- International Journal of Engineering Research

Transcription

19th
2 Days International Conference
on
“Hydraulics, Water Resources, Coastal and Environmental
Engineering( HYDRO 2014 International)”
December 18-20, 2014
Organized by
Department of Civil Engineering, MANIT Bhopal
Maulana Azad National Institute of Technology Bhopal
(Madhya Pradesh) India Pin -462051
Web : www.manit.ac.in
In association with
International journal of scientific engineering and
Technology (ISSN : 2277-1581)
Website : www.ijset.com and email : [email protected]
International journal of Engineering Research
ISSN:2319-6890)(online),2347-5013(print)
Website : www.ijer.in and email : [email protected]
Indexing of journals : google scholar, DOAJ, endnote, OALIB and
many more
S. N.
Title & Authors Names
Page
1.
Assessment Of Hydropower Potential In Nethravathi River Basin Using Swat
Model
M P Shobhita, Santosh Babar, H Ramesh
1
2.
Water And Sediment Yield Modeling For Micro Watershed
Nagargoje Sonali R, D G Regulwar
4
3.
4.
Approaches To Hydrological Modeling Of The Heterogeneous Catchment Of
The Dal Lakes
Raazia, R Khosa
Probability Analysis For Estimation Of Annual One Day Maximum Rainfall
Of Devgarhbaria Station Of Panam Catchment Area
Kapil Shah, T M V Suryanarayana
7
11
Theme: Hydraulics Of Spillway And Energy Dissipators
5.
6.
Experimental And Three Dimensional Numerical Studies For A Sluice
Spillway
A Kulhare, M R Bhajantri
Physical Model Study For Energy Dissipation Arrangements To The Pick Up
Weir Across Pachaiyar River In Tamilnadu
C Prabakar, P K Suresh, T Ravindrababu , A Parthiban, A Muralitharan
15
19
Theme: Hydraulic Structures
7.
8.
Experimental Investigations For Estimation Of The Height Of Training Wall
Of Convergent Stepped Spillway
P J Wadhai, N V Deshpande, A D Ghar
Studies For Location Of Bridges In The Vicinity Of Existing Hydraulic
Structures
B Raghuram Singh, R G Patil, M N Singh
23
27
9.
Study Of Sharp-Crested Triangular Weir
M Shaheer Ali, Talib Mansoor
31
10.
Study Of Elliptically Shaped Sharp Crested Weirs
N P Singh, R Singh
35
11.
Turbulence Characteristics Of Flow Past Submerged Vanes
H Sharma , Z Ahmad
38
12.
Hydraulic Design Aspects Of Stilling Basin With Sloping Apron
V S Rama Rao, K T More, M R Bhajantri, V V Bhosekar
42
13.
Hydraulic Design Of Barrage In Montane Terrains
Rajendra Chalisgaonkar , Mukesh Mohan, Manish S Sant, Pratibha S Sant.
46
14.
Optimal Design Of Intake Upstream Of A Weir – A Case Study
Kuldeep Malik, R G Patil, M N Singh
15.
Study Of Effect On The Stresses & Safety Of Gravity Dam With Changes In
Width Parameter
B S Ruprai, A D Vasudeo
50
55
Theme: Integrated Watershed Management
16.
17.
Assessment Of Environmentally Stressed Areas For Soil Conservation
Measures Using Usped Model
Bikram Prasad, R K Jaiswal, H L Tiwari.
A Novel Optimisation Model Applied To Godavari River Basin
R B Katiyar, Balaji Dhopte, Tejeswi Ramprasad, Shashank Tiwari, Anil
Kumar, K R Gota
58
63
18.
The Effect Of Parthenium Hysterophorus Weed On Basin Hydrology
Soham Adla,Shivam Tripathi
19.
Runoff And Sediment Yield Modeling Of An Agricultural Hilly Watershed
Using Wepp Model
Saroj Das, Laxmi Narayan Sethi, R K Singh
66
20.
Prioritization Of A Watershed Based On Spatially Distributed Parameters
C D Mishra, R K Jaiswal, A K Nema
70
Theme: Rehabilitation Of Dams
21.
Stability Assessment Of Chang Dam After Rehabilitation
R Singh, D Roy
22.
Rehabilitation And Improvement Of Sher Tank Project
Vishnu Arya.
Theme: Reservoir Operation And Irrigation Management
23.
24.
25.
26.
27.
28.
29.
Water Balance Assessment Of Krishna River Basin Through System
Simulation
N S R Krishna Reddy, S K Jain.
Minimization Of Conveyance Losses For Nashik Left Bank Canal [Nlbc] By
Closed Conduit Irrigation [Cci]
Gayatri R Gadekar, Sunil Kute, N J Sathe.
Methods For Estimation Of Crop Evapotranspiration Using Climate Data: A
Review
Gopal H Bhatti, H M Patel
Estimation Of Deep Percolation From Rice Paddy Field Using Lysimeter
Experiments On Sandy Loam Soil
Hatiye Samuel D, K S Hari Prasad, C S P Ojha, G S Kaushika.
Reservoir Modelling In Bearma Basin By Using Mike Basin
Shikha Sachan, T Thomas, R M Singh, Pushpendra Kumar
Replacement Of Field Channels With Pressurized Irrigation Systems: In Ssp
Command Area
Sahita I Waikhom, Monali Patel, P G Agnihotri
Reservoir Operation Based On Real Time Flow Data For Flood Control And
Incremental Power Generation
Rameshwar Prasad Pathak
76
81
86
92
96
99
Theme: Reservoir Sedimentation And Irrigation Management
30.
Effect Of Conservation Works On Soil Erosion-A Case Study Of Punegaon
Reservoir Catchment Area
M B Nakil, M Vkhire.
31.
Sediment Trap Efficiency Of Porcupine Systems For Riverbank Protection
Mohd Aamir, Nayan Sharma
Sedimentation Assessment In Nath Sagar Reservoir (Jayakwadi Project) Of
32. Maharashtra By Remote Sensing Technique – A Case Study
Prakash Bhamare, Manoj Bendre, Ravindra Shrigiriwar, Mahendra Nakil,
Sudhir Kalvit..
Theme: Risk Reliability Analysis And Design
103
107
112
33.
Hydrological Data Modelling Using Wavelet, Neural Network And Ar Models
G.Khadanga, B.Krishna.
115
34.
Improved Neuro-Wavelet Model For Reservoir Inflow Forecast
B.Krishna, Y R Satyaji Rao, R.Venkata Ramana.
118
35.
Application Of Particle Swarm Optimization In Multiobjective Irrigation
Planning
D V Morankar, K Srinivasa Raju, A Vasan, L Ashoka Vardhan
121
36.
Artificial Neural Network Model For Design Of Air Vessel For Controlling
The Water Hammer Pressures N Mowlali, E Venkata Rathnam
126
37.
Monthly Inflow Prediction Using Wavelet Neural Network
Rutuja Patil, J N Patel, S M Yadav, D G Regulwar.
131
38.
39.
40.
Improving Location Specific Wave Forecast Using Using Soft Computing
Techniques
S N Londhe, P R Dixit, B Nair T M, A Nherakkol
Discrete Wavelet Support Vector Conjuction Model For Significant Wave
Height Time Series Forecasting
Paresh Chandra Deka, Y N Suryadatta.
Potential Impact Of Soft Computing Techniques In Water Resources
Engineering
Satish Kumar Jain, R K Shrivastava
134
139
143
Theme: Water And Waste Water Management
41.
Typologies For Successful Operation And Maintenance Of Horizontal SubSurface Flow Constructed Wetlands
Lohith Reddy D, Dinesh Kumar, Shyam R Asolekar.
147
42.
A Mini Review On Fixed Film Reactor For Wastewater Treatment
Saraswati Rana, S Suresh
155
43. 8 Technological Utilization Of Parthenium Hysterophorus-A Review
S.Arisutha, R.B. Katiyar And S. Suresh
159
Theme: Water Quality Assessment And Modeling
44.
Water Quality And Flow Simulation Along River
Amarsinh B. Landage..
160
45.
Assessment Of Groundwater Quality Of Bah Block, Agra, India
Azmatullah Noor,Dr. Izharul Haq Farooqi
165
46.
Changing Water Quality Scenarios Of Tank Cascade System And Its
Implications
J Hemamalini, B V Mudgal, J D Sophia.
171
47.
48.
Booster Chlorination Strategy For Managing Chlorine Disinfection In
Drinking Water Distribution System – A Review
Roopali V Goyal, H M Patel
Hydrogeochemical Stuidies Of Groundwater In And Around Metropolitan
City Vadodara, Gujarat, India
M K Sharma, C K Jain
175
180
49.
Evaluation Of Various Objectives In Multi-Objective Sensor Placements In
Water Distribution Systems S Rathi, R Gupta
185
50.
Water Quality Assessment Of Dal Lake, Kashmir, J&K
Shabina Masoodi.
191
51.
52.
Spatial Water Quality Analysis Of Nagalamadike Watershed Of Pavagada
Taluk, Tumkur District Karanataka Using Geo Informatic Tools
Nandeesha, C Ravindranath, T Gangadaraiah, S G Swamy
Water Pollution In Ganga River
Susmita Saha
196
203
Theme: Water Resource And Hydrology
53.
54.
55.
56.
57.
58.
59.
Flood Frequency Analysis Using A Novel Mathematical Approach
Bidroha Basu,V V Srinivas
Performance Comparative Of Wavelets And
Savitzky-Golay Filter On Bathymetry Survey Data
M.Selva Balan1 Arnab Das2
Simulation Study On Performance Of Household
Rainwater Harvesting Systems
P.G. Jairaj1 P. Athulya2
COMMUNITY-BASED WATER RESOURCE
MANAGEMENT, STUDY AREA NAWLI VILLAGE,
MEWAT DISTRICT, HARYANA
Amit Kumar
Dogra1
Singh2SOIL WATER
SIMPLE
MODEL
TO Nitin
ESTIMATE
RETENTION LIMITS OF CHATTISGARH STATE
N.G.Pandey1, B. Chakravorty1, Sanjay Kumar2 & P. Mani1
Land Cover Classification By Ls-Svm With
Landsat Satellite Imagery
Shilpi1 R.M. Singh2
Assessing Impacts Of Landuse/Landcover Change
On Surface Runoff For Kadalundi River Basin: A
Watershed Modeling Approach
Sinha R. K.1, Eldho T. I.2, Ghosh S.2
209
213
219
222
227
230
234
60.
61.
62.
63.
64.
65.
66.
67.
68.
69.
70.
71.
72.
Impact Of Land-Use Land-Cover Changes On
Runoff Generation In A Bangalore Urban
Catchment
R. L. Gouri1
V. V. Srinivas2
Change
Detection
In Land Use/Land Cover Using
Remote Sensing And Gis – A Case Study For Ur
Basin In Tikamgarh District
S. Karwariya1*
Goyal2 V. C. Goyal3
T. Thomas4
Multi
ObjectiveS.Optimization
Of Cropping
Pattern In A Canal Command Area
Paritosh Srivastava1 and Raj Mohan Sing
Urban Watershed Rainfall Forecast Of Chennai
City
R. Venkata Ramana, B Krishna,Y. R. S. Rao and
V.S.Jayakanthan
Agriculture Water Consumption In Madhya
Pradesh – An Analysis From Virtual Water
Perspective
Vivek K. Bhatt1Of
Dr.Generalized
J.S. Chouhan2
Development
Neural Network
Based Eto Models From Limited Climatic Data
For Different Agro-Ecological Regions In India
Sirisha Adamala1
Raghuwanshi2
Mishra3
Estimating
FloodN.S.
Inundation
Using Ashok
Hec-Ras
And
Regression Models
R.S. Meena1 R. Jha2 and K.K.
Khatua3 Analysis Of Penman – Monteith
Sensitivity
Method For Estimation Of Evapotranspiration
Ch.V.S.S. Sudheer1 Dr.G.K.Viswanadh2 Dr.G.Venkata
Ramana3 In Sediment Size Of Two SubVariability
1Asst. Professor,
GRIET,
Hyderabad
Catchment
Areas
Of Ganga
Basin, Western
Himalayas
M.Y.A.
Khan* S.Study
Panwar
Comparative
Of Double Ring And Tension
Infiltrometers To Measure Infiltration Properties
And Hydraulic Conductivity
B. Ghosh1P.
Spatial
AndSreeja2
Temporal Distribution Of Rainfall
Trends In Bist-Doab Region Of Punjab (1901–
2010)
M. K.Radiation
Nema1 S. K.
Jain1 P.K. From
Mishra1
Net
Estimation
A Remotely
Sensed Data Using Sebal Model
M.V.S.S.Giridhar1 and P. Suneel2
Low Flow Analysis In Bina River Basin Of
Madhya Pradesh
V.K. Chandola1*, Sunil Kumar Yadav1, R.V. Galkate3, Palak
Mehata4
238
243
247
252
257
261
267
272
277
281
285
291
295
International Journal of Engineering Research
Issue Special3
18-19, Dec. 2014
Assessment of Hydropower Potential in
Nethravathi River Basin Using Swat Model
1
Shobhita M. P1, Santosh Babar2, H. Ramesh3
Lecturer, Dept. Civil Engineering, JSS Academy of Technical
Education, Mauritius
2
Research Scholar, Dept. of Applied Mechanics and Hydraulics,
National Institute of Technology Karnataka, Surathkal,
Mangalore-575025, India
3
Assistant Professor, Dept. of Applied Mechanics and
Hydraulics, National Institute of Technology Karnataka,
Surathkal, Mangalore-575025, India
Email: [email protected],
[email protected], [email protected],
Abstract: Hydropower plants have the advantage of producing
renewable and clean power, the renewable and reliable energy
source that serves national environmental and energy policy
objectives. Therefore, the development of hydropower plant
and improvements of water management have essential in
contributing to sustainable growth and energy reduction in
developing countries like India. The present study is concerned
with the development of methodology and assessment of hydro
power potential in Nethravathi River Basin with the help of
Remote Sensing and GIS. The catchment area covers 3200
km2, where most of the land cover is dominated by forest. The
basin was divided into six sub-basins based on hydrology and
topography using GIS tools. The climate over the basin is
coastal, humid tropical and receives an average annual
rainfall of about 4000 mm. sub-basin discharges were
estimated using SCS curve number method. To ensure the total
discharge from six sub-basins computed from SCS curve
number method, the flows were routed and simulated at the
outlet
using
Soil
and
Water
Assessment
Tool
(SWAT).Streamflow calibration was carried out at monthly
time steps for the period of 1998–2001, and validated for 2002–
2003. Flow-duration curves (FDC) were generated for
individual sub-basins. The results have shown a good
agreement between observed and the simulated flows. The
available discharge at 75%, 80% and 90% of time for each
sub-basin were extracted from the FDC. This information was
used to calculate the hydro power potential in all five subbasins at Q75, Q80 and Q90, by integrating thematic layers using
ArcSWAT.
Keywords: Flow Duration Curve, GIS, Hydropower,
Nethravathi Basin, Remote Sensing, SWAT model
1. INTRODUCTION
Energy supply is an important key parameter in the economic
development of a country. Hydroelectric Power is a form of
energy, a renewable resource. There are several sources of
energy that is being used by human beings, such as thermal,
nuclear, geothermal. One of them is hydro power which is one of
the oldest and the most reliable and environment friendly source
of all renewable energy. The use of fossil energy sources
contributes to environmental problems such as global warming,
acid rain, and desertification. Under these circumstances,
demands for the development of non-fossil energy sources grow
HYDRO 2014 International
significantly. Hydropower is a renewable energy sources that do
not emit the carbon dioxide and other flue gases that
contaminate the environment.
It has the least adverse environmental impact (i.e. greenhouse
gas, SO2, NOx emission) and has the most energy payback ratio
when compared among all electricity generation systems. One
Gega Watt of electricity produced by small hydropower means a
reduction of CO2emissions by 480 tons (Kusre et.al., 2010).
Hydropower is an indigenously available, clean and renewable
source of energy. The broad application of GIS and remote
sensing technology for digital mapping, river morphology
studies, terrain analysis, the integration of socio-economic
variables and for modeling and simulation play very crucial roles
in hydropower development (Pathak Mahesh., 2008). The
hydropower development shows the advantages based on
economic, environmental and social front as the reliable service,
long life (50 to 100 years), no atmospheric pollutants, can create
a new freshwater ecosystem with increased productivity, often
provides flood protection and it helps in sustainable
development(Nguyen Trung Dung., 2009).
The Indian economy uses a variety of energy sources, both
commercial and non-commercial. Fuel wood, animal waste and
agricultural residue are the traditional or non commercial
sources of energy that continues to meet the bulk of the rural
energy requirements even today. However, the share of these
fuels in the primary energy supply has declined from over 70%
in the early 50's with a little over 30% as of today. The Ministry
of Power has set on the objective of providing "Power for all by
2012". This will entail electrification of all villages by 2007 and
of all households by 2012. It is also a known fact that electricity
is one of the key infrastructure elements for the economic
growth of the country.
The existing power deficit and a rapid growing demand have
necessitated a large scale capacity power addition programme.
Severe power shortage is one of the greatest obstacles to any
country‟s development. Power the most important need in the
modern world. Hydropower development needs integrated
approaches to analyzing natural resources, physiographic setting
and the socio-economic indicators. GIS is also used to input,
store, retrieve, manipulate, analyze and output geographically
referenced data or geospatial data, in order to support decision
making for planning and management of land use, natural
resources, environment, transportation, urban facilities, and
other administrative records (Dudhani S, 2006). The many
studies on hydropower location and hydropower potential has
been carried out using GIS and remote sensing methodology
(Arun et al; 1995, Pannathat et al; 1998, Balance et al; 2000,
Kupakrapinyo and Chaisomphob 2003, Santasmita Das and
Paul, P.K. 2006, Choong-Sung et al; 2010, Vani 2010, Shobitha
2012).The scope of the present study is in development of
SWAT (Soil and Water Assessment Tool) model which will help
to evaluate the hydropower potential within watershed.
2. STUDY AREA
Nethravathi River is one of the major west flowing rivers in
Karnataka. The geographical location of the Nethravathi river
basin lies between 12º29'11" to 13º11´11" N latitudes and
74º49´08" to 75º47´53" E longitudes as shown in figure 1. The
MANIT Bhopal
Page 1
Issue Special3
18-19, Dec. 2014
Nethravathi River originates in the south of Samse village, at an
altitude of approximately 1200 meters from mean sea level in
the Western Ghats of Karnataka. The river flows towards
westward for about 103 kilometers with a drainage area of 3657
km2 (Shobitha, 2012) and empties into the Arabian Sea at
Mangalore city. The river is joined by Mundaja Neriya, Shishla
Uppar, Kumaradhara and Beltangady nallas from either side.
Average annual rainfall in the region is about 3930 mm with
90% of the rainfall contribution from South west monsoon (June
– September) alone and rest during pre and post monsoon.
Nethravathi River provides water supply for Mangalore city,
industries, hydropower production and agricultural activities in
the basin.
3. MATERIALS AND METHODOLOGY
3.1 Data used
Daily rainfall data were collected for six years from nine rain
gauge stations. The Daily gridded climate record for a period of
six years (1998-2003) including precipitation and temperature
were obtained from IMD (India Meteorological Department),
land use land cover of year 2003, soil data, DEM (topography
data).
3.2 SWAT model development
SWAT is a river basin scale model that operates on a daily,
monthly time-step. It was developed at the University of Texas,
USA. Major components of the SWAT model include
hydrology, weather, erosion, soil temperature, crop growth,
nutrients, pesticides, and agricultural management (Neitsch et al;
2001b).
SW = The final soil water content (mm), SW = The water
t
0
content available for plant uptake, defined as the initial soil
water content minus the permanent wilting point water content
(mm), t = Time in days, R = Rainfall (mm), Q = Surface
day
surf
runoff (mm), E = Evapotranspiration (mm), w
a
= Percolation
seep
(mm) and Q = Return flow (mm)
gw
3.3 Model calibration and validation
Understanding the model processes, checking the various
components such as rainfall to runoff ratio, ET, base flow
contribution, etc. are very important. To make sure all the major
components are represented well for a watershed before
attempting either manual or auto-calibration. The model contains
both manual and auto-calibration tools. In this study, model
parameters will calibrate using the observed daily flow.
Sensitivity analysis will be conducted for the SWAT model to
guide calibration process. Figure 2 represents the detailed
methodology which was used during the research work.
3.4 Estimating power output
Figure 2. Methodol ogy Flowchart
Figure 1. Location map of study area
The computation of hydrologic processes operates in five
phases: (1) precipitation, interception, (2) surface runoff, (3) soil
and root zone infiltration, (4) evapotranspiration and soil and
snow evaporation, and (5) groundwater flow. A water balance
equation calculates the change in soil water content (SWt) as:
(1)
where:
MANIT Bhopal
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Issue Special3
18-19, Dec. 2014
3.4.1 Head
The head is the vertical distance that waterfall. It is usually
measured in meters. The higher head consumes less water to
produce a given amount of power, and can use smaller, less
expensive equipment. Low head refers to a change in elevation
of less than 10 feet (3 meters). When determining head, both
gross head and net head need to be considered. The gross head is
found by considering the difference of head between weir and
power house. Net head equals gross head minus losses due to
friction and turbulence in the piping. Hydraulic power can be
captured wherever a flow of water falls from a higher level to a
lower level. The vertical fall of the water, known as the “head”,
is essential for hydropower generation; fast-flowing water on its
own does not contain sufficient energy for useful power
production except on a very large scale. Hence two quantities are
required: a flow rate of water (Q), and head (H). It is generally
better to have more head than more flow, since this keeps the
equipment smaller.
The Gross head (H) is the maximum available vertical fall in the
water, from the upstream level to the downstream level. The
actual head seen by a turbine will be slightly less than the gross
head due to losses incurred when transferring the water into and
away from the machine. This reduced head is known as the net
head as shown in figure 3.
3.4.2 Identification of sites having a suitable head
 The possible potential sites for power houses along
streams based on the gross head were located at the
intersection points of contour lines and streams.
 For this purpose a set of contour lines with intervals of
6, 10 and 20 m were generated from ASTER DEM.
 The flow accumulation map has been created by using
the flow direction map. The flow accumulation function
calculates accumulated flow, as the accumulated weight
of all cells flowing into each down slope cell in the
output raster.

Suitable sites were identified by using the DEM and
the flow accumulation map.
 The flow accumulation map was used to locate weir
and powerhouse on the high flow accumulated stream.
variability is through flow-duration curves. The flow-duration
curve of a stream is based on daily mean discharges (not peak
flows) and shows the percentage of time that a given daily mean
discharge is equalled or exceeded. A flow-duration curve is a
plot of discharge against the percent of time the flow has
equalled or exceeded. Flow-duration curves are extremely useful
in evaluating various dependable flows in the planning of water
resources engineering projects, the characteristic of the
hydropower potential of a river. The stream flow data are
arranged in a descending order of discharges, using class
intervals. The data can be daily, weekly or monthly values.
Chiang et al. (2002) stated that monthly stream flow data satisfy
the basic data requirement for water resource projects. If N
numbers of data points are used in this listing, the plotting
position of any discharge Q is
(2)
Where,
Pp = percentage of probability of the flow magnitude being
equalled or exceeded, m = the order number of the discharge, N
= Total count (Number of data)
3.4.4 Estimating power output
The dependable flows (Q90, Q80,Q75) and head substitute in the
power equation to determine the power output from each subbasin.
P=ηρQgH
(3)
Where,
P = mechanical power produced at the turbine shaft
(Watts), η = hydraulic efficiency of the turbine, ρ = density of
water (1000 kg/m3), g = acceleration due to gravity (9.81 m/s2),
Q = volume flow rate passing through the turbine (m3/s), H =
head of water across the turbine (m).
4. RESULTS AND DISCUSSIONS
From figure 3 represents sub-basin wise monthly average flow
during the months of June to January. It can be observed that
sub-basin 3 has the highest amount of discharge compared to the
rest of the sub-basins. This is due to the presence of C group of
HSG soil, which are moderately high runoff potential soil type
and very high rainfall. Sub-basin 4 has the lowest amount of
discharge. This is due to the presence of A group of HSG, which
are low runoff potential soil type
Figure 3. Measurement of head (Sale Michael et al., 2006)
3.4.3 Flow-Duration Curves (FDC)
It is well known that the stream flow varies over a water year.
One of the popular methods of studying the stream flow
Figure 4. Monthly average flow (cms) Sub-basin wise
MANIT Bhopal
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Issue Special3
18-19, Dec. 2014
4.1 Flow duration curves (FDC)
The stream flow data are arranged in a descending order of
discharges, using class intervals. The data used can be daily,
weekly or monthly values. If N numbers of data points are used
in this listing, the plotting position of any discharges Q in
equation 2. Table 1 gives the dependable flows of all sub-basins,
which are derived from flow duration curves. Table.1 shows the
flow quantiles derived from the flow duration curves for 90%,
80% and 75% for each sub-basins. These flow quantiles were
used for power estimation.
Table 1. Flow quantiles from each sub-basin
Discharge Q in
Cumecs
Subbasin
No
Q90
Q80
Q75
1
380
540
625
2
364
508
580
3
389
564
651
190
272
313
4
4.2 Estimation of power (P)
The flow quantiles estimated from FDC and hydraulic head
determined from DEM are substituted in equation 3 to estimate
power in watts for each sub-basin. The turbine efficiency (η) was
taken as 85% for Kaplan turbine. The value of power in mega
watts is tabulated in Arc GIS, as shown in Table 2.
Table 2. Sub-basin wise power potential
Sub-basin
No
Power in Mega Watts
Q90
Q80
Q75
1
2
63.37
60.70
90.06
84.72
104.23
96.73
3
64.87
94.06
108.57
4
31.69
45.36
52.20
5. CONCLUSIONS
 In the study area it was found from the hydrographs that
the months June, July, August and September had shown a
greater amount of discharge and so shows maximum
hydropower potential.
 Sub-basin - 3 leads to the highest amount of average
monthly discharge during June to January months about 3000 m3
/s when compared to the remaining sub-basins. This is mainly
due to presence of C group of HSG soil, which are moderately
high runoff potential soil type and very high rainfall.
 Using the flow duration curve, it is possible to estimate
the percentage of time that a specified flow is equalled to or
exceeded, once we know the amount of discharge that will be
available for 90% of the time from the flow duration curve, we
can calculate the power that can be produced.
 SWAT model was calibrated and validated, the R2
(coefficient of correlation) in calibration equal to 0.91 and in
validation equal to 0.92. The discharge of June, July and
gradually reduces towards August, September and October, with
little discharge during the months of November and December
and January.
6. REFERENCES
i.
Arun K.S (1995) ―GIS in small hydro planning resource
management‖ Department of Earth Sciences, University of Roorkee,
Alternate Hydro Energy Centre, University of Roorkee.
ii.
Ballance, D, Stephenson, R.A, Chapman, Muller, J (2000) ―A
geographic information systems analysis of hydro power potential in
South Africa‖ Journal of Hydro-informatics
iii.
Choong S.Y, Jin, H.L, Myung, P.S (2010) ―Site location
analysis for small hydropower using geo-spatial information system‖
Journal of Renewable Energy, 35: 852–861.
iv.
Dudhani S. (2006). ―Small hydropower and GIS for
sustainable growth in energy sector‖ Map India 2006.
v.
Kupakrapinyo C, Chaisomphob T (2003) ―Preliminary
Feasibility Study on Run-of-River Type Hydropower Project in Thailand:
Case Study in Maehongson Province‖ Proceedings of the 2nd Regional
Conference on Energy Technology towards a Clean Environment 12-14
February 2003, Phuket, Thailand.
vi.
Kurse, B.C., Baruah, D.C., Bordoloi, P.K., Patra, S.C (2010)
―Assessment of hydropower potential using GIS and hydrological
modeling technique in Kopili River basin in Assam India‖ Journal of
Applied Energy, 87: 298–309.
vii.
Mahesh, P. (2008) ―Application of GIS and Remote Sensing
for Hydropower Development in Nepal‖ Hydro Nepal Issue No. 3, 1-4
viii.
Michael, S. (2006) ―Hydropower Summary‖ Department of
Energy Biennial report.
ix.
Nguyen, T.D. (2009) ―Sustainable hydropower development‖
DAAD, Germany.
x.
Neitsch, S.L., Arnold, J., Kiniry, J.R., Srinivasan, R.,
Williams, J.R. (2001b). ―SWAT theoretical documentation version
2009.‖
xi.
Pannathat R, Taweep C, Thawilwadee B. (2009) ―Application
of GIS to site selection of small run-of-river hydropower project by
considering engineering/economic/environmental criteria and social
impact‖. Journal of Renewable and sustainable energy reviews, 13:
2336–2348
xii.
Santasmita D, Paul, P.K. (2006) ―Selection of Site for Small
Hydel Using GIS in the Himalayan Region of India‖. Journal of Spatial
Hydrology, 6: 18-28.
xiii.
Shobitha, M.P. (2012) ―Estimation of hydropower potential in
Nethravathi river basin using RS and GIS.‖ M.Tech thesis, NITK
Surathkal.
xiv.
Vani, S. (2011) ―Mapping of suitable sites for small
hydropower generation using RS and GIS‖, M.Tech thesis, NITK
Surathkal.
Water and Sediment Yield Modeling for Micro
Watershed
Nagargoje Sonali R1 & D.G.Regulwar2
1. Research scholar, Dept. of Civil Engineering, Govt.
college of engineering, Aurangabad, Maharashtra state
India
2. Associate Professor, Dept. of Civil Engineering, Govt.
college of engineering, Aurangabad, Maharashtra state
India
Email: 1. [email protected] 2.
[email protected]
ABSTRACT: Land is the most important natural resource,
which embodies soil, water and associated flora and fauna
involving the total ecosystem. Now a days degradation of land
from water-induced soil erosion is becoming a serious global
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problem, which is not only eroding the top fertile soil but is
also responsible for swelling of river beds and reservoirs
thereby causing floods and reduction in the life span of costly
reservoirs and dams. Reliable estimates of soil erosion,
sediment and water yield are, therefore, required for design of
efficient erosion control measures, reservoir sedimentation
assessment, and evaluation of watershed management
strategies. Watershed parameters such as channel network,
location of drainage divides, water and sediment yield of the
catchment etc are obtained from maps or field surveys
traditionally. Since last two decades this information has been
increasingly derived directly from digital representations of the
topography. Measurement of sediment yield on a number of
watersheds is operationally difficult, expensive, time
consuming, and tedious. Therefore modeling is carried out for
generating the sediment yield data base. Present study explores
development of sediment and water yield model for micro
watershed (627 ha) located in Khuldabad village of
Aurangabad District, Maharashtra state India. For this
topographical features such as LULC, soil map and DEM are
prepared under GIS environment and meteorological data like
temperature and rainfall has been made in gridded format.
SWAT divided watershed into HRUs by merging digital
elevation model land use and soil pattern. Annual average
basin value for water and sediment yield for present study are
24.96 mm and 1.035 T/ha respectively. The study reveals the
values and areas of sediment sources from the watershed
which helps in adopting suitable soil conservation practices in
basin.
Keywords: Land use/ Land cover, Digital Elevation Model, Soil
and Water assessment tool, Hydrological response units
1. INTRODUCTION
Soil erosion/sedimentation is an immense problem that has
threatened water resources development in all over the world.
An insight into soil erosion/sedimentation mechanisms and
mitigation methods plays an imperative role for the sustainable
water resources development. This paper presents daily sediment
yield and water yield simulations in micro watershed under
different Best Management Practice (BMP) scenarios. The Soil
and Water Assessment Tool (SWAT) was used to model soil
erosion, identify soil erosion prone areas and assess the impact
of BMPs on sediment reduction. For the existing conditions
scenario, the model results showed a satisfactory agreement
between daily observed and simulated sediment concentrations
as indicated by Nash-Sutcliffe efficiency greater than 0.83.
However, a precise interpretation of the quantitative results may
not be appropriate because some physical processes are not well
represented in the SWAT model. Literature review shows there
are many catchment models that include the soil
erosion/sedimentation processes and simulate the effect of
mitigation measures.
2. MATERIALS AND METHODS
2.1 Description of study area
Location of case study Watershed GV-41 is a significant
drainage system contributing to river Godavari. The watershed
lies between longitude 74° 58' 55” and 75° 07' 24” East and
latitude 19° 53' 33” and 19° 45' 27” North. It is included in
Survey of India topographic sheet No. 47 I/13 and 47 M/1 on 1:
50,000 scale.
Fig.1: Location Map of Study Area
2.2. MODEL INPUT
The spatially distributed data (GIS input) needed for the Arc
SWAT interface include the Digital Elevation Model (DEM)s
2.2.1 Digital Elevation Model
Topography was defined by a DEM that describes the elevation
of any point in a given area at a specific spatial resolution. A 90
m by 90 m resolution DEM (Fig. 2) was downloaded from
SRTM (Shuttle Radar Topography Mission). The DEM was
used to delineate the watershed and to analyze the drainage
patterns of the land surface terrain. Sub basin parameters such as
slope gradient, slope length of the terrain, and the stream
network characteristics such as channel slope, length, and width
were derived from the DEM.
Fig.2. DEM of Study Area (Source: SRTM)
2.2.2 Land Use/ Land cover Map and Soil Map
Detailed classification of land use /land cover is shown in fig.
3.Database of LULC collected from Bhuvan (NRSC) .
Distribution of various soil types among the study area is shown
in fig.No.4.
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Fig.3. Detailed LULC classification of study area
Fig.4. Classification of Soil in Study area
Table No. 1 Spatial model input data for the Watershed.
soil temperature, crop growth, pesticides agricultural
management and stream routing. The model predicts the
hydrology at each HRU using the water balance equation, which
includes daily precipitation runoff, evapotranspiration, and
percolation and return flow components. The surface runoff is
estimated in the model using two options (i) the Natural
Resources Conservation Service Curve Number (CN) method
(USDA-SCS, 1972) and (ii) the Green and Ampt method (Green
and Ampt, 1911). The percolation through each soil layer is
predicted using storage routing techniques combined with crackflow model (Arnold et al., 1995). The evapotranspiration is
estimated in SWAT using three options (i) Priestley-Taylor
(Priestley and Taylor, 1972), (ii) Penman-Monteith (Monteith,
1965) and (iii) Hargreaves (Hargreaves and Riley, 1985). The
flow routing in the river channels is computed using the variable
storage coefficient method (Williams, 1969), or Muskingum
method (Chow, 1959). The SWAT model uses the Modified
Universal Soil Loss Equations (MUSLE) to compute HRU-level
soil erosion. It uses runoff energy to detach and transport
sediment (Williams and Berndt, 1977). The sediment routing in
the channel (Arnold et al., 1995) consists of channel degradation
using stream power (Williams, 1980) and deposition in channel
using fall velocity. Channel degradation is adjusted using USLE
soil erodibility and channel cover factors.
2.4 SWAT model setup
The SWAT model inputs are Digital Elevation Model (DEM),
land use map, soil map, and weather data, which is shown in
Table 1. The ArcGIS interface of the SWAT2005 version was
used to discretize a watershed and extract the SWAT model
input files. The DEM was used to delineate the catchment and
provide topographic parameters such as overland slope and slope
length for each sub basin. The land use map of the Global Land
Cover Characterization (GLCC) was used to estimate vegetation
and their parameters input to the model. The GLCC is part of the
United States Geological Survey (USGS) database, with a spatial
resolution of 1 km and 24 classes of land use representation. The
parameterization of the land use classes is based on the available
SWAT land use classes. The soil types of the study area were
extracted from the soil map obtained from NBSS database.
3.
RESULTS AND DISCUSSIONS
Using above materials and models SWAT model is performed.
DEM and LULC,soil map having 8 classification each taken as
input. Output is obtained for each subbasin in delineated
watershed of study area. Whole SWAT procedure is followed
using SWAT Manual 2005. Results of the present study are as
shown in Table No.2.
Table No.2 Average monthly basin output
2.3 SWAT model description
The Soil and Water Assessment Tool (SWAT) is a physical
process based model to simulate continuous-time landscape
processes at a catchment scale (Arnold et al., 1998; Neitsch et
al., 2005). The catchment is divided into hydrological response
units (HRUs) based on soil type, land use and slope classes that
allows a high level of spatial detail simulation. The major model
components include hydrology, weather, soil erosion, nutrients,
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Month
01
02
03
04
05
Water Yield
(mm)
30.36
32.61
33.86
43.18
60.56
Sediment Yield
(T/Ha)
0.19
0.29
0.21
0.43
0.18
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06
47.07
0.02
07
22.86
0.00
08
10.15
0.00
09
12.90
0.01
10
19.70
0.08
11
27.63
0.18
12
27.59
0.16
SWAT gives the average monthly basin values of water and
sediment yield in mm and Tonnes/Hector respectively. From the
output it seems easier to estimate sediment yield using
hydrological model i.e. SWAT. Using this model identification
of the soil erosion area becomes easier from which management
of sediment yield can be done. Thus SWAT gives each basin
values present in watershed through which Soil and Water
conservation practices can be done for sustainable development
of water resources.
4. CONCLUSIONS:
ARC-SWAT is powerful hydrological model to identify erosion
prone areas and it is also useful for watershed prioritization.
Using hydrological models identification and solution of such
critical soil erosion areas is in water resources engineering can
be achieved for sustainable development.
5. REFERENCES
i.
Chen,B. (2012) ―Development of an integrated adaptive
resonance theory mapping classification system for supporting watershed
hydrological modeling‖ Journal of Hydrologic Engineering, ASCE, vol. 17, pp
679-693
ii.
Gabriel, G., 2008 ―Fitting of time series models to
forecast stream flow and groundwater using simulated data from SWAT‖,
Journal of Hydrologic Engineering, ASCE, pp: 554-562.
iii.
Gong Y., 2010 ―Effect of watershed subdivision on SWAT
modelling with consideration of parameter uncertainty‖, Journal of Hydrologic
Engineering, ASCE, December, pp: 1070- 1074.
iv.
Kim, N.W., 2012 ― Assessment of flow regulation effects
by dams in the Han River, Korea, on the downstream flow regimes using
SWAT‖, Journal of water resources planning and management, ASCE, pp: 2435.
v.
Kirby, J.T. and Durrans, S.R., 2007 ―Modelling the
combine effect of forests and agriculture on water availability‖, Journal of
Hydrologic Engineering, ASCE, pp: 319-326.
vi.
Mishra A. and Kar S., 2012 ―Modelling hydrologic
processes and NPS pollution in a small watershed in sub humid subtropics
using SWAT‖, Journal of Hydrologic Engineering, ASCE, pp: 445- 454.
vii.
Pikounis M. (2003) ―Application of the SWAT model in
the Pinios river basin under different land-use scenarios‖ 8th International
Conference on Environmental Science and Technology, Vol 5, pp 71-79
viii.
Sang, X., and Chen Q, 2010 ―Development of SWAT tool
model on human water use and application in the area of high human
activities‖, Journal of Irrigation and Drainage Engineering ASCE, pp: 23-30.
ix.
Setegn, S. G. (2008) ―Hydrological modeling in the Lake
Tana Basin, Ethiopia using SWAT model‖ Journal of Hydrology, ASCE, vol.2,
pp. 49-62
Approaches to Hydrological Modeling of the
Heterogeneous Catchment of the Dal Lake
S. Raazia1
R. Khosa2
Department of Civil Engineering, Indian Institute of Technology
Delhi, New Delhi, 110016, India
2
Department of Civil Engineering, Indian Institute of Technology
Delhi, New Delhi, 110016, India
Email: [email protected]
1
ABSTRACT: Dal Lake situated in the state of J&K along with
its associated wetland system, forms a highly complex and
vulnerable hydrological system. The lake catchment comprises
of gently to steeply sloping mountains on three sides and a low
relief, highly urbanized landscape on one side. Owing to these
differences in physical features of the landscape, the
catchment exhibits a spatially varying hydrological behaviour.
The study identifies the catchment components with dissimilar
hydrological response and, in recognition of these distinct but
dominant hydrological features, has proposed similarly distinct
approaches to hydrological modeling for these appropriately
designated sub areas of the overall catchment. Briefly, the
entire catchment was divided into 3 subbasins namely (i) DaraDachhigam subbasin with a mild to steep mountainous relief
and a prominent network of drainage channels, (ii) Zabarwan
subbasin with gently sloping foothills along the lake shore
having a backdrop of highly steep mountains further from the
lake, and (iii) the urban subbasin consisting of a nearly plain
urbanized area and wetlands spread over an undulating
topography. In the Dara-Dachhigam subbasin, runoff
generation has been modeled in accordance with the
Hortonian mechanism using the hydrological model SWAT.
The hydrology of the Zabarwan basin is characterized by
saturated foothills and presence of springs in the lower
reaches. Presence of preferential flow paths is likely on the
forested peaks. A dual porosity hillslope runoff model that
quantifies Hortonian overland flow, saturation overland flow
and lateral subsurface flow as well as extent of foothill
saturation was used to simulate the hydrology of this region.
The urban subbasin, having historically been a wetland, has a
shallow water table with high surface water-groundwater
interactions and, accordingly, the region was modeled using
hydrological model MIKE SHE.
Keywords: Heterogeneous catchment hydrology, hydrological
modeling, Dal Lake catchment
1. INTRODUCTION
The Dal Lake is s shallow, fresh water lake situated in the
summer capital Srinagar, of the state of Jammu and Kashmir.
The lake catchment extends over an area of 336 square
kilometres including the area of the wetland system which is
about 24 square kilometers. The catchment is located between
34002‟ and 34013‟ N latitudes and 74048 and 75009‟ E
longitudes. The lake is situated at an altitude of 1583 m with the
highest point in the catchment at 4390 m height above the mean
sea level. The lake forms the central body of a complex wetland
system and is connected to a number of smaller water bodies
through numerous water channels. This urban lake along with its
associated wetland system forms a highly complex and
vulnerable hydrological system. The lake is surrounded by
gentle to steep sloping mountains on three sides and a nearly
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plain urbanized area of mild topography meshed with wetlands
spread over an undulating topography on the west. Floating
gardens along the west shore of the lake are among the unique
features of this lake. The spatial diversity in the landscape of the
catchment surrounding this lake adds to the complexity of this
system. Landscape heterogeneity results in spatial variability of
hydrological states and incomplete process understanding (Troch
et al., 2008).
Catchment morphology often acts as a dominant control on
water flow paths and may be used as a clue to understand the
catchment hydrological response (Beven et al., 1988). The
present study identifies the catchment components with
dissimilar hydrological response based on the physical features
of the landscape. In recognition of these distinct but dominant
hydrological features, the study has proposed similarly distinct
approaches to hydrological modeling for these appropriately
designated sub areas of the overall Dal catchment.
2. MATERIAL AND METHODS
2.1 Data For Hydrological Modeling
Data most relevant to hydrological modeling includes
meteorological data such as precipitation, wind speed and
temperature, and catchment characteristics such as topography,
soil types and land use. For the present study, meteorological
data including daily accumulated precipitation, daily minimum
and maximum temperatures and daily wind speed was obtained
from the weather observatory of Sher i Kashmir University of
Agricultural Sciences and Technology, Kashmir situated within
the catchment. Information about the terrain was obtained from
the ASTER Global Digital Elevation Model (ASTER GDEM) of
resolution 30 m (Figure 1). Information regarding land use land
cover (Figure 2a) and soil types (Figure 2b) were taken from the
available literature.
Figure 1. DEM of the Dal Lake catchment
(a)
(b)
Figure 2. (a)Land use land cover (2005) map of the Dal
catchment (b) Map showing soil types in the Dal catchment
(Badar et al., 2013)
2.2 Delineation of the Catchment
The catchment of the Dal Lake was delineated using ASTER
DEM of 30 m resolution using the Automatic Watershed
Delineator of the hydrological model ArcSWAT. Based on the
visually observed differences in the physical features of the
landscape and thereby in the hydrological response, the
catchment was broadly divided into three subbasins (Figure 3).
The Dara-Dachhigam subbasin comprises of the mountains on
the north of the lake and those extending far in the east behind
the Zabarwan hills. The subbasin constitutes nearly 74 per cent
of the total catchment area. The Zabarwan subbasin comprises of
the steep slopes of the Zabarwan hills lying along the entire east
coast of the lake. The urban subbasin on the west comprises of
wetlands, floating gardens and urban settlements.
Figure 3. Subbasins of the Dal catchment
2.3 Hydrological characterization and selection of modeling
approach
The Dara-Dacchigam subbasin is characterised by mountains
with slopes in the range of 6 per cent to 50 per cent drained by a
very prominent network of drainage channels, the main channel
being initiated by a glacial lake known as the Marsar Lake. The
drainage pattern is dendritic in the north region of this subbasin
whereas it is of trellis type in the east region as shown in Figure
3 (Badar et al., 2013). In this subbasin, runoff generation has
been modeled in accordance with the Hortonian mechanism.
This runoff concentrates towards the drainage channels
wherefrom it is carried to the lake through a number of streams
dominated by the Telbal creek (nallah). The outflow hydrograph
for this feature constitutes mainly of the surface runoff and with
an added component, though small, of return flow from lateral
subsurface flow. Hydrological model SWAT (Soil Water
Assessment Tool) was used to model the hydrology of this
subbasin. The model incorporates an algorithm capable of
generating stream network from the topographic information.
The SCS curve number method was used to model runoff
generation. SWAT uses kinematic storage model (Sloan et al.,
1983) to compute return flow.
The Zabarwan subbasin consists of gently sloping foothills near
the lake shore that make up nearly 25 per cent of the subbasin
followed by steeply sloping mountains having upto 68 percent
slope with forested peaks as we go further from the lake shore.
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This subbasin is devoid of any prominent drainage network and
therefore, runoff flows mostly in diffused form towards the lake.
Another notable hydrological characteristic of this subbasin is
that the foothills are wet and remain inundated at many places
for a considerable part of the year. This can be attributed to the
saturation of the soil upto ground level at the lower end of the
hillslope caused as a result of vertical percolation and lateral
subsurface flows from the higher reaches (Dunne, 1978).
Saturation of soil profile upto ground surface is also evident
from the presence of springs in this region. Further addition of
subsurface flow to the saturated profile causes water to seep
through the surface and flow as overland flow, and is known as
the return flow (Pilgrim et al., 1978; Corbett, 1979; Mosley,
1979). Moreover, the saturated soil profile does not allow any
further infiltration and therefore, these regions act as source
areas for generating runoff by the mechanism known as
saturation excess overland flow (Dunne and Black, 1970;
Hewlett and Hibbert, 1963). High levels of saturation at the
foothills also points to high amounts of lateral subsurface flow.
It can be postulated that secondary porosity of the forest covered
peaks plays a major role in conducting water as subsurface flow
(Mosley, 1979; Beven and Germann, 1982). The hydrologic
behaviour of such regions can be modeled by appropriately
superimposing a macroporosity on the natural hydraulic
conductivity of the soil (Shakya and Chander, 1995; Jain et al;
2013).
A physically based lumped parameter hillslope runoff model that
calculates unsaturated zone flow in dual porosity domain was
used to model the hydrology of this subbasin. The model
incorporates a modified form of the Horton's infiltration model
which is the original Horton's infiltration equation corrected for
lesser actual antecedent infiltration than infiltration at capacity
rate and recovery of infiltration capacity. The model considers
the macropore domain to be comprised of only two size pores.
Flow in the smallest size pores is assumed to be laminar and
calculated using Stokes law. For the largest size macropores,
Mannings equation is used to quantify flow of water assuming
turbulent flow (Equation 1).
(1)
where Qm is the total flow through the macrpores, r min and rmax
are the radii of minimum and maximum size macropores,
respectively, g is the acceleration due to gravity, A m is the total
area of the macropores and ν isthe kinematic viscosity of water.
The model also takes into account the transaction through the
walls of the macropore into the soil matrix which is quantified
using Philip's absorption equation. Preciptation in excess of the
combined capacity of the soil matrix and the macropores flows
as surface runoff. The return flow is quantified using the
kinematic storage model of Sloan et al. (1983). To account for
catchment storage effects (lagged and attenuated response), the
model routes the surface runoff through a non linear reservoir of
the form given in equation 2.
(2)
where S is the storage, Q is the outflow and k and n are nonlinear reservoir parameters.
The subbasin was divided into two regions, steeply sloping
mountains with upto 68 percent slope constituting nearly 75
percent of the total subbasin area and foothills with slopes upto 9
percent to be modeled separately. The model was setup to
calculate water table fluctuation and thereby, the length of
foothill saturation besides total outflow from the subbasin.
The urban subbasin along the west coast of the lake comprises of
floating gardens, small wetlands with undulating topography and
urban setups. The subbasin has been historically a large wetland.
The subbasin has a shallow water table with the depth to water
table varying in the range of 1.1 to 1.5 m below the ground
surface (Jeelani et al., 2013). High groundwater-surface water
interactions exist in this region. Owing to the undulating
topography, there are pockets of specific flow directions.
Accordingly, the region was modeled using the hydrological
model MIKE SHE. MIKE SHE is a 3 dimensional hydrological
model having capabilities of modeling unsaturated and saturated
zone flows together with the surface flows.
The outflow hydrograph for the Dara-Dachhigam subbasin is
shown in Figure 4. The hydrograph indicates that the subbasin
shows a direct response to precipitation. Peak flows occur
mostly during the rainy months of March and April. Occurrence
of zero flows during the months of December and January
indicates that the flows are intermittent.
Figure 4. Outflow hydrograph of the Dara-Dachhigam subbasin
Hydrological modeling of the Zabarwan subbasin revealed that
the entire precipitation falling on the unsaturated length of the
hillslope is either absorbed by the soil matrix or bypassed
through the macropores, leaving zero amount of precipitation to
flow as surface runoff.
Figure 5. Saturated slope length in the steep region of Zabarwan
subbasin for different initial conditions of water table (Ls1:
Initial length of saturation, H1: Initial height of water table
above the impervious bed in the soil profile, equal to depth to the
impervious bed if Ls1> 0)
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For a number of initial conditions of the water table, it was
observed that nearly 100 m slope length (out of an average slope
length of 2300 m) at the lower end of the steep zone always
remains saturated (Figure 5), whereas the entire length of the
foothills remains wet during all seasons (Figure 6). The same is
also evident from the presence of a number of springs in the
foothill region of this subbasin.
Figure 6. Saturated slope length in the foothills of the Zabarwan
subbasin
The entire overland flow component (appearing as peaks) in the
outflow hydrograph of the steep region (Figure 7) is due to
saturation excess overland flow occurring at the saturated lower
end of the slope. The outflow hydrograph of the foothills which
also represents the outflow of the entire subbasin (Figure 8) has
a constant return flow component and peaks due to overland
flow during precipitation events.
Figure 9. (a) Overland flow depth and (b) infiltration in the urban subbasin
Figure 7. Outflow hydrograph at the lower end of the steep
region of Zabarwan subbasin
Figure 8. Total outflow from the Zabarwan subbasin
The urban subbasin exhibits a highly complex hydrology. The
hydrological response is a result of a number of factors like land
cover, soil type and depth of water table below the ground
surface. Results
Figure 10. Overland flows in (a) x and (b) y directions in the urban subbasin
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show that the overland flow depths are dominantly affected by
the infiltration rate of the soil (Figures 9a and 9b). Existence of
positive as well as negative values of overland flows in x and y
directions (Figure 10a and 10b) shows that there are pockets of
specific flow direction in this region.
4. CONCLUSIONS
Different regions of the catchment of the Dal Lake exhibit
hydrological behaviours which are markedly different from each
other. This varied response is mainly on account of the diverse
landscape across the catchment. Therefore, a single modeling
approach is not appropriate to model the hydrology of the entire
system. In the present study, an attempt was made to understand
the hydrological response in various regions of the Dal Lake
catchment and the physics underlying that response. Based on
this understanding, appropriate modeling approaches were
selected and used to model the hydrology of the system. Suitably
chosen approaches could closely represent the observed
hydrological phenomena in the three subbasins of the catchment.
More such attempts are necessary to precisely understand and
model the hydrology of heterogeneous catchments.
REFERENCES
i.
Badar B, Romshoo SA, Khan MA (2013) Modelling catchment
hydrological responses in a Himalayan Lake as a function of changing land use
and land cover. Journal of Earth System Science 122(2): 433-449
ii.
Beven K, Germann P (1982) Macropores and water flow in soils.
Water Resources Research 18(5): 1311-1325
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Beven K, Wood EF, Sivapalan M (1988) On hydrological
heterogeneity, catchment morphology and catchment response. Journal of
Hydrology 100: 353-375.
iv.
Corbett ES (1979) Hydrologic evaluation of the storm flow
generation processes on a forested watershed. Report: PB80-129133 National
Technology Information Service, Springfield
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Dunne T, Black RD (1970) Partial area contributions to storm runoff
in a small New England watershed. Water Resources Research 6(5): 1296-1311
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Dunne T (1978) Field study of hillslope flow processes. In: Hillslope
Hydrology, John Wiley and Sons, Chichester, U. K.
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Hewlett JD, Hibbert AR (1963) Moisture and energy conditions
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viii.
Jain L, Haldar R, Khosa R (2014) Hillslope runoff processes and
modelling. International Journal of Earth Sciences and Engineering 7(1): 193201
ix.
Jeelani G, Shah RA, Hussain A (2013) Hydrogeochemical assessment
of groundwater in Kashmir Valley, India. Published manuscript:
http://www.ias.ac.in/jess/forthcoming/JESS-D-13-00128.pdf
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Mosley MP (1979) Streamflow generation in a forested watershed,
New Zealand. Water Resources Research 15(4): 795-806
xi.
Pilgrim DH, Huff DD, Steele TD (1978) A field evaluation of
subsurface and surface runoff, II, Runoff processes. Journal of Hydrology 28:
319-341
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Shakya NM, Chander S (1998) Modeling of hillslope runoff
processes. Environmental Geology 35: 115-123
xiii.
Sloan PG, Moore ID, Coltharpa GB, Eigel JD (1983) Modeling
surface and subsurface stormflow on steeply sloping forested watersheds. Report
142: 167 Water Resources Institute, University of Kenya, Lexington Kenya
xiv.
Troch PA, Carrillo GA.,
Heidbüchel I, Rajagopal S, Switanek M, Volkmann TH, Yaeger M (2008)
Dealing with landscape heterogeneity in watershed hydrology: A review of
recent progress toward new hydrological theory. Geography Compass 2:
10.1111/j.1749-8198.2008.00186.x
Probability Analysis for Estimation of Annual One
Day Maximum Ainfall of Devgarhbaria Station of
Panam Catchment Area
Kapil Shah 1
T.M.V. Suryanarayana 2
PG Student, Water Resources Engineering and Management
Institute, Faculty of Technology and Engineering, The M.S.
University of Baroda, Samiala-391410, Vadodara, Gujarat,
India
2
Associate Professor, Water Resources Engineering and
Management Institute, Faculty of Technology and Engineering,
The M.S. University of Baroda, Samiala-391410, Vadodara,
Gujarat, India
1
ABSTRACT: Daily rainfall data of 30 years (1961-1990) were
analyzed to determine the annual one day maximum rainfall of
devgarhbaria situated near panam dam, Gujarat, India. The
study area receives mean annual rainfall 903.13 mm which is
distributed in 45 rainy days. The observed values were
estimated by Weibull's plotting position and expected values
were estimated by four well known probability distribution
functions viz., normal, log-normal, log-Pearson type-III and
Gumbel. The expected values were compared with the observed
values and goodness of fit was determined by chi-square test.
The results showed that the log-Pearson type-III distribution
was the best fit probability distribution to forecast annual one
day maximum rainfall for different return periods. Based on
the best fit probability distribution, the minimum rainfall of
42.69 mm in a day can be expected to occur with 99 per cent
probability and one year return period and maximum Of
481.32 mm rainfall can be received with one per cent
probability and 100 year return period.
Keywords: recurrence interval, frequency, AODMR,
probability distribution
1. INTRODUCTION
A good understanding of the pattern and distribution of rainfall
is important for water resource management of a country.
Rainfall is one of the most important natural input resources to
crop production and its occurrence and distribution is erratic,
temporal and spatial variations in nature. Most of the
hydrological events occurring as natural phenomena are
observed only once. One of the important problem in hydrology
deals with the interpreting past records of hydrological event in
terms of future probabilities of occurrence. The design and
construction of certain projects, such as dams and urban
drainage systems, the management of water resources, and the
prevention of flood damage require an adequate knowledge of
extreme events of high return periods.
In most cases, the return periods of interest exceed usually the
periods of available records and could not be extracted directly
from the recorded data. Therefore, in current engineering
practice, the estimation of extreme rainfalls is accomplished
based on statistical frequency analysis of maximum precipitation
records where available sample data could be used to calculate
the parameters of a selected frequency distribution. The fitted
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distribution is then used to estimate event magnitudes
corresponding to return periods greater than or less than those of
the recorded events, hence accurate estimation of extreme
rainfall could help to alleviate the damage caused by storms and
can help to achieve more efficient design of hydraulic structures.
The specific objective shall include the following: 1) To analyse
maximum one day rainfall in every year. And 2) To compute
severity of rainfall by various return period.
Daily rainfall data of devgrhbaria raingauge station has been
used for the present investigation. Time series rainfall records
for the period of 30 years (1961 to 1990) have been collected
from State Water Data Centre, Government of Gujarat, and
Gandhinagar. Devgarhbaria is situated in the catchment area of
panam dam in the panchmahal district of Gujarat state at 22 0 41'
N latitude and 730 55‟ E longitude with survey of india(SOI)
toposheeet (1.4 miles), no. 46/F,46/j and 46/E. The mean annual
rainfall was 903.1287 mm. Area receives 85 per cent of annual
of the rainfall during south-west monsoon i.e. from June to
September. The study area is mostly hilly and covered with
forests except near the Panam dam site where it is relatively
flatter. It has the expansion of Soils of the derived from rocks
like quartzites, schists and phyllites. Deep soils cover about 79%
of the culturable and is watered dominantly by Mahi River. The
area experiences three marked seasons – summer (Mar-May),
Monsoon (June-Sep) & winter (Oct-Feb). Project area
experiences tropical climate with minimum temperature of 12°C
in January and maximum temperature of 39°C in May.
Table 1: One day maximum daily rainfall for the period of
1961 to 1990
observed rainfall. The distribution of one day maximum rainfall
received during different months in a year is presented in Fig. 1.
Fig. 1: AODMR in different months
Annual one day maximum rainfall was sorted out from the data
collected and using statistical techniques for data analysis. The
statistical behavior of any hydrological series can be described
on the basis of certain parameters. The statistical tests were
carried out in accordance with the procedure. The computation
of statistical parameters includes mean, standard deviation;
coefficient of variation and coefficient of skewness were taken
as measures of variability of hydrological series. All the
parameters have been used to describe the variability of rainfall
in the present study.
2.1 Return period
Return period or recurrence interval is the average interval of
time within which any extreme event of given magnitude will be
equaled or exceeded at least once. Return period was calculated
by Weibull's plotting position formula (Chow, 1964) by
arranging one day maximum daily rainfall in descending order
giving their respective rank as:
T=
The daily rainfall data are sorted out and filtered to compute
annual one day maximum. The maximum (189.1 mm) and
minimum (54.8 mm) annual one day maximum
rainfall(AODMR) was recorded during the year 20 th Sep 1962
and 26th August 1981, respectively. The mean value of AODMR
was found to be 134.77 mm with coefficient of variation as
0.5281. The coefficient of skewness was observed to be 1.3464.
August month received the highest amount of one day maximum
rainfall (53%) followed by September (17%) and July (13%). it
can be observed that the estimated annual AODMR for different
probability distributions are following the same trend of
(1)
Where, N - the total number of years of record and R- the rank
of observed rainfall values arranged in descending order.
Weibull's plotting position formula was used for computation of
observed AODMR amounts at the return periods of 1.01, 1.05,
1.11, 1.25, 2, 4, 5, 10, 20 and 40 years.
2.2 Frequency analysis using frequency factors
Values of Annual one day maximum rainfall can be estimated
statistically through the use of the Chow (1951) general
frequency formula. The formula expresses the frequency of
occurrence of an event in terms of a frequency factor, KT, which
depends upon the distribution of particular event investigated.
Chow (1951) has shown that many frequencies analyses can be
reduced to the form
XT= (1+CVKT)
(2)
Where,
is the mean, CV is the coefficient of variation, is the
frequency magnitude of a factor and XT is the event having a
return period T. KT is the frequency factor which depends upon
the return period T and the assumed frequency distribution. The
expected value of annual maximum daily rainfall for the same
return periods were computed for determining the best
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probability distributions. Calculations of frequency factor of the
four distributions namely normal, log-normal, log-Pearson typeIII and Gumbel are discussed as
2.2.1 Normal distribution
The normal distribution, a two parameter distribution, has been
identified as the most important distribution of continuous
variables applied to symmetrically distributed data. The
probability density function is given by:
(3)
Where, σ is the standard deviation and µ is the mean of the
sample.
2.2.2 Log normal distribution
A random variable x is said to follow a lognormal distribution if
the logarithm (usually natural logarithm) of is normally
distributed. The probability density functions of such a variable
y=ln x:
x
0


(4)
Where, σy is the standard deviation and µy is the mean of y = ln x
2.2.3 Log-Pearson type-III
In log-Pearson type-III distribution, the value of variate 'X'
(rainfall) is transformed to logarithm (base 10). The expected
value of rainfall 'XT' can be obtained by the following formulae
XT = Antilog X
Log X = M + KTS
(5)
where, 'M' is the mean of logarithmic values of observed rainfall
and 'S' is the standard deviation of these values. Frequency
factor KT is taken from Benson (1968) corresponding to
coefficient of skewness (Cs) of transformed variate as
(6)
2.2.4Gumbel distribution
In Gumbel distribution, the expected rainfall 'XT ' is computed by
the formula given by Chow in equation (2) KT - frequency factor
which is calculated by the formula given by Gumbel (1958) as
KT= -
(7)
2.3 Testing the goodness of fit
The expected values of maximum rainfall were calculated by
four well known probability distributions, viz., normal, lognormal, log-Pearson type-III and Gumbel distribution at
different selected probabilities i.e. 99, 95, 90, 80, 50, 25, 20, 10,
5, 2.5, 2, 1 and 0.5 per cent levels. Among these four
distributions, the best fit distributions decided by chi-square test
for goodness of fit to observed values. The chi-square test
statistic is given by the equation
χ2 =
square value (Agrawal et al. 1995). If
>
2 ñ for (N - k 1) degrees of freedom. Then the difference between observed
and expected values is considered to be significant.
2.4 Regression model
Regression models were developed for estimating the AODMR
to return periods in the present study and found the coefficient of
determination (R2).
3. RESULTS AND ANALYSIS
The average, standard deviation, coefficient of variation and
skewness of Annual One Day Maximum Rainfall for 30 years
and their respective formulas are given in Table 2. These
statistical parameters can be used to find the estimated one day
maximum rainfall from different probability distribution
functions. The variation of standard deviation over the mean is
shown in Fig. 2. It was also observed that 10 years (33.3%)
received one day maximum daily rainfall above the average.
Table 2:Computation of statistical parameters of
annual one day maximum rainfall
Statistical
Parameter
Mean
Median
Mode
x = Σx / n
x=L0+
Computed
Value
134.77
106.95
152.4
Standard
deviation
71.179
Coefficient
of variance
0.5281
Coefficient
of skewness
1.3464
(8)
Where, Oi is the observed rainfall and Ei is the expected rainfall
and will have chi-square distribution with (N - k -1) degree of
freedom (d.f.). The best probability distribution function was
determined by comparing Chi square values obtained from each
distribution and selecting the function that gives smallest chi-
Formula
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Fig 2:- Standard Deviation Variation over the mean
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The AODMR for the period of 30 years was plotted against
return period in years which was calculated from Weibull's
method and presented in Fig. 3. Observed rainfall were found for
various return periods of 1.01, 1.05, 1.11, 1.25, 2, 4, 5, 10, 20
and 40 year and for different probability distributions such as
normal, log-normal, log-Pearson type-III and Gumbel were
calculated and presented in Table 3. It is generally recommended
that 2 to 100 years is sufficient return period for soil and water
conservation measures, construction of dams, irrigation and
drainage works (Bhakar et al., 2006).
It was observed that all the three probability distribution
functions fitted significantly i.e. null hypothesis accepted except
normal distribution. Log-Pearson type-III distribution was found
to be the best fitted to AODMR data by Chi-square test for
goodness of fit. A maximum of 116.84 mm rainfall is expected
to occur at every 2 years and 50 per cent probability which is
nearer to the mean AODMR. For a return period of 5,10,20,50
and 100 years the AODMR, annual one day maximum rainfall is
178.98 mm, 226.77 mm, 277.73 mm, 351.68 mm and 413.58
mm which including other return periods are shown in Table 3.
value determined the best probability distribution function. The
chi-square values (Table 4) for normal, log-normal, log-Pearson
type-III and Gumbel distributions were 2.38,-0.04, 0.20 and
2.25, respectively. Log-Pearson type-III distribution gave the
lowest calculated chi-square value that is selected among the
four probability distributions. Hence, log- Pearson type-III has
been found the best probability distribution for predicting
AODMR for Devagarhbaria station of panam catchment area.
Table 4:
Chi-square values at different probability levels for
different distributions
Table 3:
Observed and expected one day maximum
rainfall at different probability levels
The expected AODMR for different probabilities are graphically
represented in Fig. 4.Regression models were developed from
the observed AODMR against different return period by using
Weibull's method. The trend analysis (Fig. 4.) for prediction of
one day maximum rainfall for different return period was carried
out and it is found that the exponential trend line gives better
coefficient of determination (R2) = 0.9342 and the equation is: Y
= 79.95X0.428 where Y is AODMR in mm and X is Return
period in Years.
Fig. 3:Predicted AODMR with different probability
distribution vs return period
From the figure, it can be observed that the estimated annual
AODMR for different probability distributions are following the
same trend of observed rainfall. All four probability distribution
functions were compared by chi-square test of goodness of fit
and then selecting the function that gave the smallest chi-square
Fig.4:- Annual One Day Maximum Rainfall with various return
period
4. CONCLUSIONS
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The mean value of AODMR was found to be 134.77 mm with
standard deviation and coefficient of variation of 71.179 and
0.5281, respectively. The coefficient of skewness was observed
to be 1.3464. The frequency analysis of AODMR for identifying
the best fit probability distribution was studied for four
probability distributions such as normal, log-normal, logPearson type-III and Gumbel by using Chi-square goodness of
fit test. It was observed that all the three probability distribution
functions fitted significantly i.e. null hypothesis accepted except
normal distribution. Log-Pearson type-III distribution was found
to be the best fitted to AODMR data by Chi-square test for
goodness of fit. Based on the best fit probability distribution, the
minimum rainfall of 42.69 mm in a day can be expected to occur
with 99 per cent probability & one year return period and
maximum of 413.58 mm rainfall can be received with one per
cent probability & 100 year return period. This study gives an
idea about the prediction of Annual One Day Maximum Rainfall
to design the small and medium hydraulic and soil and water
conservation structures, irrigation, drainage works, vegetative
waterways and field diversions. This study also helps in
developing cropping plan and estimating design flow rate for
maximizing crop production.
5. REFERENCES:
i.
Adegboye, O.S and Ipinyomi, R.A (1995) ―Statistical tables for class
work and Examination.‖ Tertiary publications Nigeria Limited, Ilorin, Nigeria, pp.
5 – 11 1765 – 1776.
ii.
Agarwal, M. C., Katiyar, K.S. and Ram Babu (1988). ―Probability
analysis of annual maximum daily rainfall of U. P., Himalaya.‖ Indian Journal of
Soil Conservation, 16(1): 35-42.
iii.
Barkotulla, M. A. B., Rahman, M. S. and Rahman, M. M. (2009).
―Characterization and frequency analysis of consecutive days maximum rainfall.‖ at
Boalia, Rajshahi and Bangladesh. Journal of Development and Agricultural
Economics, 1: 121-126.
iv.
Benson, M. A. (1968). ―Uniform flood frequency estimating methods
for federal agencies.‖ Water Resources Research, 4(5): 891-908.
v.
Bhakar, S. R., Bansal, A. N., Chhajed, N. and Purohit, R. C. (2006).
―Frequency analysis of consecutive days maximum rainfall at Banswara, Rajasthan,
India.‖ ARPN Journal of Engineering and Applied Sciences, 1(3) : 64-67.
vi.
Bhim Singh, Deepak Rajpurohit, Amol Vasishth and Jitendra Singh
(2012). “Probability analysis for estimation of annual one day maximum rainfall of
jhalarapatan area of rajasthan,india.‖ Plant Archives Vol. 12 No. 2, 2012 pp. 10931100
vii.
Chowdhury, J.U. and Stedinger, J.R. (1991) ―Goodness of fit tests for
regional generalized extreme value flood distributions.‖ Water Resource. Res., 27(7)
:
viii.
Chow, V. T. (1951). ―A general formula for hydrologic frequency
analysis.‖ Transactions American Geophysical Union, 32: 231237.
ix.
Chow, V. T. (1964). ―Hand book of applied hydrology.‖ McGrawHill Book Company, New York.
x.
―Introduction To Probability and Statistics In Hydrology‖ a Book By
Dr. Miguel A. Medina
xi.
Murray, R.S and Larry, J.S (2000) ―Theory and problems of
statistics‖ Tata Mc Graw – Hill Publishing Company Limited, New Delhi, pp. 314 –
316, Third edition.
xii.
Olofintoye, O.O, Sule, B.F and Salami, A.W (2009). ―Best–fit
Probability distribution model for peak daily rainfall of selected Cities in Nigeria.‖
New York Science Journal, 2009, 2(3), ISSN 1554-0200
xiii.
Salami, A.W (2004). Prediction of the annual flow regime along Asa
River using probability distribution models. AMSE periodical, Lyon, France.
Modeling
C2004,
65
(2),
41-56.
(http://www.amsemodeling.org/content_amse2004.htm) New York Science Journal, 2009, 2(3), ISSN
1554-0200 http://www.sciencepub.net/newyork, [email protected]
xiv.
Singh, R. K. (2001). ―Probability analysis for prediction of annual
maximum rainfall of Eastern Himalaya (Sikkim mid hills).‖ Indian Journal of Soil
Conservation, 29: 263-265.
Experimental and three Dimensional Numerical
Studies for A Sluice Spillway
Kulhare, A.1
Bhajantri, M.R.2
Research Officer, Central Water & Power Research Station,
Pune - 411024, INDIA
2
Dr., Chief Research Officer, Central Water & Power Research
Station, Pune - 411024, INDIA
1
ABSTRACT:Hydraulic modelling of spillways can be done
through physical modelling or computer based numerical
modelling. Experimental investigation through physical model
studies is widely adopted common practice to optimize the
design of spillway components. The advent of high-speed and
large-memory computers has enabled to obtain numerical
solutions to many complicated hydraulic problems of spillways.
Numerical simulation has become a viable complementary tool
for physical modelling of spillways. In the present work,
hydraulic model tests were carried out on a 1:45 scale 2-D
sectional model. In numerical studies, a Computational Fluid
Dynamics (CFD) software 'FLUENT' was used which runs on
a Finite Volume method for simulation. The results of the
numerical model in respect of discharging capacity, pressures
at different locations over spillway profile and sluice roof
profile were compared with the physical model results. The
numerical results obtained by simulating the system as two
phase problem showed close agreement with the results
obtained from physical model studies.
Keywords: Computational Fluid Dynamics; FLUENT; VOF;
Sluice spillway; Ski-jump bucket; Discharge capacity
1. INTRODUCTION
Innovative designs of spillways have been evolved based on the
concept of flushing. The design of spillway is required to
perform the dual function of flushing of the reservoir as well as
passing of the flood discharge. Low level Breastwall/Sluice
spillways (also called Orifice spillway) combine the advantage
of greater depth of flow over the crest and moderately sized
gates. Orifice spillways have been widely recognized as the most
appropriate, especially for run-of-the-river projects for handling
both flood releases and flushing of sediment. Orifice spillway is
an effective hydraulic structure for keeping the reservoir clean
from the sediments along with the advantage of reduced gate
height and reduced overflow crest length. Though the provision
of breast wall or sluice has many advantages, there is no specific
design method for its configuration.
Spillway designs have been investigated through physical as
well as numerical modelling. The drawback of physical model
studies of spillways are the cost of construction, delay in time
for fabrication and construction of model parts and conducting
experiments and difficulty in changing structural details of
various components of spillway while doing parametric studies.
Numerical simulation has become a feasible complementary tool
to physical modelling of spillways. The data obtained from the
physical model studies can be used for model calibration and
validation of the numerical models. To simulate the actual flow
by providing an alternative cost-effective means of fluid
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dynamics, CFD complements experimental and theoretical
analysis. However, the utility of a numerical model depends on
the validity of the governing equations and numerical methods.
CFD design tool as a more reliable and in order to become
acceptable, numerical studies should be carefully validated with
experimental results. Hydraulic design of spillways with CFD is
a new application, it requires especially careful verification.
Many researchers have conducted numerical modelling
experiments on different types of spillways. But most of the
investigations have been done for the Ogee crested overflow
spillway. Savage and Johnson, Bijan Dargahi, Unami et al. and
Ho et al. have done some recent work in field of overflow
spillway and they found reasonable agreement with experimental
data. Hu Cheng Yi et al. have studied the configuration of the
spillway with breastwall and based on physical and numerical
modelling they suggested some design configuration, which has
a greater discharging capacity, less negative pressures on
profiles and having a simpler configuration of profiles.
The main concern of the present work is to investigate the flow
phenomena over the sluice spillway and to compare the results
with 3D numerical flow simulation. A commercial CFD code
known as FLUENT was used for the present study. With the
help of a numerical model, an attempt is made to investigate
hydraulic characteristics by simulating the discharge, pressure
distribution and water surface profile over the spillway.
2. MODEL INVESTIGATION
Experiments were conducted on 1:45 scale 2-D sectional model
to optimize roof profile as well as spillway bottom profile of the
sluice spillway. In the model one full span and two half spans
on either side were fabricated in transparent Perspex sheet of 12
mm thick. The fabricated spillway was installed in a one metre
wide flume. The discharge was measured by means of a
calibrated Rehbock weir. The accepted equations for similitude,
based on Froudian criteria were used to express the
mathematical relationship between the dimensions and hydraulic
parameters of the model and the prototype. Discharging
capacity, pressures distribution over the roof profile of sluice
and spillway profile were measured for the head of 26 m over
the crest of the spillway. The measurements were taken along
the centre line section of the spillway span as well as along the
side of the pier. The pressures were measured at 21 different
locations over the spillway profile and at 12 locations over the
roof profile of sluice. Pressures on the spillway were measured
using a piezometer board with plastic tubes vented to the
atmosphere. The piezometer board was leveled with respect to
the spillway elevations. The piezometer board readings provided
the average pressure readings at each pressure tab location.
Measurements on the piezometer board were readable to within
0.045 m. Detailed measurements of water surface profiles
normal to the flow were made in the centre line of the spillway
span. A pointer gauge was used to measure the free water
surface profile over the spillway structure. Figure 1 and 2 show
the section the spillway and model view of the spillway
respectively.
Figure 1. Section of spillway
Figure 2. Model view
3. NUMERICAL MODEL SET-UP
Flow over the sluice spillway was simulated with CFD software
FLUENT. FLUENT is a commercial computer program for
modelling fluid flow and heat transfer in complex geometries.
FLUENT provides complete mesh flexibility, including the
ability to solve flow problems using unstructured meshes that
can be generated about complex geometries with relative ease. It
solves the full three dimensional equations of fluid motion in
general orthogonal curvilinear coordinates for both laminar and
turbulent flows.
3.1 Computational DomainThe geometry of the spillway is
prepared with prototype dimensions by using ”GAMBIT”
software. For building the domain for upstream of the spillway
dam axis, reservoir length of 100 m chainage was taken for inlet
of flow and in the downstream side, domain was extended upto
240 m chainage with pressure outlet. The domain height is
chosen around 32.5 m above the crest at spillway surface so that
the water level can be attained in tank as well as interface with
air can be captured properly. The domain sufficiently extended
in the downstream region around distance of 180 m from the end
of spillway structure. The objective of the extension of the
domain in downstream is to capture the water behaviour after
leaving the spillway and where the water hits the bottom surface
of the domain. Figure 3 shows the Section of the domain.
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Figure 3. Section of the domain
3.2 Grid Generation
Three dimensional grid was developed in Gambit software itself.
The 3-D mesh generation consists of the geometry generation
and 3-D grid development over the spillway geometry, water
tank upstream for reservoir and downstream region. The
objective behind this grid generation is to provide the mesh to
simulate flow through two spans, mixing of flows coming out of
two spans and the flow over spillways. The full domain is
decomposed into the smaller volumes, so that they can be
meshed by structured mesh. The cells have been clustered near
the sluice roof profile and spillway surface to capture wall
bounded effects and predict the wall pressures in the flow
simulation. The grid is made finer in those regions where the
water and air have interface. Minimum height of the grid cell is
0.1 m and maximum height of the grid cell is 0.9 m in the
domain. The hexahedral cells are used for grid generation with
the cell count 885261. The surface grid is shown in Figure 4.
Figure 4. Meshing of the domain
3.3 Boundary Conditions
Air was defined as a primary phase and water as a secondary
phase. For the calculation of air water interface i.e. free water
surface, volume of fluid (VOF) model was used. For simulation
of spillway flow, two inlets were needed to define the water
inflow to domain and air inflow over the top of domain. Water
inflow was defined as a pressure inlet with the initial water level
and initial velocity at the inlet face. Also the air inflow over the
domain upstream and downstream of the spillway was defined as
pressure inlet boundary condition. The water outflow at the end
of domain was defined as a pressure outflow boundary
condition. All the solid boundaries including side walls, Sluice
walls, piers and spillway bottom were defined as wall
boundaries with no slip condition. Figure 5 shows boundary
conditions of the numerical domain.
Figure 5. Boundary conditions of the domain
4. SOLUTION PROCEDURE
The numerical model of sluice spillway was run with unsteady
free surface calculations with pressure based solver, which
enables the pressure-based Navier-Stokes solution algorithm.
The VOF method was used to capture the interface between
water and air and governing equations are solved by the Finite
volume method. For the VOF - method the Body force weighted
scheme is used for pressure interpolation as the gravity force is
high and the modified HRIC scheme is used for the volume
fraction equations in order to improve the sharpness of the
interface between the two phases i.e. water and air. Second order
upwind scheme is used for momentum and pressure equations.
The k- ε turbulence model was used to simulate the threedimensional turbulent flow. Figure 6 shows simulated flow after
run of 27.36 seconds.
They are the critical components of simulations and it is
important that they are specified appropriately. When solving the
Navier-Stokes equation and continuity equation, appropriate
initial conditions and boundary conditions need to be applied.
Setting the appropriate boundary conditions can have a major
impact on whether the numerical model results are reflecting the
actual simulation one is trying to simulate. Poorly defined
boundary conditions can have a significant impact on the
solution. A set of boundary conditions such as pressure inlet,
mass flow inlet, velocity inlet, pressure outlet, outflow, wall
boundaries etc. are available in FLUENT. It is significant that
the boundary conditions accurately represent what is actual
physics occurring, to simulate a given flow close to real.
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Figure 6. Simulated flow over the spillway
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5. SIMULATION RESULTS AND ANALYSIS
There is an unlimited level of details of the results in the
numerical model analysis. Observations and analysis can be
made very minutely for each and every component of model in
respect of fluid properties such as velocity, pressure, and water
surface profiles etc., also the forces on the various locations. In
this numerical study the main concern to obtain discharging
capacity of sluice spillway, pressure distribution over the sluice
profile and spillway profile and free water surface profile for for
26 m head over the crest.
It was found from the physical model studies that the design
maximum discharge of 2983 m3/s could be passed through two
sluices fully open with the reservoir water level (RWL) El. 26 m
above the crest. Also in the numerical model, the discharging
capacity of the spillway was found adequate to the passing
discharge of 3030 m3/s at RWL El. 26 m, which is 1.6% higher
than what we found from physical model studies. Also the
coefficient of discharge is coming around 0.63 which is closer to
0.62 that was obtained by experimental studies. The result shows
the good agreement between physical and numerical values in
respect of discharge values.
Water surface profile have been measured over the spillway
surface on the physical model and compared with the numerical
solution.
Figure 7 shows the plot of both the water
surface profile elevations. It has been observed in the numerical
model study that after the lip of the ski-jump bucket, the water
surface elevations obtained lower than what obtained in the
physical model. Numerical model was solved with prototype
dimensions and in the prototype, the jet after ski-jump bucket
has been thrown out fully into the air downstream of the
spillway structure. There may be more interaction of air and
water because there will be free surface from either side of jet.
This may be the reason of the deviation in elevation values after
the spillway structure.
Figure 7. Water surface profile
Pressure distribution were computed over the sluice bottom
profile for numerical studies and compared with the physical
model results. In both the studies water surface follow the sluice
profile and corresponding pressures having same trend over the
profile. Figure 8 shows the plots for pressure values of
experimental and numerical modelling results over the sluice
surface for comparison. It shows the good agreement between
both the values. Pressure distribution over the sluice profile were
found satisfactory and having a good agreement between
both the studies in most of the locations. Figure 9 shows
pressure contour near the sluice profile obtained from
numerical model. It shows the low pressure zone in
downstream portion of the sluice surface as observed in
physical model.
the
the
the
the
the
Figure 8. Pressures over the sluice profile
Figure 9. Pressures contour near the sluice profile
Figure 10 shows the plots for pressure values of experimental
and numerical modelling results over the spillway surface. The
plot shows good agreement between both the values except some
locations near the entrance. Due to absence of upstream curve
the separation of flow is observed at the crest of the spillway
near entrance of the sluice. Flow at the entrance of spillway in
the numerical model and the physical model are shown in Figure
11 in the form of velocity vectors. It shows the separation of
velocity vectors near the entrance, so that the pressures on the
spillway surface in this zone are reduced compared to other
locations. Whereas in the physical model the velocity
components cannot be minutely observed and also due to the
wide river valley in the model the vertical component of velocity
vectors was dominated by the horizontal components of velocity
vectors. Because of this reason the pressure distribution is not
following the same trend in this region in both the cases of
centre line as well as side of pier.
MANIT Bhopal
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18-19, Dec. 2014
ii.
Cheng, Xiangju, Yongcan, Chen and Lin, Luo. (2006), Numerical
Simulation of Air-Water Two-Phase Flow over Stepped Spillways. Science in
China Series E: Technological Sciences, Volume 49, Number 6, 674-684.
iii.
Dargahi B. (2006), Experimental Study and 3-D Numerical
Simulations for a Free-Overflow Spillway. Jour-nal of Hydraulic Engineering,
ASCE 132-9,899-907.
iv.
Fluent Manual ver. 6.3
v.
Gadge,P.P., Kulhare,A. and BhosekarV.V., Application of
Computational Fluid Dynamics in Hydraulic Structures, National Conference on
Hydraulics and Water Resources, HYDRO -2011, Dec 29-30, at SVNIT, Surat,
Gujarat.
vi.
Hu, C. Y., Wei, Y., and Zheng, Z. P. (1990), Study on Configuration of
Overflow Dams with Breast Wall. 7th Congress APD-IAHR.
vii.
Savage, B. M., and Johnson, M. C. (2001), Flow over Ogee Spillway:
Physical and Numerical Model Case Study. International Journal of Hydraulic
Engineering, ASCE 127-8, 640-649.
viii.
Unami, K., Kawachi, T, Munir, Baber M., Itagaki, H, (1999), Two
Dimensional Numerical Model of Spillway Flow, Journal of Hydrol. Engg.
ASCE 125, 369-375.
ix.
Versteeg, H. K., and Malalasekera, W. (1995), An Introduction to
Computational Fluid Dynamics-The Finite Volume Method. Longmaon Scientic
&Technical, England.
Figure 10. Pressures over the spillway profile
Figure 11. Velocity vectors at the entrance of spillway
4. CONCLUSION
In this paper, a finite volume-based CFD software FLUENT was
used to investigate the hydraulic characteristics of flow through
sluice spillway. The water surface profile, pressure distribution
and discharge characteristics of the chosen spillway were
computed and compared with existing physical model data. The
computed and experimental values of the coefficient of
discharge were 0·63 and 0·62, the computed value being 1.6 %
higher than the experimental value observed on the physical
model. As seen from the figures depicting pressure values and
water surface elevations, it shows the good matching trend and
values in case of breastwall bottom profile for both numerical as
well as experimental studies. The upstream slope was not
guiding the flow over the crest properly, as a result of which a
mild separation zone was seen forming over the horizontal crest
in the numerical model as depicted in the figure of velocity
vectors, which was not predicted by physical model. Except the
entrance the pressure distribution was found good agreement
between the physical and numerical results. Reasonable
agreement is observed with the numerical and physical model
results, showing the applicability of the CFD software for the
numerical simulation of real case study of spillway. Further
refinement in mesh generation and cell count may improve the
results of the simulation of flow through a sluice spillway.
REFERENCES
i.
Bhajantri, M.R., Eldho T.I, Deolalikar P.B. (2006), Hydrodynamic
Modelling of Flow over a Spillway using a Two-Dimensional Finite VolumeBased Numerical Model. Sadhana, Vol.31, part 6, 743-754.
Physical Model Study for Energy Dissipation
Arrangements to the Pick up Weir Across
Pachaiyar River in Tamilnadu
C. Prabakar1 P. K. Suresh2 T. Ravindrababu3 A.
Parthiban4 A. Muralitharan5
1
Assistant Engineer, Institute of Hydraulics & Hydrology
Poondi 602 023, India
2
Research Head, Centre of Excellence for Change, P W D
Campus, Chepauk, Chennai 600 005, India
3
Assistant Director, Institute of Hydraulics & Hydrology Poondi
602 023, India
4
Assistant Director, Institute of Hydraulics & Hydrology Poondi
602 023, India
5
Assistant Engineer, Institute of Hydraulics & Hydrology Poondi
602 023, India
ABSTRACT: The Agricultural development in Tamil Nadu
mainly depends upon the surface irrigation as well as lift
irrigation. But the state has almost utilized its surface water
potential and ground water potentials. Hence, the further
expansion of irrigation and agriculture in Tamilnadu depends
on inter-linking of rivers by utilizing the surplus flood water
which flows into the sea as unused. This scheme is proposed
for interlinking of rivers Tamirabarani, Karumeniyar, and
Nambiyar by connecting surplus water from Tamirabarani
through kanadian channel and a new flood carrier canal for a
length of 73km.
The diversion of surplus water of
Tamirabarani basin to its sub basin of Pachaiyar and
adjoining basin of Nambiyar and Karumeniyar will be a
milestone for linking the south flowing rivers. Under the
Formation of Flood carrier canal with a carrying capacity of
3200 cusecs crosses the river Pachaiyar. At the place of canal
crossing the river Pachaiyar, to utilise the river water of
pachaiyar to divert in the flood carrier canal the Pickup weir is
MANIT Bhopal
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18-19, Dec. 2014
proposed to be constructed across Pachaiyar for a length of
250m to pass the Maximum flood discharge of 31664 cusecs
safely. The physical model study for the energy dissipation
arrangements for the stilling basin of the proposed weir and
the scour vents is studied in this Institute and a 6 numbers of
trials were conducted to evolve efficient Energy dissipation
arrangements for the proposed pick up weir and scour vents.
Various energy dissipation structures were introduced in the
above 6 trials and the optimal performance is ascertained in
the model studies and suggested for the stilling basin is given
in this report in detail.
Keywords: Physical model, Energy dissipation, Friction blocks
1. INTRODUCTION:
The Agricultural development in Tamil Nadu mainly depends
upon the surface irrigation as well as lift irrigation. But the state
has almost utilized its surface water potential. Hence, the further
expansion of irrigation and agriculture in Tamilnadu depends on
inter-linking of rivers and their tributaries by utilizing the
surplus flood water which flows into the sea as unused.
This scheme is proposed for interlinking of rivers Tamirabarani,
Karumeniyar, and Nambiyar by diverting water from
Tamirabarani through the existing Kannadian Channel by
increasing the carrying capacity and excavating a new flood
carrier from LS 6.50km of existing Kannadian Channel through
drought prone areas of Sathankulam, Thisayanvilai in
Tirunelveli and Thoothukudi Districts respectively for a length
of 73km after fulfilling the needs existing Kannadian Channel.
The diversion of surplus flood water from Tamirabarani
basin will be effectively utilized in the farther most gross
command area but also in the adjoining basins of Pachaiyar,
Nambiar and Karumeniyar rivers. The flood carrier canal will
be operated only in the time of flash flood when the surplus flow
of Tamirabarani water through the last anicut namely
Srivaikundam anicut goes into the sea after meeting out the full
demand of Tamirabarani basin. The diversion of surplus water of
Tamirabarani basin to its sub basin of Pachaiyar and adjoining
basin of Nambiyar and Karumeniyar will be a milestone for
inter-linking the south flowing rivers.
The Director, Institute of Water studies, Chennai formulated a
proposal from tail end of Kanadian channel (nearby Melaseval)
by using remote sensing and GIS taking into consideration of the
existing Kanadian channel alignment. It is proposed to excavate
a flood carrier canal from Kannadian Channel at LS 6.50 km to
ML theru for a length of 73 km. The carrying capacity of the
flood carrier at LS 0m is 3200 cusecs (90.61 Cumecs).
On its length of run the new flood carrier crosses the river
Pachaiyar and Karumeniyar. At the place of crossing the river
Pachaiyar, a Pickup weir was proposed to be constructed at LS
20599 to LS 20690m of flood carrier canal. The design and
drawing was prepared by the Superintending Engineer, Designs
Circle. Chennai-05, it was suggested in the design that the
energy dissipation arrangements proposed for the weir and scour
vents are only tentative and should be finalized based on the
rock level available at the downstream side during execution and
conducting model studies at Institute of Hydraulics and
Hydrology, Poondi.
1.1
LOCATION
The River Thamirabarani originates from eastern slope of
Western Ghats and traverse to a length of 120 kms and it is More
Or Less Perennial River. There are 12 numbers of tributaries
confluences with this river on its length of traverse. The
following reservoirs were constructed across Thamirabarani
River and its tributaries.
1. Papanasam Reservoir
2. Manimuthar Reservoir
3. Servalaru Reservoir
4. Gadana Reservir
5. Ramanadhi Reservoir
6. Gundar Reservoir
7. Karuppanathi Reservoir
8. Adavinainarkoil Reservoir
9. Vadakku Pachaiyar Reservoir
The following anicuts were constructed across
Thamirabarani River and its tributaries.
1. Kodaimelazhagian Anicut
2. Nathiyunni Anicut
3. Kanndain Anicut
4. Ariyanayagipuram Anicut
5. Suthamalli Anicut
6. Pazavoor Anicut
7. Maruthur anicut
8. Srivaikundam Anicut.
The pickup weir and scour vents were proposed to construct
across the River Pachaiyar located at LS 20599 to LS 20690m of
the proposed flood carrier canal. The Chief Engineer, PWD,
WRO, Design Research & Construction Support has given the
approved drawing for the proposed pick up weir and scour vents.
2.0
OBJECTIVE OF STUDY
1. To evolve efficient Energy dissipation arrangements for
the proposed pick up weir and scour vents to pass the
Maximum flood discharge of 31664 cusecs safely.
2. To evolve good flow performance on stilling basin and
surplus course.
3.0
HYDRAULIC PARTICULARS
1
Maximum Flood Discharge
31664 Cusecs
2
Front maximum flood level
+ 60.175m
3
Rear maximum flood level
+53.45m
4
Crest level
58.745m
5
Length of the structure
240m
Pick up weir
1
Discharge through Weir
29773 cusecs
2
Length of Weir
226.80m
3
Crest level
58.745m
4
Downstream bed level
+52.00m
5
Stilling basin length
18.80m
6
Stilling basin level
50.60m
7
Rock level/Foundation level
+48.80m
Scour vents
1
Discharge through Scour vents
MANIT Bhopal
1901 cusecs
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Issue Special3
2
3
4
5
6
7
8
9
Sill level
Number of vents
Size of vents
Basin level at Left side scour
vents
Basin level at Right side scour
vents
Top of operating platform
Foundation level @ Right side
Foundation level @ Left side
18-19, Dec. 2014
+53.00m
4 nos
2.1m x 0.9m
+52.00
49.50m
+61.175m
+49.00m
+50.50m
4.0
MODEL SET UP
A comprehensive rigid bed, geometrically similar physical
model with a scale of 1:50 is selected and the model discharge is
calculated. Model discharge of the river was allowed through 'V'
notch. Necessary gauge well have been constructed for
measuring the water levels for the required maximum flood
discharge.
4.1 Rigid Bed Model
The model was constructed with the hydraulic components as
per the design drawings and the downstream bed levels given by
the Field officials.
Right scour vents
Spillway
Stilling basin
Left scour vents
Figure- 1. Dry Model of Pachaiyar Spillway
5. MODEL RUN
Maximum flood discharge of 31664 Cusecs for Pachaiyar River
is taken and the model discharge was computed and allowed
through "V" notch to arrive the energy dissipation arrangements
in the stilling basin.
Trial I
After incorporating the SE/Designs proposal and downstream
bed levels furnished by the field officials with embankments on
both sides of the river width, the model was run with the
maximum flood discharge.
Observation
The hydraulic jump was found satisfactory, the flow
concentrates on the central portion of the river since the banks of
the river has high bed level ranging from +54.00 to +55.00m.
The velocity ranges to 4.0 to 4.5m/sec. Cross flow was observed
in the Left and Right scour vents since the bed level in the
downstream of scour vents is in higher level, when comparing to
the stilling basin. The flow of the left side scour vent tends to
move towards the stilling basin of the weir. This trial needs
alterations.
Trial II
In this trial the downstream of the river bed below the stilling
basin is regarded to a bed slope of 1 in 400 up to a length of
500m along the river, keeping the SE/Designs proposal of
hydraulic components.
Observation
The hydraulic jump was found satisfactory; the flow spreads
uniform to the entire width of the river. The velocity in the
range was of 3.5 to4.0 m/sec on the downstream. The left side
scour vent baffle wall top has a level of +54.00m and after
regarding the downstream bed level of the river course from the
stilling basin of the river course is +52.00m to a slope of 1 in
400. The water from the left side scour vents experience a fall of
2m and water plunges in the downstream with an impact. With
this condition the river bed will experience a heavy scour, which
can damage the hydraulic structures. This trial needs additional
alterations and requires suggestions from the Design wing.
Trial III
The site officials informed that the downstream portion consists
of hard rock so that water can be allowed in the downstream bed
of the left side scour vents. To assess the site condition the site
was inspected and it is found that left side scour vents portion
has hard rock up to a level of +53.00m; hence it was suggested
that the Stilling basin of Left side scour vent can be kept as
+52.80m and baffle wall top as +54.00m. The fall of 2m in left
scour vent portion to the river portion can be provided with
necessary water cushion arrangements by extending the wing
wall and divide wall of the scour vent which can be fixed after
conducting the model trial. The design shall be got revised from
the designs wing.
The model trial was done with the suggestions made by the
officials as above with a ramp to negotiate the fall and the
Observed velocity on the left scour vents is 4.5 m/sec. Hence
this trial needs further alterations.
Trial IV
The Designs wing has revised the drawing and based on the
details, the trial was done and the velocity is in the range of 1.5
to 2.0 m/sec in the weir portion and 7.3m/sec in the left side
scour vent portion and 3.7m/sec in the right side scour vent
portion.
In order to reduce the velocity further, trial was
conducted by introducing friction blocks of size 4mx1mx1m in
the entire river width on the downstream side of the baffle wall
with two rows in zig zag arrangement, the velocity is in the
range of 1.0 to 2.0m/sec at the weir portion and 6.0 m/sec at the
left side scour sluice. This trial needs alteration.
Trial V
In this trial the following alteration were done as follows
1. Downstream of the river is provided with a reverse
slope arrangements keeping the level as +52.00m at LS 30M
and +53.00m at LS 60m and continuing the level of +53.00m up
to a distance of LS 170m on the river course.
2. Reducing the stilling basin level of Left side scour
vent portion to +51.50m and baffle wall top as +.52.00m.
The trial was conducted and the velocity ranges from 1.98 to
2.90m/sec, hydraulic jump in the stilling basin is satisfactory but
during initial period the cross flow of water is observed from the
MANIT Bhopal
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18-19, Dec. 2014
left and right side scour vents and concentrate on the weir
portion.
Trial VI
The Designs wing visited the site of the Pachaiyar and had
discussion with the field engineer about the re grading of the
river. They have finalized that the river can be re graded to the
entire width of the river as only weathered rock and soft
disintegrated rock are available in the river course portion. The
Designs wing has request to conduct the model study for the
regraded section of the river for the entire width as suggested in
the approved design and to maintain the computed Rear Water
Level(RWL) +53.45M @50m downstream of the weir
alignment.
The running model trial was inspected by
Designs wing and the model was run with the maximum flood
discharge.
Observation
The hydraulic jump formed in the stilling basin is found
satisfactory and velocity on the downstream of the weir portion
is in the range of 1.0 to 2.0 m/sec is within the permissible
range. But the downstream of left side scour vent portion
measured a velocity of 4.0 m/sec. To reduce the velocity further
in the left side scour vent, friction blocks of size 1.0x1.0x1.0m is
introduced 3 rows with 3 numbers in the first and second rows
and 2 nos in the second row. Thus making zig zag arrangements
in the left side scour vent portion. By introducing the baffle
blocks the velocity got reduced to 3.28 m/sec. Thus this trial is
giving satisfactory performance in the stilling basin and also
velocity is got reduced to the permissible range. The Velocity
observed in the model trial is furnished below. This can be taken
as the final trial and the following recommendation has been
given to incorporate in the construction of the weir at the site.
Table No.1 Statement showing the observed velocity in Trial
No. VI
SL NO
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
LS
Chainage
from the
axis of
spillway in
"m"
40
50
75
100
125
150
175
200
225
250
275
300
325
350
375
400
425
450
475
500
Observed Velocity in “m/sec”
LEFT
CENTRE
RIGHT
3.28
2.21
1.38
1.71
1.98
0.99
1.40
0.99
0.99
0.99
0.99
1.40
0.99
0.99
0.99
1.40
1.40
0.99
1.40
1.40
2.21
1.98
1.71
1.40
1.40
1.40
1.40
1.40
1.40
1.40
1.40
1.40
1.40
0.90
1.40
0.99
0.99
0.99
0.99
0.99
2.80
1.98
1.71
1.71
1.40
0.50
0.50
0.50
1.40
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
1.40
0.99
Figure 2. & 3. Running Model of Pachaiyar River
Figure 4. View of Left side scour vents
Hydraulic Jump at stilling basin
Figure 5.
CONCLUSION
The spillway and scour vents design approved by the Designs
wing is functioning satisfactorily for the given maximum flood
discharge and the following alteration is to be made in the
surplus course and the stilling basin portion.
1. Introducing friction blocks on the downstream left side scour
vent portion with three rows of friction block of size
1.0x1.0x1.0m with 3 numbers in the first and third rows and 2
nos in the second row as shown in the sketch.
2. Raising the Right downstream divide wall between stilling
basin weir portion and the Right side scour vent portion to a
level of +54.00m.
ACKNOWLEDGEMENT
The authors acknowledge the services of Designs Wing, PWD,
WRO, Chennai and the field engineers for collection of field
data and suggestions during the course of model studies.
REFERENCES
i. Allen. J Scale Models in Hydraulic Engineering
ii. Chow, V.T. (1959), Open Channel Hydraulics, McGraw-Hill,
New York, NY
iii. Elevators Key, Hydraulic Energy Dissipation.
MANIT Bhopal
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Experimental Investigations For Estimation of the
Height of Training Wall of Convergent Stepped
Spillway
P. J. Wadhai 1 N. V. Deshpande 2
A. D. Ghare 31 Associate
Professor, Department of Civil Engineering, G. H. Raisoni College of
Engineering, C.R.P.F. Gate No. 3, Hingna Road, Digdoh Hills, Nagpur
- 440 016, Maharashtra, India.,
Principal, Guru Nanak Institute of Engineering & Technology,
Kalmeshwar Road, Dahegaon,
Nagpur - 441 501,
Maharashtra, India,
3
Associate Professor, Department of Civil Engineering, Visvesvaraya
National Institute of Technology,
Nagpur - 440 010,
Maharashtra, India, (Corresponding Author),
2
ABSTRACT: Amongst hydraulic engineers worldwide, there is
enough interest generated for the construction of stepped chutes. Ease
of construction and enhanced energy dissipation of flow over the
control structure itself, are the primary reasons for its growing
popularity. There are a good number of literature references available
for the design of stepped spillways with straight side walls, but a very
limited literature is available on the design of stepped spillways with
convergent training walls. This paper presents the experimental
findings carried out on a 45o stepped spillway set up having 1:1
convergent training walls. The step height variation is accounted for,
in the proposed expressions which can be used for assessment of the
flow bulking and the requirements of the height of training walls, in
convergent stepped spillways.
Keywords : Stepped spillways, convergence angle, step height
ratio
1.
INTRODUCTION :
A stepped spillway has conventional ogee spillway profile.
However, it is provided with steps from just below the crest up
to the toe of the spillway. The provision of steps on the
downstream face of the spillway chute increases the rate of
energy dissipation and in turn, reduces the size of energy
dissipater downstream. A typical cross section of stepped
spillway is indicated in Figure 1. Thus a stepped chute not only
significantly increases the dissipation energy rate but also
decreases the construction costs of the downstream stilling basin.
h1 = Flow depth measured vertically above the extreme corner of each step along the training wall
Y = Minimum depth of flow immediate after the toe
Hd = Head
over crest of
the spillway
D = Point of tangency
E = Toe of spillway
C = Crest
h = Normal size step height
D
Down
stream
side
Up
stream
side
H’ = Drop height
h
H
h1
Crest
axis
θ
E
Y
Cavitation risk resulting from excessive sub-pressures decreases
due to lower flow velocities and occurrence of high amount of
air entrainment. But, this aeration produces flow bulking and
therefore the spillway requires higher side walls. The effect of
convergence enhances this effect due to shock waves and taller
training walls are required. In the present study, it is proposed to
experimentally determine the effect of converging training walls
on flow characteristics of stepped spillway. Literature survey
reveals that a limited literature is available on stepped spillways
with convergent training walls as compared to stepped spillways
having straight training walls. In due course of time, many of the
stepped spillways are expected to be made with convergent
training walls because of the geological or topographical
constraints or due to limited scope for right-of-way caused by
urbanization.
Sorensen (1985), Peyras et al. (1992), Christodoulou (1993),
Chanson (1994), Chamani and Rajaratnam (1999), Barani et al.
(2005), Chanson (2006), Chinnarasri and Wongwises (2006),
and others focused on study of stepped spillways. Hunt et al.
(2008) conducted a study utilizing a three-dimensional, 1:22
scale physical model to evaluate the flow characteristics over a
sloping stepped chute ( 3H: 1V) with varying training wall
convergence angles. It was found that the required training wall
height varied from critical depth for 15o convergence angle to
thrice the critical depth at 52o convergence angle. As a follow up
work, a major reference on training wall height requirements of
convergent stepped spillway was presented by Hunt et al.
(2012), wherein a simplified expression was developed to
predict the vertical height of training wall as a function of
centerline depth of flow. This expression was developed on the
basis of simplified control volume momentum analysis and
hence can be supposed to be a generalized one. However, more
testing of this expression was warranted, due to requirement of
an empirical adjustment associated with the force term during
the derivation of the proposed expression. In background of this,
it was felt necessary to conduct the experiments to develop an
expression for estimation of height of training wall for 45o
convergent stepped spillways.
2. EXPERIMENTAL FACILITY :
Experimental setup consists of a convergent stepped spillway of
ogee type 2.66 m long crested weir with stepped chute of (θ =
45° i.e. 1:1) and a toe channel of 0.5 m wide and 10 m long. The
side wall of the stepped spillway converges from point of
tangency to down the chute with a convergence angle Ø = 45 o .
A storage reservoir having 9 square meter plan area and1.75 m
depth constructed on the upstream side of the convergent
stepped spillway crest. Stepped spillway experimental set up
followed by arrangement of water recirculation system
consisting of a pump of capacity 10 HP connected with G.I.
suction and delivery pipe of 150 mm diameter. The pump fetch
the water from underground sump which in turn discharged in to
an upstream reservoir through delivery pipe provided with an
arrangement for venturimeter with U-tube manometer for flow
rate measurement.
Figure 1. Indicative cross section of stepped spillway
MANIT Bhopal
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Experimental testing was done for model step height (h) of 0.12
m, 0.06 m, 0.03 m and 0.015 m which in turn corresponds to
respective step height ratios { H* = (H‟/ h) }of 10, 20, 40 and
80. Water from upstream reservoir flow over the convergent
stepped spillway which further allowed to flows freely through a
toe channel. For volumetric measurement the flow from toe
channel empties in to a collecting tank of plan area 5.31 m2. For
measurement of different values of rate of flow in the range of
0.02 m3/sec to 0.064 m3/sec, head over crest of spillway was
measured at a distance of 0.15 m upstream of the crest. For
measurement of water surface levels along and across the steps
and also for measurement of flow depths at other locations
vernier type point gauges were used with a sensitivity of 0.1
mm.
maximum depth of flow observed along the converging training
walls in dimensionless form and the regime of flow (nappe or
skimming) for the different experimental runs.
Table 1. Experimental observations and computations for θ =
45º, Ø = 45º and H* = 10
Figure 2 shows the photographs of convergent stepped spillway
experimental setup constructed at
G. H. Raisoni College of
Engineering, Nagpur in collaboration with VNIT, Nagpur,
Maharashtra State, India.
45º, Ø = 45º and H* = 20
Dimensionl
ess
discharge,
Discha
rge, Q,
cumec
Discharge
per unit
width at
crest,
q,
cumec/m
Critical
depth of
flow,
Yc,
m
Step
height,
h,
m
0.025
0.00940
0.020735
0.06
71.31
2.1583
Partly
Skimming
0.032
0.01203
0.024444
0.06
60.08
2.3733
0.040
0.01504
0.028365
0.06
51.61
2.5867
Partly
Skimming
Partly
Skimming
0.051
0.01917
0.033352
0.06
44.07
2.8017
Skimming
0.060
0.02255
0.037168
0.06
39.75
3.0167
Skimming
hmax /h
Flow
regime
45º, Ø = 45º and H* = 40
Figure 2. Photographs of convergent stepped spillway
experimental setup during experimental runs
Training walls of convergent stepped spillway and side walls of
toe channel were fabricated with acrylic sheets for visibility of
flow. Prior to begin with the experimentation, calibration of
venturimeter and a triangular was done by the volumetric
measurements using a collecting tank.
3.
EXPERIMENTAL OBSERVATIONS AND
COMPUTATIONS :
All the data sets of observations and computations for the
experimental runs for the different step height ratios (H*) and
also for smooth ogee spillway are presented in Table 1, Table 2,
Table 3, Table 4 and Table 5. These tables also show the
Disc
harg
e, Q,
cum
ec
Disch
arge
per
unit
width
at
crest,
q,
cumec
/m
0.02
4
0.03
5
0.04
2
0.05
2
0.06
3
0.009
02
0.013
16
0.015
79
0.019
55
0.023
68
Critical
depth of
flow,
Yc,
m
Step
height,
h,
m
0.020178
0.03
0.025949
0.03
0.029302
0.03
0.033786
0.03
0.038397
0.03
Dim
ensi
onle
ss
disc
harg
e,
72.7
0
56.6
2
50.2
3
43.4
5
38.1
3
hmax /h
Flow
regime
3.7467
Skimming
3.8533
Skimming
4.0300
Skimming
4.2133
Skimming
4.4933
Skimming
45º, Ø = 45º and H* = 80
Discharge, Q,
Cumec
MANIT Bhopal
Discharge
per unit
width at
crest,
q,
cumec/m
Critical
depth of
flow,
Yc,
m
Dimensionle
ss discharge,
Step
height, h,
m
hmax /h
Flow
regime
Page 24
Issue Special3
18-19, Dec. 2014
0.020735
0.015
70.77
3.9867
Skimming
0.033
0.012406
0.024951
0.015
58.39
4.4467
Skimming
0.041
0.015414
0.028835
0.015
51.33
4.7067
Skimming
0.051
0.019173
0.033352
0.015
44.07
5.1867
Skimming
0.064
0.024060
0.038802
0.015
37.90
5.8000
Skimming
45º, Ø = 45º and smooth ogee chute
Dimensionles
s discharge,
Discharge,
Q,
cumec
Discharge
per unit
width at
crest,
q,
cumec/m
Critical
depth of
flow,
Yc,
m
Step height,
h,
m
0.026
0.00977
0.021284
0
0.035
0.01316
0.025949
0.042
0.01579
0.052
0.063
hmax ,
m
Flow
regime
69.72
0.0526
0
56.28
0.0596
0.029302
0
50.23
0.0624
0.01955
0.033786
0
43.65
0.0664
0.02368
0.03840
0
38.13
0.0726
Skimmi
ng
Skimmi
ng
Skimmi
ng
Skimmi
ng
Skimmi
ng
4. ANALYSIS OF EXPERIMENTAL DATA :
Due to convergence of the chute walls, the required training wall
height is governed by the flow run- up. Visual observations
indicated that there were no transverse waves for any of the step
height ratios and the air entrainment occurred for nearly all the
observations. Experimental data has been collected for plotting
the water surface profiles along the centerline of the spillway
and also along the convergent walls. As anticipated, the flow
depths near the wall were more than those at the centerline of the
spillway. The flow depths along wall shall form the basis for
deciding the minimum training wall height requirement so that
the flow does not overtop the convergent training walls
endangering the safety of the structure. Figure (3) illustrates the
observed water surface profiles along the wall for different
discharge for a step height ratio H* = 40. As the maximum depth
of flow along the wall (hmax) would determine the training wall
height, a dimensionless plot showing its variation with
dimensionless discharge is presented in Figure (4). The
regression equations have been obtained and are as follows.
These expressions are proposed to be used for computation of
training wall height of convergent stepped spillway with
convergence angle of 45o and chute slope of 1:1.
The maximum flow depths along wall depths were compared
with the corresponding critical depths. For H*=80, the maximum
flow depth was found to be between 2.25Yc to 2.9Yc, for H*=
40, the maximum flow depth was found to between 3.5Y c to
5.6Yc, for H*= 20, the maximum flow depth was found to lie
between 4.85Yc to 6.25Yc whereas for H*= 10, the maximum
depth of flow was observed to be in the range of 5.35Y c to
7.6Yc.
1.4000
1.2000
Stepped Spillway
Profile
1.0000
0.8000
H* = 40, Q1 = 0.024
Cumec
0.6000
Elevation, m
0.009398
0.4000
0.2000
0.0000
-0.50
0.00
0.50
1.00
1.50
Station, m
Figure 3 (a). Water surface profiles along the side wall of
spillway for step height ratio, H* = 40, Q1 = 0.024 cumec.
1.4000
1.2000
Stepped Spillway
Profile
1.0000
0.8000
H* = 40, Q2 = 0.035
Cumec
0.6000
Elevation, m
0.025
0.4000
0.2000
0.0000
-0.50
0.00
0.50
1.00
1.50
Station, m
Figure 3 (b). Water surface profiles along the side wall of
(1)
1.4000
1.2000
Stepped Spillway
(2) Profile
1.0000
0.8000
H* = 40, Q3 = 0.042
Cumec
Elevation, m
0.6000
(3)
0.4000
0.2000
0.0000
-0.50
0.00
0.50
1.00
1.50
Station, m
(4)
Figure 3 (c). Water surface profiles along the side wall of
MANIT Bhopal
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Issue Special3
18-19, Dec. 2014
1.4000
1.2000
Stepped Spillway
Profile
1.0000
0.8000
H* = 40, Q4 = 0.052
Cumec
Elevation, m
0.6000
0.4000
0.2000
0.0000
-0.50
0.00
0.50
1.00
1.50
Station, m
Figure 3 (d). Water surface profiles along the side wall of
1.4000
1.2000
Stepped Spillway
Profile
1.0000
0.8000
H* = 40, Q5 = 0.063
Cumec
Elevation, m
0.6000
0.4000
0.2000
0.0000
-0.50
0.00
0.50
1.00
1.50
Station, m
Figure 3 (e). Water surface profiles along the side wall of
1.4000
1.2000
H* = 40, Q1 = 0.024
Cumec
1.0000
H* = 40, Q2 = 0.035
Cumec
0.8000
H* = 40, Q3 = 0.042
Cumec
5. CONCLUSIONS :
Stepped spillways with convergent training walls will have to be
employed when there is limited space available for spillway
rehabilitation work. Only a few guidelines are available in the
literature for design of convergent stepped spillways, a three
dimensional experimental study has been carried out on 45 o
convergent stepped spillway having 1:1 chute slope and different
step heights. The flow over the convergent stepped spillway was
observed to be air entrained and more bulked as compared to
ogee spillway. With increase in dimensionless discharge, the
maximum flow depth at the convergent training wall normalized
by the step height, was found to decrease. Based on the
experimental observations and its analysis, the regression
equations for maximum depth of flow near the converging walls
{ Eq. (1) to (4)} have been proposed. A high value of coefficient
of determination for all the regression equations indicated that
the correlation was good. In general, the maximum flow depth
near the convergent training wall was found to lie between 2.25
to 7.6 times of the critical depth of flow, depending up on the
step height ratio. The regression equations presented in this
paper, may be useful for the hydraulic designers engaged in
estimation for deciding the appropriate training wall height for
convergent stepped spillways. However, more experimental
studies with different convergence angles shall be required, to
formulate more generalized expressions for estimation of
requirement of adequate training wall heights for convergent
stepped spillways.
6. ACKNOWLEDGEMENTS :
The research presented in this paper is based on a research
project funded by Raisoni Group of Institutions, India, which is
gratefully acknowledged.
7. NOTATION :
0.6000
H* = 40, Q4 = 0.052
Cumec
0.4000
Elevation, m
H* = 40, Q5 = 0.063
Cumec
0.2000
0.0000
-0.40
-0.20
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
Station, m
Dimensionless Maximum Depth of Flow along the Side Wall , hmax / h
Figure 3. Water surface profiles along the side wall of spillway
for step height ratio H*= 40 with varying discharge i.e. Q1 =
0.024 cumec, Q2 = 0.035 cumec, Q3 = 0.042 cumec, Q4 =
0.052 cumec, Q5 = 0.063 cumec.
7.0000
H* = 10
H* = 20
6.0000
H* = 40
5.0000
H* = 80
4.0000
y = 9.455x-0.48
R² = 0.978
3.0000
y = 20.56x-0.52
R² = 0.988
2.0000
y = 12.21x-0.28
R² = 0.929
1.0000
y = 48.87x-0.59
R² = 0.992
0.0000
0.00
20.00
40.00
60.00
80.00
100.00
Dimensionless Discharge , Q / (Hd5/2.g1/2)
Figure 4. Dimensionless maximum depth of flow along the side
wall versus dimensionless discharge
The following symbols are used in this paper :
A = L * Hd = Area of flow at crest of spillway;
A1 = B * Y = Area of flow at toe of spillway;
B = Width of flow channel;
C = Discharge coefficient;
Er = ∆E / Eo = Relative energy dissipation;
Eo = H + 1.5 Yc = Energy at crest of spillway;
Et = Y + (V12/ 2.g ) = Energy at toe of spillway;
g = Acceleration due to gravity;
h = Normal size step height;
hmax = Maximum depth of flow observed along the converging
training wall;
h1 = Depth of flow observed along the converging training
wall;
H = Datum head measured from toe up to crest of Spillway;
Hd = Head over crest of spillway;
H' = Drop height;
H* = H' / h = Step height ratio;
L = Length of crest;
Lr = Lp /Lm = Scale ratio;
n = Number of regular size steps;
q = Q / L = Intensity of Discharge;
Q = C * L * (Hd)1.5 = Rate of flow i.e. Discharge over crest
of spillway;
MANIT Bhopal
Page 26
Issue Special3
R
V
V1
Y
Yc
∆E
Ø
θ
8.
18-19, Dec. 2014
= Hydraulic radius;
= Q / A = Velocity of flow at crest of spillway;
= Q / A1= Velocity of flow;
= Depth of flow;
= Critical depth of flow;
= Eo - Et = Energy loss due to stepped spillway;
= Convergence angle;
= Chute angle;
REFERENCES :
i.
Sorensen, R. M. (1985). “Stepped spillway hydraulic
model investigation.” J. of Hydraul Eng., 111(12), 1461 - 1472.
ii.
Peyras, L., Royet, P. and Degoutte, G. (1992). “Flow and
energy dissipation over stepped gabion weirs.” J. of Hydraul Eng., 118(5), 707717.
iii.
Christodoulou, G. C. (1993). “Energy dissipation on
stepped spillways.” J. of Hydraul Eng., 119(5), 644 - 649.
iv.
Chanson, H. (1994). “Hydraulics of skimming flow over
stepped channels and spillways.” J. of Hydraul Res., 32(3), 445 - 460.
v.
Chanson, H. (1994 a ). “Comparison of energy dissipation
between nappe and skimming flow regime on stepped chutes.” J. of Hydraul
Eng., Res., IAIHR, 32(2), 213 - 218.
vi.
Chamani, M. R. and Rajaratnam, N. (1999).
“Characteristics of skimming flow over stepped spillways.” J. of Hydraul Eng.,
125(4), 361 - 368.
vii.
Barani, G. A., Rahnama M. B. and Sohrabipoor, N.
(2005). “Investigation of flow energy dissipation over different stepped
spillways.” American Journal of Applied Sciences., ISSN 1546-9239, 2 (6):
1101- 1105.
viii.
Chanson, H. (2006). “Hydraulics of skimming flow on
stepped chutes : The effects of inflow conditions.” J. of Hydraul., Res., 44(1),
51 - 60.
ix.
Chinnarasri, C. and Wongwises, S. (2006). “Flow pattern
and energy dissipation over various stepped chutes.” J. of Irrig. Drain. Eng.,
132 (1), 70 - 76.
x.
Sherry L. Hunt, Kem C. Kadavy, Steven R., and Darrel
M. Temple (2008). “Impact of converging chute walls for roller compacted
concrete stepped spillways.” J. of Hydraul Eng., ASCE, 134 (7), 1000 - 1003.
xi.
Sherry L. Hunt, Darrel M. Temple, Steven R., Kem C.
Kadavy, and Greg Hanson. (2012). “Converging stepped spillways: simplified
momentum analysis approach.” J. of Hydraul Eng., ASCE, 138 (9), 796 - 802.
Studies For Location of Bridges in the Vicinity of
Existing Hydraulic Structures
B. Raghuram Singh1 ,
Dr. R. G. Patil2 ,
M. N. Singh3
1
Research Officer, CWPRS, Pune, India;
Email:[email protected]
2
Chief Research Officer, CWPRS, Pune, India;
3
Joint Director, CWPRS, Pune, India;
Email:[email protected].
ABSTRACT: The rapid urbanization and increased traffic
volume has forced the planners to construct additional bridges
to cross the river passing through cities. These bridges are
being constructed at increased interval, adjacent to the existing
bridges, and barrages. The case being discussed here is the
River Yamuna at Delhi. The river in this reach is constricted
with the construction of series of bridges. Due to this
construction, the passage of flood and silt gets modified near
these bridges and creates problems to these hydraulic
structures over a long period, reflecting either afflux or
drawdown. The objective of the present study is to decide the
suitable location of the proposed bridge in the close vicinity of
existing bridge and a barrage. The studies were conducted on a
composite model with a horizontal scale of 1:300 and vertical
scale of 1:60 constructed at CWPRS, Pune. Series of studies
were conducted to assess the movement of sediment through
the reach by changing the location of the proposed bridge. The
results and findings of the same are presented in this paper.
Keywords: bridge pier, velocity, discharge intensity, water level
1. INTRODUCTION
Present day New Delhi, national capital of India was original
situated on the western bank of River Yamuna. After the
independence and receiving tremendous impetus, New Delhi has
developed into a populous city extending on either banks of
River Yamuna. Being national capital region (NCT),
Government of India, the state of Delhi and adjoining states have
accorded high priority for the infrastructure development to
connect the satellite cities around the city of Delhi. This
envisage construction of bridge across the River Yamuna in
addition to the existing barrages and bridges.
The River Yamuna which drains the southern Himalaya region,
originates in Yamunotri and flows through the gangetic plain
beyond Yamuna nagar, enters the state of Delhi at Palla and
leaves it after traversing a distance of about 50 km near the
village of Jaitpur. The sediment being very fine the river is
alluvial in nature. The Yamuna joins the Ganga at Allahabad.
The huge pressure of development has forced the authorities to
construct roads and bridges to connect the areas on either banks
of the river Yamuna at Delhi. These bridges are being proposed
to be constructed adjacent to existing barrages and bridges. The
water way and alignment automatically gets fixed up due to the
existing structures. However, the afflux gets accumulated and
possibly may lead to additional resistance to the flow. Afflux
may affect adversely the sensitive flooding conditions existing
on the upstream areas of Delhi. In addition the reduction in
velocities over the length of the river due to increase in depth
(Afflux) of flow may accentuate the sediment deposition, which
over a long period may lead to aggradation of river bed and
increase in the flood levels. These issues need to be assessed
before construction of bridge and avoid any such difficult
situation.
Model studies were conducted at CWPRS on a comprehensive
model of River Yamuna at Delhi built to a scale of 1:300
horizontal and 1:60 vertical for a proposed bridge to be
constructed between Okhla barrage and DMRC bridge. The
existing two structures were at a distance of around 85 m and it
was proposed to insert one more road bridge between these two
structure or adjacent to them based on the model studies.
2. PHYSICAL MODEL
The existing mobile bed model of river Yamuna constructed to a
horizontal scale (L) of 1:300 and a vertical scale (D) of 1:60
covering a river reach from Palla to Jaitpur was utilized for
present model studies.
MANIT Bhopal
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Issue Special3
18-19, Dec. 2014
Figure 3. Model prototype conformity ( Q= 7022 m3/s )
Figure 1. Plan of River Yamuna at Delhi
In order to reproduce proper bed movement and roughness, the
model bed was made mobile by laying sand having a mean
diameter (D50) of 0.34 mm. Figure 2 shows the grain size
distribution of sand used. In order to establish flood slope and to
observe water levels at various locations, gauges were installed
on the right side upstream and downstream of Wazirabad
barrage, upstream of ISBT road bridge and upstream of
Indraprastha barrage and on the left side at Kailashnagar
downstream of old rail-cum-road bridge, near Okhla weir and at
the proposed road bridge site.
Figure 2. Grain size distribution curve for the sand used in the
model
3. PROVING STUDIES:
The maximum flood discharge of 7,022 m3/s occurred in
Yamuna at Delhi in the year 1988. Discharge equivalent to 7,022
m3/s was let into the model and by controlling the gauge
upstream of the Indraprastha barrage as per the gauge discharge
curve, water levels were observed at various gauge locations.
Figure 3, shows the comparison of the water levels observed on
the model. These are in close agreement with the prototype
values. In view of this, the model was considered as "proved"
4.0 MODEL STUDIES
Studies were carried out to examine the following aspects of
design
(i)
Suitable location of the proposed bridge.
(ii)
Effect of water levels and velocities on the
proposed bridge.
(iii)
Flow conditions in the vicinity of the bridge.
The model studies were carried out for the following discharges.
(a)
7,022 m3/s (2.48 lakh cusec) (maximum
discharge observed in 1988 at
Wazirabad Barrage)
(b)
9,910 m3/s (3.5 lakh cusec) (design discharge
considered for ISBT bridge and
bridge proposed subsequently on Yamuna)
(c)
12,750 m3/s (4.5 lakh cusec) (check flood for
substructures, foundation and protection works
suggested by Central Water Commission)
Bridge location:
The project authorities were interested in locating the proposed
road bridge between the Okhla barrage and the under
construction DMRC bridge spaced about 85 m apart. This
helped in connecting the road bridge with the approach road on
either banks of the river. However, insertion of bridge between
the two existing structures would entail introduction of
additional resistance to the flow which could pose difficulties in
general movement of sediment from the Okhla barrage. This
difficulty in a long run can pose aggradation of bed on upstream
which in turn can increase the flood level. To avoid this, it was
decided to study the effect of the bridge insertion at various
possible locations which were decided after discussion with the
project engineers.Model studies were carried out for the bridge
alignment at the following locations.
Alignment - 1:
Studies for the proposed road bridge
approximately 57.5 m downstream of Okhla
barrage (i.e. 27.5 m upstream of proposed DMRC
bridge).
Alignment - 2:
approximately 185 m downstream of Okhla
barrage (i.e. 100 m downstream of proposed
DMRC bridge).
Alignment - 3:
barrage (i.e. 35 m upstream of Proposed DMRC
bridge).
Alignment - 4:
MANIT Bhopal
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Issue Special3
18-19, Dec. 2014
barrage (i.e. 35 m downstream of Proposed
DMRC bridge).
4.1 Model studies with existing condition
The preliminary model studies include, assessing and
understanding the flow conditions existing in and around the
structures to be introduced. This study is conducted by passing
the predecided
Alignm
ent1&3 Alignm
ent-2
Figure 6. Flow pattern in the vicinity of pattern in the vicinity of proposed bridge with Q = 12,750 m3/s
(Alignment –1)
Figure7. Flow proposed bridge with Q=12,750m3/s (Alignment –2)
Alignm
ent-4
Figure 4. Model set-up with existing conditions
Figure 5. Flow pattern in the vicinity of proposed
bridge with Q = 12750 m3/s (Alignment-1)
discharge through the model, but the structure of the proposed
bridge is not inserted. However, to help recognize the structure,
position and alignments are marked in such a way that it does
not affect the flow conditions. In this case all four alignments are
marked on the model as shown in Fig 4. And the experiments
were conducted for above referred three discharges. The flow
conditions were observed. The water surface elevation, and
velocities at critical points were measured. These data would be
used to compute the discharge intensities and afflux later. The
measured values are presented in Table. 1. Fig. 5 depicts flow
pattern along the proposed bridge under existing condition with
river discharge of 12,750 m3/s (Alignment – 1).
4.2 Model studies with proposed road bridge
The road bridge was proposed to cross the river Yamuna
downstream of Okhla barrage, however its exact location was
not decided. It was thought to be located between the Okhla
barrage and the under construction DMRC railway bridge. The
space available between these two structures was only 85 m. In
view of this, four alignments (Alignment-1, 2 , 3 and 4 as
discussed above) were studied on the model separately to decide
the location of bridge and its effect on the overall functioning of
barrage and movement of sediment downstream through various
structures.
The road bridge along the alignments 1 to 4 were separately
inserted on the model and the studies were conducted. The
measurements such as water levels and velocities at critical
points were taken. General flow conditions and its effect on the
river behavior was also assessed. The data in respect of velocity
and water surface elevation is presented in Table 1. Fig 6, 7, 8
and 9 show the variations of flow pattern for four alignments
from alignment-1 to 4 respectively.
Figure 8. Flow pattern in the vicinity of pattern in the vicinity of proposed bridge with q = 12,750 m3/s
( Alignment –3)
Figure 9. Flow proposed bridge with q = 12,750 m3/s (Alignment – 4)
Table 1. Maximum water levels and velocities observed during
model studies
Na
me
of
the
Str
uct
ure
Ok
hla
Bar
rag
e
Pro
pos
ed
Roa
d
Bri
dge
Ok
hla
Bar
rag
e
Pro
pos
ed
DM
RC
Bri
dge
Pro
pos
ed
Roa
d
Bri
Case 1. 27.5 m upstream of proposed DMRC bridge (Alignment -1)
Q= 7022 m3 /s
Q= 9910 m3 /s
Q= 12750 m3 /s
Without
With
Without
With
Without
With
Bridge
Bridge
Bridge
Bridge
Bridge
Bridg
e
WL
V
WL
V
W
V
W
V
WL
V
W
V
(m)
(
(m)
(m)
L
(m)
L
(m)
(m)
(
L
m
(m)
(m)
m
(
(
)
)
m
m
)
)
203
2.
203
2.0
20
2.9
20
2.9
204
3.
2
3
.19
0
.3
2
4.3
0
4.4
5
.58
4
0
.
0
8
6
4.
5
9
4
2
203
3.
203
3.0
20
3.8
20
3.8
204
4.
2
4
.14
0
.6
3
4.1
0
4.3
5
.41
5
0
.
5
4
4.
5
7
6
6
Case 2. 100 m downstream of proposed DMRC bridge (Alignment -2)
3.
203
3.1
20
3.8
20
3.8
204
4.
2
0
.23
0
4.2
2
4.4
5
.57
5
0
5
8
4
5
4.
8
0
203
3.
203
3.0
20
3.8
20
3.8
204
4.
2
.12
0
.18
1
4.1
4.3
2
.41
5
0
5
3
0
4.
6
6
203
.18
202
.98
MANIT Bhopal
2.
9
1
203
.06
2.9
4
20
4.0
5
3.6
20
4.2
5
3.6
5
204
.25
4.
0
2
0
4.
5
2
4
.
6
4
.
5
3
4
.
1
0
Page 29
Issue Special3
18-19, Dec. 2014
dge
Ok
hla
Bar
rag
e
Pro
pos
ed
Roa
d
Bri
dge
203
.19
Ok
hla
Bar
rag
e
Pro
pos
ed
DM
RC
Bri
dge
Pro
pos
ed
Roa
d
Bri
dge
203
.18
203
.14
Case 3. 35 m upstream of proposed DMRC bridge (Alignment -3)
2.
203
2.0
20
2.9
20
2.9
204
3.
2
0
.28
3
4.2
1
4.4
7
.59
4
0
2
9
5
5
4.
9
0
3.
203
3.1
20
3.8
20
3.8
204
4.
2
0
.25
0
4.1
2
4.3
5
.41
5
0
5
5
3
1
4.
7
4
Case 4. 35 m downstream of proposed DMRC bridge (Alignment -4)
3.
203
3.1
20
3.8
20
3.8
204
4.
2
0
.24
2
4.2
1
4.4
5
.57
5
0
2
8
3
2
4.
8
1
203
3.
203
3.1
20
3.8
20
3.8
204
4.
2
.12
0
.19
5
4.1
2
4.3
7
.43
5
0
5
4
1
1
4.
6
9
203
.03
3.
0
2
203
.11
3.1
0
20
4.0
8
3.6
2
20
4.2
6
3.7
1
204
.27
4.
0
2
2
0
4.
5
5
6. DISCUSSION OF RESULTS
3
.
5
5
4
.
5
5
4
.
5
8
4
.
5
5
4
.
1
5
5. QUALITATIVE STUDIES
Model studies were conducted with four alternate alignments
with and without the proposed road bridge. During model
studies of alignment -1 and alignment -3, it was observed that
the sediment was depositing at the upstream of Okhla barrage
and in between the Okhla barrage and under construction DMRC
bridge. In view of this, to assess the effect of sediment
movement through the Okhla barrage and downstream bridge
qualitative studies were conducted by feeding particular quantity
of sediment in to the flow about a kilometer upstream of the
barrage. The movement of sediment with proposed road bridge
at about 35 m upstream (Alignment -3) and 35 m downstream
(Alignment – 4) of proposed DMRC bridge was studied by the
silt injection. In case of studies with alignment-3, it was
observed that relatively large quantity of sediment was
depositing on the upstream and through the spillway bays as
shown in Fig.10 compared with the aggradation seen in respect
of alignment -4 as shown in Fig. 11.
Okhla barrage, under construction metro rail bridge and
proposed road bridge are closely located in river reach of about
85 m. These structures with a waterway of 552 m of barrage and
574 m for bridges hold the river to a fixed course at their
locations and therefore there is no possibility of any meandering.
The river is about a kilometer wide in this reach and has already
been constricted to about 552 m due to the construction of
barrage and its guide bunds.
For the alignment – 1, the maximum water levels observed at the
proposed road bridge and Okhla barrage under existing
conditions was 204.41 m and 204.58 m respectively with a
discharge of 12750 m3/s. With the proposed road bridge in
position, the maximum water levels observed at the bridge axis
and Okhla barrage was 204.76 m and 204.92 m with a discharge
of 12750 m3/s. This indicates to an afflux of about 35 cm near
the proposed bridge axis and 34 cm at Okhla barrage.
The water levels observed at the proposed road bridge
(Alignment-2) and Okhla barrage without and with the bridge
were 204.25 m and 204.52 m and 204.57 m and 204.80 m
respectively with the discharge of 12750 m3/s. This indicates to
an afflux of 27 cm near the proposed road bridge and 23 cm near
the Okhla barrage.
For alignment -3, the maximum water levels observed at the
proposed road bridge and Okhla barrage under existing
conditions was 204.41 m and 204.74 m respectively with a
discharge of 12750 m3/s. With the proposed road bridge in
position, the maximum water levels observed at the bridge axis
and Okhla barrage was 204.59 m and 204.90 m with a discharge
of 12750 m3/s. This indicates to an afflux of about 33 cm near
the proposed bridge axis and 31 cm at Okhla barrage.
The water levels observed at the proposed road bridge
(Alignment - 4) and at the Okhla barrage without and with the
bridge of waterway 574 m were 204.27 m and 204.55 m and
204.57 m and 204.81 respectively with the discharge of 12750
m3/s. This indicates to an afflux of 28 cm near the proposed road
bridge and 24 cm at Okhla barrage.
The studies conducted with alignment 3 & 4, by feeding
equivalent sediment on the upstream of Okhla barrage, indicated
that comparatively higher deposition of sediment on upstream of
Okhla barrage and through the road bridge – DMRC bridge in
case of alignment-3 when compared with alignment-4.
The afflux measured at the Okhla barrage due to the bridge
alignment-3 was 31 cm and due to bridge alignment 4, it was 24
cm. In view of this the alignment-4 is performing better than the
alignment-3.
7. CONCLUSIONS
Figure 10. A view of proposed road bridge 35
u/s of DMRC bridge (alignment - 3)
Figure 11. A view of proposed road bridge 35
d/s of DMRC bridge (alignment -4)
From the studies carried out with river discharges of 7022 m3/s,
9910 m3/s and 12750 m3/s following important conclusions were
made :
The site for the proposed road bridge 35 m downstream of
proposed DMRC bridge (Alignment – 4) was satisfactory as
revealed by model experiments from the hydraulic point of view
and silt flow conditions.
MANIT Bhopal
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The waterway of 574 m (14 spans of 41 m centre to centre each)
for the proposed bridge 35 m downstream (Alignment -4) of
DMRC bridge didn‟t cause any undesirable flow conditions at
the proposed bridge axis and at the Okhla barrage.
The movement of sediment with proposed road bridge at about
35 m upstream (Alignment -3) and 35 m downstream
(Alignment -4) of proposed DMRC bridge was studied by the
silt injection. It was seen that there was qualitatively large
quantity of sediment deposition on upstream of barrage, through
the spillway bays and at proposed road bridge in the model for
Alignment -3 rather than for the Alignment-4. This will cause
aggradations of river bed near proposed road bridge. In view of
this, the proposed road bridge in between the barrage and under
construction DMRC bridge was not recommended.
ACKNOWLEDGEMENT
We wish to express our deep sense of gratitude to Shri. S.
Govindan, Director, CWPRS for constant encouragement and
valuable suggestions during the course of this studies and kind
permission given for publishing this paper.
REFERENCES
i. CWPRS Technical Report No.5092 of July 2013, ―Hydraulic
model studies for the proposed road bridge downstream of Okhla
barrage across river Yamuna at New Delhi‖.
ii. Engelund.F.(1996).Hydraulic Resistance of Alluvial streams,
Journal Hydraulic Division, ASCE, March . PP 315-327.
iii. K.G.Ranga Raju, R.J.Garde and H.S.Yadav (1996) Modelling
Bed level variations in Alluvial Streams, ISH, Vol. 2, PP 28-43.
iv. S. B Kulkarni and V. M. Wakalkar (1998) Hydraulic Model
Studies for Improvement of flow conditions at Samal Barrage, ISH,
Vol.4, PP 24-33.
v.
SMITH D.W.,
(1977). Why do Bridges fail?, Civil
Engineering, American Society of Civil Engineers.
Study of Sharp-Crested Triangular Weir
M. Shaheer Ali1Talib Mansoor2
P. G. Student, Department of Civil Engineering, A.M.U.
Aligarh
2
Associate Professor, Department of Civil Engineering, A.M.U.
Aligarh
E-mail: [email protected]
1
ABSTRACT : Triangular weir is a simple form of weir best
suited for low discharge and is free from aeration difficulties.
It is mostly used in various branches of engineering like
hydraulics, environmental, chemical and irrigation for the
purpose of discharge measurement. Earlier studies
conducted on triangular weir indicate that the discharge
coefficient related to head or head to weir height ratio
covering a limited range of head and vertex angles. Further,
no generalized equation proposed to compute either
discharge coefficient or discharge for any head and vertex
angle. In this study, a total of 65 experimental runs were
taken for five weir vertex angles (from 30◦ to 90◦) at apex
elevation of 20cm. Using the general formula for triangular
weir dimensionless discharge and dimensionless head has
been defined that helps in merging all the data points of five
angles to one single curve. A generalized equation between
dimensionless discharge and dimensionless head has been
obtained. The maximum error obtained in the discharge
computed from this equation is ±5%. This equation also
validates the data of previous study (Wahaj, 1999).
Keywords:Weir vertex angle, Discharge coefficient,
Dimensionless discharge, Dimensionless head, generalized
equation.
1. INTRODUCTION
A weir is built across a river (or stream) in order to raise the
level of water on the upstream side and to allow the excess
water to flow over its entire length to the downstream side. Thus
a weir is similar to a small dam constructed across a river, with
the difference that a dam allows excess water to flow to the
downstream side, only through a small portion called spillway,
whereas a weir makes the excess water to flow over its entire
length. Weirs have been mostly used for flow measurement in
open channels. Since 1500 A.D. weirs have been a subject of
interest for the mankind. In 1885, the investigations of Francis
led to the application of weirs for accurate discharge
measurements. Investigations of Thomson (1858) and Bazin
(1888-1898) promoted the use of weirs.
The triangular weir is used widely for measuring the flow of
liquids in flumes and open channels. It is inexpensive, easy to
use and maintain.Several assumptions are made to obtain a
definite relationship between the actual discharge through the
weir and the head obtained on the weir. These structures have
been very often used in laboratories and in fields to know the
nature of flow, nappe profiles and to determine the coefficient of
discharge (Cd). The discharge coefficient takes into account the
effects which are ignored in the derivation of the discharge
equation for a triangular weir such as capillary action of water,
viscosity, surface tension, approach velocity and influence of
weir contraction on the nappe profiles. Thomson (1858)
recommended Cd = 0.593 and 0.617 for the 90 o and 127o notch
angles respectively. Barr (1910) concluded that the coefficient
was increased by roughness and projections on the upstream face
of the weir. Barralso concluded that the coefficient was
independent of channel width if the width was at least eight
times the head. For channel widths less than 8h, the coefficient
increased asthe width decreased. Strickland (1910) quoted
formula for 90°-notch weirs on the basis of Barr's experiments as
.
Cone (1916) gave the followi ng for mula:
,
MANIT Bhopal
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Where, S = side slope of the notch, expressed decimally, and
.
Greve (1932) gave the following formula:
Lenz (1943) gave the formula:
WhereN,
are functions of notch angle. Kindsvater-Shen
(1980) developed a formula for the discharge over a triangular
weir with angles notch angles between 20o and 100o, given
by
,
WhereCe: coefficient of discharge, he: effective head (= h +kh),
Ceis a function of three variables, i.e. Ce = f (h/p, p/B, Ɵ) where,
kh is an experimentally determined quantity in metre which
accounts for the combined effects of viscosity and surface
tension .Capetillo et. al (2013) developed a discharge
coefficient equation for sharp crested triangular weirs on the
basis of free vortex theory as described by Bagheri and
Heidarpour (2010); and measurement of the upper and lower
nappe profiles using an adaptation of the low-speed
photographic technique proposed by Salvador et al. (2009). The
equation is given by:
Where Vb: lower nappe velocity at the maximum elevation
section of the lower nappe (m/s); Rb: radius of the streamline
curvature at the lower nappe of the profile (m); k: nonconcentricity coefficient; Y: the flow depth at the maximum
elevation section of the lower nappe (m).
From the literature surveyed above, it is clear that the
coefficient of discharge is given for individual angles and most
of the investigators related Cd with h and some of them related it
with the wetted perimeter, Reynold‟s number, and Weber
number. No generalized equation exists to compute discharge
for all angles of the triangular notch. In the present study, an
attempt has been made to compute discharge covering a wide
range of notch angles and heads.
The objective of the present study is to develop a generalized
equation for the discharge through a triangular weir and to
establish a relationship between the discharge coefficient, notch
angle, h/p and p/B using an experimental data and regression
analysis.
WhereQ is the discharge (m3/s); Ɵ is the notch angle; h is the
head above the crest (m); Cdis the discharge coefficient
(dimensionless).
2. EXPERIMENTAL SETUP:
The experiments were conducted in a horizontal, rectangular (75
cm wide and 53 cm deep), prismatic glass walled channel having
cement plastered bottom (Photo 1). Weirs were made of G.I.
sheets. Weirs were installed at a distance of 8.5 m from the
upstream end of the channel. Water was supplied through an
inflow pipe from laboratory overhead tank provided with an
overflow arrangement to maintain the constant head. A sharpcrested triangular weir was installed at a desired angle, Ɵ and
apex height p. Discharge was controlled by means of a control
valve. The flow was allowed in the set-up to fill the upstream
channel up to the apex level of the triangular weir. The apex
level was recorded with a point gauge of accuracy 0.1 mm. The
discharge was allowed to flow in the channel and become steady
and then the head difference in the two limbs of differential
manometer attached to the bend meter mounted on the supply
pipe was measured. The discharge flowing in the channel was
computed using an accurate Calibration curve prepared for bend
meter. Under the same steady state flow conditions point gauge
reading at the free surface was recorded near the center of the
channel at 1 m upstream of the weir to avoid the curvature effect
of water surface (Photo 2). Five such readings were taken and
averaged to obtain a precise value of gauge reading. Head over
the apex was obtained by subtracting the apex level from
averaged free surface reading. The discharge was changed by
means of the control valve and a number of runs were taken to
cover a wide range of h/p.
The entire procedure was repeated for other weirs having
different apex angles. Table - 1 gives an account of the
different parameters of the triangular weir taken into
consideration in this study. A total of 65 experimental runs
were taken.
1.1 Governing equation:
Fig. 1: Experimental Setup
The discharge through a triangular weir is given by:
(1)
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Fig. 2: Q v/s h
Following best fit equations for Q – hhave been obtained:
R2 = 0.9999
= 30◦,
Photo 1: Upstreamview of the channel
2
= 45◦,
R = 0.9999
2
= 60◦,
R = 0.9987
2
= 75◦,
R = 0.9818
(2)
(3)
(4)
(5)
= 90◦,
R2 = 0.9968 (6)
These graphs show that there is an increasing trend for the
discharge Q with increase in head above the crest.Further the
discharge curve for 90o weir lies at the top while for 30o weir lies
at the bottom. It is obvious from this figure that for a particular
head discharge over 30o weir will be the least whereas discharge
through 90o weir will be the highest. In other words, for a
particular discharge, the head above the apex will be less in 90 o
weir and more in 30o weir.
Eqs (2) – (6) can be written as
Photo 2: Point Gauge
Table -1: Range of parameters for the triangular weir
Notch
angle
p
(cm)
h
(m)
Qo
(m3/s)
Fr
( Ɵ, 0)
30
No.
of
runs
20
20
60
20
75
20
90
20
0.00510.013
0.00760.0184
0.0070.022
0.00750.033
0.00650.0356
0.00280.0053
0.00450.0093
0.01390.0304
0.01510.0452
0.02040.0531
8
45
0.1770.2605
0.17180.2466
0.1480.2355
0.13050.2469
0.1080.2233
Q= Ahn
A generalized equation in the above form could not be obtained
due a large scatter in the values of coefficients A and n and
hence a large % error in the computed discharge.
3.2 Variation of Cd v/s h/p:
Using Eq (1) Cd was computed and plotted against h/p as shown
in Fig. 3
9
`11
21
16
3. ANALYSIS AND RESULTS:
3.1 Variation of discharge with head:
The variation of discharge with head for five triangular weirs
tested in the present study is shown in Fig. 2.
Fig. 3: Cd v/s h/p
MANIT Bhopal
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The above graphs show that the value of Cd decreases as the
angle is changed from 30o to 45o. If the value of notch angle is
further increased from 60o to 90o, the values of Cd starts
increasing. This variation is noticeable in the lower range of h/p
(i.e., 0.5 to 1). In the higher range of h/p, the variation in Cd is
insignificant.
The variation of Cd with h/p shows a decreasing trend for angles
30◦, 45◦and 60◦ and an increasing trend for angles 75 o and 90o.
However, the RMSE values for the best fit curves are small
enough. So a generalized equation of the form
could not be obtained as the trend of A and B shows a large
scatter and the percentage error in the discharge computed with
this equation is high.
Fig. 4: Qn v/s Hn
3.3 Generalized equation
Therefore an attempt has been made to make the discharge and
head dimensionless in order to obtain a generalized equation for
the discharge over a triangular weir.
Rewriting Eq. (1) as:
The resulting discharge equation is
agreement diagram in Fig. 8 shows that the computed
discharge lies within an error band of ± 5 %
. The
Dividing both sides by p5/2,
Thus, both sides are changed to dimensionless quantities and can
be expressed as:
Fig. 5: Qov/s Qc
Where,
Hn = h/p
The data obtained from the experimental work is converted in
the form of above mentioned dimensionless discharge Qn and
dimensionless head Hn and graph plotted between Qn and Hn as
shown in fig. 7:
SYMBOLS USED:
A = flow area
Cd = discharge coefficient
Ce = effective discharge coefficient
Fr = Froude number
g = gravitational acceleration
Q = discharge over weir
p = weir height
h = head above crest
B = channel width
Ɵ = included angle at the apex of the triangle
Qno = observed non-dimensional discharge
Qnc= computed non-dimensional discharge
REFERENCES
i. Bengtson H.H. (), Sharp Crested Weirs for Open Channel Flow
Measurement.
ii. Bos (1989), Discharge measurement structures.
iii. Capetillo Et.al (), Discharge coefficient analysis for triangular sharpcrested weirs using low-speed photographic technique.
iv. Chow V.T. (), Open channel flow.
MANIT Bhopal
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Issue Special3
18-19, Dec. 2014
v. Hager W. H. (), Discharge measurement structures
vi. Horton R.E. (1907), Weir experiments, coefficients, and formulas
(Revision of paper no. 150, Department of the Interior United States
Geological Survey).
vii. Jain A.K.(), Fluid mechanics.
viii. Jiwani R., Steffen P. E. (), Methods of Flow Measurement for Water
and Wastewater.
ix. King h.w.(1996), Handbook of hydraulics.
x. Larsen D.C. (1992), Water measurement.
xi. MasoudGhodsian (),Stage discharge relationships for triangular
weir.Rao N.S. (), Theory of weirs.
xii. Smith E.S., Providence, R. I. (),The v-notch weir for hot water.
Study of Elliptically Shaped Sharp Crested Weirs
N.P. Singh1 R. Singh2
Ujjain Engineering College, Ujjain, Sanwer road Ujjain (MP),
Pin 456010, India
2
Govt. Engineering College, Ahmadabad, Gujarat, Pin 380001,
India
1
ABSTRACT : This is a study about behavior of elliptically
shaped sharp crested weirs placed across open channels and
used as flow measuring devises. Effects of surface tension and
viscosity on coefficient of discharge are studied for values of
Channel Reynolds Number less than 2000. Value of coefficient
of discharge is established for Channel Reynolds Number
greater than 2000. Suitability of elliptically shaped sharp
crested weirs as flow measuring devices are analyzed. The
study has generated experimental data for a new shape and a
less studied flow regime.
Keywords: Sharp crested weir, coefficient of discharge, head
discharge characteristics
1. INTRODUCTION
Sharp crested weirs are commonly used devices for flow
measurement in open channels. Their advantage lies in the fact
that they are cheaper as compared to electronic flow devices. In
fact they become part of the same hydraulic structure in which
they are installed. The accuracy of discharge measurement
depends upon several factors such as the accuracy of fabrication
of the device, accuracy of measurement of the head, the
sensitivity of the device and also how well the control section is
maintained.
The triangular and the rectangular weirs are commonly used
flow measuring devices. They are easy to fabricate. However,
use of curvilinear weirs becomes incidental in many cases.
Parabolic weir has the distinction that in this weir the discharge
varies with the second power of head (Igathinanathane et al.
2007). This makes the calculation work easier and this also
becomes the unique feature of the parabolic shaped weir. On the
other hand the discharge in case of a triangular and rectangular
weir varies as the 2.5th and 1.5th power of the head respectively.
Baddour (2008) has described a method to determine the head
discharge equation of sharp crested weirs with openings defined
by polynomials of order n. He has described three different sharp
crested weirs following the fourth order polynomial geometry,
but each having their own head discharge equation so that for the
same head each weir gives different discharges.
Curve sectioned sharp crested weirs such as circular or elliptical
shaped weirs have an advantage that they do not have a
horizontal edge to be leveled. More over the circular shape can
easily be cut and fabricated on electrically operated lathe
machines. The ellipse is a curve drawn around two axes of
unequal length. A circle is a special case of an ellipse where the
two axes become equal in length. In fact if the eccentricity of the
ellipse tends to become equal to zero the shape tends to resemble
a circle whereas as the eccentricity of the ellipse tends to value
one it assumes the shape of a straight line. The analytical
solution of the discharge equation involves solution of similar
kind of elliptical integrals for elliptical as well as circular weirs.
For an ellipse behaving as a sharp crested weir for its major axis
placed horizontal, Sommerfeld et al. (1996) have proposed the
following equation for theoretical discharge:
Qt 
32
g ab3 / 2 21  k 2  k 4 E k   2  k 2 1  k 2 K k  
15
(1)
h
and is called the modulus of the integral,
2b
K k  and E k  are the elliptical integral of the first and
second kind respectively.
The intention of this work is to study the characteristics for
elliptical shaped sharp crested weirs.
where k 
2. THEORITICAL BACK GROUND AND FLOW
COMPUTATIONS
The theoretical discharges are computed as per the analysis
given below:
The equation 2 for an ellipse having semi major and minor axis
a and b respectively is given by:
x2 y 2
(2)

1
a 2 b2
Where a and b are the major and the minor axes respectively
of the ellipse.
The discharge equation for a shape sharp crested weir is
obtained by summing up the discharge through a small strip at a
distance x from the vertex and of thickness “dx”, the width of
the strip is obtained by the use of the equation 2. The area of the
strip is thus obtained by multiplying the chord length by the
thickness “dx” of the strip. The velocity at the elemental area is
obtained by the use of the Torricelli‟s formula in equation 3:
v
2 g h  x 
(3)
The discharge through the elemental strip is given by the product
of the area and the velocity at the elemental strip. The discharge
for a head h is obtained by summing up the discharges dq
through all such elemental strips in the range 0 to h which is
given by:
MANIT Bhopal
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Qth   2 g h  x  dA , where dA is the area of the small
elemental strip. The definition sketch is shown as per Figure1.
The discharge for an ellipse with its major axis vertical yields
the following equation for the theoretical discharge:
h
h
2b
(4)
Qth   dq 
2 g  2ax  x 2 h  x dx
a
0
0



there was no leakage through the weir section. White cement and
m-seal were used for sealing the joints. It was thus ensured that
the flow occurred only through the weir opening. To overcome
accidental errors and each discharge was measured twice so as to
make sure that there could be only one discharge corresponding
to any particular value of head. To overcome systematic errors
head values were measured once while discharges were
increasing and once when the discharges were made to decrease.
Water surface profile was determined by taking readings of the
free surface of water in the open channel upstream of the weir
section and it was observed that the flow in the channel was a
uniform flow. The minimum distance of the vertex from the
channel bottom was kept equal to 0.1 m or 10 cm.
4. THEORITICAL ANALYSIS
Figure 1. Elliptical section of wier
In the present study the above definite integral is solved by using
the Gaussian Quadrature technique so that estimates are made
till five places of decimals. The solution of the above integral is
also checked by finding the elliptical integrals of the first and the
second kind as per equation 1. The experimental discharges are
calculated by taking actual observations in the experimental
channel. The coefficient of discharge can thus be calculated.
3. EXPERIMENTAL SETUP AND METHODOLGY
Experiments were performed in a masonry channel 5 m long,
0.97m wide and 0.4 m deep. Flow was made to circulate into the
channel by means of a 10 horse power pump. Flow from the
pump entered into the channel through a stilling basin and a
baffle wall so that the water that entered the approach channel
became quiescent and without any wave formation in the
vicinity of the head measurement. At the other end of the
channel at the test section a metal frame was installed
perpendicular to longitudinal axis of the channel in which the
elliptical shaped sharp crested weirs of different sizes could be
mounted by nut bolting. It was insured that the weirs were truly
in plumb and perpendicular to the longitudinal axis of the flume.
A vernier point gauge was mounted on the channel which could
take readings up to one tenth of an mm. The point gauge was
placed along the centre line of the channel and at a distance of
five times the maximum head measured on the upstream of the
weir section. Downstream of the weir section led to a
rectangular measuring tank 0.97 m long 0.47 m wide and 0.7
meter deep. The discharge from the measuring tank drained into
an underground sump which was also the source of water to be
supplied into the channel. The discharge measurements were
done by finding the rise in water level in the measuring tank in
given time. A stop watch that could measure time up to 1/100th
of a second was used for measuring time. Streamlined entry and
exit were ensured into the channel. It was ensured that the head
measurements were not affected by any kind of local turbulences
in the vicinity of the control section. It was also made sure that
The Guassian Quadrature technique is used for solving the
discharge equation of the ellipse as represented by equation 3
iteration accuracy till 5 places of decimal was achieved. A
program was prepared in the Fortran environment to implement
the scheme of the Gaussian Quadrature technique. Heads of
flows are used as input to get the theoretical discharges. The
programme gives the coefficient of discharge as the output. The
first set of the experiments are performed for low discharges so
that the resulting flows are in the laminar transient zone for the
open channel so that the Reynolds Number was less than 2000.
The h / P ratio is varied from in between 0.25 to 0.54. To study
the variation of coefficient of discharge with head, Cd is plotted
against a dimensionless parameter h / P where P is the distance
of the weir vertex from the channel bottom.
5. RESULTS
The merit of a sharp crested weir is its simplicity of procedures
for the discharge measurement. Once the head is measured the
discharge can be read out from the calibration curves. While
measuring the head over the vertex of the sharp crested weir care
is to be taken that the head has stabilized and it is not rising or
falling when the reading is being taken. According to Falve
(2003), with the change of discharges in the channel it may take
many minutes for the head to stabilize in the channel. To
overcome this difficulty they suggested to take the head
measurement with increasing as well decreasing discharges so as
to eliminate the systematic errors. Therefore keeping this in
mind the head measurements for the present study are done for
once while the discharges are being increased and once while the
discharges are being decreased. However, a stabilizing time of
two minutes was also permitted while taking reading in either
direction. The semi elliptical sections chosen are installed to
work as sharp crested weirs with semi major axis in each case
0.25 meter and vertical minor axis of each section as 0.26 m,
0.30 meter and 0.34 meter with corresponding internal angles as
54.94o, 61.927o and 68.431o and corresponding eccentricities
0.854, 0.800 and 0.733 respectively. The lower value of the
eccentricity is an indicator of flatness of slope of the elliptical
curve. As such the eccentricity of the ellipse tends to zero for the
flattest ellipse when the ellipse tends to assume the shape of the
circle.
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Head discharge variations are plotted as shown in Figures 2 for
Channel Reynolds number from 635 to 1837 which is the
laminar transition zone. The computed head discharge curve lies
above the experimental curve as expected. However in the given
range of flow and Reynolds number it is further observed that
the two curves have not remained parallel. With increasing
Reynolds number the gap between the two curves of computed
and experimental discharges goes on widening which means that
the ratio of experimental discharge to the theoretical discharge
and therefore the coefficient of discharge goes on reducing for
the given range of Reynolds Number .
Figure 4. Variation of Cd with surface tension
Figure 2. Variation of Cd with Reynolds Number
It is observed on comparing the experimental discharges for the
three different weir sections that for lower eccentricity of ellipse
that is for the highest value of minor axis of 0.34 m the
discharges for the same and similar heads are higher.
Figure 5. Variation of Cd with head
Figure 3. Variation of Cd with viscosity
It is concluded that for the same head and similar hydraulic
conditions a wider section is capable of handling higher
discharge. This is due to the fact that for same head the flow
velocity remains same, but the area of cross section is more for a
wider section resulting into higher flows. Due to this reason the
ellipse subtending an angle of 90 degree at the vertex gives the
highest discharge as compared to elliptical sections having the
same major axis and lesser value of the minor axis. After
experimental results, the relationship between Cd and viscosity
and surface tension is presented in Figure 3 and Figure 4
respectively. Variation of Cd with head is also presented in
Figure 5.
6. CONCLUSIONS
The properties of the elliptical section as a weir are studied.
Discharge sensitivity of elliptically shaped weir section is found
to be more than the rectangular section. In fact its sensitivity lies
between that of a rectangular and parabolic weir section.
The carrying capacity of a semi elliptical section is inscribed in a
rectangle is found to be 18.28% more than that of a parabolic
weir inscribed in the same rectangle and under same hydraulic
conditions. The carrying capacity of elliptical section is 57.7%
more than that of an inscribed triangle the carrying capacity of
the section is however less and is only 78.8% of the
circumscribing rectangle.
The dependence of Cd on surface tension parameter and
viscosity parameters is studied. Their effect on Cd is more
pronounced for low depths and discharges. From the present
study it is concluded and reinforced that that coefficient of
discharge becomes independent of surface tension parameter at a
much lower depth while viscosity parameter still continues to
control Cd for higher depths.
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The values of coefficient of discharges are recorded for Re less
h
P
ratio. The rate of decrease is faster in the laminar zone for and
h
lesser values of
.The Cd values tended to approach a value
P
h
0.62 for the corresponding Reynolds number of 1837 and
P
ratio 0.54.
The Cd values are also recorded for higher Reynolds numbers
than 2000. It is concluded that the Cd value decreases with
with Re varying between 2064 and 9369 in the turbulent regime
of open channel flow. For the three sections with  = 30, 45 and
60o the average value of coefficient of discharge is found to be
0.45. The average value coefficient of discharge value for  =
90o which is actually a circular shape is found to be 0.53.
It is concluded that the general value of Cd = 0.6 cannot be used
under all circumstances for all shapes ,but will depend upon the
weir geometry, the weir dimensions in comparison to channel
dimension and the upstream flow conditions.
Apart from being of academic importance the knowledge of
elliptically shaped weir will become handy when its use
becomes incidental.
REFERENCES:
i.
Baddour RE (2008). Head discharge equation for sharp
crested polynomial weir. Journal of Irrigation and Drainage
Engineering, 134(2), 260-262
ii.
Falvey TH (2003). Hydraulic Design of Labyrinth Weir,
ASCE Publications.
iii.
Igathinanathane C, Srikant K, Prakash B, Ramesh AR (2007).
Development of parabolic weirs for simplified discharge measurements.
Journal of Biosystem Engineering, 96(2), 111-119
iv.
Sommerfeld JT, Michael P (1996). Journal of Environment
Science Health, 31(4), 905-912
Turbulence Characteristics of Flow Past
Submerged Vanes
Sharma, H., Research scholar, Department of Civil
Engineering, Indian Institute of Technology Roorkee, Roorkee,
Uttarakhand, India-247667. E-mail: [email protected]
Ahmad, Z., Professor, Department of Civil Engineering, Indian
Institute of Technology Roorkee, Roorkee, Uttarakhand, India247667. E-mail: [email protected].
generates the excess turbulence in form of helical flow
structure in the flow due to pressure difference between
approaching flow side and downstream side of vane.
Experiments were performed in a recirculating concrete flume
of width 1.0 m, 0.3 m depth and of 19 m length to observe flow
pattern around submerged vane rows. It was observed that in
the presence of submerged vanes all the turbulence quantities
were observed to increase. It was also observed that optimum
amount of flow was diverted with one vane row rather than
utilizing multiple vane rows.
INTRODUCTION
Submerged vane is basically an aerofoil structure, which
generates the excess turbulence in form of helical flow structure
in the flow due to pressure difference between approaching flow
side and downstream side of vane (Odgaard and Spoljaric, 1986;
Odgaard and Mosconi, 1987; Odgaard and Wang, 1991; Wang
and Odgaard, 1993). These vanes are in general placed at certain
angle with respect to the flow directions which is usually in
between, 10o – 40o (Fig. 1.). Submerged vane differs from the
traditional methods like groins, dikes, etc., which are usually
placed normally to the flow and produce flow distribution by
drag force and are not so much efficient in controlling the
sediment transport. Submerged vanes utilize vorticity to
minimize the drag and produce flow redistribution in the flow
such that longitudinal flow is compelled to get diverted towards
the transverse direction (Wang and Odgaard, 1993). Many
investigators like Odgaard and Wang (1991a), Wang and
Odgaard (1993), Marelius and Sinha (1998), Tan et al. (2005),
Ouyang et al.
(2008) have studied analytically and
experimentally the flow structure of the submerged vane. This
paper presents the study of flow pattern around rows of
submerged vanes.
A BRIEF REVIEW OF LITERATURE OF FLOW
AROUND SUBMERGED VANES
Odgaard and Kennedy (1983) calculated by using KuttaJoukowski theorem and verified by physical modeling the
utilization of submerged vane as bend protector. Odgaard and
Wang (1991a) studied the flow pattern around the submerged
vane by including various factors which can possibly affect the
flow pattern and developed a formula to calculate lift and drag
coefficient. Wang and Odgaard (1993) critically analyzed the
theory of tip vortex and utilized method of images for two vanes
and for multiple vane arrays they proposed a differential
equation. Marelius and Sinha (1998) observed the flow pattern
around the vane for α > 30o and also obtained the optimum angle
of attack. Tan et al. (2005) studied the flow pattern around the
vane and optimized the vane parameter so that vane can act as
sediment manager. Ouyang et al. (2008) obtained an interaction
model of vane by putting up the fact that vane interaction field
associated with multiple vane array is different for different vane
in the system in contradiction to the theory put forward by Wang
and Odgaard (1991a). Han et al. (2011) experimentally studied
the effect of submerged vanes on the flow characteristics of 90 o
channel bend.
ABSTRACT : Submerged vane is basically an aerofoil
structure placed at certain angle with respect to the flow
directions which is usually in between, 10o – 40o, which
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8H and 20H from the last vane row. Velocity was measured
initially for four vane rows and after measuring the flow pattern
around submerged vane, a vane row was removed and process
was repeated and finally the final flow pattern was measured for
plane shear flow condition.
Fig. 1. Submerged vane induced transverse irculations
EXPERIMENTAL ANALYSIS OF FLOW PATTERN
AROUND SUBMERGED VANE
Experiments were performed in Hydraulic Engineering
Laboratory of Civil Engineering Department, Indian Institute of
Technology, Roorkee. Experiments were performed in a
recirculating concrete flume of width 1.0 m, 0.3 m depth and of
19 m length (Fig.2.) The bed slope of flume was measured to be
6.32 ×10-4. The water was supplied to the flume through an
overhead tank in which the level of water was kept constant to
have constant discharge for a particular opening of the valve
fitted in delivery pipe of the tank. After the experimentation the
used water was taken to sump from where by the centrifugal
pump water again sent back to the overhead tank. Flow
strengtheners and wooden wave suppressors were provide to kill
the surfacial disturbances and for straightening of the flow. A
tail gate was provided at the end of the flume in order to
maintain the uniform flow into the flume. An orificemeter was
also provided in the delivery pipeline from overhead tank for the
measurement of discharge. Four rows of submerged vanes were
attached to the bed so as to perform experimentations of flow
pattern around submerged vanes.
RESULTS AND DISCUSSIONS
From the Fig. 4, it can be seen that in the presence of vanes, flow
near to the vane is highly unstable and chaotic. The turbulence is
clearly having heterogeneity as going up in vertical direction
from bed towards the flow surface turbulence quantities decrease
usually but in the presence of submerged vanes all the
turbulence quantities varied having a peak. This peak signifies
the area of separation and high shear stress. It was also seen that
this peak occur at z/h ≈ 0.4.
Fig.2. Line sketch of experimental setup
Fig.3. Experimental set up with submerged vanes (H = 6 cm and
L = 12 cm)
Vanes used in experimentations were viz. 6cm x 12cm whose
lateral spacing respectively was 12.5 cm (Fig. 3.). In order to
measure the velocity mini ADV was used over sections x = 3H,
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Fig.4. Variation of various turbulence quantities and velocity
profile for x = 3H for four vane rows
Fig.5. Variation of various turbulence quantities and velocity
profile for x = 3H for no vane row.
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When observed this point comes out to be z = 0.83 times height
of vane. According to observations of Odgaard and Wang (1991
a) and Wang and Odgaard (1993) the point of origin of vortice
was 0.8 times the vane height and present observation was very
near to their observation. In case of Fig. 5, it can be clearly seen
that variation of all turbulence characteristics was same in all
direction and was nearly overlapping each other. It signifies that
turbulence in case of without vanes was homogeneous in nature.
Also, the turbulence quantities varied in accordance with the
observations of Nezu and Nakagawa (1993).
0.6
b)
0.6
0.5
a)
0.5
0.4
z/H
z/H
0.4
0.3
0.3
0.2
0.2
0.1
no vanes
1 vane row
2 vane rows
3 vane rows
4 vane rows
0.1
no vanes
1 vane row
2 vane rows
3 vane rows
4 vane rows
0.0
-4
-2
0
2
4
v/u*
0.0
-6
-4
-2
0
2
4
6
v/u*
0.6
0.6
c)
b)
0.5
0.5
0.4
z/H
z/H
0.4
0.3
0.3
0.2
no vanes
1 vane row
2 vane rows
3 vane rows
4 vane rows
0.2
no vane
1 vane row
2 vane rows
3 vane rows
4 vane rows
0.1
0.1
0.0
0.0
-4
-6
-4
-2
0
2
4
6
-2
0
2
4
v/u*
v/u*
Fig. 7. Variation of transverse velocity with and without vane
row for a) y = 0.45 m; b) y = 0.5 m and c) y = 0.55 m for x = 20h
(h = vane height).
0.6
c)
0.5
z/H
0.4
0.3
0.2
no vanes
1 vane row
2 vane rows
3 vane rows
4 vane rows
0.1
0.0
-4
-2
0
2
4
v/u*
Fig. 6. Variation of transverse velocity with and without vane
rows for a) y = 0.45 m; b) y = 0.5 m and c) y = 0.55 m for x = 8h
(h = vane height).
It was seen from Figs. 6 and 7 that with one vane row more flow
was diverted in the transverse direction as transverse velocity
then two, three and four vane rows. Hence, it signifies the fact
by placing one vane row optimum diversion of flow can be done
while other vane rows did not produced effective diversion as
was expected.
CONCLUSIONS
It can thus be concluded from the experimental study that in the
presence of submerged vanes all the turbulence quantities were
observed to increased. It was also observed in the variation of
turbulence quantities a peak was observed to occur at z/h ≈ 0.4.
This represented the core of vortex having maximum turbulence.
Height of core of vortex was observed to be z = 0.83 times
height of vane which was close to value quoted in literature. It
was also observed that with one vane row more flow was
diverted in the transverse direction then two, three and four vane
rows. Hence, it signified the fact that by placing one vane row
optimum diversion of flow can be done while other vane rows
did not produced effective diversion as was expected.
REFERENCES
i.
Marelius, F., and, Sinha, S.K. 1998. Experimental analysis of flow
past submerged vanes. Journal of Hydraulic Engineering, ASCE, 124 (5), 542545.
ii.
Nezu, I., and Nakagawa, N. 1993. Turbulence in open channel flows.
IAHR, AA Balkema, Delft, Netherlands.
iii.
Odgaard, A.J., and, Kennedy, J.F. 1983. River bend bank protection
by submerged vanes. Journal of Hydraulic Engineering, ASCE, 109 (8), 11611173.
MANIT Bhopal
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iv.
Odgaard, A.J., and, Spoljaric, A. 1986. Sediment control by
submerged vanes. Journal of Hydraulic Engineering, ASCE, 112 (12), 11641181.
v.
Odgaard, A.J., and, Mosconi, C.E. 1987. Streambank protection by
submerged vanes. Journal of Hydraulic Engineering, ASCE, 113 (4), 520-536.
vi.
Odgaard, A.J., and, Wang, Y. 1991 a. Sediment management with
submerged vanes. Theory: I. Journal of Hydraulic Engineering, ASCE, 117 (3),
267-283.
vii.
Ouyang, H.T., Lai, J.S., Yu,H., and, Lu, C.H. 2008. Interaction
between submerged vanes for sediment management. Journal of Hydraulic
research, IAHR, 46 (5), 620-627.
viii.
Tan, S.K., Guoliang, Y., Lim, S.Y., and, Ong, M.C. 2005. Flow
structure and sediment motion around submerged vanes in open channel.
Journal of Waterway, Port, Coastal and Ocean Engineering, ASCE, 131 (3),
132-136.
ix.
Wang, Y., and, Odgaard, A.J. 1993. Flow control with vorticity.,
Journal of Hydraulic Research, IAHR, 31 (4), 549-562.
x.
Han, S.S., Biron, P.M., and Ramamurthy, A.S. 2011. Three
dimensional modeling of flow in sharp bends with vanes. Journal of Hydraulic
Research, IAHR, 49 (1), 64-72.
Hydraulic Design Aspects of Stilling Basin with
Sloping Apron
V.S. Rama Rao1
K.T.More2
Dr.
3
M.R.Bhajantri
Dr. V.V.Bhosekar4
[email protected] , [email protected]
[email protected] , [email protected]
Central Water & Power Research Station, Khadakwasla, Pune411 024
ABSTRACT: Stilling basins are very popular type of energy
dissipators provided for high head / low head spillways, weirs,
culverts and channels. Energy dissipation by stilling basins is
governed by various factors like intensity of discharge, head
causing flow, Froude number and tail water depth. When the
tail water levels are sufficient to cope up with the sequent depth
of hydraulic jump, stilling basins with horizontal apron are
provided. If the tail water levels are higher than the required
for sequent depth, sloping aprons are provided to contain the
jump within the spillway glacis to avoid encroachment of jump
further upstream. The design of sloping apron involves fixing
of slope of apron, calculation of length of apron and provision
of appurtenances like endsill. The slope of the apron has
influence on the tail water depth and thereby the length of the
jump and its location on the apron. The end sill is constructed
at the downstream end of the stilling basin, whether solid or
dentate and has function of reducing the length of the
hydraulic jump and controlling scour. It is not possible to
standardize design procedures for sloping aprons as for the
horizontal aprons. The slope of the apron must be determined
from economic considerations and the length must be judged
by the type and soundness of the riverbed downstream. In this
paper various aspects relating to sloping stilling basins are
discussed with reference to hydraulic model studies conducted
on Garudeshwar weir in CWPRS. Numerical modelling was
also carried out for the weir and the results were found in good
agreement with results from physical model studies.
Key Words: Horizontal apron, Sloping apron, initial depth,
sequent depth, end sill, downstream apron, maximum water
level, crest elevation.
1.0 INTRODUCTION
Energy dissipaters for spillways are required to dissipate the
excessive energy generated by impounding water when gets
released down. The huge amount of potential energy is
converted into kinetic energy due to steep slope of glacis of
spillway. This energy may cause serious erosion which depends
largely on the rate of discharge, head causing flow and
credibility of the river bed material and surrounding geological
area on the proximity of the dam and cause problems to the
downstream of spillways and sometimes create threat to the dam
complex itself. The energy of released flows can cause problems
in the following ways:
 Erosion of banks and spillway undermining
 Sedimentation problems
 submergence of downstream areas
To avoid the above mentioned problems the excess energy is to
be dissipated to an allowable limit. The various structures which
are required for this are called energy dissipators. The design of
energy dissipator plays an important role in the dam safety issue.
The common types of energy dissipators are stilling basin with
horizontal and sloping aprons, ski jump type buckets and solid/
slotted roller buckets.
2.0 STILLING BASINS AND SLOPING APRONS
Stilling basins are the most popular type of energy dissipators
provided for spillways. When the Tail Water Rating curve
matches with the Jump Height Curve, Stilling Basin is the
suitable form of energy dissipation arrangement. For spillways
on weak rock conditions and weirs and barrages on sand or loose
gravel, hydraulic jump stilling basins are recommended. Design
of stilling basins involves calculation of invert level of basin,
length of basin and appurtenances provided for basin. When Tail
water is too high as compared to the sequent depth, the jet left at
the natural ground level would continue to go as a strong current
near the bed forming a drowned jump which is harmful to river
bed. In such a case, a hydraulic jump type stilling basin with
sloping apron should be preferred as it would allow an efficient
jump to be formed at suitable level on sloping apron. Figure 1
shows a typical sloping stilling basin with endsill.
3.0 HYDRAULIC DESIGN OF SLOPING APRON
Stilling basin with sloping apron can be considered for high head
spillways when tail water depth is more to achieve economy.
The hydraulic jump may occur in different ways on sloping
apron as shown in Figure 2. Type B jump forms at toe of slope
and ends on horizontal apron, type C forms on slope and ends at
junction of slope and horizontal apron, and type D forms entirely
on slope. The length of apron required may range from 40 – 80
% of length of jump. When good rock is available downstream,
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that rock is supposed to act as apron. Figures 3 and 4 show
length of jump in terms of conjugate depth D2 and ratio of
conjugate depth D´2 to D1 (IS: 4997- 1968).
Extensive studies were done on sloping apron stilling basins
(Hager, 1974) by Kindsvater (1944), USBR (1948), Bradley and
Peterka (1957), Ariyemma (1958), Bunyan (1958), Smith
(1959), Van Beesten (1962), Rajaratnam (1963), Mahmood
(1964) and Mura Hari (1973). Procedure adopted for designing
sloping apron is given as under (Peterka, 1984):
1. Determine an apron arrangement which will give the
greatest economy for the maximum discharge condition. This is
a governing factor and the only justification for using a sloping
apron.
2. These stilling basins are provided for spillways/ weirs
whose heads are less than 15 m and intensity of flow less than 30
m3/s/m.
3. Position the apron so that the front of the jump will
form at the upstream end of the slope for the maximum
discharge and tail water condition. Several trials will usually be
required before the slope and location of the apron are
compatible with the hydraulic requirement. It may be necessary
to raise or lower the apron, or change the original slope entirely.
4. With the apron design properly for the maximum
discharge condition, it should then be determined that the tail
water depth and length of basin available for energy dissipation
are sufficient for, say,1/4,1/2 and 3/4 capacity.
Figure 1. Typical Sloping Stilling Basin with end sill
Figure 3. Length of jump in terms of conjugate depth D2 (IS:
4997- 1968)
Figure 4. Ratio of conjugate depth D´2 to D1(IS: 4997- 1968)
4.0
HYDRAULIC
MODEL
STUDIES
ON
GARUDESHWAR WEIR
Garudeshwar weir is located about 12 km downstream of Sardar
Sarovar Dam in Gujarat. The reservoir created by the weir would
function as the lower reservoir for reversible operation of the
turbines of river bed power house of Sardar Sarovar Dam. Total
length of the weir is 1137 m which includes 339 m long rockfill
dam and non overflow blocks of length 189 m. The ungated
overflow portion is 609 m long. It has an ogee profile with crest
at El. 31.75 m. The design discharge is 62,807 m3/s and the high
flood level is El. 44.65 m. The FRL and MDDL are at El. 31.5 m
and El. 25.91 m respectively. The original design of weir
consisted of roller bucket as an energy dissipater with a 40 m
long apron downstream of bucket and since the solid roller
bucket was not functioning satisfactorily for the entire range of
discharges, the design was changed to 95 m long Stilling basin
with horizontal apron as energy dissipator. As the horizontal
stilling basin was not performing satisfactorily, it was provided
with the sloping apron with dentate end sill.
Figure 2. Hydraulic jump on sloping apron and the relationship
between D´2 and D1 (Peterka, 1964)
Figure 5. Location plan of proposed Garudeshwar weir.
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Hydraulic model studies have been considered as best tool for
assessment of suitability of spillways and energy dissipators. For
Garudeshwar weir project, 1:55 scale 2-D sectional model was
built in a glass sided flume. 55 m length of the weir and stilling
basin with sloping apron as energy dissipator were constructed
in brick masonry and the surface was plastered in smooth
cement and painted with enamel paint. The upstream and
downstream beds were reproduced rigid at El. 12 m. Piezometers
were provided along the surface of the weir with sloping apron
for hydrostatic pressure measurement. Necessary arrangements
were made for measurement of discharge, water levels and
pressures. The accepted relationship of hydraulic similitude,
based on Froudian criteria were used to express the
mathematical relation between the dimension and hydraulic
quantities of the model and the prototype. The general relation
expressed in terms of model scale is as given in Table 1.
Table 1. Model Scale Relation for Various Dimensions
Dimensions
Length
Area
Velocity
Discharge
Time
Pressure in m of water
head
Manning´s ń´
Scale Relation
1 : 55
1 : 3025
1 : 7.42
1 : 22434
1:7.42
1 : 55
Figure 6. Tail Water Rating Curve and Jump Height Curves
for different aprons of Stilling Basin.
Figure 7. Pressures on profile of Sloping Stilling Basin for the
discharge of 15,700 m3/s
1: 1.95
5.0 STUDIES WITH SLOPING APRON (CWPRS T.R.No.
5027, 2012)
5.1
Studies with dentated end sill
The performance of 60 m long stilling basin with sloping apron
with dentate endsill was observed for the entire range of
discharges up to the maximum discharge of 62,807 m3/s. The
hydraulic jump on sloping basin is subjected to varied tail water
levels for different discharges. Studies indicated that weak jump
was forming for higher discharges above 31,400 m3/s but for
discharges ranging from 31,400 m3/s (50%) up to 15,700 m3/s
(25%), a clearly defined hydraulic jump was forming in the
stilling basin but slightly encroaching upstream on the rear slope
of the weir. Tail water rating curve versus jump height curve
shows that tail water levels are 0 to 5 m higher than jump heights
for entire range of discharges (Figure 6). The energy dissipation
seems satisfactory for the given tail water levels. For discharges
below 10,000 m3/s, the front of jump shifted downwards and
showed tendency of further shift for 10 % retrograded tail water
levels. The studies for pressures indicated that the pressures
were positive on the surface of the weir and stilling basin for the
entire range of discharges. Velocities observed downstream of
sloping apron are of the order of 1.1 m/s. Figures 7 and 8 show
pressure and water surface profiles on Sloping Stilling Basin for
the discharge of 15,700 m3/s with dentated endsill.
Figure 8. Water surface profiles on Sloping Stilling Basin for
the discharge of 15,700 m3/s
Photo 1. Performance of stilling basin with horizontal apron
with dentated endsill for discharge of 15,700 m3/s
5.2
Studies with Solid end sill
The end sill, either dentated or solid, located at the downstream
end of the stilling basin reduces the length of the stilling basin
by creating additional tail water depth. It also deflects the flow
along the stilling basin floor upward and away from the bed of
the downstream channel and protects it from scour. The end sill
also serves to hold the hydraulic jump in equilibrium within the
MANIT Bhopal
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basin resulting in improved efficiency. To allow a shift of toe of
jump further upstream for lower discharges, the existing
dentated endsill was converted into solid endsill and studies
were carried out. From the model studies, it was observed that
the front of the jump shifted slightly towards toe with the
provision of solid end sill as compared to the jump with dentated
end sill, though it did not form at the toe of the weir. But for
discharge of 15,700 m3/s, jump was forming exactly at the toe
without showing any tendency of shifting down as shown in
photo 3. Velocities observed downstream of sloping apron are of
the order of 1.5 m/s and were slightly more than the one with
dentated endsill as shown in Table 2.
Photo 3. Performance of Stilling Basin with solid
endsill for discharge of 15,700 m3/s
Table 2. Velocities observed downstream of sloping apron
Type of profile
Discharge, Q
(m3/s)
60 m long sloping
apron with
dentate endsill
15700
Maximum
observed velocity
d/s of end sill @
Ch. 90 m (m/s)
1.17
60 m long sloping
apron with solid
endsill
15700
1.51
6.0 NUMERICAL MODELLING
The commercial software Flow-3D, developed by Flow Science,
was used for the numerical modeling of the flow. The Flow-3D
uses finite-volume method to solve the Reynolds-averaged
Navier –Stokes (RANS) equations over computational domain
(Amorim et al, 2004). Tracking of free surface is performed
using Volume-of-Fluid method. The numerical modelling of the
flow inside the stilling basin is much complex due to the high
intensity of the turbulence and the recirculation that is associated
with the hydraulic jump. To represent these characteristics of the
flow, Re-normalized Group (RNG) turbulence model was used.
During simulation, upstream boundary was set as a Volume
Flow rate and downstream boundary as a Pressure Outlet. The
extent of the mesh in the upstream X-direction was adjusted
until any further increases had negligible effect on the discharge,
while the downstream boundary was placed past the energy
dissipator to cover tail water level conditions. The simulation
was run for 85 seconds which was found to be enough for the
hydraulic jump stabilisation. During the simulation, flow starts
from the rest and is settled by water level difference between
upstream and downstream. There is an initial time gap, for
which the hydraulic jump, still is not stabilised and
characteristics flow parameters presents a great time fluctuation.
When the jump becomes stable, these values have a small
fluctuation around an average value. Simulation was carried out
for 15,700 m3/s (25% of design discharge). Figure 9 shows
numerical simulation in Flow- 3D for Garudeshwar weir for
discharge of 15,700 m3/s with solid endsill.
Figure 9: Numerical Simulation in Flow-3D for Garudeshwar
weir for discharge of 15,700 m3/s.
7.0 COMPARISION OF RESULTS OF PHYSICAL AND
NUMERICAL MODELS.
8.0
The results obtained from numerical simulation were compared
with the results obtained from experimental (physical) model
studies.
8.1 Average Pressure
Pressure at pre-defined points were measured from numerical
simulation at 85 seconds, corresponds to occurrence of stable
hydraulic jump. Figures 10 and 11 show results from numerical
simulation and comparison of results for pressures obtained from
numerical simulation and experimental studies for discharge of
15,700 m3/s, respectively. The results are in general agreement at
most location.
8.2 Average Water Profile
Water surface profile over surface of weir measured from
numerical simulation at 85 s, corresponds to occurrence of stable
hydraulic jump. Figures 12 and 13 show results from numerical
simulation and comparison of results for water surface
elevations obtained from numerical simulation and experimental
studies with a discharge of 15,700 m3/s, respectively. The results
are in general agreement at most location with minor
differences.
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Figure 13. Comparision of Water surface profile for discharge
of 15,700 m3/s.
Figure 10. Average mean Pressure from Numerical Simulation
for discharge of 15,700 m3/s.
8.0 CONCLUSIONS
The sloping apron stilling basin is adopted when the tail water
levels are higher than the sequent depths of horizontal apron.
The design involves calculation of economical slope of stilling
basin suited to frequent disposable floods. Though codal
provisions mentioned the applicability of these for heads less
than 15 m and intensities less than 30 m3/s/m, while designing
these basins for other conditions, hydraulic model studies are
necessary for verifying its performance. Garudeshwar weir of
Sardar Sarovar Project, Gujarat was designed with sloping apron
stilling basin after testing various alternatives through hydraulic
model studies. The studies indicated that the length of apron is
sufficient for containing the jump in the sloping basin. By
carrying out numerical modelling, water surface and pressure
profile were compared with results of physical model studies and
were found in good agreement. Thus, it is inferred that the
numerical modelling can be used as a complementary tool to
physical modelling for studying various alternatives. However,
final designs needs to be studied on physical model.
ACKNOWLEDGEMENT
The authors are thankful to Shri S Govindan, Director CWPRS
for his encouragement in writing the paper. The authors are also
grateful to staff of SED Division, CWPRS for their help in
preparation of this paper.
Figure 11. Comparision of average mean Pressure for discharge
of 15,700 m3/s.
Nomenclature
D1 = Depth of flow at the beginning of the jump
D2 = Depth conjugate to D1 for horizontal apron
D´2 = Depth conjugate to D1 for sloping apron
hs = Height of endsill
L j = Length of hydraulic jump
L b = Length of basin
V1 = Velocity of flow at the beginning of the jump
V2 = Velocity of flow at the end of the jump
θ = Angle of sloping apron with horizontal
F1 = Froude Number of flow at the beginning of the jump
REFERNCES
Figure 12. Water surface profile from Numerical Simulation for
discharge of 15,700 m3/s.
i.
Amorim, J. C., Rodrigues, R.C., Marques, M. G., (2004) ―A
Numerical and Experimental Study of Hydraulic Jump Stilling Basin‖ - Advances
in Hydro-science and Engineering, Volume VI.
ii.
CWPRS Technical Report No. 5027 of Nov 2012 ―Hydraulic model
studies for Garudeshwar Weir with sloping apron of Sardar Sarovar Narmada
Project, Gujarat, 1:55 Scale 2-D Sectional Model‖.
iii.
Hager. W.H. (1992) ―Energy Dissipators and Hydraulic Jump‖.
Kluwer Academic Publishers, The Netherlands.
iv.
IS: 4997- 1968 ― Indian Standard Criteria for Design of Hydraulic
Jump Type Stilling Basins with Horizontal and Sloping Apron‖
v.
Peterka A. J. (1984) ―Hydraulic Design of Stilling Basins and Energy
Dissipators‖, Engineering Monograph No. 25, United States Department of the
Interior Bureau Of Reclamation, Water Resources Technical Publication, Denver,
Colorado.
Hydraulic Design of Barrage in Montane Terrains
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Rajendra Chalisgaonkar 1, Mukesh Mohan1, Manish S. Sant2 and
Pratibha S. Sant2
1
Superintending Engineer, Irrigation Department, Dehradun248001, Uttarakhand.
2
Assistant Engineer, Irrigation Department, Roorkee-247667,
Uttarakhand.
E-mail: [email protected]
ABSTRACT:The bouldery reach of river is characterized by
supercritical flow for the major portion of its length till it
reaches the plains where the river runs at sub-critical stage.
The river bed comprises of boulders, cobbles, gravels, etc. with
a mean sediment size ranging from 10cm to 30 cm or more.
The approach of planning and design of diversion structures
for irrigation, drinking water or power generation in upper
bouldery reaches of rivers having steep gradient and deep
pervious foundation are entirely different from the design
principles followed for structures in mild sloping lower reaches
of rivers with flat and plain terrains flowing in fine alluvial
soils and as such the existing guidelines by Bureau of Indian
Standards for design of weirs and barrages do not apply to the
planning and design issues of structures in bouldery reaches.
In this paper, authors have described in detail the hydraulic
design of barrage carried out by the prevalent BIS guidelines
and the formulae developed by many researchers for hydraulic
design of barrage in montane regions and presented a
comparison.
characterized by supercritical flow for the major portion of its
length till it reaches the plains where the river runs at sub-critical
stage. The river bed comprises of boulders, cobbles, gravels, etc.
with a mean sediment size ranging from 10cm to 30 cm or more.
Fig. 1 gives an idea of rivers flowing in bouldery reaches with
steep gradient and carrying large size boulders.
In fact, current IS code on „Guidelines for Hydraulic Design of
Barrages and Weirs: Part – I, Alluvial reaches‟ (IS: 6966 – Part
I, 1989) and other related codes by Bureau of Indian Standards
(BIS) are applicable for barrages on alluvial reaches of rivers
with fine and medium size sediments. The Engineers and other
design consultants are still using the Guidelines available for
Design of Barrages in alluvial reaches due to non-availability of
sufficient literature and guidelines of Bureau of Indian
Standards. However, the Indian rivers of large magnitude,
flowing over gravelly and bouldery beds in the Himalayan and
sub-Himalayan regions, need more accurate studies and analysis
as the planning and designing of these structures are entirely
different from the design principles followed for structures in
mild sloping lower reaches of rivers with flat and plain terrains
flowing in fine alluvial soils.
The paper describes in detail the hydraulic design of barrage
carried out by the prevalent BIS guidelines and the formulae
developed by many researchers for hydraulic design of barrage
in montane regions.
Key words: Diversion structure, Bouldery River, Supercritical
flow, Sediment size, Impervious apron, Cut-off
Depths
1.0 INTRODUCTION
A barrage, by a definition, is a weir fitted with a gated structure
to regulate the water levels in the pool behind in order to divert
water through canal. The importance of weirs or barrages to
divert river water through a canal system for irrigation and other
useful purposes in tropical and subtropical countries needs no
emphasis. Outwardly, it would appear a comparatively
straightforward task to divert water from perennial rivers. By
following the general guidelines, the location and alignment of
barrage axis and that of the canal head works may be decided
but the other details like the width of barrage and head works,
levels of weir crests, length of weir floors, river training works,
pond level etc. have to be finalized based on the hydraulic
conditions and geologic characteristics of the river bed and
banks of the site. However, it poses a considerable challenge to
hydraulic engineers to devise a safe and economical way of
tapping the mighty rivers of the Indian subcontinent, with their
highly variable flow over the year in montane terrains. A barrage
is a costly structure involving an expenditure of several hundred
million rupees. Any approach to reduce the cost of a barrage
satisfying the design criteria would be appreciated as an
innovative step.
Generally 15m to 20m high barrage type diversion structure are
constructed in bouldery reaches of a river with steep gradient
and narrow cross section. The bouldery reach of river is
Figure 1. Typical River in Bouldery Reach
2.0 DESIGN OF BARRAGE IN MONTANE REGION
From the literature survey, it has been observed by the authors
that mainly the researchers have developed rational formulae for
estimating the water way and scour depth in montane region.
Therefore in the succeeding paragraphs, only the formulae
suggested by researchers for estimating water way and scour
depth have been described.
3.0 WATERWAY
3.1 Alluvial Rivers
To minimize shoal formations in meandering alluvial rivers, the
following looseness factor, suggested by IS 6966(Part 1):1989,
shall be applied to Lacey‟s waterway for determining the
primary value of the waterway:
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Silt Factor
Looseness Factor
Less than 1
1.2 to 1
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18-19, Dec. 2014
1 to 0.6
(7), R depth of scour below the highest flood level in m; Q is
high flood discharge in the river in m3/s; q is intensity of flood
discharge in m3/s per m width; and f is silt factor which may be
Lacey‟s waterway is given by
P  4.83 Q
(1)
Where, Q is design flood discharge in cumec.
The IS 6966(Part 1):1989 also suggests that for deciding the
final waterway, the following additional considerations may also
be taken into account: (a) Cost of protection works and cutoffs,
(b) Repairable damages for floods of higher magnitudes, and (c)
Afflux constraints as determined by model studies.
3.2 Bouldery Rivers
For deciding the preliminary waterway (P) of the barrage in
Bouldery River, the following formulae developed by
different researchers may be used as guidance.
a) Using formula developed by P.Sen(1997)
f  1.76 d50
(2)
Where, q is intensity of the discharge which is given by Eq. (3)
q  6.56 D1.17 d 50 0.354
(3)
Where, D is total depth of flow (regime depth), and d50 is
average diameter of the stone in the bed.
b) Using formula developed by R.Garde(2000)
P
 3.872Qn 0.396
d50
(a) For design discharge upto 500cumec R. D. Hey(1986)
R  0.22Q 0.37 d 0.11
(b) For design discharge above 500cumec P.Sen(1997)
g  S
gb

 1)(d50 ) S  

(5)
Where, P is waterway required, d50 is median size of bed
material, Q is design flood discharge in cumec, Sgb is Specific
gravity of bed material and S is average bed slope of the river at
the location of the proposed structure. It should be noted that
Lacey‟s equation is applicable in the alluvium reach of the river.
SCOUR DEPTH
4.1 Alluvial Rivers
River scour is likely to occur in erodible soils, such as clay, silt,
sand and shingle. In non-cohesive soils, the depth of scour may
be calculated from the Lacey‟s formula which is as follows:
1/ 3
than 1)
(10)
where in Eqs. (9) and (10), R is regime depth below the HFL in
m, Q is a total discharge in the river in cumec, d is median size
of bed material in mm and q is the intensity of discharge in the
river in cumec/m. The Scour depths around a barrage
constructed on mobile gravel or bouldery bed will vary from
point to point due to various factors affecting the flow condition
at each point.
4.0 EXAMPLE OF BARRAGE DESIGN
stream power which is defined by Eq. (5) as
Q
R  0.473  
 f 
(9)
(4)
Where, Qn is Non dimensional quantity, may be called as
Qn  Q /  d50 2 

(8)
4.2 Bouldery Rivers
For calculating the regime depth of flow in gravelly or bouldery
rivers, different formulae have been developed. For average
diameter of bed material upto 0.4m(400 mm) the following
formulae may be used:
R  0.2q 0.855 d 0.3
P Q/q
as
calculated from the relationship
In order to compare the changes in the design of barrage, due to
the formulae developed for montane regions, an example has
been presented in the paper to illustrate the effects on the various
parameters of barrage design. 138m long barrage has been
designed in the montane regions using the standard guidelines
available for barrage design in alluvial regions and the formulae
described in the preceding paragraphs. The basic data adopted
for the detailed design are shown in Table 1.
5.0 COMPARISON OF METHODS OF BARRAGE
DESIGN
The perusal of detailed design of various elements of barrage,
carried out by Lacey‟s and P. Sen method, given in Table 2
indicates that
5.1 Detailed Design
Design parameters or elements of design obtained from the
formulae suggested by Lacey, Sen and Garde have described in
Table 2.
(applicable when looseness factor is more
(6)
or
1/ 3
 q2 
R  1.35  
 f 
than 1)
(applicable when looseness factor is less
(7) where, in the Eqs. (6) and
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viii
ix
5.1.4
I
Ii
iii
iv
Table 2 – Summary of Design of Barrage Elements
5.1.1
i
ii
5.1.2
Fixation of crest levels
909.50m
vii
911.00m
viii
Water way
Calculation
Lace
y's
form
ula
478.8
m
Parameters
5.1.3
i
Water way (using
Eqs. (1), (2) and (4))
ii
No. of bays
iii
Length of each bay
iv
Total overall water
way Provided
v
Looseness Factor
ii
iii
iv
V
vi
vii
P.
Sen
form
ula
139.
9m
8
8
15.0
m
140.5
0m
15.0
m
140.
50m
0.29
1.0
ix
R. Garde
formula
67.7m
x
8
15.0m
xi
140.50m
2.1
xii
Calculation
for
Depth of Cutoffs
Lacey's
formula
P. Sen formula
9829cumec
9829cumec
85.47cumec/m
85.47cumec/m
12.16m
25.19m
926.50m
926.50m
Parameters
I
vi
Crest Level of
Undersluice bay
Crest Level of the
other barrage bays
Design
Flood
Discharge
Discharge Intensity
Scour depth
Upstream water level
corresponding
a
discharge of 9829
cumec
Upstream cutoff level
corresponding
a
discharge of 9829
cumec
Assuming upstream
cutoff level to be
Downstream water
level corresponding a
discharge of 9829
cumec
xiii
xiv
908.26m
888.72m
907.25m
889.00m
924.10m
924.10m
xv
Hence, downstream
cutoff
level
corresponding
a
discharge of 9829
cumec
Assuming
downstream cutoff
level to be
Calculation
for
Length of floor
Maximum staic Head
'H' = 929.5 -908.5
GEC=
(S-1)(1-n),
where 'S' is specific
gravity and 'n' is
porosity
Safe exit gradient
„GE‟
According to Bligh's
Creep Theory, Total
Length of floor
Taking depth of
downstream cutoff
„d‟ to be
Length of sloping
glacis
Length of trough
If
the
total
downstream
slope
floor length is 95 m,
level of the floor at
the d/s with a river
slope of 0.0131
Assuming level of
the floor at the d/s
with a river slope of
0.0131 to be
Length
of
downstream
slope
from
905.00
to
908.25
Taking 2m horizontal
length
beyond
downstream slope &
1.5m length of weir
crest downstream of
the
gate,
total
essential downstream
length
Length of intake
works
on
the
upstream
side
abutments
Provide total length
of upstream side
(since
the
total
length of upstream
side comes negative
using P. Sen formula
hence
providing
minimum length 1.5
scour depth for P.
Sen)
Total length of floor
899.78m
873.73m
899.00m
874.00m
21.00m
21.00m
-
0.99
1 in 5
1 in 4
105.00m
84.00m
10.00m
35.00m
18.00m
18.00m
65.00m
65.00m
908.26m
908.26m
908.25m
908.25m
6.50m
6.50m
93.00m
93.00m
32.00m
32.00m
117.00m
38.00m
210.00m
131.00m
6.0 CONCLUSION
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The design of barrage in montane region carried out by the
prevailing Laceys technique and formulae suggested by
researchers P. Sen and others has been described in detail in the
paper and the results have been illustrated in the Tables 1 to 2.
The comparison of results, clearly indicates that there is an
improvement in looseness factor, as compared to Lacey‟s water
way, in fixing the water way of the barrage. Also for a given
discharge as the average size of bed material increases, the scour
depth and depth of cut offs increases substantially. However, it
has been observed that the length of weir floors are deccreased,
when formulae developed for montane terrains by researchers
are adopted. It has also come to the notice of the authors that the
Bureau of Indian Standards is planning to formulate Guidelines
for the Design of Barrage in hilly terrains and therefore it is also
recommended that some more studies be conducted in the
montane regions before finalizing the draft of the proposed
Guidelines for Hydraulic Design of Barrages and Weirs”, Part 2Bouldery Reaches by the Bureau of Indian Standards, New
Delhi so that the results obtained from the formulae are
authenticated.
6.1 Waterway
Length of waterway, L is equal to the regime perimeter, P. In
boulder reaches of the river, it would be economical to reduce
the waterway to about (0.6 - 0.8) times Lacey's waterway. From
the calculations, it is observed that the length of waterway,
according to R. Garde formula is 0.14 times the Lacey‟s
formula. Moreover the length of waterway, according to P. Sen
formula is 0.29 times the Lacey‟s formula which is in the
acceptable range for boulder reaches.
6.2 Looseness factor
The ratio of waterway actually provided to waterway computed
is known as looseness factor. Generally the overall width of
barrage actually provided may be more or less as has been
computed theoretically. The perusal of Table 2 indicates that the
looseness factor computed by Lacey, P. Sen and R. Garde
formulae are 0.29, 1.0 and 2.1 respectively.
6.3 Scour Depth
It is obseved from Table 2 that the scour depth computed by
Lacey and P. Sen formulae are 12.16m and 25.19m respectively
for the same discharge and silt factor. It indicates that the Scour
depth calculated by P. Sen formula is almost two times the scour
depth, what has been estimated by Lacey‟s formula and
therefore the formula suggested by P. Sen has to be validated
with further studies before using it.
6.4 Total Length of Floor
The perusal of Table 2 indicates that the the total floor length in
montane terrains shall be less as compared to the alluvial
regions, if formulae suggested for montane terrains are used. The
total floor length obtained from Lacey and P. Sen formulae are
210m and 131m respectively.
7.0 REFERENCES
i. Garde, R.J. and RangaRaju, K.G. (2000) ―Mechanics of
Sediment Transport and Alluvial Stream Problems‖ 3rd Ed. New Age
Int. Pub. Pvt. Ltd., New Delhi.
ii. Hey, R. D., and Thorne, C. R. (1986) ―Stable channels with
mobile gravel beds.‖ J. Hydraul. Div., 112(8), 671–689.
iii. Khosla, M.N., Bose, K.K.and Taylor, M.T. (1954) ―Design of
Weirs on Permeable Foundation‖, Publication No.12, Central Board of
Irrigation and Power, Malcha Marg, New Delhi.
iv. Lacey, G. (1929) ―Stable channels in alluviums‖. Journal
Institution of Engineers, Paper No. 4736, 229.
v.
Mazumder, S.K. (2004) ―Scour in Bouldery Bed – Proposed
Formula‖, Written discussion on Paper No. 508 by R. K. Dhiman,
Journal of Indian Roads Congress, Vol 65(3).
vi. Mazumder, S.K. and Yashpal Kumar (2005) ―Estimation of
Scour in Bridge Piers on Alluvial Non- Cohesive Soil by different
methods‖, IRC Highway Research Bulletin. Oct., 2006.
vii. Sen, P. (1997) ―Depth of scour in gravelly and bouldery
rivers‖, Journal of the Institution of Engineers (India), Civil
Engineering Division, Vol. 77, pp. 209-214.
viii.
(1989) ―Guidelines for Hydraulic Design of Barrages and
Weirs‖, Part 1-Alluvial Reaches (First revision), IS:6966, Bureau of
Indian Standards, Manak Bhawan, New Delhi.
ix. (1989) ―Guidelines for Operation and Maintenance of
Barrages and Weirs‖, IS:7349 (First Revision), Bureau of Indian
Standards, Manak Bhawan, NewDelhi.
x.
(1991) ―Criteria for Investigation, planning and Layout of
Barrages and Weirs‖, IS:7720, Bureau of Indian Standards, Manak
Bhawan, New Delhi
xi. Guidelines for Hydraulic Design of Barrages and
Weirs(DRAFT)‖, Part 2-Bouldery Reaches, IS 6966: Part-2, Under
formulation, Bureau of Indian Standards, Manak Bhawan, New
Delhi(Unpublished).
Optimal Design of Intake Upstream of A Weir – A
Case Study
Kuldeep Malik1, Dr. R. G. Patil2 and M.N.Singh3
1 Research Officer, Central Water and Power Research Station,
Khadakwasla, Pune 411 024, India,
2 Chief Research Officer, Central Water and Power Research
Station, Khadakwasla, Pune 411 024, India,
3 Joint Director, Central Water and Power Research Station,
Khadakwasla, Pune 411 024, India,
ABSTRACT: Intake is a very vital component in every power
project, which facilitate drawal of sufficient uninterrupted raw
water from the available water body in the vicinity. Locating
intake is a unique exercise for every project because the kind
and nature of water body differ in individual projects. An
intake for Rourkela Power Plant for drawing 0.425 m 3/s water
was to be located in the backwaters of Tarkera weir across
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INTRODUCTION
Tarkera weir was constructed across Brahmani river near
Rourkela about 50 years back to facilitate the assured supply of
raw water for Rourkela Steel Plant (RSP), Orissa. Two intakes
have been constructed near the left bank just upstream of
Tarkera weir. M/s. NSPCL has now proposed to construct an
additional intake adjacent to existing intakes to cater raw water
requirement of 0.425 m3/s needed for the expansion of Rourkela
Power Plant (Fig.1). The river Sankh and Koel join at Vedvyas
to form river Brahmani and the confluence is about 5.6 km
upstream of Tarkera weir. Mandira dam with a storage reservoir
capacity of 326 MCM supply regular water for diversion to the
intakes throughout the year.
The first intake built upstream of Tarkera weir is working nicely,
however, the functioning of second intake is not upto the mark.
The second intake has siltation problem because of limitations in
the opening levels. In view of this the project authorities were
apprehensive of the design of third intake and wanted to
properly design this intake to avoid future complications.
Koel river
Shankh river
Brahmni river
Proposed
Intake
Location
Tarkera
Weir
Figure 1 : Index plan of Intake site
The intake design is mainly dependent on the river morphology
adjacent to the intake. Since the intake is to be located upstream
Figure 1 : Index Plan
of a weir, the reservoir
is subjected to sedimentation and the
river tries to change its planform continuously. This change is
due to the movement and deposition of sediment with respect to
the flow passing downstream of the weir. To assist in proper
location of the intake, morphological studies were conducted
with the help of topo-sheet of 1970, Satellite Imageries for the
years1989, 2000 and 2012 (Fig.2). In addition hydrographic
survey data of Brahmani river, hydraulic data and observations
made during site visit were used to locate the intake.
Koel River
Keywords: Bridge; minimum water level; power plant;
river morphology; satellite imageries ; weir.
Mandira
Reservoir
Toposheet of 1970
Imagery of 1989
Imagery of 2000
Imagery of 2012
Brahmani River
river Brahmani 100 to 150 m upstream of existing intakes of
Rourkela Steel Plant near left bank. The desk studies were
conducted, in CWPRS, to locate the intake and decide various
hydraulic design parameters.
The location of intake was decided on the
basis of morphological analysis using Topo-sheet of 1970 and
satellite imageries of the years 1989, 2000 and 2012. The same
was confirmed by the analysis of river cross-section data in the
upstream of Tarkera weir. The G-Q data at upstream gauging
site and 1 in 100 year flood of 15,700 m 3/s was used to workout
expected water levels at proposed intake site using 1-D
mathematical model HEC-RAS. The maximum scour level for
the intake well of 8.0 m diameter was worked out and
foundation level was recommended considering the grip
length. To draw required quantity of water and to minimize the
entry of sediment, size of the openings of the intake structure
were decided by limiting drawal velocity to 0.2 m/s so as to
ensure minimal disturbance in the surrounding flow field.
Orientation of the openings were decided in such a manner
that drawal of sediment in the intake system is minimum and
maximum portion of sediment travels in the down stream
direction along with flow. The crest level of the opening was
decided below LWL for 90% dependability. Openings in the
intake well were suggested at two levels, one to draw surface
water during floods and another from the bottom layer during
lean flow to minimize entry of sediment into the intake system.
Formation / Pump floor level was decided considering
sufficient free board above the expected 1 in 100 year flood
level. Various intricacies involved in locating an Intake well
upstream of a weir and its design are discussed in the paper.
South Eastern Railway Bridge
Panposh
Figure 2 : Brahmani river courses for past years
STUDY
OF
TOPO-SHEETS
AND
SATELLITE
IMAGERIES
The toposheet of the year 1970 (73 B ), showing Brahmani river
from the confluence of Sankh and Koel rivers to the upstream of
proposed Intake location, was compared with satellite imageries
for years 1989 (IRS 1A), 2000 (IRS 1C) and 2012 (IRS P6) to
study the changes in the deep channel courses of river Brahmni
in the vicinity of proposed Intake site upstream of Tarkera weir.
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locate Intake about 70 to 80 m upstream of existing Intake.
Brahmani River
Figure 2 shows comparison of the river reach near proposed
Intake location as well as in its upstream and downstream during
years 1970, 1989, 2000 and 2012. It could be seen from the
toposheet of year 1970 and satellite images of later period that
there is very minimal change in the course of Brahmni river
from its origin i.e. confluence of Sankh and Koel rivers to the
Tarkera weir (near proposed Intake location). Although several
changes have been observed in the past images in upstream
reach of both rivers before the confluence, the river channel is
quite stable at the proposed intake site. In the upstream of
Tarkera weir deep channel portion is well spread from left bank
to right bank, there are some rock exposures in the centre of
channel also. In the reach under consideration, the deep channel
is along left bank for more than last 40 years. The left bank
upstream of Tarkera weir is on outer curve, therefore, deep
channel has been following it. There were several rock
exposures near right bank about 2 km upstream of Tarkera weir
acting as a nodal point, it deflects the river course towards left
bank. Afterwards river follows concave path and flows in wider
area, one channel follows left bank and another along the right
side upto Tarkera weir. Siltation between the channels is also
noticed in an area of about 600 m long and 200 m wide, about
150 m upstream of existing intakes. A close view of satellite
images ( Figure 3) shows presence of deep channel upstream of
Tarkera weir well spread over the width of river.
Toposheet of 1970
Imagery of 1989
Imagery of 2000
Imagery of 2012
Fig. 3 : A close view of Satellite images for past 40 years
STUDY OF RIVER CROSS-SECTION DATA
The cross-section data was utilized to review and finalize the
location of proposed Intake, considering location of deep
channel, river bed levels and bank slope at different locations
etc. From the cross-sections upstream of Tarkera weir, it was
observed that deepest bed level near the left bank upstream of
existing intake varied from RL 190 m to 190.5 m at a distance of
80 m to 100 m from left bank (Fig. 4) and the deep channel is
about 60-70 m wide. Whereas, further upstream, river is
showing tendency of shoal formation. Deep bed levels are more
than RL 191.3 m and width of deep channel near the left bank is
also very less. Therefore, it is considered more appropriate to
Fig. 4 : River cross-sections 180 and 235 m upstream of Nalla
confluence
EXAMINATION OF GROUND REALITY
To get familiarize with the site conditions or to know the ground
truths, it is also necessary for the designers to carry out site
inspection before finalizing the design. With this view site
inspection was also carried out. The Brahmani river reach from
confluence of Sankh and Koel rivers i.e. 5.8 km upstream of
Tarkera weir (Photo 1) to 600 m downstream of proposed intake
location was inspected along both of the banks of river. It was
noticed that deep channel of river was along left bank in most of
the portion. Within the reach under study, the river flow is
between well defined & firm high banks. There existed solid
rock exposures along river bed at number of places including
vicinity of the proposed Intake location. It was observed that a
very deep pool of water was present from Tarkera weir to about
500m upstream and deep channel was towards left side of the
river (Photo 2). Two Intakes were already constructed by RSP
just upstream of Tarkera weir to fulfill its requirement (Photo 1).
Out of these two Intakes, the old one had multiple level openings
and is working satisfactorily. Whereas, the new intake was
provided with only one lower level opening. Therefore, it was
facing severe siltation problem during monsoon.
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GQ PANPOSN
208
206
WATER LEVEL IN M
204
202
200
198
196
0
2000
4000
6000
8000
10000
12000
14000
DISCHRGE
Figure 5 : Gauge – Discharge relation at Panposh gauging site
FINALISING DIFFERENT WATER LEVELS
Daily discharge and corresponding water-level data from June
1996 to May 2010 at Panposh gauging station about 4km
upstream of Tarkera weir (Fig. 5) and daily discharge and
corresponding water-level data from June 1972 to June 1996 at
Bolani gauging station about 40 km downstream of Tarkera weir
was utilized to decide minimum and maximum expected water
levels at the Intake and thereby to decide various levels of
opening and pump floor level. Statistical analysis of discharge
data by gumble extreme value distribution for minimum yearly
flow was used to ensure availability of required discharge in the
river. It was informed by the project authority that the total water
requirement for the project would be about 0.425 m3/s i.e. 15 cfs
and sufficient water was available in the pool behind Tarkera
weir due to regular releases from the Mandira dam, about 22 km
upstream of Tarkera weir. It was informed by project authority
that Rourkela Steel Plant has assured water drawal from the
pondage created by Tarkera weir. The maximum flood discharge
and corresponding water levels in Brahmni river were available
at Panposh gauging site about 4 km upstream of Tarkera weir,
which are used to workout scour and the foundation level of
Intake.
From the Gumble extreme value analysis of the gauge-discharge
data of Panposh gauging site for 26 years, it was revealed that
for 50 years frequency, maximum discharge would be 14,138
m3/s and minimum discharge would be 7.8 m3/s. The maximum
discharge with hundred year frequency was found to be 15,700
m3/s, which is considered for design of foundation level of
Intake structure. The project authority informed that they had
never faced shortage of water supply at Tarkera Pump house for
last 40 years due to regular releases from Mandira dam in the
upstream. The Mandira dam having storage capacity of 326
million cubic meter was solely constructed for RSP and the
releases from the dam are governed by the requirement at
Tarkera weir. The requirement of water for NSPCL intake is
only 0.425m3/s, for which availability is ensured on the basis of
above data.
For deciding the sill level of the opening of the Intake well, a
realistic assessment of minimum water level is necessary. The
sill level of the lowest opening should therefore be such that it is
sufficiently below the lowest minimum water level satisfying the
criteria of submergence. Generally the concentration of sediment
near the bed is more. For minimizing the sediment entry into the
Intake, the sill level of the opening should be provided with
openings at different levels depending upon variation in water
level with the arrangement to close bottom openings at the time
of high flood. Also the area of Intake openings should be such
that at minimum water level the velocities at opening / entry
should preferably be below the standard drawl velocity of 0.2
m/s for drawl of required discharge with least disturbance to the
surrounding area.
After study of the hydraulic data and results of 1-D
mathematical model studies provision of lowest level opening in
the Intake has been considered at about 3 m above the river bed
level in the vicinity of Intake i.e. at RL 193.0 m. As per standard
drawl velocity of 0.2 m/s, one opening of size 2.2 m wide x 1.0
m high would be required at each of the levels at RL 193.0 m
and RL 199.0 m as shown in Fig. 6. During high flood period
water should be drawn from gates at higher level and lower level
gates should be kept closed, otherwise high silt concentration
bed load may enter the Intake system and clog the pump-sump.
During the lean period flow with low sediment load, water can
be drawn from low level openings. For the proper gate
operation, openings at different level should be staggered. Intake
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openings should neither be provided facing upstream nor facing
downstream, but these should be provided at the sides making an
angle of 30 to 400 to the flow direction.
for a discharge of 14000 m3/s, water level and velocity were RL
206.32 m and 2.40 m/s respectively. Considering the water level
for flood of 17,500 m3/s, the pump floor level is provided above
RL 209.04 m taking into account free-board of 2.0 m.
Table-1
Normal Depth (0.000224)
Chainage Bed Level
(m)
(m)
6762.38
5774.49
4812.92
4678.78
4523.36
4418.34
4315.33
4212.22
4089.69
3986.56
3890.89
3793.38
3704.22
3609.84
3506.16
3368.44
3268.76
3163.12
3062.24
2984.19
2902.72
2805.54
2696.16
2507.88
2452.09
2255.78
2058.92
1907.94
1805.92
1639.82
1534.96
1432.23
1304.86
1204.36
1075.39
973.22
0.00
PREDICTION
OF
FLOW
PARAMETERS
AND
HYDRAULIC DESIGN
The one dimensional mathematical model HEC-RAS was used
to predict water levels and velocities at the proposed Intake site.
The Water level and corresponding discharge data available at
Panposh gauging site of CWC about 4 km upstream of proposed
Intake site was utilised for model calibration. For different
discharges, the flow simulations were carried out by providing
normal depth condition at the downstream boundary, for which
the bed slope of river was taken as 1 in 4464 (as per available
survey drawings) and about 1 in 2460 m in downstream of
Tarkera weir. Discharges were used as the upstream boundary,
and n value was taken as 0.04. The n value was decided
considering lot of rock exposures in bed in this reach. It was
seen from the extrapolated gauge – discharge data at Panposh
that the water level was about RL 207.2 m for the discharge of
14,000 m3/s and matches well with the water level obtained by
Mathematical model for this discharge (RL 207.49 m). Similarly
water level at Panposh for discharge of 12,000 m3/s was RL
206.60 m from the G-Q curve and RL 206.50 m from the
mathematical model, which shows a very good conformity.
The high flood of 15,700 m3/s was also simulated by providing
normal depth as the downstream boundary condition and
discharge at the upstream boundary. The Table-1 shows the
water levels and velocities worked out with HEC-RAS at
different locations. From this table, it was seen that at proposed
Intake site, water levels and velocities for flood of 15,700 m3/s
were RL 207.04 m (Fig.7) and 2.55 m/s respectively, whereas
194.21
193.47
190.53
190.13
189.95
188.91
189.12
189.17
190.24
187.60
186.77
185.10
188.46
187.70
189.44
190.02
189.89
189.98
189.99
189.61
189.48
190.24
190.22
190.23
190.48
190.07
191.26
190.41
190.95
190.91
190.34
190.63
190.80
190.65
190.44
190.05
190.18
Q=14000m 3 /s
Q=15700m 3 /s
WL( m)
Vel (m/s)
WL( m)
Vel (m/s)
207.49
207.36
206.98
206.97
206.94
206.88
206.82
206.76
206.66
206.61
206.59
206.57
206.57
206.63
206.59
206.59
206.57
206.55
206.50
206.48
206.44
206.43
206.40
206.32
206.25
206.23
206.16
206.18
206.15
206.09
206.05
206.04
206.00
205.99
205.96
205.92
205.75
2.55
1.97
2.63
2.53
2.45
2.56
2.68
2.77
2.95
3.00
2.91
2.85
2.70
2.21
2.29
2.08
2.11
2.11
2.19
2.23
2.28
2.22
2.28
2.40
2.58
2.41
2.49
2.14
2.15
2.26
2.33
2.23
2.25
2.18
2.20
2.26
1.95
208.28
208.16
207.75
207.73
207.70
207.64
207.57
207.51
207.40
207.34
207.32
207.30
207.31
207.38
207.33
207.34
207.32
207.30
207.25
207.21
207.18
207.17
207.13
207.04
206.97
206.95
206.88
206.91
206.88
206.80
206.76
206.75
206.72
206.71
206.67
206.62
206.46
2.68
2.07
2.79
2.68
2.60
2.71
2.84
2.94
3.12
3.18
3.09
3.03
2.85
2.34
2.42
2.19
2.21
2.21
2.31
2.36
2.41
2.35
2.41
2.55
2.74
2.57
2.63
2.25
2.25
2.40
2.48
2.36
2.39
2.29
2.31
2.40
2.04
Remark
Panposh Gauging Station
cs 0 @ 236m U/S of Tarkera weir
cs 27 @ 180m U/S of Tarkera weir
cs 28 @ 16m D/S of Tarkera weir
Fig. 7 : Water Surface Profiles along Brahmani River
The general scour was worked out considering maximum
discharge of 15,700 m3/s and silt factor of 0.9799 for D50 = 0.31
mm of bed material (sand). Considering 450 m river width
during high flood stage (as per the cross-section data), average
discharge intensity would be 34.88 m3/s/m and increasing it by
40% for flow concentration, the maximum intensity of discharge
has been considered as 48.84 m3/s/m. Taking into account local
scour for 8.0 m diameter of Intake well, the maximum Scour
levels were worked out on the basis of criterion laid down by the
various investigators like Sir Claude Inglis, Dr. H.W. Shen etc.
Considering the HFL of RL 207.0 m with the two different
criterion, the maximum Scour levels were at RL 170.96 m and
RL 183.88 m respectively. The foundation level is to be decided
MANIT Bhopal
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considering sufficient grip length below this level. Foundation
level may however be restricted at higher level in case good
quality rock is encountered above this level.
Table-2
Maximum Scour level at Intake considering different
approaches
Sl.No.
1
Scientific
Approac
h
Inglis
General Scour
q
DL  1.34
 f
2



Well
Diamete
r
Maximu
m Scour
level
8m
HFL- 2
DL
= 170.96
m
8m
HFL- D
– 1.4b
= 183.88
m
1
3
= 18.02 m
Water depth available at
HFL=17m
1
2
Shen
 Q 3
D  0.473  =
f
11.92 m
Water depth available at
HFL=17m
1
Research scholar, Department of Civil Engineering,
Visvesvaraya National Institute of Technology,
Nagpur, 440 010, India, Email: [email protected]
2
Assistant Professor, Department of Civil Engineering,
Visvesvaraya National Institute of Technology,
Nagpur, 440 010, India, Email: [email protected]
ABSRACT: Dams are critical flood control devices and a
major source of electric power, irrigation etc. An effort has
been made in this paper to optimized the parameter of dam as
even a small variation in the length or width of the dam can
overall reduce the tremendous cost of the structure. An excel
sheet has been prepared for this purpose with procedure
followed by Indian Standard Code IS 6512 Criteria for the
design of the gravity dam in which the parameter of the dam
like length; width has been change to get the optimized
parameter with the permissible stresses within the safe limit as
per the standards of code for the hydraulic structure.
Keywords: Gravity Dam, Design, Optimization parameter,
Critical values, Safety limits criteria, stresses.
1. INTRODUCTION:
CONCLUSIONS
Morphological and one dimensional mathematical model studies
were carried out for deciding intake location in Brahmani river.
The analysis of Topo sheet, satellite imageries and cross-sections
of river Brahmni revealed that the course of river Brahmni is
stable at proposed Intake location for more than 60 years. Hence,
the proposed location of intake well about 70 m upstream of
existing RSP intake and at 80 m from left bank in deep channel
of Brahmni river was hydraulically satisfactory. The founding
level of RL 170.96 m for the 8 m outer diameter Intake well was
considered necessary from maximum scour depth analysis and
adequate provision of grip length. Intake shall be provided with
one opening of size 2.2 m X 1.0 m at each of levels at RL 193.0
m and 199.0 m and could be operated effectively during
monsoon period to minimise the sediment entry into the intake
well. The formation level / pump floor level could be kept at
least 2.0 m above the HFL i.e. at RL 209.0 m at Intake location.
The construction / sinking of intake well is to be undertaken in
such a manner that the river flow conditions are least disturbed
and cofferdams / sheet piles etc. provided during sinking should
be removed as early as possible before the monsoon flood.
ACKNOWLEDGEMENT
Authors express deep sense of gratitude to Shri S. Govindan,
Director, CWPRS for constant encouragement and valuable
suggestions during preparation of papers and consent to publish
this paper. The co-operation extended by all the CWPRS staff
members in conducting studies is great- fully acknowledged.
REFERENCES
i.
CWPRS Technical report No. 5095, August 2013, ―Water
Availability and Intake Studies for Expansion of Rourkela
Power Plant of NSPCL, Odisha‖.
Study of Effect on the Stresses & Safety of Gravity
Dam with Changes in Width Parameter
B.S. Ruprai1
A.D Vasudeo2
Hydraulic Structures are important components of Water
Resources Engineering systems. Hydraulic structures such as
dam‟s, weirs, spillways, stilling basins, energy dissipaters etc
constitute major components of water resources projects. These
are the main components of the system and the primary focus of
analysis. Conventionally these structures are designed using
standard methods and codes. The design methods adopted are
also well established. But still it has been documented by many
of the researchers that the structures do not perform well during
the design life. It has also been observed in standard literature
that these structures fail without prior warning which leads to
catastrophic events.
The hydraulic and structural analysis and methods adopted in
designing of these structures are very complex. Even a small
saving in the height or width of the dam without affecting the
safety of the structure can give a lot of saving to the structure.
The present study is aimed at proposing a research methodology
for the design of big Water Resources Engineering systems. In
India specific design codes are available which document step
wise procedure for the design of Dams, Spillways, Conveyance
channel etc. However these components of the water resources
systems are treated in isolation. An algorithm is prepared to
optimize the parameter of the design of the gravity dam in the
present case. The design procedure is adopted by Indian
Standard
IS-6512:1984, “Criteria for the design of solid
gravity dam”. To make the optimization procedure more
understandable, a Microsoft Excel Sheet program is prepared to
analyze the effects of varying dimensions and the factors on
which the design is dependent. The sheet provides a good tool to
check the permissible stresses and stability of the dam against
sliding and overturning and safety within the permissible limits
prescribed in the IS Code.
2. MATERIAL AND METHOD:
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By varying the width of dam on both the upstream side and the
downstream side the stresses are studied for all the different
cases of the dam which are discussed in brief in result and
discussion. The Design constant taken is the height of the dam
which will depend on water level and free board. Design
variables are the sloping projection of height on the upstream
side of the dam considered as X1, width of the dam on the
upstream side of the dam is taken as X2 and width of the dam on
the downstream side of the dam is considered as X3. A typical;
Diagram Showing these parameters is in Figure No : 1.
Figure No. 1 : Typical diagram showing the parameters of dam
By observing the Figure No. 1, it is evident that the upstream
and downstream slopes have a great impact on the values of X2
and X3 which in turn will govern the total base with of the dam.
Whereas X1 remains unaffected as it is the Height which already
is assumed to be constant.
By varying the slope the decrease in any of the above parameter
can directly change the dimensions of the dam and in either case
reduce or increase the total size and will affect the stresses and
stability. The above equation for design variables can be
mathematically written as:
X = f [x1, x2, x3]T
(1)
As for the geometric constraints if we consider the Y axis of the
dam, the design variables X1 which limit from the geometry of
the gravity dam can be studied from the figure no. 1 with the
minimum level of the dam ie origin to the maximum level of the
dam ie total height „H‟ of the dam and can be mathematically
written in the form of equation as given below:
(2)
Similarly, the second design variable if we follow the X axis of
the dam which will be the upstream side slope of the gravity
dam is kept to the steeper limiting angle because in the angle is
reduced the width will increase on the upstream side of the dam
which is not advise due to less contribution to the safety and
stability of the dam and also create the hindrance of the storage
capacity of the dam. Hence the slope is steeper from the above
point of the view and its limit can expressed in the mathematical
from as given below:
(3)
Regarding the third design variable which is also along the Xaxis of the Figure No. 1 that will be the downstream side slope
of the gravity dam and it will depend on the engineers decision
whether to start the slope from the top width of the dam or lower
than that. In our case the slope is not directly started from the top
width from optimization point of the view and hence to reduce
the amount of concrete the slope is kept less as that of the
upstream height and the downstream height is kept H c as shown
in the Figure No. 1 from the economy point of the view.
Mathematically the equation can be written as given below:
0.6Hc2 – X3 ≤ 0 &X3 – 0.8Hc2 ≤ 0
Or
(4)
Thus by varying the above values the optimized width is
obtained as in this paper a parameter on the downstream side of
the width is only reduced as there is significant saving in the
concrete and their by directly affecting the cost of the dam is
studied which are discussed in the results. As there is not
considerable saving in the dam if the parameter of X 2 is reduced
because already taken very steeper as they play less importance
in the stability of the dam as majority of the dam is have higher
width on the downstream side only.
For case 1, the dam is checked for the reservoir empty
conditions in which the eccentricity is less than < b/6 means no
tension will be developed and vertical stresses are checked at toe
and heel and are within the permissible limits. Also the stability
is checked by the formula as stated in Indian Standard Code IS
6512:1984 as under:
( w  u ) tan  CA

F
Fo
F
P
(5)
F=Factor of safety,
w=total mass of the dam,
u=total uplift force,
tan  = coefficient of internal friction of the material
C=cohesion of the material at the plane considered
A= area under consideration for cohesion
F  =partial factor of safety in respect of friction,
Fo=partial factor of safety in respect of cohesion, and
P= total horizontal force
Also the stability is checked for the overturning moment and
given by the formula as under:
Factor of safety against overturning = Resisting
Moment/Overturning Moment and should be less than the IS
code permissible limit.
Similarly the stresses and stability are checked considering the
reservoir full condition, considering uplift for case 2, reservoir
full condition, considering no uplift for case 3 and reservoir full
condition with drains chocked for case 4 which are discussed in
details in result and conclusion by adopting the above algorithm
for the programming.
3. RESULT AND DISCUSSION:
Form the standard literature a generalized dam section is
optimized by changing the width of the dam; stresses and
stability are checked to satisfy the Indian Standard Code. The
result and discussion are given below and by reviewing the
graphs, it can be studied that there is very small change in the
stresses as the width is reduced by 0.1m from 51m to 50m after
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which their the dam is not safe in sliding criteria. The detailed
discussion of the result is as under.
Figure No. 4: Variation of Parameter of dam & Stresses with change in the
width of toe for reservoir full condition with no uplift
Figure No. 2: Variation of Parameter of dam & Stresses with
change in the width of toe for reservoir Empty Case
Consider Case 1 for reservoir empty condition in which with the
variation in parameter of dam & stresses are studied with change
in the width of toe and as from the above Figure No. 2, the
various parameter of dam like eccentricity, various stresses are
checked for reservoir empty condition and the width of the dam
at toe is reduced from 51m to 50m with height constant and the
stresses are safe for this condition, but if we reduce further the
width the stresses does not remain safe as per the Indian
Standards. The factor of Safety against Sliding and Overturning
satisfy the safety criteria for this case.
Consider case 3 of dam for reservoir full condition with no uplift
in which with the variation in parameter of dam & stresses are
studied with change in the width of toe and as from the above
Figure 4, the various parameter of dam like eccentricity, various
stresses are checked for reservoir full condition with uplift case
and the width of the dam at toe is reduced from 51m to 50m with
height constant and the stresses are safe for this condition, but if
we reduce further the width the stresses does not remain safe as
per the Indian Standards. The factor of Safety against Sliding
and Overturning satisfy the safety criteria for this case.
Figure No. 5: Variation of Parameter of dam & Stresses with change in the width
of toe for reservoir full condition drains chocked
Figure No. 3: Variation of Parameter of dam & Stresses with
change in the width of toe for reservoir full condition with uplift
Consider case 2 of dam for reservoir full condition with uplift in
which with the variation in parameter of dam & stresses are
per the Indian Standards. The factor of Safety against Sliding
and Overturning satisfy the safety criteria for this case.
Consider case 4 of dam for reservoir full condition with no uplift
in which with the variation in parameter of dam & stresses are
per the Indian Standards. The factor of Safety against Sliding is
the only component which is not safe while the overturning
criteria satisfy safety for this case. Thus by providing Sliding
key of small size the sliding criteria can also be satisfied.
4. CONCLUSION:
The economy can be further achieved by reducing the width of
the dam as the stresses are within the safety limit. Just by
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decreasing the width of the dam by just one meter and satisfying
the stress and stability a considerable saving in the cost is
achieved. As the constraints of the width of the dam which
cannot be reduced further due to sliding of the gravity dam is not
within the permissible limit, but if we consider the structural
aspect by provision of shear key the width of the dam can be
further reduced to get more economy. As our limitation of the
research is to have normal gravity dam without shear key hence
the economy of the width can be optimized upto certain limit
only.
5. APPENDIX I
Consider the example in which the dam is design for the below
mentioned case which is safe in all the design aspect as taken
from standard literature, but if applying the algorithm stated
above the width of the heel is reduced to 60m without affecting
the safety their by achieving considerable saving in the concrete
and hence the overall economy.
Total Width of the dam
=
61m
Width of the heel
=
51m
Width of the toe
=
10m
Height of the dam
=
65m
6. REFRENCES:
i.
Cohn, M. Z. and Dinovitzer, A. S., (1994). Application of structural
optimization, Journal of Structural Engineering, ASCE, 120(2): 617–650.
ii.
E.J. Haug and J.S Arora, 1979. Applied optimal design, WileyInterscience, New York.
iii.
F. González-Vidosa, V. Yepes, J. Alcalá, M. Carrera, C. Perea and I.
Payá-Zaforteza., (2000). Optimization of Reinforced Concrete Structures by
Simulated Annealing, School of Civil Engineering,Universidad Politécnica
Valencia, Spain.
iv.
IS 6512: Indian Standard Code of practice for design of gravity dam,
2010.
v.
Kirsch, U., (1997), How to optimize prestressed concrete beams,
Guide to structural optimization. Edited by J.S. Arora. ASCE Manuals and
Reports on Engineering Practice No. 90, American Society of Civil Engineers,
New York. pp. 75–92.
vi.
Lazan, B. J., (1959). Energy dissipation mechanisms in structures
with particular reference tomaterial damping, in Structural Dynamics, edited by J.
E. Ruzcka, ASME Annual Meeting, Atlantic City, N. J.
vii.
S.S. Rao., 1977. Optimization theory and applications (Second
Edition), 1977 Wilsey Eastern Limited, New Delhi.
viii.
U. Kirsch, 1981. Optimum Structural Design, McGraw Hill, New
York.
ix.
Zienkiewicz, O. C. and Taylor, R. L., (1991). The Finite Element
Method, McGraw-Hill, London, Fourth edition.
Assessment of environmentally stressed areas for
soil conservation measures using usped model.
Bikram Prasad1, R K Jaiswal2 and Dr H.L Tiwari3
1.
Ph.D Scholar MANIT, Bhopal
2
Scientist, National Instiute of Hydrology Bhopal
3
Assistant Professor, MANIT Bhopal.
ABSTRACT: A balanced ecosystem consisting of soil, water,
and vegetation is essential for the Survival and welfare of
human. However, over-exploitation of natural resources
created disturbances in ecosystems and induces natural
hazards. Erosion and Sedimentation are major issues in
disrupted ecosystems. Soil erosion is a major environmental
and agricultural problem worldwide. The loss of soil from
farmland may be reflected in reduced crop production
potential, lower surface water quality and damaged drainage
networks. We have studied the environmentally stressed area in
a catchment using USPED model. In this attempt my study
area is The Kodar reservoir, constructed across river Kodar,
a tributary of river Mahanadi. The dam is constructed on
Raipur – Sambalpur national highway at a distance of 65
km from Raipur near village Kowajhar in Mahasamund
district. We studied the soil stresses area of the Kodar
reservoir using USPED model. This model is built on the
backbone of the Universal Soil Loss Equation (USLE) and the
Revised Universal Soil Loss Equation (RUSLE) models.. It
depends on Rainfall erosivity factor, Soil erodibility factor,
Topographic index, Cover and management factor and
Support practice factor. It predicts the spatial distribution of
erosion and deposition rates for a steady state overland flow
associated with a given rainfall input. We have generated the
thematic layers in GIS for development of USPED modelBy
using the method we have given the priorities and divided subwatersheds as very high, high, moderate, low and very low
priority. We have concluded that out of 67 sub-watershed 8 sub
watershed comes under very high, 2 under high, 3 under
moderate and rest under low and very low priority.
Keywords: USPED, Watershed, soil erodibility.
1. INTRODUCTION
Sediments deposited in the reservoir can be transported into the
headrace tunnel and can lead to the wearing of mechanical parts
of the Power station units such as buckets and the needle valves.
The silting of reservoir can reduce the storage capacity of
reservoir and high level of sediment deposited in the dam can
also raise concern for the stability of the dam. Soil Erosion and
sedimentation are the major environmental and agricultural
problem worldwide. A balanced ecosystem consisting of soil,
water and vegetation is necessary for the survival and fortunes of
human being. Nearly 12×106 ha of available land are destroyed
annually and to adequately feed people a diverse diet about 0.5
ha of arable land per capita is needed but only 0.27 ha per capita
is available. The world population is increasing and there is
continuously degradation of land by erosion resulting in food
shortages and malnutrition. However, over-exploitation of
natural resources created disturbances in ecosystems and induces
natural hazards. Although the erosion has occurred throughout
the history of agriculture it has intensified in the recent years.
Hence in this study we will identify the erosion affected area and
will conclude some preventive measures to minimize the soil
loss.
1.1 Soil erosion by water
Soil erosion is a naturally occurring process and is the wearing
of a field's top soil by the natural physical forces of water and
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wind or through forces associated with farming activities such as
tillage. Soil erosion is a slow process that continues relatively
unnoticed, or it may occur at an alarming rate causing serious
loss of topsoil. The loss of soil from agricultural land can lead to
reduction in crop production potential, lower surface water
quality and damaged drainage networks. It depends upon various
factors such as rainfall erosivity factor, soil erodibility
topographic factor vegetation and tillage practices
2. STUDY AREA
The Kodar reservoir which is constructed on river Kodar, a
tributary of river Mahanadi has been selected for the systematic
and scientific study of reservoir sedimentation, sediment yield
from catchment areas and prioritization of catchment for soil
conservation measures.
3. METHODOLOGY
3.1. USPED
This model is developed on the backbone of the Universal Soil
Loss Equation (USLE) and the Revised Universal Soil Loss
Equation (RUSLE) models. The USPED model considers
divergence and convergence of slope by modelling, in a
geographic information system environment, the entire upslope
area that contributes to the overland flow of water across every
point in the landscape. The model more fully accounts for
topographic complexity by considering both in the downhill
direction and the perpendicular to the downhill direction. It
computes both soil erosion and sediment deposition as the
change in sediment transport capacity in the direction of flow.
This paper attempts to identify the spatial patterns of soil erosion
within the catchment area on river Kodar, a tributary of river
Mahanadi in Raipur. Maps of erosion and deposition were
derived for catchment area of river Kodar, a tributary of river
Mahanadi and its individual sub-basins by implementing the
USPED model. The USPED model employs a stream powerbased sediment transport model with an expression of mass
conservation to simulate soil erosion and deposition. The model
departs from the RUSLE annual average soil loss equation
expressed by E (tons/acre/year).
(1)
Where R represents the rainfall erosivity index, K the soil
erodibility factor, LS the slope length and steepness, C the land
cover management factor, and P represents the support practices
factor.
The USPED model assumes that sediment transport rates are
determined by the erosional strength of flowing water, and never
limited by the supply of transportable soil particles. Thus it is
assumed that the sediment transport rate (capacity) is given by:
(2)
where b represents the local surface slope (degrees), m and n are
constants depending on the type of flow and soil properties,
where the constants m and n have the values 1.6 and 1.3
respectively for prevailing rill erosion and 1 for prevailing sheet
erosion. The results of the USPED model represent relative
magnitudes of the soil erosion and deposition rates rather
specific soil loss values traditionally expressed in tons/acre/year.
The net rate of soil erosion or deposition (ED) is given by the
two-dimensional (horizontal plane) divergence of the sediment
flux that expresses mass conservation:
(3)
Where, a represents the aspect of the terrain (the direction of
maximum hill slope gradient in the horizontal plane in degrees).
3.1.1 Rainfall erosivity factor (R)
The R factor is calculated by rainfall and the energy imparted to
the land surface by the impact of rain drop. Rainfall erosion
index implies a numerical evaluation of a rainstorm which
describes its capacity to erode soil from an unprotected field. It
is a function of intensity and duration of rainfall and mass,
diameter, and velocity of the rain drop.
Annual R factor,
(4)
Ra  79  0.363 * PA
where, PA is the annual rainfall in mm and Ra are annual Rfactor in MJ mmha-1yr-1. The theissen map (Fig. 1) of Kodar
catchment has been prepared using the ILWIS 3.0 software and
it observed that Kodar catchment is affected by Kodar,
Bagbahara and Bartunga R.G. stations. The weights and R-factor
for different RG stations have been presented in The value of
annual and seasonal R-factor for Kodar reservoir catchment has
been obtained as 429.39 MJmmha-1hr-1 and 402.94 MJmmha-1hr1
respectively. The weights of Kodar, Bagbahara and Bartunga
RG stations have been computed as 0.50, 0.48 and 0.02
respectively. The rainfall in the study area concentrated mainly in
the month of July, August and September. By using the operation
attribute map input as thessien polygon and table as R value
output Rmap is generated is shown Fig 2.
Fig 1: Thessien polygon map of the study area
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Fig. 3: Kmap for the Kodar catchment
Fig 2: Rmap for the Kodar catchment
3.1.2 Soil erodibility factor (K)
The soil erodibility factor relates the rate at which different soils
erode. K is expressed as soil loss per unit of area per unit of R
from a standard plot (a plot of 22.3m long with a uniform slope
of 9% under continuous fallow and tilled parallel to the slope. In
case of USLE, the standard
.14
100 K  2.1M 1Kodar
(10 4 )(12  a )  3.25(b  2)  2.5( c  3) (5)
Bagbahar
a silt, very fine sand and clay [(% of
where, M is the
percent of
Bar t unga
very fine sand+%
of silt)*(100-% of clay)], a is the organic
matter, b is the structure of the soil (very fine granular=1, fine
granular=2, coarse granular=3, lattic or massive=4) and c is the
permeability of the soil (fast=1, fast to moderately fast=2,
moderately fast =3, moderately fast to slow=4, slow=5, very
slow=6). For determination of organic matter from organic
carbon a factor 1.724 has been used (BUB, 2007; Wayne et al,
2003). The soil map of the study area has been taken from the
soil map of National Bureau of Soil Survey & Land Use
Planning (NBSS&LUP). By using the operation attribute map
and feeding the table as Kvalue, Kmap has been generated in
the ILWIS 3.0 software (Table 1 & Fig. 3).
3.1.3 Topographic index (
)
The topographic index was calculated using the Digital
Elevation Model which has been generated using contour map
and point elevation map obtained from the seamless data
distribution database. The use of DEM has been documented by
Mitasova et al (1996) to be the most reliable elevation data when
higher resolution data is unavailable because it allows for lower
levels of systematic errors and artifacts of analysis compared to
the lower resolution DEMs that are available. The interpolation
for contour map and rasterize operation for point elevation has
been performed to get two separate raster maps. The „iff‟
statement of ILWIS has been used to combine both the raster
maps to get the DEM. The points defining the flow line are
computed as the points of intersection of a line constructed in the
flow direction given by aspect angle a: and a grid cell edge. The
Map Calculation option of raster operation in ILWIS has been
used to determine topographic factor map (Fig. 4).
Table 1: Computation of K-factor for soils in the study area
573.63
515.99
Nomencl
ature
%
Fine
sand
% Silt
%
Clay
657
&670
11.03
11.32
689
8.60
23.87
1.80
12.2
2
710
6.30
5.41
0.00
733
4.47
14.12
2.14
746
3.20
26.87
3.22
747
10.03
19.83
0.00
M
2668.
59
2850.
38
1171.
00
1819.
22
2910.
32
3086.
00
a
1.6
2
2.0
3
1.6
2
1.2
1
1.9
7
0.8
6
b
c
K
Fact
or
458.35
400.72
343.08
285.44
3
1
0.15
3
1
0.20
3
3
0.09
3
3
0.15
3
2
0.20
3
2
0.24
Fig 4: Digital elevation model for Kodar catchment
3.1.4 Cover and management factor (C)
The main role of vegetation cover in the interception of the rain
drops is that their kinetic energy is dissipated by them. The crop
management factor is the expected ratio of soil loss from land
cropped under specified conditions to soil loss from clean, tilled
fallow or identical soil and slope and under the same rainfall.
Available soil loss data from undisturbed land were not
sufficient to derive C values by direct comparison of measured
soil loss rates, as was done for the development of C values for
cropland. The following equation suggested by Van der et al.
1999, 2000 has been used for estimation of C factor.
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
NDVI 
 

(6)
C  exp   NDVI 
The α-value of 2 and β-value of 1 gave good results ( Ioannis et
al, 2009) have been used in the study. It has been observed that
some values of C-factor may be reached to value greater than the
limiting value of 1.0 and hence a scaling factor Z was used to
keep the C-factor within the range of 0 to 1 (Mokua, 2009). The
equation 3.6 can be written as:

 
C  Z  exp
NDVI 

  NDVI 
(7)
For computation of value of Z, a scalar graph can be plotted
between NDVI and C-factor and value of Z has been determined
by iterations to scale the values of C-factors from 0 to 1.
NDVI has been calculated from the equation
 RED  NIR 
NDVI  
 RED  NIR 
conservation measures. The histogram of the resultant map has
been used to estimate the rate of soil erosion from the catchment.
The land use classification of the study area has been taken from
IRS LISS IV data. Using spectral signatures of various land
uses, sample sets for different land uses have been prepared. The
maximum likelihood technique of classification has been used
for generation of land use map of Kodar catchment. From the
analysis, it has been observed that the Kodar catchment is an
agriculture watershed covering nearly eighty percent of
watershed with dense forest on the ridges only. Several small
water bodies in the form of village tanks have been found in
Kodar catchment which is used for bathing, cattle, recreation and
other house hold work.
(8)
RED is Band III and NIR is Band IV of IRS satellites (IRS ID
and P6). For determination of C-factor map of the study area, the
NDVI image of LISS III data for the study area has been
generated. The C-factor-map using equation 7 has been prepared
and a graph between NDVI and C-factor values has been plotted.
From the analysis of graph, it has been observed that the some of
the C-factor values were going above the limiting value of Cfactor. Therefore, a correction factor of 0.6246 has been applied
to keep all the values between 0 and 1 (Fig. 5).
Fig 6: P map for the Kodar catchment
4. ANALYSIS AND RESULT
The map of all the factors responsible for developing the
USPED model is generated. The sediment flux is than calculated
by multiplication of all the maps separately for both rill and
sheet. The directional derivative of the sediment transport
capacity is than computed using the command Mapfilter. Finally
using the Map slicing command the Erosional Depositional map
for
sheet
and
rill
is
generated.
Fig 3.5 C map for the Kodar catchment
3.1.5 Support practice factor (P)
Conservation practice conditions consist mainly in the methods
of land use and tillage, and the agro technology. The amount of
soil loss from a given land is influenced by the land management
practice adopted. The value of P ranges from 1.0 for up and
down cultivation to 0.25 for contour strip cropping of gentle
slope. In case of USPED model, the agricultural area of
catchment has been divided in different slope ranges and
according to slope, the values of P-factor have been assigned
(Fig. 6). For other land uses, standard values considering no
conservation measures have been given. All the thematic maps
have been generated in ILWIS GIS for USPED model. After
multiplication of thematic maps R, K, LS, C and P-factors, the
annual and seasonal soil loss maps giving spatial distribution of
soil losses have been generated. It has been observed from the
field visits that presently no conservation measures are being
implemented in study area, P-factor map has been generated
using P-factor values for different land uses with no
MANIT Bhopal
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SW-29
SW-31
SW-35
SW-37
SW-40
SW-42
SW-47
SW-52
SW-54
SW-56
SW-59
Total
Fig 7: Sheet and Rill Erosion and Deposition after map slicing in
Kodar catchment.
4.1 Watershed prioritization using usped model :
From the histogram all the erosional value for the sub-watershed
has been taken and mean average value is calculated. Both the
sheet and rill erosion value has been taken and the erosional
value has been sorted0 between 0 and 1. The priorities of subwatersheds have been divided in the various ranges i.e. more
than 0.50 as very high, 0.50 to 0.30 as high, 0.30 to 0.20 as
moderate, 0.20 to 0.10 as low and less than 0.10 as very low
priority.
4.2 Overall prioritization:
The overall priority has been evaluated by taking the mean of the
sheet and rill value. The final priorities of sub-watersheds have
been divided in the various ranges i.e. more than 0.50 as very
high, 0.50 to 0.30 as high, 0.30 to 0.20 as moderate, 0.20 to 0.10
as low and less than 0.10 as very low priority, so that
environmentally stressed areas can be identified for soil
conservation measures
Table 4.1 Overall Prioritization of Sub Watershed
S.N.
Priority
Class
Range
of final
priority
No. of
watershed
1.
V. high
Up to
0.50
08
2.
High
02
3.
Moderate
4.
Low
0.50 to
0.3
0.30 to
0.20
0.20 to
0.10
5.
V. low
Less
than
0.10
47
03
07
Sub-Watershed
SW-2, SW-38,
SW-44. SW-45,
SW -46, SW-48,
SW-49 and SW63
SW-61 and SW64
SW-60, SW-62
and SW-65
SW-1,
SW-5,
SW-32, SW-50,
SW-57, SW-66
and SW-67
SW-3,
SW-4,
SW-6,
SW-7,
SW-8
SW-9,
SW-10, SW-11,
SW-12 SW-14
SW-15 SW-16
SW-17 SW-18
SW-19 SW-20
SW-21 SW-22
SW-23 SW-24
SW-25 SW-26
SW-27 SW-28
Total
area
(sq.
km)
24.29
9.93
23.65
41.08
208.76
SW-30
SW-34
SW-36
SW-39
SW-41
SW-43
SW-51
SW53
SW-55
SW-58
307.71
Fig 8: Overall Prioritization of Watershed
5. CONCLUSION
Intensified pressures on the land and an improved understanding
of human impacts on the environment are leading to profound
changes in land management. This trend has a significant impact
on the development of supporting GIS and modelling tools. In
this paper, a soil erosion model at Kodar catchment with the
integration of USPED (Unit Stream Power Erosion and
Deposition) and GIS tools has been developed to estimate the
annual soil loss. Different components of USPED were modelled
using various mathematical formulae to explore the relationship
between Rainfall emissivity, Soil erodibilty, Topographic factor,
Crop factor and Practice factor maps. The USPED model was
implemented in geographic information system (GIS) for
predicting the spatial patterns of soil erosion risk required for
soil conservation planning From the analysis of the Kodar
catchment using USPED model it has been observed that 52.22
km2 area has been subjected to sheet erosion, while the eroded
material may deposit in 42.48 km2 area of Kodar reservoir. The
areas affected by sheet erosion may be treated with agronomic
measures of soil conservation such as contour farming, contour
bunding, bench terracing etc on cropped land and afforestation,
agro- forestry on degraded forest and barren lands. Similarly,
55.25 km2 areas of Kodar reservoir may be affected by rill
erosion where suitable mechanical soil conservation measures in
the form check dams, gully plugs etc. may be constructed.
According to this model, approximately in 67.8 % of the basin
has very low erosion risk and 13.38 percent has low erosion risk
7.77 percent area has moderate risk. But erosion risk is high on
3.3% and Very High on 7.89% of the basin. In general, it is clear
from the results of this study that the developed model is
beneficial for the rapid assessment of soil erosion.
REFERENCES
MANIT Bhopal
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i.
Alejandra, Puerto, Rico., González, M, Rojas.,(2008) Soil erosion
calculation using remote sensing and GIS in río grande de arecibo watershed,
Annual Conference Portland, Oregon.
ii.
Bhattarai, Rabin., & Dutta, Dushmata., (2006) Estimation of Soil
Erosion and Sediment Yield Using GIS at Catchment Scale Springer Science
Business Media B.V..
iii.
Jones, S, David., Kowalski, G, David., and Shaw, B, Robert., (1996)
Calculating Revised Universal Soil Loss Equation (RUSLE) Estimates on
Department of Defense Lands: A Review of RUSLE Factors and U.S. Army Land
Condition-Trend Analysis (LCTA) Data Gaps.
iv.
Kumar, Suresh., and Kushwaha, SPS., Modeling Soil Erosion Risk
based on RUSLE-3D using GIS in a Shivalik sub-watershed.
v.
Liu, Jinxun., Liu, Shuguang., Tieszen L. Larry and Chen, (2014)
Mingshi Estimating Soil Erosion Using the USPED Model and Conservation
Remotely Sensed Land Cover Observations
vi.
May, Linda., and Place, Chris., (2005) GIS-based model of soil
erosion and transport Freshwater Forum
vii.
Mitasova, H. and Mitas, L., (1999) Erosion/deposition modeling with
USPED using GIS. http://www2.gis.uiuc.edu:2280/modviz/erosion/usped.html.
viii.
Mitasova, Helena., Hofierka, Jaroslav., Zlocha, Maros., & Iverson,
R, Louis., (1996) Modelling topographic potential for erosion and deposition using
GIS, International Journal of Geographical Information Systems, , VOL 10, NO 5,
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ix.
Nearing, M.A., Jetten, V. , Baffaut, C., Cerdan, Couturierd, A.,
Hernandeza, M., Le Bissonnaise, Y., Nicholsa, H, M., Nunesf, P, J., Renschlerg,
C.S., V. Souche`reh,. Oost, van, K., (2005) Modeling response of soil erosion and
runoff to changes in precipitation and cover.
x.
Paige, Ginger., and Zygmunt, Jennifer.,(2012) The science behind
wildfire effects on water quality and erosion.
xi.
Pistocchi, A., Cassani, G., and Zani, O., (2009) Use of the USPED
model for mapping soil erosion and managing best land conservation practices,
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Pricope, G, Narcisa., (2009) Assessment of Spatial Patterns of
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Journal of Spatial Hydrology Vol.9, No.1 Spring.
xiii.
Wordofa, Gossa., (2011) Soil erosion modelling using GIS and
RUSLE on the EURAJOKI watershed FINLAND.
A Novel Optimisation Model Applied to Godavari
River Basin
R.B.Katiyar2,Balaji Dhopte1, Tejeswi Ramprasad1, Shashank
Tiwari2, Anil Kumar2, K.R.Gota2
1
Department of Chemical Engineering, Jawaharlal Nehru
Engineering College, Aurangabad-431003
1
Department of Chemical Engineering, Maulana Azad National
Institute of Technology, Bhopal-462051
[email protected]
ABSTRACT: Integrated water resources management (IWRM)
is a rapidly developing field encompassing many disciplines
including ecology, engineering, economics, and policy.
Generic integrated watershed management optimization model
is developed to study efficiently a broad range of technical,
economic, and policy management options within a watershed
system framework and choose the optimum combination of
management strategies and associated water allocations for
designing a sustainable watershed management plan at
minimum cost. The watershed management model integrates
both natural and human elements of a watershed system and
includes the management of ground and surface water
sources, water treatment and distribution systems, human
demands, wastewater treatment and collection systems, water
reuse facilities, non potable water distribution infrastructure,
aquifer storage and recharge facilities, storm water, and land
use. The model was formulated as a linear program and
applied to Godavari basin in India. Results according to the
study carried out demonstrate the merits of integrated
watershed management by showing the relative effectiveness
and economic efficiency of undervalued management options ,
the value of management strategies that provide several
functions such as the benefits of increased infiltration for
meeting both storm water and water supply management
objectives and that both human and environmental water
needs can be met by simultaneously implementing multiple
diverse management tools, which in this case study led to
achieving 60-65% of the recommended in-stream flow with
only 25% decrease in net benefits.
Keywords: Optimization models; Integrated systems; Water
supply; Watersheds; Water management; Storm water
management; Land management; Wastewater management;
Groundwater recharge
1. INTRODUCTION
Water is an important resource which is used in each and every
industrial sector. But the increasing demand on water from the
sectors emphasizes the need of integrated watershed. It therefore
becomes necessary to understand what is a watershed, the
various kinds of interactions in a watershed, the side effects of
degradation of a watershed and basic approach on how to
implement a watershed management plan for a water source.
(USEPA publication.,2013)
A watershed is the area of land that delivers runoff water,
sediment and dissolved substances to a river. It a unit which
collects, stores and releases water through the networks to the
main river. It is an integration of flora, fauna, land, water and
their interacting elements.
It is quite clear that in order to study the integrated watershed
management we need to have a basic knowledge of the
hydrological principles which govern the occurrence,
distribution, movement and properties of the water. The
hydrological cycle describes the various paths water may take
during its continuous circulation from ocean to atmosphere to
earth and back to ocean. Water is temporarily stored in streams,
in lakes, in the soil and as groundwater. The basic watershed
equation is given as:
P=I + F + E + T+ Q ± S
Where, P is precipitation, I is interception, F is filtration, E is
evaporation, T is plant transportation, Q is runoff and S is
storage.
Atmospheric moisture is one of the smallest storage volumes of
the earth‟s water, yet it is the most vital source of freshwater for
humankind. The distribution and amount of precipitation (P)
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depends on air mass circulation patterns, distance and direction
from large water bodies and local topography. Precipitation may
be intercepted or captured by leaves, twigs, stems and soil
surface organic matter and returned to the atmosphere as water
vapour. This process known as interception (I) does not help to
recharge soil moisture or generate stream flow in fact it lessens
the impact of the raindrop on the soil surface and the danger of
soil erosion. When water reaches the ground surface, a portion
of it is absorbed by the soil. Infiltration (F) is the process of
water seeping into the soil and is controlled by surface soil
conditions, such as soil texture, vegetation type and land use. For
the purpose of integrated watershed management, necessity is to
develop models which focus on developing comprehensive
watershed management models as opposed to the existing
redundant hydrologic models. Such models are referred to as
integrated watershed management models. Two of these models
which are the most common models in practice are Water
Evaluation and Planning (WEAP) (Yates et.al, 2005) and Water
Ware (Jamieson and Fedra., 1996)
2. MODEL FORMULATION
The model introduced here is a generic lumped parameter model
that combines the principles of the hydrologic cycle, human
water system and a wide range of management options. The
natural components of the watershed system are depicted with
white backgrounds. These include land use, runoff, percolation,
surface water, groundwater and external surface water, and
ground water. Run off and percolation is specified as unit values
of flow per land area for each land use type for a hydrologic
design condition. The land use component specifies the existing
area of each land use type. Surface water, representing rivers and
other landscape sources of water is assumed to have negligible
channel storage and hence empties completely within each time
step. The underground water is the only natural watershed
component with a large storage capacity. The human
components of the watershed system are depicted with gray and
black backgrounds. Gray is used for components that exist and
are managed by water and waste water utilities. The human
system includes a reservoir, potable water treatment plant,
potable distribution system, wastewater treatment plant,
wastewater collection system, water reuse facility, non potable
distribution system, septic systems, and aquifer storage and
recharge facility. The reservoir may be a single reservoir or the
sum of many reservoirs assumed to be operated together as a
single reservoir system. The potable water treatment plant treats
water from surface water, reservoir, or groundwater sources to
drinking water standards. The wastewater treatment plant
provides secondary wastewater treatment to meet surface water
discharge quality standards. Its effluent may be further treated
by tertiary wastewater treatment at the water reuse facility.
(Zoltay, et.al. 2010)
Fig.1 : Schematic representation of the integrated watershed
management model
BMP‟s- Best Management Practices,
SW – Surface Water
GW- Ground Water
WTP – Water Treatment Plant
P use – Potable use
NP use – Non Potable use
ASR – Aquifer Storage and Recharge
WWTP – Waste Water Treatment Plan
3. BACKGROUND
The river Godavari is the second largest river in the country and
the largest in Southern India. It raises in the Sahyadri hills at an
altitude of about 1067 m near Triambakeswar in the Nasik
district of Maharashtra State and flows across the Deccan
plateau from the Western Ghats to Eastern Ghats. Rising in the
Western Ghats about 80 km from the shore of the Arabian sea, it
flows for a total length of about 1465 km in a general SouthEastern direction through the States of Maharashtra and Andhra
Pradesh before joining the Bay of Bengal at about 97 km south
of Rajahmundry in Andhra Pradesh.
The major tributaries joining the Godavari are the Pravara, the
Purna, the Manjra, the Maner, the Pranhita, the Penganga, the
Wardha, the Wainganga, the Indravati and the Sabari. The
Godavari basin extends over an area of 312813 km2, which is
nearly 10% of the total geographical area of the country. The
basin comprises areas in the States of Maharashtra, Madhya
Pradesh, Chhattisgarh, Andhra Pradesh, Karnataka and Orissa.
The State-wise distribution of the areas is given in table below:
Table 1: Distribution of Godavari river
Sr.
No.
Name of the
state
1.
2.
3.
4.
Maharashtra
Madhya Pradesh
Chhattisgarh
Andhra Pradesh
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Drainage
are
(km2)
152199
26168
39087
73201
Percentage of the
total
basin drainage area
48.6
8.4
12.5
23.4
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5.
6.
Karnataka
Orissa
Total
4406
17752
312813
18-19, Dec. 2014
1.4
5.7
100.0
Except for the hills forming the watershed around the basin, the
entire drainage basin of the river Godavari comprises of
undulating country, a series of ridges and valleys interspersed
with low hill ranges. Large flat areas which are characteristic of
the Indo-Gangetic plains are scarce except in the delta. The
Sahyadri ranges of Western Ghats form the Western edge of the
basin. The interior of the basin is a plateau divided into a series
of valleys sloping generally towards East. The Eastern Ghats,
which form the Eastern boundary, are not so well defined as the
Sahyadri range on the West. The Northern boundary of the basin
comprises of tablelands with varying elevation. Large stretches
of plains interspersed by hill ranges lie to the South. Important
tributaries of Godavari is given the following table : (Integrated
Hydrological Data Book.,2006)
Table 2: Tributaries of Godavari river
Sr.
No.
Name of
the river
Elevation
of source
Length
of tributary
(km)
Catchment
Area
(sq.km.)
1
Upper
Godavari
Pravara
Purna
Manjira
Middle
Godavari
Maner
Penganga
Wardha
Pranhita
Lower
Godavari
Indravati
Sabari
1,067
675
33502
Average
annual
Rainfall
(mm)
770
1,050
838
823
323
208
373
724
328
6537
15579
30844
17205
606
797
846
955
533
686
777
640
107
225
676
483
721
462
13106
23898
24087
61093
24869
932
960
1055
1363
1208
914
1,372
535
418
41665
20427
1588
1433
2
3
4
5
6
7
8
9
10
11
12
The water resources potential in Godavari basin has been
assessed to be 110.54 km3.The utilisable surface water is about
76.3 km3 ,the replenish able ground water is about 45 km3. There
is a vast potential for irrigation development and hydropower
generation in the basin. Prior to Independence only a few
irrigation projects were constructed in Godavari basin.
Important among these are Godavari delta system (with
Dowlaiswaram weir as head works), Nizamsagar reservoir,
Kadana dam and Pravara dam. After independence, under
various five year plans a large number of multipurpose and
irrigation projects have been taken up. Themost important
among them are the Jaikwadi, Sriramsagar, Kadam, Upper
Indravati, Singur and Godavari Barrage (by modernising the
existing gated weir at Dowlaiswaram). Since the mid1960's, the
Central Water Commission is conducting hydrological
observations in Godavari basin. Hydrological observation
stations have been established on main Godavari River as well
as on all the important tributaries. During the year 2008-09,
hydrological observations at 48 stations have been under
operation. Out of these, 7 stations are on the main Godavari and
the remaining 41 are on its tributaries. In addition to gauge and
discharge observations, sediment load at 16 stations and water
quality monitoring at 18 stations are also being done. There are
32 water quality measurement sites on the basin and as many as
25 of them are for sediment measurements also. In addition,
there are 24 gauge discharge observation stations in the basin.
4. IMPLEMENTATION OF MANAGEMENT OPTIONS
The different ways by which the available water resources can
be managed is by the effective application of judicious methods.
This is where the management options come into picture. Once
the above model is applied we get the following management
options which are listed in the table : (Zoltay, et.al.,2010)
Table 3: Management options
Module
Storm water run off
Usage of land
Supply of
treatment
water
Demand management
Wastewater treatment
Aquifer storage
Inter basin transfer
and
Management options
More bio retention units should be installed
Forest land and cover should be preserved
More land should be purchased depending on
need
Surface water pumping
Groundwater pumping
Treatment of water
Surface storage
Repair of leakages in the distribution system
Increasing revenues for water and wastewater
services
Secondary treatment
Reuse by treating with tertiary methods
Distribution system for non potable water
Repair in filtration into collection system
Replenish ground water with water from
reservoirs
Import potable water
Export waste water
5. RESULTS OF WATER SHED MANAGEMENT MODEL
The main storage capacity is in groundwater aquifers, which
were used through ASR and bio retention units. Another
interesting aspect of these results is that both bio retention units
and ASR were recommended even though they serve similar
functions of recharging groundwater.The utilization of the bio
retention facility and the ASR facility highlights the need to
increase the ground water recharge in the basin. ASR is more
effective and versatile than the bio retention units in terms of
source of recharge water and the quantity of water flow.
Although the repair of leaks in distribution infrastructure is
increasingly common, repairing sewer pipes to prevent the
infiltration of groundwater is generally considered too costly
because of the deeper and larger diameter pipes.
6. CONCLUSION
An integrated watershed management optimization model to
support informed decision making was introduced and used to
evaluate a wide range of management options including land-use
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management to simultaneously address numerous watershed
management objectives, which are traditionally modelled
independently. The model demonstrated that with an increasing
diversity of management options, net benefits of watershed
management can increase. In addition, our results indicated that
demand management through price changes and the repair of
leakage in water distribution and wastewater collection systems
are effective management options as they were selected in all
scenarios where they were available. The recommendation for
the joint implementation of ASR and bio retention units
demonstrated that complex interactions among components of a
watershed necessitate the evaluation of management options
within a systems framework in order to realize the full impact of
management decisions and to enable informed decision making.
REFERENCES:
i.
Integrated Hydrological Data Book, Water Planning & Projects
Wing Central Water Commission, New Delhi, September, 2006, pp 15-16
ii.
Jamieson, D. G., and Fedra, K..,The ‗Waterware‘ Decisionsupport
System For River-Basin Planning. 1: Conceptual Design .,1996., pp 163–175
iii.
UNEPA Publication,. A Quick Guide To Developing Watershed Plans
To Restore And Protect Our Waters, May 2013
iv.
Viktoria I. Zoltay, Richard M. Vogel,Paul H. Kirshen,Kirk S.
Westphal., Integrated Watershed Management Modeling: Generic Optimization
Model Applied to the Ipswich River Basin, Journal Of Water Resources Planning
And Management ., September/October 2010, Pp 566-575
v.
Yates, D, Sieber, J., Purkey, D, and Huber-Lee, A.,WEAP21-A
demand-, priority-, and preference-driven water planning model, Part 1: Model
characteristics. Water Int., 2005., pp 487–500
Runoff and Sediment Yield Modeling of an
Agricultural Hilly Watershed Using Wepp Model
1
2
3
Saroj Das , Laxmi Narayan Sethi and R. K. Singh
1. M. Tech. Student, Department of Agricultural
Engineering, Triguna Sen School of Technology,
Assam University, Silchar-788011
2. Associate Professor, Department of Agricultural
Engineering, Triguna Sen School of Technology,
Assam University, Silchar-788011
3. Principal Scientist & Head, Agricultural Engineering
Division, ICAR Research Complex for NEH
Region, Barapani (Umiam), Meghalaya-793 103
ABSTRACT: Soil erosion rates caused by water are highest in
agro systems located in hilly or mountainous regions of Asia,
Africa and Southern America, especially in less developed
countries. Each year about 10 million ha of cropland are lost due
to soil erosion, thus reducing the cropland available for food
production. The loss of cropland is a serious problem. So, a good
management practice to protect the soil from erosion to sustain
long-term productivity is imperative for meeting the world‟s
future demand for food and fiber. Thus, the present study was
undertaken to develop the best management practices for a small
hilly watershed (Mawpun, Meghalaya) in North Eastern of India.
The watershed covers around 57.17 ha and falls under high
rainfall and high land slope conditions. For quantification of
runoff, sediment yield from areas of different land uses and
conservation practices of the watershed a physically based Water
Erosion Prediction Project (WEPP) model was used. The WEPP
model was calibrated using meteorological data (2002 to 2004)
and most sensitive soil related parameters (namely, rill erodibility,
interrill erodibility, effective hydraulic conductivity and critical
shear stress) of the small treated watershed (Mawpun watershed)
and validated using data of 2005 and 2006 monsoon season. The
performance of the model was also evaluated by estimating the
daily runoff and sediment yield using the monsoon season data of
different years. Coefficient of determination (R2) (0.72–0.96),
Nash–Sutcliffe simulation model efficiency (0.71–0.95), and
percent deviation values (16.4-21.2) indicate resonable simulation
accuracy of runoff from the watershed. High value of coefficient
of determination (R2) (0.73–0.94), Nash–Sutcliffe simulation
model efficiency (0.55–0.89) and percent deviation values (16.1–
19.3) for sediment yield indicate that the WEPP model can be
successfully used in the Mawpun watershed, India.
Keywords: Runoff, Sediment yield, Watershed Management,
WEPP Model.
1. INTRODUCTION:
Land and water are the most precious natural resources, the
importance of which in human civilization needs no elaboration.
The overexploitation of these natural resources causes natural
imbalance of the ecosystem and environment degradation. Soil
erosion is one of the main reasons for degradation of soil and
water quality ultimately adversely affecting the environment.
About 99.7% of the food consumed by human beings comes
from the land (Pimentel and Pimentel, 2003) and about 1964.4
million ha area which is 12% of the world‟s total land surface
suffers from degradation problems (Koohafkan, 2000).
Therefore, to combat the problem of resource degradation and
ecological imbalance, appropriate management practices were
the most efficient factor for long term agricultural sustainability.
With these facts in mind the present study was conducted to
evaluate the WEPP model for quantification of runoff and
sediment yield from areas under different land uses and
conservation practices.
2. MATERIAL AND METHODS:
2.1 Study area:
The study site (Mawpun Watershed) is located at 250 41‟ N
latitude, 910 55‟ E longitudes and at an altitude of 1010 m in RiBhoi district of Meghalaya state of India. The location of the
study site is shown in Figure 3.1.The study area is a part of the
eastern Himalayan range is made up mostly of Precambrian
metamorphic and igneous rocks. The study area is mainly hilly
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with steep slope that ranges between 0 to 30 % and the
maximum slope of some hilly portion is nearly 100%.
2.5 Model performance evaluation:
The hydrological model was evaluated through a pair wise
comparison of the observed and simulated data to determine the
closeness of their match. Split sample calibration approach was
adopted for model‟s performance evaluation. Five-year‟ data set
pertaining to 2002 through 2006 was split into two parts. The
data of 2002-2004 were used for model calibration and that of
2005-2006 for model validation. The manual calibration based
on trial-and-error procedure (Sorooshian and Gupta, 1995) was
used in the study. Singh et al. (2011) reported that soil related
parameters namely; rill erodibility, interrill erodibility, effective
hydraulic conductivity and critical shear stress were most
sensitive in Meghalaya conditions. Therefore, only these
parameters were considered for calibration. The calibrated
values of these parameters reported by Singh et al. (2011) were
taken as base value and fine tuned for the Mawpun watershed.
2.2 Meteorological and hydrological data:
The weather data such as daily rainfall, maximum and minimum
temperature, morning and evening relative humidity, wind
speed, pan evaporation and sun shine hours for a period of 5
years (2002–2006) were collected from the Agricultural
Engineering Division, ICAR Research Complex for North East
Hill Region and analyzed for making the model input files. The
observed hydrological data such and daily sediment yield for the
periods of five years (2002 to 2006) were collected from the
Agricultural Engineering division, ICAR Research Complex for
NEH Region and analyzed for making model input file.
2.3 Topographic data and soil properties:
Topographic information pertaining to the Mawpun watershed in
the form boundary map, contour map, drainage map, soil map,
and land use/land cover maps were collected from the
Agricultural Engineering Division, ICAR Research Complex for
North Eastern Hill Region, Barapani and used for delineation of
watershed. Physical and chemical properties of soil for the study
watershed were collected from Agricultural Engineering
Division, Indian Council of Agricultural Research Complex for
NEH Region.
Figure-1: Location map of Mawpun watershed
2.4 WEPP model:
The USDA – WEPP (Water Erosion Prediction Project) Hillslope
is a physically based, distributed parameters model based on
fundamentals of stochastic weather generation, infiltration theory,
hydrology, soil physics, plant science, hydraulics and erosion
mechanics. Date, amount, intensity and duration of rainfall,
minimum and maximum temperatures, wind velocity and direction
at 8 and 14 h of the day, daily values of radiation and dew point
temperatures for the period of 2002–2006 were used as input to
create climate input files for WEPP model using Break Point
Climatic Data Generator (BPCDG). The delineation of watershed
using WEPP model was presented in Fig. 2. Slope and soil files
were created using slope and soil file builder within the WEPP
interface. The management input file was built using file builder
within the model interface.
Figure-2: Delineated hillslopes and channels of the Mawpun
Watershed using WEPP model
(Martinec and Rango, 1989), Nash and Sutcliffe (1970)
simulation coefficient (ENS) and coefficient of determination
(R2) were determined. Performance of the model was evaluated
for runoff as well as sediment yield simulations. The underprediction/over-prediction by the model within or equal to ±25%
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of observed values were considered acceptable level of accuracy
for the simulations as suggested by Bingner et al. (1989).
3. RESULTS AND DISCUSSIONS:
3.1 Simulation of runoff and sediment yield:
The daily observed runoff and sediment yield hydrographs for
the calibration (May–October) 2002 to 2004 and the validation
periods (May–October) 2005 and 2006 are shown in Figure-3
through Figure-7 and Figure-8 through Figure-12, respectively.
It is observed that the trend of the simulated values closely
matches the trend of the measured values for calibration periods
and validation periods. However, the measured daily runoff and
sediment yield of higher magnitude is under-predicted by the
model during simulations for calibration periods and validation
periods. Based on the goodness-of-fit test statistics (Table-1,
Table-2, Table-3 and Table-4), it can be concluded that the
WEPP model simulates daily runoff from the Mawpun
watershed with acceptable accuracy.
Figure-5: Observed and simulated daily runoff hydrograph of
Mawpun watershed during model calibration for the
period of May to October 2004.
Mawpun watershed during model validation for the
Mawpun watershed during model validation for the
Figure-8: Observed and simulated daily sediment yield of
Mawpun watershed during model calibration for
the period of May to October 2002.
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parameter
Obs
erve
d
4.16
Sim
Obser
ulate ved
d 2004
4.87
4.24
Simulat
ed
Std.Dev.
Obser
Sim
ved
ulate
20
2 d
02
0 5.81
5.00
0
5.04 3 4.70
7.49
6.29
5.08
4.93
Maximum
33.2
20.1
46.3
30.1
30.3
25.0
Total
465.5
512.
2
93
442.3
90
437.
2
93
382.2
No
ofevents
%Dv
541.
0
90
102
102
Mean
Mawpun watershed during model calibration for
16
0.
.2
60
0.
70
R2
ENS
1
0.
7.
92
0.
0
8
8
4.91
-15.7
0.76
0.74
Table-2: Goodness-of-fit statistics of observed and simulated daily runoff
simulation during validation periods 2005 and 2006 (May to October).
Statisti
cal
parame
ter
Mean
Std.De
v.
Maxim
um
Total
Mawpun watershed during model calibration
for the period of May to October 2004.
No
ofevent
%Dv
s
R2
Sediment yield(t/ha)
Ob
ser
ved
0.2
0
0.3
1
2
Simulated
Observ
Simu
ed
lated
2003
Observed
0.24
0.16
0.19
0.19
0.27
0.31
0.29
1.11
1.99
1.65
22.7
17.2
20.1
90
93
2002
2004
-16.4
-16.8
0.81
0.79
0.
2
0.31
0.
2
2
2.0
1.
8
2
16.8
12
9.
102
16
0
-16.7 2
0.74
0.55
0.76
0.73
19.
5
90
ENS
93
S
i
m
u
l
a
t
e
d
Table-3: Goodness-of-fit statistics of observed and simulated daily sediment
yield simulation during calibration periods 2002 through
2004 (May to October).
Mawpun watershed during model validation for
Stati
stical
para
mete
r
Mea
n
Std.
Dev.
Maxi
mum
Total
No
ofeve
%Dv
nts
R2
Mawpun watershed during model validation for
Table-1: Goodness-of-fit statistics of observed and simulated
daily runoff simulation during calibration periods 2002
through 2004 (May to October).
Statistical
Runoff (mm)
Observe
d
Simulated
Observed
2005
2006
Simula
ted
3.31
3.90
2.42
2.83
4.36
3.76
4.06
3.76
32.2
18.0
23.1
17.0
337.7
398.3
247.5
289.5
105
105
102
102
ENS
-17.9
-17.0
0.67
0.80
0.73
0.80
Table-4: Goodness-of-fit statistics of observed and simulated
daily sediment yield simulation during validation
periods 2005 and 2006 (May to October).
Statistical
Sediment yield(t/ha)
Runoff (mm)
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parameter
Obse
rved
Mean
Std.Dev.
Maximum
Total
No
ofevents
%Dv
R2
ENS
0.11
0.17
1
11.5
105
Simulated
2005
0.13
0.16
0.7
13.9
105
-20.9
0.69
0.62
18-19, Dec. 2014
v.
Pimentel, D., Pimentel, M., 2003. World population, food, natural
resources and survival. World Future, Vol. 59(3-4); 145-167.
vi.
Singh, R.K., Panda, R.K., Satapathy, K.K., Ngachan, S.V., 2011.
Simulation of runoff and sediment yield from a hilly watershed in the eastern
Himalaya, India using the WEPP model. Journal of Hydrology, Vol. 405(3-4);
261-276.
vii.
Sorooshian, S., Gupta, V.K., 1995. Model calibration. In: Singh, V.P.
(Ed.), Computer Models of Watershed Hydrology. Water Resources
Publication, Highlands Ranch, Colorado, USA; 23–68.
Obser Sim
ved 2006ulat
ed
0.09
0.1
10.1
0.17
60.9
1.00
4
9.4
11.
2102
102
-19.1
0.80
0.57
Prioritization of a Watershed Based on Spatially
Distributed Parameters
4. CONCLUSSIONS:
1
In the present study, we tested the WEPP model for its efficacy
to predict runoff and sediment yield in high rainfall and steep
slope conditions of eastern Himalaya. The model was used to
develop vegetative and structural control measures to enhance
agricultural sustainability in the Mawpun watershed. Based on
results of the study the following conclusions were drawn:
1. The WEPP model simulates runoff and sediment yield
satisfactorily in high rainfall and high slope conditions of
Meghalaya with Nash–Sutcliffe coefficients > 0.50 and percent
deviations < ± 25.0. Comparison between WEPP–simulated and
measured values of runoff and sediment yield revealed that the
model tends to under-predict the values of higher magnitude.
2. Toposequential cropping on hill slope with graded
bunding and terracing at appropriate locations reduced the
sediment yield by 52%.
3. Crops cultivation in mild sloped and valley lands with
graded bunding, crop cultivation in bench terraces in medium to
high slope up to 30%, horticultural fruit crops from 30 to 60%
slope and forest or timber farming on land slope above 60%
yielded sediment at the rate of 9.4 t/ha.
4. Thus topo-sequential land use reinforced with graded
bunding and terraces at appropriate locations will bring the
sediment yield within the safe limit enhancing the sustainability
and profitability of agricultural system in hilly ecosystem.
5.
REFERENCES:
i.
Bingner, R.L., Murphee, C.E., Mutchler, C.K., 1989. Comparison of
sediment yield models on various watershed in Mississippi.Trans, ASAE, Vol.
32 (2); 529–534.
ii.
Koohafkan, A.P., 2000. Land resources potential and sustainable
land management- An overview. Lead paper of the International conference on
Land Resource Management for Food, Employment and Environmental
Security during November 9-13, New Delhi(India); 1-22.
iii.
Martinec J, Rango A (1989) Merits of statistical criteria for the
performance of hydrologic models. Water Resour Bull AWRA, Vol. 25; 421–
432.
iv.
Nash, J.E., Sutcliffe, J.V., 1970. River flow forecasting through
conceptual models Part 1-A discussion of principals. J. Hydrol. 10 (3), 282–
290.
C. D. Mishra1, R.K. Jaiswal2, A. K. Nema1
Institute of Agriculture Sciences, Banaras Hindu University
Varanasi (U.P.) -221005
2
National Institute of Hydrology, Regional Center, Bhopal
(M.P.) – 462001
Abstract: Identification of erosion prone and runoff generation
areas of a watershed is essential for the effective and efficient
implementation of best management practices for conserving the
natural resource in favour of sustainable development. In this
study, an effort has been made to identify critical erosion-prone
areas of the Nagwan watershed (89.44 km2) of Upper Damodar
Valley situated in Hazaribagh District in Jharkhand state India,
using the spatially distributed parameters responsible for hazard
of erosion. A geographical information system and remote
sensing was used for generating these parameters including
slope factor, soil erodibility factor of Universal Soil Loss
Equation (USLE), stream power index, sediment transport index
and curve number (CN) value, topographic wetness index for
water conservation. Using supervised classification method with
a maximum likelihood (ML) technique was applied to three
multi-spectral bands to generate the land use/cover map from
IRS-P6 (LISS-IV) satellite data and found six land use classes
such as agricultural land (55.78 km2), dense forest (1.47 km2),
open forest (11.63 km2), barren land (0.25 km2), water body
(1.26 km2), shrubs land (3.46 km2) and built up land (4.76 km2).
The soil erodibility factor map was prepared from the soil map,
and K factor values from a soil survey data. The Watershed
priorities have been divided in four categorizes namely very
high, high, moderate, and low priority. From the analysis, 13.45
km2 and 22.81 km2 have been found under very high and high
priority classes respectively where immediate attention for soil
and water conservation measures are required.
Keywords: GIS, remote sensing, wetness index, stream power
index, sediment transport index, Watershed prioritization
1. INTRODUCTION
Watershed is an ideal unit for management of natural resources
that also supports land and water resource management for
achieving sustainable development. The significant factor for the
planning and development of a watershed are its physiography,
drainage, geomorphology, soil, land use/land cover and available
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water resources. The concept of watershed management
recognizes inter-relationship among land use, soil, water and the
linked between uplands and downstream areas (Tideman, 1996).
The deterioration occurs generally in terms of forest loss and
land degradation by soil erosion. Among several factors, the
major one is deforestation followed by unsuitable agricultural
practices. Watershed characteristics, such as land use/land cover,
slope, and soil attributes, affect hydrologic and water quality
processes and hence regulate sediment and chemical
concentration (Basnyat et al. 2000). Knowledge of the basic
hydrologic processes occurring in watersheds give a better
understanding of land use impacts on soil and water resources.
Change in land use/land cover is considered as an important
hydrologic factor affecting storm runoff generation and sediment
yield (Calder 1992; Naef et al. 2002; Bakker et al. 2005). This is
especially true for humid and sub-humid subtropical areas in
India which are affected by heavy monsoon rains during four to
five rainy months (Sharma et al. 2001). With reference to nonpoint source (NPS) pollution, the critical areas are those areas
where either soil erosion exceeds the soil loss tolerance limit or
where the maximum improvement in the quality of water
resources can be attained with the minimum capital investment
through best management practices (Mass et al. 1985). Land and
water are the two basic natural resources for the survival of
living systems. These two resources have been interacting with
each other in various phases of their respective cycles. The
future of the nation depends largely on the effective utilization,
management and development of these resources in an
integrated and comprehensive manner.
Soil erosion has been accepted as a serious problem arising from
agricultural intensification, land degradation and possibly due to
global climatic change (Yang et al.,2003). Accelerated soil
erosion has been globally recognized as a serious problem since
people took up agriculture (Renschler et al., 1999). In India,
annual soil erosion (displacement of soil) rate is about 5334
million tones out of which about 1572 million tones is carried
away by the river systems into the sea and 9% of total annual
soil erosion i.e. about 480 million tones is deposited in the
various reservoirs reducing their carrying capacity (Dhruva
Narayan and Ram Babu,1983). Under Indian conditions, an
average soil loss value of 16.4 t/ha-yr (Narayana 1993) may be
considered as the limit for identifying critical watershed areas
(Singh et al. 1992).
Satellite based remote sensing technology meets both the
requirements of reliability and speed and is an ideal tool for
generating spatial information needs. However, the use of
remote sensing technology involves large amount of spatial data
management and requires an efficient system to handle such
data. Thus, blending of remote sensing and GIS technologies has
proved to be an efficient tool and have been successfully used by
various investigators for water resources development and
management projects as well as for watershed characterization
and prioritization (Chalam et al. 1996; Chaudhary and Sharma
1998; Kumar et al. 2001;Ali and Singh 2002; Singh et al. 2003;
Pandey et al. 2004; Suresh et al. 2004). A few more studies are
reported where remotely sensed data had been used for the
assessment of soil degradation to devise cost effective methods
for soil conservation (Jain and Kothyari 2000; Jain et al. 2001;
Baba and Yusof 2001; Fistikoglu and Harmancioglu 2002;
Sekhar and Rao 2002; Chowdary et al. 2004; Pandey et al.
2007).
Digital elevation models (DEMs) are already widely used and
play an increasing important role in geomorphology, hydrology,
soil erosion and many related geoanalysis fields (Moore et al.,
1991; Goodchild et al., 1993; Wise, 2000). Topography is a firstorder control on spatial variation of hydrological conditions. It
affects the spatial distribution of soil moisture, and groundwater
flow often follows surface topography (Burt and Butcher, 1986;
Seibert et al., 1997; Rodhe and Seibert, 1999; Zinko et al.,
2005). The TWI is usually calculated from gridded elevation
data. Different algorithms are used for these calculations; the
main differences are the way the accumulated upslope area is
routed downwards, how creeks are represented, and which
measure of slope is used (Quinn et al., 1995; Wolock and
McCabe, 1995; Tarboton, 1997; Guntner et al., 2004). The
topographic wetness index (TWI) has been used to describe the
spatial soil moisture patterns and zones of saturation or variable
sources for runoff generation is obtained (Beven and Kirkby,
1979; Wilson and Gallant, 2000) and also used to study spatial
scale effects on hydrological processes (Beven et al., 1988;
Famiglietti and Wood, 1991; Sivapalan and Wood, 1987;
Siviapalan et al., 1990) moreover to identify hydrological flow
paths for geochemical modelling (Robson et al., 1992) as well as
to characterize biological processes such as annual net primary
production (White and Running, 1994), vegetation patterns
(Moore et al., 1993; Zinko et al., 2005), and forest site quality
(Holmgren, 1994a). The locations of higher TWI host more
favorable conditions for landslide formation (Conoscenti et al.,
2008). The stream power index could be used to identify the
erosive effects of concentrated surface runoff (Wilson and
Gallant, 2000), to identify suitable locations for soil
conservation measures and reduce the effect of concentrated
surface runoff. The sediment transport index accounts for the
effect of topography on erosion. The two-dimensional catchment
area is used instead of the one-dimensional slope length factor as
in the Universal Soil Loss Equation.
2. Description of the study area
Nagwan watershed (89.44 km2) is located the Upper Damodar
Valley, situated in Hazaribagh district of Jharkhand, India, the
second most seriously eroded area in the world (EI-swaify et al.
1982), was selected for the study. The watershed lies between
85016′41″ and 85023′50″ E longitudes and between 23059′33″
and 2405′37″ N latitudes. Location map of the study area is
shown in Figure 1. The test watershed is just 7 km from the soil
conservation department of Damodar Valley Corporation (DVC)
at Hazaribagh, Jharkhand; is well connected by road/rail
network. Geologically, the area is quite complex, having rocks
of varying composition. The soils of the area are mainly of clay
loam and silty loam type. The topography of the watershed is
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undulating and maximum and the minimum elevations of the
area are 667 m and 560 m, respectively. The area experiences
sub-humid sub-tropical monsoon type of climate, characterized
by hot summers (40◦C) and mild winters (4◦C). The watershed
receives an average annual rainfall of 1256 mm, out of which
more than 80% rainfall contributes during monsoon season
(June–October). The average storm intensity, by considering
storms of more than 30 min duration, is about 10 cm/hr. The
daily mean relative humidity varies from a minimum of 40% in
the month of April to a maximum of 85% in the month of July.
The main agricultural crops grown during kharif season are
paddy and maize and in rabi season are wheat, gram and
mustard. The agriculture is mostly rainfed as only 20% irrigation
is available in the area through sources other than rain and the
cropping intensity is also quite low at 98%. The irrigation is
received mainly by wells. Prevalence of conventional cultivation
practices, characterized by conventional tillage or no tillage; low
fertilizer/manure consumption and local varieties of the crops is
mainly responsible for the low crop productivity in the area. All
this information on the test area was obtained through secondary
sources such as Directorate of Economics and Statistics,
Ministry of Agriculture; Directorate of Census (data Center);
DVC, Hazaribagh officials and Sadar block office of Hazaribagh
district.
3.2 Generation of GIS data base
For the generation of GIS data base of their spatial distribution
different thematic maps such as base map, digital elevation
model map, delineation of watersheds, soil group map,
topographic wetness index map, stream power index map,
sediment transport index map and land use map are prepared
with the help of GIS based software ILWIS (3.6). A base map
has been generated by digitizing the Survey of India (SOI)
toposheet as reference map for all other purposes. The watershed
covered by 1:50,000 scale SOI topographic maps NO.72H8 and
73E5. The watershed boundary was marked on the basis of the
contours and the drainage lines available on the SOI topographic
map and also using the procedure described by Jenson and
Domingue (1988).
3. 2. 1 Slope map
Generation of slope map, the contour map and point elevation
map of study area has been used. Using the GIS based software
ILWIS (3.6), the slope map for the region is generated.
3. 2. 2 Digital elevation model (DEM)
The contour map (20 m interval) and spot height map of the area
are merged together and a composite map having information
about contours as well as spot height is formed. This combined
map is further interpolated at 20-metre pixel resolution using
map interpolation function available in Integrated Land and
Water Information System (ILWIS) to generate a DEM of the
area. Slope map was calculated using contour line map using
script function available in ILWIS 3.6.
3. 2. 3 Soil erodibility factor (k) map
The soil maps of the study area in the scale of 1:250,000 were
traced, scanned and exported to ILWIS 3.6..The scanned maps
were loaded in ILWIS 3.6. and georeferenced. Boundaries of
different soil textures as per the soil conservation service soil
classification system were digitized and the polygons
representing various soil categories were assigned with different
colours for identification. This information is then transferred on
to the base map for preparation of the soil map and assign the K
factor values from a Soil Survey data which is given in table 1.
Figure 1. Location map of Nagwan watershed.
3. MATERIALS AND METHODS
3.1 Data used
Table: 1. Soil texture of Nagwan watershed
Topographic maps at 1 : 50 000 scale from the Survey of India,
Calcutta and soil resources data from Damodar Valley
Corporation (DVC), Hazaribagh were used in this study for
digitization of contour lines, construction of Digital Elevation
Model (DEM). IRS-P6 (LISS IV ) satellite data having sensor
scenes 23.5 m resolution(Path-105 and Row-55), with pass dates
of 22 December 2012, were used for land-use/land-cover
classification maps. soil map collected from National Bureau of
Soil Survey and Land use Planning (NBSS&LUP), Government
of India for identification of soil types of the study area.
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Map
unit*
16
32
Taxonomy*
Fine, mixed, hyperthermic Typic
Haplustalfs
Loamy, mixed, hyperthermic Lithic
Ustorthents
Fine loamy, mixed, hyperthermic
Typic Paleustalfs
Fine-loamy, mixed, hyperthermic
Typic Rhodustalfs
K
value
0.19
0.33
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*Department of Agriculture & Cane Development, Govt. of
Jharkhand
(Burroughet al., 1998). The sediment transport index is defined
by the equation below.
3. 2. 4 Land use map
LU/LC map was developed by supervised classification
techniques with maximum likelihood algorithm were used for
the classification of digital data of an IRS-P6 (LISS IV ) satellite
in which an area or group of pixels that belongs to one or more
categories of specific land use and land cover was classified. The
land uses were classified into five classes namely agriculture,
water, dense forest, fallow land and urban settlement and assign
the standard curve curve number (CN) value numbersfor the
Indian conditions(ministry of agricultural, Govt. of india 1972).
(3)
3. 3 Priority assessment
For the determination of priority of the critical erosion-prone
areas in the watershed values of the parameters are normalized in
a standard scale such as 0 to 1. The following equation has been
used to normalize all the parameters on the 0 to 1.
3. 2. 5 Topographic Wetness Index Map
The topographic wetness index (TWI), also known as the
compound topographic index (CTI), is a steady state wetness
index. It is commonly used to quantify topographic control
on hydrological processes (Sorensen, 2006) The index is a
function of both the slope and the upstream contributing area per
unit width orthogonal to the flow direction. The index was
designed for hill slope catenas. Accumulation numbers in flat
areas will be very large, so TWI will not be a relevant variable.
The index is highly correlated with several soil attributes such
as horizon depth, silt percentage, organic matter content,
and phosphorus (Moore, 1993) wetness index map prepared by
using ILWIS 3.6 software with DEM raster map. The WI is
defined as
(4)
Where,
is the Normalized value of a parameter for
parameter,
(1),
ia the Upper value in the standard scale
is the Lower value in the standard scale (0),
is
the Maximum value of the parameters,
is the Minimum
value of the parameters respectively and
is the Observed
value of parameters for
parameter. After computing the
normalized values of different parameters and then getting
average of parameters for the final priority. After determining
the final priority critical area it has been grouped in four classes
of priority namely very high, high, moderate and low on the
basis of priority ranking.
4. RESULTS AND DISCUSSION
(1)
4. 1 Development of thematic map
where As is the contributing area draining to the grid cell per unit
length of a side of the grid cell (m2/m) and β is the slope angle
of the cell (degrees). Slope values of zero were substituted with
a value of 0.001 to avoid returning an undefined index value.
The thematic map of Nagwan watershed has been prepared
using satellite image, toposheets and soil map in GIS. These are
discussed below:
3. 2. 6 Stream power index (SPI)
4. 2. 1 Slope factor
Using ILWIS 3.6 software with raster map of wetness index
generate the stream power index map. it is reflect the erosive
power of the stream terrain (moore, 1993). it is defined as:
The factors of slope steepness (S) are in the present study area
varied from 0.06 to 1.0 as shown in a Figure 2.
4. 1. 2 Topographic wetness index (TWI)
(2)
3. 2. 7 Sediment transport index (STI)
The Sediment Transport Index characterizes the process of
erosion and deposition. it reflect erosive power of the overland
flow. Unlike the length-slope factor in the Universal Soil Loss
Equation (USLE) it is applicable to three-dimensional surface
In the analysis found maximum area 3.88 km2 with value of
TWI is 11.28. The maximum, minimum, average and standard
deviation of TWI is 21.68,5.74, 13.72 and 4.27 respectively.
The DEM and flow accumulation map have been used as inputs
and STI map was prepared in ILWIS (3.6) for the watershed as
shown an in Figure 3.
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Supervised classification techniques with maximum likelihood
classifier were used for the land use classification with average
accuracy 89.98 %, average reliability 85.09 % and overall
Accuracy 90.78 %. Seven major land use categories namely
agriculture land (with & without crop and grass land), barren
land, builtup land, dense forest, open forest, scrubs, water bodies
were identified and then assign CN value . The land use map of
the watershed is shown in Figure 7 and the land use details are
shown in Table 2.
Figure 2. Slope map of the Nagwan watershed
Figure 3. TWI map of the Nagwan watershed
4. 1. 3 Stream power index
The stream power index are calculated by using the eq. 2, the
value are varies 0.70 to 50. SPI map was generated by ILWIS
(3.6) for the watershed and shown an in Figure 4.
4. 1. 4 Soil erodibility (K) factor
The soil map of the catchment area was used to prepare the
digitized soil map. The predominant soil textural classes were
clay loam and silty loam type, found in the watershed. Soil
group of the study area shown in Figure 5 and soil erodibility
value given by Table 1.
Figure 6. STI map of the Nagwan watershed
Figure 7. Land use map of nagwan watershed.
Table 2. Land use pattern of nagwan watershed.
Land use
Area in km2
Agricultural land (with
crop)
Agricultural land (with
no crop)
Barren land
Built up land
Dense forest
Grass land
Open forest
Shrubs
Water body
27.40
Curve
number
95
28.39
95
0.26
4.74
1.47
10.84
11.63
3.46
1.26
85
91
58
79
60
64
100
4. 2. Final priority map
Figure 4. SPI map of the Nagwan watershed
Figure 5. Soil erodibility map of Nagwan watershed.
4. 1. 5 Sediment transport index
The sediment transport index was calculated for watersheds
using the Eqn.3. These values ranged from 0.03 to 5. STI map
was prepared in ILWIS (3.6) for the watershed as shown an in
Figure 6.
Not all watershed contribute erosion and at same rate. the
identification of erosion prone area within the watershed which
contribute maximum sediment yield obviously should determine
our priority to go forward appropriates conservation
management strategy for maximum benefit. Also prioritization
is required for proper planning and management of natural
resources for catchment area treatment plan in the watershed.
Determination of priority for the watersheds have been
determined and normalized and give weight. The final priorities
of spatially based for watershed are determined and priorities of
critical erosion prone area for watersheds are grouped in four
categories as shown in Table 3 and spatially depicted in Figure
8
4. 1. 6 Land use/ land cover based on curve number
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Figure 8. Final priority map of the Nagwan watershed
Table 3. Final priority of Nagwan watershed
Priority category
Low
Moderate
High
Very high
Area (km2)
19.83
33.36
22.82
13.45
5. CONCLUSIONS
The compound indices such as topographic wetness, stream
power and sediment index these indices can be used to derive
spatilly meaningful parameterisations of a landscape like
potential for erosion. In land use classification the maximum
area comes under agricultural land (62%) with minimum in
barren land (< 1%). The use of GIS and remote sensing data
enabled the determination of the spatial distribution paramets (
slope map, soil erodibility map, topographic wetness index map,
stream power index map, sediment transport index map and land
use map) and prioritization of watersheds was done. The
watershed prioritization indicated that the critical erosion area
under high (25.50%) and very high (15%) priority class where
requires immediate attention for soil conservation treatment.
Hence, remote sensing and GIS technology can be used as an
alternative to conventional method of soil loss estimation and
subsequent prioritization of spatilly erosion prone area of
watershed for implementing soil conservation practices. The best
management practices proposed for nagwan watersheds are;
afforestation, trenching, bunding, stone wall fencing, brushwood
check dams, earthen check dams, gabian structures and masonry
structures.
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Minimization of Conveyance Losses for Nashik
Left Bank Canal [NLBC] by Closed Conduit
Irrigation [CCI]
Gayatri R. Gadekar1, Dr. Sunil Kute2 Dr. N. J. Sathe3,
1
ME Hydraulics, Civil Engineering, Sinhgad College of
Engineering, Pune, University of Pune.
2
Professor, Civil Engineering, K. K. Wagh Institute of
Engineering and Research, Nashik, University of Pune.
3
Assistant Professor, Civil Engineering, Sinhgad College of
Engineering,Pune, University of Pune.
E-mail:[email protected]
2
, [email protected] [email protected]
MANIT Bhopal
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Issue Special3
18-19, Dec. 2014
ABSTRACT:The present paper focuses on the minimization of
conveyance losses for Nashik Left Bank Canal [NLBC]
originating from Gangapur dam of Nashik District of
Maharashtra state located at 20° 38‟ Lattitude and 73° 19‟
Longitude. This is an unlined canal of 64 km stretch having
design discharge 8.92 cumecss. NLBC has conveyance losses of
about 57% and 55% in rabi and hot weather season,
respectively. To minimize these conveyance losses of NLBC,
Closed Conduit Irrigation [CCI] system has been suggested and
analysed in this paper. This CCI system will consist of a conduit
line of 1.82m diameter of Glass Fibre Reinforced Pipe [GFRP]
running as an open channel i.e. under atmospheric pressure for
total 64 km length of the canal with longitudinal slope of
1:4000. The CCI system of NLBC with free board of 0.5m has
3.02m of head losses for the entire length of the canal. For the
Full Supply Depth [y] of 1.32m in GFRP of
, the
Froude number [Fr] of flow is 0.4149; which indicates
Subcritical flow for CCI. The CCI for NLBC will save of about
15.55 Mm3 of irrigation water which constitutes a part of
conveyance loss for the present Open Canal Irrigation [OCI]
system of NLBC for the entire canal length.
2.
CASE STUDY OF NASHIK LEFT BANK CANAL
[NLBC]
2.1 Study area
Nashik district of Maharashtra state is one of the leading districts
in the field of agriculture. The new experiments and use of
advanced technology have empowered the farmers to increase
export of agro based products. Gangapur Dam is most important
and the oldest earthen dam in Nashik. It was constructed in 1965
on Godavari River. Two canals namely Nashik Right Bank Canal
[NRBC] and Nashik Left Bank Canal [NLBC] take off from the
dam. The GRBC is closed due to high civilization in the area.
The present paper focuses on the case study of Nashik Left Bank
Canal of Nashik district, Maharashtra state which is located at
20° 38‟ Lattitude and 73° 19‟ Longitude. The reach of this canal
is 64 km which is running open to atmosphere. The alignment of
canal and its command area is shown in Figure 1.
Keywords- Open Canal Irrigation [OCI], Conveyance losses,
Hydraulic Design of Conduit, Closed Conduit Irrigation [CCI],
Glass Fibre Reinforced Pipe [GFRP].
1.
INTRODUCTION
1.1 Open canals are used to convey the water from storage
reservoir to the agricultural land for irrigation. Water has to travel
from its head to fulfill the needs of agriculture; irrigation
channels with poor maintenance causes heavy losses during its
conveyance phase. It is observed that the losses due to
evaporation, infiltration, percolation and water thefts in open
canal reduce the efficiency and yield of irrigation. Therefore, it is
necessary to check these conveyance losses in case of irrigation
canals. Discharge of water through the canals is utilized for
irrigation purposes only. During its passage from canal head up
to the agriculture land, there are various types of losses
occurring; these losses are termed as conveyance losses. Major
amount of irrigation water is lost during this conveyance phase.
Figure 1: Command Area of Nashik Left Bank Canal [NLBC]
Source: Nasik Irrigation Departmen
1.2 Many researchers have tried to quantify these conveyance
losses. Kolhe, P. S. (2012), in his paper has suggested the
Pressurized Pipe Distribution Network [PDN] for Nagthana-II for
optimal utilization of Irrigation Water. Ghazaw,Y.M. (2010) has
developed the design charts and computer programme to
facilitate the design of optimal water loss section. Burt, C.M.
et.al. (2008) have given the solution for reduction in canal
seepage by in place compaction of canal banks and bed. Swamee,
P.K. et.al. (2002) have given the minimum water loss canal
sections that have been obtained using the explicit equations for
seepage loss and evaporation equation for flowing channels.
MANIT Bhopal
TABLE I: General Information of NLBC [6]
Sr.
No.
Description
1
Cross-Section
2
3
4
5
Shape
Canal Bed Level [CBL]
Design Discharge
Chainage [Location]
Data
2.44 m X 2.44
m
Trapezoidal
589.94 m
8.92 cumecss
801.83 m
6
Bed Width
3m
7
Bed Gradient
1:4000
8
9
10
11
12
13
Length
Full Supply Depth
Type
Canal Top Width
Depth of Canal
64 km
1.65 m
Unlined
4.67m
2m
Side Slopes
1:0.5
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Issue Special3
18-19, Dec. 2014
2.2 Design of NLBC
2.3 Crop water requirement for NLBC
The data for the case study is collected from Nashik Irrigation
Department [NID]. The general information of NLBC is given in
Table-I wherein Table -II represents the crop pattern and crop
water requirement details for NLBC.
In Table II, the yearly crop water requirement is calculated as
V= 24.5 Mm3. This is the crop water requirement for base period
of 72 days.
Therefore, Discharge in cumecs corresponding to volume of 24.5
Mm3
= [24.5 * 106] / [72 *24*60*60] = 3.96 cumecs. From Table II, it
is clear that the crop water requirement in cumecs for NLBC is
3.96 cumecs.
2.4 Conveyance losses and Diameter of Conduit of NLBC
The conveyance losses for NLBC are calculated by applying the
general water budget equation to the open canal for rabi and hot
weather season. The values of the conveyance losses and
efficiency for the rabi and hot weather season are represented in
Table – III.
TABLE II: Crop Pattern and Crop Water Requirement Details
for NLBC [6]
Season
Crop
Pattern
Area
[Ha]
Rabi
[54
Days]
Grapes
Sugarcane
Vegetables
Wheat
Others
965.10
137.04
86.02
81.03
304.03
Water
Requirement
Mm3 Cumecs
5.76
0.93
4.201
0.68
1.28
0.21
0.888
0.14
3.641
0.59
Grapes
1125.07
7.88
1.27
Sugarcane
162.15
0.85
0.14
Hot
Weather
[18
Days]
A=
2860.44
Ha
V=
24.5
Mm3
W=
3.96
Cumecs
It can be seen from the Table III, that there are huge conveyance
losses for the NLBC. NLBC has yearly conveyance loss of 15.55
Mm3, in which rabi season has the conveyance loss of 10.69 Mm3
and conveyance loss of 4.862 Mm3 has observed in hot weather
season. It is clear from the Table III that the conveyance loss for
NLBC is more than 55 %, which is very huge. The efficiency is
calculated from the details of conveyance losses for rabi and hot
weather season. The efficiency of NLBC is 43.02 % for rabi
season wherein 44.71% for hot weather season.
S
r.
N
o.
MANIT Bhopal
Seas
on
Area
under
Crop
[Ha]
No. of
Days
water
suppli
ed
Quan
tity of
water
suppli
ed at
head
of
canal
[Mm3
]
Quan
tity of
water
used
[Mm3
]
Conve
yance
Losse
s
Mm3
Efficiency
[%]
%
Page 78
Issue Special3
1
Yea
rly
2860.
44
72
27.55
12.00
15.55
56.44
2
Rab
i
1573.
22
54
18.76
1
8.073
10.69
56.98
3
Hot
Wea
ther
1287.
22
18
8.793
3.931
4.862
55.29
18-19, Dec. 2014
43
.5
6
43
.0
2
44
.7
1
= 1.82 – 0.5
= 1.32 m
Table III: Actual Details of Conveyance Losses and Efficiency
for NLBC [6]
Therefore, the actual discharge [QActual] required in NLBC can
be calculated by considering the designed discharge [QD] and
efficiency of NLBC.
Figure 2: Closed Circular Conduit [GRP]
∴ Actual Discharge [QActual]
= Designed discharge [QD] X Efficiency [ ]
= 8.92 X 0.4356 = 3.886cumecs.
2.6 Velocity and Type of Flow of NLBC
This discharge is to be supplied to the area of 2860.44ha which
is under crop.
1) For closed conduit irrigation of GRP 1.82m,
From Table I, the longitudinal slope[S] of the canal is 1:4000.
The conduit to be used for NLBC irrigation has to be designed
for the Actual Discharge [QActual] of 3.86 cumecs. The conduit
which will be used for NLBC‟s Closed Conduit Irrigation [CCI]
needs to be durable and strong. Hence, for NLBC, the Glass
Fibre Reinforced Pipe [GRP] is recommended as it has working
life of about 70 years, its C value is 140, it is light weight and
the Glass Fibres structure increases its strength to a great extent
[7]. The diameter [D] of GRP conduit section is obtained with
the relation of discharge [Q], area [A] and velocity [V]. For the
velocity of flow, Chezy‟s formula is used.
Velocity [V] of flow through GRP 1.82m [5] = C√RS
V = C √ {[D/4] S}
V = 140 √ {[1.82/4] * [1/4000]}
V = 1.493 m/s
Froude Number [Fr] of flow for GRP ϕ1.82m [5] = V/ √ [gy]
Fr = 1.493 / √ [9.81 * 1.32]
Fr = 0.4149 < 1 Subcritical flow.
2) For an open canal flow in NLBC,
Therefore, Actual Discharge [Qactual]
= Area [A] X Velocity [V]
= {[П / 4] * D2} X {C X √[R*S]}
Substituting the values of Qactual [3.886 cumecs], C of GRP [140]
and longitudinal slope [1:4000], and simplifying above equation,
the diameter of GRP for NLBC is calculated which comes out to
be 1.82m. This is the diameter of equivalent closed conduit
section for NLBC for supplying the discharge of 3.886cumecs.
Diameter of Equivalent GRP for CCI of NLBC = 1.82m
Figure 3: Open canal cross-section of NLBC
2.5 Freeboard for CCI of NLBC
But, the closed conduit irrigation [CCI] which is suggested in
this paper is an open channel flow i.e. the flow inside the conduit
will be running under atmospheric pressure. Hence sufficient
free board should be available in a GRP of 1.82m diameter. The
free board for the discharge range of 1-5 cumecs is assumed as
0.5 m.
Full supply depth [y] through conduit of 1.82m
= Diameter of GRP – Freeboard
Velocity [V] of flow through open NLBC [5] = C√RS
= C √ [A/T] S
= 40 √ [6.0866/4.38] * [1/4000]
= 0.7456 m/s
Froude Number [Fr] of flow for open channel NLBC [5]
= V/ √ [gy]
= 0.7456 / √ [9.81 * 1.65]
MANIT Bhopal
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Issue Special3
= 0.3058 < 1
18-19, Dec. 2014
Hot
Weather
[18 days]
Subcritical flow.
For open canal NLBC, the velocity is 0.7456 m/s whereas for
GRP 1.82m, it is 1.493 m/s. The Froude Number for open
channel NLBC is 0.3058 whereas for GRP 1.82m it is 0.4149.
3.
4.862
3.13
3.31
1592.08
ADVANTAGES
OF
CLOSED
CONDUIT
IRRIGATION [CCI] OVER OPEN CANAL
IRRIGATION [OCI] FOR NLBC
2.7 Head losses of NLBC for GRP
In a Closed Conduit Flow through ϕ 1.82m GRP, there will be
the Head losses.
1) For the head loss due to friction [5],
hf = [fLV2] / [2gD]
Friction factor for GRP ϕ1.82m [f] = 2.13 X 10-3 [7]
Now,
hf
= [2.13 X 10-3 * 64000* 1.49532] / [2*9.81*1.82]
= 8.535m
Friction loss per meter [hf] =8.535/6400 = 1.33 X 10-4m
Considering the total length of the canal i.e. 64 km, the friction
head loss is very less.
2) Head loss at entry of GRP ϕ1.82m [5] = 0.5 [V2/2g]
= 0.5 [1.49532 / (2*9.81)] = 0.057m
3) Head loss at exit of GRP ϕ1.82m [5] = [V2/2g]
= [1.49532 / (2*9.81)] = 0.114m
NLBC has conveyance losses of about 15.55 Mm3. Due
to the conversion of open canal into closed conduit
section; these losses of water in each season will be
minimized. This can be considered as the saving of
water. Thus, the water saved can be utilized for
improving duty.
II. As CCI increases the duty of water by 2.29 cumecs and
3.31
cumecs
for
rabi
and
hot
weather
season,respectively. Hence, more area can be brought
under irrigation for NLBC.
III. Conveyance losses have resulted into
decreased efficiency of canal ranging
from 57% in rabi and 55% in hot
weather season. Hence, the use of CCI
will save 15.55 Mm3 of water, thus
increasing the efficiency of NLBC.
IV. NLBC sites have problems like breeding of mosquitos,
fly nuisance, water logging and salinity which can be
stopped if CCI system is implemented
I.
4. CONCLUSIONS
4) Head loss at entry of GRP ϕ1.82m for each branch [5]
= 0.5 [V2/2g] = 0.5 [1.49532 / (2*9.81)] = 0.057m
Head loss at entry of GRP ϕ1.82m for 50 branches
= 50 X 0.057 = 2.85 m
Therefore, total head lost in GRP ϕ1.82m is calculated as,
HLoss = 1.33 X 10-4 + 0.057 + 0.114+2.85
HLoss = 3.02m
The head losses are calculated for NLBC‟s CCI by considering
the loss at entry and exit of conduit, friction losses and loss at
entry of each branch. The head loss is 3.02m for the entire 64km
stretch of NLBC.
2.8 Saving in water of NLBC by CCI
Due to conversion of OCI into CCI, conveyance losses of 15.55
Mm3 are saved, which can be used for improving the duty of
water. The following table IV shows the details of improvement
in the duty.
Table IV: Details of Improvement of duty
Extra
Extra
Water Saved
water
land that
Season
made
can be
Mm3 Cumecs available
irrigated
[cumecs]
[ha]
Rabi [54
2083.2
10.69
2.29
2.29
days]
It is revealed from the hydraulic analysis, that the
conversion of open canal into circular closed
conduit is technically feasible and there is impact of
water saving of 10.69 Mm3 for rabi season and
4.862 mm3 for hot weather season for improving
irrigation potential by reducing the conveyance
losses. In addition to saving in water, there is 50%
increase in the velocity of flow because of increased C-value of
GRP. A case study of Nashik Left Bank Canal [NLBC] of length
64 km shows that 57% losses during rabi season and 55% of
conveyance losses during hot weather can be stopped by
adopting this system. Thus, the net saving of 15.55 Mm3 can be
achieved by adopting CCI. The capital cost of such conversion is
justified on the basis of water saving of 15.55 Mm3 for the 64
km stretch of NLBC and increased irrigation potential of 2083.2
ha and 1592.08 Ha for Rabi and Hot Weather season
respectively. Hence, it is recommended to use CCI in place OCI
to save the valuable water.
ACKNOWLEDGEMENT
A paper of this nature calls for intellectual nourishment,
professional help and encouragement from many quarters. I
would like to extend my sincere gratitude towards the Nashik
Irrigation Department (NID) and Graphite India Ltd. for
providing me with the necessary authentic data required for the
paper.
MANIT Bhopal
Page 80
Issue Special3
18-19, Dec. 2014
REFERENCES
i.
Kolhe P.S. (2012) ―Optimal Utilization of Irrigation Water by Use of
Pipe Distribution Network (PDN) Instead Of Canal Distribution Network
(CDN) In Command Area‖, India Water Week 2012, New Delhi.
ii.
Ghazaw Y. M. (2010), ―Design Charts of Optimal Canal Section for
Minimum Water Loss.‖ Journal of Engineering and Computer Sciences,
Qassim University, Vol. 3, No. 2, pp. 73-95
iii.
Burt C. M. et. al. (Nov 2008) ―Canal Seepage Reduction by Soil
Compaction‖, IA Technical Conference, ITRC Paper No. P 08-002.
iv.
Prabhata K. Swamee, Govinda C. Mishra, Bhagu R. Chahar (2002),
―Design of Minimum Water-Loss Canal Sections‖, Journal of Hydraulic
Research, Vol. 40, 2002, No. 2.
v.
Garg S. K., (2005), ―Irrigation Engineering and Hydraulic
Structures‖ 19th Edition, Khanna Publishers, Delhi, India. Pp 1141,1162.
vi.
Annual Report (June 2013): ―Annual Water Account of Major and
Medium Projects‖, Nashik Irrigation Department. Pp. 7
vii.
IS 12709: 2009, ―Glass Fibre Reinforced Plastics (GRP) Pipes,
Joints and Fittings for Use for Potable Water Supply — Specification.‖
methods which can give reasonably good accuracy. In view of
the recent development in data acquisitions and techniques to
model soil water crop interaction, selection of appropriate
model has become very important step. The objective of the
study is to review all the methods available to estimate first
reference evapotranspiration based on climate. For estimating
reference evapotranspiration (ETref) various empirical
methods, radiation based equations and methods based on
radiation as well as dynamic factors are discussed. The paper
suggests points to be considered for selection of appropriate
method. ASCE Standardized PM Equation and dual crop
coefficient provide precise estimation of ET under varied
climates.
Keywords:Evapotranspiration,Reference
Penman-Monteith
evapotranspiration,
1.0 INTRODUCTION:
BIOGRAPHIES
Ms. Gayatri R. Gadekar is pursuing her post graduation in Hydraulics from
Sinhgad College of Engineering. Her research area includes water resources
engineering.
Dr.Sunil Kute is currently Professor of Civil Engineering. Also, he is Chairman,
Board of Studies (Civil Engineering) and member of Academic Council and
Senate of University of Pune .He has experience of 23 years in teaching,
administration and research. He is Ph.D. guide of University of Pune and North
Maharashtra University .His 60 research papers are published in journals and
conferences .His research areas are structural engineering and water resources
engineering .Currently, 6 students are pursuing Ph.D. under his guidance.
Dr.N. J. Sathe is currently M. E. Hydraulics coordinator in civil engineering
department of Sinhgad College of Engineering, Pune. . Also, he is Chairman of
Geoinformatics and Engineering Geology subjects of University of Pune. He is
member of Board of Studies of Shivaji University. He has experience of 15 years
in teaching and research. He is Ph.D. guide of University of Pune. His 37
research papers are published in journals and conferences. His research areas are
Geoinformatics, Engineering Geology and Water Resources Engineering.
The irrigated agriculture uses large chunk of water, thus a big
responsibility lies with irrigation managers to efficiently use the
water. The large quantity of water is lost as evaporation and
transpiration from the fields. Evaporation and transpiration
usually happen at the same time and is hard to separate the two
processes. To match the irrigation supply with demand,
estimation of the evapotranspiration is required to
be done with appropriate methods which can give
reasonably good accuracy. FAO presented two
publications to describe various model for
estimating crop water requirements (Doorenbos
and Pruitt, 1977; Allen et al., 1998). In view of
the recent development in data acquisitions and techniques to
model soil water crop interaction selection of appropriate model
needs the understanding of capabilities and limitations of each
available model. This paper reviews most of the widely used
methods available to estimate reference evapotranspiration based
on climate data. The paper also suggests points to be considered
for selection of appropriate method.
2.0 EVAPOTRANSPIRATION:
Methods for Estimation of Crop
Evapotranspiration Using Climate Data: A Review
Gopal H. Bhatti 1, H.M. Patel2
Research Scholar and Associate Professor, Civil Engineering
Department, Faculty of Technology & Engg, The M.S University
of Baroda, Vadodara. 390 001, Gujarat, India.
2
Head and Professor, Civil Engineering Department, Faculty
of Technology & Engg, The M.S University of Baroda,
Vadodara. 390 001, Gujarat, India.
Email: [email protected], [email protected]
1
ABSTRACT: As water being the limited resource, its optimum
utilization is of great concern in irrigated agricultural sector as
it is the largest user in most part of the world. To match the
irrigation supply with demand, estimation of the
evapotranspiration is required to be done with appropriate
Evapotranspiration is the combined process through which water
is lost by evaporation from the soil surface and from the crop by
transpiration. The crops require a fixed quantity of water to
meet the water losses through evapotranspiration for bumper
crop production under standard conditions. The crop
evapotranspiration (ETc) under standard conditions refers to
crops that are disease-free, well fertilized and are grown in
large fields under optimum soil water with excellent
management and environmental conditions, so as to attain full
production under the given climatic conditions Allen et al.
(1998). ETc measurement is not easy and requires sophisticated,
expensive equipment and trained research personnel with varied
range of systems. Lanthaler (2004) reported measuring
evapotranspiration using lysimeter. Evapotranspiration data
could be obtained from varied range of measurement systems
which included lysimeters, eddy covariance, Bowen ratio,
scintillometry, sap flow, satellite-based remote sensing, direct
modeling and soil water balance such as gravimetric, neutron
MANIT Bhopal
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Issue Special3
18-19, Dec. 2014
probes, electromagnetic types of soil sensors, time domain
reflectometry etc. Phene et al.,(1990); Cammalleri et al. (2010);
Allen et al., (2011); and Evett et al., (2012). Direct measurement
techniques are not feasible for estimating evapotranspiration in
large irrigated area. Mostly they are used for research purposes
by trained personnel. Evapotranspiration is generally estimated
by using different methods which requires measurements of
climatological parameters.
especially in the higher latitudes. Radiation method would be
more reliable than Blaney Criddle in equatorial regions, on small
islands, or at high altitudes even if measured sunshine or
cloudiness data were available (Doorenbos and Pruitt, 1977).
The empirical and temperature based methods have been used
for estimating evapotranspiration for longer periods i.e. monthly
or weekly.
4.0 RADIATION METHODS:
3.0 EMPIRICAL
METHODS:
AND
TEMPERATURE
BASED
3.1 Pan Evaporation method
Evaporation pan provided measurement of integrated effect of
temperature, radiation, wind and humidity on evaporation from a
particular open water surface. Evaporation pan data were utilized
to convert evaporation from free-water surface with pan
coefficient to estimate potential evapotranspiration (Allen et al,
1998). Incorrect accounting for pan environment and local
climate could cause errors in estimation of crop water use upto
plus or minus 40 percent (Cuenca 1989). However pan
evaporation has been one of the widely used methods due to
simplicity and minimum data requirements.
3.2 Temperature based methods
Hedke (1924) developed a method for estimating valley
consumptive use based on “heat available” defined as degreedays (number of days multiplied to temperature). Blaney and
Morin (1942); Lowry and Johnson (1942) developed a method
for roughly calculating seasonal consumptive use. Blaney-Morin
term included relative humidity term which was useful index for
measuring vapour transport component of evaporation process.
Lowry and Johnson method was developed based only on
temperature. Thornthwaite (1948) developed a method with an
assumption of an exponential relationship existing between
mean monthly consumptive use and mean monthly temperature.
The formula did not take into account the wind effect which
could be an important factor at many places. Blaney – Criddle
(1950 and 1962) developed method for areas where available
climatic data covered air temperature data only. The mean air
temperature was considered to be a good measure of solar
radiation. It was considered one of the popular procedures for
estimating potential evapotranspiration due to its simplicity and
readily available temperature data. In this method monthly
consumptive use crop coefficient k had to be developed for each
and every crop under the climatic condition of particular area.
Phelan (1962) developed a procedure for adjusting monthly k
values as a function of air temperature which is known as SCS
Blaney Criddle method. Doorenbos and Pruitt (1977) suggested
including other meteorological variables by using specific data
or general estimates of sunshine hours, relative humidity and
wind speed to have an improved estimate of potential
evapotranspiration which is known as FAO Blaney-Criddle
method. Blaney Criddle method had a limitation of selecting
percent of daytime hours instead of solar radiation as an index of
solar energy. It is observed that daytime hours obtained from
sunshine tables did not properly accounted for solar angle effects
Evapotranspiration occurs only when energy is available and
hence estimation of solar radiation can give better estimation of
ET by using Energy Balance equation which includes Rn
(radiation from sun and sky), G (heat to ground), H (heat to air).
Makkink (1957) proposed a formula for estimating ET from air
temperature and sunshine or cloudiness or solar radiation. The
Makkink equation was the base of the subsequent FAO 24
Radiation method. Turc (1961) developed a formula based on
ten-day mean air temperature and solar radiation. The Turc
equation had limitation to be applied only if Tmean > 10 .
Jensen-Haise (1963); Hargreaves-Samani (1985) developed a
relationships between temperature and solar radiation using the
observations of consumptive use of water. In spite of sufficient
energy available, ET could be less due to aerodynamic resistance
in form of Wind speed and Humidity as for the atmosphere‟s
ability to remove water vapour, an “Aerodynamic” strength also
plays a crucial role.
5.0 COMBINATION METHODS:
Penman (1948, 1963) utilized Bowen ratio principle and derived
a “combination equation” by coalescing two terms, one
(radiation) term which was for the energy required to uphold
evaporation from open water surface and second (wind and
humidity) term for the atmosphere‟s ability to remove water
vapour, an “aerodynamic” strength. Penman formula could be
used for estimation of potential evapotranspiration by using a
reflection coefficient (r) value of 0.25 for most crops. Monteith
(1965, 1981) extended Penman‟s basic concept to plants and
cropped areas by introducing resistance factors, including
surface resistance and aerodynamic resistance by clearly
identifying the reliance of transpiration on canopy controls
known as Penman-Monteith evapotranspiration equation.
Priestly and Taylor (1972) proposed a well- known
simplification of Penman‟s equation for humid environments
where the aerodynamic term was put at a constant value (0.26)
of the energy term. Doorenbos and Pruitt (1975, 1977) proposed
a modified Penman method with a revised wind function term
and an adjustment for mean climatic data for estimating
reasonably accurately the reference crop ET by giving tables and
graphs to facilitate computation. Wright (1982) modified the
original Penman equation and adapted 1982 Kimberly-Penman
equation. Kizer et al., (1990) developed hourly
evapotranspiration prediction model by calibrating the Penman
equation for an alfalfa reference crop. Allen et al., (1998) used
the equation on hourly basis with the rs term having a constant
value of 70 s m-1 throughout the day and night. They
recommended FAO-56 Penman Monteith method as the sole
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standard method for determining reference evapotranspiration in
all climates, especially when there was availability of data.
Allen, (2000) developed REF-ET program which provided
standardized reference evapotranspiration calculations in
different time steps for more than 15 methods commonly used
such as
Pan Evaporation, Temperature methods, Radiation
methods, Combination methods. Allen (2002) compared the
seasonal ET obtained by reference evapotranspiration estimated
by ASCE standardized Penman-Monteith with 1982 Kimberly
Penman and found the differences to be low. Walter et al.,
(2005) developed a standardized reference evapotranspiration
equation which could be applied to two types of reference
surfaces alfalfa and clipped grass for daily and hourly
calculation time step. The ASCE Standardized Reference
Evapotranspiration Equation based on FAO-56 PenmanMonteith equation was developed by ASCE-EWRI task
committee with aforesaid purpose. The equation is also
recognized as ASCE-EWRI standardized Penman-Monteith
equation. Allen et al. (2006) reviewed the functioning of FAOPM method using surface resistance parameter rs = 70 sm-1 in
hourly time step while using a constant rs = 50 sm-1 during day
and rs = 200sm-1 during night for hourly period. The various
widely used equations discussed above are depicted in Table 1.
Values for Cn and Cd in FAO-PM and ASCE-EWRI
standardized PM equations are given in Table 2.
6.0
COMPARISON
METHODOLOGIES
STUDIES
OF
Table1. Equation and Measured data required for ET o prediction
for various methods.
Name of Prediction
Method
Equation
Data used
Empirical and
Temperature
Methods
Hedke (1924)
Heat available =
Temp x days
FEW
Many comparison studies have been carried out worldwide
regarding the functioning of various methods to estimate
reference ET. Each method has its own strengths and weakness
under the particular set of conditions. Here only few studies have
been discussed to just give a brief idea about their functioning.
Hatfield and Allen (1996) compared ET estimates under
deficient water supplies with Priestly-Taylor and PenmanMonteith equations. Penman-Monteith gave more consistent
results, while Priestly-Taylor overestimated ETc. Dodds et al.,
(2005) reviewed various methodologies to estimate ETref. (i)
Evaporation Class-A pan tended to be 7-8% higher than the
locally calibrated ETo values for evaporation rates < 10mm day1
and for values > 10mm day-1the pan overestimated the values
by upto 30%. (ii) Two methods of Penman combination
Equation with certain variation in it were compared with
lysimeter. a). Kohler-Parmele variation was with a purpose of
calculating the long wave radiation from the soil-plant system
using the air temperature instead of evaporating surface
temperature, b) Morton gave an iterative variation of the Penman
equation to calculate a suitable evaporating surface temperature;
where both methods performed well. Berengena and Gavilan
(2005) compared measured ETo using lysimeter with estimated
ETref in a highly advective semi arid environment. They found
that locally adjusted Penman and ASCE-PM gave the best
results; followed by FAO-PM. Hargreaves equation under
predicted for high ET values and the Priestly-Taylor equation
was found to be too sensitive to advection and the values
improved only after the application of correction of the Jury and
Tanner. Er. Raki et al.(2010) compared three empirical
methods Makkink, Priestley-Taylor and Hargreaves-Samani for
computing reference evapotranspiration (ETo ) to those with
FAO Penman-Monteith in semi arid climate. Hargreaves
equation tended to under estimate ETo upto twenty percent for
daily periods. Makkink and Priestly& Taylor methods clearly
under estimated the values of ETo during dry periods in
comparison to FAO-PM model, since values of α = 1.26 and Cm
= 0.61 that used are suitable for humid conditions. Artificial
Neural Networks (ANNs) could be a useful tool to estimate
reference evapotranspiration as a function of climatic elements
Kumar et al., 2002; Jothiprakash et al., 2002. Chauhan and
Shrivastava, (2012) reported that ANNs performance when
compared with lysimeter measured values were better than those
obtained from Penman-Monteith method for estimation of ET ref.
Ojha and Bhakar (2012) carried out the comparison between
daily ETref estimated by Penman Monteith (PM) method and that
of estimated by ANNs and found the ANNs results encouraging.
T
Blaney and Morin
(1942)
PET = rf(0.45
Ta+8)(520 – R1.31)/
100
T,SS,RH
Lowry and Johnson
(1942)
CU = 0.00185 HE+
10.4
T
Thornthwaite
(1948)
T,SS
Blaney and Criddle
(1945,1962)
T,SS
SCS-Blaney Criddle
Phelan(1962)
T,SS
;
US Weather Bureau
Class A pan
RH,E,W
FAO-Blaney
Criddle Doorenbos
& Pruitt (1977)
T,SS,RH,W
Temperature and
Radiation Methods
FAO
radiation
(Makkink, 1957)
T,SS,RH,W,Rs
Turc(1961)
T,RH,Rs,
Jensen and Haise
(1963)
T, Rs
Hargreaves
and
Samani (1985)
T, Rs,/(SS1,Ra)
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1
= mean air temperature (o F and
).
Combination
Methods
-1
extraterrestrial radiation (mm d ) ,
Penman
(1948,1963)
T,SS,RH,W,Rs
Penman-Monteith
method (Monteith
1965)
T, RH, Rn
Priestly
Taylor(1972)
T, RH, Rn
and
C),
=
= maximum and
minimum daily air temperature difference.
-2
o
= evaporative
-1
latent heat flux (MJ m day ),
= slope of saturated vapour
o -1
pressure curve ( kPa C ), Rn= net radiation flux (MJ m-2 day-1),
G = sensible heat flux into the soil (MJ m-2 d-1),
=
psychrometric constant ( kPa o C-1),
= vapour transport of
flux (mm d-1).
= density of air ( kg m-3),
-1 o
= specific heat of
-1
Modified Penman
method, Doorenbos
and
Pruitt
(1975,1977)
T, W, Rn
moisture ( J kg C ), VPD = vapour pressure deficit,
= canopy surface resistance and aerodynamic resistance ( sm-1).
W = temperature related weighting factor,
= wind related
1982
Kimberly
Penman
Method,
Wright (1982)
T, RH, W, Rn
Penman
equation
for hourly ET for
alfalfa, Kizer et
al.,(1990)
T, RH, W, Rn
function,
= difference between saturation vapour
pressure at mean air temperature and the mean actual vapour
pressure of air (both in mbar), c = adjustment factor to
compensate for the effect of day & night weather conditions. ETr
= reference evapotranspiration (MJ m-2d-1),
= wind function.
LE = mean hourly latent heat flux (Wm-2), U2 = wind speed at
2m (km h-1),
= coefficients.
= saturation vapour
;
pressure (k Pa),
FAO-56 PenmanMonteith Method,
Allen et al.,(1998)
T, RH, W, Rn
ASCE-EWRI
standardized -PM
method, Walter et
al., (2005)
T RH, W, Rn
and =
numerator constants and denominator constants
respectively that change with reference type and calculation time
step
.
Table 2. Values for Cn and Cd in Equation for the FAO-PM and
ASCE-EWRI standardized PM equations (as reported in Allen et
al., (1998) and ASCE-EWRI (2005))
T = Temperature, SS = Sun shine hours, RH = Relative
Humidity, W = Wind, E = Evaporation, Rs= Solar Radiation, Rn
= Net Radiation.. PET= Potential evapotranspiration (mm day-1),
Ta= Mean monthly temperature in o C, R= Mean monthly
Relative humidity, rf = ratio of monthly to annual radiation.
CU= Annual consumptive use (inches), HE= Effective heat, in
degree days above 32o F. e = unadjusted potential ET
(cm/month)( month of 30 days each and 12 hrs daytime), t=
mean air temperature(o C), I = annual or seasonal heat index, α=
an empirical exponent. = monthly consumptive use factor, T =
mean monthly temperature (o F), p = monthly per cent of total
daytime hrs of the year. ET= Seasonal crop water requirements
(inches),
= monthly Blaney Criddle coefficient
,
=
monthly consumptive use factor ,
= mean temperature for
month i, (o F). ETo= Reference evapotranspiration (mm day-1),
Kp= Pan coefficient, Epan = Pan evaporation (mm day-1). , b =
climatic calibration coefficients , = mean daily percentage of
total annual daytime hours, = mean daily temperature in o C
over the month considered. = adjustment factor depending on
mean humidity and daytime wind conditions, W = function of
the temperature & altitude, Rs= solar radiation (mm day-1).
=
coefficient depending mean relative humidity, Rs= solar
radiation (MJ m-2 day-1), = latent heat of vaporization (MJ kg-
= mean actual vapour pressure (k Pa),
Method
Calculation time
step
Cn
Cd
FAO-PM
(ETo) &
24-h
900
0.34c
Hourly
37
0.24/0.96a
24-h
1600
0.38
Hourly
66
0.25/1.7a
ASCE-PM
(ETo)
ASCE-PM
(ETr)b
a
The first value for daytime periods (when Rn>0) and the second
value is for night time. b ETr is reference ET from 0.5m tall
alfalfa. c The Cd= 0.34 is now recommended to be changed to
0.24 for daytime and 0.96 for night time for hourly or shorter
time steps.
7.0 DISCUSSION
Irrigation is supplied to compensate the moisture deficit in soil
occurred due to evapotranspiration. Hence precise estimation of
ET is very much required. The factors affecting potential ET are
radiation, temperature, relative humidity and wind speed. The
measurement techniques just provide the point value of moisture
content and it cannot be used to estimate the crop water
requirement of large irrigated area with varied climate. The
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empirical and temperature based methods performed suitably
under specific climatic and agronomic conditions for which they
were originally developed and could not be used under different
conditions, other than that for which they were developed.
Transferring these to other regions led to either under/over
estimation causing substantial errors. The radiation methods
which considered the radiant energy provides better estimates in
humid climate but were less precise in advective conditions in
arid and semi arid climates, and hence it needed adjustment or
correction. The combination methods take into account the
radiant energy term as well as aerodynamic term the ability to
remove water vapour hence it improved upon the ET estimation.
FAO-PM was considered the sole standard method in case all
the climate data are available. ASCE-PM method was
standardized for different reference crops and also for different
calculation time step. The ASCE- PM standardized reference ET
equation is widely accepted for precise estimation of ET. This
method can provide important tool for developing decision
support system for irrigation scheduling. The relationship of ET
and climate parameters is complex and hence many researchers
have resorted to data modelling such as ANN technique.
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Allen, R. G. (2000). Using the FAO-56 dual crop
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Jarlan, L., Ouldbba, A., . . . Allen, R. (2010). Assessment of reference
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Evett, S. R., Schwartz, R. C., Howell, T. A., Baumhardt, R.
L., & Copeland, K. S. (2012). Can weighing lysimeter ET represent
surrounding field ET well enough to test flux station measurements of daily
and sub-daily ET? Advances in Water Resources, Article in Press.
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Hargreaves, G. H., & Samani, Z. A. (1985). Reference
crop evaporation from temperature. . Applied Engineering in Agriculture
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Kizer, M. A., Elliott, R. L., & Stone, J. F. (1990). Hourly
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Kumar, M., Raghuwanshi, N. S., Singh, R., Wallender, W.
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Lanthaler, C. (2004). Lysimeter Stations and Soil
Hydrology Measuring sites in Europe – Purpose, Equipment, Research
results, Future Developments. Graz: Department for Water Resources
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xxv.
Lowry, R. L., & Johnson, A. F. (1942). Consumptive use of
water for agriculture. Transaction American Society of Civil Engineers
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xxvi.
Makkink, G. F. (1957). Testing the Penman Formula by
Means of Lysimeters. In Jensen, M. E. Historical evolution of ET
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xxvii.
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xxviii.
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of Indian Water Resources Society, Vol. 32, No. 1-2,, 13-21.
xxx.
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water, bare soil, and grass. Proceedings Royal Society of London, A193,
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Penman, H. L. (1963). Vegetation and hydrology. In:
Farhani, H. J., Howell, T. A., Shuttleworth, W. J. and Bausch, W. C.
Evapotranspiration: Progress in measurement and modeling in
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Blaney-Criddle formula. In: Jensen, M. E. Historical evolution of ET
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estimating methods, A Century of progress. CSU/ARS Evapotranspiration
Workshop, Fort Collins, Co, 12-Mar-2010, p.4.
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(1990). Sensing Irrigation needs. In: Farhani, H. J., Howell, T. A.,
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of surface heat flux and evaporation using large-scale parameters. .
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requirements, potential ET : A simple climatic formula evolved up
todate.In: Jensen, M. E. Historical evolution of ET estimating methods, A
Century of progress. CSU/ARS Evapotranspiration Workshop, Fort
Collins, CO, 12-Mar-2010, p.8.
xxxvii.
Walter, I. A., Allen, R. G., Elliott, R., Itenfisu, D., Brown,
P., Jensen, M. E., . . . Wright, J. L. (2005). The ASCE Standardized
Reference Evapotranspiration Equation. USA: Task Committee on
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Wright, J. L. (1982). New evapotranspiration crop
coefficients. . Journal of Irrigation and Drainage Division. 108 (IR1), 5774.
Estimation of Deep Percolation from Rice Paddy
Field Using Lysimeter Experiments on Sandy
Loam Soil
Hatiye, Samuel D.1, K.S.Hari Prasad 2, C.S.P. Ojha 2 and G.S.
Kaushika 3
1,3
Ph.D. Scholar, Civil Engineering Department, IIT Roorkee,
247667 Roorkee, India; 2Professor Department of Civil
Engineering, IIT Roorkee, 247667 Roorkee, India.
ABSTRACT: In this study, variation and characteristics of
deep percolation from irrigated rice paddy field using drainage
type lysimeter set up has been presented. The water intensive
lowland rice paddy has been grown from July to November
2013. Water balance components including irrigation size,
rainfall, soil moisture and deep percolation were monitored on
daily bases. It has been observed that quite a large volume of
water is returned as deep percolation flow as physically
demonstrated from twin lysimeter measurements. We employed
a simple tipping bucket type water balance model to validate
the experimental data. The deep percolation monitored on
daily bases does not agree with the model computed value,
however it agrees well on an extended time interval in an order
of seven days (weekly bases). On average more than 80% of the
input volume of water goes on the account of deep percolation
in non puddled, continuously irrigated rice field.
1
Corresponding Author: email: [email protected]; phone +918266802124
Our study proves that locally constructed lysimeters could
effectively be utilized in water balance study of a cropped area
when used in combination with root zone soil moisture
monitoring devices and can contribute to the further water
resources management of an irrigated field. We deduce from
this study that deep percolation process is one of the most
important factors lowering surface method irrigation efficiency
in general and rice paddy fields in particular in course
textured soils. We recommend, revisit of irrigation scheduling
options besides the already practiced water saving options in
water intensive crops for better utilization of water resources.
Key words: Deep percolation, Lysimeter experiment, Rice
paddy, Root zone depletion, Water balance model
1. INTRODUCTION
Deep percolation phenomena from frequently irrigated fields
such as paddy rice seriously diminish irrigation efficiency,
jeopardise proper water management and minimize water
productivity. This is quite sound in coarse textured soils where
water holding capacity is relatively less. Seepage and percolation
losses of water are major reasons behind the poor water
productivity in wetland rice (Patil et al. 2011). Percolation loss
of water from irrigated field is not only reducing irrigation
efficiency but also becoming a haphazard to an environment by
carrying agriculture-based chemicals to the surrounding water
bodies, especially to the groundwater aquifer systems (Tafteh
and Sepaskhah 2012).
Various studies were conducted to estimate deep percolation
from irrigated fields. Large volume of deep percolation loss
could exist during the continuous flooding operation of rice
paddy, even in under puddled conditions (Kukal and Aggarwal
2002; Bouman et al. 2007; Yadav et al. 2011). Bouman and et
al. (2007) reported that around 70% of input water could go for
percolation loss when groundwater depth is equal to or more
than 2m. Yadav et al. (2011) observed that, about 81% of water
added was drained beyond the root zone (0–60 cm) from
continuously flooded rice field. Many factors influence
percolation phenomena through the bottom of the crop root
zone. Ponding size, water table depth, evapotranspiration,
antecedent soil moisture condition, soil texture and structure
characteristics, shrinkage behaviour of soil and biotic activities
in soil root zone, irrigation size and time, climatic condition,
crop type and characteristics, water management and agronomic
practices, puddling intensity and depth, etc… (Kukal and
Aggarwal 2002; Bouman 2007; Bethune et al. 2008; Selle et al.
2011). Sizable efforts have been made so far to reduce deep
percolation from rice fields: alternate wetting and drying (AWD)
( de Vries, et al. 2010; Bouman et al. 2007; Belder et al. 2004;
), aerobic rice (Nie et al. 2012), delayed application of
continuous flooding (Dunn and Gaydon 2011), puddling (Kuakal
and Aggarwal 2002; Kukal and Sidhu 2004). However,
consideration and effort to reduce deep percolation under non
puddled rice paddy field was not dealt significantly.
There are various ways available to quantify and estimate deep
percolation. Drainage type lysimeters are considered to be the
most important facilities, at field level, to measure percolation.
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However, lysimeters are criticized to be costly to install,
maintain and operate; and so they often are used singly such that
adequate replication of measurements is not possible (Evett et al.
2012; Bethune et al. 2008; Hillel 2004). Apart from the direct
measurement of percolation using lysimeter set up, various
models have also been developed to estimate deep percolation
from agricultural areas. Deep percolation, the water that passes
below the crop root zone, is usually calculated based on the
conventional water balance equation (Peng et al. 2012; Bethune
et al. 2008; Huang et al. 2003).
Estimation of deep percolation from rice paddy has not
commonly been determined using drainage type lysimeters. The
reason may be due to the fact that drainage volume at the bottom
of water intensive crops is quite large which may not be easy for
continuous monitoring. This particular study aims, therefore, to
quantify deep percolation in continuously monitored irrigated
fields of paddy rice to understand and characterize deep
percolation. The experimental data is planned to be evaluated
using simple water balance model after FAO. In the course of
the exercise, we try to examine the major influencing factors of
deep percolation from cropped area employing drainage type
lysimeters in sandy loam soils.
2.
MATERIALS AND METHODS
The study site is located in the Utterakhand state of India, a field
experimental plot situated in Department of Civil Engineering,
IIT Roorkee in the geometric grid of 77 o53‟52” East Longitude
and 29o52‟00‟‟ at an average altitude of 274m above mean sea
level. The area experiences hot summer season with monsoon
rainfall and cold winter. The monthly average maximum
temperature of the study area is recorded in the range of 19.33
(January) to 37.73oC (May) and monthly average minimum
temperature in the range of 7.2(January) to 25.6 oC (July)
according to the data from National Institute of Hydrology
(NIH) at Roorkee. The average relative humidity runs from
52.2% (May) to 89.7% (January). The average annual daily
sunshine duration is 7.7hrs. The average annual rainfall of
Roorkee is 1060 mm out of which almost 80% is recorded
during the monsoon season (June to September).
experimental conditions have been maintained inside and outside
the lysimeters throughout the growing period of the crop.
Twenty one days old seedlings were transplanted in a soaked
field. Basal doses of zinc sulphate, superphosphate and urea (N
fertilizer) were applied in two equal instalments during
transplanting and 6 weeks after transplanting. Weed control has
been undertaken manually by hand removing all the weeds from
field three times during the growth period of the crop. Irrigation
water size of 20mm to 100mm has been applied to the paddy
field during the growth stages except the final late stages when
the crop was matured to harvest. The soil physical and hydraulic
characteristics have been determined in the laboratory for three
representative spots of the plot and replicate depths from 0 to
140cm following standard procedures. The soil physical
properties determined are indicated in the table below (table 1).
Irrigation water was applied for a specific area by measuring
discharge and calculating time required to provide a
predetermined depth of water. The soil moisture status was
monitored by using soil moisture probe (Profile Probe-2; Delta T
Devices, Cambridge) through access tubes installed both inside
and outside the lysimeters. The profile probe sensor which is
connected to HH2 meter provides soil moisture content data at
10, 20, 30, 40, 60 and 100cm depths. It enables to measure the
soil moisture content in volumetric bases for different soil types
ranging from clayey to sandy soils with accuracy between +0.04
(after soil specific calibration) and +0.06(after generalized soil
calibration in normal soils). The soil moisture was measured on
daily bases and before and after irrigation or rainfall whenever
these events took place.
Deep percolation was measured twice in a day at bottom of the
lysimeters early in the morning (07:00 a.m.) and evening
(around 07:00 p.m.). The lysimeter rim was kept 10cm above the
ground to avoid run-on or runoff. Tipping buckets in access
caisson hall were used to collect the drainage water. Climatic
data (temperature, relative humidity, pan evaporation, wind
speed and rainfall) for the growth period of the crop was
obtained from nearby metrological station, National Institute of
Hydrology (NIH), India located at distance of 0.8 kilometres
from the experimental site.
Table 1. Soil physical characteristics of the experimental plot
The field experiment consisted of growing paddy rice ((Oryza
Sativa L.) , var. Surbati Basmati) from July 23 (day of
transplanting) to 02 November (day of harvest) of the 2013
kharif season. The area of lysimeters is 1m2 having a depth of
1.5m repacked soil monolith of the experimental field. The
construction of the lysimeters took place in 2007 and hence they
are considered to replicate the surrounding root zone soil
environment. The soil monolith is a repacked soil material
consisting of the upper 1.3m filled with a sandy loam textured
soil, moderately homogeneous throughout the profile,
characterized by an organic content of 1.1 to 1.2%. The bottom
0.08m was filled with a very course gravel of size more than
3cm diameter overlain by 0.12m thick gravel of about 2cm in
diameter. This bottom arrangement allows drainage towards
imbedded perforated pipes which carry percolating water
towards tipping buckets (Shankar 2007) (Fig 1). The same
2
3
Soil depth
(below
GL2),cm
Bulk
density(
g/cm3)
San
d
(%)
Silt
(%)
Cl
ay
(
%
)
Soil Class
(USDA)3
Satura
ted
Water
conten
t, θsat
1.58
Particl
e
densit
y
(g/cm3
)
2.55
0-30
73.
40
22.7
0
Sandy
Loam
0.38
30-60
1.55
2.57
66.
89
28.3
9
Sandy
Loam
0.40
60-80
1.54
2.56
68.
57
26.5
4
Sandy
Loam
0.40
80-100
1.54
2.58
69.
10
26.5
4
2.
9
6
4.
0
1
4.
3
3
3.
8
Sandy
Loam
0.40
Ground level
USDA=United States Dep‟t of Agriculture
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4
100-140
1.59
2.62
68.
01
27.3
8
4.
5
8
Sandy
Loam
0.39
when the root depth has grown deeper, water contents measured
at deeper depths has been taken into account in computation of
soil moisture deficit besides the soil water content in the surface
layer. Consequently, the deep percolation is computed as:-
DPi  10R j  ( i 1   i )  Pi  I i  ETci  Ri
3. MODEL DESCRIPTION
The soil water balance is the concept, derived from the law of
conservation of mass, used in quite many studies dealing with
water flow in the soil root zone, solute transport, groundwater
flow and recharge, etc.…. It is dealing with quantification and
analysis of each inflow and outflow components while
accounting for storage in the system environment (Kim, et al.
2009; Chien and Fang 2012; Peng et al. 2012).
A FAO based simple tipping bucket soil water balance model
(Allen et al. 1998) is used in this study to test the validity of field
experimental observation. The lysimeter water balance can be
given by (Hillel 2004):-
Di  Di 1  Pi  I i  ETci  DPi  Ri
(1)
(4)
Irrigation and precipitation are usually inputs to the field and
obtained from actual field measurements. Evapotranspiration
could be calculated from various models. Among the various
methods developed so far, the FAO Penman Monthieth approach
has been applied in this study.
Evapotranspiration for standard conditions, Etc, is estimated by
incorporating a crop coefficient, Kc,
ETc  K c  ETo
(5)
Where ETc for standard conditions assumes hypothetical
conditions where there is no short of water, actively growing
disease free crops in an extensive area. However, this imaginary
condition seldom occurs in a practical field condition. Detailed
procedures to estimate Kc and attached parameters are given by
FAO paper 56 (Allen et al., 1998), Rallo et al., 2012)
Where D (mm) = root zone moisture depletion; P (mm) =
precipitation; I(mm) = applied irrigation; ETc (mm) = actual
evapotranspiration; DP (mm) = deep percolation of water
moving out of the root zone; Ri(mm) is surface runoff ; i and i-1
are, respectively, considered to be the current and previous time
steps (days in this study).
The soil moisture deficit in the root zone is obtained from
monitored water contents at respective depths. It is usually
referenced with the field capacity of a given soil and may be
given by:-
Di  10 R j   fc   i 
Figure-1. Lysimeter set up details (All dimensions are in mm)
(2)
Where θi is the soil moisture content (%) in the root zone depth
Rj (m) at the end of day i ; θfc is the soil moisture content at field
capacity (%). The deep percolation is computed taking into
account the root growth of the crops. The field observed root
length has been interpolated for each day of the crop growth
period and used as an input in the computation of the soil water
balance model. In particular, the root growth has an effect on the
soil moisture deficit as portrayed in the following equation.
Di  Di 1  10R j  ( fc   i )  R j  ( fc   i 1 )
Di  Di 1  10R j  ( i 1   i )
Runoff component of the water balance in lysimeter studies is
often neglected since it is either minimal or controlled in such a
way that there exists no run-on and run-off. If the top level of the
lysimeter is constructed a slightly above the ground elevation,
surface water inflow or outflow could be eliminated. However,
in certain torrential storms it is advisable to consider runoff from
a lysimeter since water could overflow the lysimeter rim. Runon in our experimental site did not occur since the field
surrounding is constructed of earthen bunds covered with plastic
sheets. Therefore, surface runoff in our experimental field has
been considered only when rainfall magnitude overflows above
the lysimeter rim level according to the following algorithm:
(3)
Where
is the average root depth (m) in the time interval i and
i-1 and other terms are as defined earlier. If the depth of root
zone is small,
< 10cm, as in the early growth stages of the
crops, the soil moisture content on the top layer is considered;
(6)
Where Ri= runoff generated (mm); P i= rainfall (mm) and Lrh =
the lysimeter rim height measured from ground surface inside
the lysimeter (mm).
MANIT Bhopal
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4.1. Diurnal Deep Percolation
Figure 2 shows the measured deep percolation for the day and
night times. During the period of the experimental run, the
observed deep percolation on day time (measured around 7:00
p.m. in the evening) is lower than the deep percolation occurred
during night time (measured around 7:00 a.m. in the morning).
Although we could clearly observe such variation of day and
night time percolation which took place due to the effect of
evapotranspiration during day time, the comparison of
evapotranspiration with deep percolation shows poor correlation.
The correlation coefficient between daily deep percolation and
actual evapotranspiration is nearly 0.13 (not shown here). The
less dependence of percolation on ET refers that deep
percolation is more dependent on some other factors such as
input water volume (Selle et al. 2011; Bethune et al. 2008;
Ochoa et al. 2007; Smith et al. 2005), soil hydraulic
characteristics (Smith et al. 2005), final infiltration rate (Selle et
al. 2011; Bethune, et al. 2008), Groundwater depth (Bouman and
et.al. 2007; Bethune, et al. 2008), antecedent root zone soil
moisture condition (Ochoa et al, 2007), irrigation management
techniques (Smith et al. 2005); crop type and cropping pattern
(Smith et al. 2005). The input depth of water, antecedent soil
moisture conditions, groundwater depth and irrigation
management techniques have eventually influenced the deep
percolation in the experimental field.During the crop period, the
deep percolation event was observed to follow the input water
pattern. Occurrence of intense storms caused high deep
percolation than event irrigations (fig. 4). Irrigation could be
controlled to minimize deep percolation but it is hardly possible
to manage percolation from storm rainfall. The process of
puddling would enhance the soil water retention capacity
(Kuakal and Aggarwal 2002; Kukal and Sidhu 2004).
However, effectiveness of this technique in ensuring lateral flow
through side bunds and deep percolation thereof is being
debatable.
The antecedent soil moisture condition is obviously another
factor which could characterize the deep percolation. Whenever,
the soil is at or above field capacity, the input water added would
contribute to deep percolation balance. Since, the wetting event
in this particular study was frequent (fig 4); the soil was
remained near field capacity for most of the growing period and
hence large deep percolation. Generally, the deep percolation
showed a decreasing trend from the monsoon season (JulySeptember) to late season stage of the crop season (OctoberNovember). The decreasing trend would be due to the coupled
effects of reduced irrigation sizes, frequency and the ending of
monsoon rainfall season.The performance of the two lysimeters
in metering deep percolation has also been investigated. It has
been seen that the observed amount of deep percolation from
both lysimeters is fairly similar showing the repacked soil
monolith exhibit the same property in both lysimeters
particularly during the non-storm periods. During
Figure 2. Deep percolation at lysimeter 1(L1) for day (broken)
and night times (solid) lines
storm periods, the lysimeters were observed to demonstrate
variations in allowing percolation (fig. 2 and 3). This may be due
to the fact that the lysimeters depict differences in preferential
flow which is significant during rainy days.
4.2. Model
Percolation
Predicted
and
Measured
Deep
The model predicted and measured deep percolation is shown
below (fig. 6) for various time steps. The deep percolation
computed using the simple water balance model on daily time
step poorly agrees with the field measured daily deep
percolation. This would be due to the inherent nature of the
model in which it assumes the deep percolation to occur on the
day of event irrigation or rainfall. However, in practical field
situations deep percolation could take place starting on the day
of triggered irrigation or rainfall occurrence and in the next
consecutive days (Liu et al. 2006; Peng et al. 2012). Peng et al.
2012, has indicated that percolation would cease after seven
days (a weekly time step). Liu et al. (2006) has shown that deep
percolation would follow a sort of power law function. Apart
from that, till the percolating water finds way out to tipping
buckets
in
to
the
Figure 3. Relation of Deep Percolation (DP) Measured in the
two lysimeters
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Figure 4. Input (Irrigation-IRR and Rainfall) water and deep
percolation
atmosphere, there is a time lag between incidence of irrigation or
rainfall and drainage of water. This time lag could not be
perfectly one day as assumed in the simple tipping bucket
model. In actual situations, construction of lysimeters could only
be done for specific depth of the root zone, mostly considering
the maximum depth of root lengths of major crops in an area.
Therefore, whenever the root zone of a particular crop is less
than the outlet level of the lysimeter, we would expect certain
effects of storage which reasonably cause time lag for
percolation to occur. The important thing is that the ability of the
lysimeter set up in monitoring deep percolation beyond the root
zone.
The statistics with regard to the lumped time step deep
percolation is shown in the table below (table 2). Deep
percolation computed on weekly (7 days) time step showed very
good agreement with the measured cumulative deep percolation.
This shows that consideration of smaller time steps (in order of
few days or less than a day) would yield erroneous results
particularly in computing the deep percolation component of the
water balance from drainage type lysimeters. In fact, the storage
effect of the lysimeter monolith could not be disregarded.
However, it is only possible to construct drainage type
lysimeters whose outlets are located at certain fixed position
below the root zone (usually below 1m depth from ground
level).
Table 2. Statistical parameters for measured and computed deep
percolation
Time interval, days
C
O
D
C A
O R
V E
1
0
.
1
1
3
1 . 0.
0 06
2
0
.
6
9
0 . 0.
3 11
7
0
.
9
0 0.
. 01
3
After observation of the deviations between model predicted and
field observed deep percolation besides the temporal
characteristics of measured percolation, we extended time
interval from daily time step to 5, 7 and 10 days interval to apply
the water balance. The results of this time lumping exercise,
commencing from the day of transplanting to crop harvest,
showed that there is a good
L1=Lysimeter 1; L2 =lysimeter 2; cum = cumulative;
ETA=Actual Evapotranspiration
Figure 5. Cumulative Water Balance Components during the
crop period
5
agreement between measured and predicted deep percolation
values.
7
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10
18-19, Dec. 2014
1
2
0
.
7
3
0 0.
. 04
2
4
COD=Coefficient of determination; COV= Coefficient of
Variation; ARE=Average relative error
The statistics shows that there occurs a good agreement between
model predicted and measured values of deep percolation when
applied on extended time steps. Thus we deduce from these
results that locally constructed drainage type lysimeters could
provide detailed information in characterizing deep percolation
phenomena in an irrigated farm.
Deep percolation
measurements could be undertaken in the time intervals of 5 to
10 days in further investigation of deep percolation researches
unless drainage outlets are installed at very shallow depths
which may, however, not be practicable.
(a) Time Interval = 1day
Interval = 5days
(b) Time
(c) Time Interval = 7days
Interval = 10days
(d) Time
Deep percolation from rice field has been investigated. The deep
percolation varies mainly in response to the input water depth
and frequency of application/occurrence when groundwater table
is assumed deep. Intense and continuous storms particularly
caused high percolation rate and depth owing to the saturated
antecedent moisture conditions during and after these
incidences. Evapotranspiration is observed to have some
influence on deep percolation as daily measurements reveal,
although there is a weak correlation between evapotranspiration
and deep percolation.
The FAO based simple tipping bucket water balance model
poorly simulates the daily deep percolation measured at drainage
type lysimeters. However, the model better predicts the
cumulative deep percolation on lumped time step of the order of
7 days (weekly time interval). Overall, in this study it has been
investigated that deep percolation is the most important process
in the water balance of irrigated paddy field diminishing
irrigation efficiency. Comparable volumes of deep percolation
from rice cultivated areas have been reported earlier, even under
puddled root zone conditions. Therefore, it is advisable to seek
alternative irrigation scheduling strategies to minimize deep
percolation and hence increase irrigation efficiency and further
enhance the water resource utilization of a region.
REFERENCES
Figure 6. (a-d) Measured (solid lines) and model predicted
(Dots) deep percolation
The overall share of deep percolation in the water balance is
quite high. We observed that there occurred above 80% of the
volume of water input goes as deep percolation. The total
amount of input water during the growing season was 3078.1mm
and the total measured deep percolation was 2506.5mm (fig. 5)
while the model computed deep percolation was 2646.60mm.
This shows that quite a significant volume of water is percolated
during frequent irrigation of the paddy growth period, although
it could be quite possible to reduce the amount of water input by
appropriate irrigation scheduling.
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Reservoir Modelling in Bearma Basin by Using
Mike Basin
Shikha Sachan1*, T. Thomas2, R.M. Singh3,
Pushpendra Kumar4
1
Department of Farm Engineering, Banaras Hindu University
Varanasi 221005, India
*
E-mail : [email protected]
ABSTRACT: MIKE BASIN is an integrate water resource
management and planning computer model that integrates GIS
with water resource modelling (DHI, 2006). The Bundelkhand
region in Central India has been in the grip of severe drought
in the last decade mainly due to poor, limited and untimely
rainfall and its high variability coupled with improper water
resources development and management. Bearma river is one
of the important tributary of river Ken lies completely in
Madhya Pradesh. In Bearma basin, Irrigation planning and
management has been carried out for drought year (2002).
Study has been conducted and analysed under two different
Scenarios,
(1) : without provision of reservoir in the Bearma basin
(2) : with provision of reservoir in the Bearma basin
In the first scenario, all demands of water users on 10 daily
basis from july 1 to November 10 are fulfilled through river
whereas in second scenario all demands of users on 10 daily
basis are fulfilled by river as well as from the reservoir RS
directly connected through user WU7. In the present study, the
irrigation management in the command of Bearma basin has
been carried out from reservoir releases. In this study “rule
curve reservoir” method was used for addition of reservoir in
Bearma basin. Irrigation demands for soybean crop during the
monsoon period (June to October) on a 10- daily basis for all
users namely WU1, WU2, WU3, WU4, WU5, WU6 and WU7
existing in sub-basins namely SW1, SW2, SW3, SW4, SW5,
SW6 and SW7 have been computed by using CROPWAT. It
can be seen that in scenario (1) there is no provision of
reservoir in the basin, user WU7 used maximum water as
125.55 MCM and deficit is also maximum in this sub-basin
with 88.48 MCM. In scenario (2) with provision of reservoir in
basin, it can be seen that that reservoir RS has used maximum
water of 218.05 MCM and deficit of 42.41 MCM also occurs.
The performance is more noticeable that demand deficits have
greatly reduced from 88.48 MCM to 42.41 MCM for WU7 by
construction of reservoir. It can be appreciated that all the
users that have not been connected to the reservoir are facing
deficits of varying magnitudes under drought situation.
Therefore, it will be prudent to explore additional sites for
reservoirs on different locations so that the deficits can be
minimised to the minimum extent possible.
Key words : Bearma basin, MIKE BASIN, rule curve method.
The Bundelkhand region was once known for its large natural
resources, abundant water resources including perennial streams,
large number of traditional tanks and rich forests. However,
large scale exploitation of all these resources has made the area
to be the poorest by which pressure on water resources in the
Bearma basin is likely to increase dramatically in the near future
as a result of high population growth. It is required to protect
rivers from degradation caused by hydrological conditions (Cui
et. al., 2010). However, the water demand is increasing whereas
water resources are expected to decrease because of climate
warming and the same or decreasing precipitation (Bates et. al.,
2008).Climatic variability, changes and uneven distribution of
resources create water shortages and interrupt the usual water
linked activities posing serious threat to nature, quality of life
and economy (Hisdal and Tallaksen, 2003). The recurrent
droughts in the last decade had led to large scale migration from
the Bundelkhand due to non-availability of water for domestic
and agricultural activities. The low stream flows are indicative
of rainfall situation (Galkate et. al., 2010).
In fact, drought is estimated to be the most costly natural disaster
in the world, wide range of detrimental effects associated with
precipitation deficits include: decreased crop yields, increased
wildfires, death of cattle and wildlife, water shortages, and rising
food prices (Witt, 1997) and the most complex and least
understood of all natural hazards, affecting more people than any
other hazard (Wilhite, 2000). Drought impacts the poorer
economies to a larger extent and may cause fatalities as
compared to developed countries. A drought is an extended
period when a region notes a deficiency in its water (Beran and
MANIT Bhopal
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Issue Special3
18-19, Dec. 2014
Rodier, 1985). The consequences of drought vary greatly
depending on its location, timing, extent and the type of society
or societal sector impacted by the drought (Gleick, 1993).The
different types of droughts have each their own specific
spatiotemporal characteristics (Peters et al., 2006; Tallaksen et
al., 2009). Different types of drought are meteorological,
hydrological, agricultural and socio-economic (Hisdal and
Tallaksen, 2003). Meteorological drought simply refers to the
atmospheric conditions that result in the absence or reduction of
precipitation and since its definition only relies on rainfall. Due
to its reliance on plant and soil conditions, agricultural drought
usually has a lag time in response to precipitation changes (Park
et al., 2005), and the impact depends greatly on the timing of the
drought in relation to crop growth.
In light of the grim scenario in the region, the Bearma basin is a
major tributary of the Ken river system, the life line of
Bundelkhand has been selected to study the drought scenario in
the recent years and for planning to cope up with such situation
in future through reservoir modeling. The monsoon rainfall is
the only possible source of irrigation in Bundelkhand region of
semi-arid Central India. A continuous spell of poor rainfall in
combination with high temperature in successive years hinders
water availability and imparts stress on ground water resources
leading to severe drought in many parts during both, the
monsoon and the non-monsoon seasons. Therefore, in present
study irrigation model has been simulated especially for drought
year 2002, under two different scenarios (without provision of
dam in the main stream and with provision of dam in the stream)
has been carried out to analyse irrigation deficit for soybean
crops. The model was simulated from observed flow by
preparing Bearma basin model in MIKE BASIN software.
Methods
Irrigation Management Planning for Bearma basin
For determination of suitable sites for construction of reservoirs
in study area, one location has been identified selected on the
main river. To develop drought mitigation strategies through
scientific planning of water resources and management, MIKE
BASIN model has been developed. In the present study, the
irrigation management in the command of Bearma basin has
been carried out from reservoir releases; therefore, reservoir,
irrigation nodes and transfer of water through channel have been
specified.
Figure 1. Schematic representation of reservoirs and the various water users
drawing water from reservoir as well as from the river (Scenario-2)
Description of MIKE BASIN Model
Rivers and their main tributaries are represented by a network
consisting of branches and nodes in the model. The model
requires the entire catchment to be segmented into a series of sub
catchments. The river system is represented in model by
digitized river network which can be generated directly on the
computer screen in Arc Map (DHI, 2003). A nodal
representation of case study of Attanagalu Oya Basin, Sri Lanka
was prepared using MIKE BASIN to estimate stream flow at
each node, (K. R. J. Perera et al., 2010).
Reservoir
MIKE BASIN can accommodate multiple multi-purpose
reservoir system and individual reservoir to simulate the
performance of specified operating policies using associated
operating rule curves. In present study rule curve reservoir
method was used for addition of reservoir in Bearma basin. Rule
curve reservoir regards a single physical storage and all users are
drawing water from the same storage.
Reservoir properties
The reservoir characteristics, operating rules, upstream and
downstream connections to users and control nodes are specified
in the reservoir properties dialog. The level-area-volume table is
used to compute reservoir volume at any level in reservoir.
Reservoir operation properties
The most common operating rule is the rule curve (standard
reservoir method). Rule curves define the desired storage
volumes, water levels, and releases at any time as a function of
existing water level. Present study has been carried out using
rule curve method.
Channels
The channels are the segments that connect water users,
irrigation nodes and hydropower nodes to a river or a reservoir.
In the present study the channel segment was used for
connecting water users and reservoirs.
Simulation
MIKE BASIN Model has been simulated for drought year
(2002). In first case, the model simulated after setting up all
water users „without any reservoir‟ and in second case the model
is simulated after setting up „reservoir‟ and water users. The
output time series contain water used, demand deficit, stored
volume in reservoir, water levels in reservoir, and channel flows
at given time span assigned during simulation. The schematic
representation of the reservoir and the various water users
drawing water from reservoir as well as from the river
(Scenario-2) is given in
Results and Discussion
MANIT Bhopal
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Issue Special3
18-19, Dec. 2014
11
323
31.70
157.70
Irrigation Management Planning for Bearma basin
The development of irrigation management plan depends on its
scientific resource action plans and their proper implementation.
Reservoir Characteristics
For application of MIKE BASIN model, reservoir properties
such as reduction level, high flood level, dead storage level, bed
level and reduction factor are required which were determined
using GIS and is given in Table 2. By using DEM of the study
area and the drainage pattern of the catchment, one suitable site
has been selected for construction of reservoir and incorporated
in the analysis. Reduction level for reservoir has been fixed
between high flood level and dead storage level, from where a
specified reduction from demand user node was applied by the
software, high flood level for a reservoir has been fixed and area
below that has been extracted and histogram has been used to
determine area elevation capacity Table. Dead Storage Level has
also been fixed for the reservoir because below DSL water
cannot use for irrigation.
Table 2. Reservoir characteristics for reservoir RS
S.No.
Reservoir Properties
1
Reduction Level
316m
2
Reduction Fraction
0.9
3
Dead Storage Level
314m
4
Bed Level
306 m
5
High Flood Level
323m
The cone formula (Murthy, 1968) has been used to compute the
capacities between two successive levels, which in turn gave the
cumulative capacities at different levels of reservoir. The area
elevation capacity (AEC) table for reservoir RS has been given
below in Table 3. The total command area of each user with
respect to total command area in each sub-basin is given in table
4. The water demands for soybean crop for each of the water
users on ten daily basis is given in Table 5.
Table 3. Area Elevation Capacity Table for reservoir RS
Sr. No.
Reduction
Level (m)
1
2
3
4
5
6
7
8
9
10
306
309
312
315
317
318
320
321
322
323
Cumulativ
e Area
(km2)
0.51
1.42
2.95
5.88
9.88
12.58
19.43
23.42
27.53
31.53
Cumulativ
e Capacity
(MCM)
1.25
3.96
10.34
22.92
38.37
49.57
81.29
102.68
128.13
157.64
Table 4. Total command area of each water user with respect to
total command area in each sub-basin
Sr. No.
Sub-basin
SW1
Water
Users
WU1
Total Command Area
(km2)
284.19
1
2
3
4
5
6
7
SW2
SW3
SW4
SW5
SW6
SW7
WU2
WU3
WU4
WU5
WU6
WU7
54.31
121.26
121.16
356.13
25.95
707.91
Table 5. Water demands of all users for soybean crop in Bearma
basin in different ten daily period
Date
01071995
10071995
20071995
31071995
10081995
20081995
31091995
10091995
20091995
31101995
10111995
20111995
WU1
WU2
WU3
WU4
WU5
WU6
WU7
(m3/sec) (m3/sec) (m3/sec) (m3/sec) (m3/sec) (m3/sec) (m3/sec)
7.57
1.45
0.00
0.00
0.00
0.73
19.91
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.35
1.03
0.31
8.36
4.77
0.91
0.00
2.15
6.31
0.00
0.00
7.63
1.46
7.33
0.00
0.00
1.05
28.59
10.26
1.96
5.09
2.93
8.61
1.38
37.53
13.91
2.66
5.94
0.00
0.00
0.61
16.71
8.52
1.63
5.04
0.77
2.27
0.90
24.42
12.63
2.41
0.94
6.30
18.51
1.35
36.79
3.45
0.66
0.79
0.00
0.00
1.20
32.61
1.32
0.25
0.72
3.94
11.58
0.84
22.94
0.00
0.00
0.00
2.20
6.47
0.41
11.31
Simulation of model under scenario-1(without reservoir)
MIKE BASIN model is simulated for all sub-basins and water
users without any reservoir during the period of July 1 to
November 10 for the drought year, 2002. The analysis has been
carried out to obtain the used water and deficit volume in
different 10-days period at all seven sub-basins. The water used
by different users and their deficit for different 10- days period
have been presented in Table 6. From the analysis it can be seen
MANIT Bhopal
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Issue Special3
18-19, Dec. 2014
that user WU7 used maximum water as 125.55 MCM and deficit
is also maximum in this sub-basin with 88.48 MCM, and WU1
user faces maximum deficit of 62.07 MCM without any water
being used. Amongst all the users, user WU3 faces a minimum
deficit as 2.64 MCM. Other water users WU2, WU4 and WU6
also face low demand deficits of 11.86 MCM, 16.61 MCM and
7.86 MCM. Therefore it is imperative to harness the excess
surface water by constructing reservoirs of larger capacity at
suitable locations in the basin to meet the all demands of various
water users, so as to provide buffer storage during drought
situation.
with provision of reservoir. Table 6. Used water and Demand
deficit water for all users without reservoir
Dat
e
De
ca
de
WU1
WU2
WU3
WU4
WU5
W WU7
U
6
Used D
Wate efi
r
cit
(MC
(
M)
M
C
M
)
Us
ed
W
ate
r
(M
C
M)
De
fici
t
(M
C
M)
Us
ed
W
ate
r
(M
C
M)
De
fici
t
(M
C
M)
Us
ed
W
ate
r
(M
C
M)
De
fici
t
(M
C
M)
Us
ed
W
ate
r
(M
C
M)
De
fici
t
(M
C
M)
Used
Wate
r
(MC
M)
D
efi
cit
(
M
C
M
)
Use
d
Wat
er
(MC
M)
De
fici
t
(M
C
M)
0.0
0
0.0
0
0.0
0
0.0
0
0.0
0
0.0
0
0.0
0
0.0
0
0.00
0.
00
0.00
0.0
0
0.00
0.
00
Jul
y1D
0.0
0
6.5
4
0.0
0
1.2
5
0.0
0
0.0
0
0.0
0
0.0
0
0.00
0.
00
0.00
0.6
3
0.27
16
.9
4
Jul
y2D
0.0
0
0.6
5
0.0
0
0.1
3
0.0
0
0.0
0
0.0
0
0.0
0
0.00
0.
00
0.00
0.0
6
0.09
1.
63
Jul
y3D
0.0
0
0.0
0
0.0
0
0.0
0
0.0
0
0.0
0
0.0
0
0.3
3
0.98
0.
00
0.00
0.2
9
2.47
5.
47
Au
g1D
0.0
0
4.1
2
0.0
0
0.7
9
0.0
0
0.0
0
0.0
0
1.8
9
3.46
2.
08
0.00
0.0
3
0.12
0.
60
Au
g2D
0.0
0
6.5
9
0.0
0
1.2
6
3.6
9
2.6
4
0.0
0
0.0
0
0.00
0.
00
0.00
0.9
1
11.9
4
12
.7
7
Au
g3D
0.0
0
9.7
5
0.0
0
1.8
6
4.8
4
0.0
0
0.0
0
2.7
8
8.18
0.
00
0.00
1.3
1
35.6
7
0.
00
Se
p1D
0.0
0
12.
02
0.0
0
2.3
0
5.1
3
0.0
0
0.0
0
0.0
0
0.00
0.
00
0.00
0.5
3
14.4
4
0.
00
Se
p2D
0.0
0
7.3
6
0.0
0
1.4
1
4.3
5
0.0
0
0.0
0
0.6
7
1.96
0.
00
0.00
0.7
8
21.1
0
0.
00
Se
p3D
0.0
0
10.
91
0.0
0
2.0
8
0.8
1
0.0
0
0.0
0
5.4
4
15.4
7
0.
53
0.00
1.1
7
23.6
4
8.
15
Oc
t1D
0.0
0
2.9
8
0.0
0
0.5
7
0.6
8
0.0
0
0.0
0
0.0
0
0.00
0.
00
0.00
1.0
4
5.60
22
.5
8
Oc
t2D
0.0
0
1.1
4
0.0
0
0.2
2
0.6
2
0.0
0
0.0
0
3.4
0
4.95
5.
06
0.00
0.7
3
3.14
16
.6
8
Oc
t3D
0.0
0
0.0
0
0.0
0
0.0
0
0.0
0
0.0
0
0.0
0
2.0
9
5.99
0.
16
0.00
0.3
9
7.09
3.
66
No
v1D
0.0
0
0.0
0
0.0
0
0.0
0
0.0
0
0.0
0
0.0
0
0.0
0
0.00
0.
00
0.00
0.0
0
0.00
0.
00
To
tal
0.0
0
62.
07
0.0
0
11.
86
20.
13
2.6
4
0.0
0
16.
61
40.9
9
7.
83
0.00
7.8
6
125.
55
88
.4
8
Simulation of model under scenario-2 (with reservoir)
In this analysis, one reservoir has been suggested and simulation
in MIKE basin has been conducted considering supply from this
reservoir also. After simulation run, with reservoir during the
period of July 1 to November 10 for all these seven users, the
model provides used water, deficit water, reservoir volume,
reservoir level. Analysis has been carried out to obtain the used
water and deficit volume in different 10-days periods. In the
analysis, the water user WU7 was directly drawing water for
meeting their demands from reservoir RS whereas the remaining
users WU1, WU2, WU3, WU4, WU5 and WU6 were
simultaneously withdrawing water from the river directly to
meet their demand requirements. The result of the water used
and deficit for user SW7 has been presented in Table 7. From the
analysis it can be seen that reservoir RS has used maximum
water of 193.95 MCM and deficit of 66.51 MCM also occurs. It
can be appreciated that all the users that have not been connected
to the reservoir are facing deficits of varying magnitudes in
drought years and therefore, it will be prudent to explore
additional sites for reservoirs on different locations of the main
Bearma river so that the deficits can be minimised to the
minimum extent possible.
01
–
07200
2
1007200
2
2007200
2
3107200
2
1008200
2
2008200
2
3108200
2
1009200
2
2009200
2
3009200
2
1010200
2
2010200
2
3110200
2
1011200
2
Comparison of performance between scenario (1) and scenario
(2)
Initially, when the planning is carried out for a „no reservoir
condition‟, it can be seen that all users face higher demand
deficit in varying extents. The total demand deficit is 197.35
MCM in the drought year. The provision for constructing
reservoirs helps to drastically reduce the demand deficit. The
comparison of the demand deficit for water user WU7 drawing
water through reservoir RS3 reveals that there is the demand
deficit of 42.41 MCM after the provision of the reservoir RS3.
However, when we compare the performance between Scenario1 (no reservoir) and Scenario-2 (with reservoir), it is seen that
the maximum demand deficit of 88.48 MCM for WU7 with no
reservoir scenario drastically gets reduced to 42.41 MCM with
the provision of reservoir RS3. The comparison of demand
deficit for both scenarios clearly demonstrates that the provision
of reservoir RS3 with the basin has greatly helped to reduce the
impact of drought as can be seen by the significant reduction in
demand deficit with the reservoir supplies for water user. The
performance is more noticeable because the demand deficits
have greatly reduced from 88.48 MCM to 42.41 MCM for WU7
ble 7. Water used-demand deficit for user WU7 directly
connected through the reservoir RS for drought year
Date
01-07-
MANIT Bhopal
Decade
Used
Water
(MCM)
0.00
Deficit
(MCM)
0.00
Stored
Volume
(MCM)
1.25
Reservoir
Level
(m)
306.00
Page 95
Issue Special3
2002
10-072002
20-072002
31-072002
10-082002
20-082002
31-082002
10-092002
20-092002
30-092002
10-102002
20-102002
31-102002
10-112002
Total
18-19, Dec. 2014
July-1D
0.00
17.20
1.35
306.11
July-2D
0.00
1.72
2.06
306.90
July-3D
0.00
7.95
4.27
309.15
Aug-1D
0.00
0.72
10.75
312.10
Aug-2D
9.88
14.82
157.64
323.00
Aug-3D
35.67
0.00
157.64
323.00
Sep-1D
14.44
0.00
157.64
323.00
Sep-2D
21.10
0.00
157.64
323.00
Sep-3D
31.79
0.00
156.72
322.97
Oct-1D
28.18
0.00
140.60
322.42
Oct-2D
28.18
0.00
122.46
321.78
Oct-3D
30.99
0.00
109.84
321.28
Nov-1D
17.84
0.00
96.82
320.73
218.05
42.41
From the results obtained, it is concluded that the demands are
not fully satisfied and there were demand deficit under both
scenarios for drought year. In second scenario one reservoir was
planned and water was drawn from the reservoir as well as from
the river and the analysis performed. The model was run to see
the performance of the model and its ability to cope up during
droughts. The model run in Scenario-1 shows that the demand
deficits have increased significantly in all of the sub-basins as
the supply in the river was very less. The maximum deficit was
observed in sub-basin SW7.This indicates that the gravity of the
situation magnifies as seen by the abrupt increase in the demand
deficit in a drought year. Subsequently, the planning was carried
out with the provision of one reservoir and model run in a
drought years. Here it can be observed that the demand deficit
has reduced considerably to 42.41 MCM which was aiming to
achieve under such study. This study clearly demonstrates that
planning for drought mitigation can be carried out by
constructing small reservoir in the sub-basin to cater the
increased demand during periods of intermittent dry spells
during drought years.
References
i.
Bates, B. C., Z. W. Kundzewics, S. Wu, J. P. Palutikof. 2008. Climate
Change and Water Technical Paper of the Intergovernmental Panel on
Climate Change. IPCC Secretariat, Geneva, 210.
ii.
Beran, M.A. and Rodier, J. A. 1985. Hydrological Aspects of
Drought: a Contribution to the International Hydrological Programme, World
Meteorological Organization, Studies and reports on hydrology 39, Paris.
iii.
Carter, D.B., Mather, J.R. 1966. Climatic classification for
environmental biology. Publications in
Climatology,
Laboratory
of
Climatology 4 (19).
iv.
Cui, B. X. Li, K. Zhang. 2010. Classification of hydrological
conditions to access water allocation schemes for Lake Baiyangdian in North
China. Journal of Hydrology, 385:247-56,
v.
DHI. 2008. MIKE BASIN User Manual, Water and Environment, Inc.
and Council of Governments.
vi.
Galkate, R.V., Thomas, T., Pandey, R.P., Singh, S. and Jaiswal, R.K.
2010. Drought Study in Chhindwara District of Madhya Pradesh, India. Third
International Conference on Hydrology and Watershed Management
(ICHWAM-2010), February 3-6, 2010, JNTU, Hyderabad, India.
vii.
Gleick, P.H. 1993. Water in Crisis: A Guide to the World‘s Fresh
Water Resources. Oxford University Press, New York, NY.
viii.
Hisdal, H. and Tallaksen, L. M. 2003. Estimation of Regional
Meteorological and Hydrological Drought Characteristics: A case study for
Denmark, Journal of Hydrology, 281,230-247.
ix.
K. R. J. Perera,N.T.S. Wijesekera. 2012.Potential on the use of GIS
Watershed Modelling for River Basin Planning – Case Study of Attanagalu Oya
Basin, Sri Lanka, Vol. No.04, pp(13-22).
x.
Murtthy, B.N. 1968. ―Capacity survey of storage reservoirs‖, Central
board of irrigation and power, publication no. 89.
xi.
Park, S., Feddema, J.J., Egbert, S.L. 2005. MODIS land surface
temperature composite data and their relationships with climatic water budget
factors in the central Great
Plains. International Journal of Remote
Sensing 26 (6), 1127–1144.
xii.
Perera, K. R. J, & Wijesekera, N.T.S. 2010.Identification of the
Spatial Variability of Runoff Coefficients of Three Wet Zone Watersheds of Sri
Lanka for Efficient River Basin Planning. ASCE: EWRI Conference on
International Perspective on Current and Future State of Water Resources and
the Environment.
xiii.
Wilhite, D.A. 2000. Drought: A Global Assessment (2 volumes, 51
chapters, 700 pages).
Hazards and Disasters: A Series of Definitive
Major Works (7 volume series), Routledge Publishers.
xiv.
Witt, J.L. 1997. National Mitigation Strategy: Partnerships for
Building Safer Communities. Federal Emergency Management Agency, p. 2.
xv.
Yodre, R.E., Odhiambo, L.O., Wright, W.C. 2005. Evaluation of
methods for estimating daily reference crop evapotranspiration at a site in the
humid southeast United States.American Society of Agricultural Engineers
ISSN 0883-8542,Vol.21(2).pp.197-202.
Replacement of Field Channels with Pressurized
Irrigation Systems: in Ssp Command Area
Mrs Sahita I. Waikhom1, Monali Patel2, Dr P.G Agnihotri3
Asst. Professor, CED, Dr. S. & S. S. G.G.E.C, Surat-395001,
Gujarat, India
2
M.E Water Resources & Mgmt., Dr. S. & S. S. G.G.E.C, Surat395001, Gujarat, India
3
Asso. Professor, CED, S.V.N.I.T, Surat-395007, Gujarat, India
1
[email protected]
2
[email protected]
3
[email protected]
1
ABSTRACT: To irrigate the entire command area of SSP
through conventional flow irrigation is no possible. There is
Strong need for efficient and cost effective use of limited delta
to cover the entire command area where optimization of water
use is the prime consideration. It has been recognized that use
of modern irrigation methods like drip and sprinkler irrigation
is the only alternative using Pressurized Irrigation Network
System (PINS). This is primarily, a pipe network carrying
required discharge at adequate pressure, finally delivering it to
the attached MIS network. Design of this network is suitably
framed incorporating features of water distribution under the
Canal Command Area (CCA). Pressurized Irrigation Network
MANIT Bhopal
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Issue Special3
18-19, Dec. 2014
System (PINS) is used as a substitute for sub-minors and field
channels in an open canal network.
2. NECESSITY OF PRESSURIZED
NETWORK SYSTEM (PINS)
In present study, the design of PINS using Spreadsheet with
multiple outlets at emitters with two alternatives i.e., 24 hrs
power supply and 8 hrs power supply is carried out. The study
area is selected in agro-climatic zone no.6 of SSP Command
area which is situated at village Rampur. Rampur village is
served by Dholka direct minor canal. Analysis is carried out
for both the alternatives using Darcy-Weisbach formula and
diameter of PINS pipe, connecting pipes, storage, pumping
requirements and number of filters is computed using
spreadsheet.

Adoption of PINS with MIS in the VSAs in the SSP area can
assure water availability to each farmer and uneven
distribution and tail end problems can be overcome. It is
envisaged that where the Narmada water has reached but the
sub-minors are yet to be constructed – is the most preferable
situation where such pilot projects can be attempted.
Keywords- PINS, Sub-minors, Command area, Sardar Sarovar
Project (SSP), Conventional Irrigation.





To make the Micro-Irrigation System (MIS) adoption
technically viable in the canal command areas, a
pressurized water conduit system act as bridge by
drawing water from the canal, storing in a place.
To minimize the land acquisition problem.
Not possible to irrigate the entire command area of SSP
through conventional flow irrigation. Strong need for
efficient and cost effective use of limited delta to cover
the entire command area
Limited availability of water - optimization of water use
is the prime consideration
Adverse soil characteristics in certain areas - low
application of water is imperative
Flood/ flow irrigation not desirable to problematic
areas. To restrict unregulated water lifting from canals.
Conjunctive management of pipe distribution with
ground water.
To improve overall farm efficiency.
2.1 Objective
1. INTRODUCTION
Water is one of the most critical inputs for agriculture which
consumes more than 80% of the water resources of the country
(Sen, 2012). Agriculture is the largest user of water, which
consumes more than 80% of the country‟s exploitable water
resources. The overall development of the agriculture sector and
the intended growth rate in GDP is largely dependent on the
judicious use of the available water resources. While the
irrigation projects (major and medium)have contributed to the
development of water resources, the conventional methods of
water conveyance and irrigation, being highly inefficient, has led
not only to wastage of water but also to several ecological
problems like water logging, salinization and soil degradation
making productive agricultural lands unproductive (MoA, 2006).
There is a strong need for efficient and cost effective use of
limited delta to cover the entire command area of SSP. It has
been recognized that use of modern irrigation methods like drip
and sprinkler irrigation is the only alternative using Pressurized
Irrigation Network System (PINS) (Carlos, 2009). Pressurized
Irrigation Network System (PINS) is substitute arrangement for
sub-minors and field channels in an open canal network
(SSNNL, 2009). This is primarily, a pipe network carrying
required discharge at adequate pressure, finally delivering it to
the attached MIS network. Design of this network is suitably
framed incorporating features of water distribution under the
Canal Command Area (CCA). In present study, the design of
PINS using Spreadsheet with multiple outlets at emitters with
two alternatives i.e. 24 hours power supply and 8 hours power
supply is carried out.
Sardar Sarovar Project (SSP) is one of the major irrigation
projects in Gujarat state of India. The main thrust of command
development activities is on the empowerment of beneficiary
farmers in sustainable water resource management (SSNNL,
2009).


IRRIGATION
The objective of the study is to Design Pressurized Irrigation
Network System (PINS) with 8 hours & 24 hours power supply
to make the Micro-Irrigation System (MIS) adoption technically
viable in the canal command area.
3. SSP COMMAND AREA
Sardar Sarovar Project (SSP) is one of the major irrigation
projects of Gujarat state of India. Sardar Sarovar (Narmada)
Project Phase –IIA covers Culturable Command Area (C.C.A of
20, 42, 39 Ha) between Mahi and Surashtra Branch Canal off–
taking from Narmada Main Canal. The study area is selected in
agro-climatic zone no.6 of SSP Command area which is situated
at village Rampur.
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Figure 1 Location of Study area in Gujarat
(iii) Design of PINS pipe
Rampur Village is served by Dholka Direct Minor through
Dholka branch canal located at Dholka taluka, Ahmedabaddistrict. The Dholka Minor is off-taking @ Ch. 52140 m of
Dholka Branch Canal having C.C.A. of 789.82 Ha. Out of which
C.C.A. of Rampur is 124 ha. Thus C.C.A of Dholka minor is
divided in 19 chaks. It is divided into two chak; chak 1 & chak 2
with areas as 27.74 & 32.09 C.C.A. respectively, each of which
is further divided into 4 sub-chaks. Darcy-Weishbach formula is
used to carry out analysis to decide diameter of PINS pipe,
connecting pipes, storage, pumping requirements and number of
filters required by using spreadsheet. Discharge has been
computed using basic discharge co-efficient (BDC) taken as 0.65
for agro-climatic zone VI.
Presently the farmers of the proposed project area have limited
irrigation facilities. There is only one bore well in the proposed
area of the study area. The power supply can be made available
by Uttar Gujarat Vij Company Limited (UGVCL).
3.1 Data Requirement
The data needed to carry out design are meteorological data,
region map, index map, soil map & salient features of Dholka
direct minor.
4. METHODOLOGY FOR DESIGN OF PINS
For pressurized pipe network, three types of pipes like Polyvinyl
Chloride (PVC), High Density Polyethylene (HDPE) and Fiber
Reinforced Pipe (FRP) can be used.
They carry water at an adequate pressure, to deliver it to the
attached MIS network. Here in study for pressurized flow HDPE
pipe is preferred.
Design discharge
Q = (6 / n) x [BDC x CCA 1] /2
(8 hrs)
Q
= BDC x CCA1/ 2
(24hrs)
(iv) Pumping Efforts
Pump HP
= (Q x H) / (75 x)
Where, Q = design discharge in lps
H = Pressure head in m; n = no. of sub-chaks
(v) Filters
Capacity of media filter (m3/ hr)
PINS pipe X 3.6 (8 hrs)
Capacity of media filter (m3/ hr)
PINS pipe X 3.6 (24 hrs)
= Design discharge of
= Design discharge of
5. OUTCOME
As per above steps design is carried out and result is obtained as
shown below(Table-1 & 2) for both alternatives along with
schematic (Fig. 2 & 3)
For 8 hours Power Supply PINS Pipe designed:
Table 1 Design of PINS Pipe (8hrs Power Supply)
For design of pressurized irrigation network system components
like connecting pipes, storage facility, PINS pipe, pumps, filters,
and intake well and pump house are required. Same design can
also be prepared for the regions which face severe water scarcity
and areas where natural water bodies exist can be identified and
PINS can be adopted there. The design for PINS at Rampur
village is carried out by following steps. In the distribution
design of PINS, storage well is considered in the start of
command area and pump house close to well. PINS design for
all chak area is prepare using Spreadsheet for 8 hours & 24
hours power supply.
(i) Connecting Pipes
Cha
k No
1
2
CCA
(ha)
27.7
4
32.9
6
Discharg
e (lps)
Sub
cha
k
No
Designe
d
Available
Pipe
OD
(mm
)
1
117.3
127.6
140
2
177.15
182.6
200
3
165.8
182.6
200
4
149.9
164.2
180
1
148.86
164.2
180
2
148.86
164.2
180
3
183.8
205.4
225
4
187.3
205.4
225
Pipe Inside Dia (mm)
13.52
15.93
Connecting pipe is an arrangement necessary to connect the
source of water to the storage with the intake well i.e. Initial
point of PINS. In our case for non-pressurized gravity flow we
prefer PVC pipe. For this, generally low pressure gravity mains
of PE 80 class of PN 2.5 (2.5 kg/cm2) would be sufficient.
(ii) Storage Facility
Facility is required for 8 hrs power supply. For practical
purpose, 1 day storage facility is to be designed.
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performance and improvement in efficiencies of the irrigation
systems, it is necessary to adopt a self-sustainable system. A
modernization of canal command area is, therefore, necessary
through micro irrigation system. To bring more area under
irrigation it has become extremely necessary to introduce new
irrigation techniques like micro irrigation system for
economizing the use of water and increase productivity per unit
of water. Micro irrigation system need be promoted in a holistic
manner involving appropriate methods like “PRESSURIZED
IRRIGATION NETWORK SYSTEM” (PINS).
Figure 2 Schematic Diagram of PINS (8 hrs Power Supply)
(Source: SSNNL, Gandhinagar)
For 24 hours Power Supply PINS Pipe designed:
Table 2 Design of PINS Pipe (24 hrs Power Supply)
Chak
No
1
2
CCA
(ha)
27.74
32.96
Discharge
(lps)
18.03
The PINS along with MIS will result in many advantages like
increase in crop productivity (20-30%), water saving (30-50%),
fertilizer savings (approximately 40%) and bringing more area
under irrigation with the same quantity of available water, equity
in distribution of water, both spatially and temporary.
Designed
Available
1
117.46
127.6
140
Adoption of MIS with PINS in the VSAs in the SSP area can
assure water availability to each farmer and uneven distribution
and tail end problems can be suitably overcome. In addition to
the above tangible financial benefits, the conversion of irrigation
method from flooding to MIS and its integration with PINS in
SSP will also have other important intangible benefits.
2
152.58
164.2
180
REFERENCES:
3
129.1
145.8
160
4
142.7
145.8
160
1
148.3
164.2
180
2
148.3
164.2
180
3
183.8
205.4
225
4
187.3
205.4
225
Sub chak
No
Pipe Inside Dia (mm)
Pipe
OD
(mm)
21.42
Figure 3: Schematic Diagram of PINS (24 hrs Power Supply)
(Source: SSNNL, Gandhinagar)
6. CONCLUSIONS
The country is likely to be more water stressed in the coming
years. Therefore technologies for water harvesting and storage
and technologies for precision water application methods need to
be adopted (Mehta, Sharma, Kathuria, 2012). For effective
i.
Carlos Estrada, César González,Ricardo
Aliod, and JaraPano (2009), Improved Pressurized Pipe Network Hydraulic
Solver for Applications in Irrigation Systems, American society of Civil
Engineering.
ii.
LakhdarZella,
Ahmed
Kettab,
Gerard
Chasseriaux (2006), Design of a Micro-irrigation system based on the control
volume method, Biotechnology, Agron. Soc. Environment volume10
iii.
Literature from Sub-division Office (FO),
SSNNL, Dholka
iv.
Mamta Mehra, Devesh Sharma, Prachi
Kathuria (2012) Groundwater use dynamics: analysing performance of microirrigation system - a case study of Mewat District, Haryana, International
Journal of Environmental Sciences Volume 3, no 1.
v.
Micro-irrigation (drip & Sprinkler irrigation) guidelines (January
2006) by Ministry of Agriculture, Department Of Agricultre (DoA) &
Cooperation, Govt. of India.
vi.
Paper on Pressurized Irrigation System by Sardar Sarovar Narmada
Nigam Limied (SSNNL 2009).
vii.
Sen, Somanth Project Report, (2012), Impact
Assessment of Micro Irrigation scheme in Madhya Pradhesh.
Reservoir Operation Based on Real Time Flow
Data for Flood Control and Incremental Power
Generation
Rameshwar Prasad Pathak
B-474, Sarita Vihar,New Delhi, 110076,India
[email protected]
ABSTRACT: The floods are most frequented natural disasters
in the world. The water management and flood control shall be
on top priority in National Development plan. The monsoon
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Rivers and snow melt rivers have its special features and
characteristics. In all cases to meet the annual irrigation and
power requirement and also for regulation needs, water
storages of varying magnitude are created. The water
storages/reservoirs are also instrumental in flood moderation.
However providing additional reservoir capacity for flood
moderation has advantages and disadvantages, but flood
moderation by reservoir operation based on real time flow data
is more effective method. The pragmatic approach also results
in increased power generation in downstream power projects.
The river basin planning, intercepted catchment flow, silt load,
available command area are amongst various factors to be
considered in deciding principle levels of reservoir which
imposes limits on reservoir operation and thereby limiting the
flood moderation. The paper also considers the elements of
dam safety, which are prime factor in the studies. The
submergence of land and property and draw down cultivation
are another issues relevant to the subject, while framing
reservoir operation rules. During monsoon, the inflows in to
and outflows from reservoir are not only unpredictable but are
subjected to great variations, creating flood like situations. The
real time data collection gives new dimension to solution to
flood moderation. The paper founded on case study and
literature available on the subject, deals with this solution
which will help to control flood and add to power generation by
utilizing reservoir capacity optimally.
Keywords: Reservoir operation, pragmatic approach, Real time
Flow data, Forecast, Predepletion, Flood moderation,
incremental generation, submergence
1.0 INTRODUCTION:
The rainfall and snowfall are two important elements for growth
of life on this planet. But there is tremendous variation in space,
time and quantum of rainfall. The less rainfall in an area creates
draught condition not only affecting human being but all living
creatures, flora and fauna in that area. The rivers drain surface
water and even underground stream water to sea. The wishful
rainfall in terms of space, time and quantum is still in dreams of
scientists except for some very expensive methods in very
limited way and very limited weather conditions. Consequently
we have to live with it and device methods and means to face
such adverse situations, keeping in view human comfort. The
excessive rainfall creates flood like situation, inundating the area
sometime inhabited or agricultural fields or both or other
important establishments. Similarly excessive snowfalls may
paralyze life and block roads, streets and cover the affected area.
As the snowfall has variation so also snow melt has variation in
summer and winter as per intensity of the seasons. Therefore
planning has to be done for these different categories of the
rivers. In China in one of the instant the extreme summer,
causing excessive snow melt flow in the river was followed by
consecutive rain storms, which resulted in unprecedented floods
in the area. Geographically in India following three categories
exist:
1.
2.
Rivers in hilly region of Himalayas/snow melt
Rivers in hilly region excluding in category no. 1
3.
Rivers in plain region
1.1 Occurrence of Flood
In brief the floods and draught are resultant of variation of
rainfall/snowfall in space, time and quantum and also variation
of intensity of summer and winter seasons. The rain precipitate
in the catchment area and through Nullahs and tributaries rain
water flows to the main river channel, all along its length. The
river bed is lowest contour of the area and mostly in fault zone.
Over the years, erosion and cutting by river water has shaped its
banks. Within these banks the river channel is located and most
of the time river is confined within these banks. The flood like
situation is encountered, when river overflows its banks. This so
happens that the inflow to rivers exceeds the channel capacity
and afflux is above the height of the banks. In this scenario the
high afflux level obstruct the flow from Nullahs and tributaries
and the levels in Nullahs and tributaries also rises above normal
due to back water levels causing flood like situation in those
areas also. All this causes submergence of neighboring lands of
the rivers, Nullahs and tributaries. The snow melt river will have
some additional issues to be considered. The storage reservoir
created intercepting this flow can accommodate this additional
quantum of water and release it in regulated manner to minimize
submergence in the downstream areas. In the event reservoir
gets filled up to the level as specified by reservoir rule curve, the
gates are opened to release extra inflows and the maximum
outflow that would be possible would correspond to the level
available above the crest level. If the inflows to the reservoir
increase, the reservoir level will also build up to the required
afflux above the crest to matching outflow is developed and
outflow shall balance the inflow. Incase inflow approaches
highest flood the reservoir will touch the maximum water level,
by this increased capacity the quantum of downstream flood will
be reduced. The time required for opening the gates and also
uncertainty of estimation of inflows result in to excessive
releases from reservoir causing flood like situation in
downstream areas, may it be devoid of precipitation of that
magnitude. On these events critics raises eye brows against
construction of dams. Whereas in extreme conditions, the floods
are inevitable, dams or no dams. Only in most exceptional case,
dam break can cause additional flood fury, which is disaster
beyond control of technology adopted or operator deployed. As
such limits of dam safety must be clearly defined.
2.0 AMBIT OF CONSIDERATION
With the foregoing discussions it evolves that various elements
are responsible for flood downstream of storage/reservoir. This
is complex phenomenon, which needs in depth study by expert
of the field. Broadly the inflow depends upon the characteristics
of upstream portion of the basin, including direct draining areas.
Similarly the downstream portion gets affected in accordance
with its characteristics. Therefore the holistic approach shall be
adopted, considering complete basin, and all other elements for
study of the subject. The reservoirs are control node and its
parameters are first elements of control, and decide flexibility in
the system. There are other factors which add to accuracy of
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control, like information about siltation of reservoir and
consequent reduction in capacity of reservoir. The information
about inflows to the reservoir both in quantum and time will
multiply the flexibility of control available at reservoir. Even the
information of downstream structure/ channel will enable the
control of upstream reservoir to be effective in mitigating
adverse situation downstream. This is made possible by real time
flow data system. This pragmatic approach will not only reduce
the fury of flood but will enable the River Bed Power plant to
generate additional power, utilizing flood water. The paper deals
with the major factors for monsoon rain fed rivers. For snow
melt river study is similar except some additional factors need to
be considered.
3.0 RIVER BASIN
The river basin is the expanse of land from which all surface
water from rain or snow melt drains through a sequence of
streams, rivers and lakes, into the sea at the stated single river
mouth, estuary or delta. Apart from physiograpy and Geological
and geographical features, the land use of the basin land is very
important. Govt. have also constituted various agencies
responsible for development of Basin. The developmental
activities would include water management, planning for
reservoirs, canals, power plants. For this developmental work the
land use may change calling for rehabilitation and resettlement
or environment mitigating measures.
For the present study the major River Basin Characteristics can
be summarized as follows:


Description of River basin: The Geographical and
Physical Features and Natural Elements of the basin.
Physiography,
Geology,
Geochemistry,
Soils,
vegetative cover, water resources, etc.
Land use: Forestry, Agriculture, Biodiversity, wild life,
Aquatic life, Population, Roads, Cities, Existing
Structures,


ydro Meteorological features: Climate, temperatures,
humidity, rainfall, snowfall, surface and under ground
water.
Water Management: Hydrological details, river
network, irrigation, power, flood control
3.1 Description of River Basin
The expanse of river basin from origin of river including its
tributaries to confluence to sea is defined by longitude and
latitude. The large river basin may be divided into sub basin
also. The physical characteristics of the River basin include its
location, physiography, soils, climate, surface water and ground
water resources, and natural water quality. The geochemistry of
the River Basin is based primarily on stream sediment and
stream geochemical data. The regional geologic grouping of
rocks of similar compositions, porosity, permeability, are of
greater importance in stream hydrogeochemistry. The parent
materials in which the soils formed, the subsoil in various depth,
the major groups of soils in the area, and other details need to be
studied. The River basin is a dynamic hydrological system
containing interactions between aquifers, streams, reservoirs,
floodplains, and estuaries. Because water is transmitted through
faults and fractures, each surface water drainage basin or
watershed is also a ground water drainage basin or watershed.
Surface and ground water are in such close hydraulic
interconnection that they can be considered as a single and
inseparable system. All these elements are responsible to
establish relationship between precipitation and runoff.
3.2 Land use
The Description of river basin gives clear understanding of what
we are dealing with in terms of physiographic details, which is
resultant of nature‟s action, and in case of study of land use we
have to deal with what environment and life and nature we have
to protect. The urban development and rural areas have
encroached in the flood zone. The approach road, bridges,
culverts, important structures, etc, are also very sensitive items.
These shall be indicated on contour maps clearly indicating
populated area, agricultural fields and all other details. The
forest area, reserved forest, pilgrimage activities, sanctuary, area
important for biodiversity, aquatic life, etc needs to be also
indicated in the contour maps for complete basin. The water
resource studies must include it while planning a project or
operating it. The runoff characteristics changes with such
developmental activities.
3.3 Hydro Meteorological features
Three main elements of the climate that significantly affect the
water availability and present grounds for development, use and
conservation of this resource are air temperature, precipitation
and evapotranspiration. The orographic features reflect upon
these most important climatic events. Depending on variation in
climate, the large basin can be divided in different zone for
convenience of studies. Its variations are the result of land and
sea distribution and closeness, as well as of various orographic
features. Considerably more precipitation
H
occurs in mountainous
parts of the basin than in the plains winter temperatures
(December to February) are low, while high temperatures occur
during the summer season (June – September). Average annual
temperatures in the region vary in a wide boundaries depending,
in the first place, on elevation. The lowest long-term annual
average temperatures at measured points take place on the
mountain ridges With regard to air temperatures, it can be
roughly assessed that within-the-year variations exhibit a
common pattern for majority of the catchments in plains.
Dividing lines between these different zones are not sharp, due
to different degree of influence of various factors that determine
the climate. At high altitude the precipitation falls in form of
snow so that relatively long periods with snow cover are
common characteristic of the region. Generally there are too few
reliable data available about impact on climate changes on
flows, large pressure to land use change, lack of non-structural
measures. The study should comprise collection and analyses of
data at meteorological and hydrological gauging stations at the
basin-wide level, evaluate flood characteristics and drought
properties in meteorological and hydrological aspects, flow
forecasting and climate change. Precipitation amount and its
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annual distribution varies widely within the basin. It, however,
can roughly be asserted that the form of precipitation has a
common feature: rainfall of different duration is likely to occur
all over the whole catchment where low mountains, hilly terrain
and plains dominate. Most precipitation occurs in summer
monsoon season and part during autumn monsoon.
3.4 Water Management:
Water is renewable source. Hydrologic cycle involves the
continuous circulation of water in the Earth-atmosphere system.
Of the many processes involved in the water cycle, the most
important are evaporation, transpiration, condensation,
precipitation, and runoff. Although the total amount of water
within the cycle remains essentially constant earth as unit, its
distribution among the various processes is continually
changing. The various steps involved for hydrologic evaluation
of details are as under:
 Extension of records
 Transferring of records
 Statistical analysis of historic records
 Hydrological Modeling
Hydrology is not an exact science. The meteorological data
combined with characteristics of river basin including land use,
are fundamentals for analysis of hydrological details and
working out equation for rainfall and runoff relationship. A
typical water balance analysis will compare meteorological input
data to a measured (or transferred) set of flow data within the
receiving stream. The precipitation-runoff process is complex as
it involves numerous flow routing interactions in the watershed.
Additionally, the spatial and temporal characteristics of
precipitation also make the prediction of runoff a challenge.
Additionally, the spatial and temporal characteristics of
precipitation also make the prediction of runoff a challenge to
engineers.
For sustainable development of the earth water management is
challenge to the planners. Its scarcity or abundance both creates
havoc in the system. Its spatial and temporal variation leads to
storage of water to meet the various requirements of society of
irrigation, drinking, industry, power, waterways, sanitation, and
many such other requirements. This paper deals with the event
when there is heavy precipitation and runoff has flooded the
channel.
4.0 STORAGE PLANNING
There are very few reservoirs planned for flood control. Even no
additional capacity is provided in any of the reservoir for flood
storage is provided (exceptions are there). Only temporary flood
storage exists between the maximum Water Level and Full
Reservoir Level. The spillway capacity is designed to pass the
Highest Flood and the corresponding highest water level at crest
shall not exceed Maximum Water Level.
5.0 RESERVOIR OPERATION
The reservoir capacity is designed to meet various requirements
of drinking, industrial use, irrigation, power, downstream
releases, etc. and to meet losses by way of evaporation losses,
seepage losses, silt load, etc. Reservoir Rule Curve envelopes,
the Lower Rule Curve, and Upper Rule Curve, which defines the
range of Operating Regimes. Lower Rule Curve defines the
operation to match the flow that can be maintained through out
the dry season under the lowest hydrologic condition so that the
reservoir reaches its minimum operating level. These are the
minimum elevations that the reservoir should maintain in order
to guarantee to meet the required output. Upper Rule Curve
defines the limit of operation with the minimum spills, which
exceed the regulating capacity of the reservoir combined with
the discharge capacity of the power plant These operations are at
the maximum elevations that the reservoir should maintain in
order to guarantee to meet the required output and safety of
dam. In addition to this the priority is set in which order the
various requirements are met. The downstream releases are
usually on instructions of tribunal or the court and gets first
priority. However drinking is basic need of Human and this gets
top priority, and almost at par is the downstream release,
Industry use is second priority followed by irrigation. Power
release trails behind. At the same time downstream releases are
through the river bed power releases. During monsoon period,
the unpredicted quantum of rain fall adds to inflows, which add
to uncertainty to the operations. Excess inflows give opportunity
to increase generation. However power releases are limited to
machine discharge capacities. A pragmatic approach shall be
adopted using the befitting software.
6.0 REAL TIME FLOW DATA
The availablity of Real Time Flow Data, supported by extensive
hydro meteorological network add new dimension to the
solution to the problem. Imposition of competitive water
charges, restriction on water releases to control fluctuation of
water levels in downstream, environmental aspect, safety of fast
growing urbanization, safety of rail- road transport network,
Safety of hydraulic Structure are many such factors which
warrants for reservoir operation in close margins and accounting
and monitoring of releases from reservoirs. This requires to
make correct assessment of inflows and out flows from the
reservoirs. For this stream-flow data, real time information on
impoundment or variation in impoundment at the reservoir
projects, estimation of evaporation losses and monitoring of
withdrawal from reservoir are required. This requires a strong
Hydro meteorological net work, with proper communication
preferably satellite communication system which will remain
operative in remote areas and in most adverse condition
6.1 Hydro meteorological net work
In this paper it is stressed that the complete river basin planning
shall be done and not for a reservoir in isolation or State wise.
Hydro meteorological network shall cover the complete river
basin from origin to confluence covering tributaries and other
major drainage system which has come up with growing
urbanization. The cover area of rain gauge station normally
depends upon the topographic characteristics of different part of
basin, intensity, distribution and rainfall, storm areas, the
number of streams draining the catchment area, etc. The river
basin characteristics need to be considered, some of which have
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been discussed in foregoing paragraphs. The spatial distribution
of network would be influenced by the setting up of
developmental scenario comprising of a number of artificial
interceptions of flows by way of hydraulic structures or storage
and diversion. Meteorological network shall be equipped with all
modern equipments and facilities specially well distributed rain
gauges capable of collecting and reporting precipitation in terms
of time and quantity of occurrence, with adequate number of
observatories to monitor the dew point, wind velocity,
temperature, relative humidity, radiation/sun shine hour etc. In
order to assess evaporation losses pan evaporation data shall be
collected. As wind velocity, temperature, relative humidity,
radiation have parametric effects on evaporation, these data shall
be collected at each storage sites as well. The silt load
characteristics of stream flow are required during operation stage
to monitor the effect of storage interception, for assessment of
realistic quantity of water stored. The hydrological observation
network be also equipped with all modern equipments and
facilities for measuring runoff, stream velocity water levels,
specifically during flood. The rainfall and runoff equation be
evolved considering the watershed characteristics, and be
revised on regular basis on developmental activities changing
landscape, and comparing the runoff so calculated with
measurement at various hydrological observatories.
7.0 FLOOD ABSORPTION BY PREDEPLETION AND
INCREMENTAL POWER GENERATION
Pre depletion of reservoir is not an element of consideration at
design and planning stage. But supported by strong Hydro
meteorological net work and communication system, and based
on computerized realistic assessment the pre depletion of
reservoir can be safely implemented with negligible risk of loss
of precious water and also resultant reduction of fury of flood
otherwise endangering neighboring and downstream areas of
submergence, and also for safety of hydraulic structure in case
meteorological conditions further worsen. The regulated release
of water on account of predepletion can further be planned
through power house for incremental generation. Extended time
available for depletion would not only result in reduction of
intensity in downstream release but will allow more water to be
released through power house resulting in incremental
generation. The depletion of reservoir would depend upon the
degree of accuracy of assessment and time lag assessed. Both
these factors would be governed by the detail study conducted
on characteristics of river basin, including river channel, and
how authenticated rainfall and runoff equation is formulated.
This exercise is relevant not only for reservoir but also for
natural lakes as well. In recent flooding of J&K such an
approach would have reduced the adversity to some extent
8.0 CONCLUSION:
Water is much needed commodity and it shall be conserved. But
surplus of water in terms of floods can disrupt the life killing
persons and damaging the properties, submerging the area,
causing deceases due to stagnant water. Draught and flood both
are curse and reservoirs are answer to both these problems.
Storage of water with hedging will help in fighting draught and
flood control possible to an extent by pre depleting reservoir
based on Real time Flow data, supported by strong Hydro
meteorological network, uninterrupted communication and
befitting software, and river basin characteristics are updated
along with reservoir parameters, to get realistic assessment for
incremental power generation and flood control. Such studies
and its implementation had made the reservoir projects boom to
the society
Effect of Conservation Works on Soil Erosion-A
Case Study of Punegaon Reservoir Catchment
Area
M.B. Nakil1
M.V. Khire2
PhD student, CSRE, Indian Institute of Technology Bombay,
Powai, Mumbai 400076, India
2
Associate Professor, CSRE, Indian Institute of Technology
Bombay, Powai, Mumbai 400076, India
1
ABSTRACT: Better analysis of the erosion causing factors,
knowledge of terrain uses are necessary for implementing soil
and water conservation practices. The researchers have carried
out experiments to quantify the effect of conservation practices
in terms of value P used as a parameter in Revised Universal
Soil Loss Equation (RUSLE). Conventional farm management
practices implemented as per land-uses and the terrain slopes.
To these land uses appropriate values of conservation practice
factor P ranging between one and zero are assigned. Knowing
the area occupied by various land use classes weighted mean
(WM) value of P is calculated for micro watershed. The
Government departments do implement erosion control works
on Government land. These works make add on effect and
reduce conventional values of P. The present paper deals with
quantification of add-on effect of major soil and water
conservation works carried on Government land. The
effectiveness of works executed can be represented in terms of
ratio of actual cost incurred to the estimated cost of
conservation works. This ratio ranges 0 to 1 as per physical
progress of works. Thus it has values 0 for not doing any work
and 1 for all works completed. The modification factor defined
as (1-ratio) is applied to weighted mean (WM) value of P to get
modified value Pm. This value is used in RUSLE (A=RKLSCP)
model, for predicting soil loss. The use of this methodology in
soil loss prediction of Punegaon Reservoir catchment area
shows good result.
Keywords: Soil erosion, catchment area, RUSLE, Management
practice factor, Soil Conservation
1. INTRODUCTION:
The water induced soil erosion involves detachment,
transportation and deposition of soil particles. The overall
erosion process depends on six basic parameters viz. rainfall
energy, properties of soil, land topography (slope steepness and
slope length), land-use and cover, and support practices. These
parameters decide quantity and extent of soil erosion. The
support practices reduce the process of detachment of soil
particles. The effectiveness of these practices is represented by
MANIT Bhopal
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the term P in the soil erosion prediction model. The farm
practices are implemented by individual stakeholders. At the
same time Government agencies do implement soil and water
conservation works on government as well as on private lands.
These works do reduce the value of term P and reduce overall
soil erosion. This indirect effect has not been considered by any
of the earlier model. The completed works in the microwatershed are considered for evaluation. This compound effect is
evaluated in the present case study, in the form of modified value
of support practices P. This modified value is used in soil loss
prediction model for estimating the erosion quantity.
2. DETAILS OF STUDY AREA
The Punegaon is medium project dam is situated in Western
Ghat near Dindori town of Nashik district of Maharashtra State,
on river Unanada, a tributary of Godavari river. It was built in
year 1995 and functioning for 19 years. The dam is having
catchment area of 63.84 km2. The area is having both hilly and
gentle slope terrain. The elevation ranges from 700m to
maximum 1088m. The slope varies from 0% to 80%. Most of
the rock is Deccan trap basalt. The climate of the area is
tropically humid with three seasons of four months duration
namely rainy, winter, summer. The annual rainfall variation
ranges from 450 mm to 2500 mm. The soil depth of the area
varies from few centimeters to over 50 cm. The soils mainly are
clay, clay-loam, sandy clay loam, gravelly loam, sandy loam.
The Land use / Land cover classes prevailed in this area are
namely forest plantations, deciduous forest, waste land, scrub
land, built up land, paddy fields, fallow lands and wet lands and
water. Agriculture is prominent.
3. SOIL LOSS ESTIMATION MODEL
The support practices are implemented by the farmers to reduce
the soil erosion. There are various types of practices which are
based on soil type, terrain slope and sustainability. Mechanical
types like contouring, strip cropping and terracing are
predominant in the study area. The reduction in soil loss from
unity to fraction because of farm management practices is
represented by the term ―P‖. The term is quantitative indicator
of effect of management practices. The experiments on the
effects of types of management practices on soil erosion have
been carried out and the values of ―P‖ are estimated through the
models. The values of “P” range from unity (no conservation
works) to zero (no erosion). In practice it is not possible to get
no erosion condition due to sustainability of the conservation
practices. It is thus not possible to get 100% reduction in soil
loss.
4. MATERIALS
The Punegaon reservoir catchment is marked topographic sheets
from Survey of India. The Indian Satellite IRS LISS III image of
May 2011 is used for LU/LC supervised classification. The
image analysis is carried out using ERDAS software. The
ground survey data has been used as training sets. The classes
identified are deciduous forest, forest plantations, waste land,
scrub land, built up land, paddy fields, fallow fields, wet lands
and water bodies. The support practices are crop and terrain
specific. Government agencies have carried out the conservation
measures on Public and private lands. The data of such
conservation works are collected from Government agencies.
This data is in terms of amount and is with regard to total
estimate of conservation works and works actually implemented.
The data of conservation scheme is village wise so the
computation of factor ―P‖.
The researchers through simulation have carried out the
experiments to estimate soil loss on small plots. Thus various
estimation models got evolved. The model RUSLE (Renard et
al., 1997) is revised Universal Soil Loss Equation model which
was initially established by Wischmeier and Smith (1978)
through USLE. The hybrid of USLE and RUSLE model is used
in present case for estimating annual soil loss from study area.
The revision of the model is with regard to revised methods of
evaluation of factors. The RUSLE (Renard et al., 1997) is
expressed same as USLE as shown in equation (1),
5. METHODS
A  ( R  K  L  S  C  P)
(2)
(1)
where Pt= average annual rainfall,
There are five rain-gauge stations near to catchment of reservoir;
however the Thiessen polygon shows only one station influences
entire area. The equation is applied to average annual rainfall of
35 years. The average R value is considered for analysis. The
vector layer of catchment in GIS environment is rasterised for
the average R value.
where “A” is estimated annual soil loss (t/ha/yr). The factors
which affect the erosion process are considered in this equation.
These factors are namely ―R‖ a rainfall erosivity factor, ―K‖ the
soil erodibility factor, ―L‖ the slope length factor, ―S‖ the slope
steepness factor, ―C‖ the land cover management factor and
―P‖ the support practice factor. Like USLE and RUSLE many
models are in practice. These models have modified approaches
in evaluating the affecting factors.
The erosion causing parameters namely R, K, L, S, C, P are
evaluated. The methods are illustrated in following paragraphs.
R symbol is used for rainfall erosivity parameter. It is having
unit as (MJ mm ha-1 h-1). In the present case the R value is
derived as per equation (2) developed by Nakil (2014).
R  (906.77  exp 0.0009  Pt 
The soil erodibility parameter ―K‖ is expressed in units as (t ha-1
MJ-1 mm-1 ha h). The K value is evaluated by following equation
(3) as given by Wischmeier & Smith (1978).
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K  1.313 * [((2.1  10 4  M 1.14  (12  a))  ((3.25  (b  2))  ((2.5  (c  3))]  1000
LU/LC. These values and prevailing farm support practices are
considered while assigning the “P” values to the LU/LC fields in
the present case. The weighted mean value of parameter “P” is
calculated for each micro watershed. It is denoted as “Pw”
(3)
where M=[(% silt + very fine sand) x (100-% clay)], a=%
organic matter, b= soil structure code number and c=
permeability class number. The relevant properties of soil are
used in the equation (3) to get value of “K” for each class. The
soil class‟s areas are vectorized and then rasterised around “K”
values.
The slope length parameter “L” is evaluated by equation (4) and
the slope steepness parameter “S” is evaluated by equation (5).
These equations are given by Wischmeier & Smith (1978).
L  (  22.13) m
6.2 Deriving modification factor
The Government funding is cost-estimated to execute all soil and
water conservation works in a micro-watershed. The details of
such overall cost estimate and the actual amount spent on works
are made available. The % effectiveness of the works executed is
calculated in terms of the ratio of expenditure done to the
estimated cost of overall works. This ratio of works “Rw‖ and
the modification factor “Mf” for these works are calculated for
each micro-water-shed using equations (6) & (7) respectively.
Rw  (Cw  Ew)
(4)
S  (0.0065 s  0.045  s  0.065)
(6)
(5)
Where Rw=Ratio of works, Cw= Cost of executed works, Ew=
Estimated cost of works
2
where , m=exponent, s=% slope, λ = slope length= 23.5m
adopted pixel size in GIS
The value of ―m‖ adopted are 0.5 for slope >= 5%, 0.4 for
slope= 5% to 3.5%, 0.3 for slope= 3.5% to 1%, 0.2 for slope= <
1%. These values have been suggested by Wischmeier & Smith
(1978). The values of factor “L” thus are derived using equation
(4) out and are assigned in GIS environment to respective slope
classes.
The contours are digitized using topographic sheets and are
converted to percentage raster map as percentage value of “s”.
This percentage slope raster for “s” is used in equation (5) to get
rater map for “S” parameter.
The researchers have assigned the values of cover management
parameter “C” as per land use and land cover (LU/LC), after due
experimentation. The values range in between 0 to 1. These
values are assigned to respective matching LU/LC units.
6. ANALYSIS OF CONSERVATION PARAMETER
Large numbers of micro conservation works are carried out
using Government funding in a catchment area. Each funded
work is executed fully in a season, once it is commenced. Thus
if funding is utilized say 30% then it means 30% number of
works are fully completed. It does not mean that all works are at
30% progress level. These conservation works make add-on
effect and reduce soil loss from catchment. As such the add-on
effects result in modification of Conservation Practice parameter
“Pm”. This modification of the parameter “Pm” is made as per
following procedure as given by Nakil (2014).
Mf  (1  Rw)
(7)
Where Mf= Modification factor, Rw =Ratio of works
Ideally when all proposed works in a catchment are executed
(that is when expenditure on works is equal to estimated cost) it
can be assumed that soil conservation is achieved fully for that
catchment (the ratio of works “Rw” is one and modification
factor “Mf” is zero). However such situation occurs
occasionally. When no works are carried out, the ratio of works
is zero and modification factor becomes (1-0=1). Thus the value
of modification factor ranges between one to zero.
6.3 Deriving modified parameter
The weighted mean value of conservation practice parameter
“Pw” derived in 6.1, is multiplied by the modification factor
―Mf‖, as per equation (8), to account for compounded effect of
Government conservation works.
Pm  ( Pw  Mf )
(8)
where Pm= modified value of P, Pw = weighted mean value of
P, Mf= Modification factor
The modified values of conservation practice parameter are
derived using equation (8) for each micro-water-shed. The
derived values for each watershed as shown in Table 1 are
vectorized and rasterised in GIS environment.
6.1 Deriving weighted value “Pw”
In a micro water-shed the farmers adopt different practices as per
land form and as per LU/LC. The researchers after due
experimentation have assigned the “P” values according to
The Hybrid model of RUSLE and USLE are used in the present
case to estimate the soil erosion of a reservoir catchment. The
parameters namely R, K, L, S, C and P derived are integrated in
the model. The model is run in GIS for conservation practice
MANIT Bhopal
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parameter ―P‖ and for modified conservation practice parameter
“Pm”. The map of erosion rates “A” derived for study area is
shown in Figure1 while the raster maps for the parameters R, K,
L, S, C, P are shown in Figure 2. The integration of parameters
in both the cases resulted in following erosion rates.
Nakil M.B. (2014), Analysis of parameters causing water
induced soil erosion, annual progress seminar Indian Institute of
Technology Bombay,
Sedimentation rate with conventional conservation practice
factor “P‖=0.072 Million tons per year
Sedimentation rate with modified conservation practice factor
―Pm‖ =0.065 Million tons per year
The observed average rate of sedimentation for this catchment
=0.048 Million tons per year.
The results reveal that the sedimentation rate estimated using
conventional ―P‖ is 50% higher than the observed value. While
the estimated sedimentation rate using modified ―Pm‖ is 35%
higher than the observed value. The evolved method has helped
to quantify the use of the soil conservation works executed
through Government funding. It is seen here that the prediction
of soil loss associated with modified conservation parameter
“Pm” is reduced by 15%. The revised value of predicted soil loss
is nearer to the observed value. While the soil loss estimation by
using unrevised value of “P” results in over-prediction. The
higher prediction even after using modified value “Pm” attracts
refinement in other parameters of model.
Figure 1. Erosion rates of Punegaon Catchment
7. CONCLUSIONS
The soil & water conservation works carried on Government /
common lands in the watershed modify the conservation practice
parameter used in soil loss equation model. These public works
reduce the conservation parameter “P”. The methodology is
evolved here to derive the modified parameter “Pm”. Normally
it is examined that the soil loss estimated for catchment area
using soil loss equation model is on higher side. The use of
modified conservation practice parameter “Pm”, in-place of
conventional value “P” helps to overcome this over-estimation.
This approach can make the soil loss equation more accurate and
thus acceptable particularly for large catchment area. Here the
refinement and accuracy in quantification of soil loss estimation
in view of public conservation works helps to correctly assess
the reservoir sedimentation. The methodology can be used for
any soil loss prediction model.
REFERENCES:
i.
Feb. 2014: 42
ii.
Renard, K.G., Foster G.R., Weesies G.A., Mc Cool
D.K., and Yoder D.C. (1997) Predicting Soil Erosion by
Water: A Guide to Conservation Planning with the Revised
Universal Soil Loss Equation (RUSLE). Agriculture
Handbook No. 703, U.S. Department of Agriculture,
Agriculture Research Service, Washington, District of
Columbia, USA.
iii.
Wischmeier, W.H. and Smith, D.D., (1978),
Predicting Rainfall erosion losses- A guide to conservation
planning, Agricultural Handbook number 537, USDA,
Science and Education Administration, washington, District
Columbia, USA
MANIT Bhopal
Figure 2. Erosion causing parameters R, K, L, S, C, P
of Punegaon reservoir catchment
Table 1. calculation of values of „Pm” on the basis of LU/LC
Page 106
Issue Special3
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3
Sediment Management in Reservoir of
Hydroelectric Power Projects - Numerical
Simulation Studies for Punatsangchhu – I, Bhutan
Neena Isaac1
T.I. Eldho2
S.B. Tayade3
Chief Research Officer, Central Water and Power Research
Station, Khadakwasla, Pune-411024, India
Research Scholar, Department of Civil Engineering, IIT
Bombay,Mumba-400076, India
2
Professor, Department of Civil Engineering, IIT Bombay, India
3
Assistant Research Officer, Central Water and Power Research
Station, Khadakwasla, Pune-411024, India
1
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18-19, Dec. 2014
14
ABSTRACT: Run-of-the-river hydroelectric power projects in
the Himalayan Region are developed on the principle of
sustaining reservoir life by sediment management. Sediment
management is generally achieved by sluicing or drawdown
flushing through low level outlets during peak flows. The
sedimentation in reservoirs depends on various factors such as
reservoir geometry, flow and sediment characteristics and
reservoir operation schedule. Hence, design and operation of
such projects is highly site specific and simulation using
numerical and physical models is essential for optimizing the
design during planning stage. One dimensional numerical
model is useful for predicting long term sediment deposition
pattern in elongated reservoirs. In this paper, reservoir
sedimentation studies carried out using numerical model
simulations the run-of-the-river Punatsangchhu- I Hydro
Electric Project (1200 MW) located on Punatsangchhu river in
Wangdue District, Bhutan is presented. Simulations using one
dimensional model HEC-RAS 4.1 were carried out to predict
the sedimentation profiles along the reservoir covering a reach
of about 18.5 km upstream of dam. Sediment rating curve was
developed from available suspended sediment data.
Simulations were carried out to predict the sedimentation
profile after various duration of reservoir operation. It was
observed that sedimentation in the reach from about 10.5km to
12.5km upstream of dam axis is very high. Simulations were
continued for reservoir operating at MDDL till the sediment
deposition at dam reached the spillway crest level. Hydraulic
flushing is proposed to restore the reservoir capacity.
Keywords: Run-of-the-river, sediment management, numerical
model, reservoir sedimentation, Punatsangchhu- I H.E.
Project
1. INTRODUCTION:
Hydropower projects in Himalayan region are nowadays
developed as run-of-the river schemes. The rivers in this region
carry huge quantity of sediment load during monsoon season and
the reservoirs gets silted up within a few years of operation. The
life of such projects can be sustained by proper sediment
management. Sediment management is generally achieved by
sluicing or drawdown flushing through low level outlets during
peak flows. The choice of the most efficient method depends on
various factors such as reservoir geometry, flow and sediment
MANIT Bhopal
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characteristics and reservoir operation schedule. Run-of-the-river
hydropower projects are generally developed at head reaches of
perennial rivers by diverting available water to utilize the high
elevation difference for power generation. Since sediment
concentration is generally very high during peak flow season,
these reservoirs are operated at MDDL by sluicing during
monsoon. Since sedimentation problems of such projects are
highly site specific, design of various components and operation
schedule required to be optimized by hydraulic model studies
Sediment deposition pattern in reservoirs can be estimated using
mathematical models. Many such models have been developed
and are being applied to simulate sediment deposition in
reservoirs. One dimensional (1D) numerical model can be
applied to predict long term deposition in reservoirs. A detailed
description of 1D modelling and review of some of the available
sediment models were presented by Morris and Fan (1997).
Detailed review of the reservoir sedimentation and flushing
processes including case studies, numerical, and physical models
are reported by Batuca and Jordan (2000). Guidelines for
predicting long term reservoir sedimentation and description
with representative case studies of 1D and 2D sediment transport
models were presented by Basson (2007). Mike 11(1D),
RESSASS (1D), GSTARS (Quasi 2D), Mike 21(2D) (Quasi 3D)
and a 3D model applied to the sedimentation studies of Three
Gorges Project, China were described (Basson, 2007). Jungkyu
Ahn and C T Yang (2010) studied the reservoir sedimentation
and flushing processes of Xiaolangdi Reservoir on the Yellow
River in China using GSTARS3 model. Nils Reidar B. Olsen
and Stefan Haun (2010) reported application of a 3D numerical
model with an adaptive grid for flushing of the Kali Gandaki
reservoir in Nepal. Seyed Hossein Ghoreishi, et.al (2010)
simulated the process of sediment flushing by a three
dimensional numerical model based on Reynolds Averaged
Navier-Stokes (RANS) equations. Application of a 2D
numerical model (CCHE-2D) to simulate the sedimentation
along a 150 km reach of the Aswan High Dam Reservoir, Egypt,
was presented by Ahmed and Ahmed (2013). Isaac et al. (2013)
reported application of 1D numerical model for predicting the
reservoir sedimentation and geometrically similar scale physical
model for hydraulic flushing of sediment from reservoir of
Chamera-II reservoir, India.
In this paper, 1D numerical model based on HEC-RAS used to
simulate the reservoir sedimentation of Punatsangchhu –I H. E.
project on Punatsangchhu river, Bhutan is presented. The project
has been proposed as a run-of-the-river project with the
provision for annual flushing of reservoir through low level
sluice spillways to remove deposited sediment. The project site
is in the Himalayan ranges where the sediment load in rivers is
generally high during monsoon season. 1 D numerical model has
been used to predict the long term sedimentation profiles.
2. STUDY AREA DESCRIPTION:
The Punatsangchhu –I project is located on Punatsangchhu river,
between 8 km and 16 km downstream of Wangdue Bridge,
Bhutan. The dam site is about 80 km from Thimphu. The rivers
Phochhu and Mochhu rises from the snow covered peaks of the
Himalayan ranges in the North-West Bhutan at an elevation of
about 7000m and join at Punakha to form the river
Punatsangchhu. The Punatsangchhu River has a total length of
about 320 km from its source in Bhutan to its confluence point
with Brahmaputra in Assam. Its course in Bhutan has a length of
about 250 km. The catchment area of Punatsangchhu river upto
dam site extends from latitude 27015‟N to 28030‟N and
longitude 89015‟ E to 90030‟ E. The total Catchment area upto
the project site is 6390 km2 out of which 3115 km2 is snowfed
area and the remaining 3275 km2 is rainfed area.
Figure 1. The location plan for Punatsangchhu-I H. E. Project is
presented in.
The project complex consists of a 136 m high (from deepest
foundation level) concrete gravity dam, 7 numbers of sluice
spillways (8 m width and 14.65 m height with crest at El.1166
m), 4 intakes with crest at El. 1182 m, 300 m long desilting
basins and 9 km long and 10m diameter circular Head Race
Tunnel (HRT). The sluice spillways are designed for the
Probable Maximum Flood (PMF) of 11500 m3/s and 4300 m3/s
GLOF. The reservoir is to be operated between Full Reservoir
Level (FRL) of El.1202 m and Minimum Draw Down Level
(MDDL) of El. 1195 m. The gross storage capacity of the
reservoir is 25 Mm3 and live storage is 16 Mm3 with
3. NUMERICAL MODEL:
Sediment transport and deposition in reservoirs are three
dimensional in nature. The physical processes are very complex
and could be simulated using three dimensional (3D), two
dimensional (2D) or one dimensional (1D) numerical model. A
number of such commercial or free models are available. The
selection of the model depends on the objectives of the study,
availability of data and computational resources. 3D numerical
models are essential to reproduce complex flow patterns and
flow near hydraulic structures. However, simplification with 1D
approach is well suited for narrow and gorge type reservoirs
where longitudinal processes are prevailing and if long periods
need to be simulated. Based on the above criteria, the one
dimensional model, HEC-RAS 4.1 (USACE, 2010) developed
by the U.S. Army Corps of Engineers at the Army‟s Hydrologic
Engineering Centre was selected in the present study to simulate
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the sediment deposition in the reservoir of Punatsangchhu –I
H.E. project.
The sediment transport module of HEC-RAS simulates
streambed profile changes resulting from varying river flow and
tail water conditions. The model is based on 1D gradually varied
flow hydraulics and sediment transport theory. Water surface
profiles and other hydraulic parameters such as water velocity,
hydraulic depth, hydraulic roughness, energy slope, and width at
each cross section are computed from one cross section to the
next by standard step method according to the energy equation.
If water surface profiles are rapidly varied, momentum equation
is applied. HEC-RAS uses the quasi-unsteady flow approach for
sediment transport simulation. The continuous flow hydrograph
is approximated with a series of discrete steady flows of specific
durations. Hydrodynamic computations are performed for each
of these steady flows and transport parameters are generated at
each cross section. Flow durations are subdivided into
computational time steps, since bathymetry updates are required
more frequently than the flow increment durations. The
geometry file is updated and new steady flow hydrodynamics are
computed at the beginning of each computational time step
(Gibson et. al., 2006). The sediment continuity (Exner) equation
is then solved over the control volume associated with each
cross section, computing from upstream to downstream. At the
end of each computational time step, the aggregation or
degradation is translated into a uniform bed change over the
entire wetted perimeter of the cross section. The cross sectional
station-elevation information is updated and new hydrodynamic
computations performed before the transport capacity is
computed for the next sediment routing iteration.
Figure 2. River system schematic
For hydraulic computations, the roughness coefficient was
simulated by Manning‟s „n‟. In the present study, steady flow
computations were carried out for calibrating the model by
adjusting the „n‟ value. Water levels observed during flood on
26th May 2009 and 3rd July 2010 were used for calibration of
the model. Water levels along the river reach was matched with
the model results. The results are presented in Fig. 3. Manning‟s
„n‟ was assumed as 0.048 for the channel portion.
The main input data required for HEC-RAS include cross
sections of river reach, inflow hydrograph, grain size distribution
curve of bed material, sediment Vs discharge relation, rule curve
for reservoir operation and sediment transport equation
(USACE, 2010).
Figure 3. Observed and computed water surface profiles
4. MODEL SETUP:
The 1D numerical model of river Punatsangchhu covering a
reach of about 18.5 km upstream of dam and 1.5 km downstream
was developed using HEC-RAS. The river schematic was
developed as per the river plan. The river geometry was
reproduced in the model using the river schematic and the cross
section data. Cross sections data was available at 35 m interval
near the dam axis and at 500m interval in the remaining reaches.
Fig. 2 gives the river schematic with the locations of cross
sections.
4.1 Upstream boundary:
The inflow discharge hydrograph and total sediment load data
were specified as upstream boundary condition for the
simulations.
Daily observed discharge data was available at the Wangdue
rapid gauging site for the period from July 1992 to July 2009.
The above daily discharge hydrograph after correcting errors and
filling the gaps was used as the upstream boundary in the
simulation runs. The inflow hydrograph was repeated for longer
duration simulations. The inflow hydrograph is presented in Fig.
4.
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material gradation curves (Fig.6) at five locations upstream of
dam axis were available and hence used in the simulations.
Figure 4. Inflow hydrograph
Figure 6. Bed material gradation curve
Suspended sediment concentration along with corresponding
discharge observations were made at the Wangdue rapid gauging
site for the period from July 1992 to July 2009. Using the above
data sediment rating curve was developed and the same is
presented as Fig.5. The sediment rating curve was verified with
the sediment data available from gauge site at 1.5 km upstream
of dam axis established by M/s WAPCOS. Sediment data from
January 2010 to June 2010 was available and used for
verification.
Simulations were carried out to predict the sedimentation profile
after different durations of reservoir operation. Initially, the
sediment rating curve developed from observed data at Wangdue
rapid gauging site was used as the upstream boundary for
sedimentation runs. Since no measured data was available, the
bed load was assumed as 20% of the suspended load and the
total load was specified at the upstream boundary. The gauging
site was located at the pool area just upstream of the rapid such
that most of the incoming sediment settles in the reaches
immediately upstream. Hence the sediment concentration
measured at the gauging site was observed to be less. In order to
account for the unmeasured sediment load, the observed values
were increased by 4 times and the rating curve was modified.
Simulations were conducted for reservoir operating at FRL and
MDDL.
It was observed from the sedimentation profile obtained after 5
years of reservoir operation that deposition takes place in the
reaches between 4km to 6km and 9km to 13 km from dam axis.
The river slope is mild and the cross sections are comparatively
wider in the upstream reaches. Hence sedimentation in the above
reach was observed to be high. The area near the dam and
intakes remains clear of sediment deposition.
Figure 5. Sediment rating curve
4.2 Downstream boundary:
The reservoir operation level at dam was specified as
downstream boundary in simulations. Simulations were carried
out by maintaining the reservoir water level at the FRL of El.
1202 m and at the MDDL of El.1195 m.
4.3 Bed material gradation curve:
In HEC-RAS, the sediment continuity equation is solved
separately for each grain size and material is added or removed
to the active layer. Hence it is required to specify the initial grain
size distribution of the bed material. In the present study, bed
To get the pattern of bed profile near dam and intake area,
simulation runs were carried out by specifying equilibrium
sediment load condition at the upstream boundary. The bed
profiles obtained by the simulation of daily hydrograph for a
period from January 1992 to July 2025 (about 33 years), and
reservoir operating at MDDL is presented in Fig. 7. It was
observed that the sedimentation level at the dam axis reached
about the spillway crest level of El.1166 m. The delta deposition
in the pool area between 1.5 km and 5.5km was progressing. The
cross section of river in the reach from about 10.5km to 12.5km
upstream of dam axis is very wide compared to the sections just
upstream and downstream. Hence sedimentation in the above
reach was observed to be very high.
MANIT Bhopal
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load at upstream boundary indicated that sediment deposition
will reach the dam and spillway crest level after about 33 years
of reservoir operation without annual flushing. Simulations with
annual flushing indicated that sediment deposition will move
from upstream towards the dam during drawdown flushing. It
was observed from the results of simulation that due to the flatter
bed slope and wider river sections, sedimentation is high in the
upstream reaches of reservoir.
ACKNOWLEDGEMENTS:
Figure 7. Bed Profiles after different years with reservoir at
MDDL
Sediment management in the reservoir of Punatsangchhu – I
project is proposed by drawdown flushing during monsoon when
the sediment concentration exceeds the design value of desilting
basins. Hence in order to obtain the sedimentation profile with
annual flushing, simulations were carried out by lowering the
water level at dam axis during annual peak flows. The bed
profiles obtained after 5 years with and without annual flushing
are presented in Fig. 8. It was observed that during flushing, the
sediment deposition from the area around 5 km is moving
downstream towards the dam axis.
Figure 8. Bed Profiles after 5 years with and without drawdown
flushing
6. CONCLUSIONS:
The Punatsangchhu – I H. E. project is planned as a run-of-the
river scheme. Sediment management in the reservoir is proposed
by annual drawdown flushing during peak flow and sluicing
during monsoon by operating the reservoir at MDDL. In this
study, one dimensional numerical model was used to obtain the
sedimentation profile of reservoir under different operating
conditions. Simulations with the measured sediment inflow rate
indicated very low deposition when the reservoir was operated at
FRL and MDDL. No sediment deposition was observed near the
dam and intake area. Simulations with equilibrium sediment
Kind permission given by Shri. S. Govindan, Director CWPRS,
Pune for publishing the paper is acknowledged with thanks.
Support and input given by Dr.(Mrs.) V. V. Bhosekar, Joint
director, CWPRS, officials of WAPCOS India and PHPA,
Bhutan are thankfully acknowledged. Authors also express
thanks to officers and staff members of HAPT division,
CWPRS, Shri. P. S. Kunjeer, and Shri. S.A. Kamble, Research
Officers for their co-operation and support in conducting the
studies.
REFERENCES:
i.
Ahmed Moustafa, Ahmed Moussa (2013). Predicting the deposition in
the Aswan High Dam Reservoir using a 2-D model. Ain Shams Engineering
Journal 4, 143–153.
ii.
Basson, G. (2007). Mathematical Modelling of Sediment Transport
and deposition in Reservoirs, Guidelines and Case Studies. ICOLD Bulletin
No.140. International Commission on Large dams, 61,avnue Kleber, 75116,
Paris.
iii.
Basson, G. (2009). Sedimentation and sustainable Use of Reservoirs
and river Systems. ICOLD Bulletin No.147. International Commission on Large
dams, 61,avnue Kleber, 75116, Paris.
iv.
Batuca, D.G., and Jordan, J.M. (2000). Silting and desilting of
reservoirs, A.A. Balkema, Rotterdam.
v.
Gibson, S., Brunner, G., Piper, S., and Jensen, M. (2006). Sediment
Transport Computations with HEC- RAS, Proceedings of the Eighth Federal
Interagency Sedimentation Conference (8thFISC), April2-6, 2006, Reno, NV,
USA.
vi.
Isaac, N., Eldho, T. I., Gupta, I. D. (2013). Numerical and physical
model studies for hydraulic flushing of sediment from Chamera-II reservoir,
Himachal Pradesh, India, ISH Journal of Hydraulic Engineering, DOI:
10.1080/09715010.2013.821788.
vii.
Jungkyu Ahn, Chih Ted Yang, (2010). Simulation of Xiaolangdi
Reservoir Sedimentation and Flushing Processes, 2nd Joint Federal Interagency
Conference, Las Vegas, NV, June 27 - July 1.
viii.
Morris, G.L., and Fan, Jiahua (1997). Reservoir sedimentation hand
book. McGraw-Hill Book, New York.
ix.
Nils Reidar B. Olsen & Stefan Haun (2010). Free surface algorithms
for 3D numerical modelling of reservoir flushing. River Flow 2010 - pp 11051110.
x.
Seyed Hossein Ghoreishi, Mohammad Reza Majdzadeh Tabatabai
(2010). Model study reservoir flushing. Journal of Water Sciences Research,
ISSN: 2008-5338 Vol.2, No.1, Fall 2010, 1-8, JWSR.
xi.
Sonam Choden (2009).Sediment Transport Studies in Punatsangchu
River, Bhutan. Water Resources Engineering, Department of Building and
Environmental Technology, Lund University, P.O.Box 118, SE-221 00 Lund
xii.
USACE. (1993). Scour and deposition in river and reservoirs: HEC 6
– User‘s manual. US Army Corps of Eng., Hydrol. Eng. Center, 690 Second
Street, Davis, CA, 95616–4687.
xiii.
USACE. (2010). HEC-RAS River analysis system – Hydraulic
reference manual and user‘s manual. US Army Corps of Eng., Hydrol. Eng.
Center, 690 second street, Davis, CA, 95616.
xiv.
USBR. (2006). Erosion and sedimentation manual. US Department of
the Interior Bureau of Reclamation.
MANIT Bhopal
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Sedimentation Assessment in Nath Sagar
Reservoir (Jayakwadi Project) of Maharashtra by
Remote Sensing Technique – A Case Study
Prakash Bhamare1
Manoj Bendre2 Ravindra
3
Shrigiriwar Mahendra Nakil4 Sudhir Kalvit5*
Maharashtra Engineering Research Institute
Dindori Road, Nashik – 422004, Maharashtra, India
Phone
0253-2534676
E mail – [email protected]
*Corresponding Author
ABSTRACT: Jayakwadi irrigation project is a major project
in Maharashtra State constructed on the river Godavari in the
year 1975-76 with a gross and live storage potential of 2909
Mm3 and 2171 Mm3 respectively. The reservoir has been
named as Nath Sagar reservoir after well known Marathi Saint
Eknath of the 16th Century. The project has been instrumental
in the economic development of Marathwada region of the
State. However, since last few years, due to vagaries of
monsoon and inadequate run off from the catchment, the
reservoir has been facing shortage of water. The reservoir was
filled up to F.R.L. hardly three to four times in last decade.
More over sedimentation in this reservoir has been another
issue before the reservoir management authority. Inadequacy
of water storage and the reduction in storage potential of the
reservoir on account of sedimentation have forced the
reservoir authority to conduct sedimentation assessment survey
of this reservoir for assessing the net storage available in the
live storage zone. The sedimentation assessment survey was
entrusted to Maharashtra Engineering Research Institute
(M.E.R.I.) Nashik by Jayakwadi reservoir authorities. In April
2014, M.E.R.I., conducted the survey by satellite remote
sensing technique using IRS LISS III images, and the present
live storage capacity between Full Reservoir Level (FRL) and
Minimum Draw Down Level (M.D.D.L.) had been estimated. A
revised Elevation –Area – Capacity table at 0.10 meter interval
had been prepared for the live storage zone which can be very
useful for the reservoir management authority while operating
the reservoir.
(Key words – Dead Storage, Live storage, D.G.P.S., M.D.D.L,
bathymetry.)
1.0 INTRODUCTION
Apart from the hydrological factors, deforestation, rapid
urbanization, developmental activities in the catchment area
such as construction of roads, railway lines, land leveling and
terracing excessive quarries and mining etc are some other
important factors responsible for rapid erosion of the land in the
catchments of reservoirs. The soil erosion in the catchment
accelerates the process of sedimentation in reservoirs. The
sedimentation results in reduction of storage capacity of
reservoirs. Many lakes and reservoirs, which are important fresh
water resources, are under the threat of sedimentation today. The
reduction in water storage potential of the multipurpose
reservoirs affects the entire irrigation and domestic water
planning. Therefore, sedimentation is a matter of concern for the
reservoirs in the context of their utility and useful life. The
siltation in reservoirs does not have uniform pattern everywhere.
It is obvious because climatic and topographical conditions and
the land use pattern in the catchment area are different in
different regions of the state. Periodic reservoir capacity
assessment surveys provide useful information about storage
availability at different levels in different periods which is
important scheduling the water use effectively. Remote sensing
based reservoir sedimentation surveys are essentially based on
mapping of water-spread areas at the time of satellite over pass.
It uses the fact that water-spread area of the reservoir reduces
with the sedimentation at different levels. The water-spread area
and the elevation information are used to calculate the volume of
water stored between different levels. These capacity values are
then compared with the previously calculated capacity values to
find out change in capacity between different levels.
The Maharashtra Engineering Research Institute which is the
Research Wing of the State‟s Water Resources Department has
done substantial work in the field of reservoir sedimentation
assessment. First sedimentation assessment survey of Nath Sagar
reservoir was conducted by Maharashtra Engineering Research
Institute, Nashik by satellite remote sensing technique using
digital images of IRS 1B satellite with LISS II sensor (36 m
spatial resolution) for the period between years 1994 - 1997.
The next survey of Nath Sagar reservoir was conducted using
most of the digital images of RESOURCESAT 1 satellite with
LISS III sensor (24 m spatial resolution) for the period between
years 2011-2013. Temporal sedimentation assessment surveys
are useful to keep the content table of reservoir updated which is
a pre requisite for realistic planning of reservoir storage.
2.0 OBJECTIVES OF THE SURVEY
The sedimentation assessment study was conducted with the
following objectives

To estimate the present live storage capacity of
reservoir

To update Elevation-Capacity curve for the live storage
zone of reservoir.

To estimate storage capacity loss in reservoir since it‟s
first impounding.

To update the content table of the reservoir for live
storage zone.
3.0 STUDY AREA
The Nath Sagar reservoir lies between Latitude 19 0: 19‟: 13” and
190: 41‟: 46” N and Longitude 740: 49‟: 23” and 750: 24‟: 22” E.
The reservoir is constructed on river Godavari, near village
Jayakwadi in Paithan Taluka of Aurangabad district. The project
comprises earthen dam of nearly 10.5 Km in length. Total
catchment area of the reservoir is 21750 sq. km. The designed
gross storage capacity of the reservoir at FRL 463.906 m is 2909
Mm3 and live storage capacity between FRL & MDDL is
2170.92 Mm3. The MDDL of the reservoir is at R.L. 455.524 m.
Designed dead storage capacity is 738.08 Mm3. The reservoir
was first impounded in the year 1975-76.
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4.0 DATA USED FOR THE PRESENT STUDY
RESOUR
CESAT 1
LISS III
16 Dec 2007
462.8
97
(A) Field Data
RESOUR
CESAT 1
LISS III
22 Nov 2007
463.3
27
RESOUR
CESAT 1
LISS III
23 Oct 008
463.7
9
RESOUR
CESAT 1
LISS III
5 Oct 2007
463.9
06
Following field data required for this survey was obtained from
Jayakwadi Project authority.
i) Reservoir Levels for given dates of the satellite pass
ii) Reservoir F.R.L. and M.D.D.L.
iii) First year of reservoir impounding.
(B) Satellite Data
NRSC website was browsed and a list of cloud free dates of
RESOURCESAT 1 and RESOURCESAT 2 satellite pass over
Nath Sagar reservoir was prepared for the period between Year
2011 and 2013. The selection of the satellite images was done
after studying the draw down pattern of the lake levels and
selected satellite data was procured from the NRSC Hyderabad.
In all, total 19 images of RESOURCESAT 1 and
RESOURCESAT 2 satellites together, with LISS III sensor
having a spatial resolution of 24 m are used for this survey.
These satellite images were of different water levels between
R.L.455.066 m to F.R.L 463.906 m. Out of these, 14 images
were of period between Oct 2011 to Jan 2014 and 5 images were
of the year 2007-08. Since Nath Sagar reservoir did not have full
storage in last 6-7 years, the images of old period (year 2007-08)
had to be used to cover the study up to F.R.L. avoiding
extrapolation of result. Thus the present study covered 100% of
live storage zone. Table 1 gives the dates of satellite passes with
respective water levels.
5.0 METHODOLOGY
The satellite images procured from National Remote Sensing
Centre were already rectified (geo-referenced).
Hence
preprocessing of images was not necessary. The images were
analysed digitally using standard image analysis software.
Classification technique was adopted for the analysis and the
water spread areas of the reservoir in all the images were
measured.
The following flow chart describes the methodology in brief.
Table- 1.Details of Satellite pass, sensor, path and row and water levels
Satellite
Sensor
Date of Pass
RESOUR
CESAT 1
LISS III
06 May 2013
RESOUR
CESAT 1
LISS III
12 Apr 2013
455.3
59
RESOUR
CESAT 2
LISS III
19 Nov 2012
455.9
45
RESOUR
CESAT 2
LISS III
11 Feb 2013
456.1
24
RESOUR
CESAT 1
LISS III
30 Jan 2013
456.2
80
RESOUR
CESAT 1
LISS III
13 Dec 2012
456.7
60
RESOUR
CESAT 2
LISS III
05 Apr 2012
457.7
91
RESOUR
CESAT 2
LISS III
24 Mar 2012
Elev
ation
m
455.0
66
457.9
44
RESOUR
CESAT 1
LISS III
1 Jan 2014
458.6
94
RESOUR
CESAT 1
LISS III
21 Oct 2013
459.2
67
RESOUR
CESAT 2
LISS III
24 Jan 2012
459.4
59
RESOUR
CESAT 1
LISS III
19 Dec 2011
460.2
88
RESOUR
CESAT 2
LISS III
13 Nov 2011
461.1
62
RESOUR
CESAT 1
LISS III
8 Oct 2011
461.7
00
RESOUR
CESAT 1
LISS III
9 Jan 2008
Table 2 shows the Water Spread Areas (WSA) of Nath Sagar
reservoir in all the images corresponding to their water levels
Table 2. Water spread areas estimated from satellite data
462.3
95
MANIT Bhopal
Date
of
pass
06
May
2013
12
Apr
2013
19
Nov
2012
11
Feb
2013
30
Jan
2013
13
Dec
2012
05
Apr
2012
24
Mar
12012
Jan
2014
21 Oct
2013
24 Jan
2012
19
Dec
2011
13
Nov
82011
Oct
2011
9 Jan
2008
16
Dec
2007
Elevation in
m.
Area in
Mm2
455.066
112.54
455.359
124.47
455.945
137.84
456.124
141.78
456.280
147.72
456.760
160.81
457.791
182.38
457.944
185.46
458.694
202.97
459.267
223.05
459.459
231.34
460.288
248.71
461.162
271.05
461.700
290.2
462.395
311.89
462.897
325.31
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22
Nov
2007
23
Oct
008
5 Oct
2007
463.327
343.27
463.79
367.52
463.906
371.69
18-19, Dec. 2014
Figure -2 Satellite images of Nath Sagar Reservoir of different dates in order of
reducing water levelimage
Jayakwadi Project (Nathsagar reservoir)
Satellite Remote Sensing based Elevation Vs revised area curve for live storage zone
400
Interpolation of Water spread area (WSA) at Regular
Interval
y = 0.1776x3 - 1.6011x2 + 29.146x + 111.87
R2 = 0.9991
300
Revised Area in Mm2 ---->
For the present survey cloud free satellite images of
different water levels for the reservoir portion between R.L.
455.066 m and F.R.L. 463.906 m were available. Water
levels on the date of satellite pass for selected satellite data
were not at regular interval. To get WSA values at regular
elevation interval, a curve was plotted between Elevation
and the Revised Area and a best fit polynomial equation of
third order was derived for the graph. The best fit equation
is as follows.
y = 0.1776x3 - 1.6011x2 +
29.146x + 111.87
R2 = 0.9991 (R = Coefficient
of co-relation)
where x = Elevation difference in meters (measured above
R.L. 455.00 m)
y = Water spread area in Mm2
Using this equation, the Water Spread Areas at regular
interval of elevation between R. L. 455.00 m and F.R.L.
463.906 m have been worked out.
Third order polynomia equation for best fit
curve for the graph is as below
350
where R = Coefficient of co-relation
x = elevation measured above R.L. 455.00 m
y = revised water spread areas
250
200
150
100
50
0
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
8.00
9.00
10.00
Elevation measured above R.L. 455.00 m considering R.L. 455.00 m as datum ----->
Figure -3 Graph between R.L. and respective water spread areas (estimated from
satell
Calculation of Reservoir Capacity
Computation of reservoir capacity at different elevations has
been done using following prismoidal formula.
V = h/3*(A1 + A2 + SQRT (A1 * A2)).
Where V- Reservoir capacity between two successive
elevations h1 and h2
h- Elevation difference (h2 – h1)
A1 and A2 are areas of reservoir water spread at elevation h 1
and h2.
Figure -4Graph showing comparison of Live storage capacity as per different
surveys
Sr.
No
1
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
MANIT Bhopal
Elevation
Original
Capacity
in meters
2
455.524
455.600
456.000
456.499
456.999
457.499
457.999
458.499
458.999
459.499
459.998
460.499
460.998
461.498
461.997
462.498
463.000
463.500
3
0
11.171
75.559
157.896
240.234
333.743
434.51
535.377
648.323
771.203
894.082
1028.98
1178.431
1327.038
1486.624
1659.565
1833.561
2013.35
Revised
live
storage
capacity
as per
1994-96
survey
4
0
10.288
66.305
140.744
220.658
306.180
397.586
495.150
599.149
709.857
827.308
952.504
1084.722
1225.008
1373.075
1530.115
1696.166
1870.487
Revised
Live
storage
Capacity
as per
2012-13
survey
5
0
9.711
63.379
136.247
215.655
301.294
393.064
490.930
594.924
705.144
821.516
944.992
1074.884
1212.315
1357.167
1510.833
1673.649
1845.251
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Issue Special3
19
463.906
2170.935
2018.782
18-19, Dec. 2014
1991.987
6.0 RESULT AND DISCUSSION
The result of the present survey and its comparison with
original survey and year 1994-96 survey are given in
following table.
Table – 3 showing the comparison of original live storage capacity with that
of year 1996 survey and year 2012-13 survey
Table 3
Description
As per original
survey year 197576
Live storage
capacity
As per survey of year
1994-96
2170.935
2018.782
-------
0.35
As per survey of
year 2012-13
1991.987
in Mm3
Average
Annual
loss
0.23
%
their prediction have become an important process in the part
of a hydrologist. In recent years various models like
autoregressive (AR) integrated with Moving Average (MA),
Neural Network (NN), Fuzzy Theory, Genetic Algorithm (GA),
Support Vector machines (SVM) and Wavelet Transformation
have been used in analysis and prediction of the hydrological
data. These models were not only used individually for analysis
of the data, but also a combination of these models were used
for better representation of the data and subsequent
predictions. The models can be developed using the standard
software packages as available and with R/Matlab. In this
paper the ARIMA, ANN and the Wavelet combined with ANN
was analyzed for better performance and validated with the
data as available for the catchment. The model efficiency was
also reported in various parameters like root mean square
error (RMSE), coefficient of correlation (R) and other model
specific parameters.
Keywords: Wavelet, Neural Network, Autoregressive Model
Revised content table for the live storage zone has been
prepared at 0.1 m contour interval which can be of great use
for the reservoir management authority during reservoir
operation. Revised Live storage capacity of Nath Sagar
reservoir between M.D.D.L. 455.524 m and FRL 463.906 m
is estimated to be 1991.987 Mm3 for the year 2012-13 as
against Original Live storage capacity of 2170.935 Mm3
between these levels, with a loss of 178.948 Mm3 (8.24 %).
The average annual percent loss in live storage for the
period of 36 years between 1976 and 2012 works out to
0.23% which is not severe. Sedimentation in the dead
storage zone i.e. below M.D.D.L. 455.52 m could not be
estimated by remote sensing method. For this, a
hydrographic survey is necessary.
7.0 REFERENCES
i.
Technical Report on revised storage capacity assessment of
Jayakwadi reservoir by satellite remote sensing technique. (year 2014),
Maharashtra Engineering Research Institute, Nashik – 4
ii.
Figure -3 Graph between R.L. and respective water spread areas
(estimated from satellite images
Hydrological Data Modelling Using Wavelet,
Neural Network And Ar Models
G.Khadanga1, B.Krishna2
Scientist, National Informatics Centre, CGO Complex, New
Delhi
2
Scientist, NIH, Kakinada, Deltaic Regional Center, Kakinada
1
ABSTRACT: Hydrological data like rainfall, runoff,
evapotranspiration, water table, reservoir water level etc. and
1. INTRODUCTION:
A time series is a sequence of observations that are arranged
according to the time of their outcome. In time series the
physical quantity and the sequence and the order of the data
collection is very important. Meteorology records like hourly
wind speeds, daily maximum and minimum temperatures, daily
monthly and annual rainfall, discharge data of a river or dam are
few examples of the time series data. Various statistical
approaches like regression, auto regression, auto regressive
integrated moving average time series modeling, stochastic
approaches, machine learning, data mining, ANN, fuzzy set,
neuro fuzzy, support vector machine, fourier transform, wavelet
combines with ANN have been used to model the time series
data. The analysis of the nonlinear behavior and raise the
forecast precision and lengthen the forecasted time are a
challenging task in time series modeling. In this paper the
ARIMA model is explored with the sample data using R as
modeling tool. Then the other modeling tools like ANN,
Wavelet and combined with ANN are used for the rainfall data
analysis and the models output was interpreted.
1.1 Arima Model in R:
The acronym ARIMA(p,d,q) stands for "Auto-Regressive
Integrated Moving Average." Lags of the differenced series
appearing in the forecasting equation are called "autoregressive" terms, lags of the forecast errors are called "moving
average" terms, and a time series which needs to be differenced
to be made stationary is said to be an "integrated" version of a stationary series. In ARIMA the p is the number of
autoregressive terms, d is the degree of first differencing, and q
is the order of the moving average part.
The auto.arima() function of R (open source software package
for statistical modeling) uses the Hyndman and Khandakar
algorithm which combines the unit root tests, minimization of
the AICs (Akaike‟s Information Criterion) and MLE to obtain
the ARIMA model. The daily rainfall data of the test location is
MANIT Bhopal
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0.8
0.4
Daily Rainfall in MM
ACF
0.0
collected for 10 years and the plot of the data and the 1st
difference is shown in fig1. The decomposition of the daily time
series data is shown in fig2. The auto correlation and the partial
auto correlation is shown in fig3. As the ACF is dropping to zero
the time series is stationary. The PACF is exponentially
decaying and sinusoidal and there is a significant spike at lag 2
in ACF, but none beyond lag 2.
0
10
20
30
Days
0.3
0.2
0.1
0.0
Daily Rainfall in MM
PACF
0
10
20
30
Days
Figure 4. The ACF and PACF of the Rainfall Data
Forecasts from ARIMA(2,0,3) with non-zero mean
20
50 100
10
0
-10
6
0
50 100
remainder
200
2
4
trend
8
0
5
seasonal
10
0
data
200
30
Figure 1. Daily Rainfall Data of the series with 1st difference
1990
1995
2000
2005
2010
7600
7620
7640
7660
7680
7700
Figure 5. The forecast of the rainfall data using auto.arima R
function
time
Figure 2. Decomposition of the Daily Rainfall Data
The summary of the fit is shown below:
Coef
ar1 ar2 ma1
ma2
ma3 intercept
-0.0727 0.6202 0.4345 -0.5172 -0.1872 3.1970
s.e. 0.1570 0.0920 0.1579 0.1183 0.0378 0.1823
sigma^2 estimated as 97.96:
log likelihood=-28465.15,
AIC=56944.29 AICc=56944.31 BIC=56992.91
Figure 3. The summary of the fitness of the ARIMA Model
2. Artificial Neural Network (ANN)
The Artificial neural network (ANN) offers a quick and flexible
means of modeling hydrologic data analysis and prediction.
ANN tolerate imprecise or incomplete data, approximate results
and are less vulnerable to outliers. The ANNs can be described
either as a mathematical and computational model for non-linear
relationship, data classification, clustering and regression or as
simulations of the behavior of collections of the biological
neutrons.
The feed-forward multilayer perceptron (MLP) is the most
commonly used ANN in hydrological applications. The first step
in back propagation learning is the initialization of the network.
The structure of the network is first defined. In the network,
activation functions are chosen and the network parameters,
weights and biases, are initialized. The parameters associated
with the training algorithm like error goal, maximum number of
epochs (iterations), etc, are defined. Then the training algorithm
is called. After the neural network has been determined, the
result is first tested by simulating the output of the neural
network with the measured input data. This is compared with the
measured outputs. Final validation is carried out with
MANIT Bhopal
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independent data. The input values were normalized before use
in the ANN. The result of the training using the feed forward
network is shown in the table.
The first two year daily data was taken for training and one year
data was taken for validation. The regression coefficient is found
out to be 0.397 (Fig6) after lots of trial with different models.
The model with 4 input layer, one hidden layer found with
highest regression coefficient. The result is not very encouraging
however moderate predictions can be taken up with this model.
As the result is not very encouraging an attempt is made for
analyzing the data using both wavelet and neural network.
Figure 7. Original data set is broken into wavelets
3. Wavelet
The wavelet analysis has been used as alternative to Fourier
transform. The fourier transform mainly concentrate on the
frequency domain where as the wavelet analysis can provide the
exact locality of any changes in the dynamic patterns of the
sequences. Wavelet analysis is the breaking of a signal into
shifted and scaled version of the original data. Sometimes it is
also called as multi resolution analysis.
The original signal is passed through loss pass and high pass
filters and emerges as two signals as Approximations (A) and
Details (D). The approximations as low–scale and high
frequency components of the signal. The details are the highscale and low frequency components. The Daibechies and
Morlet wavelet transforms are more frequently used for
hydrological time series data.
Figure 8. Forecast & correlation coeff.
The Decomposed details (D) and approximations (A) are taken
as inputs into a neural network and then resultant wavelets were
combined to form the original data. Optimal structure of the
neural network (input layers, number of hidden, optimal
parameters of the neural network for train, transfer functions)
nodes was used to get the best performance. The output node is
taken as one step ahead of the original time step.
Figure 9. Training data and model output data
The daily rainfall data for the first two year is taken as the
calibration data and one year data is taken as validation data.
The original time series data is decomposed in to details and
approximate components using the wavelet transform algorithms
(DB5, D1, D2, D3, A4). The original timeseries and
decomposed parts are shown in fig 7.
Figure 6. Regression coeff in ANN
Table 1. Staistics of WNN and ANN for Calibration and
Validation period
Model
x(t)=f(x[t-1],x[t2],x[t-3],x[x-4])
WNN
ANN
MANIT Bhopal
Validation
Calibration
RMSE
R
20.05
0.697
13.79
0.419
RMSE
13.79
8.918
R
0.734
0.197
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4. CONCLUSIONS
The performance of the model were experimented with various
combinations and the best performance is found with regression
coefficient 0.985 (Fig7). This is much better than the ANN case.
The observed and model output is shown in the fig 9. The table 1
shows the various statistical parameters for the ANN and the
WNN case. The coefficient of correlation is better in WNN.
From the figure it is observed that the peak rainfall data is
predicted with minimum errors. The forecasted values are well
fitted to the 45 degree line. It was concluded that the best
predication of the data is possible with WNN model.
REFERENCES:
i.
Box, G. E. P. and G. M. Jenkins,(1976), ―Time Series analysis,
forecasting and control‖ Holden day, Oakland, California.
ii.
Goel N.K., Stochastic Modeling of Hydrological Process, Training
Course on Integrated Catchment Modelling, NIH, Roorkee, Nov. 2013.
iii.
Haykin, S. (1994), Neural Network: a comprehensive foundation.
MacMillan, New York.
iv.
J.S. Yang, S.P. Yu, and G.-M. Liu.,‖ Multi-step-ahead predictor
design for effective long-term forecast of hydrological signals using a novel
wavelet neural network hybrid model‖, Hydrol. Earth Syst. Sci., 17, 4981–4993,
2013.
v.
Kottegoda N.T.(1979), Stochastic Water Resources Technology, John
Wiley and Sons New York.
vi.
Nayak, P.C., Sudheer, K.P., and Ramasastri, K.S.( 2004a)‖ A Neurofuzzy Computing Techniques for modeling Hydrological Time Series:, Journal of
Hydrology, 291(102):52-66.
vii.
Shumway, H. Robert., Stoffer S. David., ―Time Series Analysis and its
Applications with R Examples‖, Third Edition, Springer, 2011.
viii.
Yevjevich, V., Stochastic Processes in Hydrology, May 1971.
Improved Neuro-Wavelet Model for Reservoir
Inflow Forecast
B.Krishna, Y.R.Satyaji Rao and R.Venkata Ramana
Scientists, Deltaic Regional Center, National Institute of
Hydrology, Siddartha Nagar, Kakinada-3, Andhra Pradesh. Email: [email protected]
ABSTRACT : There is a need for forecasts of reservoir inflow
events in order to: a basin wide consistency in management
operations based on a thorough knowledge of variation in
inflows, an improved capability for predicting and monitoring
flood events. Using hybrid model or combining several models
has become a common practice to improve the forecasting
accuracy. The combination of forecasts from more than one
model often leads to improved forecasting performance. An
attempt has been made to find an improved method for
accurate prediction of inflow by combining the wavelet
technique with Artificial Neural Networks (WNN). Wavelet
analysis effectively decomposes the main signal and diagnoses
its main frequency component and abstract local information.
The observed time series is decomposed into sub-series using
discrete wavelet transform and then appropriate sub-series is
used as an independent variable for the Neural Network
model. Several hybrid models have been developed to forecast
the inflow into Malaprabha reservoir in one day advance. The
calibration and validation performance of the developed
models is evaluated with appropriate global statistics. The
results were compared with the standard models with
undecomposed data. The application of wavelet based neural
network models were found to be more effective as its
prediction efficiency is more and its peak value is closer to
observed value.
Keywords: Inflow, Neural Networks, Training, Wavelet
Decomposition
1. INTRODUCTION:
Inflow is an important data for an optimal reservoir operation.
The importance of an accurate flow forecast, especially in floodprone areas, has increased significantly over the last few years as
extreme events have become more frequent and more severe due
to climate change and anthropogenic factors. Data based
forecasting methods are becoming increasingly popular in flood
forecasting applications due to their rapid development times,
minimum information requirements, and ease of real-time
implementation. Using hybrid model or combining several
models has become a common practice to improve the
forecasting accuracy. The combination of forecasts from more
than one model often leads to improved forecasting
performance. An attempt has been made to find an alternative
method for accurate prediction of inflow by combining the
wavelet technique with Artificial Neural Networks (WNN).
Artificial Neural Network (ANN) is widely applied in hydrology
and water resource studies as a forecasting tool. In ANN, feed
forward backpropagation (BP) network models are common to
engineers. It has proved that BP network model with three-layer
is satisfied for the forecasting and simulating in any engineering
problem. Three-layered feed forward neural networks (FFNNs),
which have been usually used in forecasting hydrologic time
series, provide a general framework for representing nonlinear
functional mapping between a set of input and output variables.
Although ANN had been used extensively as useful tools for
prediction of hydrological variables, it has also many drawbacks
to deal with non-stationary data (Cannas et al., 2006).
Wavelet analysis is a useful tool for non-stationary processes
such as hydrological time series (Rajaee et al., 2011). Wavelet
transform, which is a pre-processing decomposed technique,
showed successful performance in hydrological applications.
Several studies have been published that developed hybrid
wavelet–ANN models. Wang and Lee (1998) developed a hybrid
wavelet–ANN model to forecast rainfall–runoff in China. Rajaee
et al., (2011) applied wavelet combined with neuro-fuzzy and
ANN for sediment load prediction, Cannas et al. (2005)
developed a hybrid model for rainfall–runoff forecasting. Okkan
(2012) developed different models as Wavelet Neural Network
(WNN) in combination with Discrete Wavelet Transform
(DWT) and Levenberg-Marquardt based Feed Forward Neural
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Networks (FFNN) and Wavelet Multiple linear Regression
(WREG) for monthly reservoir inflow forecasting.
2. WAVELET ANALYSIS
The wavelet transform is the tool of choice when signals are
characterized by localized high frequency events or when signals
are characterized by a large numbers of scale-variable processes.
Because of its localization properties in both time and scale, the
wavelet transform allows for tracking the time evolution
processes at different scales in the signal. The continuous
wavelet transform of a time series f (t) is defined as
1
f (a, b) 
a

t b
f
(
t
)

(
)dt

a

(1)
Where  (t ) is the basic wavelet with effective length (t) that is
usually much shorter than the target time series f (t). The
variables are a and b, where a is the scale or dilation factor that
determines the characteristic frequency so that its variation gives
rise to a `spectrum'; and b is the translation in time so that its
variation represents the `sliding' of the wavelet over f(t). The
wavelet spectrum is thus customarily displayed in timefrequency domain. For low scales i.e. when |a| << 1, the wavelet
function is highly concentrated (shrunken compressed) with
frequency contents mostly in the higher frequency bands.
Inversely, when |a| >> 1, the wavelet is stretched and contains
mostly low frequencies. For small scales, thus a more detailed
view of the signal (known also as a “higher resolution”) whereas
for larger scales a more general view of the signal structure can
be expected. However, in practical the hydrologic time series
does not have a continuous – time signal process but rather a
discrete – time signal. The Discrete Wavelet Transform (DWT)
is to calculate the wavelet coefficients on discrete dyadic scales
and positions in time. Discrete wavelet functions have the form
by choosing
and
in equation (1). The
Eq. (1) has takes the form
g
m, n
(t ) 
a
t  n b0 a0
m
1
m
g(
0
a
m
)
o
(2)
where m and n are integers that control the wavelet dilation and
translation respectively;
is a specified fined dilation step
greater than 1; and
is the location parameter and must be
greater than zero. The appropriate choices for
and
depend
on the wavelet function. A common choice for them is
=2,
=1.
The original signal X(n) passes through two complementary
filters (low pass and high pass filters) and emerges as two
signals as Approximations (A) and Details (D). The
approximations are part of low pass filter, high-scale and low
frequency components of the signal. The details are part of high
pass filter, low-scale, and high frequency components.
Normally, the low frequency content of the signal
(approximation, A) is the most important part. It demonstrates
the signal identity. The high-frequency component (detail, D) is
nuance. The decomposition process can be iterated, with
successive approximations being decomposed in turn, so that
one signal is broken down into many lower resolution
components (Figure 1). Thus, DWT allows one to study different
investigating behaviours in different time scales independently
(Rajaee et al., 2011).
Decomposition level is generally based on signal characteristics
and experiences to selection. Mohammad, (2012) used int[lgn]
as resolution level number, where n is the length of daily stream
flow sequences and lg denotes the logarithm to base 10. The P
may be selected from the range of 2 and int[lgn], that is, 2 ≤ P ≤
int[lgn]. Based on this concept, three decomposition levels were
used in this study. In this study, wavelet function derived from
the family of Daubechies wavelets with order 5 (db5) used for
the selection of best architectures of ANN.
Based on the physical knowledge of the problem and statistical
analysis, different combinations of antecedent values of the
inflow, rainfall and stream flow time series were considered as
input nodes. The output node is the inflow data to be predicted in
one step ahead. The time series data of all variables was
standardized for zero mean and unit variation, and then
normalized into 0 to 1. The activation function used for the
hidden and output layer was logarithmic sigmoidal and pure
linear function respectively. For deciding the optimal hidden
neurons, a trial and error procedure started with two hidden
neurons initially, and the number of hidden neurons was
increased up to 10 with a step size of 1 in each trial.
2.1 Method of combining wavelet analysis with ANN
The decomposed details (D) and approximation (A) were taken
as inputs to neural network structure as shown in Figure 2. To
obtain the optimal weights (parameters) of the neural network
structure, Levenberg–Marquardt (LM) back-propagation
algorithm has been used to train the network. A standard MLP
with a logarithmic sigmoidal transfer function for the hidden
layer and linear transfer function for the output layer were used
in the analysis. The number of hidden nodes was determined by
trial and error procedure. The output node will be the original
value at one step ahead.
3. STUDY AREA AND DATA
In the present study, the daily data of rainfall, stream flow at
Khanapur gauging station and reservoir inflow for 11 years
(from 1986 to 1996) were used to forecast the inflow in
Malaprabha reservoir. The model was calibrated using 7 years of
data from 1986 to 1992 and validated by using the remaining 4
years of data from 1993 to 1996.The input vectors to models are
selected based on the procedure described by Sudheer et al.
(2002). The following data sets identified as input neurons to
ANN and WNN model were examined (i) daily inflow (at t 0 and
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t0-1), daily rainfall (at t0) and daily stream flow at Khanapur
gauging station (at t0) [4 input nodes] (ii) daily inflow (at t 0 and
t0-1), daily rainfall (at t0) and daily stream flow at Khanapur
gauging station (at t0 and t0-1) [5 input nodes] (iii) daily inflow
(at t0, t0-1and t0-2), daily rainfall (at t0) and daily stream flow at
Khanapur gauging station (at t0 and t0-1) [6 input nodes].
4. MODEL EVALUATION
To find out the optimal model developed in estimating reservoir
inflow, different statistical indices are introduced. The indices
employed are the coefficient of correlation (R), root-meansquare error (RMSE) between the observed and forecasted
values and the coefficient of efficiency (Nash-Sutcliffe) (COE).
For the above application, the data is divided into training and
testing data sets. In this application, the first 7 year daily data
(from 1986 to 1992) are used for training and the remaining 4
year (from 1993 to 1996) are used for testing. The standardized
observed data was taken as input to ANN. ANN was trained
using backpropagation (BP) with LM and Radial basis (RB)
neural network algorithms. The optimal number of hidden
neurons were determined by trial and error procedure. Table 1
shows the performance of ANN models for different datasets of
inputs in calibration and validation periods. The decomposed
data of different datasets of inputs was taken as input to ANN
which makes the WNN. The number of hidden nodes were
determined by trial and error procedure and the performance of
these were shown in Table 1. From this table, the best performed
architectures of WNN (20-3-1) was selected.
inflow values of Malaprabha reservoir. Daily rainfall, antecedent
inflow values and stream flow data at upstream gauging station
used in this study. The observed time series are decomposed into
sub-series using discrete wavelet transform and then appropriate
sub-series is used as inputs to the neural network and regression
models for forecasting the reservoir inflow. Model parameters
are calibrated using 7 years of data and rest of the data is used
for model validation. The results were compared with the
standard ANN. From this analysis, it was found that efficiency
index is more than 97% for Wavelet based NN and regression
models whereas it is 88% and 86% for ANN and regression
models respectively. It may be noted that hydrological data used
in the WNN model has been decomposed in details and
approximation, which may lead to better capturing the rainfall
and runoff processes.
Table 1. The performance statistics for the calibration and
validation period
Mo
del
D
at
a
se
t
AN
NBP
i
(4
)
WN
NBP
i
(1
ii
6)
(5
)
ii
(2
iii
0)
(6
)
Iii
(2
4)
No.
of
Hid
den
neu
ron
s
3
RMSE
(cumecs
)
19.78
Validation
R
COE(
%)
0.962
92.60
6
4
18.21
9.21
0.968
0.992
93.73
98.39
3
3
18.86
9.81
0.966
0.991
93.27
98.18
3
8.57
0.993
98.61
R
M
S
E
29
.2
6(c
u
18
m
.1
29
ec
3.4
9s)
18
.0
29
7.3
2
20
.5
4
CO
E(
%)
88.2
2
R
0.953
0.955
0.978
95.4
88.0
84
0.951
0.978
95.5
88.1
17
0.974
94.2
Table 2. Statistical moments of the observed and modeled
0
inflow during validation period
Parameter
An analysis to assess the potential of each of the model to
preserve the statistical properties of the observed inflow series
was carried out for each year of validation period and shown in
Table 2. From Table 2, it was revealed that inflow series
computed by WNN model with dataset (iii) reproduces the first
three statistical moments (i.e. mean, standard deviation and
skewness) better than that computed by the other models. The
maximum value in the testing period is fairly well estimated by
the WMLR method. Table 2 shows that the percentage error in
annual peak flow estimates for the validation period for all
models and found that the WNN model improves the annual
peak flow estimation and the error was limited to 13.4%. It was
also observed that the peak flow estimation by wavelet based
models is much better (% error is less than 21) than ANN. The
error plots for these models in validation period are shown in
figure 3. From Figure 3, it is obviously seen that the peaks could
be estimated closely by the WNN model. From this analysis, it
was worth to mention that the performance of wavelet based
WNN models was much better than ANN models in forecasting
the reservoir inflow in one-day advance.
The main purpose of the study presented is to examine the
applicability and generalization capability of the wavelet based
neural networks with back propagation for forecasting the
MANIT Bhopal
Year
Observed
WNN
ANN
1993
35.98
34.82
30.64
1994
60.84
56.54
46.32
1995
24.42
22.84
21.37
1996
24.23
22.96
21.79
1993
78.45
75.40
64.96
1994
126.63
121.17
99.94
1995
58.05
50.49
50.37
1996
51.71
47.65
44.41
1993
4.22
4.73
4.58
1994
3.83
4.28
4.05
1995
5.22
4.81
5.18
1996
3.48
3.35
3.67
1993
669.58
-11.5
1.1
1994
1016.00
-1.1
25.3
1995
567.53
21.5
18.3
Mean
Standard
Deviation
skewness
6. SUMMARY
Calibration
% Error in
Peak
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1996
382.90
18.7
18-19, Dec. 2014
10.4
Figure 3. Distribution of error plots along the magnitude of flow during
validation period
REFERENCES:
i.
Cannas, B., Fanni, A., Sias, G., Tronei, S., Zedda, M.K., 2005. River
flow forecasting using neural networks and wavelet analysis. In: EGU 2005,
European Geosciences Union, Vienna, Austria, 24–29 April, 2005.
ii.
Cannas, B., Fanni, A., See, L. & Sias, G. (2006). Data preprocessing
for river flow forecasting using neural networks: Wavelet transforms and data
partitioning. Physics and Chemistry of the Earth, PartsA/B/C, 31(18): 11641171.
iii.
Mohammad Nakhaei and Amir Saberi Nasr, (2012). ―A combined
Wavelet- Artificial Neural Network model and its application to the prediction of
groundwater level fluctuations‖ JGeope 2 (2), 2012, P. 77-91
iv.
Okkan, U. (2012) ―Wavelet neural network model for reservoir
inflow prediction‖, Scientia Iranica, 19(6), pp.1445-1455.
v.
Rajaee, T., Nourani, V., Mohammad, Z.K. and Kisi, O. (2011). ―River
suspended sediment load prediction: application of ANN and wavelet
conjunction model‖, Journal of Hydrologic Engineering, 16(8): 613-627.
vi.
Sudheer, K.P., Gosain, A.K., Rangan, D.M, Saheb SM. 2002.
Modeling evaporation using an artificial neural network algorithm.
Hydrological Processes 16: 3189–3202.
Figure 1. Diagram of multiresolution analysis of signal
Figure 2. Wavelet based multilayer perceptron (MLP) neural network
Application of Particle Swarm Optimization in
Multiobjective Irrigation Planning
D V Morankar1 , K Srinivasa Raju2 , A Vasan3, L
Ashoka Vardhan4
1
Faculty of Civl Engineering, College of Military Engineering,
CME(PO) Pune 411031
2,3,4
Centre of Excellence in Water Resources Management,
Department of Civil Engineering
Birla Institute of Technology and Sciences, Pilani Hyderabad
Campus, Hyderabad-500078
ABSTRACT: Particle Swarm Optimization (PSO) is applied to
the case study of Khadakwasla Complex reservoir system,
MANIT Bhopal
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Maharashtra, India in multiobjective irrigation planning
environment. Three objectives, namely, Annual Net Benefits
(ANB), Annual Crop Production (APD) and Annual Labour
Employment (ALE) are considered in maximization
perspective for 90% dependable inflow level scenario with
groundwater. Uncertainty in objectives is tackled through
nonlinear membership functions which are also used as the
basis to formulate the problem in multiobjective environment.
It is observed from result analysis that ANB, APD, ALE in
multiobjective environment respectively are `1458.12 Million,
1.30 Million tons, 4.74 Million man-days with degree of
satisfaction 0.26.Various combinations of PSO parameters
such as randomness amplitude of roaming particles (  ),
speed of convergence (  ), randomness control parameter
(  ), inertia (θ), penalty value and population of particles were
tried and the optimal set of  ,  , θ respectively are arrived at
0.10, 1.17, 0.28. Sensitivity analysis is performed to study the
influence of population size, number of iterations, penalty
value on ANB, APD, ALE, degree of satisfaction, α, ω, θ and
CPU Run Time (CPURT). It is observed that CPURT
increases with increase in population, number of iterations,
while it is almost constant with increase in penalty. ANB shows
no appreciable change with increase in population, with
increase in number of iterations however, it decreases with
increase in penalty.
Keywords: PSO, Optimization, reservoir system, irrigation
planning, membership function.
1. INTRODUCTION
Irrigation planning is becoming complex due to increase in
irrigation, municipal and industrial demands and dwindling
supplies. The problem becomes aggravated in multiobjective
situations where more than one objective is to be satisfied
simultaneously. An optimization approach is thus essential to
achieve efficient cropping pattern, reservoir operating policies in
the multiobjective framework. On the other hand, Particle
Swarm Optimization (PSO) is gaining familiarity in
multiobjective environment due to its flexibility and handling
practical problems (Morankar, 2014). Numerous authors studied
irrigation planning in multiobjective environment. Some of the
studies are as follows:
Raju and Nagesh Kumar (2000) analyzed the irrigation planning
problem in multiobjective framework with net benefits,
agricultural production and labour employment as objectives for
the case study of Sri Ram Sagar Project, India. Objectives were
considered as fuzzy in nature. Sahoo et al. (2006) developed
linear programming and fuzzy optimization models for planning
and management of available land-water-crop system of
Mahanadi-Kathajodi delta in eastern India. The models were
used to optimize the economic return, production and labour
utilization, and to arrive at the related cropping pattern. Consoli
et al. (2008) proposed minimization of reservoir release deficit
to meet the irrigation demands and the maximization of net
benefits from Pozzillo reservoir, Eastern Sicily. They used
nonlinear programming, constraint method and interactive
analytical step method to find the best compromise solution. It
was concluded that the interactive approach allows improving
the performance of the reservoir. Deep et al. (2009) developed
fuzzy interactive method for efficient management of
multipurpose multireservoir problems and applied to a realistic
multipurpose multireservoir. Two objectives, namely, irrigation
and hydropower generation were considered in fuzzy
environment. These objectives were combined into a single
objective using the product operator and nonlinear optimization
was adopted using Genetic Algorithm. It was concluded that the
interactive approach was found to be satisfactory. Yang and
Yang (2010) applied an interactive fuzzy satisfying method to
solve multiobjective optimization problem for the case study of
Yellow River Delta, China. Mirajkar and Patel (2013) applied
multiobjective fuzzy linear programming approach to a case
study of Ukai irrigation project Gujarat, India. Four objectives
were considered. The model was solved for four situations of
90%, 85%, and 75% and 60% exceedance probability. It was
concluded that probable inflow corresponding to 75%
exceedance probability was marginally sufficient to meet the
requirements of the study area.
No efforts have been made till now to explore Particle Swarm
Optimization in multiobjective fuzzy irrigation planning
environment for a real world environment. Keeping this in view,
present study adopts nonlinear membership function in PSO
environment to deal with uncertainty aspects in objective
functions. The main outcome from solution methodology is
reservoir operating policy, cropping pattern, ANB, APD, ALE in
compromise solution and degree of satisfaction. Following
sections/subsections describes particle swarm optimization,
mathematical modeling followed by results and discussion
which includes sensitivity analysis.
1.1 Particle Swarm
Membership Function
Optimization
and
Nonlinear
Particle Swarm Optimization (PSO) is a metaheuristic
computational procedure (Kennedy and Eberhart, 1995;
Morankar, 2014) which simulates the locomotion of swarm
based organisms. PSO iteratively tries to improve a solution by
moving the potential solutions called particles, through the
solution space by directing them towards the present iterations
optima and global optima throughout all iterations. Here, the
particles keep track of their past coordinates thus keeping track
of the swarms best solution (fitness) achieved so far and use this
for altering direction and speed in the next iteration. The
swarm‟s best position in the entire search domain is assumed to
be gbest and last generations best position is pbest. In every
iteration, each particle location is altered based on its current
position (x), velocity (v), distance between itself and pbest, and
the distance between itself and gbest which can be summarized
by the following equation:
vijk 1   vijk  n1 ( pijk  xijk )  n2 ( gijk  xijk )   n3
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xijk 1  xijk  vijk 1
(2)
Where, i = number of particles, j = number of decision variables,
k = iteration count, g = gbest particle, p= pbest particle, θ =
inertia, n q where q=1, 2, 3 is Gaussian distributed random
complex project has three storage reservoirs, Panshet,
Warasgaon, and Temghar with the gross storage of 871Mm3,
New Mutha Right Bank Canal (NMRBC) serving 62146 ha
command area (of length 202 km), Janai-Sirsai Lift Irrigation
Scheme (JSLIS) (14080 ha command area), Purandar Lift
Irrigation Scheme (PLIS) (25100 ha command area) (refer Fig
2.)
variable ranged between 0 and 1,  = randomness amplitude of
roaming particles,  = speed of convergence,  = randomness
control parameter.
The inertia factor is used for refining the swarm‟s behavior
towards the magnitude of the search domain. The velocity of the
particles is directly proportional to inertia; larger values of
inertia increases the search domain while smaller values of
inertia narrow the scope of search. The velocity of all the
particles significantly reduces as the iteration count increases
resulting in initial rapid search for optima in the beginning
followed by a convergence towards the end. Number of
iterations was specified as termination criteria.
Nonlinear membership function for any objective function Z can
be expressed as (Fig 1):
0


 Z  Z L 
 Z  X   

 Z U  Z L 
1

for
Z  ZL
for Z L  Z  Z U
for
Z  ZU
(3)
Where β provides the basis for desired shape of membership
function (β=1 for linear; β >1 and β <1 for nonlinear) and Z U, ZL
are maximum and minimum acceptable levels of the objective.
Introducing a new variable  , the problem in multiobjective
environment is stated as (Raju and Nagesh Kumar, 2014):
Maximize λ
Subject to

GJ
(X )



for each objective function j =1,2,..,n
0    1
(4)
G
represents the membership functions for objective. Higher 
and all other existing bounds and constraints. Here
Figure 2. Schematic diagram of Khadakwasla Com
The Pune city generates around 451 Million Liter per Day
(MLD) of sewage. Pimpri-Chinchwad city generates 287 MLD
of sewage (Tirthakar et al., 2009). At present, 68% of the total
sewage generated by PMC and 63% sewage generated by Pimpri
Chinchwad Municipal Corporation (PCMC) is treated before
being discharged into the rivers. This water is proposed to be
used for irrigation through pumping in existing canal system and
an independent lift irrigation scheme, PLIS. The model
developed adopts conjunctive use concept; in addition it also
uses treated waste water as a supplement to irrigation water.
3. MATHEMATICAL MODELING
Three objectives are considered in the present study. The first
objective of the model is to maximize the Annual Net Benefits
(ANB) from the Khadakwasla, JSLIS and PLIS, after meeting
the cost of groundwater. The Annual Net Benefits include
irrigation benefits, revenue generated from domestic water
supply to PMC and water supplied to industries. Annual Net
Benefits are expressed in ` as:
36
12
i 1
t 1
12
12
t 1
t 1
ANB   Bi Ai  PGW  GWt PDW  DWt PIND  INDt
(5)
J
value is desirable. Morankar et al.(2013) and Morankar (2014)
discussed nonlinear membership function in detail.
2. CASE STUDY
The second objective is to maximize Annual Crop Production
(APD) of crops under Khadakwasla, JSLIS and PLIS and
expressed (in tons) as:
36
Khadakwasla project is meant for providing irrigation facility to
the scarcity areas of Pune district as well as drinking water
supply to Pune Municipal Corporation (PMC), Pune
Cantonment, Daund Nagar Palika and surrounding villages
(Water Resources Department, 2008). The water stored is also
utilized for industries in and around Pune. The Khadakwasla
Figure 1. Nonlinear Membership Function
APD   PDi Ai
i 1
(6)
The third objective is to maximize Annual Labour Employment
(ALE) so that the employment generated can minimize the
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migration. Annual labour employment (in man-days) is
expressed as:
36
ALE   LEi Ai
i 1
(7)
Ai = Area irrigated under ith crop (i=1,2,3…,36; crops are listed
in figures 3, 4, 5 respectively for Khadakwasla, JSLIS and
PLIS); t = Index of the month in water year, t = 1,..,12;
(1=June,..,12=May); PGW  Cost of groundwater pumping
(`/Mm3); PDW , PIND  Revenue from drinking water (domestic
water supply) and industrial water supply respectively (`/Mm3);
Ai  Area of ith crop in ha; Bi  Return from crop i (cost of
seed, fertilizer, pesticides, labour charges, implements, interest
on capital is considered in working out the benefit Bi for the
crop i) ( ` / ha); DWt , INDt  Domestic and industrial water
supply in Mm3 for the month t (22.93 and 1.18);
PDi  Production of crop i (tons/ha); LEi  Labour
employment of crop i ( man-days/ha);. GWt  Groundwater use
in Mm3 for the month t. Seasons in which these crops grown are
Kharif (K), Rabi (R), Hot Weather (HW), Two Season (TS).
The model is subjected to the constraints representing limitations
of the project resources and the relation within various
parameters. This includes mass balance equation on monthly
basis, land requirements of crop, water requirements of crop,
canal capacity, minimum and maximum reservoir storage,
groundwater withdrawal, etc. Mathematical expressions of the
constraints are not presented due to space limitations.
A scenario of 90% dependent inflow with groundwater is
developed as water scarcity scenario (drought situation), using
nonlinear membership function (equations 3 and 4) for three
objectives ANB, APD and ALE for PSO (from now termed as
PSO-NM). Initially the mathematical model is solved,
independently for three objectives using linear programming
approach which gives the upper and lower bound for the
respective objective functions. These bounds are used in forming
nonlinear membership function for each objective. A computer
program is developed in MATLAB (www.mathworks.com)
environment for the solution of PSO problem. The optimal set of
parameters governing PSO has been established after numerous
trials, discussion with experts and referring to the available
literature. Swarm size of 1000 particles, number of iterations of
5000, penalty function value of 2000 and randomness control
parameter (γ) value of 0.005 are chosen after such process.
Randomness amplitude of roaming particles (α), speed of
convergence (ω) and inertia are assigned values randomly from
0 to 5 and are iterated for about 100 runs. The output (α, ω,
inertia) with best fitness was averaged and values of α, ω, inertia
arrived after such process are 0.10, 1.17, and 0.28. These
parameters were used for further process.
Typical results are presented in the form of graphs. Figures 3, 4,
5 give cropping pattern suggested by the PSO solution for
Khadakwasla, JSLIS and PLIS. It is observed that:
 Area under each crop is less than the corresponding
area in existing cropping pattern, except for Groundnut (HW).
 Area under crop for Khadakwasla command is 44671
ha, and the intensity of irrigation is 43.93%, which is 17.18%
less than the existing irrigation intensity of 61.11%.
 Area under crop for JSLIS command is 17231 ha,
indicating 8.84% increase in existing intensity of irrigation of
78.6%. There is increase in command even in case of drought
situation.
 PLIS command shows decrease in intensity of irrigation
by 36.74 % with crop area and intensity of irrigation as 10595 ha
(26.38%). Existing intensity of irrigation is 63.12%.
 Overall intensity of irrigation is observed to be 44.88%
with total irrigable area as 72497 ha. This shows decrease in
intensity of irrigation by 18.86% over existing intensity of
irrigation of 63.74%. This decrease is in expected lines as
amount of surface water is less.
 The annual net benefits, annual crop production and
annual labour employment is `1458.12 Million, 1.30 Million
tons, 4.74 Million man-days with corresponding degree of
satisfaction  value of 0.26.
Figures 6, 7 and 8 shows monthly irrigation water use policy for
Khadakwasla and JSLIS, monthly groundwater use policy for
Khadakwasla and Treated Waste Water use policy for
Khadakwasla/JSLIS and PLIS respectively. It is observed that,
total water requirement of crops in command area is 423.05
Mm3. Of the total demand, only 33.77 % (142.9 Mm3) is
satisfied by canal water, 39.36 % (166.52 Mm3) is satisfied by
treated waste water and 26.85 % (113.63 Mm3) is satisfied by
groundwater. This outcome clearly suggests lesser availability of
irrigation water, which is compensated partially by treated waste
water and groundwater.
4.1
Sensitivity Analysis
Sensitivity analysis is performed to study the influence of
population size, number of iterations, and penalty value on
ANB, APD, ALE and λ, randomness amplitude of roaming
particles (α), speed of convergence (ω) and the weighting
function (inertia), CPU Run Time (CPURT). Population size
chosen for sensitivity analysis is 100, 200, 500, 1000, 1500 and
2000 whereas these are 500, 1000, 2000, 5000, 7000 and 10000
in case of number of iterations and 500, 1000, 2000,5000,10000
and 20000 in case of penalty values. Each time only one
parameter was changed keeping the other two values constant.
However, selected combinations of population size, number of
iterations and penalty are studied and discussed in Table 1. Table
1 shows the outcome of sensitivity runs. Important observations
emanated are:

Variations in population size show a random trend of
change in λ. λ increases with increase in number of
iterations and decreases with increase in penalty.
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CPURT increases with increase in population, number
of iterations, while it is almost constant with increase in
penalty. There is substantial increase in CPURT with
increase in number of iterations.
Figure 6. Monthly Irrigation Water Use Policy
Figure 7. Monthly Groundwater Use Policy
Figure 8.Monthly Treated Waste Water Use Policy
Figure-3.KhadakwaslCroppingPattern
Figure 4. JSLIS Cropping Patter
*K, R, TS, HW, P, TP denotes Kharif, Rabi, Two Seasonal, Hot
Weather, Perennial seasons and Transplanted respectively
Table 1. Influence of Population Size, Number of
Iterations and Penalty value on λ , α, ω, Inertia ,
ANB,APD and ALE

Figure.5. PLIS Cropping Pattern


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α value has minimum variation in between 0.08 and
0.13.
There is no definite trend of variation of ω with
population, number of iterations and penalty value. The
overall range of variation of ω value is small (0.06).
Inertia value varies in a small range of 0.26 and 0.32,
with a change in population, number of iterations and
penalty value.
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

18-19, Dec. 2014
ANB shows no appreciable change with increase in
population and iterations, while it decreases with
increase in penalty.
APD and ALE shows no specific trend with change in
population, iteration and penalty.
5 CONCLUSIONS
Irrigation intensity reduces by 18.86% as compared to existing
intensity of 63.74%. This stresses the need of integrated water
resources management in water scarce situations. There should
be reduction in irrigable command at annual irrigation planning
stages itself in drought situation, so that the farmers at later stage
do not face the water crisis. PSO parameters need to be
established with more runs in high dimensional environment.
The model developed is generalized in nature and any given
situation can be extended with minor modifications.
ACKNOWLEDGEMENTS
Authors are grateful to all the officials of Pune Irrigation Circle,
Pune, GSDA, Pune and Agriculture Directorate, Pune Division
for providing necessary data, practical inputs and encouragement
for the study and thankful to professors and officials at Mahatma
Phule Krishi Vidyapeeth, Rahuri, Maharashtra for providing
valuable inputs.
REFERENCES
i.
Consoli S, Matarazzo B, Pappalardo N (2008) Operating rules of an
irrigation purposes reservoir using multiobjective optimization.Water
Resources Management 22(5):551-564
ii.
Deep K, Singh KP, Kansal ML, Mohan C (2009) Management of
multipurpose multi-reservoir using fuzzy interactive method.Water Resources
Management 23(14): 2987-3003
iii.
Kennedy J, Eberhart R (1995) Particle swarm optimization.
Proceedings of IEEE International Conference on Neural Networks. IV:1942–
1948
iv.
Mirajkar AB,Patel PL (2013) Planning with multi-objective fuzzy
linear programming for ukai–kakrapar irrigation project, Gujarat, India.
Canadian Journal of Civil Engineering 40(7): 663-673
v.
Morankar DV, Srinivasa Raju K, Nagesh Kumar D (2013) Integrated
sustainable irrigation planning with multiobjective fuzzy optimization
approach. Water Resource Management 27(11):3981-4004
vi.
Morankar DV(2014) Fuzzy based approach for integrated planning
and performance evaluation of an irrigation system. PhD thesis, Birla Institute
of Technology and Science, Pilani, India.
vii.
Raju KS,Nagesh Kumar D (2000) Irrigation planning of sri ram
sagar project using multiobjective fuzzy linear programming. Indian Society of
Hydraulics 6(1):55-63
viii.
Raju KS,Nagesh Kumar D (2014) Multi-criterion analysis in
engineering and management. PHI learning private limited, New Delhi.
ix.
Sahoo B, Lohani AK, Sahu RK (2006) Fuzzy multiobjective and linear
programming based management models for optimal land-water-crop system
planning. Water Resources Management 20(6): 931-948
x.
Tirthakar SN, Deshpande MS,Nirbhawane PS (2009) Master plan
2025 of pune municipal corporation for sewage treatment and disposal.
Institution of Public Health Engineers 2(2):13-19
xi.
Water Resources Department (2008) Khadakwasla complex project
note, Government of Maharashtra
xii.
Yang W,Yang Z (2010) An interactive fuzzy satisfying approach for
sustainable water management in the yellow river delta, china. Water
Resources Management 24(7): 1273-128
Artificial Neural Network Model for Design of Air
Vessel for Controlling the Water Hammer
Pressures
N.Mowlali1, E.Venkata Rathnam2
M. Tech Student, Water Resources Engineering, National
Institute of Technology, Warangal
2
Associate Professor, Department of Civil Engineering, National
Institute of Technology, Warangal
1
ABSTRACT: Air vessels, surge tanks, pressure relief valves,
are some of the mostly used devices for controlling the water
hammer pressures which may causes from sudden change in
velocity due to sudden operation of gate valves in hydroelectric
power schemes and or tripping of power to pumps in hydraulic
conveyance systems. Graphical and other heuristic methods
are available in the literature for the design of air vessels
which are generally installed on the downstream of pumps.
The air vessel design variables include initial air volume, total
volume of water and total air vessel volume. The paper
presents a regression based artificial neural network (ANN)
model for investigating optimised values of air volume and
vessel volumes from the system parameters viz., pipeline
length, pipe diameter, flow velocity, friction factor, wave
celerity, maximum and minimum pressure heads. The system
parameters were used as input variables and the corresponding
air vessel volume as output variable to train the neural network
model. The training has been done by feed forward back
propagation algorithm. The ANN model developed in the study
has one input layer (8 system parameters), ten hidden layers
(log sigmaoid function) and one output layer (air vessel
volume). The trained neural network model was applied to
large conveyance system (Pumping main of Devadula Lift
Irrigation Project) to obtain optimal air vessel volume. The
neural network model predictions were compared with the sizes
obtained from application of software, SAP2 (Surge Analysis
Package version2.0) and observed that ANN models provides
economical sizes.
Key words: water hammer, air vessel, ANN, regression, system
parameters
1.0 INTRODUCTION
Transient protection of water conveyance systems may require
use of devices such as open surge tanks, air vessels, air/vacuum
valves, pressure relief valves etc. Selection and design of
suitable transient protection devices is dictated by the severity of
transient causing events. Design of transient protection systems
is a challenging problem and selection, installation, and
operation of these hydraulic devices depend on the layout,
alignment, pipe and pump characteristics and flow rates. Air
vessels, also known as closed surge tanks, are effective in
protecting the pipe system against negative as well as positive
pressures (Stephenson 2002). Typical arrangement of an air
vessel, shown in Figure 1 consists of three components (i) the
vessel (ii) the connector pipe and (ii) inlet and outlet orifices
controlling flow to and from air vessel. Decision variables
associated with optimal sizing of air vessels are total volume of
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air vessel, initial gas volume, inflow resistance, outflow
resistance, and a polytrophic exponent. The resistance (R) is
defined as
R  H / Q 2
(1)
Where, ∆H = head drop in m; Q = flow rate in m3/s. The
resistance (R) is correspond to the orifice sizes (and pipe
diameters) that are provided for inflow and outflow from pipe
system to air vessel. Inflow resistance governs the rate of flow
into the pipe system where as outflow resistance governs the rate
of flow into the air vessel. Value of polytrophic exponent or the
gas expansion constant has significant influence on the required
air vessel size. The air vessel design problem can be stated as a
constrained optimization problem in which objective is to find
total volume of air vessel and initial air volume and the
constraints to be considered as range of negative and positive
water hammer pressures. For a typical cylindrical and
vertically mounted vessel the design variables are vessel
volume C = hS, and initial air volume, which can be given by the
initial height of air into the vessel(ZR). The typical sketch of air
vessel is shown in Fig.1. The following physical, functional and
fluid parameters dictate the size (volume) of air vessel for a
given problem. (i) Physical parameters of the pipe: static or
geometric elevation to overcome (H),Length(L), diameter(D),
friction factor(f); (ii) Fluid-pipe mixed parameters: celerity of
the pressure waves (a); (iii) Parameters related to the steadystate: velocity(VR); (iv)Functional parameters that represent the
extreme piezometric heads or pressures desirable at the upstream
(without loss of generality) end: Hmax and Hmin.
required. As a tool to determine such a volume, it represents an
important time saving aid for users. For the training of the neural
network, the input data taken are representative patterns of the
above mentioned parameters, together with the suitable volumes
obtained following the trial and error process mentioned above.
1.1 WATER HAMMER EQUATIONS
Equations (2) and (3) shown below are two basic water hammer
equations (Wood et al. 2005, Almeida and Koelle 1992, Wylie
and Streeter 1993).
Continuity equation
H a 2 Q

0
t gA x
(2)
Momentum equation
H
1 Q

 f (Q)
x
gA t
(3)
Where Q = flow rate, H = pressure head, f(Q) = friction slope
expressed as a function of flow rate, A = pipe flow area, a =
pipe celerity or wave speed, g = gravitational acceleration, x and
t the space-time coordinates. Advective terms are neglected in
the above equations as they are negligibly small for most water
distribution problems of practical importance. Solution of
Equations (1) and (2) with appropriate boundary conditions will
yield head (H) and flow (Q) values in both spatial and temporal
coordinates for any transient analysis problem. The above
equations are first order hyperbolic partial differential equations
in two independent variables (space and time) and two
dependent variables (head and flow).
2.0 Study Area
Fig.1. Schematic sketch of Air Vessel (Source: Stephension, 2002)
There is no explicit and direct relationship between these
parameters and the size of air vessel required. Sever approaches
based on different tests and heuristic criteria can be found in the
literature. However, professionals find that air
vessel
optimization is usually a trial and error process, generally
performed the transient simulation using surge software that,
eventually, provides the minimum air vessel volume required so
that the maximum and minimum developed pressures at the
pumping station do not exceed Hmax and Hmin respectively. A
neural network that encapsulates this unknown trial and error
process from a relevant number of cases, already solved, that
allows to directly obtaining the minimum air vessel volume
J. Chokka Rao Godavari Lift Irrigation scheme has been
envisaged to lift 14m3/s of Godavari water to EL. 308 m &
partly up to EL. 540 m to irrigate approximately 2.85 Lacks
Acres of Command area. Project Envisages Lifting of water
from Godavari River at Gangaram village, Eturnagaram,
Warangal District in Telangana state in 7 stages with for water
conductor system 200.340 Kms approximately, long steel
pipelines connecting 8 Nos. of existing tanks (i) intake to
Dharmasagar via., Bhimghanpur, Salivagu (ii) from R.S.
Ghanpur to Chittakodur via., Aswaraopalli, (iii) from
Dharmasagar to Tapashpally via., Gandiramaram, Bommakur.
The hydraulic details of the lift irrigation project are provided in
table1. The transmission line alignment and steady state
hydraulic gradient line for the pumping discharge of 14m3/s is
shown in Fig.2.
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Table1. Transmission line and Pump details
Design discharge of transmission
line
Length of transmission line
Diameter
and
thickness
of
transmission line
Pipe material
Internal lining material
External coating/guniting thickness
Low water level and minimum
14 m3/s
38252m
Diameter=3m,
Thickness=16mm
Steel
Epoxy
25mm
RL 71.0m and
RL
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Issue Special3
water level at intake
Discharge level at upper reservoir
Number of parallel pumps
Pump rated head and rated
discharge
18-19, Dec. 2014
93.5m
RL 166.94m
2
Head=131m,
discharge=7m3/s
non-linear), called transfer or activation function, such as a
sigmoid or a hyperbolic tangent, etc.
Table2. Transient Pressures and size of Air Vessel from
results of SAP2.0
Location
Pump1
delivery
Pump2
delivery
Transmission
line
Transmission
line
Transmission
line
Transmission
line
Transmission
line
Transmission
line
Transmission
line
Transmission
line
Transmission
line
Diameter
(mm)
Transmission
line
chainage
(m)
Transient
Pressures
Hmax(m) Hmin(m)
Air
vessel
volume
(m3)
2000
0
210.06
97.33
2000
0
210.06
97.33
3000
35.8
210.02
94.89
3000
4972.8
204.45
136.36
3000
9945.5
198.53
139.87
3000
15109.5
191.99
140.82
360.4
368.2
368.2
384.7
The most frequent learning method for the multilayer
perceptron is called “generalized delta rule” or “back
propagation” of error. This type of learning is called
supervised since to be performed it is necessary to provide
the network with the correct answer that the output layer has
to produce for a number of cases already solved. For the
network to learn correctly, the output ZK produced by the
network should be close to the correct response, t K, called
target, which will be provided to the network during the learning
phase. This is achieved by adjusting the weights associated to
the links (synapses) between units (neurons) and the links
between certain inputs in the units called biases. We will call wJI
the weights of the hidden layer and w , those of the output
layer. The biases will be, 𝜃𝑗 𝑎𝑛𝑑 𝜃′𝑘. The performance of the
network can thus be expressed by equation(4).
378.7
372.3
3000
20273.6
184.24
137.25
3000
25246.3
178.18
159.09
355.6
Being the activation function of a sigmoid, such as the
following:
f ( x) 
349.9
3000
30984.1
173.22
168.91
3000
35765.6
169.09
165.01
3000
38252
166.94
166.94
340.3
328
308.2
1
orf ( x)  tanh(x)
1  e x
(4)
The generalized delta rule performs the adjustment of the
weights by calculating the value of the error for a specific
input and then transfers it, by back propagation (BP), to
previous layers, so that each unit adjusts its associated weights to
minimize an error function These steps are repeated for each
input pattern of the so-called training set, what is known as
online learning. Alternatively, if the updating of the weights is
performed upon presenting all the training patterns to the
network, the process is known as batch learning. In any case, the
error function decreases gradually and the network learns.
Given an input pattern xV(v =1,..., p), the components of the
network output, zv are given by.If tv is the target, the correct
output, corresponding to xv , the function to be minimized.
The mean square error
E (Wij , j ,WKJ , K ) 
Fig 2. Longitudinal alignment of pipeline and HGL
3.0 NEURAL NETWORKS
The multilayer perceptron (MLP) is one of the most widely
used feed forward artificial neural networks. This network
consists of a layer (input layer) of inputs, xi, another layer
(output layer) of outputs, zK, and one or more intermediate layers
(hidden layers). Figure 2 which takes into consideration only
one hidden layer with outputs noted by y J, and only one
unit (neuron) in the output layer. Each unit of the hidden
and output layers has a function assigned, f, (which may be
1
  (tv  Z r )2
2 v K
(5)
The minimization can be performed by means of different
algorithms, which range from simple gradient descent
algorithms, to conjugate gradient methods, second order
Newton, which do not require the Hessian matrix, and the
Newton method itself . The next section describes the methods
used and their capabilities to solve the problem under study.
There are several error minimization algorithms (2) that allow
the progressive adjustment of the weights in the learning
process. For this work, we have used some of the functions of
Matlab® Toolbox, nnet. The convergence rate of the different
algorithms depends on the technique used and it is closely
related to the mathematical foundations it is based on network
design.
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net=newff(minmax(Pn[nn1,nn2,1],{tansig,tasig,purelin},trainlm)
;
net.trainparam.show
=50;
net.trainparam.epoches =2000;
net.trainparam.goal
=1e-5;
for i
=1:2;
net.layers{i}.initFcn =initwb;
net.biases{i}.nitFcn =rands;
end;
Once the training data have been correctly loaded in the working
space, any of the functions implemented by Mat lab must be fed
with a number of parameters defining; (a) The design of the
network structure and (b) The training algorithm. A typical set
of commands to perform these tasks is shown above. The first
command creates the neural network ready to be trained within
the object net.In the example corresponding to the figure, the
network is created with three layers; the first one has nn1
neurons, the second nn2 and the third 1. Vector minmax(Pn)
contains the maximum and minimum values of each one of the
input data. The transfer functions are tansig in the first two
layers and linear (the identity functions) in the output layer. The
training function used in the example, trainlm, implements the
Levenberg–Marquardt algorithm.
Once the network structure has been created, some parameters
associated to the training function are initialized., in particular,
defines the number, show, of iterations between two consecutive
displays of the training status; the total number of iterations,
epochs, which will be performed in the process; and a level,
goal, of the error function value (2) to drop below. The two latter
are mechanisms to stop the learning process.Next, it is
performed the initialization of the network weights to random
values ranging from -1 to 1. Different initializations were
performed and very close behaviours of the network were
obtained. Finally, the training function, train, is called by passing
to it the object net, which defines the network, and the matrices
that contain the inputs, Pn, and the targets, Kn, which will be
used to carry out the learning. According to this basic procedure,
several trials have been performed with different networks,
changing the number of layers, the number of neurons per layer
and the training function.
With regard to the training function, the best results have been
obtained, as would be reasonably expected, with some functions
that implement the most powerful optimization techniques. As
far as first order methods concern, the conjugate gradient in its
Polak–Ribiere version (traincgp) has shown excellent
performance. As for second order methods, the Levenberg–
Marquardt (trainlm), which allows quadratic approach, and
therefore is a QuasiNewton method,although it does not require
the calculation of the Hessian matrix, presents also an excellent
behaviour.
3.1 Data Analysis
As mentioned above, to train the network we have used a set of
data from almost 150 real cases previously studied that had been
recorded, and other 150 simulations specifically performed to
cover cases not taken into consideration in those cases. On the
other hand, the set of data has been completed with another
150 patterns obtained from the Graze and Horlacher‟s charts.
These data have been shuffled and distributed into three parts:
training, validation and test data. The networks response can be
assessed to a certain extent by the errors provided by the test
data. The response for the test data has been perfect, as stated
above. Nevertheless, it is interesting to study with more detail
the network s response. One possibility is to carry out a
regression analysis to assess such response. All the training,
validation and test data have been used and a regression
analysis between the values used and the network s output
performed. The Matlab toolbox nnet also provides an
appropriate tool: the postreg function.
a) Before doing the ANN the entire input and target data are
normalised in between 0 and 1
using the following equation.
 ( R  Rb )*( P  A) 
Pa   a
  Rb
( B  A)


(6)
Where; Pa is a matrix of normalized data; Ra is 0.9 and Rb is 0.1;
P is a matrix of raw data;
A is minimum value of
matrix P; B is maximum value of matrix P.
b) ANN is performed by using MATLAB. In this 75% of the
data has been used as training
and 30% data used as testing and validation data.
c) In the present study feed forward back propagation network is
used.
d) MATLAB provides built-in transfer functions which are used
in this study; linar (purelin),
Hyperbolic Tangent Sigmoid (logig) and Logistic Sigmoid
(tansig).
4.0 Regression Based ANN Model
A model has been developed based on the input data used for the
training of neural network model.In this model grid (8 0x 2030)
based key parameters has been used as input. It is normalized by
using the following normalizing factor, The neural network
toolbox in Matlab 7.0 is used for training. The Neural Network
model is three layered network with eight inputs, one hidden
layer, the hidden layer consist of ten neurons to that of one in the
output layer as shown in fig 3. The training has been done by
feed forward backpropogation algorithm. Backpeopagation
algorithm updates the network weights and biases in the
direction in which the performance function decreases most
rapidly. One iteration of this algorithm can be written as
X k 1  X k   k g k
(7)
Where XK is a vector of current weights and biases, gk is the
current gradient, and a is the learning rate. There are two
different ways in which this gradient descent algorithm can be
implemented incremental mode and batch mode. In the
incremental mode, the gradient is computed and the weights are
updated after each input is applied to the network. In the batch
mode all of the inputs are applied to the network before the eight
are updated.
MANIT Bhopal
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TRAINLM is used as training function for the network;
TRAINLM is a training function that updates weights and bias
values according to backpropagation. Trainlm can train any
network as long as its weight, net input, and transfer functions
have derivative functions. Backpropagaton is used to calculate
derivatives of performance with respect to the weight and bias
variables X. Each variable is adjusted according to the following
(8)
dX  deltaX * sign(gX)
Where the elements of deltaX are all initialized to delta0 and gX
is the gradient. At each iteration the elements of deltaX are
modified. If an element of gX changes signs from one iteration
to the next, then the corresponding element of deltaX is
decreased by delta dec. If element of gX maintains the same sign
frm one iteration to the next, then the corresponding element of
deltaX is increased by delta Inc. Log-Sigmoid transfer is used by
the neurons to generate the output. The function log sigmoid
generates output between 0 and as the neuron net input goes
from negative to positive infinity.
The performance of training data is compared by using sum
squared error. The targeted error is set to zero. The network is
simulated by using testing data and the generated data is
compared to observed data at each location by calculating
Regression coefficient. Now the network is ready to be trained.
The samples are automatically divided into training, validation
and test sets. The training set is used to teach the network.
Training continues as long as the network continues improving
on the validation set. The test set provides a completely
independent measure of network accuracy. The NN Training
Tool shows the network being trained and the algorithms used to
train it. It also displays the training state during training and the
criteria which stopped training will be highlighted in green. The
buttons at the bottom open useful plots which can be opened
during and after training. Links next to the algorithm names and
plot buttons open documentation on those subjects.To see how
the network's performance improved during training, either click
the"Performance" button in the training tool, or call
PLOTPERFORM. Performance is measured in terms of mean
squared error, and shown in log scale fig 4. It rapidly decreased
as the network was trained. Performance is shown for each of
the training, validation and test sets. The version of the network
that did best on the validation set is was after training.
Tr
ans
mi
ssi
on
lin
e
Ch
ain
ag
e
(m
)
Input
1
Input 2
Elevat
ion
(m)
Transi
missio
n
length
(m)
0
134.5
0
35.
8
49
72.
8
99
45.
5
15
10
9.5
20
27
3.6
25
24
6.3
30
98
4.1
35
76
5.6
38
25
2
Another measure of how well the neural network has fit the data
is the regression plot. Here the regression is plotted across all
samples. The regression plot shows the actual network outputs
plotted in terms of the associated target values. If the network
has learned to fit the data well, the linear fit to this output-target
relationship should closely intersect the bottom left and top-right
corners of the plot. If this is not the case then further training, or
training a network with more hidden neurons, would be
advisable. The sample data for ANN model is provided in table
3.
Table 3. Sample Data for ANN Model
MANIT Bhopal
In
pu
t3
Di
a
m
ete
r
(m
)
Inp
ut 4
Input
5
Inp
ut 6
Input7
Input 8
Out
put
frict
ion
fact
or
(f)
Wav
e
celeri
ty, C,
(m/s)
Vel
ocit
y,
V
(m/
s)
Hmax (m)
Hmin
(m)
38252
3
0.0
1
837.6
4
1.9
8
210.06
97.33
Opt
ima
l
airv
esse
l
vol
um
e
(m3
)
377
.9
132
38252
3
3
210.02
94.89
118.7
38252
3
1.9
8
1.9
8
1.9
8
97.33
38252
837.6
4
837.6
4
837.6
4
210.06
132
0.0
1
0.0
1
0.0
1
204.45
136.36
116.1
38252
3
0.0
1
837.6
4
1.9
8
198.53
139.87
359
.4
117.6
38252
3
0.0
1
837.6
4
1.9
8
191.99
140.82
341
.9
123.3
38252
3
0.0
1
837.6
4
1.9
8
184.24
137.25
323
.0
118.5
38252
3
0.0
1
837.6
4
1.9
8
178.18
159.09
307
.9
115.0
38252
3
0.0
1
837.6
4
1.9
8
173.22
168.91
294
.0
114.7
38252
3
0.0
1
837.6
4
1.9
8
169.09
165.01
281
.6
118.2
38252
3
0.0
1
837.6
4
1.9
8
166.94
166.94
270
.7
375
.7
375
.7
371
.9
Fig 3 . Three layered Neural Network model
Page 130
Issue Special3
18-19, Dec. 2014
i.
Chandramouli, V., Lingireddy, S., and Brion, G.M. (2007) A Robust
Training
Terminating Criterion for Neural Network Modeling of Small
Datasets, ASCE Jl.of Computing in Civil Engineering.
ii.
Combes, G. and Borot, R. (1952). ―New graph for the calculation of
air reservoirs account being taken of the losses of head.‖ La Houille Blanche,
Grenoble, France, October-November.
iii.
Stephenson, D (2002).‖Simple guide for design of air vessels for
water hammer protection on pumping lines‖, J. Hydr. Eng., ASCE 128 (8) 792–
797.
iv.
Di Santo, A.R., Fratino, U., Lacobellis, V. and Piccinni, A.f. (2002).
―Effects of free outflow in rising mains with air chamber.‖ Journal of Hydraulic
Engineering, American Society of Civil Engineers, 128(11), 992-1001.
v.
Graze, H.R. and Forrest, J.A. (1974). ―New design charts for air
chambers.‖ Fifth Australasian Conference on Hydraulics and Fluid Mechanics,
December.
vi.
Jung, B.S., and Karney, B.W. (2006) ―Hydraulic optimization of
transient protection devices using GA and PSO approaches‖, ACSE Jl. of Water
Resources Planning and Management.
vii.
Kim, C.Y., Bae, G.J., Hong, S.W., Park, C.H., Moon, H.K. and Shin,
H.S. (2001). ―Neural network based prediction of ground surface settlements
due to tunneling.‖ Computers and Geotechnics, 28, 517-547.
Fig4. Performance plot
Monthly Inflow Prediction Using Wavelet Neural
Network
Rutuja Patil 1
Dr. J. N. Patel 2
Dr. S. M. Yadav3
4
Dr. D.G.Regulwar
1
Research Scholor, Civil Engg. Dept.,SVNIT, Surat, India,
[email protected]
2
Proffessor, Civil Engg, Dept., SVNIT, Surat, India,
[email protected]
3
Proffessor,Civil Engg. Dept., SVNIT, Surat, India,
[email protected]
4
Asso. Prof, Civil Engg. Dept., Govt. College of Engineering,
Aurangabad, India, [email protected]
Fig.5 Regression Plot
5.0 CONCLUSIONS
Surge protection devices like Air vessels are necessary for the
pumping mains. Mathematical formulation of water hammer
equations and boundary conditions of air vessel are presented.
A regression based ANN model is demonstrated for sizing the
economical air vessel. A case study of pumping main of Reach-1
in Phase-I of JCR Devadual Lift irrigation project of Telangana
state is considered. The information on ranges of water hammer
pressures occurs while tripping of power to pumps was obtained
using SAP2.0. These system parameters are used to train the
neural network model. The neural network model predictions
were compared with the sizes obtained from application of
software, SAP2 (Surge Analysis Package version2.0) and
observed that ANN models provides economical sizes.
References
ABSTRACT: Prediction of accurate inflow is very important in
optimal reservoir operation and planning. In this paper study
has been carried out for developing Wavelet Neural Network
for predicting one month ahead inflow using different time
lags for reservoir inflows. For illustration of WNN technique
the model has been developed using monthly inflow data of
Jayakwadi Reservoir stage – I, Paithan, Maharashta. Wavelet
neural network is an improved hybrid model which combine
the benefit of discrete wavelet transform and artificial neural
network model. For Wavelet Neural Network model the input
signal have been decomposed into sub series using discrete
wavelet transform up to three resolution level by Daubechies 5
(DB5) wavelet. Summation of detail and approximation of
signal is considered as input to typical three layer feed forward
neural network. Levenberg-Marquardt back propagation
algorithm is used for training the network in which seventy
percent is used for training and thirty percent data is used for
testing. The number of hidden neuron has been fixed to five
for better result by trial and error procedure. The accuracy of
WNN model has been compared with conventional Feed
Forward Neural Network model. When statistical based
evaluation criterion has been observed it was found that WNN
model performed better than ANN model and WNN model can
MANIT Bhopal
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be used as successful tool for predicting monthly reservoir
inflow.
Keywords: Neural Network, Wavelet, Inflow Prediction,
Discrete Wavelet Transform
1. INTRODUCTION
Reservoir prediction plays very important role in optimal
reservoir operation and management. Different methods have
been used for this purpose which includes physical and
conceptual models also. In last decade many data driven
techniques such as Artificial Neural Networks have been
successfully applied for forecasting various hydrological events.
S. K Jain et. al (1999) have studied applicability of ANN in
inflow prediction and reservoir operation. Ahmed El-Shiefie et.
al (2009) have forecasted inflow at Aswan high dam using radial
basis neural network with the use of upstream data. Different
learning algorithms have been used successfully for prediction
of inflow. Ozgur Kisi (2009) have compared different algorithm
such as back propagation, conjugate gradient and cascade
correlation for prediction of one month ahead inflow of
reservoir.
Flipea Prada et. al (2009) have linked the
geomorphologic conditions of basin to weights of ANN
architecture to improve the prediction accuracy. All these studies
were carried out taken into consideration of only time domain
content of signal. But many times the useful information is
hidden in frequency content of signal. So it is necessary that
along with time domain the frequency domain should be taken
into account for better prediction. Due to ability of wavelet to
provide time – frequency information, in recent years many
studies have been carried out combining wavelet transform with
Artificial Neural Network for prediction of hydrologic events.
Wang and Ding (2003) has studied the wavelet network model
and its different application to prediction in hydrology. They
developed a hybrid WNN model for short term and long term
hydrological predictions. Ozgur kisi (2008) have applied a
neuro-wavelet technique for modeling monthly stream flows on
Canakdere River and Isakoy Station on Goksudere River, in the
Eastern Black Sea region of Turkey. Ozgur Kisi (2011) has
investigated the accuracy of the wavelet regression (WR) model
in monthly stage forecasting for same study area. Venkata
Ramana et al. (2013) have predicted monthly rainfall by
combining wavelet technique with Artificial Neural Network
(ANN). Umut Okkan and Zafer Ali Serbs (2013) have combined
wavelet transform with different black box models in reservoir
inflow modeling. It have been suggested that DWT – FFNN and
DWT – LSSVM models can be used as successful tools for
modeling inflow of Demirkopru dam.
neurons. The neural network maps input layer to output layer
with the help of connecting weights between nodes. For ANN
model identification of number of factors such as model
structure, training algorithm, training data set, data
standardization, number of training iterations, play an important
role.
Figure-1: Feed Forward Neural Network
In recent study Multilayered Feed Forward Neural Network
(FFNN) Back Propagation learning algorithm is used. In FFNN
the signal is passed in forward direction from input layer to
output layer. FFNN are simple to build and requires less
computational time than recurrent networks. (Jain et. al. 1999)
An optimal FFNN is the one which gives the minimum model
error. For this determining optimal number of hidden neuron is
most essential task. A trial and error procedure have been
adopted for the same though some algorithms have been
proposed to do this. (Shouke Wei et. al. 2013)
Many different learning algorithms are used to train ANN,
among which Back Propagation is widely used because of its
robustness BP calculates the error between targeted and actual
output and propagates error back to input layer. The weights of
neuron are again adjusted to minimize the model error.
2.2 Wavelet Neural Network
Wavelets are the waves of zero mean and are effectively for
short duration. Wavelet analysis used shifting windowing
technique with various scale. Long time interval gives low
frequency information where as short time interval gives high
frequency information.
There are two types of wavelet transform: Continuous wavelet
Transform (CWT) and Discrete wavelet transform (DWT)
2. METHODOLOGY
In practical applications in hydrology researchers have access to
a discrete time signal rather than continuous time signal.
Therefore in present study Discrete Wavelet Transform has been
used. The discrete wavelet transform (DWT), provides sufficient
information both for analysis and synthesis of the original signal,
with a significant reduction in the computation time.
2.1 Artificial Neural Network
In discrete wavelet transform the time series xt is defined as:
In present study comparison between Artificial Neural Network
and Wavelet Neural Network has been done for forecasting
monthly inflow of Jayakwadi Reservoir Stage - 1
An ANN is a structure of simple interconnected operating
elements known as nodes; these are inspired from biological
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(1)
Where t is integer time step, j and k are integers that control
scale and time respectively, Wj,k is wavelet coefficient for scale
factor 2j and time factor
There are many algorithms used for discrete wavelet transform.
In present study Mallat algorithm is used.
(2)
(5)
Where Qp is predicted inflow
Qt is targeted inflow
These statistical parameters are calculated using total predicted
and observed data from WNN and ANN models. The model
which will give minimum value RMSE and Maximum value R
can be selected as best model.
In present study the hybrid model was formed combining
discrete wavelet transform and feed forward neural network.
Comparative study has been performed between ANN and WNN
model for one month ahead inflow prediction. For WNN model
development the input data was decomposed into different
subseries up to three resolution levels. After decomposition the
reconstructed series that is summation of all details and one
approximation signal were used as input to feed forward neural
network. Other important issue in wavelet analysis is to choose
the wavelet type. Daubechies wavelets are one of the widely
used wavelet family, which is written as dbN, where db is
surname and N is order of wavelet. We have used Daubechies
wavelet 5 (db5).
(3)
Figure-2: Decomposition till resolution level 3
In discrete wavelet analysis the signal is passed through high
pass and low pass filters to analyze high frequency and low
frequency without losing information. Mother wavelet giving the
detail coefficients represents low scale and high frequency
components. Father wavelet giving approximation coefficients
represents high scale and low frequency components. In general
the resolution level of decomposition is decided by formulae
INT (log n), where n is length if time series and INT is integer
number, log is normal Logarithm (Wang and Ding 2003).
The results of WNN model was compared with conventional
ANN model which are presented in Table 1.
Table-1: Comparison between ANN and WNN Model
ANN
WNN
Training
RMSE
R
177.0042 0.9273
166.5624 0.9018
Testing
RMSE
R
350.2433
0.8565
194.3533
0.9499
Figure- 4: Expected and Predicted Inflow for ANN Model for
Testing Period
2.3 Model Evaluation
Appropriate model evaluation methods are essential because the
developed models can be used in management and planning.
Two performance evaluation criteria used in this study are
computed as in the following section.
Coefficient of correlation (R):
(4)
Root mean square error (RMSE):
Figure- 5: Expected and Predicted Inflow for WNN Model for
Testing Period
4. CONCLUSIONS
MANIT Bhopal
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The main purpose of this study is to compare the results of ANN
and WNN model for one month ahead inflow prediction. The
comparison of two graphs show that ANN model has failed to
predict peak inflows where as WNN model has fairly predicted
the peak inflows but has failed to predict low inflows.
Based on evaluating statistical parameters it can be said that
WNN model perform better than ANN model. The study shows
that for peak inflow prediction which is necessary for flood
management can be effectively predicted by a hybrid WNN
model.
This study has been carried out for one month flow prediction.
For future the work can be extended for daily or hourly inflow
prediction using different wavelets other than Daubechies such
as Haar wavelet.
Also by combining with different neural networks such recurrent
neural network, time lagged networks with wavelet transform.
Along with inflow as input other hydrological data such as
rainfall, evaporation can be used for improving the results.
REFERENCES
i.
Ahmed El-Shafi, Alaa E. Abdin Aboelmagd Noureldi and Mohd R.
Taha (2009) "Enhancing Inflow Forecasting Model at Aswan High Dam
Utilizing Radial Basis Neural network and Upstream Monitoring Stations
Measurements" J. Water Resource Management, Springer, (l23), 2289–2315.
ii.
Felipe Prada-Sarmiento and Nelson Obregón-Neira (2009)
―Forecasting of Monthly Stream flows Based on Artificial Neural Networks", J.
Hydrologic Engg, ASCE , 1390- 1396
iii.
Ozgur Kisi (2007) "Stream flow forecasting with different Artificial
neural network algorithm." J. Hydrologic Engg, ASCE, (12), 532-539
iv.
Ozgur Kisi (2009) ―Wavelet regression model as an alternative to
neural networks for monthly stream flow forecasting‖ J. Hydrological Process,
(23), 3583–3597
v.
Ozgur Kisi 2011. ― Wavelet Regression Model as an Alternative to
Neural Network for River Stage Forecasting‖ J. Water Resource management,
(25), 579 – 600.
vi.
R. Venkata Ramana, B. Krishna, S.R. Kumar, N.G. Pandey.( 2013).
―Monthly Rainfall Prediction Using Wavelet Neural Network Analysis‖. J.
Water Resource management, (27), 3697 -3711
vii.
Robi Polikar, ― The Wavelet Tutorial ‖, Part 1-4,
http://users.rowan.edu/~polikar/WAVELETS/WTpart4.html
viii.
S. K. Jain, A. Das and D. K. Srivastava. (1999). ―Application of ANN
for Reservoir Inflow Prediction and Operation.‖ J. Water Resour. Plng. and
Mgmt., ASCE, 125(5), 263-271.
ix.
Shouke Wei, Hong Yang, Jinxi Song, Karim Abbaspour, Zongxue Xu,
(2013) ―A wavelet-neural network hybrid modeling approach for estimating and
predicting river monthly flows‖ Hydrological Science Journal, 58 (2), Pg. no.
374 – 389
x.
Umut Okkan, Zafer Ali Serbes, (2013) ―The combined use of wavelet
transform and black box models in reservoir inflow modeling‖. J. Hydrol,
Hydromech, (61), 112-119
xi.
Wang and Ding, (2003) ―Wavelet Network Model and Its Application
to the Prediction of hydrology‖ Nature and Science, 1(1), 67- 71
Improving location specific wave forecast using
Soft computing techniques
S.N. Londhe1, P.R. Dixit2, B. Nair T.M3, A. Nherakkol4
Professor, Civil Engineering, Vishwakarma Institute of
Information Technology, Pune, India
(Tel: 9126932300, Fax: 912026932500, email:
[email protected]) Member IAHR
2.
Assistant Professor, Civil Engineering, Vishwakarma Institute
of Information Technology,
Pune, India (Tel: 9126932300, Fax: 912026932500 email:
[email protected])
3.
Scientist E and Head Information Services and Ocean Sciences
Group (ISG), Indian National
Centre for Ocean Information Services (INCOIS), Ocean Valley,
Pragathi Nagar, Hyderabad,
India (Tel: 040-23886007, Fax: 040-23895001 email:
[email protected])
4.
Scientist: Information Services and Ocean Sciences Group
(ISG), Indian National
Centre for Ocean Information Services (INCOIS), Ocean Valley,
Pragathi Nagar, Hyderabad,
India (Tel: 040-23886007, Fax: 040-23895001 email:
[email protected])
1.
ABSTRACT: Presently Indian National Centre for Ocean
Information Services (INCOIS) provides wave forecasts on
regional and local level ranging from 3 hours to 7 days
ahead using numerical models (www.incois.res.in). It is
evident from real time observations that the predicted
SWHs by a physics based model vary randomly and have
non-linear relationship with observed values due to many
reasons. Consequently predicted and actual values deviate
significantly from each other with an „error‟ which has to
be removed to cater the needs of safe and secure lives
residing along Indian coastline. Present work aims in
reducing this error in numerical wave forecast made by
INCOIS at Pondicherry station. For this „error‟ between
forecasted and observed waves at current and previous
time steps were taken as input to predict the error at 24 to
48 hr ahead lead time in advance using a hybrid Neuro
Wavelet Technique. Separate neural networks were
trained with approximate and detail wavelet coefficients
and the output of networks were reconstructed back using
inverse DWT. This predicted error was then added or
subtracted from numerical wave forecast to improve the
prediction accuracy. It is observed that numerical model
forecast improved considerably when the predicted error
was added or subtracted from it. It will add to the
usefulness of the wave forecasts given by INCOIS to its
stake holders. The performance of improved wave heights
is judged by correlation coefficient and other error
measures like RMSE, MAE and CE, the details of which
are provided in the paper.
Key words: Wave forecasting, Numerical model of wave
forecast, Wavelet Transform, Neuro Wavelet Technique
(NWT).
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1. INTRODUCTION:
As more than a quarter of the country‟s population residing
along the coastlines of India, estimation and prediction of all
oceanographic parameters is of the utmost importance to cater
the needs of safe and secure life . Consequently accurate
forecasting of significant wave heights is at the most priority and
is of vital importance in all oceanographic activities as almost all
ocean engineering applications inevitably depends on it.
Presently Indian National Centre for Ocean Information Services
(INCOIS) provides wave forecasts on regional and local level
ranging from 3 hours to 7 days ahead using numerical models
(www.incois.gov.in). It is clear from the results of numerical
model forecast that the predicted significant wave heights by a
physics based wave model vary randomly and have non-linear
relationship with the observed values. There are many known
and unknown reasons like malfunctioning of the wave rider
buoy, dismantling of rider buoy due severe wind conditions
because of which the predicted and actual wave heights deviate
from each other. As the numerical model requires exogenous
data inputs and works on larger grid size making it the major
impediment in employing it particularly for location specific
forecasts even though it works reasonably well for regional level
and it is apparent that as the error modeling could not be
effectively done without the complete knowledge of these
random processes, other alternative methods are welcome to
bring this error down.
The soft computing techniques which do not require a priori
knowledge of the underlying phenomenon and give meaningful
solutions by using the readily available measured data and their
antecedent values, can be employed to bridge the gap between
these wave forecasts and observed values by developing a wave
forecast improving model using the observed and forecasted
waves. The technique of ANN is now an established technique
in the field of Hydraulic Engineering as well as coastal and
Ocean Engineering as evident from a plethora of publications in
the journals of international repute. Jain and Deo (2006)
presented a comprehensive review of these applications. Some
of the researchers have used ANN in single form (Deo and
Naidu (1999), Deo et al. (2001), Makarynskyy (2004), Londhe
and Panchang (2006)) while others have done so in combination
with numerical approaches to increase the accuracy of the latter.
The works aimed at improving the power of numerical models
include those of Kazeminezhad et al. (2011),Makarynskyy and
Makarynskaa (2006), Zhang et al. (2006), Zamani et al. (2008),
Mahjoobi et al. (2008), and Gunaydin (2008). Jain and Deo
(2007), Kambekar and Deo (2010, 2012) and Londhe (2008)
employed Genetic Programming (GP) for wave modeling.
Londhe (2008) in his work on estimation of missing wave
heights had shown that GP performs better than the numerical
model WAVEWATCH III.
It can be seen from the above citations that many of the research
workers are from India and to the author‟s best knowledge none
of them have worked on reducing the above mentioned „error‟ in
between the observed and forecasted waves but all of them have
tried to improve the forecast only by applying different soft
computing techniques either sole or in combination with one or
two. And it therefore forms a strong case for the present work
to be carried out seeing that the research work mentioned above
have shown that the soft tools of ANN and GP can forecast the
oceanic parameters reasonably well but not with highest
precision. Present work aims in reducing the „error‟ of numerical
wave forecast made by INCOIS at Pondicherry station. For this
„error‟ between forecasted waves (by numerical model) and
observed waves, at current and previous time steps are taken as
input to predict the error at 24 to 48 hr ahead lead time in
advance using a hybrid Neuro Wavelet Technique (NWT). The
Neuro Wavelet Technique (NWT) is in fact a combination of
two methods, Discrete Wavelet Transform (DWT) and Artificial
Neural networks (ANNs). The predicted error by NWT is then
added or subtracted from the numerical wave forecast to
improve the prediction accuracy. Thus the improved predictions
are then compared with the observed wave heights to see that
whether the hybrid Neuro Wavelet Technique can suffice the use
of it in improving the prediction accuracy or not.
The outline of the paper is as follows. Details of study Area and
Data are described in the next section followed by the brief
information about both ANN and Wavelet techniques. The
methodology for model formulation by Neuro- Wavelet
Technique is described in next section followed by results and
discussions. Concluding remarks are presented at the end.
2. STUDY AREA AND DATA:
Present study is done at Pondicherry Station (11°56' N 79°53' E)
in Tamil Nadu, India which is owned and maintained by Indian
National Centre for Ocean Information Services (INCOIS) of
India. For this, 24hr and 48 hr ahead numerically forecasted
values of significant wave heights (provided by INCOIS) and the
previously measured significant wave heights at the same time
steps of 24hr and 48hr for 3 years from 2011 to 2013 at
Pondicherry station were used.
The difference between
measured and forecasted wave heights is the „error‟ and that of
only to be minimized. A time series of this „error‟ is used as
input to calibrate and test the models for forecasting the error at
24hr and 48hr in advance at the same location. Readers are
referred to http://www. incois.gov.in for more details.
Figure 1: Location Map of Pondicherry Station.
3. ARTIFICIAL NEURAL NETWORK
It is a systematic arrangement of system‟s causative variable
(input neurons) and the output variables (output neurons) mostly
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connected by one or more hidden layers with neurons which
works similar to the biological neural network in the human
brain. The mapping of input and output to the required accuracy
is done by using an iterative procedure for minimizing the error
between the observed and network predicted variables (outputs).
The calibration („training‟ as per ANN terminology) is done on a
set of data using a training algorithm which minimizes the error
and makes the network ready to face the unseen data kept aside
for testing the model. The ANN was first introduced and
applied in last decade of the twentieth century, and is now an
established technique in modeling water flows and therefore
now a day‟s readers are well versed with the terminology,
working of ANN. Hence detail information about working of
ANN, its component is avoided in the current paper. The readers
can refer text books like Bose and Liang (1996), Wassarman
(1993) and research papers by The ASCE Task Committee
(2000), Maier and Dandy (2000) and Dawson and Wilby (2001)
for understanding the preliminary concepts and working of
ANN.
4. WAVELET TRANSFORM
As wave series is non stationary, highly complex and time
dependent phenomenon, its analysis is to be done using time and
frequency domain. For the analysis of such time varying signals,
these signals are often transformed into frequency domain.
Using Fourier transformation, the signal is decomposed into
different frequencies, but this transform only presents the signal
frequencies and not the time instance at which particular
frequency occurs. Another drawback of Fourier transform is that
it works better with stationary signals.
This frequency localization problem is overcome by Short Time
Fourier Transform (STFT), in which the signal is analyzed in
particular time interval taking Fourier transform in that interval.
For analysis of the low frequency signal the time interval should
be large and for high frequency it should be small. Thus, for
decomposing the time interval, scale must be varied. This
problem of analysis with different time intervals is overcome by
Wavelets.
A Wavelet transformation is a signal processing tool with the
ability of analyzing both stationary as well as non-stationary data
series, and to produce both time and frequency information with
a higher (more than one) resolution, which is not available from
the traditional transformation; Fourier and Short Term Fourier
Transform (Deka et al (2012). It decomposes the signal using a
small wave like function called as Mother Wavelet, which is
translated over the signal with different scales to obtain
decomposed signals. Thus the wavelet transform breaks the
signal into its wavelets (small wave) which are scaled and
shifted versions of the original wavelet (mother wavelet). Here
the Scaling function of the wavelet and wavelet function serves
as low and high pass filters respectively. Thus the signal is
passed through the low and high pass filters, and sub - sampled
to separate low (approximation) and high (detail) frequencies.
The low frequency can further passed through Low and high
pass filter to get more resolution in the analysis. This analysis is
called as Multi- resolution analysis (MRA). The wavelet
transformation is classified under two heads; continuous wavelet
transformation (CWT) and discrete wavelet transformation
(DWT). As the scope of the present work is limited to the use of
discrete wavelet transform, it is briefly explained below.
4.1 Discrete Wavelet Transform (DWT): The Discrete
Wavelet Transform (DWT) is presented as in Eq.1
k
2

kl t   2  2k t  l

(1)
where „ψ‟ is mother wavelet.
Here the scale is represented in terms of the 2 k and the
translation in terms of 2k l. The coefficients of the DWT
represent the projection of the signal over a set of basic functions
generated as translation and dilatation of a prototype function,
called mother wavelet. There are several mother wavelets like
Haar, Debauchies (db), symlets, biorthogonal etc. In the present
study Debauchies (db) wavelet types (1 to 35) are used. Readers
are referred to Mallat (1998) or Labat et al. (2000) for further
details.
5. MODEL FORMULATION
As mentioned above, the data of significant wave heights
forecasted by numerical model (Provided by INCOIS) and the
measured wave heights from 2011 and 2013 (3 years) at
Pondicherry station was used in the present work. The difference
between the observed and forecasted wave height is the „error‟
which is necessarily to be minimized to improve the location
specific wave forecast at Pondicherry. As maximum as the
reduction in this error, the maximum accuracy can be achieved
in forecasting the waves. To improve the numerical model
prediction at a particular lead time, this „error‟ necessarily
predicted for that particular lead time should be used. In the
same regards , time series of calculated „errors‟ was used to
predict the corresponding error at particular lead time ( 24hr and
48 hr ahead lead times). Thus to improve the numerical model
forecasts at 24hr and 48hr ahead lead time the corresponding
„errors‟ at 24 hr , 48 hr ahead respectively must be predicted.
This is achieved by developing a hybrid Neuro –Wavelet
technique (NWT). Figure 2 explains the working principle of
NWT, where the discrete wavelet transform decomposes time
series of error into low (approximate) and high (detail)
frequency components. In the present study the decomposition
of approximate is carried out further up to fifth level in order to
provide more detail and approximate components which
provides relatively smooth varying amplitude series. For the
further details of multi level decomposition technique readers
are referred to www.mathworks.com. The neural network is then
trained with decorrelated approximate and detail wavelet
coefficients. The outputs of networks during testing are
reconstructed back using inverse DWT. Thus the effect of
autocorrelation mentioned earlier by Vos and Rientjes (2005)
was removed by the use of Neuro Wavelet Technique.
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Figure 2: Algorithm of NWT
Development of these models to predict the errors at 24hr, 48hr
ahead lead times, the current and previous errors at current time
step and previous time steps upto 24hr, 48hr back respectively
were used as inputs. 70% of the data from the total data set of
errors was used to train the model while remaining 30% data
was used for validation (15%) and testing (15%) to develop each
model. Separate models were developed to predict error at 24 hr
& 48hr ahead lead time. Table 1 presents the inputoutput and
architectural details of these models.
Table 1: Model details for prediction of errors
Model
for
24 hr
ahead
48 hr
ahead
Input
Output
t-24, t-21, t-18, t15, t-12, t-9, t-6, t3, t (total 9 inputs)
t+24 (24hr ahead
predicted error)
t-48, t-45, t42,………,t-9, t-6,
t-3, t (total 17
inputs)
t+48 (24hr ahead
predicted error)
Architecture of
detail & approx
9:2:1 LM, 0-1,
mse, logsigpurelin, 30
epochs
17:5:1 LM, 0-1,
mse, logsigpurelin, 40
epochs
coefficient of efficiency (CE) as suggested by The ASCE Task
Committee (2000) between the observed and improvised wave
heights were also calculated. Readers are referred to Dawson and
Wilby (2001) for their formulae. Table 3 presents the model
assessment done using RMSE, MAE and CE. Figures 3 and 4
shows wave plots for 24 hr and 48 hr ahead forecasts
respectively by both numerical model and by improvised wave
forecasts after application of NWT for Pondicherry station while
figure 5 shows scatter plots for 48 hr forecasts. It is clear form
Table 2 that for all the forecasting intervals the correlation
coefficient „r‟ values of Improvised SWHs are superior to the
numerical model predictions („r‟24 hr:0.82 of Improvised
forecast as against 0.78 of numerical model) . Also it can be
observed that even at higher lead times of 48 hr „r‟ is increased
from 0.74 to 0.80 which is significant achievement in the
forecasting of wave at higher lead times. Table 3 indicates that
the Root Mean Squared Error (RMSE) values of Improvised
models are at lower end than the numerical model. High values
of coefficient of efficiencies (CE) than the CE values of
numerical model confirm the superiority of newly improvised
SWHs over the original waves. The wave plots presented in
figures 3, 4 demonstrated the clear attainment of the exercise of
„forecasting of the errors at the particular lead time by using
NWT‟ to improve the forecast made by the numerical model at
that particular lead time. It is manifested from the newly
improvised SWH series that the values of peaks and troughs are
imprisoned well due to the correction made by the adding or
subtracting the forecasted errors from the original forecasts of
numerical model.
These predicted errors were then added or subtracted from the
numerical model forecasts of respective lead times and forecasts
done by numerical models for 24hr and 48hr ahead lead times
were improvised. Subsequently these improvised forecasts were
compared with the observed wave heights to perceive the model
competency and the results of which are discussed in next
section.
Table 2: Correlation coefficients
Sr.
No
MANIT Bhopal
1
24 hr
Observed
and
Numerical
model
0.78
2
48 hr
0.74
Observed
and
Improvised
0.80
0.82
Table 3: Model Assessment
Sr.
No
6 RESULTS
All the models were tested with unseen inputs and the errors
were forecasted for the respective lead times. The numerical
model forecasts were then improvised by using these predicted
errors as mentioned in model formulation. The accuracy in
forecasting the significant wave heights was then judged by the
correlation coefficient (r) between the observed and improvised
wave heights, scatter plots between the same and the wave plots.
The correlation coefficients between the observed and
improvised wave heights for the developed models are given in
Table 2. Additionally other error measures such as root mean
squared error (RMSE), Mean absolute error (MAE) and
„r‟ Correlation Coefficient
Forecast
Interval
Fo
re
ca
st
Int
er
val
RMSE
CE
MAE
Observ
ed and
Numer
ical
model
Obs
erv
ed
and
Im
pro
vise
d
Observ
ed and
Numer
ical
model
Obs
erv
ed
and
Im
pro
vise
d
1
24
hr
0.21
0.04
0.65
0.98
2
48
hr
0.20
0.18
0.58
0.74
Obs
erv
ed
and
Nu
mer
ical
mo
del
O
bs
er
ve
d
an
d
Im
pr
ovi
se
d
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18-19, Dec. 2014
Figure 3: Wave plot of 24 hr ahead forecast
It is evident from the above mentioned results that there is an
elevated improvement in the 24hr, 48 hr forecast and hence it
can be said that the use of NWT is worthwhile in similar kind of
research works in improvising the forecasting accuracy. The
forecast done by newly improvised SWHs is more superior than
the original numerical model wave forecasts for both the lead
times of 24 hr and 48 hr which indicates that for higher lead
times greater than 12 hr also (for 24hr and 48 hr) , the exercise
of „ forecasting the error‟ for particular lead time to improve the
numerical model wave forecast gives the considerable results.
To the authors best knowledge, as this is the first kind of effort
to improve the wave prediction done by the numerical model by
improvising the „errors‟ specifically, It will definitely an
significant addition to the usefulness for the wave forecasts to
INCOIS and its stake holders. High correlation coefficient („r‟)
and coefficient of efficiency (CE) values and low RMSE values
proves the proficiency of new hybrid technique of NWT and
hence it is pretty clear that this technique can be explored further
in analogous class of research area.
7. ACKNOWLEDGEMENT
The authors would also like to thank INCOIS, Hyderabad
(Indian National Centre for Ocean Information Services,
Ministry of earth sciences, Govt. of India.) for funding the
research project under the Ocean State forecasts Scheme (OSF).
REFERENCES
Figure 4: Wave plot of 48 hr ahead forecast
r = 0.80
Figure 5: Scatter plot for 48 hr forecast
6. CONCLUSIONS
The present paper portrays use of a hybrid Technique, Neuro
Wavelet with multilevel decomposition for improving the
location specific wave forecasts at Pondicherry station, Tamil
Nadu, India. The forecast done by the numerical model for 24 hr
and 48hr ahead lead times were improvised by adding or
subtracting the „errors‟ which were forecasted by the use of
hybrid Neuro Wavelet Technique for the respective lead times.
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Discrete Wavelet Support Vector Conjuction
Model for Significant Wave Height Time Series
Forecasting
Paresh Chandra Deka1 & Suryadatta Y N 2
Associate Professor, National Institute of Technology
Karnataka,Surathkal-575025,India
2
M.Tech student, National Institute of Technology
Karnataka,Surathkal-575025,India
1
ABSTRACT: In this study, a hybrid model of wavelet and SVM
(WSVM) has been developed to forecast significant waveheight for different wavelet transformations namely
Daubechies 2, 3& 4 with decomposition levels 5,6 &7. The
whole process was carried out at station SW4 (Mangalore port)
at west coast of India near Mangalore for 3 hour leadtime.
Here the wavelet transformation is used to decompose the
original significant wave height (Hs) data into its sub signals
in the form of approximation and detail coefficients. Further,
these coefficients were fed to SVM as inputs and targets and
the results obtained from the hybrid model are then
reconstructed to obtain the predicted significant wave heights.
The predicted results from the proposed model were compared
with the single SVM results. It was shown that the proposed
model, WSVM that makes use of multiresolution time series as
input, allows for more accurate and consistent predictions with
respect to the SVM models.
Keywords: Support vector machine, Wavelet transforms, Time
series forecasting, Significant wave height, Hybridization,
decomposition level
1. INTRODUCTION
Time series prediction of a ocean wave data or any other fields
that falls under time series category in a real scenario is much
complex rather than non time series prediction. Recently,
various artificial intelligence computing techniques like Fuzzy
logic, Artificial Neural Networks (ANN) and Genetic
programming (GP),Support vector machine(SVM) etc. were
used efficiently in time series prediction to improve the
forecasting accuracy. These computing techniques normally
utilizes tolerance to uncertainties, imprecision, and partial truth
associated with input information in order to cope up the draw
backs in mathematical models. Application of these computing
techniques has been reported from different authors to forecast
time series significant wave heights for multiple lead times (Deo
and Naidu, 1999; Rao et al. 2001; Makarynskyy et al. 2005; Jain
and Deo, 2007; Gaur and Deo, 2008). Even though reliability of
these models some times lacks in satisfactory performance, that
is may be due to high non linearity and non-stationarity in the
data or may be due to gaps within the data set. In this context,
data normalisation techniques has been attempted to reduce the
statistical variations in the data in recent few years to improve
the performance of existing models though these techniques
seems to be time consuming and trial and error based methods.
Apart from this, to improve the model performance,
hybridisation of different models has been carried out from the
different authors (Kim and Valdes, 2003; Deka and Prahlada,
2012).
Recently, support vector machines (SVMs) which is one of the
soft computational techniques has been successfully used in
different research areas (Smola, 1996; Vapnik et al., 1997; Gao
et al., 2001; Yoon et al., 2004; McNamara et al., 2005; Awad et
al., 2007; Kaheil et al., 2008). In the last decade, wavelet
transform has become an useful technique for analysing
variations, periodicities, and trends in time series. In the past,
hybridisation of wavelet transformation with other models has
been reported in different fields. Chen et al. (2007) used the
same combination to forecast tides around Taiwan and South
China Sea, and concluded that the proposed model can
prominently improve the prediction quality. Recently, Ozger
(2010) used Wavelet-fuzzy model to forecast significant wave
height and average wave period for higher lead times up to 48 h
and results were satisfactory. Deka et al. (2010) used hybrid
Wavelet-ANN model to forecast significant wave height of
station near marmugaoport, Arabian Sea, and the results
obtained for two steps ahead prediction was satisfactory. Kisi
&Cimen. (2011). used a wavelet & support vector conjunction
model in monthly stream-flow forecasting. They obtained the
conjunction model by combining the two methods discrete
wavelet transforms & support vector machine and compared
with the single support vector machine. The test results were
compared with the single support vector regression model. The
comparison results showed that the discrete wavelet transforms
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could significantly increase the accuracy of the SVR model in
forecasting monthly stream-flows.
In this study, a hybridisation of Wavelet and SVM has attempted
to make the model perform in a better way in terms of
consistency and accuracy.
2.1Wavelet theory
A Wavelet transformation is a signal processing tool like Fourier
transformation with the ability of analysing both stationary as
well as non stationary data, and to produce both time and
frequency information with a higher resolution, which is not
available from the traditional transformation.The wavelet
transform breaks the signal into its wavelets (small wave) which
are scaled and shifted versions of the original wavelet (mother
wavelet).There are many wavelet functions are available for
wavelet analysis such as Haar wavelet, Daubechies wavelets,
Coiflet wavelets, Morlet wavelet, etc. and all these wavelets are
slightly differ in their shape properties.
In the WSVM model, the raw signals (significant wave height
time series) must be decomposed into multi-scale sub signals
before proceeding to the SVM. From this point of view, the Hs
signals are first decomposed into sub signals with different
scales (decomposition levels), i.e., a large scale sub signal and
several small-scale sub signals in order to obtain temporal
characteristics of the input time series. For a given time series,
the time series corresponding to a(t) is the approximation sub
signal (large scale) of original signal and the i-th detailed sub
signal (small scale) is identified by i where i is the
decomposition level of significant wave heights time series.
Thereby, the number of input variables for the SVM
model is determined as i+1 because the model uses 1 variable
and the time series is decomposed into i+1 sub signals. In this
structure, the annual or seasonal data are decomposed into largescale sub signals and the small periods such as daily, monthly
and weekly data are decomposed into detailed sub signals.
In the Discrete wavelet transform (DWT), filters of different
cutoff frequencies are used to analyse the signal at different
scales. The signal x (t) is passed through a series of high pass
filters and low pass filters and down sampled (i.e. throwing
away every second data point) to analyse the high frequencies
and low frequencies respectively . The output from the high pass
and low pass filters are the approximation coefficients (A1, A2…
An) and detail coefficients (D1, D2…Dn) respectively. The
process of decomposing a signal into its sub-bands or sub signals
is also termed as multi resolution signal decomposition.
2.2 SVM theory
The support vector machines are developed based on statistical
learning theory and are derived from the structural risk
minimization hypotheses to minimize both empirical risk and the
confidence interval of the learning machine in order to achieve a
good generalization capability.SVM is simple enough to
understand and found better than neural networks, decision trees.
The basic idea behind SVM is to map the original data sets from
the input space to a high dimensional, or even infinite
dimensional feature space so that classification problems
become simpler in the feature space. The main advantage of
SVM is that, it uses kernel trick to build expert knowledge about
a problem so that model complexity and prediction error are
simultaneously minimised.
2.3 WSVM Model
The wavelet-support vector machines (WSVM) model combines
the strengths of discrete wavelet transform and SVM processing
to achieve powerful nonlinear approximation ability. Thus, the
WSVM approach can be applied as a forecasting model. The
schematic structure of WSVM model is illustrated in Figure1
below.
Fig 1 Schematic Structure of WSVM Model
In the present work only significant wave height (Hs) of
previous time steps were used as predictors. Here, wave height
values up to previous 12hour were taken into consideration as
predictor variables to predict Hs (t+n) where Hs (t+n) is the
future significant wave height and „n‟ denotes the lead times in
hours with t as current significant wave height. Only 3hr lead
time datasets were used.
The data used in the current study is processed significant wave
height (Hs) of the station SW4 (Latitude 12°56 ´31´ánd
longitude 74°43´58´´) located near west coast of India which
was collected from New Mangalore Port Trust (NMPT) during
the year 2003 from January 1st to December 31st. The frequency
of the data was 3 hourly significant wave heights. The statistical
properties of dataset presented in Table 1.
Table 1 Statistical properties of the data for station SW4
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Min(m)
Max(m)
Mean(m)
Skewness
Curtosis
Standard
deviation(m)
0.24
3.06
1.015
0.77
0.4864
0.6278
figure 3 and figure 4 for reference check with observed value.
WSVM model was closely following the observed value as
compared to SVM model. The scattered plot also clearly reflects
the better performance of WSVM model in figure 5 as compared
to SVM model in figure 6.
Table 2 Performance of WSVM & SVM models
The results obtained from the both single WSVM model and
single SVM model are presented in the form of various
performance indices like R, RMSE, Scatter, Bias etc. through
tables, and various graphs. Figure 2 shows the representative
decomposition of signal of significant wave height for mother
wavelet Daubechies of order 2 with level of decomposition
5.The subseries of decomposed time series in the shape of
approximations and detail coefficients are with single series “s”
can be clearly understood from the figure 2.
Wavelet
R
RMSE
SCATTER
BIAS
Db2L5
Db2L6
Db2L7
Db3L5
Db3L6
Db3L7
Db4L5
Db4L6
Db4L7
SVM
0.9959
0.9960
0.9963
0.9966
0.9967
0.9970
0.9969
0.9969
0.9969
0.9714
0.0515
0.0515
0.0515
0.0499
0.05
0.0501
0.0499
0.05
0.04
0.151
0.0502
0.0502
0.0502
0.049
0.049
0.049
0.0487
0.049
0.049
0.1474
1.002
1.002
1.002
1.001
1.001
1.001
1.001
1.002
1.002
1.0135
Fig 3 Time Series Plot of WSVM of Db2, Decomposition
Level 5
Fig .2 Decomposition of signal using mother wavelet db2 and
L5
The model testing results are presented in the table 2.The
various WSVM hybrid model linked with various mother
wavelet db of order 2,3 and 4 with various decomposition level
such as 5,6 and 7 are shown in the same table.It was observed
that the single SVM model perform poorly compared to all
different WSVM model considering various statistical
performance indices.The correlation coefficient close to 1 with
low RMSE values confirms the higher forecasting accuracy of
WSVM models compared to SVM model.Also,scatter index
nearer to zero and bias value close to 1 are the performance
indicator which reflects better forecasting accuracy for WSVM
model.
The Db wavelet with various order and various levels shows
insignificant contribution to the forecasting accuracy as appeared
in the table 2 considering various performance criteria.
Fig 4 Time Series Plot of SVM
Fig 5 Scatter Plot of Observed vs WSVM Prediction of Db2
Level 5
The graphical representation of time series forecasting for
WSVM (best model) and SVM models are also presented in
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Fig 6 Scatter Plot of Observed vs SVM prediction
4. CONCLUSIONS
The proposed hybrid WSVM model outperformed single SVM
model for a 3hr lead time prediction. The improvement of
results in WSVM model is due to dividing the dataset into multifrequency bands using DWT to make data as a stationary data.
SVM is good at handling non-stationary data, but it has shown
excellence in handling stationary data and hence the proposed
model performed very well. It was noticed that as the
decomposition level for different wavelets was increased, the
performance of the hybrid WSVM model also increased. This
enhancement was minute for some wavelets but was noticeable
for all the different wavelets. Also, Level 7 of decomposition
had less error than level 5 and 6. While Db4 of Levels 5, 6& 7
giving similar results R=0.9969. Selection of proper mother
wavelet was also carried out in the present study. Db3 wavelet
performed better at decomposition level 7 suggesting that at
higher decomposition level it can perform better. The
conjunction model of WSVM can also be tried with various lead
times such 6hr, 12hr, 24hr and 48hr lead time as a future scope
of study. Also, Conjunction of SVM with other mother wavelets
like Haar ,Sym, Coif etc can also be tried.
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Potential Impact of Soft Computing Techniques in
Water Resources Engineering
Satish Kumar Jain1 R. K. Shrivastava2
Research Scholar, RGPV and A.P., UIT, RGPV, Bhopal (M.P.),
462036, India
2
Professor, SGSITS, Indore (M.P.), 452003, India.
1
ABSTRACT: Soft computing techniques like Artificial Neural
Network (ANN), Fuzzy Logic and Genetic Programming (GP)
etc. which drawn their inherent characteristic from biological
system are very competent in prediction of most of the
variables of Water Resources Engineering (WRE), which are
highly nonlinear in nature due to spatial and temporal
variations. Problems of sedimentation discharge, flood
forecasting, draught prediction, power generation, irrigation,
society development etc. cannot be understand effectively
because of nonlinear nature of these variables. Soft computing
techniques are being used widely now a day in prediction of
behaviors of rainfall, runoff, sediment discharge, and water
quality etc. These variables are directly associated with the
problems of water resources engineering. This paper has
examined some of the important studies on use of soft
computing techniques in water resources engineering
published in high impact journals since 2002-2013. Soft
computing techniques are based on modeling the input output
variables. These models learn from a set of examples and then
train themselves for predicting the required results more
effectively to any other conventional method. Further these
results obtained from soft computing techniques have been
used in planning and designing the infrastructure in water
resources engineering to resolve the problems and satisfactory
results have been found.
Key Words: Soft computing techniques, Artificial Neural
Network, Fuzzy logic, Genetic programming.
1. INTRODUCTION
In last two decades soft computing techniques such as Artificial
Neural Network, Fuzzy Logic and Genetic Programming etc.
have emerged as very popular tools for prediction and estimation
of various parameters in all fields of science and technology.
Water resources engineering is also very much influenced by use
of these techniques. Many problems of water resources
engineering which were not being solved precisely by use of
conventional empirical methods due to non linearity and short
data length are presently being solved by use of these soft
computing techniques. The prediction and estimation of rainfall,
runoff, sediment yield, permeability, water quality etc. is very
important in management of water resources projects and
problems such as life of reservoir, flood control, draught
management, irrigation and water quality etc. Earlier the
solution of these problems was based on conventional methods
but now use of soft computing techniques is giving encouraging
results.
Artificial neutral network
Artificial Neural Networks are very much inspired by the
biological neuron system. These are massively parallel
distributed processing system which is highly inter-connected
neural computing elements which have learning ability that can
be used. This learning process is termed as training of the
network. After training the network is validated and then tested
over other set of data. Fixed rules have not been framed for
development of ANN model. Trial and error approach is applied
for optimization of the appropriate network.
1.1 Fuzzy logic
Fuzzy set theory suggested by Lotfi A. Zadh (1965) is the base
of fuzzy logic. Fuzzy logic try to capture the logic as humans do
for real world knowledge in the face of uncertainty raised due to
generality, ambiguity, vagueness, chance or incomplete
knowledge or any other reason. Fuzzy sets support a range of
membership of elements to a set. Fuzzy sets express the gradual
transition from membership to non membership and vice versa
and this capability is used widely. The important characteristics
of fuzzy logic approach are ability to learn and generalize ability
to cope up with noise, the distributed processing which
maintains robustness.
1.2 Genetic programming
Genetic Programming (GP) is evolutionary computation based
technique. Evolutionary computation forms a group of
techniques which are inspired by natural process and also
emulate them. All varieties of organisms present on the earth
have resulted out of these evolutionary natural processes.
However GP is chiefly used for mathematical optimization of
complex nonlinear problems and desired solution of inputoutput relationship. An initial population of randomly generated
programme is considered by GP which is derived from random
combination of input variables, random numbers and functions
including arithmetic operators (+, -, *, /) mathematical functions
(Sin, Cos, exp, log), logical functions (or, and) etc. This
population is then operated through evolutionary process and the
fitness measure of formed programme is evaluated. The best fit
model is then selected from initial population. GP performs over
symbolic expression or formula rather than over numbers which
represent the candidate solutions. For developing the time series
forecast simple models, GP is considered suitable than ANN.
Also capability of GP about parsimonious selection of the
variables for model development from the potential inputs helps
to prevent redundancy in model development (Sreekanth and
Datta, 2011).
REVIEW OF LITERATURE
This paper presents review of various important studies
conducted in field of water resources engineering since 20052013 which used ANN, GP and Fuzzy Logic soft computing
techniques.
Feyzolhpour et al. (2012) used neural differential evolution
(NED), multilayer perceptron (MLP), and radial basis function
(RBF) models for prediction of daily suspended sediment
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concentration in Givi Chay River in the northwest of Iran. Input
parameters were available discharge and sedimentation
concentration. For training testing of networks, wet period data
from January to June 2009 and January to June 2010 were used
respectively. In NDE, various input combinations were used.
Programme code was written in MATLAB language. Different
NDE architectures were tried and after testing results were
compared on the basis of values of root mean square error
(RMSE) and determination coefficient (R2). In artificial neural
network (ANN) also different architectures were tried and best
models were determined. In RBF model, the number of units for
single hidden layer and the spread parameter value with 17 and
0.39 provided the best testing performance. In MLP, 4 numbers
of hidden nodes were found appropriate after employing trial
and error method. These results were compared and it was found
that NDE model (1-3-1) performs better than ANN models. The
results were also compared with sediment rating curves (SRC)
and it was concluded that, ANN performance was better than
SRC.
Mustfa et al. (2012) applied ANN in prediction of suspended
element discharge in Pari River at Slibin in Peninsular Malaysia.
MLP feed forward neural network was adopted with Gradient
Decent (GD), Gradient Decent with momentum (GDM), Scaled
Congugate Gradient (SCG) and Levenberg Marquardt (LM)
algorithms for training purpose. Five years daily data of
discharge and suspended sediment were used as input and output
parameters in 3-3-1 ANN model. Input discharge parameter was
divided in present, first and second antecedent times discharge.
Statistical measures as mean, standard deviation and coefficient
of variance were found higher for training than testing. This
indicated that training data contain more complexity and
variability. Learning rate for GD and GDM was kept 0.01 and
0.03. Epochs for LM, SCG, GD, and GDM were 24, 585, 5000
and 5000 respectively. The error measures RMSE, R2, Mean
Squared Relative Error (MSRE) were estimated for different
algorithms and it was concluded that SCG and LM performed
better than GD and GDM. The performance of SCG and LM
was similar however LM was faster (1/7 of SCG convergence
time).
Adhikari et al. (2012) proposed a Fuzzy Logic Controller (FLC)
method based on fuzzy control for hydropower generation and
reservoir operating system in dams for safe and efficient
performance in Himalayan region in India. In this method,
spillway gates were opted for safe reservoir control of dams.
Input parameters were taken as water level and flow rate while
output parameter was turbine valve openness. All models used
Tabu Search Algorithm (TSA), Fuzzy Delphi method and
Mamdani Interface method to evaluate for evaluation by manual
“C.O.G. Defuzzi-friction” and MATLAB FIS editor validation.
Initially the various variables, membership functions and rule
base were defined randomly then TSA was used to choose the
most appropriate parameters values charactering the fuzzy
membership function. It was concluded that fuzzy model
performs well and drawbacks of the human based control
systems do not appear in this method.
Shamsudin et al. (2013) estimated the long term phosphorous
loading rates using Vollenweider model and evaluated
eutrophication status using MATLAB fuzzy logic toolbox. The
uncertainty of phosphorous loading rates was also demonstrated
using MATLAB fuzzy logic Simulation for detention pond at
Kolam Tadahar 1 located within University Teknologi Malaysia
(UTM) South Branch Campus, S Kudai. Fifteen water samples
were collected in three visits of study field. Annual hydraulic
loadings with other parameters such as twelve year rainfall data
since 2000 to 2012, annual runoff coefficient and drainage area
and temporate data were collected. Identification of unit
hydrographs and components flows from rainfalls, Evaporation
and stream flow data (IHACRES) model was used to refined
runoff inflowing discharge, hydraulic loadings and pond storage
volume. The value of coefficient of determination R2 was found
very close to 1. In evaluation of eutrophication status Fuzzy
Interface System (FIS) editor in fuzzy logic tool box was
updated to define new names as Part Per Billion (PPB) and
Hydraulic Residence Time (HRT) as input and Trophic state for
output. Total 16 rules were created for each variable in triangular
membership function. Thus the study initiated the use of
MATLAB fuzzy logic in detention pond uncertainty.
Bhist et al. (2013) used ANN and fuzzy Logic soft computing
techniques in for forecasting of water table elevation in region of
Budaun district of Uttar Pradesh in India. Five ANN models
with one hidden layer and five ANN models with two hidden
layers were developed with ground water recharge, ground water
discharge, and water table elevation for previous year as input
parameters with different combinations for all five types but
similar for one and two hidden layer models. Output parameter
was water table elevation in all models. Five fuzzy models were
also developed in the study. For fuzzy models recharge and
discharge with specified time legs were as input parameters and
water table elevation was output parameter. Results were
compared with observed data on the basis of estimation of
statistical measures such as Coefficient of correlation (R),
Coefficient of Determination (R2), Mean Absolute Deviation
(MAD) and Root Mean Square Error (RSME). It was concluded
that Fuzzy model 2 is the best model among all with values 0.99,
0.98, 0.26 and 0.31 of R, R2, MAD and RMSE respectively. It
was also found that ANN works better with more inputs while
fuzzy works well with fewer inputs are available.
Sreekanth and Datta (2011) (ref C1) Compared GP and ANN
predictive modeling techniques by developing models for
saltwater intrusion levels in eleven ground water pumping wells.
The pumping rates with three stress periods were taken as inputs
and salinity levels as output. Training and validation data was
generated by three dimensioned coupled flow and transport
simulation model FEMWATER which was used to train GP and
ANN models. Training and validation sets were random for both
GP and ANN. Neuroshell software was used to develop ANN
model using feed forward back propagation algorithm.
Minimization of RMSE of prediction was taken as objection
function for training in both GP and ANN. In ANN models
sigmoid transfer function and 0.1 learning rate were used. The
ANN architecture was optimized by trial and error. GP models
were developed with 500 population size and frequencies of
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mutation and cross over were 95 and 50 respectively. Initially
addition and subtraction operators were added alone and later
multiplication, arithmetic and data transfer operators were
added. The preference of GP and ANN models was evaluated on
the basis of estimation of R and minimized RMSE values. It was
concluded that GP models are simpler with less inputs for
simpler prediction than ANN models.
Selle and Muttil (2010) developed GP models to predict the deep
percolation responses under surface irrigated pastures with
different soils, water table levels and water ponding durations
for surface irrigation. Data was obtained by lysimeter
experiments. The aim of this study was to test the compatibility
of different structures of GP models in comparison to conceptual
models in field of hydrology. It was concluded that GP models
give comparable results. The recurrence of developed models in
multiple runs was also investigated and it was found that these
models consistently come up with same model complexity but as
the level of complexity increases the recurrence of generated
models vary.
Kalteh (2008) adopted feed-forword multilayer perceptron
(MLP) ANN to develop a model for rainfall- runoff relationship
in Northern Watershed in Iran. Input variables rainfall and
temperature of five station points and output was runoff at
station situated at downstream to the above five stations. Time
span of data was fifteen years. Developed model contained one
hidden layer with six neurons. Back propagation algorithm was
used for training purpose. RMSE and R values were estimated to
define the performance of network and these values were found
quite satisfactory. Kalteh also described the mechanism of
learning process of ANN model by Neural Interpretation
Diagram (DIM), Garson‟s algorithm and randomization
approach and results were very encouraging.
Chouhan and Shrivastava (2009) predicted reference
evapotranspiration (ETo) for Mahanadi Reservoir project
Chhattisgarh State in India through application of Levenberg
Marquardt (LM), Quasi Newton (QN) and Back Propagation
with adaptive learning rate algorithms for training a feedforward ANN model. One hidden layer network models with
different combinations of input variables were made and their
performance was checked for above three training algorithms.
The average monthly max and minimum temperature , relative
humidity , wind speed and sunshine data since January 1986 to
December 2005 was used in this study. Average monthly
reference evepotranspiration data were estimated by FAO
Penman Monteith (p-m) method and compared with output of
produced by ANN. Statistical measures Mean Square Error
(MSE), Raw Standard Error of Estimates (RSEE), Standard
Error of Estimates (SEE)and Correlation coefficient (R) were
estimated for performance evaluation. It was concluded that a 39-1 model trained with QN algorithm performed accurately with
model efficiency 93%.
Hang and Suetsugi (2013) estimated sediment load in ungauged
catchments of Tonel Sap River Basin in Combodia. Monthly
average standard sediment load (SSLm) of four catchments has
been simulated in this study. Also the applicability of trained
ANN models was assessed in three ungauged catchment
representative (UCR) before their use for prediction of monthly
suspended sediment load. Data mainly used in this study was
suspended sediment load (SSL), discharge (Q), rainfall (R) and
digital elevation map (DEM). Total suspended sediment load
(SSLt) was also predicted to check the model performance on
the basis of determination R2. RMSE, mean absolute error
(MAE) and absolute percentage bias (APBIAS). It was
concluded that models of this study can be used for estimating
SSLM and SSLt of ungauged catchments with an accuracy of
0.61 in terms of R2 and 34.06 in terms of APBIAS respectively.
Garg and Jothprakash (2010) estimated trap efficiency (Te) of
Pong Reservoir on Beas River in Kangra district of Himanchal
Prades in India by using ANN model. The annual rainfall,
annual inflow and age of the reservoir were the input variables
since 1980 – 2006, while Te was single output. Te was estimated
by multi-layered perceptron (MLP) ANN model with one hidden
layer of four neurons. This appropriate ANN model was selected
on the basis of trial and error approach. The sigmoid and
hyperbolic Tangent (tan h) transfer functions were used. Back
propagation algorithm was used for training of 70% data length
with Momentum, Conjugate Gradient (CG) and Levenberg
Marquardt (LM) as learning rules. The statistical measures such
as Coefficient of correlation (R), Mean Square Error (MSE),
Root Mean Square Error (RMSE), Mean Absolute Error (MAE)
and Nash Sutcliff Efficiency (E) were estimate to evaluate the
performance of the models. It was concluded that the 3-4-1 feed
forward BPANN model estimated Te very well with sigmoid
activation function, 0.7 as momentum factor and 1.0 as learning
rate. The results of ANN models very well matched with results
obtained from empirical methods.
Garg and Jothiprakash (2010) applied ANN and Genetic
programming (GP) approaches in estimating trap efficiency (Te)
of Govind Sagar Reservioir at Satluj River in Bilaspur district of
Himanchal Pradesh in India. Input variables as annual rainfall
(Rt), annual inflow (It), annual sediment yield (St) and age of the
reservoir (at) of 32 years were taken for single output Te in
development of ANN models. MLP, Elman Recurrence Neural
Network (RNN) and Radial Basis Function (RBF) ANN were
tried in MATLAB environment. A RBF 4-4-1 architecture
model was found best in all three types ANN models at spread =
0.8 and R = 0.955. This model was generalized for data of 10
years of Pong Reservoir on Satluj River in Himanchal Pradesh in
India. In GP modeling, population size was provided 500. The
statistical parameters estimated shown that with short length
data, the GP models perform well than ANN model.
Agrawal et al. (2009) forecasted daily and weekly runoff and
sediment yield by using ANN. Ten years data consists of
rainfall, runoff and sediment yield of Vamsadhara River Basin in
South India. In forecasting runoff, the single input linear transfer
function (SI-LTF) models, multi-input linear transfer function
(MI-LTF) models and ANN models were developed. In runoff
forecasting, SI-LTF models, rainfall (R) - runoff (Q) models
were developed for daily and weekly time pass, while in MILTF modeling MI-LTF models, rainfall values of all rain gauge
stations were considered. In ANN modeling for runoff
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forecasting for daily and weekly basis, sigmoid function by
pattern learning were subjected to maximum 5000 iterations.
The learning rate (α) and momentum rate (β) were taken as
constant as 0.5 for error convergence. Data from 1984 to 1987
was used in testing and cross validation. Daily model was found
better than weekly model. It was concluded that ANN model
with three hidden layers performed best with nodes twice in
hidden layer to nodes in input layer and less 5000iterations. For
forecasting sediment yield, all models SI-LTF, MI-LTF and
ANN performed equally well.
Muhammadi et al. (20120 used ANNand neural – fuzzy
inference system for estimation of suspended sediment
concentration in Karaj River in north – west of Tehran. The
Input parameters as water temperature, base and flow discharge
of 40 years were considered. Sediment density was single
output. Input data was normalized between 0.1 and 0.9. MLP
three layered network was used with LM learning rule. In
MATLAB environment, coding system was performed to design
Artificial Neural Fuzzy System. Fuzzy inference system was
generated by genfis 2 (datin, datout, r) order. Cluster radius
varied between 0 and 1. The performance criteria of model were
R and RMSE. It was found in the study that accuracy of neural –
fuzzy inference system is much more than the accuracy of
sediment rating curve or any other methods.
Haghizadeh et al. (2010) proposed an ANN model for estimation
of yield sediment at Sorkhab River in upstream DEZ basin in
Iran. The MLP with feed forward back propagation (FFBP)
approach was adopted in the present study. The input data was
standardized between ranges 0-1. Performance of the model was
evaluated by values of R2, E, MAE, RMSE and Theil‟s
inequality coefficient (U). Analysis of the results revealed that
ANN-MLP with FFBP approach performed better than multi
regression approach. It was also concluded that ANN and
regression models developed for one watershed cannot be
adapted to the watersheds at different locations.
Shabani and Shabani (2012) estimated daily suspended sediment
yield through ANN in Kharestan Watershed in Iran. Twenty five
years water and sediment discharge data of Shoor Kharestan
River was used in this study. The performance of MLP neural
networks with error back propagation algorithm was evaluated
for prediction and simulating suspended yield from available
water discharge. First data was normalized between ranges 0-1
then 80% of normalized data was trained and 20% data was used
for testing. For information modeling, Qnet -2000software was
used. Trial and error method was adopted to select best
architecture by changing numbers of neurons in hidden layer.
Performance of the models was checked on the basis of RMSE,
MAE and R2 and best model was decided with values 19.27,
12.14 and 0.98 respectively. These values were also compared
with values obtained from rating curves and it was concluded
that performance of ANN models is far better than rating curves
for prediction of daily suspended sediment yield.
Handhel (2009) predicted reservoir permeability through ANN
in horizontal and vertical directions of Mishrif lime store
reservoir at Nasyria oil field in south of Iraq. A MLP model was
selected and trained with back propagation algorithm. Well logs
data of two exploration wells and their 103 core permeability
measurements in both horizontal and vertical directions were
used with input variables as Gama ray log, Bulk Density log,
sonic log, Neutron log, Deep Induction log and output variable
as log horizontal permeability and log vertical permeability. First
data was normalized then 60% of normalized data was trained
while 20% data was used for testing and remaining 20% data
was used for validation. The best network architecture was
selected on trial and error basis. A three layered network was
opted with 20 neurons in hidden layer. The logistic sigmoid
activation function was used in hidden layer and linear activation
function was used in output layer. The performance of network
was tested by values of R2. The R2 for predicted vertical and
horizontal permeability was 0.86 and 0.90 respectively. It was
concluded that ANN can be used effectively for prediction of
permeability.
3. CONCLUSIONS
After thorough review of various important researches in field of
water resources engineering during time since 2005-2013, it
may be concluded that soft computing techniques such as ANN,
GP and fuzzy logic have been used successfully for solutions of
various problems. MLP neural network trained with back
propagation algorithm has been most suitable in most of the
problems. Some studies have revealed that fuzzy logic and GP
models perform better than ANN models. If the input variables
are less in number then preference can be given to GP in place of
ANN.
REFERENCES:
i.
Garg, V., and Jothiprakash, V. (2010). ―Modeling the Time Variation
of Reservoir Trap Efficiency.‖ Journal of Hydrologic Engineering, 15(12), 10011015.
ii.
Haghizadeh, A., Teang shui, L., and Goudarzi, E. (2010). ―Estimation
of Yield Sediment Using Artificial Neural Network at Basin Scale.‖ Australian
Journal of Basic and Applied Sciences, 4(7), 1668-1675.
iii.
Handhel, A. M. (2009). ―Prediction of Reservoir Permeability from
Wire Logs Data Using Artificial Neural Networks.‖ Iraqi Journal of Science,
50(1), 67-74.
iv.
Heng, S., and Suetsugi, T. (2013). ―Using Artificial Neural Network
to Estimate Sediment Load in Ungauged Catchments of the Tonle Sap River
Basin, Cambodia.‖ J. of Water Resource and Protection, 5, 111-123.
v.
Kalteh, A. M. (2008). ―Rainfall-runoff modeling using artificial
neural networks (ANNs) : modeling and understanding.‖ Caspian J. Env. Sci.,
6(1), 53-58.
vi.
Muhammadi, A., Akbari, G., and Azizzian, G. (2012). ―Suspended
sediment concentration estimation using artificial neural networks and neuralfuzzy inference system case study: Karaj Dam.‖ Indian Journal of Science and
Technology, 5(8), 3188-3193.
vii.
Mustafa, M. R., Rezaur, R. B., and Isa, M. H. (2012). ―River
Suspended Sediment Prediction Using Various Multilayer Perceptron Neural
Network Training Algorithms- A Case Study in Malaysia.‖ Water Resour
Manage, 26, 1879-1897.
viii.
Ref Sreekanth, J., and Datta, B. (2011). ―Comparative evaluation of
Genetic Programming and Neural Networks as potential surrogate models for
coastal aquifer management.‖ Journal of Water Resources Management, 25,
3201- 3218.
ix.
Selle, B., and Muttil, N. (2010). ―Testing the structure of a
hydrological model using genetic programming.‖ Journal of hydrology, 397 (12), 1-9.
MANIT Bhopal
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18-19, Dec. 2014
x.
Shabani, M., and Shabani, N. (2012). ―Estimation of Daily
Suspended Sediment Yield Using Artificial Neural Network and Sediment Rating
Curve in Kharestan Watershed, Iran.‖ Australian J. of Basic and Applied
Sciences, 6(12), 157-164.
xi.
Shamsudin, S., Rahman, A., Haron, Z., and Ahmed, A. A. P. (2013).
―Detention Pond Phosphorous Loadings Uncertainty Using Fuzzy Logic.‖ Int
Journal of Soft Computing and Engineering, 3(2), 1-5.
climatic variations by changing the operational patterns are
also briefly discussed.
Keywords: Wastewater Treatment and Reuse, Constructed
Wetlands, Natural Treatment Systems, Clogging,
Biofilm
1. INTRODUCTION:
Typologies for Successful Operation and
Maintenance of Horizontal Sub-Surface Flow
Constructed Wetlands
Lohith Reddy D, Dinesh Kumar* and Shyam R
Asolekar
Centre for Environmental Science and Engineering,
Indian Institute of Technology Bombay, Mumbai, 400076, India
*Corresponding author: [email protected]
ABSTRACT: This paper reviews the current trends of
technical and operational limitations of sub-surface flow
constructed wetlands for treating domestic wastewaters.
Considering the long-term effectiveness of constructed
wetlands, aging contributes to decrease in contaminant
removal rates over time. Also, temperature fluctuations
especially given in the Indian conditions affect the constructed
wetland efficiency and functioning over time (Vymazal, 2010).
Fluctuations in inflow due to wide range of changes in
precipitation magnitude lead to reduction in treatment
efficiency of the system. Colder conditions affect the rate at
which the contaminants get metabolized. Heavy influent flow
results in overload to the system and driving it to perform
inefficiently. On the other hand, lower flow (dry conditions)
damages plants and hence severely limits the system
performance (Pedescoll et al., 2009). The most commonly
observed operational problem in constructed wetland is the
clogging of the wetland media. The clogging affects the
infiltration capacity of the filter media which resulted in
inefficient use of the system. Also, clogging cause the
deterioration of hydraulic conductivity inside the system
(Knowles et al., 2009). The clogging can be minimized by
implementation of efficient pre-treatment process and units
before the wastewater enter into the wetland (Varga et al.,
2013). The other methods for reducing the rate of clogging
include washing the clogged medium and replacing it back,
exposing the clogged medium to oxidizing agents like H 2O2,
anaerobic pre-treatment, flow direction reciprocation,
minimization of inlet cross sectional area, and implementation
of step-feeding etc. The important issues of successful O&M of
constructed wetlands and there remedial measures have been
discussed in this paper – which are the highlights of this paper.
Also the effect of climate change on wetland efficiency and
strategies to be implemented for effective tackling of the
Increase in population and urbanization, there is a rampant
increase in wastewater generation. Wastewater treatment
facilities are not developed at the same place especially in
developing nations like India. According to CPCB reports
(2008), about 38,254 Million Liters per Day (MLD) of sewage is
generated from class I and class II Towns in India. However,
waste treatment facility is limited to 12,000 MLD, which is
merely 30% of total generation. Therefore large volume of
wastewater runs into natural water bodies leasing to pollution of
coastal zones and ground drinking water. CPCB (2009)
calculated the economic value of municipal wastewater in terms
of nutrient value to land for agriculture and realized that
fertilizers along with wastewater worth Rs 1091.20 million are
discharged in to the coastal waters from coastal cities and towns
annually. In the recent years, Natural Treatment Systems (NTSs)
have been accepted as distinct treatment technologies with low
construction and operation and maintenance cost. NTSs have
been proven a better alternative of wastewater treatment
worldwide because it has minimum energy requirement, reduced
maintenance and higher degree of treatment as compared to
conventional treatment systems for sanitation of small
communities. The different types of NTSs are available and the
most common include hyacinth and duckweed ponds, Lemna
Ponds, Waste Stabilization Ponds, Oxidation Ponds and Lagoons
and Algal-bacterial Ponds etc. The wastewater treated from
NTSs, especially from constructed wetlands (CWs) gives a
substantially good quality treated water interims pollution
indicator like BOD, COD, TSS and coliforms (Vymazal, 2007).
A large volume of wastewater continues to be discharged into
natural watercourses leading to pollution of the coastal zones
and drinking water reservoirs in India (Asolekar, 2001).
Disposal of partially treated and mostly untreated effluents into
rivers and lakes and runoff from urban and agricultural areas are
the two main reasons responsible for deterioration of drinking
water resources. In addition, excessive withdrawal of water for
agricultural and municipal utilities as well as use of rivers and
lakes for religious and social practices, and perpetual droughts
limits the capacity of river for dilution of wastes (Asolekar,
2002). The other sources of pollution which is also responsible
for pollution of ground water and surface water resources are
diffused pollution. The diffuse pollution generally occurs when
potentially polluting substances leach into surface waters and
groundwater because of rainfall, soil infiltration, and surface
runoff (Vymazal, 2008). The typical examples of diffuse
pollution include application of fertilizers in agricultural
activities and forestry, use of pesticides in wide range of land
uses, contaminant pollution from roads and paved areas,
atmospheric deposition of contaminants from industrial activity,
etc. (References Needed)
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Constructed wetlands (CWs), also called as reed beds, are
artificially engineered eco-systems designed and constructed to
maintain and manipulate physico-chemical and biological
processes within a semi-controlled natural environment
(Vymazal, 2010). These systems are robust, have low external
energy requirements (especially when compared with
conventional wastewater treatment technologies like activated
sludge process), and are easy to operate and maintain which
makes them suitable for decentralized wastewater treatment in
areas that do not have public sewage systems (Wu et al., 2014).
Contaminant removal processes in CWs are very complex and
depend on various interrelated physico-chemical and biological
processes such as sedimentation, filtration, precipitation,
volatilization, adsorption, plant uptake etc. (Vymazal, 2007).
These processes are again indirectly or directly affected by
different loading rates, temperatures, soil types, operation
strategies and redox conditions in wetlands. The present trends
in urbanization make it difficult for intensive use of CWs due to
large area requirements but considering the fact that the wide
range of pollutant removing ability of CWs including nitrogen,
phosphorous, organics, solids, metals and coliforms makes it the
most sought after technique of wastewater treatment in recent
days.
Based on the experienced gained during India wide survey of
operating CWs systems as well as the state of the art of the
current knowledge, the article has been constructed to
summarize the various affecting operating parameters and
environmental conditions that affect the performance of CWs.
The prime objectives that were focused during this study are as
follows:
1. Strategies need to be implemented in alteration of operating
patterns of CW in order to reduce the problem of clogging,
2. To understand and study the effect of tidal operation (flow) in
wetland‟s treatment efficiency,
3. Study of changes in wetland media and treatment efficiency in
response to artificial aeration.
2. Classification of constructed wetlands
CWs may be classified according to the type of macrophyte
community dominating into free-floating, floating leaved, rooted
emergent and submerged macrophytes. Also division can be
made on basis of wetland hydrology and also flow direction
(Vymazal 2010).
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Table 1. Typologies of classification of wetland systems
S.
No.
Type of
System Description
Macrophyte
(A) Classification of wetlands based on type of vegetation
1
Emergent
Emergent macrophytes usually growing in
saturated soil, and can grow in water depth of 0.5m
or more
2
Submerged
Macrophytes having their photosynthetic tissue
submersed by water, can grow well in oxygenated
water
3
Floating
Macrophytes rooted in submersed sediments
leaved
having water depth of 0.5-3.0m and having slightly
aerial leaves
4
Free
Macrophytes freely floating on the surface of water
floating
(B) Classification of wetlands based on their hydrology inside the system
1
Free water
Similar to natural wetlands with shallow flow of
surface flow wastewater (which is less than 60cm deep) over
saturated soil substrate (Saeed and sun, 2012)
2
Sub-Surface Mostly employ gravel as main media, wastewater
flow
comes in contact with microorganism growing on
plat roots and substrate allowing pollutant removal
from bulk liquid (Saeed and sun, 2012)
i) Vertical flow systems
ii) Horizontal flow systems
iii) Hybrid flow systems
Table 2. Mechanisms and process in pollutant removals in CWs
S.No.
1
2
3
1
2
3
Process parameter
Mechanism involved
(A) Physical processes
Sedimentation
Involved during precipitation of
suspended particulate matter in
the matrices of CW system
which later on processed by
microorganisms and root
systems. Design and operation
of CWs for the treatment of
precipitation water heavily
depends on extent of
sedimentation.
Filtration
Media used for plant growth act
as a conventional filtration unit
in removing pollutants.
Suspended solids are filtered by
plant roots and voids that present
in between gravel and sand.
Pebbles, gravel, plants help to
stabilize flow and slow down
water velocity.
Adsorption
Adsorption (apart from
precipitation) plays a vital role
in removal of most of the
phosphorous from the
wastewater.
(B) Chemical processes
Precipitation
Accounts for removal of
phosphorous, inorganic
pollutants and also heavy metals
from the industrial and domestic
wastewater.(Qi et al., 2014)
Chemical
Involved in breaking down of
decomposition
complex chemical compounds
into simpler forms which can be
removed by other process.
Ammonification
If the incoming water is rich
with organic nitrogen,
Ammonification initiates the
first step of nitrogen
1
2
3
transformation if the incoming
water is rich with organic
nitrogen. It is an energy
releasing, complex biochemical
process where amino acids are
subjected to oxidative
deamination producing ammonia
(Saeed and Sun, 2012)
(C) Biological processes
Bacterial
Soluble organic matter is
metabolism
degraded aerobically by
heterotrophic bacteria. Also,
ammonifying bacteria help in
removal of nitrogen by
degrading organics with
nitrogenunder aerobic
environment.(Vymazal 2010)
Plant
Plants generally help in removal
metabolism
of nutrient by providing the
substrate (rhizomes and roots)
which help in bacterial growth
as well as nutrient uptake.
Plant uptake
Plant uptake represents only
temporary storage since the
absorbed nutrients are returned
back to system after plant die-off
Processing of pollutants inside the CWs
The various processes taking place in CWs includes, physical,
chemical and biological which takes places through the
combined actions of all the wetland components. The various
crucial process taken places during the course of treatment of
wastewater are surmises in Table 2.
Nutrients removals in CWs systems
The major nutrient in domestic wastewater includes nitrogen and
phosphorus which are being removed through various
mechanisms summarized in table 2. Phosphorous in wetlands
treating is generally removed by precipitation and adsorption (by
media, generally soil). Precipitation is generally a stimulated
condition whereas adsorption occurs naturally under conditions
prevailing in wetland. Phosphorus sorption capacities of soils are
directly proportional to the amount of amorphous forms of
aluminium and iron content in the soil (Reddy et al., 1998; Axt
and Walbridge, 1999). Sorption is reported to be higherin
aerobic soil/sediment conditions than anaerobic conditions (Ann
and Delfino, 2000). However, fluctuating aerobic and anaerobic
conditions of soils and sediments can also cause transformation
of crystalline Al and Fe compounds to more amorphous forms
under anaerobic conditions, which have greater surface areas for
phosphorous sorption reactions to occur. Nitrogen content in
wastewater and influent treated by wetland is basically removed
by denitrification. The coexistence of aerobic and anoxic layers
facilitates biological nitrogen removal via the coupling of
nitrification and denitrification reactions. This process involves
the carbonand nitrogen cycles inCWs as the denitrifying bacteria
obtainenergy from organiccompounds at the same time, as
nitrate and nitrite is used as an e-acceptor. Denitrification
occurred through heterotrophic aerobic facultative bacteria that
are able to use nitrate and nitrite as an e- acceptor under anoxic
conditions. These bacteria use oxygen preferentiallyover nitrate
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as an e- acceptor when it is available in thesurrounding
environment. Significant denitrification rates areobserved in
depleted oxygen environments only. (Garcia et al, 2010).
Potential technological intervention
1
Organic
carbon
availabili
ty
2
Hydrauli
c
Loading
3
Feed
mode
4
Retentio
n time
Apart from numerous benefits offered by the constructed
wetland technology, it also has several limitations for successful
operation and maintenance which needs to be highlight to make
technology successfully projected at large scale of
implementation for wastewater treatment and reuse. The primary
factors which affect the performance of the system may be
included under environmental factors and operational issues
(summarized in Table 3).
During the operation of CWs based systems some common
problems have been observed world-wide which ultimately
affect the performance and acceptability of systems by the
designers for long-term operation. The most commonly observed
operational problem in constructed wetland is the clogging of the
wetland media. Clogging can be defined as a process of
accumulation of solids of different types basically found in
dissolved or suspended form in the influent to be treated by
wetland which results in progressive loss of initial hydraulic
characteristics, mainly porosity and hydraulic conductivity. The
main disadvantage of clogging is it eventually leads to reduction
in the infiltration capacity of the filter media. Also this results in
deterioration of hydraulic conductivity over time (Knowles et
al., 2009). The indicators of clogging of wetland media include,
solids accumulation in between the pores, reduction in drainage
porosity, saturated hydraulic conductivity, appearance of water
on surface of medium near the inlet zone, formation of bad/foul
smells (Turon et al., 2009), presence of mosquitoes (Turon et al.,
2009), etc.The clogged CW bed resulted in various effects
including, decrease the hydraulic conductivity and porosity,
causes preferential water flows along the wetland, formation of
dead zones and short circuits (Pedescoll et al., 2009), ponding of
wastewater on surface of the system (Knowles et al., 2010),
diminishment of hydraulic retention times (HRT) (Morales et al.,
2014), poor plant growth and weed infestation (Knowles et al.,
2009), reduction in treatment efficiency of the wetland (Turon et
al., 2009) etc. The indicative parameters and their effects on the
clogging of CW system have been summarised in Table 4.
Table 3: Factors effecting wetland performance of constructed
wetland systems
S.
No.
Causativ
e factor
1
pH
2
Dissolve
d oxygen
3
Tempera
ture
Effect of influencing factor on overall performance
of system
(1) Environmental factors
Since nitrification consumes alkalinity and results
in drop of pH it indirectly affects denitrification.
An optimum pH between 6-8 with a highest rate of
denitrification at pH between 7.0-7.5 is reported
(U.S EPA, 1975)
Lack of oxygen inhibits denitrification and it can
be overcome by employing forced aeration into the
wetland matrix (Zhang et al., 2010) but on the
flipside this can account for little extra operational
costs for smaller wetland systems.
Temperature affects nirtrification as well as
denitrification with peak denitrification rates
observed between 16-32˚C. Denitrification attains
maximum rate between 20-25˚C (U.S EPA, 1975).
Higher temperatures during summer showed
considerable increase in nitrification-denitrification
rates compared to winters.
(2) Operational factors
Organic carbon availability is the factor governing
nitrogen removal mechanisms. In case of absence
of organic carbon, external carbon source is
generally added to the Horizontal flow wetland
system. A C/N ratio of 2.5 was found to have
highest total nitrogen removal (Zhao et al., 2010).
Denitrification rates are also influenced (improved)
by external carbon addition. Rustige and Nolde
(2007) proposed that addition of acetic acid
achieved denitrification rates of around 75% with a
C/N ratio varying from 0.1-0.8 in case of landfill
leachate.
Greater hydraulic loading does not ensure required
contact time for proper nitrogen removal and hence
reduces the treatment efficiency. Trang et al., 2010
noticed the reduction in both organics and nitrogen
removal when the HL was increased by 300%
attributing it to overland flow resulting from high
HL.
The various feed modes in practice across the
world include batch, intermittent, step and tidal
feed modes. Each have their own advantages and
disadvantages but amongst them tidal mode
showed efficient performance at higher organic
loading and hence advisable for treatment of
wastewater with high organic loads (Saeed and
Sun, 2012)
Higher retention times usually increase the
nitrogen removal as more contact period ensures
efficient nitrification and denitrification cycles
taking place inside the wetland matrix
The degree of clogging may be assessed by the traditional
methods which comprise of tracer testing and chemical analysis
of composition of clog matter. Now a day‟s the recent methods
are being employed for assessing the degree of clogging are the
constant-head permeameter tests and falling-head permeameter
tests. These in-situ tests provide valuable insight on the
assessment and evaluation of the extent of clogging (Nivala et
al., 2011). Further, accuracy in the results may be achieved by
adopting even more sophisticated methods like the finite element
analysis models (Knowles and Davis, 2011). Being noncohesive, gravel samples cannot be extracted non-destructively
to use for analysis by standard laboratory tests (Ranieri, 2003).
Hence the need for advanced non-conventional methods arises.
The quantity of TCOD and BOD5 indirectly account for
clogging as this will result in solids accumulation through
microbial growth. Wastewater containing soluble organic
biodegradable constituents account for most of the TCOD and
BOD5.
Theoretically several models of clogging in sub-surface flow
treatment wetlands have been proposed for by many researchers.
Some of these models include reactive-transport model. (Samso
et al., 2013) which mimics the working of HSSF constructed
wetland which is most preferred natural wastewater treatment
technology in use. HSSF constructed wetlands are generally
simulated by considering the wastewater flow saturation. And
for describing the hydraulics taking place in the wetland system,
we generally adopt a continuously stirred tank reactor network
(Mthembu et al., 2013). However, the purification process of
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wetlands is quite difficult to understand and hence limits the
wide scale application of these models. However, CWs are
known to be complex systems, the behavior of which depends on
various physical as well as chemical factors. To understand the
effect of all these factors on the functioning of CWs, further
iterations and studies have to be conducted.
Table 4: Indicative parameters and their effect on clogging of
CW systems
S.
No.
1
2
3
4
5
Indicative
Parameter of
Clogging
Accumulation
of wastewater
solids and
vegetal debris
in the CW
media
Growth of bio
film on CW
medium
Rhizomes and
roots
Chemical
processes
Hydraulic
overloading
Description of Causative Effect
Reference
Similar to flocculation where the
transport mechanism results in
collision between the particles and
the particles adhere onto the
medium upon impact with it. The
retention of particles on the media
is due to the electrochemical effect
of adsorption i.e. the summation of
electrical double layer interactions
and Van der waals forces.
The microbes follow the same
principles of transport and
attachment followed by the
suspended solids. Biofilm clogging
reduced the inlet hydraulic
conductivity as much as 64% when
compared to the outler hydraulic
conductivity.
Root growth would counteract the
clogging phenomenon contrary to
the normal belief. Macro-porous
network of flow is provided by the
roots due to their tubular structure
which could reduce clogging. Root
material contributed to sub surface
clogging whereas leaf litter-fall
contributed to surface clogging.
Processes like physico-chemical
adsorption associated with removal
of metals and phosphorous and
deposition of chemical precipitates
contribute to clogging.
This can also be considered as one
of the contributing factors for
clogging. Hydaulic overloading
basicallyresults
in
reduced
detention timeswhich results in
partial degradation of organics and
also hydraulic overloading leads to
increased rate of TSS inflow into
the wetland ultimately resulting in
faster rate of clogging.o
CasellesOsorio and
García(20
06)
fluctuations
3
Fluctuations in
inflow
4
Colder conditions
5
Heavy influent
flow
6
Dry flow damage
CasellesOsorio and
García(20
06)
Kickuth
andKönem
ann,
(1988),
Kadlec
and
Wallace
(20009)
Wallace
and
Knight,
2006
Knowles
et al., 2010
S.
No.
1
2
3
4
5
6
7
9
Temperature
Description of Causative Effect
Reference
Aging contributes to decrease in
contaminant removal rates over
time
Especially given in Indian
Vymazal,
2005
Claudiane
et al, 2006
Saeed and
Sun
(2012)
Pedescoll
et al.,
2009
Table 6.Strategies in minimization of clogging of CW systems
8
2
Saeed and
Sun
(2012)
The most widely found functional limitation of constructed
wetland-clogging can be minimized by adopting various
approaches during operation and maintenance practices of CW
systems which have been summarized in Table 6.
Table 5. Potential interventions in deprived performance of CW
systems
Causative factor
for clogging
Age effect
2010
Strategies for minimization of clogging
The potential interventions which found mainly responsible in
depriving the systems have been listed in Table 5.
S.
No.
1
conditions affect the wetland
efficiency and functioning
The wide range of changes in
precipitation magnitude lead to
reduction in treatment efficiency
of constructed wetland
Affects the rate at which the
contaminants are broken down
Results in overload to the
wetland driving it to perform
inefficiently
Dry flow damages plants and
hence severely limits the
wetland function. Also
intervenes with the growth cycle
of plants as it takes considerable
amount of time for the plants to
grow back and function at full
potential.
10
11
Vymazal,
MANIT Bhopal
Potential activity to minimize the clogging
References
Implementation of efficient pre-treatment
process and units before the wastewater enters
the wetland
Washing the clogged medium and replacing it
back in the wetland
Partially or completely replacing the clogging
medium with new one. This has to be adopted
only when the degree of obstruction is
considerable (> 75% stagnancy)
Exposing the clogged medium to oxidising
agents like H2O2
High TSS removal in anaerobic pretreatmentwould effectively reduce or avoid
the wetland clogging problem by a
considerable extent
Changes in operation strategies (resulting in
performance intensification) like flow
direction reciprocation which results in
effective prevention of organic matter
accumulation were effectively implemented
by Shen et al.,2010 with in-situ application of
flow direction reciprocation.
Minimization of inlet cross sectional area
which reduces the cross sectional loading
would also result in minimization of the
clogging considerably but has its own effects
on the design modifications that result in
reduction of overall treatment efficiency
Implementation of step-feeding can also
avoid clogging since the organic load and
suspended solids would be distributed along a
greater section of the wetland
Increasing the granulometry of filler materials
by gradual increment in the size of the stones
of the bed along the length of the bed
Implementing structural modifications in
water distribution channel
Raking: Scraping the initial length of the bed
with a small rack proves to be advantageous
Varga et al.,
2013
Pedescoll et al.,
2009
Pedescoll et al.,
2009, Turon et
al., 2009
Pedescoll et al.,
2009
Varga et al.,
2012
Saeed and Sun
(2012)
Munoz et al.,
2006, Nivala et
al., 2011
Wu et al., 2014
Morales et al.,
2014
Wu et al., 2014
Turon et al.,2009
Page 151
Issue Special3
18-19, Dec. 2014
in removing accumulated solids layer and
improving the hydraulic conductivity of the
matrix
Potential modifications to improve the overall system
performance
The long-term effectiveness of CW systemremains a problem
which can be improved by adopting various corrective measures.
Application of designed pollution load during course of
treatment
The wetland cells which are having size to deal with heavy
rainfall conditions, will be subjected to insufficient water during
the summer and spring seasons to maintain plant growth and
microbialactivity in the wetland (Suliman et al., 2005) which
eventuallycause wetland becomes dry and microbes will be lost.
This type of situation reduced the performance of the treatment
system due to lack of microbes and it again need time to reestablish the system microorganisms (Suliman et al., 2005).This
kind of problem can be dealt by constructing a water storage
facility at the upstream of the wetland (so that flow occurs due to
gravity). This also allows for wastewater to be collected during
monsoon period of the year when wetland system cannot provide
a high level of treatment. This stored water can be utilized
during the summer which supports excellent plant and microbial
growth and hence enhances the treatment efficiency to a
considerable extent.
Design and operation of CW systems according to the
climatic conditions
Constructed wetland performance is either studied in a
Mediterranean climate or in a continental climate. Generally,
there are no studies comparing CW performance of two or more
systems in different climates in any part of the world (Garfi et
al., 2012). Also, there prevails a general assumption that CW‟s
are more suitable in tropical areas than in temperate conditions,
because in warm conditions there is continuous plant growth and
biological activity throughout the year, which indirectly results
in increased efficiency which is not possible in colder climates.
The results obtained by Garfi et al., 2012 clearly show that for
both tropical and temperate climatic conditions, horizontal
subsurface flow constructed wet lands serve as a successful
technology. However, efficiencies can be increased in colder
climates also by changing the operating conditions like
increasing the hydraulic retention time and also decreasing the
pollutant mass loading rates (Akratos et al., 2008; Garfi et al.,
2012). Increasing the HRT reduces the differences in efficiency
between cold and warm periods to be less than 10% for all
parameters. Hence the wetland is not utilized to its full potential
(Wu et al., 2014).
It has been observed that climatic variations do not have a
considerable effect on removal of TSS. This is because TSS
removal occurs in CW‟s mainly due to physical processes like
sedimentation and filtration which are not sensitive to season or
temperature. However, according to Garfi et al., 2008, season
had an effect on the mass removal rate of TSS. This may be
linked to an increase of water retention time caused by increase
in water loss inthe warm season (Dušek et al., 2008). Climatic
variations had a clear effect on NH4 mass removal rate efficiency
which shows that NH4 removal mechanisms are temperature
dependent. NH4 removal efficiencies were very poor in winter
and in fact negative in some cases. This meant that the NH4
concentrations increased from inlet to the outlet indicating
activity of methanogenic bacteria (Dušek et al., 2008); due to
which nitrogen mineralization occurs, increasing NH4
concentration. Finally it can be summarized that biological
processes depend on climate and temperature,and winter
removal performances of horizontal subsurface flow constructed
wetland for nitrogenand soluble organic matter, which are
completely driven by biologicalactivity, may be reduced (Kadlec
and Reddy, 2001).
Anti-clogging design of wetland bed
Since porosity will significantly affect the hydraulic conductivity
of the wetland medium, it will be illogical to keep a constant
grain size throughout the medium (Morales et al., 2014). An
increase in 45% of porosity has resulted in a corresponding
decrease upto 60% hydraulic conductivity (Morales et al., 2014).
Also since the aeration of system is necessary, it must be made
sure that the wetland must have simple operation that will attain
natural aeration so as to avoid clogging as a result of anaerobic
activities and simultaneously ensure aerobic processes on the
surface so as to account for degradation of organic matter
(Morales et al., 2014).
Generally the major process that is responsible for natural
exchange of gases like hydrogen sulphide and oxygen is the gasliquid mass exchange between the water and the atmosphere.
The amount of aeration happening by this process is generally
not enough to maintain aerobic conditions if DO of the influent
is very less. So in order to ensure these pre-requisite conditions
for an efficient wetland performance we must adopt a dynamic
and anti-clogging stone layout which will not only avoid
clogging to a maximum extent, but also creates natural forced
aeration along the entire length of the wetland media (Morales et
al.,2014). Morales et al (2014) has analyzed the effect of stone
layout by testing and comparing the operation of two wetland
beds, one with normal rolled-edge stones with no layout
organization having an effective porosity of 14.9% throughout
all cross sections., and other wetland bed consisting of sharp
stones with organized layout with an effective porosity of
51.46% at the input and 23.57 at the output section. He found
out that the later setup had experienced negligible clogging
compared to the former setup.
Installation of primary treatment units
Anaerobic pre-treatment generally involves the treatment of
primary wastewater by anaerobic digesters before allowing it for
treatment by CW. The major contributor of clogging is the TSS,
soluble organic matter which are biodegradable also result in
clogging due to microbial growth (Varga et al., 2013). Since
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these two contributors can easily be avoided by effective pretreatment of influent wastewater, this method is of utmost
significance for successful abatement of clogging. The
combination of constructed wetland and anaerobic digester
proves to be very effective since both these operations are ecofriendly, sustainable and cost-effective, considering the low
construction and maintenance costs of the same (Pedescoll et al.,
2011). The combination of these two technologies work on a
complimentary basis considering the fact that anaerobic pretreatment removes considerable amount of suspended solids and
on the other hand CW contributes towards ensuring better
effluent quality (Alvarez et al., 2008).
Reversing clogging by H2O2 treatment
Most part of the treatment operation by CWs is done by the
wetland bed or so called filter media of the wetland. But as time
passes, the wetland bed gets clogged eventually and reduces the
overall treatment efficiency of wetland. The conventional
method of handling this situation is by removing the clogged
material and replacing it or renewing it with fresh material. This
method is advisable if the size of the wetland is comparatively
small. But for treatment on a large scale this method does not
serve the purpose of reversing clogging and proves to be very
costly as well. In recent years, others methods such as
implementing resting period for each wetland cell to restore the
clogged pores and gaining back the hydraulic conductivity, have
been proposed. But this method also proves to be impractical for
small systems (Nivala and Rousseau, 2009). Studies conducted
by Nivala et al., 2009 shows that replenishing the clogged
wetland bed by oxidising agents such as H2O2was promising
enough to be implemented on a regular basis since it did not
have any long term effect on the wetland plants and biofilms. It
was observed that H2O2acts as an oxidising agent and liberates
the particulate biomass accumulated in the bed by chemical
oxidation. This also resulted in increased TSS content in the
effluent for a small period of time due to chemical oxidation by
H2O2 and physical process like heat and bubbling that generally
occurs due to H2O2 application.
Changes in operating patterns
Tidal Flow: This is a recently developed practice in which the
flow into the wetland is controlled so as to maintain aerobic as
well anaerobic zones in the wetland media facilitating
simultaneous nitrification and denitrification. Nitrification
occurs when the aerobic conditions are prevailing in the media
which is achieved by draining the wetland cells allowing oxygen
to enter the media pores, while denitrification along with
sulphate reduction is achieved when the anoxic conditions are
maintained by flooding the wetland cells with influent until
saturation is achieved(Sun et al., 2005; Wu et al., 2011). This
methodhas observed to be cost effective, efficient as well as
robust but requires skilled manpower to cater for complicated
operational demands.
Effluent recirculation: Considering the fact of low nitrogen
removal rates of CW compared to other treatment technologies,
there is a need for operational modifications of the wetland in
this aspect. The main reason for low nitrogen removal rates in
the wetlands is short hydraulic retention time. For this the
wetland is divided into two stages wherein the first stage in
characterized by implementation of low retention time for BOD
removal. After considerable amount of organic load is degraded
in the first stage, the wastewater is recirculated into the second
stage wherein the hydraulic retention time is increased to
approximately 3.5 days. Recirculation of wastewater effluent
basically dilutes the concentration of incoming wastewater and
increases the contact time of the wastewater with the biofilms in
the substrate and hence improves the denitrification process in
the presence of organic matter (Saeed and Sun, 2012). As a
result increased nitrogen removal takes place due to higher NO 3N reduction attaining higher N- removal efficiencies of the
wetland. This method can be fruitful when applied for horizontal
subsurface flow wetlands receiving high strength wastewater.
On the other hand it has its own limitations. Some of them
include increased operational costs, reduced pollutant removal
for wetlands receiving medium to low strength waste water (Wu
et al., 2014).
Step feeding: Step feeding in simpler terms means introducing
the wastewater inflow at number of input points along the length
of the wetland bed. In order to ensure uniform distribution of
flow, the inlet and outlet structures must be designed properly
especially for wetland systems with small L:Wratio (Stefanakis
et al., 2010). Hence introducing more number of inlet points will
theoretically increase the L:W ratio and hence uniform
distribution of influent into the wetland is ensured. Stefanakis et
al. (2010) has conducted experiments for 3 years with
introduction of step feeding in last year of wetland operation. He
found out that instead of uniform step feed hydraulic loading, the
gradual decreasing pattern in the flow at 3 step feeds has given
more supporting results, where in the organic and nitrogen
removal improved significantly. Phosphorous also shown
increasing trend in removal. This successful step feed
distribution was reported to be as 60%, 25%, and 15% of the
total influent volume.
Flow direction reciprocation: This method can be adopted
provided the wetland is temporarily divided into 2 or more parts
each connected in series and having a cyclic configuration.
According to Behrends, 1999, in this method adjacent cells
sequentially and continuously drained and filled in order to
achieve aerobic, anoxic and anaerobic conditions necessary for
sufficient removal of BOD, TAN (total ammonia nitrogen), and
total phosphorus. This cyclic process of draining and filling the
wastewater in reciprocation is achieved by using gravity, pumps
or a combination of both. The physical parameters
corresponding to this setup like detention time, depth of filter
bed, and reciprocation frequency depend on the effluent quality
required, type of wastewater treated, land availability and
hydraulic loading rate. Behrends (1999) suggested that the
retention time should be significantly longer that the cycle time
of reciprocation. In case of domestic wastewater treatment the
retention time is advisable to be kept between 0.5 to 15 days.
Accordingly the reciprocation cycle time must be restricted to
fluctuate anywhere between six times an hour to twice a day.
MANIT Bhopal
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Insulation during cold climate: The wetland performance
deteriorates during winter due low nitrification-denitrification
rates, low DO levels etc. This problem can be overcome by
taking a wise choice during macrophyte selection. According to
Saeed and Sun (2012) plant species such as Phragmites have a
very high below, above ground biomass. Also they have better
aerial tissue growth. This aerial tissue acts as insulation during
winter. Similarly, the high growth of plant tissue inside the water
provides oxygen hence increasing the available DO during the
winter conditions. Hence overall treatment efficiency of the
system can be enhanced in a natural way during winter without
any changes in the structure of the wetland design. Apart from
this artificial insulating material such as straw or rock wool can
be used for thermal insulation during very cold conditions.
Artificial aeration: To tackle the situation of extremely low
oxygen availability in HSS constructed wetland during winter
due to plant dormancy, we need to provide an alternate source of
oxygen to ensure proper nitrification-denitrification cycles. One
way to achieve this is through artificial aeration. Artificial
aeration on one hand improves the TSS removal by increasing
reaction kinetics and by maintaining empty spaces through the
process of escape of bubbles in the initial portion of the wetland
bed. On the other hand it enhances TKN removal both in
summer as well as in winter. (Claudiane et al, 2006) due to
creation of more favorable nitrification conditions as a result of
added oxygen availability. Also due to increased oxygen
availability sulphur reduction can also be avoided (Faulkner and
Richardson,1989). Clogging is also avoided by artificial aeration
due to enhanced mineralization of accumulated organic matter
(Wu et al., 2014)
Summary
Operating patterns can play a significant role in increasing the
effluent quality of horizontal subsurface flow constructed
wetlands especially considering the year-long varying climatic
conditions in India. Long term problems such as clogging can be
effectively addressed by planned implementation and execution
of these operational changes. Effective biological pre-treatment
and flow direction reciprocation can be adopted so as to avoid
clogging. Similarly in order to tackle with oxygen content
regulation we can adopt measures like tidal operation, artificial
aeration. Measures like effluent recirculation and step feeding
can be practiced to deal with fluctuations in organic loading or
hydraulic loading or both. Few changes (like artificial aeration)
may result in increased operational costs but considering the
contrast achieved in effluent quality it is advisable to implement
these changes on a large scale basis.Various types of constructed
wetlands may also be combined so as to achieve higher
treatment efficiency by complimenting the existing advantages
and drawbacks of each of them.
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Axt, J. R. and Walbridge, M. R. (1999) ‗Phosphate removal capacity
of palustrine forested wetlands and adjacent uplands in Virginia‘, Soil Sci. Soc.
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Behrends, M. (1999) Rekonstruktion von Meereisdrift und terrigenem
Sedimenteintrag im Spa¨tquarta¨r: Schwermineralassoziationen in Sedimenten
des Laptev-See-Kontinentalrandes und des zentralen Arktischen Ozeans. Reports
on Polar Research 310, Bremerhaven: Alfred Wegener Institute for Polar and
Marine Research, 167 pp.
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Chazarenca, F., Gagnon, V., Comeau, Y., Brisson, J. (2009) Effect of
plant and artificial aeration on solids accumulationand biological activities in
constructed wetlands. Ecological Engineering 35 (2009), 1005–1010.
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Christos, S. A., Vassilios, A., Tsihrintzis (2006) Effect of temperature,
HRT, vegetation and porous media on removal efficiency of pilot-scale
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Claudiane, O. P., Florent, C., Yves, C., Jacques, B., (2014) Artificial
aeration to increase pollutant removal efficiency of constructed wetlands in cold
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Technology 47(9): 49-55.
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Fu, G., Zhang, J., Chen, W., Chen, Z. (2013) Medium clogging and
the dynamics of organic matter accumulationin constructed wetlands. Ecological
Engineering 60 (2013): 393–398.
viii.
Huaguo, W., James, W., Jawitz (2006) Hydraulic analysis of cellnetworktreatment wetlands. Journal of Hydrology (6) 330, 721– 734.
ix.
Jaime, N., Tom, H., Scott, W., Katy, B., Brix, H., Manfred V. A.,
Müller, R. A. (2013) Comparative analysis of constructed wetlands: The design
and construction of the ecotechnology research facility in Langenreichenbach,
Germany. Ecological Engineering 61 (2013) 527–543.
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Kadlec, R. H. and Reddy, K.R. (2001) Temperature effects in
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Kadlec, R.H., (2008) Comparison of free water and horizontal
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Knowles, P., Dotro, G., Nivala, J., Garciae, J. (2011) Clogging in
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Knowles, P.R., Griffin, P., Davies, P. A. (2010) Complementary
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Marianna, G., Anna, P., Eloy, B., MaríaHijosa, V., Ricardo, S. C.,
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A Mini Review on Fixed Film Reactor for
Wastewater Treatment
Saraswati Rana and S. Suresh*
Department of Chemical Engineering, Maulana Azad National
Institute of Technology Bhopal-462 051
*Corresponding Author's E-mail: [email protected]
ABSTRACT: This paper reviews on presents rotating
biological contactor (RBC) treatment method for dye/textile,
pharmaceutical, refinery and distillery industrial effluents and
their applications. Wastewater from these industries is most
difficult to treat due to the presence of complex aromatic
chemical structure, which makes them highly stable and hence
recalcitrant for degradation. Physico-chemical treatment
methods are costly due to high price or large quantity of
consume chemicals and equipments, and excessive amounts of
sludge production. Thus, RBC is the biological methods which
preferred due to simple, cheap, process stability and ecofriendly operations and also offers high interfacial area
generated in the rotating disc to establish good contact between
the microbial species and pollutants. In this mini-review,
summarized the performance of RBC in industries like in
petroleum refinery, COD removal is 42%, in dye/textile
industry COD removal is 84%, in distillery industry, COD
removal is 57%, and in chemical industry, COD removal is
93%. From the literature, found that RBC would be a best
treatment choice for dye/textile and chemical industries
effluents.
Key words: Rotating biological contactor; wastewater; loading;
Chemical oxygen demand.
1. INTRODUCTION
Dye, Pharmaceutical, Refinery, Distillery industries are
major contributors to worldwide industrial pollution and creates
an adverse effect eco-system/human being (Suresh and
Rameshraja 2011). Effluent of these industries represents on
environmental problem due to its high organic load, colour,
turbid, suspended solids, presence of synthetic dyes of complex
aromatic chemical structure, phenolic compounds (Metcalf and
Eddy, 2004). Which make them highly stable and hence
recalcitrant for degradation these wastes require appropriate and
comprehensive management approach environmental regulatory
agencies are setting strict criteria for discharge of wastewaters
from industries (Suresh et al., 2011). Colour from dye/textile
industry causes a reduction of sunlight penetration in rivers
because of this decreases both photosynthetic activity and
dissolved oxygen concentration causing harm to aquatic life
(Suresh and Kumar, 2013).
The wastewater generally treated with primary,
secondary and advance treatment methods. The primary
treatment includes neutralization, equalization, sedimentation,
screening, etc. whereas the secondary treatment process includes
the biological and chemical treatment process (Metcalf and
Eddy, 2004). The advanced treatment method are carbon
adsorption, denitrification, ion exchange, reverse osmosis,
electrodialysis etc. and the biological treatment process such as
activated sludge process, trickling filter, oxidation ditch,
sequential batch reactor, rotating biological contactor etc
(Metcalf and Eddy, 2004; Suresh et al., 2011).All the treatment
method have its own advantage and disadvantages, due to ecofriendly, more stability and high interfacial area, low
maintenance and power consumption throughout the process,
RBC is the have addition advantages over other treatment
method (Ghawi and Kris, 2009; Waskar et al, 2012). Biological
wastewater Treatment process can be divided into types-attached
growth process and suspended growth process. Attached growth
process is more stable than suspended growth process because of
its capacity to endure fluctuations in flow rate and organic
matter.
A rotating biological contactor is an aerobic and
anaerobic fixed film biological treatment. This treatment method
is generally used as secondary treatment of industrial and
domestic wastewater. In rotating biological contactor, disc
biomass is liable for the degradation of organic materials (Ghawi
and Kris, 2009). Rotating biological contactor consist a different
size glass container called reactor and a series of circular disks
of polymer materials like polystyrene, polyvinyl chloride,
polyethylene, acrylic plastic. These discs are submerged in
wastewater and rotated through it. These discs are mounted on
horizontal shaft and rotated by a variable-speed electric motor.
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250
Percent removal of Phenol
RBC consist single or multiple stage (shown in Fig. 1). There are
many parameters affecting RBCs performance like organic
loading, hydraulic loading, biomass, rotational speed,
wastewater temperature, staging, RBC media, Dissolved oxygen
levels, medium submergence.
200
Co (mg/l)
150
160
180
200
220
100
50
0
0
10
20
30
40
Time (h)
Figure 2. Effect of phenol concentration on rotating biological
reactor (Pradeep et al. 2011).
Figure 1. Three stage Fixed film Biological Contactor.
This mini review focus on various discussions on
performance for treatment of wastewater from dye, distillery,
pharmaceutical and refinery industries by using Fixed bed
reactor.
2. LITERATURE DISSCUSSIONS
Pradeep et al. (2011) studied on phenol degradation
with the using of rotating biological contactor which consisted of
six polymethacrylate discs, each of diameter was 18 cm, was
covered with polyester cloth. A 10 litres working capacity tank
was made by glass. Different phenol concentrations were studied
from 160 to 220 mg/l phenol with an increment of 20mg/l. For
every increase in phenol concentration the removal efficiency of
phenol and the residence time were examined. The time profile
of phenol removal is as shown in Fig.2. It was observed that the
phenol removal was 99 % for concentrations from 40 to 180
mg/l. At the concentration of 200 mg/l, decrease in removal
efficiency was observed. When reactor was fed with the
concentration of 220 mg/l, the phenol degradation rate and
phenol removal efficiency dropped significantly as the
microorganisms were acclimatized till 200 mg/l of phenol.
Alemzadeh et al (2002) obtained 99% phenol removal using
RBC at an initial phenol concentration of 100 mg/l.
Pakshirajan et al. (2009) investigated that treatment of
decolourising of azo dye containing synthetic wastewater in
continuously operated RBC reactor. Initial dye concentration
was varied between 50mg/l and 100mg/l and the disc rotation
speed ranged varied from 5 rpm to 11 rpm. Results revealed that
containing synthetic wastewater by a mixed culture in RBC
reactor was more than 92% at all experimental conditions.
Initial dye concentration showed significant negative effect as
compared to disc rotation speed on decolourisation efficiency of
RBC.
Kapdan and Kargi (2002) investigated the role of C.
versicolor, white-rot fungi on decolorization efficiency of a
textile industry wastewater and concluded that removal
efficiency depends on biofilm thickness, rotational speed and
concentration of carbon source or glucose. For better growth of
fungi and higher decolorization efficiency, optimum glucose
concentration was 10g/l compare to 5g/l, with the decreases of
glucose concentration color removal efficiency also decreased
due to loss of fungal activity. Color removal efficiency found
77% with 5 g/l glucose concentration. It was found that
rotational speed also played important role in color removal
efficiency. Decolorization increased with the increasing of
rotational speed such as 35% efficiency found at 10-20 rpm and
75% at 30-40 rpm because with the increases of rpm Dissolved
oxygen also increased TOC removal efficiency found 65% at 20
rpm and 80% at 40 rpm. Total decolorization efficiency obtained
33% with 500 mg/l dye concentration and 80% with 50-100 mg/l
dye concentration. At high concentration of dye decolorization
efficiency decreased due to the adverse effect of dye on fungi.
Goyal et al. (2010) studied the four stage model of RBC
for treatment of textile wastewater. Material used for RBC
compartments was stainless steel, number of disks in each stage
was 4, disks made by polystyrene. The synthetic wastewater
contained a mixture of three commercially available reactive
dyes- procion brilliant yellow, procion brilliant blue and procion
brilliant red. As the concentrations of glucose decreased
gradually from 3.0 to 1.0 g/l, the color removal efficiency varied
from 95% to 85%. The color and COD removal efficiency of the
RBC system decreased sharply (15% and 40%, respectively) as
the glucose concentration was further decreased (from 1.0 to 0.5
g/l and then to 0.0 g/l). It indicated that 1.0 g/l is the minimum
concentration of glucose, which is required for the RBC system
to effect color (90±5%) and COD (95±3%) removal. At the
optimized dose (1.0g/l) of glucose media, at 12 h retention time,
dye concentration was increased from 25 to 125 ppm. The result
shows that the efficiency of color and COD removal was varied
from 87% to 97% and 70% to 96%, respectively, when the dye
concentration was increased 25 to 100 ppm. The treatment
efficiency for color and COD removal fell down immediately as
the dye concentration was further increased (from 100 to
125ppm). So, it was observed that the 1.0 g/l of glucose
MANIT Bhopal
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concentration and 100 ppm dye concentration in the textile
wastewater are the optimum dosage for the best treatment
efficiency of the RBC system in terms of color and COD
removal.
Emerenshiya et al. (2011) reported that treatment of
distillery wastewater using RBCs. They found that dissolved
oxygen (DO) level is nearly enhanced 40% which is
contradictory of normal treatment that DO concentrations drop
during the experiment. This indicated that treatment with RBC
reduces the organic load after secondary treatment. The COD
values showed nearly 60% reduction after treating with RBC‟s
and the effluents was suitable to be reused. Guimaraes et al.
(2005) investigated that rotating biological contactor (RBC)
containing P. chrysosporium immobilized on PUF disks with
optimized decolourization medium (basal medium without both
thiamine and exogenous nitrogen) in continuous mode with a
residence time of 3 days. The RBC reactor was monitored to
determine the active life of the biocatalyst (Fig. 3). During the
initial 17 days an average decolourization of 54% and an
average total phenols reduction of 62% were observed. From the
17th day of continuous operation, a progressive decrease in
colour removal was observed while the reduction of total
phenols was reasonably stable. Minimum values of 27 and 56%
were recorded on the 24th day, for colour and total phenols
reduction, respectively. Guimaraes et al. (2011) suggested that,
the decrease in efficiency with the increase in the treatment
period recorded was probably due to the loss of mycelial
activity, primarily in the first stage, caused by diffusion
limitations.
70
60
Percent removal
50
40
30
Colour
20
Total Phenols
10
0
0
5
10
15
20
25
30
Time (day)
Figure 3. Colour and total phenols removal performance of
continuous RBC reactor operated in one way feeding mode
(Guimaraes et al., 2005).
Pakshirajan and Kheria (2012) studied that
decolourization of synthetic wastewater by using two stage RBC
reactor with made of polymethyl methacrylate. Reactor was
operated at a temperature 30 ± 2°C, disc speed 6 rpm with 48h
hydraulic retention time. Further experiments were performed
with the wastewater containing no glucose and glucose at
different concentrations (1-10g/l) and glucose in the media was
replaced by molasses (4g/l) and mixed with the wastewater (1:1)
in order to evaluated its utility as a cheaper carbon source than
glucose in treating the wastewater. Results revealed that
decolourization efficiency was 64% at the end of 2 day, which
however further reduce to 53% at the end of this operation stage.
This reduction in decolourization efficiency correlated with the
enzyme activity profiles which shows that LiP (Lignin
peroxidise) and MnP (manganese peroxidise) activities were
high at the beginning of the stage with lower values towards the
end of the stage. The initial value of the COD removal efficiency
of the wastewater was 73% at the end of two days and it reduced
to 57% at the end of the stage. In wastewater was diluted with
media (no glucose) and it was feed to the bioreactor. Results
revealed that low decolourization efficiency (52.49%) was
achieved but COD removal was slightly higher (65%) than the
first stage. They found that media containing glucose < 5g/l, the
decolourization efficiency value was low due to low MnP
activity. Maximum decolourization efficiency 83% was achieved
when 10g/l of glucose was used in the media for dilution.
Decolourization achieved 80% at 5g/l glucose in the media.
Molasses at 4g/l was used in the media, it could seen that
although COD removal was very high, due to complete
mineralization of highly oxidisable substances present in the
molasses decolourization of the wastewater was quite low due to
insufficient enzyme activities. Results indicated that costly
carbon source glucose in the decolourization media with the
more cheap molasses, however, revealed very high COD
removal efficiency, but low decolourization efficiency of the
industry wastewater. Malandra et al. (2003) investigated that
treatment of winery wastewater using RBC. It was observed that
extensive bio film developed on the RBC discs and contained a
number of yeast and bacterial species that displayed a dynamic
population shift during the evaluation period. The COD
reduction attained 43% with a retention time of 1 hour. It
reported that one of the yeast isolates MEA5 was able to reduce
COD from synthetic wastewater by 95% and 46% within 24
hours under aerated and non-aerated conditions respectively.
Coetzee et al. (2004) studied two stage model of RBC for the
treatment of the winery wastewater. RBC compartment was
stainless steel, media dimension was 23cm and disks were made
by Polyurethane material with discs rotated at 6 rpm with 40%
discs submerged. Result revealed that after retention time of 1
hour COD reduction was attained 23 % (from 3828mg/l to
2910mg/l).
Deshpande et al. (2012) investigated that treatment of a
pharmaceutical wastewater which pretreated in a first stage by
electrocoagulation (EC), using an anaerobic fixed-film fixed-bed
reactor. The reactor was operated at an organic loading rate
(OLR) ranging from 0.6 to 7.0 kg COD/(m3 d) at an HRT of 1–3
d, and the resulting experimental data is shown in Fig. 4. Under
these operating conditions, best removal efficiencies were
obtained at OLRs ranging from 0.6 to 4.0 kg COD/(m3 d) and an
HRT of 2 d, at which COD removals were in the range of 80%
to 90%. Further increases in OLR to 5.0, 6.0 and 7.0 kg
COD/(m3 d) resulted in a drastic reduction in COD removal
efficiency to 72.6%, 64.0% and 46.0%, respectively.
Vasiliadou et al. (2014) studied on RBC for removal of
pharmaceutical compounds under continuous operation. A twostage RBC was used, providing a total surface area of 1.41m2.
Four pharmaceuticals of different therapeutic classes; caffeine,
sulfamethoxazole, ran-itidine and carbamazepine were studied.
The different conditions resulted to different solid retention
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Percent removal of VOC (MEK, MIBK, Toluene mixture)
times (SRT: 7–21 d) in each scenario. The increase of SRT due
to variations of the operating conditions seemed to has a positive
effect on pharmaceuticals‟ removal and negative correlation was
observed between substrates‟ loading and pharmaceuticals‟
removal. An increase of initial pharmaceuticals‟ concentration
resulted to decrease of SRT and pharmaceuticals‟ removal,
suggested that toxic effect to the biofilm. The maximum
removals achieved were greater than 85% for all
pharmaceuticals. The model predicted the contribution of
sorption and biodegradation on pharmaceuticals elimination
taking into account the diffusion of pharmaceuticals inside
biofilm.
100
95
90
85
80
75
70
160
170
180
190
200
210
Time (day)
Figure 5.. Removal efficiency of VOC (MEK, MIBK, Toulene mixture) in the
rotating biological contactor (Datta and Philip, 2014).
100
Percent removal of COD
90
80
70
60
50
40
30
20
10
0
0
50
100
150
200
250
300
350
Time (day)
Figure 4. Performance of rotating biological reactor for treating
electro-coagulation pretreated substrate (Deshpande et al., 2012).
Tyagi et al. (1993) investigated that treatment of the
petroleum refinery wastewater in fabricated four stage RBC
reactor. Polyurethane foam was attached to discs. Media
dimension was 25cm and 42.5% discs submerged in waste and
RBC rotated at 10 rpm. HRT values were taken 7.6, 3.8, 2.53,
and 1.89h, respectively, in each successive stage and substrate
concentration was 2.3-5.3, 4.7-10.7, 9.5-18.8, and12.725.1g/m2d for respective hydraulic loading 0.01, 0.02, 0.03 and
0.04m3/m2.d. After treatment with these values the COD
removal rate was 87.5%, 84.9%, 81.5% and 80.2% for respective
loading of inlet COD 2.3-5.3, 4.7-10.7, 9.5-18.8 and12.7-25.1
g/m2.d). Datta and Philip (2014) studied on removal of complex
mixture of VOCs commonly found in surface coating
manufacturing and application facilities in the RBC. Methyl
ethyl ketone (MEK), methyl iso-butyl ketone (MIBK),
ethylbenzene, o-xylene and toluene (T) were taken as model
pollutants. Overall removal efficiency dropped to 88.1% for a
total mixture of 672 g/m3/h (Fig. 5). The elimination capacity
was hundred percent initially with increasing individual as well
as total ILR, however, with further increase of ILR, the total
elimination capacity decreased (Fig. S5). The concentration
profile of these three compounds along the length of the reactor
showed that while MEK and MIBK was biodegraded mostly in
the first 18 cm of the RBC, biodegradation of toluene took place
along the entire length of the RBC.
CONCLUSION
Fixed film reactor/RBCs have been widely used by various
investigators for the treatment of industrial wastewater
especially from dye/textile, pharmaceutical, distillery and
refinery. Numbers of studies have been done by varying the
various controlling parameters like organic loading, hydraulic
retention time, speed of rotation, dissolve oxygen, staging,
temperature, submergence etc. From the literature discussion and
results showed that Fixed film reactor is effectively used for
treatment of wastewater of even at very high organic. Fixed film
reactor does not require recirculation of secondary sludge and its
hydraulic retention time is low is an important advantage when
compared with other biological treatments like activated sludge
processes, trickling filter and other treatment methods.
REFERENCES
i.
A. M. Deshpande, S. Satyanarayan and Ramakant (2011), Kinetic
analysis of an anaerobic fixed-film fixed bed-reactor treating wastewater arising
from production of a chemically synthesized pharmaceutical, Environmental
Technology, Vol. 33, pp: ( 1261–1270)
ii.
A. Datta and L. Philip (2014), Performance of a rotating biological
contactor treating VOC emissions from paint industry, Chemical Engineering
Journal, 251, pp: 269–284
iii.
Suresh S and Rameshraja D. Treatment of Tannery Wastewater by
Various Oxidation and Combined Processes, Int. J. Environ. Res., 5(2):349-360,
2011.
iv.
Suresh S, Ravi Kant Tripathi and M. N. Gernal Rana. Review On
Treatment Of Industrial Wastewater Using Sequential Batch Reactor. Int. J. Sci.
Technol. Manage. 2 (1), 64-84, 2011.
v.
Suresh S, Sachin Kumar, Removal of Dyes from Textile Wastewater
using Photo-Oxidation: A review paper on current technology. BS publication
(ISBN: 978-81-7800-286-6) 5nd chapter, Vol. 1, 2013.
vi.
Metcalf and Eddy, 2004 Wastewater Engineering, Tata McGraw-Hill
Publishing, Fourth Edition.
vii.
A.H.Ghawi and J.Kris 2009 Use of rotating biological contactor for
appropriate technology wastewater treatment. Slovak Journal of Civil
Engineering,
viii.
V.G Waskar, G.S. Kulkarni, V.S. Kore (2012), Review on Process,
Application and Performance of Rotating Biological Contactor (RBC),
International Journal of Scientific and Research Publications, Volume 2, ISSN
2250-3153
ix.
L. Malandra, G. Coetzee, Marinda-Bloom (2003), Microbiology of a
Rotating Biological Contactor for Winery Wastewater Effluent, water research,
Vol.37, pp: 4125-4134
x.
Guimaraes, P. Porto, R. Oliveira, M. Motab (2005), Continuous
decolourization of a Sugar Refinery Wastewater in a Modified Rotating
Biological Contactor with phanerochaete Chrysosporium Immobilized On
Polyurethane Foam Disks, Process Biochemistry, Vol.40 pp:535-540
xi.
K. Pakshirajan, E.R. Rene, and T. Swaminathan (2009),
Decolourisation of azo dye containing synthetic wastewater in a Rotating
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Technological Utilization of Parthenium
Hysterophorus-A Review
S.Arisutha1, R.B. Katiyar2 and S. Suresh*
Department of Energy, Maulana Azad National Institute of
Technology Bhopal- 462 051
*Department of Chemical Engineering, Maulana Azad National
Institute of Technology Bhopal- 462 051
2
Department of Chemistry, Govt. Motilal Vigyan Mahavidyalaya,
Bhopal
*Corresponding authors E-mail: [email protected]
1
ABSTRACT: Parthenium hysterophorous is a terrestrial
weed and growing wild in many parts of India creating
agricultural and health hazards. Currently, parthenium
hysterophorous is used for different purposes like
composting, vermi-composting, biogas production and
sorption of heavy metals etc. Toxins (parthenin) and other
phenolic acids such as vanillic acids, chlorogenic acid, caffeic
acid, anisic acid, parahydrobenzoic acid were major
components of parthenium hysterophorous. It causes
asthmas, bronchilis, dermatitas etc in humans and animals.
Generally, this weed uprooted and destroyed by burning in
air without any use. This reviews focuses on different
technology for utilization of parthenium hysterophorous.
From the literature point of view, methane content of gas
varied between 62 to 70 % and sorptive removal of Cd(II)
and Ni was found to be 99.7% and 97.54% respectively on
to parthenium hysterophorous ash. Seeds germinations and
radical growth were inhibited by parthenium solids and also
decreases biogas production.
Keywords: Parthenium hystophorous, compost, biogas,
biosorption, heavy metals.
LITERATURE DISCUSSIONS
By accidentally parthenium hysterophorus introduced into India
through PL480 food grains from USA two decades ago.
Agriculture chemist found that parthenium hysterophorus is a
weed came by imparted food grains which affecting food, fodder
crops and causes serious problem to humans/animals such as
allergic, asthama, bronchilis etc (Hausen,1978; Narasimhan et al.
1977; Gunaseelam, 1987; 1997). Rajan(1973) reported growth of
seminal roots and coleoltiles in wheat seeding inhibited by P.
hysterophorus weed. Parthenium is reported to have insecticidal
hematicidal and herbicidal properties and used for producing
biogas, paper and compost (Gunaseelam, 1987; Katiyar, 2014).
In recent years, energy generation from animal waste/weeds/leaf
litters by anaerobic digestion have attracted because of the oil
crisis (Kumar et al., 2013; Arisutha et al., 2014a-b). Gunaseelan
(1997) reviewed that hand- and mechanically-sorted municipal
solid waste and nearly 100 genera of fruit and vegetable solid
wastes, leaves, grasses, woods, weeds, marine and freshwater
biomass for anaerobic digestion to methane. Adsorption is the
one of promising technology for removal of organic pollutant
onto activated carbon but it is costly and requires high cost to
regenerated (Suresh et al., 2013). Therefore, these is need for
development of low cost and easily available material, which
can absorb organic pollutants.
Gas production from the mixture of P. hyserophorus with cattle
dung shown in Fig.1. Gas production was started only from the
sixth week. Maximum production was found to be 2.4 litre per
week. During the five week fermentation period, the reductions
of total solid and organic carbon were 15.4% and 18.4%
respectively.
2.5
Total gas production (L/week)
Biological Contactor reactor: a factorial design study, International Journal of
Environment and Pollution.Vol. 5, pp: 266-275
xii.
R. A. Emerenshiya, S.Kalavathy and R.Rajendran (2011), Analysis of
Physico-Chemical Parameters of WashWater from Distillery before and after
treatment using rotating biological contactor. Journal of Plant Sciences Feed,
Vol.10, pp: 183-185
xiii.
K. Pakshirajan, Kheria S., (2012), ―Continuous treatment of coloured
industry wastewater using immobilized Phanerochaete chrysosporium in a
rotating biological contactor reactor‖, Journal of Environmental Management,
Vol. 101, pp: 118-123
xiv.
Goyal R., T.R. Sreekrishnan, M. Khare, S. Yadav, and M. Chaturvedi
(2010) ―Experimental Study on Color Removal from Textile Industry Wastewater
Using the Rotating Biological Contactor‖, Journal of Practice Periodical of
Hazardous, Toxic, and Radioactive Waste Management, Vol. 14, pp: 240-245
xv.
I.A. Vasiliadou, R. Molina, F. Martinez, J.A. Melero (2014),
Experimental and modelling study on removal of pharmaceutically active
compounds in rotating biological contactors, Journal of Hazardous Materials,
Vol.274, pp:473-482
xvi.
G. Coetzee, L Malandra, GM Wolfaardt and M Viljoen-Bloom
(2004), Dynamics of a microbial biofilm in a rotating biological contactor for
the treatment of winery effluent, Water SA, Vol. 30
xvii.
R.D.Tyagi, F.T. Traiq and A. K. M. M. Chowdhury (1993),
Biodegradation Of Petroleum Refinery Wastewater in a Modified Rotating
Biological Contactor With Polyurethane Foam Attached to the Discs, Water
Research, Vol. 27, pp: 91-99
xviii.
S. Cortez, P. Teixeira, R. Oliveira, M. Mota (2008), Rotating
biological contactors: a review on main factors affecting performance, Rev
Environ Sci Biotechnol 7: 155-172
xix.
K. Stalin (2014), Performance of Rotating Biological Contactor in
Wastewater Treatment- A Review, International Journal of Scientific &
Engineering Research, Volume5, ISSN 2229-5518, pp: 520-524
xx.
I. K. Kapdan, F. Kargi (2002), Biological decolorization of textile
dyestuff containing wastewater by Coriolus versicolor in a rotating biological
contactor, Enzyme and Microbial Technology 30, 195-199
xxi.
N.V. Pradeep, Anupama, U.S. Hampannavar (2011), Biodegradation
of phenol using Rotating Biological Contactor, International Journal of
Environmental Sciences, Volume 2, 105-113
2
1.5
1
0.5
0
0
2
4
6
8
Time (Weeks)
Figure 1. Total gas production during anaerobic
digestion of P. hyserophorus (adopted from Gunaseelan (1987)
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4.93
100
4.92
98
96
4.91
94
Cd (II) adsorbed (mg/g)
Percent removal of Ni (II)
4.9
92
4.89
90
4.88
88
4.87
86
4.86
Percent removal of Ni (II)
Cd (II) adsorbed (mg/g)
Gunaseelam (1995) investigated that effect of inoculam size and
pretreatments on methane production from parthenium weed in
2L fermentes at 26±1 oC temperature. Maximum yield of
methane was observed (152±15ml/gVS). Fresh parthenium at
1000ml cattle manure slurry (inoculums) for 21 days conditions
and 140±8ml of methane per g of V s as dried parthenium weed.
The methane yields from HCl and NaOH treated parthenium
were 45 and 69% respectively, higher than untreated
parthenium. Gunaseelam (1994) reported that 23% of volatile
solid in terms of lignin content and should be pre-treated before
use as feed stock for methane production. Abbasi et al (1990)
investigated eight common aquatic weeds such as Salvinia
molesta, Hydrilla verticillates, Nymphala stellata, Azolla
pinnata, Ceratopteris sp. Scirpus sp. Cyperus sp. and
Utricularia reticulate for production of energy. They found
methane yield in the order of 108 Kcal per ha per year as in
Salvinia weed. Abbasi and Nipaney (1991) studied biogas
production from Pistia stratiotes weed and found 58-68 %
average methane content in the 10 days period. They also
observed different chemicals such as propionic acid, butyric,
isobutyric, valeric acid along with biogas production.
Gunaseelan (1987) investigated methane yield on the
parthenium hysterophorus weeds mixed with cattle manure
(10% v/v) at 30 ±10C in 3L batch digesters. They found methane
content of the gas varied between 60% and 70%. Nizani et.al
(2009) reviewed grass biomethane process with multiple stages.
One ton of volatile solid produces 300m3 of methane when mass
of volatile solids in the grass as a feedstock.
and dried mass of parthenium in the form of powder may be
added in soil to request heavy metal and pollutants
REFERENCES
i.
Abbasi S.A, Nipaney P.C Biogas production from the aquatic weed
Pistia (Pistia stratiotes), Bioresource Technology, 37(3), 1991, 211-214
ii.
Abbasi S.A, Nipaney P.C, Sahaumberg, G.D. Bioenergy potential of
eight common aquatic weeds. Biological waste, 34(4), 1990, 359-366.
iii.
Arisutha S., Suresh S., Prashant Baredar, D.M. Deshpande,
Evaluation of Methane from Sisal Leaf Residue and Palash Leaf Litter. Journal
of The Institution of Engineers (India): Series E., Springler, In press, 2014b.
iv.
Arisutha S., Suresh S., Prashant Baredar, D.M. Deshpande, R.B.
Katiyar, R. Nithyanandam, M. H. Nassir. Utilizing Earthworm Eisenia Fetida in
Vermicomposting of Biogas Slurry with Mixed Crop Litter. Procedia
Engineering, 2014a (In press).
v.
Gunaseelan V.N. Impact of Anaerobic Digestion on Inhibition
Potential of Parthenium Solids. Biomass and Bioenergy 14, 2, 179-184, 1998.
vi.
Gunaseelan V.N. Anaerobic Digestion of Biomass for Methane
Production: A Review. Biomass and Bioenergy Vol. 13, Nos. l/2, pp. 833114,
1997
vii.
Gunaseelan V.N., Parthenium as an Additive with Cattle Manure in
Biogas Production. Biological Wastes 21 (1987) 195-202.
viii.
Gunaseelan, V. N., Effect of inoculum/substrate ratio and
pretreatments on methane yield from Parthenium, Biomass and Bioenergy, 1995,
8, 39-44.
ix.
Gunaseelan, V. N., Methane production from Parthenium
hysterophorus L., a terrestrial weed in semi-continuous fermenters, Biomass and
Bioenergy, 1994, 6, 391-398.
x.
Hausen B.M, Parthenium hysterophorus allergy. A weed problem in
India, Derm Beruf Umwelt. 1978; 26(4):115-20.
xi.
Katiyar R.B., Optimization of engineering and process parameters for
vermicomposting. PhD Thesis, Department of Chemical Engineering, MANIT
Bhopal, India. p. xx + 187 (2014).
xii.
Kumar S, Suresh S, Arisutha S., Production of Renewable Natural
Gas from Waste Biomass. J. Inst. Eng. India Ser. E (2013) 94:55-59.
xiii.
Narasimhan, T. R., Ananth, M., Narayanaswami, M., Rajendrababu,
M., Mangala, A. & Subba Rao, P. V. (1977). Toxicity of Parthenium
hysterophorus. Current Science, 46, 15.
xiv.
Nizami,A-S, Korres,N.E and Murphy, J.D. Review of the integrated
process for the production of grass bio-methane. Environmental science and
technology, 43(22), 2009, 8497-8508.
xv.
Rajan, L., Growth inhibitor(s) from Parthenium hysterophorus L,
Current Sci., 1973, 42, 729.
xvi.
Suresh S, Srivastava V.C., Mishra I.M. Studies of Adsorption
Kinetics And Regeneration Of Aniline, Phenol, 4-Chlorophenol And 4Nitrophenol By Activated Carbon. Chem Ind. Chem. Eng. Q. 2013, 19 (2)
195−212
84
4.85
82
0
20
40
60
80
100
Water Quality and Flow Simulation along River
Time(min)
Figure 2. Effect of time on the sorption of Ni (II) on P.
hysterophorus ash (Singh et al., 2009) and Cd (II) removal onto
P. hysterophorus (Ajmal et al., 2006).
Fig. 2 shows effect of time on the adsorption of Ni(II) maximum
value at 50 min attained ( 97.54 %) (Singh et al., 2009). The
effect of contact time on the adsorption of Cd(II) at 50 mg/l
initial Cd(II) concentration is shown in Fig. 2. The rate of
adsorption is very fast initially and maximum removal of Cd(II)
occurs with 20 min (Ajmal et al. 2006).
P. hysteophorous ash has shown great potential for the removal
of Ni (II)/Cd (II) from aqueous solution. P. hysteophorous is a
problem creating weed. Instead of burning, they may be dried
Prof. Amarsinh B. Landage1
Assistant Professor, Government College of Engineering,
Karad, Maharashtra, 415 124, India
1
ABSTRACT: Many rivers are the primary source of water. In
the last few decades there is a serious problem of deterioration
of water quality. River water quality models need to represent
the physical, chemical, and biological transformations, which
occur within a river such as bacterial biodegradation, chemical
hydrolysis, physical sedimentation etc. The water quality
control can be possible if know biochemical oxygen demand
BOD, chemical oxygen demand COD, dissolved oxygen, total
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phosphorous, toxic substances etc. at different location and
time. For estimating these water quality variables we solve the
mass transport equation by finite difference method, but for
unsteady nonuniform channel we don‟t have the depth of flow
at different cross sections therefore we use the Saint Venant
Equation solution by finite difference method for water depth
at various cross sections and thus by measuring the water
depth at various sections of a river we solve the mass transport
equation for same for estimating different water quality
measures. Thus simulate the dynamic behavior of flow in a
river. Water quality both within the river reaches and at the
outflow can be determined for a given set of inputs. The finite
difference method for solving the mass balance equation of
dissolved oxygen (DO) and BOD. However numerical solution
does not give the results as compare to analytical solution but
the system is very complicated for unsteady nonuniform river
and including each term in mass balance of DO and BOD so
our purpose of this project is that the results should be very
close to analytical solution and practical. A mathematical
model for estimating the different water quality measures
along a river at different cross sections by using MATLAB for
simulation of these measures.
Keywords: Water Quality, BOD, DO, MATLAB simulation
applies. Gour-tsyh yeh & Fan Jhang developed a model to
simulate reactive chemical transport in river network. Through
the decomposition of the system of species, transport equations
via Gauss Jourdan column reduction of the reaction network.
Kachiashvili, Gordeziani, Lazarov and Melikdzhanian developed
mathematical modeling and computer simulation of diffusion
and transport of chemicals in rivers. They developed these
models in terms of time-dependent convection-diffusion-reaction
differential equations and solve these equations by finite
difference method.
For the solution of saint Venant equation we use the
implicit finite difference method and for unsteady case we need
initial boundary conditions so we solve the these equations first
for steady case then for unsteady case. The matrix formed by
these nonlinear algebraic equations, which is solved by NewtonRaphson method.
1. INTRODUCTION:
   K   A  x, t   C  x, t     Cl  x, t   A  x, t   CD
Numerous researchers developed water quality models mostly
they assume uniform channel and steady case or they used
empirical relationship however due to complex system of
processes in water flow in a river it is difficult to estimate the
water quality measures along a river at different sections but we
can analyze biochemical oxygen demand BOD, chemical
oxygen demand COD, dissolved oxygen, total phosphorous,
toxic substances etc along a river by solving the mass transport
equation using finite difference method and for estimating the
water depth at different cross sections we can use the solution of
saint Venant equation using finite difference method. Various
models also developed such as QUASAR, QUALE2E, CEQUAL-RIV1, etc.
A combined flow and process based river water quality
model is QUASAR. In this model six classes of river-quality
problems are defined. This model was originally developed for
application to the Bedford use to simulate the dynamic behavior
of flow and water quality along the river system (Whitehead et
al., 1979; Whitehead et al., 1981). Initial application involved
the use of the model within a real time forecasting scheme
collating telemetered data and providing forecasts at key
abstraction sites along the river (Whitehead et al., 1984).
QUAL2E water quality model is applicable to well mixed
dendritic streams. It simulates reactions of nutrient cycles, algal
production, benthic and carbonaceous demand, atmospheric
rearation and their effects on dissolved oxygen demand balance.
In this model implicit finite difference method is used. This
model is used only steady state stream flow and contaminant
loading conditions. In CE-QUAL-RIV1 model the
hydrodynamic portion is solving the Saint Venant equation by
four point finite difference method. This model does not allow
for super critical flow. The transport equation is solved using
Holly-Preissmen scheme, the courant number restriction still
2. METHODOLOGY:
2.1 Mass transport equation:
The governing equation for mass transport in a river as:





 A  x, t   C  x, t     A  x, t  V  x, t   C  x, t    A  x, t   DL  C  x, t  
t 
x
x 
x

(1)
Where, x and t represent space and time respectively, A = cross
sectional area, C = contaminant concentration, V = velocity of
flow, DL = longitudinal dispersion coefficient, K = decay rate,

1 dQ
A dx , C l = concentration in the infiltrating flow, C D =
distributed sources or sinks.
2.2 Saint Venant equation:
The continuity and momentum equations of Saint-Venant
equation can be expressed as follows:
A Q

 ql
t
x
…… (2)
Q
 Q 
y

 gAS f  gAS0   qvx

  gA
t
x  A 
x
…… (3)
Where t = Time, x = Longitudinal distances, =momentum
correction factor Q = Discharge, y = water depth, A = Cross
sectional area, B = Free surface width, S0 = bed slope, Sf =
Friction slope and g = Acceleration due to gravity, ql = lateral
flow.
2
The friction slope term Sf can be estimated using Manning‟s
equation for different nodes
Sf 
n 2Q Q
A2 R 4 / 3
…… (4)
Where n is Manning‟s roughness coefficient and R is hydraulic
radius.
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2.3 Finite difference scheme:

Implicit finite difference scheme is used for solving the SaintVenant equation. The Preissmann Scheme, which has been used
extensively since early 1960‟s will be used here. It‟s a four point
weighted implicit difference approximation which is used to
transform the nonlinear partial differential equations of SaintVenant equation into a nonlinear algebraic equations. The partial
derivatives and other coefficients are approximated as follows.
…… (10)
In the matrix form the equation (9) & (10) can be represented as
i
i
 X  
f

x
f
j 1
i
 fi j11    fi j  fi j1 
2t
  fi 1 j 1  fi j 1 
x
…… (5)

1     fi 1 j 
X 
0


  g  Ai  A(i 1)  


 x 
2



i
x
…… (6)

 0
i
Where
Y 
fi j 
 Y
…… (11)
i
f

t
( ql v x ) i  ( ql v x ) i 1
0
2
1
0
 1  Q  gn 2  Q    g  Ai  A(i 1)  
    
 4/ 3    

2

 x  A i 2  AR i   x 
qi  qi 1


2
 
1
  Ai  A( i 1) 
  qv x
g
s



0

2
2 

 
 
i
1


 1 Q
gn 2  Q   
   

 x  A (i 1) 2  AR 4/ 3   
( i 1)  






   qv x i 1 


i
Where
is
an
yi , Qi , yi 1 , Qi 1  Matrix
T
1
1
f    f i j 1  f i j11   1     f i j  f i j1 
2
2
…… (7)
The subscript i designates position on x axis and the subscript j
denotes position on the time axis. where  is weighting
coefficient. The scheme is stable provided >0.5 i.e. the flow
variables are weighted towards the j+1 time level. An
unconditional stability means that there no restriction on the size
of ∆x and ∆t for stability or in general the scheme is stable for
0.55 <  ≤ 1, this scheme can be made totally implicit by taking
 = 1 and explicit by taking  = 0.
array
X 
i
of
nodal
variables
is of size of 2 x 4 and Y  is
i
of size 2 x 1.
On combining the equation for subsequent reaches the complete
equation of the river is obtained as follows.
 X   Y   0
…… (12)
  is the vector having 2N nodal variables for that particular
river and the matrix
[ X] of size 2n x 2n and [Y] of size 2n x 1. [X] And [Y] being
non-linear function of   so the equation (12) is solved by
Newton Raphson method discussed later.

2.4 Boundary conditions:
2.6 Unsteady state formulation of Saint Venant equation:
Q
o
  Qi  ql L
…… (8)
Q
From this equation we can take o discharge at downstream as
known value and depth at upstream is also known value or input
value.
2.5 Steady state formulation of Saint Venant equation:
For solving Saint Venant equation for unsteady state condition
we require an initial steady state solution corresponding to the
initial condition. The steady state equations are derived using
equation (2) & (3), after neglecting time derivatives.
Q( i 1)  Q( i )
x

q l ( i )  q l ( i 1)
2
…… (9)
{[  (Qi 1 ) 2 / Ai 1 ]  [  (Qi ) 2 / Ai ]}
x
( Ai 1  Ai ) ( y i 1  y i )
 g
2
x
g
( Ai 1  Ai ) ( S f ) i 1  ( S f ) i
2
2
g
( Ai 1  Ai ) ( S 0 ) i 1  ( S 0 ) i
2
2
The finite difference form of unsteady state Saint Venant
equations is derived from equations 2 & 3 using four point
weighted finite difference Preismenn scheme.
( Ai j 11  Ai j 1 )  ( Ai j 1  Ai j )

2t
 (Qi j 11  Qi j 1 )  (1   )(Qi j 1  Qi j ) qli  qli 1

x
2
… (13)
(Qi j 11  Qi j 1 )  (Qi j 1  Qi j )

2t
{[  (Qi j 11 ) 2 / Ai j 11 ]  [  (Qi j 1 ) 2 / Ai j 1 ]}

x
{[  (Qi j 1 ) 2 / Ai j 1 ]  [  (Qi j ) 2 / Ai j ]}
 (1   )
x
( Ai j 11  Ai j 1 ) ( y ij11  y ij 1 )
 g
2
x
( Ai j 1  Ai j ) ( y ij1  y ij )
2
x
j 1
j 1
j 1
j 1
(
S
)
( Ai 1  Ai )
f
i 1  ( S f ) i
 g (1   )
 g
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Where T= temperature in degree Celsius, θ= 1.024 for oxygen
reaeration, θ=1.047 for BOD decomposition,
θ=1.08 for
sediment oxygen demand (SOD)
2.8 Finite difference formulation:
j
j
( Ai j 1  Ai j ) ( S f ) i 1  ( S f ) i

2
2
j 1
j 1
j 1
 Ai ) ( S 0 ) i 1  ( S 0 ) i
2
2
 g (1   )
g
( Ai j 11
( Ai j 1  Ai j ) ( S 0 ) ij1  ( S 0 ) ij
( ql v x ) i  ( ql v x ) i 1

0
2
2
2
 g (1   )
Finite Difference formulation of dissolved equation for steady
state and unsteady state condition is derived from equation () by
four point finite difference scheme as:
Steady state:
…… (14)
(S f ) i 
n 2 Qi Qi Pi
Ai
Where
( 4 / 3)
(10 / 3)
2.7 Newton Raphson method:
The computational procedure at any time starts form assigning
the trial values to the 2p unknowns at that time. The trial values
may be the values known from initial conditions or from
calculated values from the previous time steps in case of
unsteady flow problems. Using this trial values we determine the
residuals or corrections

(1)
i, j

(0)
i, j
 
i , j
such that
Unsteady state:
i, j
…… (15)
Where
 (1)i, j

is the better estimate for the flow depth at section
(1)
i, j
(i,j) and
(j=1,2…p/2) are the initial estimates for the
variables (depth and discharge) , the subscript in the parentheses
indicates number of iterations. The solution is obtained by
finding values for the unknowns y and Q such that the residuals
are forced to approach very close to aero or less than prescribed
values. Following is the algorithm of Newton-Raphson method.
 J  
 F 
…… (16)
f ( )  0 the Jacobian matrix [J] and the
Denoting eq (7) as
column vector [F] is formed as
f ( )
…… (17)
…… (18)
3.8 Mass Balance Equation for Dissolved Oxygen:
Mass balances for dissolved oxygen in natural river can be
written as:
C
C
 2C
 v  DL 2  (  K d ) L  (  K a )(C s  C )  Pa  R  S B'  C D
t
x
x
…… (19)
where C = Concentration of dissolved oxygen (mg/l), v =
Velocity of flow (m/day), DL= Longitudinal dispersion (square
meter/day), Kd = Deoxygenating rate(per day), Ka = Aeration
rate (per day), Cs = Concentration of saturated DO (mg/l), Pa =
Average gross photosynthetic production of DO (mg DO/l.day),
R = Respiration by plants (mg DO/l.day), SB‟ = (SB / y)
(mg/l.day), SB = Sediment oxygen demand (g/square metre.day)
Temperature effect on reaction kinetics:
K (T )  K (20 ) (T  20)
(C i j 1  C i j11  C i j  C i j1 ) (vij11  vij 1 ) (C i j11  C i j 1 )

2t
2
x
j 1
j 1
j 1
j 1
j 1
j 1
( D  DL i 1  DL i  2 ) (C i  2  2C i 1  C i ) ((  K d ) L) ij 1  ((  K d ) L) ij11
 Li

3
2
x 2
j 1
j 1
j 1
j 1
((  K a )C s ) i 1  ((  K a )C s ) i ((  K a ) i  (  K a ) i 1 ) (C i j11  C i j 1 )


2
2
2
j 1
j 1
j 1
j 1
j 1
j 1
P  Pa i 1 Ri  Ri 1 S ' B i  S ' B i 1
 ai


0
2
2
2
……
(21)
2.9 Mass Balance equation for BOD
Mass balances for BOD in natural rivers can be written as:
L
L
2L
 v
 DL
 (  K r ) L  C D
t
x
x 2
…… (22)
 f 

 y 
J   
F  
(vi 1  vi ) (Ci 1  Ci ) ( DL (i )  DL (i 1)  DL (i  2) ) (Ci 2  2Ci 1  Ci )

2
x
3
x 2
((  K d ) L) i  ((  K d ) L) i 1 ((  K a )C s ) i 1  ((  K a )C s ) i


2
2
(  K a ( ) i  (  K a ) i 1 (Ci 1  Ci ) Pa (i )  Pa (i 1) Ri  Ri 1 S B (i ) ' S ' B (i 1)




0
2
2
2
2
2
…...
(20)
where L = Concentration of BOD (mg/l)
Kr = Ks + Kd
Ks = effective loss rate due to settling (per day)
Finite Difference formulation of BOD for steady state and
unsteady state condition is derived from equation () by four
point finite difference scheme as:
Steady state:
(vi 1  vi ) ( Li 1  Li ) ( DL (i )  DL ( i 1)  DL (i  2 ) ) ( Li  2  2 Li 1  Li )

2
x
3
x 2
( K L)  ( K r L) i 1 (L) i  (L) i 1 C Di  C Di1
 r i


0
2
2
2
…… (23)
Unsteady state:
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( Lij 1  Lij11  Lij  Lij1 ) (vij11  vij 1 ) ( Lij11  Lij 1 )

2t
2
x
j 1
j 1
j 1
j 1
j 1
j 1
( D  DL i 1  DL i  2 ) ( Li  2  2 Li 1  Li ) ( K r L) ij 1  ( K r L) ij11
 Li

3
2
x 2
(L) i  (L) i 1 C Di  C Di1


0
2
2
BOD VS DISTANCE
18
16
14
BOD (mg/l)
12
10
Numerical solution
8
Analytical solution
6
4
…… (24)
3. RESULTS AND ANALYSIS:
2
0
0
20000
DO (mg/l)
Discharge
m3/d
7.5
500000
KP 100-80
20.59
8.987
1.842
0.764
0.514
0.0002
2
10
200 at 100
KP
2 at 100 KP
540000
KP 80-60
20.59
8.987
1.842
0.514
0.514
0.0002
2
10
5 at 60 KP
120000
DO Variation along a channel
9
8
7
6
5
DO from analytical soln
4
DO from numerical soln
3
2
9 at 60 KP
540000
1
640000
0
0
20000
40000
60000
80000
100000
120000
Distance (m)
N = 10
B = 10m
L = 100 Km
Δx = 10000 m
Δt = 0.1 day
n = 0.035
h
1 = 1.24m
Depth on upstream side
Channel longitudinal bottom slope So = 0.0002
For second point source
h
1 = 1.42m
Depth on upstream side
Channel longitudinal bottom slope So = 0.00018
100000
Figure 2. Distance Vs BOD
KP <60
19.72
9.143
1.494
0.494
0.494
0.00018
2
10
Boundary conditions:
1. For Saint Venant Equation I used the discharge at downstream
as downstream boundary condition, and depth at upstream as
upstream boundary condition. 2. For mass transport equation I
used the constant boundary condition at upstream and
downstream both.
Solution and graphical representation of results
Total no. of nodes
Width of channel
Total length of channel
Space increment
Time interval
Manning roughness factor
For first point source
80000
Figure 1. BOD Vs Distance
DO (mg/l)
KP > 100
20
9.092
1.902
0.5
0.5
0.0002
2
10
2
60000
Distance (m)
For verification of the results hypothetical problem solved by
finite difference method and determined the depth and discharge
at various nodes by using the Saint Venant equation. The
problem solved by numerical method and compares it by
analytical solution. We have no data for unsteady state problem,
so compared unsteady state solution to the steady state solution.
Problem: A river receives a sewage treatment plant effluent at
kilometer point (KP 100) and a tributary inflow at KP 60. The
channel is trapezoidal. The deoxygenation rate for BOD is equal
to 0.5 per day at 20 degree Celsius. For 20 KM downstream
from the treatment plant, there is a BOD settling removal rate of
0.25 per day.
Parameter
T (0C)
DO Sat.(mg/l)
Ka (per day)
Kr (per day)
Kd (per day)
Channel slope
Side slope
Bottom width
BOD (mg/l)
40000
Figure 3. DO Vs Distance
Saint Venant Equation, mass transport equation for BOD and
DO solved by finite difference method and compares with
analytical solution. The results are found closed to analytical
solution. The dispersion term is in mass transport equation
whereas in analytical solution dispersion term is not included.
Thus results obtained by this methodology for BOD and DO
mass transport equation along a channel could be used at field
with boundary conditions.
4. CONCLUSIONS:
The mass transport equation for DO and BOD solved by finite
difference implicit scheme and the Saint Venant Equation for
depth and discharge at various nodes is also solved by finite
difference scheme which used in solving of mass transport
equation. On the basis of results it is observed that there is no
problem of Courant condition and it gives very good results.
Dispersion is not much significant for steady state problems but
it has significance for unsteady state condition. Thus we prepare
MANIT Bhopal
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a mathematical model for estimating the different water quality
measures along a river at different cross sections by using
MATLAB for simulation of these measures. By estimating these
measures by using this model we can control the water quality at
different cross section along a river as required.
REFERENCES:
i.
Brown LC & Barnwell TO (1987) The enhanced stream water quality
models QUAL2E and QUAL2E-UNCAS: Documentation and User Manual,
Report PA/600/3-87/007, U.S. EPA, Athens, GA, USA.
ii.
Cox BA (2003) A review of currently available in-stream waterquality models and their applicability for simulating dissolved oxygen in lowland
rivers. Science of the Total Environment 314: 335-377.
iii.
Downer CW and Ogden FL, 2004, GSSHA: A model for simulating
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Mujumdar PP (2002) Mathematical tools for irrigation water
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Supriyasilp T, Graettinger AJ & Durrans SR (2003) Quantitatively
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Advances in Water Resources 26: 1029-1037.
Assessment of groundwater quality of bah block,
agra, india.
Azmatullah Noor1Dr. Izharul Haq Farooqi2
Assistant Professor, Vivekananda College of Technology and
Management, Mathura Bye pass, Near Khair road, Aligarh202002, U.P., India.
2
Associate Professor, ZakirHussain College of Engg. & Tech.,
A.M.U, Aligarh-202002, U.P., India.
Email:[email protected],
1
ABSTRACT:The study was conducted in the month of May
and June 2012, to evaluate the water quality in the rural areas
of Agra. A total of 60 groundwater samples from 28 locations
which comprises of villages of Bah block. The samples were
collected from tube wells, bore wells, and hand pumps with
recording the position of sampling point, by Global Positioning
System (GPS) device. The samples were examined for physicochemical parameters of water such as pH, alkalinity, total
hardness, electrical conductivity, turbidity, iron, fluoride,
chloride, nitrate, total dissolved solid, and dissolved oxygen.
The main objective of the study was to get information on the
distribution of water quality on a regional scale as well as to
create a background data bank of different chemical
constituents and their quantities in ground water. All data were
statistically analyzed by SPSS package for mean, median,
mode and standard deviation. The Pearson correlation was
also established between physico-chemical parameters of
groundwater. The mean value for pH-8.1098, alkalinity-455.70
mg/l, total hardness-439.94 mg/l, electrical conductivity1541.68 (µS/cm), turbidity-5.86 NTU, iron-.5166 mg/l,
fluoride-1.5672 mg/l, chloride-336.5269 mg/l, nitrate-5.4703
mg/l, total dissolved solid- 737.38 mg/l, and dissolved oxygen4.4342 mg/l. When Pearson correlation was established it was
seen that thereare positive correlation of conductivity with
dissolve oxygen, fluoride, iron, total hardness, alkalinity,total
dissolved solid, and chloride. The correlation of fluoride with
iron, total hardness, alkalinity, total dissolved solid, nitrate,
and chloride is also positive. The result so obtained reveals that
the groundwater is contaminated because of penetration of
chemicals from river Yamuna which is passing along Bah
block.
Keywords: Groundwater, physico-chemical parameters, SPSS,
Agra, Bah.
1.
INTRODUCTION
Groundwater is important for human water supply and, in Asia
alone, about one billion people are directly dependent upon this
resource (Foster SSD., 1995). The groundwater resources play a
very significant role in meeting the ever increasing demands of
the agriculture, industry and domestic sectors (Saleem R., 2007).
India supports more than 16% of the world‟s population with
only 4% of the world‟s fresh water resources (Singh AK., 2003).
The potable nature of groundwater is mainly based on the
physico-chemical characteristics of the water sample. The
impact of industrial effluents is also responsible for the
deterioration of the physico-chemical and bio-chemical
parameters of groundwater.In a reporton "Status of groundwater
quality in India part-1"by (Center Pollution Control Board,
2006-2007) it is mentioned thatin Agra there are 73 industries
and 2 industrial clusters, which discharges their effluent into the
river. Of these industries, only 64 industries have effluent
treatment plants.Other industries which discharge their effluent
directly into the river, playsvital role in groundwater
contamination.The wide range of contamination sources is one
of the many factors contributing to the complexity of
groundwater assessment. It is important to know the geochemistry of the chemical-soil-groundwater interactions in order
to assess the fate and impact of pollutant discharged on to the
ground. Pollutants move through several different hydrologic
zones as they migrate through the soil to the water table. The
serious implications of this problem necessitate an integrated
approach in explicit terms to undertake ground water pollution
monitoring and abatement programs.
The intensive use of natural resources and the large production
of wastes in modern society often pose a threat to ground water
quality and have already resulted in many incidents of ground
water contamination. Pollutants are being added to the ground
water system through human activities and natural processes.
Solid waste from industrial units is being dumped near the
factories, which is subjected to reaction with percolating rain
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water and reaches the ground water level. The percolating water
picks up a large amount of dissolved constituents and reaches
the aquifer system and contaminates the ground water. The
problem of ground water pollution in several parts of the country
has become so acute that unless urgent steps for detailed
identification and abatement are taken, extensive groundwater
resources may be damaged.
Table 1. List of panchayats of study area
S.No.
1.
2.
3.
4.
5.
6.
1.1 Objective and scope of study:
Panchayat
Derak
Kenjra
Dodapura
Badous
Veri
Bitholi
Station
I
II
III
IV
V
VI
The main objective of present study was to carry out ground
water quality monitoring of 60 groundwater samples from 28
locations which comprises villages of Bah block inAgra and to
get information on the distribution of water quality on a regional
scale as well as to create a background data bank of different
chemical constituents and their quantities in ground water. One
of the main objectives of the ground water quality monitoring
was to assess the suitability of ground water for drinking
purposes. The physical and chemical quality of ground water is
important in deciding its suitability for drinking purposes.
2.
MATERIAL AND METHOD
2.1 Collection of sample:
To study the physical and chemical quality of ground water of
the area for deciding its suitability for drinking purposes. A
survey of villages of Bah was conducted in the month of May
and June, 2012 by collecting 60 samples of groundwater from 28
villages. The groundwater samples were collected by grab
sampling after flushing hand pumps for 5 to 10 minutes. The
samples were collected in 1litre plastic bottle. Groundwater
samples were immediately transferred to the laboratory and were
stored at 4˚C to avoid any major chemical alteration.
2.2 Study area:
Agra district occupies the southwestern part of the state of Uttar
Pradesh (India) and is bounded by the state of Rajasthan in the
west and the state of Madhya Pradesh in the south. Bah Tehsil is
the easternmost part of Agra district and belongs to both the
marginal and central alluvial plain (Ganga Plain). The Bah
Tehsil area is situated between 26˚45' and 27˚ 0'N latitudes and
between 78˚10' and 78˚50'E longitudes at approximately 178 m
above sea level. The study area has a semi-arid to arid climate
with an average monthly temperature varying between 38˚C and
46˚C in the summer and between 25˚C and 32˚C in the winter.
The average weather conditions allow recognizing six well
marked traditional seasons, i.e. spring (March–April), summer
(May–June), monsoon (July–August), sharada (September–
October), hemanta (November–December) and winter (January–
February). The average annual rainfall variation is between 600
and 650 mm(Misra, A. K. et al. 2007). In present study, samples
of groundwater were taken from six panchayats, which are
mentioned in Table 1. From each station 10 samples were
collected. The coordinate position of sampling point is located
by GPS device, which is further plotted by ArcGIS 10 on the
map of study area as shown in Figure 1.
Figure 1.Map of study area with sampling points.
2.3 Analytical methodology:
The groundwater samples were analyzed for total hardness, total
alkalinity, chloride using(APHA,1995) procedure, and suggested
precautions were taken to avoid contamination. The electrical
conductivity, pH, dissolve oxygen, total dissolve solids were
determined by LDO probe (HACH) and turbidity by Digital
Nephlometer. The fluoride, iron, nitrate were determined by
Spectrophotometry (DR 5000- HACH).
2.4 Statistical analysis:
The observed data of physico-chemical parameters were
analyzed by SPSS 19.0 software to measure its central tendency,
and deviation of the values from its mean i.e., standard
deviation. The Pearson correlation was also established among
them to identify their relation with each other.
The result obtained after the analysis of physico-chemical
characteristics of groundwater sampleare tabulated below from
Table 2a to Table 2f.
The number of samples which are exceeding IS: 10500 (2003)
for physico-chemical parameters are mentioned in Table 3.
Station I
The fluoride concentration is exceeding the limit as per IS:
10500 (2003) in six samples of groundwater out of ten samples.
Total alkalinity and turbidity is in excess in three samples. The
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EC is exceeding the limit in all samples of the station. In this
station the correlation between TH and TDS is significant which
indicates the concentration of Ca2+ and Mg2+ salts in
groundwater. TDS is also correlated with Fe and TA. The
correlation between TA and TH is in significance which
indicates the presence of carbonate and bicarbonate salts of Ca 2+
and Mg2+. Significant correlation of turbidity with pH and F, TH
with Fe, and pH with D.O. has also been noticed.
Station II
In this station eight samples are having higher concentration of
fluoride. The groundwater is brackish in taste which is indicated
by the range of EC lying between 1000 to 1500 micro
mhos/cm.The correlation of D.O. with turbidity and Cl is
significant.
Ganga Plain. The main characteristics of soil horizons of the
area are the high content of carbonate, distributed throughout the
depth of the profile. In addition, the study area shows frequent
alternations of mud and clay layers in the subsurface lithology
and has very low hydraulic conductivity (Misra 2005). These
factors together constitute a favourable condition for the
maximum absorption of Na+, K+,
and
by the clay
minerals in the soil of shallow and intermediate
aquifers.Generally, Na+, K+,
and
are added to the soil
from several anthropogenic sources both directlythrough
phosphate fertilizers, and indirectly, through atmospheric
pollution from industries and burning of fossil fuels (Drury et al.
1980).
Table 2a.Physico-chemical characteristics of groundwater of
Station I.
Station III
In this station TA is correlated with pH which indicates that
groundwater is alkaline in nature.
The correlation of nitrate with Fe, TDS and EC is the indication
of presence of nitrate salts of iron.
Consequently, the correlation between EC and TDS has been
noticed.
Station IV
The correlation of nitrate with TDS and EC has been noticed in
this station and EC with TDS which is the sign of presence of
nitrate salts.
Table 2b.Physico-chemical characteristics of groundwater of
Station II.
Station V
About 80% of the sample is contaminated with fluoride in this
station. The salts of chloride are present in the groundwater of
this station which is indicated by the correlation of chloride with
EC and TDS. Trace of nitrate salts of iron is present in this
station.
Station VI
There is noteworthy relation of F with Cl, D.O. and EC.
Turbidity is caused mainly due to nitrate concentration in
groundwater at this station. The correlation matrix also shows
the relation of TDS with Cl, turbidity, pH, and EC.
Table 2c.Physico-chemical characteristics of groundwater of
Station III.
The maximum and minimum values, standard deviation and
central tendencies are tabulated in Table 4a to 4f. Karl Pearson
correlation was established among all the parameters, it was
observed that TDS and EC are having positive correlation
coefficient, except in Station II. The correlations of all
parameters have been given in Table 5a to 5f.
The groundwater of the study area are characterized by a high
concentration of Na+, K+,
,
and TDS in shallow and
intermediate aquifers due to some factors which is postulated
that salt-rich geological formations have contributed to these
alluvial deposits (Kumar et al. 1993, 1995; Kumar 1998) of the
Table 2d.Physico-chemical characteristics of groundwater of
Station IV.
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Table 2e.Physico-chemical characteristics of groundwater of
Station V.
Table 4a.Statistical data of physico-chemical parameters of
Station I.
Table 2f.Physico-chemical characteristics of groundwater of
Station VI.
Table 4b.Statistical data of physico-chemical parameters of
Station II.
F-Fluoride, Fe-Iron, N- Nitrate, TH-Total hardness, TA-Total
alkalinity, TDS-Total dissolve solid, Cl-Chloride, D.O.-Dissolve
oxygen, EC-Electrical conductivity.
Table 3.Number of samples exceeding IS: 10500 (2003) limit in
all stations.
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Table 4c.Statistical data of physico-chemical parameters of
Station III.
Table 5a. Pearson correlationmatrix for physico-chemical
parameters of Station I.
Table 4d.Statistical data of physico-chemical parameters of
Station IV.
Table 5b. Pearson correlationmatrixfor physico-chemical
parameters of Station II.
Table 4e.Statistical data of physico-chemical parameters of
Station V.
Table 5c. Pearson correlationmatrix for physico-chemical
parameters of Station III.
Table 4f.Statistical data of physico-chemical parameters of
Station VI.
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Table 5e. Pearson correlationmatrix for physico-chemical
parameters of Station V.
Table 5f. Pearson correlationmatrix for physico-chemical
parameters of Station VI.
4. CONCLUSION
ii.
Bhargava GP, Abrol IP, Kapoor BS, Goswami SC (1981)
Characteristics and genesis of some sodic soils in the Indo- Gangetic alluvial
plains of Haryana and Uttar Pradesh. J Indian Soc Soil Sci 29(1):61–70
iii.
BIS Bureau of Indian Standards Drinking water-specification (2003)
IS:10500, New Delhi
iv.
Central pollution control board, Report on Status of groundwater
quality in India part-1. Groundwater quality series:Gwqs/ 09/2006-2007
v.
Drury JS, Ensminger JT, Hammonds AS, Hollem JW, Lewis EB,
Elemental and mineralogical composition of the coarse Environmental effects of
Pollutants, IX Flouride. US Environmental Protection Agency, Cincinnati, 549 p
vi.
Foster SSD., 1995 Groundwater quality, 17th Special Report.
Chapman and Hall, London
vii.
Kruawal, K., Sacher, F., Werner, A., Mu¨ller, J., &Knepper, T.P.
(2005). Chemical water quality in Thailand and its impacts on the drinking
water production in Thailand. The Science of the Total Environment, 340, 57–
70. doi: 10.1016/j.scitotenv.2004.08.008.
viii.
Kumar R (1998) Role of Himalayan Orogeny in the formation of salt
affected soils of the Indian sub-continent. In: Proceedings of 16th World
Congress of Soil Science, held at Montpellier, August 20–26, 1998. Symposium:
15 Reg No: 277
ix.
Kumar R, Ghabru SK, Ahuja RL, Singh NT, Jassal HS (1993) Clay
minerals in the alkali soils of Ghaggar river basin of Satluj–Yamuna divide in
North-West. Clay Res 12:43–51
x.
Kumar R, Ghabru SK, Ahuja RL, Singh NT, Jassal HS (1995)
Elemental and mineralogical composition of the coarse fraction of the normal
and alkali soils of the Satluj–Yamuna divide of North-West India. Clay Res
14:29–48
xi.
Misra AK (2005) Integrated water resource management and
planning for its sustainable development, using remote sensing and GIS
techniques in dark areas of Agra and Mathura districts of Uttar Pradesh.
Dissertation. University of Lucknow
xii.
Misra, A. K. and Mishra, A., (2007). Escalation of salinity levels in
the quaternary aquifers of the Ganga alluvial plain, India. Environ. Earth Sci.
Journal. 53(1), 47.
xiii.
Mor, S., Ravindra, K., &Bishnoi, N. R. (2007). Adsorption of
chromium from aqueous solution by activated alumina and activated charcoal.
Bio resource Technology, 98, 954–957.
xiv.
Ravindra, K., &Garg, V. K. (2007). Hydro-chemical survey of
groundwater of Hisar city and assessment of defluoridation methods used in
India. Environmental Monitoring and Assessment, 132, 33–43. doi:
10.1007/s10661-006-9500-6.
xv.
Robins, N. S. (2002). Groundwater quality in Scotland: Major ion
chemistry of the key groundwater bodies. The Science of the Total Environment,
294, 41–56. Doi: 10.1016/S0048-9697(02)00051-7.
xvi.
Saleem R., (2007) Groundwater management—emerging challenges.
Water Digest
xvii.
Singh AK., (2003) In: National symposium on emerging trends in
agricultural physics, 22–24 April 2003. Indian Society of Agro physics, New
Delhi.
xviii.
World Health Organization (WHO). (2006). Guidelines for Drinkingwater Quality. Third Edition. 1st Addendum to Vol. 1. WHO Press, 20 Avenue
Appia,
1211
Geneva
27,
Switzerland.
(http://www.who.int/water_sanitation_health/dwq/gdwq0506.pdf).
The quality of the groundwater of the study area is critical due
to
,
and TDS contamination from; dissolve salts in
rainwater, the canal network, low precipitation and high
evaporation due to arid climatic conditions. Among all the
station, the groundwater of station III is slightly potable and rest
of samples are having higher concentration of fluoride which can
cause skeletal fluorosis to the human life of that area. The
electrical conductivity of almost all samples is having higher
values which indicate the level of salinity in groundwater.
REFERENCES:
i.
APHA. (1995). Standard methods for the examination of water and
wastewater (19th ed., pp. 1–467). Washington, DC: American Public Health
Association.
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CHANGING WATER QUALITY SCENARIOS OF TANK
CASCADE SYSTEM AND ITS IMPLICATIONS
J.HEMAMALINI 1, B.V.MUDGAL2 , J.D.SOPHIA 3
1
3
Research Scholar, Centre for Water Resources, Anna
University, Chennai 600025.
2
Professor, Centre for Water Resources, Anna University,
Chennai 600025.
Principal Scientist, M S Swaminathan Research Foundation,
Chennai 600113.
Correspondence to: [email protected]
ABSTRACT
The changing scenarios of tank cascade reveal that the
livelihoods of rural community and tank ecosystem are under
severe threat which needs immediate attention. A cascade
constituting four non-system tanks viz. Athimanjeri,
Konasamudram, Podatturpet, Pandravedu located in Pallipet
Taluk of Thiruvalore district, Tamil Nadu is chosen as study
area. Water samples drawn from four tanks, bore and open wells
adjacent to tanks during rainy and summer seasons was tested
for its physico-chemical and biological parameters. Water
quality index calculated for the tanks to assess its suitability for
drinking shows that the status of four tanks is eutrophic and
needs proper care and interventions to improve its quality. The
irrigation water quality of the four tanks, bore wells andopen
wells are assessed using the irrigation water quality indices
namely Sodium Absorption Ratio (SAR), Soluble Sodium
Percentage (SSP), Magnesium Absorption Ratio (MAR) and
Kelly‟s Ratio (KR). The results indicate that in the Pandravedu
tank,the change in water quality isdue to discharge of untreated
sewage and dyeing unit wastewater. The community perception
on changing water quality and its impact was ascertained
through qualitative research methods like focused group
discussion and one to one interactions which confirms that due
to water quality changes in Pandravedu tank there is reduction in
paddy yield to about 40%, the water is also not suitable for
livestock drinking as it causes diseases, noneof the fish species
are consumed since it causes vomiting and diarrhea.
utilization compared to the groundwater system or even the
major irrigation projects. (Lenin B 2006).The cascade approach
should be followed in restoring tanks if the full benefits of
harvesting the runoff from a micro watershed and effective
groundwater recharge are to be realized. Another concept that
can ensure the sustainability of tanks cascade system is to have
ecological andsocio-economic harmonywhere the village society
and its economy can evolve and thrive on the judicious
utilization of the local resource base.The current study aims
atinterlinking ecosystem and the tank cascades with the
following objectives
1. To analyze and ascertain the suitability of surface and ground
water quality for drinking and irrigation.
2. To conduct an in depth quality analysis of water used for
multiple purposes in Pandravedu village.
3. To elicit community perceptions on the implications of
changing water qualityand coping strategies.
2. MATERIALS AND METHOD
Description of study area
The study area is located in state of Tamil Nadu, India and is a
part of Nagari watershed. It spreads out in Pallipattu block of
Thiruvallur district. The study area comprises four non-system
tanks in a cascade namely Athimanjeri, Konasamudram,
Podatturpet and Pandravedu. There are nine villages benefiting
from this tank cascade. In addition to agriculture most of the
villagers largely depend on non-farm activity like weaving and
dyeing. The area is generally hilly and sloppy with hard rock
formations overlain by top sandy soil. Figure-1 shows the index
map of the study area.
KEYWORDS:Tank Cascade, Physico-chemical parameters,
water quality index, irrigation water quality indices, community
perception, focused group discussion.
1. INTRODUCTION
Tanks have been the main source of irrigation in many parts of
India for centuries. Conserving the tank eco-systems for multiple
uses such as irrigation, domestic, livestock use and groundwater
recharge is a way to provide a safety net to protect the livelihood
of millions in a semi-arid India (Sakthivadivel 2004). Tanks are
eco-friendly and proper management ensure protection and
preservation of the micro ecosystem which in turn provides
services like recycling of nutrients, purification of water,
recharge of groundwater and habitat provision for a wide variety
of flora and fauna in addition to aesthetic values. Further, it
serves as flood moderators during heavy rains and serves as
water points during drought conditions. Tank irrigation was
superior in distributing water, economical in terms of energy
Figure-1.Index map of study area
Water quality analysis
Water samples collected from four tanks, nearby irrigation wells
both bore and open were tested for its physico chemical and
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bacteriological parameters for two seasons namely rainy and
summer. During water sample collection it was observed that the
Pandravedu tank receives untreated wastewater generated from
the dyeing processes along with untreated domestic wastewater
from the Podatturpet households through a lined channel. The
community expressed that in the recent years the water quality
of the Pandravedu tank has deteriorated which in turn affect their
livelihoods including environment. Therefore in addition to
water quality analysis the community perception on changing
water quality and its implication on economic uses of water,
ecological functions for healthy environment as well as sociocultural uses was ascertained.The samples are coded as given in
Table-1:
Table-1.Abbreviations of Sampling Stations
= Ideal value for nth parameter in pure water i.e 0 for all
parameters and 1.0 for pH
Vio
Sn
= Standard permissible value for nth parameter
Water quality index =
Wnqn /
Wn(2)
Where
Wn = Unit weight for nth parameter
The water quality index obtained for the four tanks Athimanjeri,
Konasamudram, Podatturpet and Pandravedu are 178, 1148, 151
and 261 respectively. Comparison of Drinking water quality
parameters are expressed in Table-2.
Table-2. Seasonal Variation of Drinking water quality
parameters
Water quality index
Water quality index is calculated for the four tanks using
equations 1 and 2 as it is a useful tool to assess the present
drinking water quality status andto compare with the BIS
standards (Yogendra et al, 2007).
(1)
Where
qn = quality rating for nth parameter
Vn
= Estimated value for nth parameter
Water quality analysis of water samples confirm that at certain
locations the values exceeded the permissible limits of drinking
standards. The presence of E coli in tank water and in
groundwater at certain places indicates that the water is polluted
with waste water. The higher values of TDS ranging between
188 mg/l to 1133 mg/l prove that water is unfit for drinking. The
total hardness and presence of chlorine is very high in the
Pandravedu tank which made unfit for domestic use and cattle
drinking. The BOD, COD and DO also exceeded the permissible
values at certain locations.
Irrigation water quality indices
Irrigation water quality of the four tanks, bore wells and open
wells are assessed using the indices namely Sodium Absorption
Ratio (SAR), Soluble Sodium Percentage (SSP), Magnesium
Absorption Ratio (MAR) and Kelly‟s Ratio (KR) (Raihan et al,
2008). Comparison of irrigation water quality indices with the
standard values are expressed in Figure-2.
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The SAR for all the tanks and wells fall within the range of 26
for both the seasons. All the four tanks exceeded the standard
permissible value of 40 for SSP whereas the bore wells and open
wells are found within the limit. The MAR values for all the
locations fall within the standard permissible limit of 50%. The
Kelly‟s Ratio is found to be greater than 1 in all the tanks
whereas the well samples are within the permissible range.
(Ramesh et al, 2010). The Table-3gives the seasonal variation of
irrigation water quality indices for rainy and summer seasons.
Table-3. Seasonal Variation of Irrigation Water Quality Indices
Qualitative research method
Qualitative research methods like group discussion with farmers‟
and one to one interaction with the general public including
landless labourers was used to collect community perceptions. A
checklist was designed comprising of questions relating to (i)
people‟s observation in changing water quality over a period of
time (ii) causes for the changes in water quality (iii) its
implications on multiple uses like agriculture, livestock,
drinking, other domestic uses and biodiversity and (iv) specific
issues affecting women due to water quality changes. The
qualitative information generated through group discussion and
one to one interaction is analyzed and presented in the
subsequent section.
Community perception on changing water quality
During group discussion with farmers they expressed that a
decade before the tank water was crystal clear in its physical
appearance and tasted very good which was directly used for
drinking, cooking, and bathing, washing and feeding animals.
However, they could observe gradual deterioration in water
quality since the year 2000 and became worst in the last five
years. Major factor attributed by the community is that in
addition to the fresh water sources the domestic and dyeing
wastewater from Podatturpet village is directly discharged into
Pandravedu tank through a drainage canal hence it is the worst
affected tank in the chain.
Cause of the problem
Figure-2. Comparison of Irrigation water quality indices
They further explained that there are about 150 dyeing units in
Podatturpet village. Weaving and dyeing is one of the
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predominant nonfarm activities in this village.Previously
weaving alone was done in Podatturpet and dyeing was done in
Kanchipuram which is located in a different taluk.After some
time people started dyeing in their own units and the untreated
effluent was let into a barren land in Podatturpet itself. It posed
lot of health issues therefore a lined channel of 5 km is
constructed by the local people to fetch to discharge untreated
effluent from the dyeing units to a nearby tank namely
Thamaraikullam. From there effluent water goes into a small
pond called Thangal which in turn drains into Pandravedu tank.
The waste water that runs through the channel is of dark brown
colour and has bad odour.
Implications of the waste water
Earlier they use to rear fish in the tank water and harvest when
the water reduces during summer. Major varieties were Koravai
(snake head), Kelluthi (cat fish), Keandai (carp) and Veral
(Murrel) but in the last few years they could harvest only tilapia
and could not find other species. Tilapia is the only variety
which survives in poor quality water. Some of the farmers
expressed that the colour of the fish has also changed and if it is
consumed it causes vomiting and diarrhea. Secondly due to
continuous availability of water, harvesting fish has become an
issue therefore fish rearing is almost stopped in Pandravedu
tank. Due to contamination of tank water, the culture of fish
rearing and consuming is total affected.
Drinking and domestic uses
According to the farmers and general public views mixing of
wastewater into fresh water tank has various implications on
productive uses of water like agriculture, livestock rearing,
fishing and other uses like ground water recharge and
biodiversity which is presented below:
Agriculture
From the farmer‟s and the landless agricultural laborerspoint of
viewsthe physical quality of tank water is affected because of the
untreated effluent from the dyeing units.The quality of water has
deteriorated due to both drainage water from houses and effluent
from the dyeing unit is sent together without treatment. The taste
of water has changed and people are not using it for drinking.
The bore wells in and around the tank is also being impacted of
the same problem. Panchayat erected four bore wells around the
tank and supplied it for drinking to the village by storing in the
overhead tanks and establishing a common distribution system.
But during last year the colour and odour of the water pumped
into the overhead tank was dark brown and hence the Panchayat
decided to change the source point.Accordingly bore hole is
erected near Kosasthalaiyaru River and pumped to overhead and
supplied for three days per week which is not sufficient and they
depend on mineral water for drinking. Even the milk gets
spoiled if the tank water is used directly. The people face
irritation in their skin if they use the tank water to bath or wash
their clothes. So they use other small fresh water ponds called as
Thoppaiamman and Vannarakulam for washing clothes.
Ground water recharge
There are four irrigation channels irrigating the agricultural
lands. Five years back the cropping pattern was paddy, paddy
followed by chilli, groundnuts, ragi and other dry crops
depending on tank water availability. Generally the tank receives
water during monsoon and dries out in summer. In fact even the
tank water spread area and tank bund was used by farmers to
grow short term vegetable crops during summer. But from last
five years due to constant flow of wastewater from upstream
village Pandravedu tank had become perennial but with poor
water quality.Therefore the farmers have no option of cultivating
summer crops except of paddy that too very few specific
varieties like ADT 37 which is a fat type of rice.Farmers are
using both surface water from tank and ground water through
open and bore wells conjunctively as a coping strategy. Farmers
expressed that the lands irrigated with tank water alone resulted
in stuntedcrops and the soil is also affected. Comparatively the
middle and tail end farmers are better as the water quality
changes in the natural process through conveyance. Farmers feel
that the entire ayacut is being affected due to the polluted tank
water and the paddy yield is also reduced from 40 bags/acre to
15 bags/acre. The worst implication is the rice grown by the
farmers is not consumed by them due to the fact that it will cause
health problems so they buy rice from outside. But earlier,a
portion of the produce was stored for their household
consumption.
Livestock
Similarly livestock which is the secondary source of living for
farmers and landless community, tank water was the main
source for cattle drinking and cleaning. But the farmers now
suspect that the cattle fall sick when it drinks water directly from
tank. Also they expressed that milk production is gradually
declining but in depth studies to be done to analyze the cause
and effect relationship. They also attributed that due to the odour
some livestock is not drinking it. So they are very particular and
providing only the drinking water supplied by Panchayat.
Fishing
Unlike any other tanks this tank is also recharging the
groundwater and majority of the farmers‟ are using it for various
purposes.However, water in open wells is polluted and is not
used presently for domestic purposes.Previously ground water
was at 200 feet depth and it was good. But now that water is also
polluted and farmers go in for bore for a depth of 300 feet.In
addition to groundwater recharging generally tanks also
contribute for conservation of biodiversity.
Biodiversity
Farmers expressed that a decade before this tank maintained a
very healthy environment including floral and faunal
biodiversity but gradually it is declining due to wastewater. For
example there were lot of crabs in the tank as well in paddy field
after monsoon especially during November but now they could
not find crabs in tank as well as fields. Community used crabs as
medicine mainly to treat over cold and breathing problems.
Similarly they expressed that some of changes are observed in
floral diversity. Another important fact is that earlier when the
water quantity reduces during summer they maintain the system,
do social forestry and other activities and all these are affected
due to continuous flow of wastewater. As a result the entire
environment and ecosystem is getting affected.
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reduce the total disinfectant dose while managing minimum
residual chlorine across the system.
4. CONCLUSION
The water quality index shows that the water is unfit for drinking
and status of four tanks is eutrophic and needs proper care and
interventions to improve its quality. The values of irrigation
water quality indices prove that the tank water quality has
deteriorated and has become unfit for irrigation. Untreated waste
waterfrom dyeing units is a major cause for the pollution of
Pandravedu tank. Continuous disposal of wastewater without
proper treatment makes the tank water unfit for any use. The
wastewater before let into the tank should undergo the necessary
treatment and the industry should strictly follow the same.
Urbanisation of the villages in and around the tank resulted in
discharging the sewage directly into the other three tanks. The
sewage system in the villages should be well designed and the
domestic sewage should be treated properly.
REFERENCES:
i.
Lenin Babu K and Mansi S, Estimation services of Rejuvenated
irrigation
tanks.
A
case
study
in
mid
Godavari
Basin
(http://publications.iwmi.org/pdf/H042911.pdf )
ii.
Ramesh K and Elango L (2011, July) Groundwater qualityand its
suitability for domestic and agricultural use in Tondiar river basin, Tamil nadu
India; Environmental Monitoring Assessment: DOI 10.1007/s10661-011-22313/Springer Science business Media B.V.
iii.
Raihan F, Alam J B (2008) Assessment of groundwater quality in
Sunamganj of Bangladesh; Iranian Journal of Environmental Health Science
and Engineering , Vol. 5, No.3, pp. 155 – 166.
iv.
Sakthivadivel R, Gomathinayagam P and Tushaar, S (2010, July 31)
Rejuvenating Irrigation Tanks through local institutions, Economic and
political.
v.
Yogendra K, Puttaiah E.T (2007) Determination of water quality
index and suitability of an urban water body in Shimoga Town, Karnataka
(Paper presented at the 12th World Lake Conference), 2007.
Booster Chlorination Strategy For Managing
Chlorine Disinfection In Drinking Water
Distribution System – A Review
Roopali V. Goyal 1, Dr. H.M. Patel2
Research Scholar , The M.S. University of Baroda, Vadodara ,
Assistant Professor, Civil Engineering Department, Sardar
Vallabhbhai Patel Institute of Technology Vasad, Dist . Anand
388 306. Gujarat, India.
2
Head and Professor, Civil Engineering Department, Faculty
of Technology & Engg, The M.S University of Baroda,
Vadodara. 390 001, Gujarat, India.
1
ABSTRACT
The amount of residual chlorine in a Drinking water distribution
system (DWDS) is commonly used as an indicator of water
quality supplied to the consumers. Adequate amount of residual
chlorine ensures the microbiological safety, and excess
chlorination leads to taste, odour, or by-product problems.
Compared to conventional methods that apply disinfectant only
at the source, in booster chlorination, chlorine is supplied at
strategic locations throughout the distribution network can
The objective of this paper is to review the work of various
researchers who have effectively applied the strategy of booster
chlorination for managing chlorination by modeling of booster
chlorination using various modeling tools. Further the available
literature is extended to explore the work done by various
investigators who have applied the different optimization
methods for (i) Optimal scheduling of injection rates of chlorine
and optimal operation of booster stations and (ii) Optimal
location of booster stations in the water distribution network. In
addition to the normal operation of booster stations a limited
amount of research that has explored the application of booster
stations to the contamination incident problem and other
applications is also included in this review. After reviewing the
work of all researchers it is found that coupling of water quality
modeling tool with advanced optimization methods can serve as
important decision making tool for management of water quality
in the DWDS.
Keywords: Booster chlorination, Drinking water distribution
system, Optimization methods.
1. INTRODUCTION:
Inadequate chlorine residual in drinking water distribution
increases potential for the breakthrough of organisms and can
ultimately result in public health and regulatory compliance
problems. As chlorine is reactive, it reacts with natural organic
and inorganic matter in water which decreases the chlorine
concentration with time called chlorine decay. The long term
chlorine decay in distributed drinking water and in natural
waters receiving chlorinated discharges can be modeled by using
first order kinetics ( Johnson 1978 ; Hass and Kara 1984;
Rossman 1994; Powell et al. 2000) is given by,
C = Co e(−K t)
(1)
Where,
Ct= Chlorine concentration at time t, mg/l
Co= Initial chlorine concentration, mg/l
T= Time ( hour)
K= First order reaction rate coefficient (hr -1)
As seen from the above equation the residual chlorine
concentration is the function of initial chlorine concentration,
travelling time and decay coefficient. To maintain the adequate
residual chlorine at the farthest end more amount of chlorine is
supplied at source in conventional methods to compensate the
loss of chlorine. But this can generate higher disinfection by
products ( DBPs), and bring odour and taste complaints. Booster
chlorination is the best strategy to maintain the balance between
lower and upper limit of the residual chlorine concentration in
which, disinfectant is applied at strategic locations within the
distribution system to compensate the losses that occur as it
decays over time (Boccelli et al.,1998; Tryby et al., 2002).
Many researchers have worked on the modeling of booster
chlorination using the water quality modeling tool for the
prediction of residual chlorine concentration in DWDS as it is
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the prerequisite for the modeling of the Booster chlorination.
Although booster disinfection is commonly practiced, a
standardized procedure for the location and operation of booster
stations has not been adopted in the water utility community.
Booster stations are often located near areas with low levels of
disinfectant residual, and they are operated with regard to the
local goals of increased residual which often ignores the systemlevel interactions (Haxton et al. 2011). In area of water
distribution system analysis, Optimization models are used for
calibration, design, and operation purpose using various kinds of
algorithms. The coupling of such water quality model with
advanced optimization methods can serve as an important
decision support model for the water supply authority for
scheduling and mass rate application of chlorine at storage
reservoir for maintaining chlorine with range in DWDS at all the
nodes.
2. MODELING AND OPTIMIZATION OF BOOSTER
CHLORINATION:
For the effective modeling of the Booster Chlorination station,
the accurate prediction of the residual chlorine concentration is
required, for which many water quality modeling tools are
available. The usability of these models was greatly improved in
the 1990s with the introduction of the public domain EPANET
model (Rossman, 1994). The model considers first-order
reactions of chlorine to occur both in the bulk flow and the pipe
wall as mentioned in equation 1. It is used by most of the
researchers to find out the residual chlorine concentration in
DWDS (Boccelli et al,1998; Tryby et al., 2002; Munavalli and
Kumar 2003; Prasad et al. 2004; Tryby et al. 1999; Uçaner and
Ozdemir 2003; Propato and Uber 2004a,b; Ostfeld and
Salomons 2005, 2006; Kang and Lansey 2010; Haxton et al.
2011). The booster stations are introduced in EPANET by water
quality sources nodes where the quality of external flow entering
the network is specified. EPANET can model the four types of
sources. (i) A concentration source fixes the concentration of
any external inflow entering the network at a node (ii) A mass
booster source adds a fixed mass flow to that entering the node
from other points in the network.(iii)A flow paced booster
source adds a fixed concentration to that resulting from the
mixing of all inflow to the node from other points in the network
(iv)A set point booster source fixes the concentration of any
flow leaving the node. (EPANET user‟s Manual, 2000). A new
version of EPANET, the EPANET Multi-Species Extension or
EPANET MSX (Shang et.al., 2008) which can be utilized for the
modeling of two source chlorine decay uses the same first order
chlorine decay equation as EPANET is also utilized by different
researchers (Carrico and Singer 2009; Parks and Van Briesen
2009; Ohar, Z. and Ostfeld, A. 2010, 2014; Haxton et al. 2011)
for the prediction of residual chlorine.
There is wide application of optimization methods for various
engineering applications including Booster Chlorination Station.
The optimization methods can be utilized for minimizing of the
mass rate of chlorine applied at booster station, optimization of
location of booster station and its operation with the constraint
of minimum residual chlorine at the locations of DWDS.
Available Literature on the application of various methods of
optimization for the booster chlorination stations is divided into
two major categories (i) Optimal scheduling of disinfectant
injection and operation of Booster Station (ii) Optimal location
of Booster Stations.
3. OPTIMAL SCHEDULING OF DISINFECTANT
INJECTION AND OPERATION OF BOOSTER STATION:
The purpose of optimum scheduling of chlorine injection is to
minimize the total dose of chlorine at source and booster stations
at the same time to satisfy the constraint of maintaining the
minimum residual chlorine at all the locations of DWDS.
Boccelli et al. (1998) formulated a linear optimization model for
the scheduling of disinfectant injections into water distribution
systems. They used EPANET water quality model to quantify
disinfectant transport and decay as a function of the booster dose
schedule using the principle of linear superposition and firstorder reaction kinetic to avoid the computational burden of
water quality simulations during optimization and booster station
operation problem .
Tryby et al. (2002) extended the linear programming (LP)
booster disinfection scheduling model presented by Boccelli et
al. (1998) to incorporate booster station location as a decision
variable within the optimization process. The formulation was
similar to the general, mixed-integer linear programming, fixedcharge facility location problem, and was solved using a branchand-bound solution procedure using coupling the data using
EPANET water quality simulator.
Munavalli and Mohan Kumar (2003) formulated a optimal
scheduling model in terms of a nonlinear optimization problem
to determine the chlorine dosage at the water quality sources
using (GA) approach in which decision variables (chlorine
dosage) were coded as binary strings and solved by linking
EPANET with a genetic algorithm (GA). For the linear chlorine
reaction kinetics (first-order reaction kinetics) the principle of
linear superposition was utilized to compute dynamic chlorine
concentrations without running the dynamic water quality
simulation model.
Uçaner and Ozdemir (2003) studied, the locations, injection
rates and scheduling of chlorine booster stations using genetic
algorithms by coupling the hydraulic solution and chlorine
concentration distribution using EPANET software.
Prasad et al. (2004) investigated the booster facility location and
injection scheduling problem formulated as a multi objective
genetic algorithm optimization model using the theory of linear
super position in water quality modeling for calculating
concentration profiles at network nodes. A multi objective
genetic algorithm called NSGA-II was used in solving the twoobjective problem.
Ostfeld and Salomons (2004) presented the methodology and
application of a genetic algorithm (GA) scheme, tailor-made to
EPANET for simultaneously optimizing the scheduling of
existing pumping and booster disinfection units, as well as the
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design of new disinfection booster chlorination stations, under
unsteady hydraulics.
carried out to find out the optimal locations of booster stations is
presented in the following paragraph
Propato and Uber (2004 a) formulated a linear least-squares
problem to determine the optimal disinfectant injection rates
that minimize variation in the system residual space-time
distribution with assumption of known locations of booster
stations . To investigate the performance and limitations of the
proposed LLS problem was applied on a Cherry hill/Brushy
plains DWDS .
Tryby M. and Uber J. (1999) developed a mixed integer linear
programming method to provide optimal locations and operating
data for booster disinfection stations in drinking water
distribution systems. The problem formulation was related to the
general fixed charge facility location problem, requiring that a
branch and bound solution procedure be used.
Propato and Uber (2004b) extended their previous work to
include the locations of the booster stations as decision variables
and formulated a mixed-integer quadratic programming (
MIQP) problem to locate booster stations and to identify their
dosage schedules for maintaining disinfectant residual in
DWDS. Solution of the problem was done via the branch-andbound technique with quadratic programming sub problems.
Ostfeld and Salomons (2006)
presented two different
optimization objectives for optimal pump operation and booster
disinfection. The proposed objectives were (1) minimization of
the cost of pumping and the booster stations operation and (2)
maximization of the chlorine injected in order to maximize the
system protection. The problem was solved using a GA linked
with EPANET.
Gibbs et al. (2010) studied the booster disinfection dosing
problem, including daily pump scheduling, for a real system in
Sydney, Australia using GA to optimize the operation of the
Woronora WDS.
Kang and Lansey (2010) formulated a real-time optimal valve
operation coupled with booster disinfection problem as a single
objective optimization model. The problem was solved using a
genetic algorithm (GA) linked with EPANET.
Ohar Z and Ostfeld A. (2010) extended the authors previous
work on the usage of chlorine - TTHM multi species model for
optimal design and operation of booster chlorination stations. An
alternative model formulation was suggested by adding
constraints requiring that the concentrations of all species at the
beginning and end of the design period be the same
Ohar, Z. and Ostfeld, A. (2014) formulated and solved model to
set the required chlorination dose of the boosters for delivering
water at acceptable residual chlorine and TTHM concentrations
for minimizing the overall cost of booster placement,
construction, and operation under extended period hydraulic
simulation conditions through utilizing a multi-species approach.
The developed methodology linked a genetic algorithm with
EPANET-MSX.
4. OPTIMAL LOCATION OF BOOSTER STATIONS:
Constans S. et al (2000) proposed linear programming
formulations to determine the optimal locations where
disinfectant must be added and optimize the injection patterns.
Solution of the proposed optimization problem not only gave the
best booster stations locations and injection patterns, but also
calculated the corresponding chlorine patterns at all the nodes of
the network.
Avi Ostfeld (2005) determined the optimal location of a set of
monitoring stations aimed at detecting deliberate external
terrorist hazard intrusions through water distribution system
nodes: sources, tanks, treatment plant intakes.The methodology
implemented in a non commercial program entitled optiMQ-S
linking optiGA and EPANET.
Lansey et al. (2007) assumed first-order reaction kinetics and
formulated an integer linear programming optimization problem
to determine the optimal location of booster stations as well as
their injection rates. The problem was solved using a GA.
Wang Hongxiang et al (2010) formulated an optimization model
in the presence of partial coverage based on the maximum
covering location problem for locating optimal booster
chlorination stations in water distribution systems. A hybrid
PSO, combined with GA algorithms, was proposed to get the
solution which was applied to a hypothetical network .
Wang Hongxiang ( 2010) introduced an optimization model to
identify optimal booster chlorination stations in water
distribution systems in the presence of partial coverage based on
the maximum covering location programming model (MCLP).
Ant Colony Optimization Algorithms was applied to optimize
the booster chlorination stations model. To improve the
optimization ability of ACOAs and avoid getting in the local
optimal solution, the Max-Min ACOAs were adopted, and a
sensitivity-based visibility factor was applied to the ACOAs to a
case study .
Table no 1 gives the summary of various optimization methods
used for the optimal scheduling, operation and location of
booster stations. Table no 2 gives the summary of various
objectives proposed by different researchers.
Table 1. Optimization methods for optimal scheduling,
operation and location of Booster Station
Optimal Locations of the booster station is equally important as
the operations and scheduling of chlorine doses. The work
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efficient and realistic than boosting on a preset schedule by
assuming that the sensor network is detecting a low
concentration of chlorine due to contamination or unpredictable
demand.
Brian Carrico and Philip C. Singer (2009) checked the effect of
conventional and booster chlorination on chlorine residuals and
Trihalomethans (THM) formation in drinking water distribution
systems using EPANET and EPANET -MSX model.
Table 2. Objective functions used for the optimization methods
Haxton et al. (2011) studied the problem of locating booster
stations to support booster disinfection in the context of a
contamination incident with objective to locate a given number
of booster stations using two different ways of formulating a
booster station optimization. The first optimization formulation
was using multi-species EPANET-MSX software to evaluate the
effects of chlorine utilization and contaminant reactions. The
second optimization formulation used an algebraic model for
modeling the flow of contaminants and chlorine in the network.
Nilufar Islam et al. ( 2013) proposed an innovative scheme for
maintaining adequate residual chlorine with optimal chlorine
dosages and numbers of booster locations was established based
on a proposed WQI for The City of Kelowna , Canada water
distribution network using EPANET software and later coupled
with an optimization scheme. Table no 3 narrates the major
findings of various researchers.
Table 3. Major Findings of Various researchers by application
of Booster Chlorination
5. BOOSTER CHLORINATION RESPONDING TO A
CONTAMINATION
INCIDENT
AND
OTHER
APPLICATIONS:
Various investigators worked on the different field to check the
effect of applications of booster chlorination towards
contaminant events and formation of disinfection by-products.
Some of the studies are mentioned here.
Propato and Uber (2004c) applied the booster chlorination
strategy to two example networks under a worst-case deliberate
intrusion scenario. Results saw that the risk of consumer
exposure is affected by the residual maintenance strategy
employed. They found that addition of a booster station at
storage tanks may improve consumer protection without
requiring excessive disinfectant.
Parks and Van Briesen (2009) tested the hypothesis that a
booster disinfection system used in conjunction with a sensor
network boost-response system could provide substantial
protection to allow for uninterrupted high quality water service
during an intrusion event using EPANET EPANET-MSX to
perform the water quality simulations. The hypothesis was
evaluated that a reactive booster schedule would be more
6. DISCUSSIONS AND CONCLUDING REMARK:
After reviewing the work of most of the researchers it is found
that coupled water quality modeling tool with advanced
optimization methods can serve as important decision making
tool for the operation of booster chlorination station to manage
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effective residual chlorine in the DWDS. Investigators utilized
different methods of optimization for optimal scheduling,
operation and locations of booster stations to maintain adequate
levels of residual chlorine throughout the DWDS. Many
researchers have linked the water quality model such as
EPANET or EPANET- MSX with optimization methods to
achieve the balance between the upper and lower limit of
residual chlorine. As seen from summary it is observed that
linear programming model , mixed integer linear programming
and Genetic Algorithm is widely used by many researchers.
Limited research papers are found with applications of
evolutionary algorithms like Particle Swarm Optimization
(PSO). The investigation carried out by various researchers
suggests that the application of booster chlorination strategy can
maintain the balance between the upper and lower limits of
residual chlorine. Studies of most of the researchers show that
the booster chlorination can reduce the amount of disinfectant
required to satisfy concentration constraints, when compared to
conventional disinfection only at the source. This reduced
concentration may help in reduction of harmful disinfection byproduct formation. Thus, the application of linked water quality
and optimization model serve as the important decision
supporting tool for the water supply mangers for effective
management of residual chlorine in DWDS. This will ultimately
provide the protection against the pathogens and harmful
disinfection by-products to consumers.
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Resources Congress , Philadelphia, Pennsylvania, United States: American
Society of Civil Engineers.
xxx.
Wang, Hongxiang ,Guo Wenxian ; Xu Jianxin ; Gu Hongmei (2010),
A Hybrid PSO for Optimizing Locations of Booster Chlorination Stations in
Water Distribution Systems. International Conference on Intelligent
Computation Technology and Automation (ICICTA), China Univ. of Water
Resources & Electr. Power, China, Volume 1 , 126- 129
xxxi.
Wang, Hongxiang. ( 2010) Ant Colony Optimization for Booster
Chlorination Stations of Water Distribution Systems, International Conference
on Computer Application and System Modeling ( ICCASM). China, VI 166-VI
170.
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Hydrogeochemical Stuidies Of Groundwater In And Around
Metropolitan City Vadodara, Gujarat, India
M.K. Sharma
C.K. Jain
National Institute of Hydrology, Roorkee – 247667, India
E. mail: [email protected]
ABSTRACT : Geo-environmental conditions have a marked
influence on the groundwater quality. Hydrogeochemical
studies relevant to the water quality explain the relationship of
water chemistry to aquifer lithology. Such relationship would
help not only to explain the origin and distribution of dissolved
constituents but also to elucidate the factors controlling the
groundwater chemistry. In the present investigation,
hydrogeochemical study was carried out in and around the
metropolitan city Vadodara, Gujarat, India to identify and
delineate the important geochemical processes which were
responsible for the evaluation of chemical composition of
groundwater. The study area is a part of Indo-gangetic Plains,
composed of Pleistocene and subrecent alluvium. The
groundwater in the study area occurs under both the unconfined and confined conditions. Groundwater conditions in
the alluvial terrains are considerably influenced by varying
lithology of subsurface formations. The rainfall is main
recharge source of groundwater body besides infiltration from
river, canals and return flow from irrigation. Thirty five
groundwater sources viz; open wells, tubewells, piezometric
wells, bore wells and hand pumps in and around Vadodara city
in pre- and post-monsoon seasons during 2008 and 2009 were
collected and analysed for major constituents. Data has been
processed as using Piper Trilinear Diagram and it was
observed that majority of the groundwater samples of the study
area belong to Ca-Mg-Cl-SO4 or Na-K-Cl-SO4 hydrochemical
facies in both pre- and post-monsoon seasons. Gibbs ratio plot
indicate that the chemistry of groundwater in the study area is
controlled mainly by the chemical interaction between aquifer
rocks and groundwater, and to some extent by processes like
evapo-transpiration etc. The process of evaporation might have
incorporated some components of sodium and chlorine ions.
The scatter plots of ions show that the relatively high
contribution of (Ca+Mg) to the total cations (TZ +) and high
(Ca+Mg)/(Na+K) ratio indicate that carbonate weathering is a
major source of dissolved ions in the groundwater of the study
area. The plot of (Ca+Mg) vs HCO3 for most of the samples in
study area indicates an excess of Ca+Mg over HCO3 inferring
an extra source of Ca and Mg. This requires that a portion of
the (Ca+Mg) has to be balanced by other anions like SO 4
and/or Cl. Plot of (Ca+Mg) vs HCO3+SO4 shows the ion
exchange process activated in the area, which may be due to
the excess bicarbonate. The plot of Na vs Cl indicates
contribution of silicate weathering through the release of Na.
Key words: Groundwater, Hydrogeochemical process,
Vadodara, Gibbs Plot, Scatter Plot
1. INTRODUCTION
Ground water plays an important role in our life support system
as it is being used for different designated uses specially for
drinking purpose. Groundwater situation in different parts of
India is diversified because of variation in geological,
climatological and topographic set-up. The prevalent rock
formations, ranging in age from Archaean to Recent, which
control occurrence and movement of groundwater, are widely
varied in composition and structure. Further, significant
variations of landforms from the rugged mountainous terrains of
the Himalayas, Eastern and Western Ghats to the flat alluvial
plains of the river valleys and coastal tracts, and the aeolian
deserts of Rajasthan are also responsible non-uniform
distribution of ground water. The rainfall patterns too show
similar region-wise variations. The topography and rainfall
virtually control run-off and groundwater recharge (Master Plan,
2002).
Growing demand of water in various sectors viz; agriculture,
industrial and domestic sectors, has brought problems of overexploitation of the groundwater resource, continuously declining
groundwater levels, sea water ingress in coastal areas, and
groundwater pollution in different parts of the country. The
falling groundwater levels in various parts of the country have
threatened the sustainability of the groundwater resource, as
water levels have gone deep beyond the economic lifts of
pumping.
Geo-environmental conditions have a marked influence on the
groundwater quality. Hydrogeochemical studies relevant to the
water quality explain the relationship of water chemistry to
aquifer lithology. Such relationship would help not only to
explain the origin and distribution of dissolved constituents but
also to elucidate the factors controlling the groundwater
chemistry. Kumar et al. (2006) also studied the
hydrogeochemical processes in NCT Delhi to identify the
geochemical processes and their relation with groundwater
quality as well as to get an insight into the hydrochemical
evaluation of groundwater and reported that salinity and nitrate
are two major problem from drinking point of view. The
prevailing hydrochemical processes operating in the study area
are simple dissolution, mixing, weathering of carbonate minerals
(kankar) and of silicate, ion exchange, and surface water
interaction. Limited reverse ion exchange has been noticed in a
few parts of the study area especially in post-monsoon periods.
Periodic seasonal switch-over has been clearly noticed in these
hydrogeochemical processes that control groundwater quality of
the area.
Reddy and Kumar (2010) carried out hydrogeochemical studies
in Penna-Chitravahi river basins in Southern India to identify
and delineate the geochemical processes responsible for the
evolution of chemical composition of ground water and reported
that the groundwater in general is of Na +-Cl-, Na+-HCO3-, Ca2+Mg2+-HCO3- and Ca2+-Mg2+-Cl- type . Na+ among cations and
Cl- and/or HCO3- among anions dominate the water; Na+ and
Ca2+ are in the transitional state with Na+ replacing Ca2+ and
HCO3- Cl- due to physicochemical changes in the aquifer and
water rock interactions. Further, Gibbs plots indicate that the
evolution of water chemistry is influenced by water-rock
interaction followed by evapotranspiration process. Vijaykumar
et al. (2010) studied hydrogeochemistry in the part of Ariyalur
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region, Perambalur district, Tamil Nadu, India and reported that
Ca+Mg, SO4+Cl and HCO3+CO3 are high facies during pre- and
post-monsoon season and evaporation process dominates the
groundwater chemistry as explained by Gibbs plot. The quality
of water for irrigation was estimated by USSL classification
indicating high salinity and low sodium hazard, satisfactory for
plants having moderate salt tolerance on soils.
Obiefuna and Orazulike (2011) characterized groundwater in
semiarid Yola area of northeastern Nigeria employing chemical
indicators and reported that alkaline earths (Ca+Mg)
significantly exceed the alkali (Na+K) and week acids
(HCO3+CO3) exceed the strong acids (Cl+SO4), suggesting
dominance of carbonate weathering followed by silicate
weathering. Chemical fertilizers and anthropogenic activities are
contributing to sulphate, nitrate and chloride concentrations in
surface and ground water of the study area. Srinivasamoorthy et
al. (2012) made an attempt to identify the major geochemical
process activated for controlling the ground water chemistry of
Sarabanga minor basin of river Cauvery, situated in Salem
district, Tamil Nadu, India and inferred that water chemistry is
guided by complex weathering process, ion exchange along with
influence of Cl ions from anthropogenic impact.
In the present paper, hydrogeochemical study in and around the
metropolitan city Vadodara, Gujarat, India is carried out to
identify and delineate the important geochemical processes
which were responsible for the evaluation of chemical
composition of groundwater by collecting groundwater samples
in pre- and post-monsoon season.
2. STUDY AREA
The metropolitan city Vadodara is the graceful city of Gujarat
State. It is bounded by 22°18′ N latitude and 73°16′ E longitude
(Fig.1). Vadodara urban agglomeration covers an area of about
140 km2. The rivers Jambua, Surya, Vishwamitri and Dhadhar,
which flow through central part of the district and empty into
Gulf of Khambat, are also part of Mahi Basin. The climate of the
metropolitan city is moderate tropical type. The temperature of
the city varies from 8˚C to 46˚C. The average annual rainfall is
recorded as 900 mm. The study area is a part of Indo-gangetic
Plains, composed of Pleistocene and subrecent alluvium. The
earliest geological evolution of the basement rocks, exposed in
northern and eastern parts, had been controlled by the
Precambrian orogenies (Arvalli and Delhi cycles), and the older
crystalline rocks ideally shows folds, faults and magmatism
related to the two orogenies. After Precambrian orogenies, major
geological events of Vadodara district were confined to
Mesozoic and Cenozoic Eras which can be related with the
breaking up of the Gondwana land and the subsequent northward
drift of the Indian sub-continent, involving formation of
sediments and Deccan Trap Volcanism with uplifts and
subsidence along the two major lineaments – Narmada and
Cambay rift system. The groundwater in the study area occurs
under both the un-confined and confined conditions.
Groundwater conditions in the alluvial terrains are considerably
influenced by varying lithology of subsurface formations. The
rainfall is main recharge source of groundwater body besides
infiltration from river, canals and return flow from irrigation.
There is no yield of water upto 50 feet, sandy aquifer was found
from 50 to 70 feet. The principal industrial areas within
Vadodara Urban areas are at Makarpura and Nandesari.
Thirty five groundwater samples from open wells, tubewells,
piezometric wells, bore wells and hand pumps in and around
Vadodara city (Fig. 1) were collected for physico-chemical
analysis in polypropylene bottles in pre- and post-monsoon
seasons during 2008 and 2009. All the samples were stored in
sampling kits maintained at 4oC and brought to the laboratory for
detailed chemical analysis. All general chemicals used in the
study were of analytical reagent grade (Merck/BDH). Deionized water was used throughout the study. The physicochemical analysis was performed following standard methods
(APHA, 1995).Ionic balance was calculated, the error in the
ionic balance for majority of the samples was within 5%.
4.1 Physico-chemical characteristics of groundwater
The hydro-chemical data of groundwater samples of premonsoon, 2008 is presented in Table 1. The pH values in the
groundwater of metropolitan city of Vadodara mostly fall within
the range 7.6 to 8.6. The pH values for most of the samples are
well within the limits prescribed by BIS (2012) for various uses
of water including drinking and other domestic supplies. The
electrical conductivity and dissolved salt concentrations are
directly related to the concentration of ionized substance in
water and may also be related to problems of excessive hardness
and/or other mineral contamination. The conductivity values in
the groundwater samples of the metropolitan city vary widely
from 760 to 5480 S/cm with almost 80% of the samples having
conductivity value above 1000 S/cm. The maximum
conductivity value of 5480 S/cm was observed in the sample of
Harni. In the metropolitan city of Vadodara, the values of total
dissolved solids (TDS) in the groundwater varies from 486 to
3507 mg/L. Almost all the samples were found above the
acceptable limit but within the maximum permissible limit of
2000 mg/L and only 14% of the samples exceed the maximum
permissible limit of 2000 mg/L. Water containing more than 500
mg/L of TDS is not considered desirable for drinking water
supplies, though more highly mineralized water is also used
where better water is not available. For this reason, 500 mg/L as
the acceptable limit and 2000 mg/L as the maximum permissible
limit has been suggested for drinking water (BIS, 2012). Water
containing TDS more than 500 mg/L causes gastrointestinal
irritation (BIS, 2012).
The presence of calcium and magnesium along with their
carbonates, sulphates and chlorides are the main cause of
hardness in the water. A limit of 200 mg/L as acceptable limit
and 600 mg/L as permissible limit has been recommended for
drinking water (BIS, 2012). The total hardness values in the
study area range from 79 to 1144 mg/L. About 20% of the
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samples fall within acceptable limit of 200 mg/L and 29%
sample cross the permissible limit of 600 mg/L. In groundwater
of the study area, the values of calcium range from 12 to 313
mg/L. The values of magnesium vary from 12 to 127 mg/L. The
acceptable limit for calcium and magnesium for drinking water
are 75 and 30 mg/L respectively (BIS, 2012). Further, only few
samples exceed maximum permissible limit of calcium as 200
mg/L and magnesium as 100 mg/L. The concentration of sodium
in the study area varies from 54 to 1110 mg/L. High sodium
values in the city may be attributed to base-exchange phenomena
causing sodium hazards. Such groundwater with high value of
sodium is not suitable for irrigation purpose. The concentration
of potassium in groundwater of the study area varies from 1.0 to
77 mg/L. As per EEC criteria, ten samples exceed the guideline
level of 10 mg/L.
45 mg/L and six samples even cross the permissible limit of 45
mg/L. In higher concentrations, nitrate may produce a disease
known as methaemoglobinaemia (blue babies) which generally
affects bottle-fed infants. The higher nitrate concentration in the
metropolitan city at few locations may be attributed due to
combined effect of contamination from domestic sewage,
livestock rearing landfills and runoff from fertilized fields. The
fluoride content in the groundwater of the study area varies from
0.00 to 1.26 mg/L. Almost all the samples of the metropolitan
city fall within the acceptable limit of 1.0 mg/L and none of the
samples exceeded the maximum permissible limit of 1.5 mg/L.
From the above discussion, it is clearly indicated that in the
groundwater of metropolitan city of Vadodara, the concentration
of total dissolved solids exceeds the acceptable limit of 500
mg/L in almost all the samples but within the maximum
permissible limit of 2000 mg/L. From the hardness point of
view, about 20% of the samples fall within acceptable limit of
200 mg/L and 29% sample cross the permissible limit of 600
mg/L. The chloride content exceeds the desirable limit in more
than 40% of the pre-monsoon samples. Sulphate contents are
within the desirable limits in about 89% samples. The nitrate
content in more than 84% samples is well within the permissible
limit. The concentration of fluoride in almost all the samples is
well within the desirable limit. The violation of BIS limit could
not be ascertained for sodium and potassium as no permissible
limit for these constituents has been prescribed in BIS drinking
water specifications.
Table 1. Hydro-chemical characteristics of the Groundwater
during Pre-monsoon 2008
Parameters
Figure 1. Map showing location of sampling sites
The concentration of chloride varies from 20 to 1464 mg/L.
More than 60% samples of the metropolitan city falls within the
desirable limit of 250 mg/L and only three samples of the city
exceeds the maximum permissible limit of 1000 mg/L. The
concentration of sulphate in the metropolitan city varies from 6
to 600 mg/L. Bureau of Indian standard has prescribed 200 mg/L
as the desirable limit and 400 mg/L as the permissible limit for
sulphate in drinking water. In the study area, 89% of the samples
analysed fall within the desirable limit of 200 mg/L and only two
samples exceed the maximum permissible limit of 400 mg/L.
The nitrate content in the metropolitan city of Vadodara varies
from 0.0 to 252 mg/L. About 84% of the samples of the
metropolitan city of Vadodara fall within the permissible limit of
pH
Conductivity,
S/cm
TDS, mg/L
Hardness, mg/L
Chloride, mg/L
Sulphate, mg/L
Nitrate, mg/L
Fluoride, mg/L
Sodium, mg/L
Potassium, mg/L
Calcium, mg/L
Magnesium, mg/L
Mini
mum
Maxim
um
Aver
age
7.6
760
8.6
5480
8.0
2013
486
79
20
6.0
0.0
0.0
54
1.0
12
12
3507
1143
1464
600
252
1.3
1110
77
313
127
1288
435
320
112
36
0.6
250
11.7
103
43
BIS (2012) Limit
Accepta
Permis
ble
sible
6.5
8.5
500
200
250
200
45
1.0
75
30
2000
400
1000
400
1.5
200
100
4.2 Mechanism Controlling the Groundwater Chemistry
Geo-environmental conditions have a marked influence on the
groundwater quality. Hydrogeochemical studies relevant to the
water quality explain the relationship of water chemistry to
aquifer lithology. Such relationship would help not only to
explain the origin and distribution of dissolved constituents but
also to elucidate the factors controlling the groundwater
chemistry. Gibbs (1970) proposed a hypothesis to elucidate the
major natural mechanisms controlling world water chemistry.
Three mechanisms – atmospheric precipitation, rock dominance
and the evaporation-crystallization process – are the major
factors controlling the composition of dissolved salts of the
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world waters. Other second-order factors, such as relief,
vegetation and composition of material in the basin dictate only
minor deviations within the zones dominated by the three prime
factors.
Gibbs plot is a diagrammatic representation of the mechanisms
responsible for controlling the chemical composition of various
bodies of water on the surface of the earth. The major cations
that characterize the end-members of the world surface waters
are Ca for freshwater bodies and Na for high-saline water
bodies. Gibbs plotted the weight ratio Na/(Na+Ca) on the x-axis
and the variation in total salinity on the y-axis (Fig. 2). This
ordered arrangement can serve as a basis for discussion of the
several mechanisms that control world water chemistry.
The first of these mechanisms is the atmospheric precipitation.
The chemical compositions of low-salinity waters are controlled
by the amount of dissolved salts furnished by precipitation.
These waters consist mainly of the rivers having sources in
thoroughly leached areas of low relief in which the rate of
supply of dissolved salts to the rivers is very low and the amount
of rainfall is high – much greater in proportion to the low
amount of dissolved salts supplied from the rocks. In addition,
the composition of this precipitation differs from that of rockderived dissolved salts. The second mechanism is the rock
dominance controlling world water chemistry. The waters of this
rock-dominated end-members are more or less in partial
equilibrium with the materials in their basins. Their positions
within this grouping are dependent on the relief and climate of
each basin and the composition of each basin. The third major
mechanism that controls the chemical composition of the earth‟s
surface waters is the evaporation-fractional crystallization
process. This mechanism produces a series extending from the
Ca-rich, medium-salinity (freshwater), „rock source‟ endmember grouping to the opposite, Na-rich, high-salinity endmember.
Figure 2. Gibbs plot (Source: Gibbs, 1970)
Almost all collected groundwater samples from study area in
both seasons fall in rock dominance zone followed by
evaporative zone suggesting precipitation induced chemical
weathering along with dissolution of rock forming minerals. It
may be inferred that the chemistry of groundwater in the study
area is controlled mainly by the chemical interaction between
aquifer rocks and groundwater, and to some extent by processes
like evapo-transpiration etc. The process of evaporation might
have incorporated some components of sodium and chlorine
ions.
4.3 Classification of Ground Water
Data has been processed as using Piper Trilinear Diagram and it
was observed that majority of the groundwater samples of the
study area belong to Ca-Mg-Cl-SO4 or Na-K-Cl-SO4
hydrochemical facies in both pre- and post-monsoon seasons.
Such water has permanent hardness and does not deposit
residual sodium carbonate in
irrigation use and generally
creates salinity problems both in irrigation and drinking uses.
4.4 Scatter Plots between Ions
The scatter plot of (Ca+Mg) vs TZ+ shows that all the points fall
above 1:1 equiline (Fig. 3). The relatively high contribution of
(Ca+Mg) to the total cations (TZ+) and high (Ca+Mg)/(Na+K)
ratio indicate that carbonate weathering is a major source of
dissolved ions in the groundwater of the study area (Fig. 3).
The scatter plot of (Na+K) vs TZ+ shows that all the points fall
above 1:1 equiline with a low ratio indicating a relatively low
contribution of dissolved ions from silicate weathering (Fig. 4).
Na+, K+ and dissolved silica in the drainage basin are mainly
derived from the weathering of silicate minerals, with clay
minerals as by-products. The plot of Na vs Cl indicates most of
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the points lie below the 1:1 equiline reflecting contribution of
silicate weathering through the release of Na. The plot of
(Ca+Mg) vs HCO3 for most of the samples of the study area
indicates an access of alkalinity over Ca+Mg content (Fig. 5).
The excess of Ca+Mg over HCO3 in some of the sample of the
upper part of basin indicate an extra source of Ca and Mg. This
requires that a portion of the (Ca+Mg) has to be balanced by
other anions like SO4 and/or Cl.
The plot of (Ca+Mg) vs HCO3+SO4 is a major indicator to
identify the ion exchange process activated in the study area. If
ion exchange is the process, the points shift to right side of the
plot due to excess of HCO3+SO4. If reverse ions exchange is the
process, points shift left due to excess Ca+Mg. Plot of (Ca+Mg)
vs HCO3+SO4 shows that most of the plotted points clusters
around the 1:1 equiline and fall in HCO3+SO4 indicating the ion
exchange process which may be due to excess bicarbonate (Fig.
5).
Figure 5. Scatter plot of (Ca+Mg) vs HCO3 and (Ca+Mg) vs
(HCO3+SO4) (Pre- and Post-monsoon)
5. CONCLUSION
Hydrogeochemical studies relevant to the water quality
successfully explain the relationship of water chemistry to
aquifer lithology. It is concluded that the problem of hardness in
groundwater at few location was attributed due to dissolution of
rock forming minerals and dominance of carbonate weathering.
The ion exchange process is dominating in the study area, which
may be due to excess bicarbonate. High concentration of sodium
and chloride may be attributed to the process of evaporation and
contribution of silicate weathering through the release of Na.
REFERENCES
Figure 3. Scatter plot of (Ca+Mg) vs TZ+ and (Ca+Mg) vs
(Na+K) (Pre- and Post-monsoon)
i.
APHA (Clesceri LS, Greenberg AE, Trussel RR, 1995) Standard
Methods for the Examination of Water and Wastewater, APHA, Washington DC.
ii.
BIS (2012) Indian Standard Drinking Water – Specification (Second
Revision). IS:10500:2012, Bureau of Indian Standards, New Delhi.
iii.
Gibbs Ronald J. (1970) Mechanisms controlling world water
chemistry. Science 170(3962): 1088-1090.
iv.
Kumar Manish, Ramanathan AL., Rao MS, Kumar Bhishm (2006)
Identification and evaluation of hydrogeological processes in groundwater
environment of Delhi, India. Environ. Geol. 50(7): 1025-1039.
v.
Master Plan (2002) Master Plan for Artificial Recharge to
Groundwater in India Central Ground Water Board, New Delhi, February 2002,
p. 115.
vi.
Obiefuna GI, Orazulike DM (2011) The hydrochemical
characteristics and evolution of groundwater in semiarid Yola area, Northeast,
Nigeria. Res. J. of Environ. Earth Sci. 3(4): 400-416.
vii.
Piper AM (1944) A Graphical Procedure in the Geochemical
Interpretation of Water Analysis. Trans. Am. Geophysical Union, 25: 914-923.
viii.
Reddy AG, Kumar KN (2010) Identification of the hydrogeochemical
processes in ground water using major ion chemistry: a case study of PennaChitravahi river basins in Southern India. Environmental Monitoring
Assessment 170(1-4): 365-382.
ix.
Srinivasamoorthy K, Vasanthavigar M, Chidambaram S, Anandhan
P, Manivannan R, Rajivgandhi R (2012) Hydrochemistry of groundwater from
Sarabanga minor basin, Tamil Nadu, India. Proceedings of the International
Academy of Ecology and Environmental Sciences. 2(3): 193-203.
x.
Vijaykumar V, Vasudevan S, Ramkumar T, Shrinivasamoorthy K,
Venkatramanan S, Chidambaram S (2010) Hydrogeochemistry in the part of
Ariyalur region, Perambalur district, Tamil Nadu, India. J. Applied Geochemists
12(2): 253-260.
Figure 4. Scatter plot of (Na+K) vs TZ+ and Na vs Cl (Pre- and
Post-monsoon)
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optimal number of sensors and their locations in the network is
desirable.
EVALUATION OF VARIOUS OBJECTIVES IN MULTIOBJECTIVE SENSOR PLACEMENTS IN WATER
DISTRIBUTION SYSTEMS
S. Rathi1
R. Gupta2
1
Research Scholar, Visvesvaraya National Institute of
Technology, Nagpur-440010, India.
2
Professor, Visvesvaraya National Institute of Technology,
Nagpur-440010, India.
ABSTRACT
Online monitoring of water quality in distribution network
through sensors are of pronounced interest for early detection of
contamination event. Since the online monitoring of network is
costly affair, the limited numbers of sensors are placed at crucial
locations to cover the entire network. Several objectives have
been proposed to decide the location of sensors. However,
including all of them in deciding location of sensors is a difficult
task. Sensor locations are obtained by considering single or few
objectives at a time. How far the other objectives not considered
during the design are satisfied can be obtained by analysis of
sensor network design. This paper aims at explaining evaluation
of various objectives for a set of known sensor locations. The
objectives evaluated are Demand Coverage, Detection
Likelihood, Time of Detection, Population Exposed, Extent of
contamination, Volume of contaminated water consumed,
Number of failed detection and Risk. The evaluation of above
objectives is carried out by considering: (i) hydraulic simulation
for dominating demand pattern; and (ii) both hydraulic and
water quality simulation over a period of time. EPANET is used
for both hydraulic and water quality simulation.
The methodology for evaluating various objectives is explained
with an illustrative network. The values of various objectives
evaluated through water quality simulations provided more
realistic and accurate results as compared to that obtained
through only hydraulic simulations. However, water quality
simulation require more efforts and computation time along with
calibrated network to rely on the modeled output.
Keywords: contamination, monitoring,
distribution system, water quality.
objectives,
water
Lee and Deininger et al. (1992) were perhaps the first to suggest
a methodology for location of monitoring stations (MSs) in a
WDN using the objective of maximizing the demand coverage
(DC) for routine monitoring of water quality. The demand
coverage was defined as the percentage of total demand
monitored by the set of MSs. The demand coverage does not
quantify the impact of contamination events. Kessler et al.
(1998) suggested total volume of contaminated water consumed
by population as an objective to be restricted to a desired level
while selecting location of MSs. Kumar et al. (1999) suggested
time of detection as level of service (LOS). In the last decade,
the purpose of water quality monitoring has completely changed
and early warning system with several other objectives like
population exposed to contamination, extent of contamination,
detection likelihood, number of failed detections, risk and
redundancy of monitoring system etc. were suggested to protect
the human from deliberate contamination events. These
objectives have been considered independently or jointly by
different researchers to propose algorithms for location of
monitoring stations/sensors (Chastain 2006; Ostfeld et al. 2004;
Watson et al. 2004; Wu and Walski 2006; Berry et al. 2005,
2006; Propato 2006; Ostfeld et al. 2008; Peris and Ostfeld 2008;
Aral et al. 2010; Weickgenannt et al. 2010; Krause et al.2008;
Dorini et al. 2010, Hart and Murray 2010; Shen and McBean
2011; Kansal et al. 2012).
It is observed that various researchers have considered different
objectives in the design of sensor network. Some of the
objectives like maximizing detection likelihood would probably
locate the sensors at the far end of the system or at the
downstream network nodes in order to detect more number of
contamination events while the objective like minimizing
expected time of detection would locate the sensors as close as
possible to the source of contamination. Thus, optimizing sensor
locations with different objectives will give different sensor
locations. Further, different objectives can be evaluated by: (i)
considering only hydraulic simulation, in which network is
analyzed for flow and velocities for most dominating demand
pattern and
it is assumed that contamination in any
concentration is detected by sensor as it reaches the sensor node;
and (ii) water quality simulations to predict the more realistic
temporal evaluation of contaminant concentration.
1. INTRODUCTION:
Water distribution network (WDN) is an important part of the
city infrastructure and its primary aim is to provide safe and
adequate drinking water to consumers. A network consists of
several pipes connected to each other and other components used
to control and measure flows and pressures. Water
contamination can occur at any time due to several reasons. The
reasons for deterioration of water quality in WDN may be
classified as natural, accidental or intentional. In order to detect
contamination event at the earliest and to reduce the impact of
contamination event, online water quality monitoring in a WDN
through sensors is desirable. However, installation of sensors
and continuous monitoring is costly affair, therefore selection of
The sensor network designed for one or more objectives may
required to be checked for its efficacy for other objectives not
considered in the design. Further, in GA based designs, few
alternative designs are required to compared for fulfillment of
different objectives during the design itself. This paper aims at
explaining evaluation of various objectives for a set of known
sensor locations. The objectives evaluated are Demand
Coverage (DC), Detection Likelihood (DL), Time of Detection
(TD), Population Exposed (PE), Extent of Contamination (EC),
Volume Consumed (VC), Number of Failed Detection (NFD),
and Risk.
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2.
PERFORMANCE
DEFINITIONS
OBJECIVES
AND
18-19, Dec. 2014
THEIR
Let S be the number of sensors installed in a WDN at different
nodes. The total number of nodes are J and number of flow
patterns are P. Let us considered that contamination takes place
only at the nodes and for any event x, the probability of
occurrence during flow pattern p is αxp. The contamination event
x at node j will certainly be detected, if j is one of the sensor
node s. If j is not a sensor node then contamination event x may
be detected at one or more of downstream sensor nodes, if there
exists flow paths from j to downstream sensor nodes s. The
event x will remain undetected if there are no sensor nodes on
the downstream of contamination node.
Demand Coverage (DC) - The term DC is defined as the
percentage of network demand monitored by a particular and/or
set of sensor nodes. If the quality of water is good at any node, it
can be presumed good at upstream nodes, if sufficient quantity
of water has passed through upstream nodes. An upstream node
is assumed as covered by a sensor node if a desired fraction of
flow has passed through that node. In general the demand
coverage of sensor network would be
P
DC 
J
 a
p 1 j 1
P J
j
p 1 j 1
t xp for detected events and  for
undetected events. The TD is an important parameter of sensor
network. It can be noted that other parameters quantifying the
impact of contamination event are dependent on TD.
Population Exposed (PE) –It is defined as the number of people
exposed to the contaminant before detection by a sensor. In case
when only hydraulic simulation is carried out, it is assumed that
sensor is capable of detecting any small concentration of
contaminant. Thus, population exposed during contamination
event x would be the addition of population of all the nodes
which gets contaminated in time txp. Therefore, population
exposed is given by
J
P
PE   xp
x1 p 1

jp
jcontaminated
nodes
(3)
Where,  jp
 population associated with node j during pattern p.
In case of water quality simulation, PE is mathematically
expressed as
 q j, p
 q
where, DT (x,p) = Travel time
J
p
PE   xp
x 1 p 1
j, p
J
C
 jp
xpj
jcontaminated
node
(1)
(4)
where aj =1 if node j is covered by set of sensor nodes, else aj =
0; q is the nodal demand. It can be noted that DC indicates the
property of sensor nodes.
Time of Detection (TD) –The detection time for a particular
contamination scenario is given by the time elapsed between the
start of contamination event and its detection by the first sensor
location. Thus, the detection time for any event x at node i which
is detected first at node j would be the minimum travel time
required by contaminant to reach from node i to node j during
flow pattern p and represented as txp. There could be some
scenarios in which contaminant may not be detected by any
sensor. The Time of Detection for undetected events may be
considered as 24 hrs or more (say  ) based on time of simulation
(Watson et al. 2004) or when it is indirectly detected in public.
The time of detection for the sensor network can been
represented by including or excluding the undetected events. In
the simplest way, it is the average time necessary for a sensor to
detect a substance.
Where, Cxpj - Contamination concentration indicator variable;
Cxpj = 1, if concentration is more than threshold concentration, 0
otherwise. It can be observed that during water quality
simulation for a contamination event the concentration of
contaminant at sensor node may be less than threshold
concentration. Herein, the event remains undetected at sensor
nodes. However, the population at all the nodes at which
concentration is more than the threshold concentration are
included in population exposed.
Extent of Contamination (EC) – It is defined as the length of
pipe contaminated by a contamination event. Length of pipe
contaminated during contamination event x would be the
addition of contaminated length of all pipes which gets
contaminated in time txp under the flow pattern p. It is
mathematically defined as
J
p
EC  ip
x 1 p 1
TD   xp DT ( x, p)
J

jp
jcontaminated
node
(5)
xJ pP
(2)
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 jp 
18-19, Dec. 2014
L
population exposed. Following these definitions Risk of
contamination can be expressed as
e
epipes between
contaminated nodes
(6)
Where, Le – Length of pipe e. The expression similar to Eq. (4)
also can be written for the case of water quality simulation for
other objectives.
Volume Consumed (VC) - The amount of contaminated water
consumed by the population before detection by a monitoring
station. Mathematically, it can be written a
p
J
VC   xp
x 1 p 1
J
q
jp
jcontaminated
node
( DT ( x, p)  t xp )
(7)
Where, qjp - demand at contaminated node j; (DT(x,p) - txp) –
Consumption time – The duration before detection by the time
water reaches to the sensor node defined as duration before
detection over which contaminated node consumes contaminated
water injected at a specific node for pattern p. (Detection time at
a sensor node minus minimum flow time to the contaminated
nodes from the contaminant injected node); DT (x, p) –travel
time to a detection point and tijp - Minimum flow time between x
and j for p.
Number of Failed Detections (NFD) - The proportion of
attacks that are undetected by all monitoring stations.
Mathematically it is expressed as
p
J
NFD   xpbi 0 p
x 1 p 1
(8)
Where, bi0p = 1, if contamination event is undetected; else 0.
Detection likelihood (DL) – It is defined as the probability of
detection of a contaminant or it is defined as complement of
NFD. For a given sensor network design i.e. by knowing the
known number and locations of sensors,
J
p
DL   xpbijp
x 1 p 1
(9)
Where,
bijp =1, if contamination event is detected else 0.
Risk – Weickgenannt et al. (2010) defined Risk as the product
of the probability of not detecting the contaminant intrusion and
the corresponding consequence in terms of water demand
consumed. Berry et al. (2005) defined Risk as fraction of
R( S )  (1 
1 J
1 J
max  ( S p , K l ))(  min  ( S P , kl ))

x x 1
x x 1 S P S
S P S
(10)
where, S - set of sensor locations; R(S) - associated
contamination risk; Sp - elements in S (Sensor location); x number of contamination scenarios; kl - a contamination
scenario index; ξ (Sp, kl) - a binary function with variables Sp
(which is a sensor location) and kl (which is a scenario); ξ(Sp,kl)
= 1 if the sensor at Sp can detect the scenario kl and 0 otherwise;
χ(Sp, kl) - Volume of water that is contaminated prior to network
shutdown following the intrusion detection at a specific sensor.
Monitoring Stations response delay (MSRD) – It is defined as
possible monitoring Stations response delay in revealing a
hazard intrusion. Herein, we assume that MSRD = 0 means it is
assumed that monitoring stations are capable of providing real
time detection data.
3.0 Common Assumptions
3.1 In hydraulic as well as water quality simulation:
Following assumptions have been made to evaluate the
objective values.
1. One contamination event is considered at any time. The
contaminant intrusion is considered at the nodal point only.
2. The probabilities of contamination at all the nodes are
assumed to be equal.
3. Sensor locations are considered only at the nodal points in
the network.
4. Sensors are assumed to be perfect in the sense that above a
specified concentration, the sensor is 100 % reliable and
below that concentration the sensor always fails to detect the
contaminant and they are accurate i.e no false positives and
no false negatives. It is also assumed that the alarm is raised
by the sensors at detection time and without considering any
response delay.
3.2 Additional assumptions during hydraulic simulation
only:
1. Hydraulic analysis is carried out for only one demand
pattern, i.e. peak demands.
2. The contaminant travels in the pipeline with the velocity of
water. Further, it is assumed that contamination is detected
by sensor as it reaches the sensor node how-so-ever small is
the concentration, thus ignoring the effect of dilution on
contaminant concentrations.
3. Sensor protects downstream populations. A population is
considered exposed if it could be reach by a flow path from
the attack point without passing a sensor.
4. The contaminated water moves in the pipeline and travel in
different pipes connected at the junctions. All points on
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downstream of attack point are assumed to be contaminated
until contamination is reached at one of the sensors, i.e. up to
time when the contaminated water will reach at least one
sensor.
Figure 1. Example water distribution system
Table 1. Pipe details peak demand hour results
3.3 Additional assumptions during water quality simulation:
1.
2.
3.
4.
5.
The pollutants are assumed to be conservative type where
contaminant does not react with water and its dynamics is
determined by water flows, dilution and mixing from the
intrusion site to consumers nodes.
Water quality simulation is performed to predict
contaminant concentration with time resulting from a
particular contamination scenario and various objectives are
evaluated by developing a pollution matrix with mass rates
of 5000 mg/min with duration of injection is of 2 hours.
It is assumed that contamination event occur at peak
demand time where more number of people consuming
water at that time.
The hazard concentration threshold is taken as 0.3 mg/l.
The contaminant reaches in the network at different nodes
with different concentrations from the contaminated source
node and effect will continues till contaminated water
reaches to one sensor in a set with concentration greater
than the threshold concentration. The effect of
contamination will be used for determination of objectives
up to that time.
Table 2. Demand pattern
4. ILLUSTRATIVE EXAMPLE
A single source WDN (Kessler et al. 1998) as shown in Figure.1
is considered for the evaluation of various objectives for a set of
known sensor locations. The network has 12 pipes and 8
consumer locations- the number of consumers at each location
(given in parenthesis) is shown in Figure 1, a source, a storage
tank and a pump. The average nodal demands are given in Lps.
The pipe diameters are given in Table 1. The demand multiplier
(ratio of actual demand to average demand) for different periods
in 24 hours are given in Table 2. Total length of the pipe in the
network is 19364 meters and total consumers of the network are
7600. The pipe 10 is 3209 m long, while all other pipes are 1609
m in length. Hazen-Williams coefficient for all pipes is 100.
Other details can be obtained from the Kessler et al. (1998).
5. EVALUATION OF VARIOUS OBJECTIVES
Various performance objectives are evaluated for two sets of
known sensor locations ─ (1) Sensors at nodes 32 and 23; and
(2) Sensors at nodes 32, 23 and 31. Further, objective function
values are obtained by considering: (1) only hydraulic
simulation; and (2) both hydraulic and water quality simulation.
5.1 Evaluation considering hydraulic analysis:
Case 1 : Sensors at nodes 32 and 23.
5.1.1 Evaluation of DC:
Lee and Deininger (1992) suggested a methodology for
maximizing DC which is based on development of water
fraction matrix and coverage matrix based on chosen coverage
criteria. In order to determine the upstream nodes covered by a
monitoring station, the coverage criteria is used and defined as
the minimum percentage of total water received at a monitoring
node that should have passed through an upstream node to be
able to consider it “covered”. The lower the coverage criteria,
the more the demand coverage of monitoring nodes increases.
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Let us consider two coverage criteria of 60 % and 30 %. With
60% coverage criteria only those upstream nodes through which
60% of the total flow has passed will be included, while with 30
% coverage criteria all the upstream nodes through which 30%
total quantity of water passed will get included. Thus, demand
coverage of monitoring station is based on coverage criteria,
which brings subjectivity in the design as its value has to be
fixed by the designer according to his own experience.
Therefore, here to evaluate the demand coverage objective we
used a simple methodology (Rathi et al. 2014) which avoids
subjectivity owing to coverage criteria as it considers all the
nodes on the shortest path from source to sensor node as covered
based on the assumption that major flows are along shortest
path.
The set of sensor at nodes 32, 23 are given. First, identify the
shortest path for nodes 32 and 23. Shortest path for 32 is 10-1112-22-32 and for 23 is 10-11-12-22-23. Now calculate DC by
adding the demands of all nodes on shortest path. Therefore, DC
of 32 = 0+7.57+7.57+10.09+7.57 = 32.8 Lps and similarly DC
for 23 = 5.046 (without adding demand of previously covered
nodes twice). Therefore, total DC of 32 and 23 = 37.846 Lps
(68.18% shown in Table 5 and 6) out of a total of 55.508 Lps. It
can be noted that DC is the attribute of sensor network that is not
affected by number or probabilities of contamination events.
5.1.2 Evaluation of TD:
To evaluate TD for the sensor network, the detection time for
individual contamination events are required. A general travel
time matrix as shown in Table 3 is developed which can be used
for evaluation of other objectives also as discussed later. The
element in travel time matrix is the shortest travel time (in Hrs.)
from the contaminated node to the other nodes.
5.1.3 Evaluation of PE, EC, DL and NFD:
To determine PE, EC, DL and NFD for sensors at nodes 32, 23
we make use of general travel time matrix shown in Table 3.
Consider contamination at node 10. The event is first detected at
sensor node 32 in 6.98 hrs. Therefore, all the nodes to which
travel time is less than 6.98 hrs. will get contaminated.
Considering the row 1 in Table 3, all the nodes except the two
sensor nodes have travel time less than 6.98 hrs and therefore
they are included in the list of contaminated nodes shown in
Table 4. The PE and EC are also given in Table 4. Average
values of PE and EC with equal probability of contamination at
all nodes are obtained as 2328 and 4847.336 meters.
Whether a contamination scenario is detected or not is shown in
Col. 5. Thus, total 9 out of total 10 number of events are
detected by sensors at nodes 32 and 23. Therefore, detection
likelihood is 90 % and NFD as complement of DL is 10 %.
Table 4. Calculation of objectives for Sensor location at nodes
32, 23
Now, to evaluate TD for sensors at nodes 32, 23 various
contamination scenarios are considered at different nodes. For
example, from the shortest travel time matrix it is observed that
for contamination event at node 10, event is be detected by both
the sensors at nodes 32 and 23 in time 6.98 hrs and 8.53 hrs,
respectively. Therefore, the detection time for this event is 6.98
hrs. The contamination at all nodes except that at node 2 is
detected at least by one of the sensor node. The event at node 2
is undetected by sensors at nodes 32, 23.
The average time of detection is calculated by considering only
the detected events with equal probability of occurrence and
found as 5.94 hours. The TD is also evaluated by considering
both detected and undetected events in which detection time for
the undetected events is the time when such events are
indirectly noticed in public. Herein, detection time for
undetected events is taken as twice of the maximum simulation
duration. The average TD is obtained as 10.14 hrs.
Table 3. Calculation of objectives for Sensor location at nodes
32, 23
5.1.4 Evaluation of VC:
Contaminated volume consumed is the actual consumption up to
the event is detected. It is calculated by aggregating the product
of the nodal demands at contaminated node by the time
difference between time of detection at sensor node and the time
required by contaminant to reach the contaminated demand node
from the point of intrusion. Thus, in this example if
contamination takes place at node 10 and first detected at sensor
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node 32, the contaminated nodes are 10,11,12, 2, 21,22,31, and
13. These nodes are arranged in ascending order of travel times
as nodes 10, 11, 12, 2, 21, 22, 31, and 13. Minimum travel time
from node 10 to 32 is 6.988 hours. The contaminated water
consumed by the time water just reaches node 32 will be given
by: {[0×(6.98-0) + 7.57×(6.98-1.3) + 7.57×(6.98-1.88) +
0×(6.98-1.93) + 7.57×(6.98-2.1) + 10.09×(6.98-3.8) +
5.046×(6.98-4.03) + 5.046× (6.98-5.84)} × 3600 = 203640 litres.
Multiplier outside the brackets are the demands (L/s) at nodes
10, 11, 12, 2, 21, 22, 31, and 13. In this way, consumption is
calculated by assuming the contamination event at each node
and assuming equal probability of attack to all nodes gives a
volume consumption of 220128 litres.
with hydraulic simulation over 24 hr period. The obtained values
of the objectives are shown in Tables 6 and 7.
Table 6. Evaluation of various objectives for a set of known
sensor locations using
Water quality analysis
5.1.5 Evaluation of Risk:
Risk values for PE (%) are evaluated considering fraction of
population exposed. The average PE is 2328 (Table 4) and the
total population is 7600. Therefore, PE (in %) is 30.63 %
(=2328x100/7600). Risk for VC (%) is 32.37 %, (i.e. 220128
Litres of contaminated water consumed out of total volume
consumption of 679937.4 Litres).
Table 7. Evaluation of Risk objective
Case 2. Sensors at nodes 32, 23 and 31.
The evaluation of various objective are also carried out similarly
for case 2. The obtained values are shown in Table 5 along with
those obtained for case 1 for easy comparison.
Table 5. Evaluation of various objectives for a set of known
sensor locations
using hydraulic analysis
Performance objectives evaluated through water quality
simulations provides more realistic results as they are obtained
by considering variation in demands over time and concentration
of pollutant. However, water quality simulation require more
efforts and computation time. From Table 5 and 6 it can be
observed that the difference between the various objective
values under hydraulic simulation and water quality simulation
are not much.
5. CONCLUSIONS
It can be observed from table 5 that with one additional sensor at
node 31, coverage increases and other parameters such as PE,
EC, TD and VC decreases. The DL and NFD remains the same
as the contamination event at node 2 still remains undetected
with addition of sensor at node 31.
Risk values for PE (%) and VC (%) are 24.33 % and 11.59 %,
respectively.
5.2 Evaluation considering water quality simulation along
with hydraulic simulation:
Performance objectives are evaluated for both the cases of
known sensor locations using water quality simulations along
This paper aims at explaining evaluation of various performance
objectives of a WDN equipped with a set of sensors at known
locations. The objectives evaluated are Demand Coverage (DC),
Detection likelihood (DL), time of detection (TD), population
exposed (PE), extent of contamination (EC), volume consumed
(VC), Number of failed detection (NFD), and Risk. It is
observed that DC is an attribute of sensor network that is not
dependent on number of contamination events and their
locations. The PE, EC, and VC are the attributes governed by
TD. With the increase in average TD, these parameters
decreases. The evaluation of above objectives is carried out by
considering only hydraulic simulation and also with water
quality simulation. The values of objectives evaluated after
performing water quality simulations provides more realistic and
accurate results as compared to considering simply hydraulic
simulations. However, water quality simulation requires more
efforts and computation time along with calibrated network to
rely on the modeled output.
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Such evaluation are also required in sensor network design using
GA with multiple objectives where several alternative designs
are compared based on performance objectives and their fitness
is quantified.
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Systems Analysis Symp., ASCE, Reston, VA.
Water Quality Assessment Of Dal Lake, Kashmir,
J&K.
Shabina Masoodi
Associate Professor, SSM College of Engineering and
Technology, Parihaspora, Pattan, Kashmir, J&K. 193121
ABSTRACT: Dal Lake is one of the prized lakes of world; it is
part of India‟s beautiful national heritage and has been the
centre of Kashmir‟s civilization. It has suffered a lot due to the
impact of pollution and the present paper is an attempt to
assess its water quality. The water quality of the Dal Lake has
been seriously altered over a period of time because of human
interventions which include agricultural activities within and
on the periphery of the lake, urbanization and mushrooming of
hotels besides waste discharge into it. The lake thus has turned
Eutrophic and is under great stress. Since the lake water is
also been harvested for public distribution (potable purposes)
this problem has gained significance keeping in view the public
health. The zones at the periphery and close to the effluent
discharge depict temporal variations. Around fifty percent of
the observed maximum specific conductivity, dissolved oxygen,
nitrate-nitrogen, ammonical–nitrogen and total phosphorus
have been noticed in the spring season. Summer season has
twenty five percent of such observations and the remaining
twenty five percent are distributed in autumn and winter
seasons. This may be possibly due to the start of activities in
the catchment, mixing or re-suspension. A comparison of
values over a period of time shows that the Dal Lake has
passed through several stages of eutrophic evolution. Extensive
data establishes far reaching changes in the physico-chemical
environment. Dal Lake receives large quantities of nitrogen
and phosphorus from incoming sewage drains from non-point
sources like seepages and diffused runoff. Of the total
phosphorus and inorganic nitrogen inflow from all sources,
the quantity contributed by the drains works out to be thirty
five percent. Similarly a sizeable quantity of total phosphates
and nitrogen are added to the lake from non point sources.
Various
engineering
interventions
like
catchment
management, dredging, de-weeding, sewerage treatment plants
etc have been taken but their efficacy is under assessment
since the results are not very positive for the health of the
Lake.
Keywords: Water quality, Human interventions, Waste
discharge, Eutrophication, Engineering intervention.
1. INTRODUCTION:
The valley of Kashmir is bordered to the South and West by Pir
Panjal ranges and to the North and East by the Himalayan foot
hills. Numerous freshwater lakes are found within the state of
Jammu and Kashmir which covers an altitudinal range of 600m
and 500m. These lakes have been originated as a result of
earthquakes, damping of glaciers, weathering, denudation,
floods and meandering of alluvial deposits. DAL LAKE is one
such prized moderate altitude lake located within the
geographical coordinates of 340 6 N 740 45‟ East of Srinagar
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spreading over an area of 25 sq Km (1895 AD) and reduced to
merely 11.5 Sq Km (2009). It is at an altitude of 1587msl.
Dal Lake has been the centre of Kashmir civilization and is
one of the most beautiful spots of tourist attraction. This
shallow-post glacial freshwater body is bounded on Southwest
and West by Srinagar city, and its remaining sides are
surrounded by gentle terraced slopes at the base of precipitous
mountains. Dal Lake is unique because of:

Floating Gardens with the lake.


Habitation within the lake.
Biodiversity.
The Dal Lake lies in the flood plains of river Jhelum whose
broad meanders have cut swampy low lands out of the Karewa
terraces. The inflow Telbal nallah channel enters the lake from
the North bringing water from the high altitude Mansar Lake.
During its downward journey the inflow stream collects large
quantities of silt from the denuded catchment and deposits it in
the lake. Numbers of ephemeral water channels, surface drains
enter the lake from the human settlements discharging large
quantities of wastes. An estimated load of 12.30 x10 6m3 of liquid
waste with 18.17 tons and 25 tons of phosphorus and inorganic
nitrogen is enriching the lake annually. Within Lake Basin itself
a number of freshwater springs (mostly choked at present) act as
permanent source of water to the lake. Towards the South-west
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side an outflow channel Tsuant-Kul discharges lake water into
Jhelum river at Gawkadal. The outflow is regulated by a sluice
gate to prevent the entry of Jhelum water into the lake during the
floods. On the Eastern side the Nigeen basin of the lake is
connected by nallah or a channel dug by Afghan Governor Amir
Khan up to Khushalsar lake, which in turn connects with Anchar
lake. This channel also serves an additional outflow channel,
particularly during floods. Influx of waste and silt and excessive
weed growth in the Lake has affected the quality of its water and
the present study it aimed at assessing it.
1. MARGINAL DREDGING AND ITS IMPACT ON
WATER QUALITY
The main purpose of dredging is to increase the area of
open water to improve water circulation, navigational routes, to
create more attractive mosaic and to define margins. As part of
the Dal Lake conservation proposals under taken National Lake
Conservation Program, NLCP as per the proposals of IRAM
consultants, marginal dredging along the shore lines of Dal near
Nishat basin and Habak basin was done using suction cutter
dredgers. Similar peripheral dredging was also undertaken in the
Nigeen basin of the lake. Another consultant AHEC (Roorkee)
also had favored marginal dredging but with the remarks that
there should be pre-implementation evaluation of lake settings,
proper equipment and disposal sites and its effect on lake
ecology and long term productivity should be continuously
evaluated. AHEC identified 38 channels within the lake which
were clogged or reduced in width and proposed to excavate
them. Similarly 57 fresh water springs were identified around
the lake whose water got polluted during the intervening period
they reached the lake. The post dredging changes and a
comparative limnology of Dal Lake reported a decrease in
Nitrate-nitrogen and total phosphorous content after dredging
while increase in Ammonical and ortho-phosphates. The
plankton diversity did not show any significant change in
dredged and un-dredged sites.
Table1. Comparative changes in Physio-Chemical
parameters at dredged and Un-dredged sites in Dal Lake
Kashmir.
Fresh water lakes usually are abound of aquatic vegetation and
constitute one of the important components of biodiversity. It is
also an established fact that the aquatic plants (Macrophytes) are
the bio-indicators of pollution and have an important role in
removal of nutrients from the lake sediment and help in
pollution abatement. At the same time excessive growth of
aquatic weeds impede boat transport hinder irrigation and
increase sediment deposition besides effect the lake aesthetics.
Thus the most sound and reasonable management approach is to
control their growth. In Dal Lake the lake dweller have been
doing de-weeding through traditional pole method where in they
would whirl the wooden pole in such a skilled way that they
would extract the weeds and use them for preparation of
vegetation gardens or as bio fertilizers. They would also take out
the bottom mud and use it for vegetable garden preparations. But
when the weed infestation in the lake basins increased beyond
proportion the authorities concerned had to deploy mechanical
harvesters which also became an issue of controversy among the
lake scientists.
According to the consultants the de-weeding in Dal should be
selective. AHEC, Roorkee (2000) states; based on the
information available, it is recommended that de-weeding has to
be selective and limited to certain areas only especially areas
which are useful to repeated harvesting. According to the
consultants de-weeding should be limited to backwaters, areas
where exotic water ferns, water lilies abound and areas where
water skilling or swimming takes place. They further suggest
that in areas selected for de-weeding it is very important that
only 40% - 50% weed is removed and the rest is left untouched.
Efforts should be directed towards harvesting undesirable plant
species such as Ceratophyllum demersum, Nymphaea Stellata
Salvinia natans and Hydrocharis morus ranae.
According Trisal (1977, 1987) Typha Agustata and Phragmites
communis were the chief occupants of littoral zone of Dal and
Nigeen Lake and extended all along the Eastern part of the
Southern side of the Hazratbal basin. In the Nishat basin and
Nigeen basin the emergents are scattered towards the shorelines
and formed large stands in the arms of the lake basin.
According to the author rooted floating leaf macrophytes
(Aquatic plants) occupy 29.2% of total area of the lake free
floating aquatic ones were distributed throughout the lake area in
sheltered pockets. Submerged aquatic species due to their
aggressive capacity cover the maximum area of 57.6% in all the
basins of the lake.
Zutshi and Tickoo (1990) while studying the impacts of
mechanical de-weeding in Dal Lake recorded the reduction in
Seechi transparency of water and attributed it to the suspension
of sediments due to impact of harvesters. The authors however
noted the increase in dissolved oxygen content by 23 % in the
surface waters and by 36% in bottom waters. They further
recorded significant temporal change in nitrate nitrogen but little
horizontal and vertical difference as a result of de-weeding.
2. DE-WEEDING AND ITS IMPACT ON WATER
QUALITY.
Kundangar (1996) while studying the impact of waste waters on
the vegetation pattern of Dal Lake reported surprising changes in
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the Dal Lake basins and reverted the increase in abundance of
some eutrophic species. He attributed the luxuriant entering the
lake besides the enrichment of sediments through leaching of
fertilizers in the immediate agricultural lands surrounding the
lake.
Kundangar (2003) while studying the impact of de-weeding in
Dal Lake estimated liquid wastes carrying 18.7 tons of
phosphorus and 25 tons of inorganic nitrogen into the lake which
results in increase in fertility of lake waters and resulting in
accelerated weed growth. They also added that major part of
phosphorus and nitrates coupled with other nutrients get locked
up in the roots and rhizomes of the aquatic weeds. Thus these
aquatic weeds play significant role in keeping the water crustily
more or less in stable condition. But these aquatic weeds on
decaying during autumn-winter go on enriching the sediment
with nutrients and play an active role in re-growth of aquatic
weeds in the next spring.
The authors recorded a slight shift in pH of water in Nehru Park
and Nigeen basin (Table 2). After de-weeding the authors
concluded that with overall 55% of manual aquatic weed
removal in various basins of Dal Lake, there was decrease in
specific conductivity, iron, and phosphorus. The authors also
recorded that the full scale de-weeding (8-100%) enhance the
release of nutrients from the enriched sediment and result in
serious and hazardous algal blooms in a Lake ecosystem
particularly in Dal Lake. The authors stressed on long term
studies to establish a set of standards both for water quality and
biodiversity changes as a result de-weeding practices in the Lake
ecosystem.
Table 2a: Pre and post de-weeding changes in water quality by
Mechanical de-weeding in Dal Lake Kashmir (after Kundangar
2003)
Table 2b: Pre and Post de-weeding changes in water quality by
Manual de-weeding in Dal Lake (after Kundangar 2003).
3. SEWERAGE AND SEWAGE TREATMENT AND ITS
IMPACT ON WATER QUALITY
Sewerage and sewage treatment constitutes a major
component of the Dal lake conservation plan for preventing the
pollution of the lake. The Dal Lake receives water from fifteen
major drains besides inflow from the Telbal and Bota-kadal
Nallahs. The drains bring in 40 mld of sewage and join the lake
at locations identified. Two alternative plans for sewage
treatment were envisaged. One proposed conceptualized a
centralized sewage treatment where in all the waste will be
collected by sewers (gravity mains) and trunk sewers with 15
immediate pumping stations (IPS) and a main pumping station,
at Brarinambal. This unit of about 41 mld will treat the sewage
through an activated sludge process and release treated waste
effluent through Brainambal cut into Jhelum. This system
through theoretically very sound has some inherent weakness,
such as power dependence (in pumping and treatment) large size
trunk sewers and large distance of transport. The power scenario
in Srinagar town is dismal and utilizing it for pumping sewage as
against domestic requirements seems as far cry. Moreover,
failure of system or any component will put the entire machinery
out of gear. To obviate these difficulties a decentralized system
is preferred and has been proposed, which could do away with a
large amount of pumping and trunk sewers. The bulk of the
sewage will flow by gravity and pumping will be restored to
only when there is no alternative.
The STP‟s will be provided at least at six sites in Dal Lake
and two or three at Nigeen. The treated effluent of three STP‟s
will flow out of the lake and the rest after tertiary treatment will
be discharged into the lake (around 40%).
The total sewage
generated in all three zones worked out to be 36.7 mld in the
year 2017. A total of nine IPS, one in zone one, six (under
construction) in zone 2 and two (existing) in zone 3 are
proposed. The decentralization has resulted in a significant
reduction in the cost of sewers and of operation and
maintenance.
Sewerage treatment.
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There are numerous options available to treat the waste water.
These include dispersed and attached growth aerobic systems.
(Activated Sludge process, Aerated Lagoon, Oxidation ditch,
Trickling filter and Rotating Biological Discs), suspended and
attached growth anaerobic systems (up flow anaerobic Sludge
Blanket, expanded bed, fluidized bed) and pond processes. In
recent past the artificial wetland compartment technology has
also gained momentum in the developed countries where in
aquatic plant species are exploited for waste water treatment.
According to the AHEC consultancy the FAB (Fluidized
Aerobic Bed and Bio-filters) technology was considered and
recommended for Dal Lake.
FAB technology consists of screening, grit removal, biological
treatment (bioreactors), tertiary treatment of clarifloculator (with
alum), centrifuge and chlorination. The six units were proposed
of which five have been made operational.
Habak
3.2mld
STP 1 (a)
STP 1 (b)
REC
7.5mld
STP 1 (c)
Nallah Amir Khan
5.4mld
STP 2
BrariNambal
9.5mld
STP 3 (a)
Hotel Heemal
6.6mld
STP 3 (b)
Laam
4.5mld
Total
36.7mld
The treated effluent of STP 1 (c) and 3 (a) is discharged in
channels leaving Dal Lake via Amir Khan. Dalgate exit and
Brari-Nambal cut). Thus only 40% of the total of 36.7 mld finds
its way into the Dal Lake.
Controversy regarding FAB Technology
Kundangar (2003) while maintaining the FAB based sewage
treatment plant, of one of the hotels in the immediate vicinity of
Dal Lake recorded reversed trend i.e, instead of expected
decrease in nutrients, a significant increase was observed in the
treated sewage. According to the author 90-98% increase was
recorded in ortho-phosphate and total phosphorus respectively
while 32% increase was recorded in nitrate-nitrogen during
winter months.
In their studies during April 2008 (Table 3a) regarding the
functioning of FAB based STP reported 44% increase in nitratenitrogen content of the treated sewage indicating the
malfunctioning of the STP‟s installed.
Table 3(a) Efficiency of nutrient removal through FAB – STP
(April 2008)
Water quality of the Dal Lake has been seriously altered over
a period of time because of human interventions which include
agricultural activities within and on the periphery of the lake,
urbanization and mushrooming of hotels besides waste
discharge. The lake thus has turned Eutrophic and is under great
stress. Since the lake water at Nishat and Nigeen is also
harvested for public distribution (Potable purposes), the quality
of water has therefore assumed a great significance keeping in
view the public health.
The zones at the periphery and close to the effluent discharge
depict temporal variations. Around 50% of the observed
maximum specific conductivity, dissolved oxygen, nitratenitrogen, ammonical–nitrogen, PO4 and total phosphorus have
been noticed in the spring season. Summer season has 25% of
such observations and the remaining 25% are distribution in
autumn and winter seasons. This may possibly be due to the start
of activities in the catchment, mixing or re-suspension
(LAWDA, 2000 report).
A comparison of values over a period of time (Table 4) shows
that the Dal Lake has passed through several stages of trophic
evolution. Extensive data establishes far reaching changes in the
physico-chemical environment. Dal Lake receives large
quantities of nitrogen and phosphorus from incoming sewage
drains, Telbal Nallah and that of Bhota Kadal as well as from
non-point sources like seepages and diffused runoff. The lake
being peculiar in having human habitations within the lake either
in hamlets (Islands), boats, house boats etc of the total
phosphorus inflow 156.62 tons from all sources, the quantity
contributed by the drains works out to be 56.36 tons. In the case
of inorganic nitrogen (NO3 and NH3-N) these figures are 241.18
tons and 77.60 tons with a flow of 11.70 million cum/yr.
Similarly 4.5 tons of total phosphates and 18.14 tons of nitrogen
are added to the lake from non point sources.
Table 4a: Water Quality changes in Hazratbal Basin of Dal Lake
over a period of time
.
Table 4b: Water Quality changes in Nishat Basin of Dal Lake
over a period of time
CONCLUSION-WATER QUALITY ASSESSMENT
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Table 4c: Water Quality changes in Nehru Park basin of Dal
Lake over a period of time
xii.
2003, De-weeding practices in Dal Lake & impact
assessment.Kundangar.
xiii.
2004, Thirty years of Ecological Research on Dal Lake, Kundangar.
xiv.
2004, Groundwater quality of downtown Srinagar, Adnan, Neelofer,
Nuzhat and Kundangar. 2005.
xv.
2004, Bacterial Dynamics of Dal Lake, a Himalayan temperate fresh
water lake, Adnan & Kundangar.
xvi.
2005. Ecology of peripheral springs of Dal lake, Kashmir Adnan &
Kundangar.
xvii.
2009, Monitoring of Dal-Nigeen Lakes & other water bodies (J&K
PCB).
xviii.
2009. Three decades of Dal Lake, Adnan & Kundangar.
xix.
2010, Sanative role of macrophytes in Aquatic Ecosystems, Adnan.
xx.
2011, Water quality changes in Nigeen Lake, Shariqa Maryam.
xxi.
2011. Ecological studies & uses of valued aquatic plants in Kashmir
wet lands, Adnan, Afsha & Kundangar.
xxii.
2012, Impact of mechanical de-weeding on Macrozoobenthic
community in Dal Lake, Basharat, Rajini, AR Yousuf &Ashwani.
Spatial Water Quality Analysis Of Nagalamadike
Watershed Of Pavagada Taluk, Tumkur District
Karanataka Using Geo Informatic Tools
Table 4d: Water Quality changes in Nigeen Lake over a period
of time
Nandeesha1, Ravindranath.C2, T.Gangadaraiah3, and S.G
Swamy4
1
Professor, Civil Engineering Department, Siddaganga Institute
of Technology, Karnataka, India
2
Research Scholar, Civil Engineering Department, Siddaganga
Institute of Technology, Karnataka, India
3
Professor Civil Engineering Department, Siddaganga Institute
of Technology, Karnataka, India
4
Fellow KSCST Bangalore Karnataka, India
[email protected] [email protected]
[email protected] [email protected]
ABSTRACT
REFERENCES:
i.
1978, Pollution of Dal Lake, Enex.
ii.
1990. Impact of mechanical de-weeding on Dal lake eco system,
Zutshi & Tickoo.
iii.
1993. Effects of weed cutting on species, composition and abundance
of plankton population, Zutshi & Tickoo.
iv.
1996, Impact of waste water on the vegetational pattern of Dal Lake,
Kundangar.
v.
1996. Aeration of Dal lake (an interim report) HRL.
vi.
1997, Dal Lake conservation & rehabilitation. (J&K LAWDA).
vii.
1998, Technical report on Dal Lake (J&K LAWDA).
viii.
ix.
x.
2000. DPR conservation and management plan for Dal –Nigeen
lake-AHEC Roorkee.
xi.
2001, Post dredging changes & comparative limnology of Dal Lake,
Kundangar.
Ground water samples from 25 locations of the watershed
bounded by latitude N 1405‟to 14015‟ and longitude E 77015‟
to77025‟ were collected. The samples collected are distributed
over Precambrian rocks such as closepet granite and gneissic
terrines. Red sandy and loamy soil covers the major area of the
watershed. The samples were analyzed for pH, Electrical
Conductivity (EC), Total Dissolved Solids (TDS), Total
hardness, Fluorides, Iron, Nitrite, Sodium and Chloride. The
results of all the samples analyzed as per standard method and
compared with the BIS and WHO, drinking water standards out
of 25 samples 23 samples of Fluoride showed more than
permissible limit, and 15 samples of nitrate showed more than
permissible limit, and 20 samples of sodium shoved more than
permissible limit the permissible range of Fe, pH, EC, Cl, TDS,
TH, are in permissible limit. The most of the samples are lie
within the permissible limits. Arc View Ver.9.2 software and
ERDAS Ver. 9.1 was used to get watershed map, land use/land
cover map, litho logical map and Iso contour maps of major
parameter are generated and overlayed on the thematic map to
study the spatial variation of the parameters in the watershed and
causes for the pollution from various sources.
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KEY WORDS: Spatial variation, permissible limit, Arc View
Ver.9.2, ERDAS Ver. 9.1
1. Introduction
Due to the ever increasing demand for potable and irrigation
water and inadequacy of available surface water the importance
of ground water is increasing everyday. In the natural
Hydrological cycle the rainwater gives us sample of good
quality of the water but as the Urbanization and Industrialization
the natural cycle of the water is disturbed resulting in less
rainfall or runoff of the good quality of the water into sea as
there is no open space left in cities to allow rain water to get
absorbed in earth due to concretization. Drinking water is a
basic requirement for life and a determinant of standard of
living. Around 22 per cent of households in India lack of access
to safe drinking water sources, like tap, hand pump and tube
well (Census 2001). Hence, significant efforts are being made
by the central and state governments for increasing the coverage
of households with adequate and safe drinking water supply,
along with sanitation services, which coincide with the
Millennium Development Goals. In the recent past, several parts
of our country have been experiencing drought conditions very
often due to vagaries of the nature, mainly monsoon. In
Karnataka, Tumkur district comes under this category.
Depending on the ground water resources available even at the
times of severe drought conditions, when major part of this
surface water resources are exhausted, it has been conceived to
develop ground water base irrigation system in certain part of
the district. Nearly 2/3rd of the state receives less than 750mm of
rainfall. Many parts of the south and north interior Karnataka
depends on ground water for its domestic and agricultural needs.
Fig 1 Location map of study area
Sample collection points and location of study area
Study area
The Nagalamadike gram panchayat is located in eastern part of
the pavagada taluk 10.9 km from the main pavagada town and
99 km from Tumkur town. The gram panchayat has a total area
of 74.6 sq. km. and a population of 1500. The area consists of
14 micro watersheds that constitute a mini watershed. This is
situated in the Pennar river basin. The sources of water in this
area include bore well, hand pump, water tanks etc. The study
area is reported to be facing a lot of problems regarding the
quality of water. The residing people are facing acute problems
of fluorosis which is due to deficient of excessive quality of
water. Thus an effort has been made to survey the study area
and analyze the quality of water by sampling and presenting the
results in an interesting and attractive way so that the need for
reforms is highlighted. The technology involved in this project
plays a major role in the analysis. The use of sophisticated
instruments such as the Water Analyzer 371, Colorimeter DDR
2010, flame photometer is used for the analysis and AAS
(Atomic Absorption Spectrophotometer) have made the tests
very simpler and quicker. Moreover the use of G.P.S. devices
such as GARMIN 12 channel made it much easier to locate a
particular water source so that any person can identify the point.
Arc
GIS Ver. 9.2 is used
for
representation
of
results.
Fig 2: Sample Location map study area.
Details of the latitude and longitude points of Nagalamadike
watershed, sample collection of 25 points shown in table no1.
Table 1. Details of
sample locations
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Methodology
For studying the chemical quality of groundwater 25
groundwater samples were collected and the sample locations
are shown in fig 2. Water samples collected from bore wells in
use and samples collected in the one litre pre-washed polythene
bottles, were analysed in the chemical labaroatoty of the
Department of Civil Engineering, SIT, Tumkur and the results
are given in table no 3.
Chemical Analysis of Ground water: Groundwater is the main
source of water that meets the agricultural, industrial and
household requirements. Population growth, socioeconomic
development, technological and climate changes has increased
the demand for potable water manifolds in the past few years
(Alcamo et al. 2007).One of the internationally accepted human
rights is the access to safe drinking water which is the basic
need for human health and development (WHO 2001). The
general health and life expectancy of the people is reported to be
adversely affected due to lack of the availability of clean
drinking water in many developing countries of the world (Nash
and McCall 1995). In irrigation, the poor water quality not only
affects the crop yield but also affects the physical conditions of
the soil (Ayers and West cot 1994). Since the dependence on
groundwater has increased tremendously in India due to
vagaries of monsoon and scarcity of surface water in recent
years, therefore groundwater quality and surface water needs to
be monitored and managed. The water sample is analysed by
using BIS 1983 permissible limits which is shown in table no 2.
Fig.3. Water Analysis Methodology chart.
The above methodology is used to find the chemical
contamination of water samples of 25 location in Nagalamadike
watershed of the Pavagoda taluk of Tumkur district karanataka
state india.
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Soil map: In the pavagada taluk the soil is consisting of fine
grained
and
loamy
soil.
The
soil
map
is
shown
in
fig
4
fig 6 and also the fluoride concentration is as shown in same
figure. Here the spatial distribution map of fluoride from the Iso
contour map of the study are shows the southwest zone having a
rich content of fluoride and the central ,norhtenzone consisting
limited quantity the lithology of these shows the granite belt and
northern
shows
the
PGC
belt.
Fig: 4 soil map of pavagada taluk
Lithology map of study area: In the Lithology map the study
area consists of Granite and PGC.is shown in fig 5
Fig:6 Lu/Lc map and overlay of Iso
concentration map of Fluoride.
Table 2 : Permissible limits (BIS-1983) of potable water in
the study area
PARAMETER
HIGHEST
DESIRABLE
LIMIT
(in ppm)
MAXIMUM
PERMISSIBLE
LIMIT
(in ppm)
FLUORIDE
0.6-1.2
1.5
NITRATE
45
NO RELAXATION
TOTAL
HARDNESS
CHLORIDE
300
600
250
1000
pH
6.5-8.5
8.5-9.5
IRON
0.3
1.0
SODIUM
0-60
100
Fig: 5 Lithology map of the study area
Land use and Land cover map of study area: In the study
area the five groups of land use and land cover is as shown in
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Results and Discussions: Chemical concentration analysis of
water samples is collected from the study area. In the study area
25 water samples are water collected from various locations and
analyzed in chemical lab the following details is shown in table
3.
permissible limit as shown in table 2, and fluoride isoconcentration map is shown in fig 8. Fluoride is more in south
west region and remaining zone is less.
Table 3: Chemical analysis of water
samples.
Fig 8. Spatial distribution map of
fluoride (Iso contour map of fluoride)
Iso concentration map of Nitrate: Spatial distribution of
Nitrate and Iso contour map is prepared using of Arc GIS tool.
Nitrate content is more in most of the water samples out of 25
samples the Nitrate present in 15 samples is more than the
permissible limit as shown in table 2, Nitrate concentration is
shown in figure no 9. In North east and south east ,the nitrate
contamination is more due to more application of artificial
manure (NPK) in agriculture.
Fig:7 Chemical concentration of water sample
Iso Concentration map of fluoride: Spatial distribution of
fluoride and Iso contour maps is prepared using of Arc GIS
tool. Fluoride content is more in most of the water samples out
of 25 samples the fluoride present in 23 samples is more than the
Fig: 9 Spatial distribution map of
Nitrate (Iso contour map of Nitrate)
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Iso concentration map of Iron: Spatial distribution of the iron
is prepared using Arc GIS and concentration of Iron is ranges
from 0 to 0.4 is within the permissible limit the Iron
concentration map is shown in fig 10.the iron concentration is
distributed almost equal in all places.
Iso concentration map of Total Hardness: Spatial distribution
of Iso counter map is developed by using the Arc GIS tool is
shown in fig 12 .TDS is more in the south central and north
central part of the study area and
remaining area is less.
Fig 12: Spatial Distribution map of Total
Hardness (Iso contour map of Total Hardness)
Fig 10: Spatial Distribution map of Iron
(Iso contour map of Iron)
Iso concentration map of pH scale: Spatial distribution map
pH is developed by using Arc GIS tool and pH is ranges from
6.28 to 8.26. All the samples in the study area falls within the
permissible range.
Fig: 11 Spatial distribution map of pH (Iso
contour map of pH)
Iso concentration map of Electrical Conductivity: Spatial
distribution and Iso counter map is developed by using the Arc
GIS tool and, Electrical Conductivity shown in fig 13. The
electrical conductivity is more in Northern part of the study area
where as remaining part the electrical conductivity is less.
Fig 13: Spatial distribution map of Electrical
Conductivity (Iso contour map of Electrical Conductivity)
3.6: Iso concentration map of Cl: Spatial distribution and Iso
counter map is developed by using Arc GIS tool and
concentration level shown in fig 14. The chloride is more in the
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north central part and south part of the study area and remaining
area is less.
dissolved materials. In natural waters, salts are chemical
compounds comprised of anions such as carbonates, chlorides,
sulphates, and nitrates (primarily in ground water), and cat ions
such as potassium (K), magnesium (Mg), calcium (Ca), and
sodium (Na). In ambient conditions, these compounds are
present in proportions that create a balanced solution. If there are
additional inputs of dissolved solids to the system, the balance is
altered and detrimental effects may be seen. Inputs include both
natural
and
anthropogenic
source.
Fig 14: Spatial distribution map of chloride (Iso
contour map of chloride)
Iso concentration map of Sodium: Spatial distribution of
Sodium and Iso contour maps is prepared using of Arc GIS tool.
Sodium content is more in most of the water samples out of 25
samples the Sodium content in 20 samples is more than the
permissible limit as shown in table 2.the sodium content is more
in north central part and remaining of the study area is less.
Fig 16: Spatial distribution map of Total
Dissolved Solids (Iso contour map of TDS)
CONCULSIONS
Fig 15: Spatial Distribution map of sodium (Iso
contour map of sodium)
Iso concentration map of Total Dissolved Solids: Spatial
distributionof TDS and Iso counter map is developed by using
Arc GIS tool. TDS concentration is shown in fig 16.The total
dissolved solids (TDS) in water consist of inorganic salts and
It is observed that the study area is basically composed of hard
and compact lithologies and to add to the conclusions the
distribution of rainfall in the state with time and space is highly
variable. Moreover, limited surface water resources and non
uniform rainfall as increased the dependence on the ground
water resources. This mounting pressure has resulted in excess
utilization of the ground water resource. Thus, the ground water
resources have reached critical stages. Geographic Information
Systems are rapidly developing as primary technologies for the
investigation of large scale patterns and processes. The use of
Arc GIS software not only improves the analytical capabilities
for water resource management but also the ability to
communicate work results and research findings to the decision
makers and general public. The advantage of GIS software‟s has
made it possible to update, modify or revalidate data at any
location. This tool will help the public and decision makers to
understand, assess and actively participate in issues pertaining to
water bodies‟ pH, Electrical Conductivity, Iron content, Total
Hardness and Chloride content in all the samples is within the
maximum permissible range. Fluoride content, nitrate content,
and sodium is more in most of the water samples. Samples
exceeding Fluoride limit- 23/25Samples exceeding nitrate limit15/25Samples exceeding sodium limit- 20/25as per permissible
table.
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Suggestions:
To prevent the entry of nitrate in the groundwater sources, the
use of chemical fertilizers in agriculture should be minimized
and the use of natural manure should be encouraged. The people
of the area should make the awareness programmers about water
quality management and rain water harvesting, artificial
groundwater recharge, etc. Frequent quality checks of water ,
Soil analysis, Rock analysis shall be made for betterment of
water quality analysis.
Scope of further study:
Sampling is to be more representative because of the vast area
covered and more samples are needed to be taken to give more
accurate results. The samples have to be analyzed
for
bacteriological parameters, heavy metals such as lead, and also
radioactive metals to know more about the affects of water for
various purposes .Spatial distribution maps have to be overlaid
on geomorphologic information. By overlaying the map of the
study area over the drainage map, soil map and lithology map
the drainage pattern; soil of the area can be assessed respectively
for future generation.
xvi.
Kazi T.G., Arain M.B., Jamali M.K., Jalbani N., Afridi H.I., Sarfraz
R.A.,
Baig
J.A.,
and
Shah
A.Q.,
(2009),
Assessment of water quality of polluted lake using multivariate statistical
techniques: A case study, Ecotox. Environmental Safety, 72(20), pp 301-309
xvii.
S.F. Mulgundmath (1974) , Dept of Mines and Geology, Bangalore. A
report on ―GROUND WATER RESOURCES OF TUMKUR TALUK, TUMKUR
DISTRICT‖.
xviii.
Statistical abstract. (2008). State statistical abstract. Chandigarh,
India: Government of Haryana Publication.
xix.
Todd, D. K., & Mays, L. W. (2005). Groundwater hydrology (3rd
ed.). New York: Wiley.
xx.
U.S. Salinity Laboratory (USSL) (1954). Diagnosis and improvement
of saline and alkali soils; USDA Handbook No. 60. pp. 160 Richards LA (ed)
(1954)
xxi.
WHO (2001). Water health and human rights, world water day
http://www.worldwaterday.org/wwday/2001/thematic/ hmnrights.html
xxii.
WHO (2008). Guidelines for drinking water quality incorporating Ist
and
2nd
addenda
Vol.1
Recommendations,
(3rd
edit)
http://www.who.int/water_sanitation_health/dwq/ gdwq3rev/en.
xxiii.
Wilcox, L. V. (1948). The quality of water for irrigation use, USDA
Technical Bulletin No 962, pp. 1–40
Water Pollution In Ganga River
Susmita Saha
Asst. Professor
Sagar Institute of Research & Technology
References:
i.
Abbasi, S.A., (2002), Water quality indices, state of the art report,
National Institute of Hydrology, scientific
ii.
Contribution no. INCOH/SAR-25/2002, Roorkee: INCOH, pp 73.
iii.
Ahmed, S., David, K.S. and Gerald, S., (2004), Environmental
assessment: An innovation index for evaluation water
iv.
quality in streams, Environment Management., 34 pp 406-414.
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Bajpai, A., Vyas, A., Verma, N. and Mishra, D.D. (2009). Effect of
idol immersion on water quality of twin Lakes of
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Bhopal with special reference to heavy metals. Poll. Res.,
28(3):433-438.
vii.
Bhavana, A., Shrivastava, V., Tiwari, C.R. and Jain, P. (2009).
Heavyvmetal contamination and its potential risk with
viii.
special reference to Narmada River at Nimar region of M.P. (India).
Res. J. of Chem. &Env. 13 (4), 23-27.
ix.
Chaudhary, B. S., Kumar, M., Roy, A. K., & Ruhal, D. S. (1996).
Applications of RS and GIS in groundwater
investigations in
Sohna Block, Gurgaon District, Haryana, India. International Archives of
Photogrammetry and Remote Sensing, 31(B-6), 18–23. Eaton, F. M. (1950).
Significance of carbonates in irrigation water. Soil Science, 69, 123–133.
doi:10.1097/00010694-195002000-00004.
x.
―DISTRICT PROFILE AND RESOURCES ATLAS OF TUMKUR
DISTRICT‖. – N.R.D.M.S Centre, Z.P, Tumkur ―Ground Water quality
evaluation of Tumkur town- By Ajay K.C., Pawan kumar P.M. ,Sanjeev Saurabh.
Year 2006-07
xi.
―Ground water quality assessment using GIS‖:-by Channabasabanna
A. Year 2005-06
xii.
Goyal, S. K., Chaudhary, B. S., Singh O., Sethi, G. K., & Thakur, P.
K. (2010) GIS Based Spatial Distribution Mapping and Suitability Evaluation of
Groundwater Quality for Domestic and Agricultural Purpose in Kaithal Distirct,
Haryana State, India. Environmental Earth Science. In press,
doi:101007/s12665-010-0472-z.
xiii.
Indian Standard Specification for Drinking Water (1983), IS-105001983, Indian Standards Institution, New Delhi,
xiv.
Jain, C. K., & Sharma, M. K. (2000). Regression analysis of
groundwater quality of Sagar District, Madhya Pradesh. Indian Journal of
Environmental Health, 42(4), 159–168.
xv.
Lloyd, J. W., & Heathcote, J. A. (1985). Natural inorganic
hydrochemistry in relation to groundwater: An introduction. Oxford, New York:
Clarendon Press, Oxford University Press.
Abstract : There is a universal reverence to water in almost all
of the major religions of the world. Most religious beliefs
involve some ceremonial use of "holy" water. The purity of
such water, the belief in its known historical and unknown
mythological origins, and the inaccessibility of remote sources,
elevate its importance even further. In India, the water of the
river Ganga is treated with such reverence. The river Ganga
occupies a unique position in the cultural ethos of India.
Legend says that the river has descended from Heaven on
earth as a result of the long and arduous prayers of King
Bhagirathi for the salvation of his deceased ancestors. From
times immemorial, the Ganga has been India's river of faith,
devotion and worship. Millions of Hindus accept its water as
sacred. Even today, people carry treasured Ganga water all
over India and abroad because it is "holy" water and known
for its "curative" properties. However, the river is not just a
legend, it is also a life-support system for the people of India. It
is important because the densely populated Ganga basin is
inhabited by 37 percent of India's population. The entire
Ganga basin system effectively drains eight states of India.
About 47 per cent of the total irrigated area in India is located
in the Ganga basin alone. It has been a major source of
navigation and communication since ancient times. The IndoGangetic plain has witnessed the blossoming of India's great
creative talent.
Keywords: Pollution in Ganga, Pollution free by Ganga Action
Plan, Treatment of water of Ganga.
1. INTRODUCTION
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The Ganga rises on the sourthern slopes of the Himalayan
ranges(fig 1.1) from the Gangotri glacier at 4,000 m above mean
sea level. It flows swiftly for 250 km in the mountains,
descending steeply to an elevation of 288 m above means sea
level. In the Himalayan region the Bhagirathi is joined by the
tributaries Alaknanda and Mandakini to form the Ganga. After
entering the plains at Haridwar, it winds its way to the Bay of
Bengal, covering 2,500 km through the provinces of Uttar
Pradesh, Bihar and West Bengal ,. In the plains it is joined by
Ramganga, Yamuna, Sai, Gomti, Ghaghara, Sone, Gandak, Kosi
and Damodar along with many other smaller rivers.
The Ganga river carries the highest silt load of any river in the
world and the deposition of this material in the delta region
results in the largest river delta in the world (400 km from north
to south and 320 km from east to west). The rich mangrove
forests of the Gangetic delta contain very rare and valuable
species of plants and animals and are unparalleled among many
forest ecosystems.
In the recent past, due to rapid progress in communications and
commerce, there has been a swift increase in the urban areas
along the river Ganga. As a result the river is no longer only a
source of water but it is also a channel, receiving and
transporting urban population lives in the towns of the Ganga
basin. Out of the 2,300 towns in the country, 692 are located in
this basin, and of these, 100 are located along the river bank
itself.
The belief the Ganga river is “holy” has not, however, prevented
over-use, abuse and pollution of the river. All the towns along its
length contribute to the pollution load. It has been assessed that
more than 80 per cent of the total pollution load (in terms of
organic pollution expressed as biochemical oxygen demand
(BOD)) arises from domestic sources, i.e. from the settlement
along the river course. Due to over-abstraction of water for
irrigation in the upper regions of the river, the dry weather flow
has been reduced to a trickle. Rampant deforestation in the last
few decades, resulting in topsoil erosion in the catchment area,
has increased silt deposits which, in turn, raise the river bed and
lead to devastating floods in the rainy season and stagnant flow
in the dry season. Along the main river course there are 25 towns
with a population of more than 100,000 and about another 23
towns with populations above 50,000. In addition there are 50
smaller towns with population above 20,000. There are also
about 100 identified polluting areas. Fifty-five of these industrial
units have complied with the regulations and installed effluent
treatment plants (ETPs) and legal proceedings are in progress for
the remaining units. The natural assimilative capacity of the
river is severely stressed.
The principal sources of pollution of the Ganga river can be
characterized as follows:




The purity of the water depends on the velocity and the dilution
capacity of the river. A large part of the flow of the Ganga is
abstracted for irrigation just as it enters the plains at Haridwar.
From there it flows as a trickle for a few hundred kilometers
until Allahabad, from where it is recharged by its tributaries. The
Ganga receives over 60 per cent of its discharge from its
tributaries. The contribution of most of the tributaries to the
pollution load is small, except from the Gomti, Damodar and
Yamuna rivers, for which separate action programmes have
already started under Phase II of “The National Rivers
Conservation Plan”.


Domestic and industrial wastes. It has been
estimated that about 1.4 x 106m3d-1 of domestic
wastewater and 0.26 x 106 m3 d-1 of industrial
sewage are going into the river.
Solid garbage thrown directly into the river.
Non-point sources of pollution from agricultural
run-off containing residues of harmful pesticides
and fertilizers.
Animal carcasses and half-burned and unburned
human corpses thrown into the river.
Defecation on the banks by the low-income people.
Mass bathing and ritualistic practices.
Causes of pollution in Ganga
It provides water to drinking purpose and irrigation in
agriculture about 40% of India‟s population in 11 states. After
27 years and Rs. 1000 crore expenditure on Ganga river, it has a
critical situation. In modern times, it is known for being much
polluted, 30 polluted nalas flows in Ganga river from Varanasi
city within seven kilometers.
2.1 Human Waste
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The river flows through 29 cities in which cities population
living above ten lakh. A large proportion damp the solid and
liquid wastes in Ganga river like domestic usage (bathing,
laundry and public defecation), Sewage wastes, unburnt dead
bodies through in Ganga river. Patna and Varanasi cities are
more responsible to water pollution of Ganga.
2.2 Industrial Waste
Countless industries lies on the bank of the Ganga river from
Uttrakhand to West Bengal like chemical plants, textile mills,
paper mills, fertilizer plants and hospitals waste. These
industries are 20% responsible to water pollution and run off
solid waste and liquid waste in the Ganga river. It is very
dangerous to water quality, their chemical properties and
riverine life.
2.3 Religious factor
Festivals are very important and heartiest to every person of
India. Owing festival seasons a lot of peoples come to Ganga
Snans to clean themselves. After death of the people dump their
asthia in Ganga river it is a tradition of India because they think
that Ganga gives mukti from the human world. Khumbha Mela
is a very big festival of the world and billion peoples come
"Ganga Snans at Allahabad, Hardwar in India. They throw some
materials like food, waste or leaves in the Ganges for
spiritualistic reasons.
2.4 Riverine Life
The Ganga river pollution increased day by day and from this
pollution marine life have been going to lost in near future and
this polluted water disturb the ecosystem of the river. And
irrigation and Hydroelectric dams give struggle to life in their
life cycle.
2.5 Bio Life
Some dams are constructed along the Ganges basin. Dams are
collected a huge volume of water and this is hazard for wild life
which are moving in Ganga river. The Kotli Bhel dam at
Devprayag will submerge about 1200 hectors of forest. In India
wildlife has been warning that the wild animals will find it
difficult to cope with the changed situation.
2.6 Human beings
An analysis of the Ganges water in 2006 showed significant
associations between water-borne/enteric disease occurence and
the use of the river for bathing, laundry, washing, eating,
cleaning utensils, and brushing teeth. Exposure factors such as
washing clothes, bathing and lack of sewerage, toilet at
residence, children defecating outdoors, poor sanitation, low
income and low education level also showed significant
associations with enteric disease outcome. Water in the Ganges
has been correlated to contracting dysentery, cholera, hepatitis,
as well as severe diarrhea which continue to be one of the
leading causes of death of children in India.
2.7 The Ganga Action Plan
The Ganga Action Plan or GAP was a program launched by
Rajiv Gandhi in April 1986 in order to reduce the pollution load
on the river. Under GAP I, pollution abatement schemes were
taken up in 25 Class-I towns in three States of U.P., Bihar and
West Bengal. GAP I was declared complete on 31.03.2000 with
an expenditure of Rs. 452 crore.
As GAP I addressed only a part of the pollution load of Ganga,
GAP II was launched in stages between 1993 and 1996, 59
towns along the main stem of river Ganga in five States of
Uttarakhand, U.P., Jharkhand, Bihar and West Bengal are
covered under the Plan and included the following tributaries of
the Ganges, Yamuna, Gomti, Damodar and Mahananda.
According to Hindustan Newspaper, January 11, 2013, the Prime
Minister has been monitoring the availability of adequate water
from Tehri Dam in river Ganga at Allahabad during the Kumbh
Mela. Directions have been given to control the pollution load
flowing in river Yamuna during the Kumbh Mela period.
Tehri Hydro Development Corporation (THDCIL) has agreed to
release 250 cumecs water from 21st December 2012 to 20th
February 2013 to 28th February 2013 in view of demand of water
for Allahabad „Kumbh Snans‟. Instructions have also been given
by PMO that Delhi Jal Board should ensure that the performance
of the 72 MGD STP (Sewage Treatment Plant) at Keshavpur
renovated / commissioned recently is stabilized so that it
functions optimally and the effluent meets the norms. The Delhi
Government has been asked to ensure that the performance of
the STPs and CETPs (Common Effluent Treatment Plants) is
optimized to meet the effluent quality norms.
At Sangam, Allahabd, the Biochemical Oxygen Demand (BOD)
of Yamuna and Ganga is generally less than 6 mg/ltr but the
main issue is of the color of effluents discharged by the pulp and
paper industries into the river Ram Ganga and Kali (both
tributaries of Ganga). Monitoring of water quality in river Ram
Ganga and river Kali and their tributaries is being initiated on a
daily basis by the State Boards of Uttrakhand and Uttar Pradesh
with the coordination of CPCB. Action will be taken against the
industries for violating the norms.
Spiritual dip in holy Ganga at Kumbh is not clean. The pollution
level in the sacred river has risen since Kumbh started at the
historical city of Allahabad on January 14, 2013 and the water is
not fit for bathing purposes, latest evaluation by country‟s
pollution watchdog the Central Pollution. The level of the
Biochemical Oxygen Demand (BOD) level – used to measure of
the level of organic pollution in the water – had increased to 7.4
milligram per litre at the main bathing place, known as Sangam,
since the Kumbh started.
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A day before the Kumbh, the pollution level was 4.4 milligram
per litre slightly more than the national standard for bathing
quality of water of 3 miligram per litre. “Higher the BOD level
worse it is for one‟s skin,” said a CPCB expert. High exposure to
dirty water can result in skin rashness and allergies. The official
reason for the sudden rise of contaminants in the river was
sudden increase in flow of human waste because of increased
bathing during Kumbh. Around 10 million people have already
visited the Kumbh and the UP government has employed around
10,000 sweepers to keep the city clean. Off the record officials
admit that their drive to check sewage from industries in Ganga
upstream of Allahabad has not worked as dirty sewage was still
flowing into the river.
The Board has been asked by the environment ministry to
monitor the pollution level in Ganga under its National Ganga
Basin River Authority and conduct periodic check on pollution
industries along the river bank. But, the dirt in the river is not a
deterrent for people to take a dip at Allahabad. Hindus believe
that the Ganga water has ability to clean and purify itself, a
claim not scientifically proven. And, this belief has drive
millions to the world biggest Hindu congregation and another 15
million are expected to visit in the 55-day long festival to end on
March 10.
state governments, under the supervision of the GPD. The GPD
was to remain in place until the GAP was completed. The plan
was formally launched on 14 June 1986. The main thrust was to
intercept and divert the wastes from urban settlements away
from the river. Treatment and economical use of waste, as a
means of assisting resource recovery, were made an integral part
of the plan.
The GAP was only the first step in river water quality
management. Its mandate was limited to quick and effective, but
sustainable, interventions to contain the damage. The studies
carried out by the CPCB in 1981-82 revealed that pollution of
the Ganga was increasing but had not assumed serious
proportions, except at certain main towns on the river such as
industrial Kanpur and Calcutta on the Hoogly, together with a
few other towns. These locations were identified and designated
as the “hot-spots” where urgent interventions were warranted.
The causative factors responsible for these situations were
targeted for swift and effective control measures. This strategy
was adopted for urgent implementation during the first phase of
the plan under which only 25 towns identified on the main river
were to be included. The studies has revealed that:

3. RESULT AND ANALYSIS

3.1 Scientific awareness

There are 14 major river basins in India with natural waters that
are being used for human and developmental activities. These
activities contribute significantly to the pollution loads of these
river basins. Of these river basins the Ganga sustains the largest
population. The Central Pollution Control Board (CPCB), which
is India‟s national body for monitoring environmental pollution,
undertook a comprehensive scientific survey in 1981-82 in order
to classify river waters according to their designated best uses.
This report was the first systematic document that formed the
basis of the Ganga Action Plan (GAP). It detailed land-use
patterns, domestic and industrial pollution loads, fertilizer and
pesticide use, hydrological aspects and river classifications. This
inventory of pollution was used by the Department of
Environment in 1984 when formulating a policy document.
Realizing the need for urgent intervention the Central Ganga
Authority (CGA) was set up in 1985 under the chairmanship of
the Prime Minister.
The Ganga Project Directorate (GPD) was established in June
1985 as a national body operating within the National Ministry
of Environment and Forest. The GPD was intended to serve as
the secretariat to the CGA and also as the Apex Nodal Agency
for implementation. It was set up to co-ordinate the different
ministries involved and to administer funds for this 100 per cent
centrally-sponsored plan. The programme was perceived as a
once-off investment providing demonstrable effects on river
water quality. The execution of the works and the subsequent
operation and management (O&M) were the responsibility of the

75 per cent of the pollution loads was from
untreated municipal sewage.
88 per cent of the municipal sewage was from the
25 Class I towns on the main river.
Only a few of these cities had sewage treatment
facilities (these were very inadequate and were
often not functional)
All the industries accounted for only 25 per cent of
the total pollution (in some areas, such as Calcutta
and Kanpur, the industrial waste was very toxic
and hard to treat).
3.2 Attainable objectives
The board aim of the GAP was to reduce pollution and to clean
the river and to restore water quality at least to Class B (i.e.
bathing quality: 3 mg l-1 BOD and 5 mg l-1 dissolved oxygen).
This was considered as a feasible objective and because a unique
and distinguishing feature of the Ganga was its widespread use
for ritualistic mass bathing. The other environmental benefits
envisaged were improvements in, for example, fisheries, aquatic
flora and fauna, aesthetic quality, health issues and levels of
contamination.
The multi-pronged objectives were to improve the water quality,
as an immediate short-term measure, by controlling municipal
and industrial wastes. The long-term objectives were to improve
the environmental conditions along the river by suitably
reducing all the polluting influences at source. These included
not only the creation of waste treatment facilities but also
invoking remedial legislation to control such non-point sources
as agricultural run-off containing residues of fertilizers and
pesticides, which are harmful for the aquatic flora and fauna.
Prior to the creation of the GAP, the responsibilities for pollution
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of the river were not clearly demarcated between the various
government agencies. The pollutants reaching the Ganga from
most point sources did not mix well in the river, due to the
sluggish water currents, and as a result such pollution often
lingered along the embankments where people bathed and took
water for domestic use.
3.3 The strategy
The GAP had a multi-pronged strategy to improve the river
water quality. It was fully financed by the Central Government,
with the assets created by the Central Government to be used
and maintained by the industrial wastes. All possible point and
non-point sources of pollution were identified. The control of
point sources of urban municipal wastes for the 25 Class I towns
on the main river was initiated from the 100 per cent centrallyinvested project funds. The control of urban non-point sources
was also tackled by direct interventions from project funds. The
control of non-point source agricultural run-off was undertaken
in a phased manner by the Ministry of Agriculture, principally
by reducing use of fertilizer and pesticides. The control of point
sources of industrial wastes was done by applying the polluterpays-principle.
A total of 261 sub-projects were sought for implementation in 25
Class I (population above 100,000) river front towns. This would
eventually involve a financial outlay of Rs 4,680 million (Indian
Rupees), equivalent to about US$ 156 million. More than 95 per
cent of the programme has been completed and the remaining
sub-projects quality, although noticeable, is hotly debated in the
media by the certain non-governmental organizations (NGOs).
The success of the programme can be gauged by the fact that
Phase II of the plan, covering some of the tributaries, has already
been launched by the Government. In addition, the earlier action
plan has now evolved further to cover all the other major
national river-basins in India, including a few lakes, and is
known as the “National Rivers Conservation Plan”.
3.4 Prevention of pollution of river Ganga
Training cum Awareness programme on Saltless Preservation of
Hides / skins was organized by CPCB at Lucknow and Kanpur,
which was attended by representatives from slaughter houses,
tannery & allied units and officers of UPPCB. The programme
was oriented towards the ongoing efforts pursuing basin-wise
approach for reduction of dissolved solids in wastewater from
leather processing industries in particular by invoking salt less
preservation of hides / skins.
CPCB has initiated a Techno-Economic Feasibility for setting up
of Common Recovery Plant & Common Effluent Treatment
Plant for Pulp & Paper Industries identified clusters at Muzaffar
nagar, Moradabad and Merut. CPCB also made a reconnaissance
survey from Gomukh to Uluberia (West Bengal) for identified
the point source and its impact on River. This reconnaissance
survey is conducted in association with Shri Rajinder Singh,
Member, NGRBA.
CPCB issued direction to UPPCB and Uttrakhand PCB in the
matter of Prevention and Control of Pollution from agro based
Pulp & Paper Sector Mills. As a result 31 industries have been
issued directions in U.P., 25 digester sealed at Uttrakhand, 8
industries were directed and 4 were stop chemical pulping.
CPCB conducted monitoring of 26 industrial units in the
strength of river Ganga between Kannauj to Varanasi in the
month of September 2010. Of these 7 were found closed during
inspection, 2 were complying to the prescribed discharge norms,
9 were requiring minor improvements, 4 have been issued
directions (under section 5 of Environment Protection Act 1986)
for closure, 3 have been issued directions for corrective
measures (under section 5 of Environment Protection Act 1986)
and I have been issued Show Cause notice for closure (under
section 5 of Environment Protection Act 1986).
3.5 Integrated improvements of urban environments
Apart from the above, the GAP also covered very wide and
diverse activities, such as conservation of aquatic species
(gangetic dolphin), protection of natural habitats (scavenger
turtles) and creating riverine sanctuaries (fisheries). It also
included components for landscaping river frontage (35
schemes), building stepped terraces on the sloped river banks for
ritualistic mass-bathing (128 locations), improving sanitation
along the river frontage (2,760 complexes), development of
public facilities, improved approach roads and lighting on the
river frontage.
3.6 Applied research
The Action Plan stressed the importance of applied research
projects and many universities and reputable organizations were
supported with grants for projects carrying out studies and
observations which would have a direct bearing on the Action
Plan. Some of the prominent subjects were PC-based software
modeling, sewage-fed pisciculture, conservation of fish in upper
river reaches, bioconservation in Bihar, monitoring of pesticides,
using treated sewage for irrigation, and rehabilitation of turtles.
Some of the ongoing research projects include land application
of untreated sewage for tree plantations, aquaculture for sewage
treatment, disinfection of treated sewage by Gamma radiation.
Expert advise is constantly sought by involving regional
universities in project formulation and as consultants to the
implementing agencies to keep them in touch with the latest
technologies. Eight research projects have been completed and
17 are ongoing. All the presently available research results are
being consolidated for easy access by creation of a data base by
the Indian National Scientific Documentation Centre (INSDOC).
3.7 Public participation
The pollution of the river, although classified as environmental,
was the direct outcome of a deeper social problem emerging
from long-term public indifference, diffidence and apathy, and a
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lack of public awareness, education and social values, and above
all from poverty.
In recognition of the necessity of the involvement of the people
for the sustainability and success of the Action Plan, due
importance was given to generating awareness through intensive
publicity campaigns using the press and electronic media, audio
visual approaches, leaflets and hoardings, as well as organizing
public programmes for spreading the message effectively. In
spite of full financial support from the project, and in spite of a
heavy involvement of about 39 well known NGOs to organizing
these activities, the programme had only limited public impact
and even received some criticism. Other similar awarenessgenerating programmes involving school children from many
schools in the project towns were received with greater
enthusiasm. These efforts to induce a change in social behaviour
are meandering sluggishly like the Ganga itself.
The Action Plan started as a “cleanliness drive” and continues in
the same noble spirit with the same zeal and enthusiasm on other
major rivers and freshwater bodies. Its effectiveness could
however be enhanced if these efforts could be integrated and
well-accepted within the long-term objectives and master plans
of the cities, which are consultancy under preparation without
adequate attention to the disposal of wastes. More information
on polluted groundwater resources in the respective river basins
will prove useful, because the existing levels of depletion and
contamination of groundwater resources, which are already
overexploited and fairly contaminated, will increase the
dependency in the future on the rivers, as the only economical
source of drinking water. This aspect has not been seriously
considered in any long-term planning.
4.2 Recommendations


3.8 River water quality monitoring
Right from its inception in 1986, the GAP started a very
comprehensive water quality monitoring programme by
obtaining data from 27 monitoring stations. Most of these river
water quality monitoring stations already existed under other
programmes and only required strengthening. Technical help
was also received for a small part of this programme from the
Overseas Development Agency (ODA) of the UK in the form of
some automatic water quality monitoring stations, the associated
modeling software, training and some hardware. The monitoring
programme is being run on a permanent basis using the
infrastructure of other agencies such as the CPCB and the
Central Water Commission (CWC) to monitor data from 16
stations. Some research institutions like the Industrial
Toxicology Research Centre (ITRC) are also included for
specialized monitoring of toxic substances. The success of the
programme is noticeable through this record of the water quality
over the years, considered in proportion to the number of
improvement schemes commissioned. To evaluate the results of
this programme an independent study of water quality has also
been awarded to separate universities for different regional
stretches of the river.
4. CONCLUSION
4.1 The future
Apart from the visible improvement in the water quality, the
awareness generated by the project is an indicator of its success.
It has resulted in the expansion of the programme over the entire
Ganga basin to cover the other polluted tributaries. The GAP has
further evolved to cover all the polluted stretches of the major
national rivers, and including a few lakes. Considering the huge
costs involved the central and state governments have agreed in
principle to each share half of the costs of the projects under the
“National Rivers Action Plan”. The state governments are also
required to organize funds for sustainable O&M perpetuity.
Initially, the plan was fully sponsored by the central government.





A white paper on the status of Ganga and GAP.
Self purifying power of the river should be
ascertained.
People should be warned that the river is not worth
aachman and bathing.
Army should be involved in cleaning the river in
Cantonment stretches.
A Ganga Restoration Fund should be constituted.
Additional resources should be generated by
charging the Ganga usesrs, through sand mining
etc.
Campaign like clean Ganga, sare Ganga should be
introduced.
References
i.
Cleaning-up the Ganges: A cost-Benefit Analysis of the Ganga Action
Plan by A Markandya and M.N. Murty.
ii.
On the Banks of the Ganga: When Wastewater Meets a Sacred River
by Kelly D Alley.
iii.
The River Goddess (Tales of Heaven & Earth S.) By Vijay Singh
(Author) and Pierre De Hugo (Illustrator)
iv.
Tare, Dr. Vinod. ―Pulp and Paper Industries in Ganga River Basin:
Achieving Zero Liquid Discharge‖. Report Code: 14_GBP_IIT_EQP_S&
R_04_Ver 1_Dec 2011.
v.
K. Jaiswal, Rakesh. ―Ganga Action Plan-A critical analysis‖. (May,
2007).
vi.
A report ―Status Paper on River Ganga‖ State of Environment and
Water Quality, National River Conservation Directorate Ministry of
Environment and Forests Government of India, Alternate Hydro Energy Centre
Indian Institute of Technology Roorkee, (August, 2009).
vii.
Singhania, Neha. ―Cleaning of the Ganga‖. Journal Geological
Society of India, Vol. 78, pp.124-130, August 2011.
viii.
Das, Subhajyoti. ―Cleaning of the Ganga‖. Journal Geological
Society of India, Vol 78, pp. 124-130, August 2011.
ix.
A report of Central Pollution Control Board, Ministry of Environment
and Forest ―Ganga Water Quality Trend‖, Monitoring of Indian Aquatic
Resources Series, Dec., 2009.
x.
A report of Water Resources Planning Commission, ―Report on
Utilization of Funds and Assets Created through Ganga Action Plan in States
under GAP‖, May, 2009.
xi.
http://en.wikipedia.org/wiki/pollution_of_the-Ganges
xii.
Report for improvement in GAP, March 1999 MOE&F.
xiii.
Ganga : A Journey Down the Ganges River by Julian Crandall
Hollick, Published October 15th 2007 by Island Press.
xiv.
Jaya Ganga : In Search of the River Goddess By Vijay Singh.
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18-19, Dec. 2014
The Ganges By Raghubir Singh.
Flood Frequency Analysis Using A Novel
Mathematical Approach
Bidroha Basu1V.V. Srinivas2
Research Scholar, Department of Civil Engineering, Indian
Institute of Science,
Bangalore - 560012, India.
2
Associate Professor, Department of Civil Engineering, Indian
Institute of Science,
Bangalore-560 012, India
1
ABSTRACT
Regional frequency analysis (RFA) is often considered to
estimate design flood quantile at target site(s) in river basins
when there is paucity of data. The analysis involves use of flood
related information from a homogeneous region (group of sites
that are hydrologically similar to the target site) to arrive at the
estimate. Conventionally RFA is based on Index-flood approach
in L-moment framework. Very recently, shortcomings associated
with assumptions of Index-flood approach motivated authors to
develop a novel mathematical approach to RFA. The approach
involves
(i) identification of an appropriate frequency distribution to fit
the random variable (flood) being analysed for homogeneous
region, (ii) use of a proposed transformation mechanism to map
observations of the variable from original space to a
dimensionless space where the form of distribution does not
change, and variation in values of its parameters is minimal
across
sites,
(iii) construction of a growth curve in the dimensionless space,
and (iv) mapping the curve to the original space for the target
site by applying inverse transformation to arrive at required
quantile(s) for the site. Effectiveness of the proposed approach
in predicting quantiles for ungauged sites is demonstrated
through a case study on watersheds in Godavari basin, India,
using a jackknife procedure. Formation of homogeneous regions
is based on region-of-influence method. Results are compared
with those obtained by using conventional index-flood
procedure.Results indicate that the proposed approach
outperforms conventional index-flood approach.
Keywords:Regional Frequency Analysis, Design flood, Lmoment, Region-of-influence
1. INTRODUCTION
Estimation of design quantile of hydro-meteorological events
such as floods at target locations in river basins having sparse/no
records is one of the major challenges for hydrologists. To
obtain the required design quantile, Regional Frequency
Analysis (RFA) gained wide recognition The analysis involves
(i) use of a regionalization approach for identification of
locations that are similar to the target location (site), in terms of
mechanisms influencing the variable being analyzed, to form a
homogeneous region, and (ii) use of a RFA approach to fit a
distribution to information pooled from the region for arriving at
design estimate. Among the various RFA approaches developed
in the past, conventional index-flood (CIF) approach
(Dalrymple, 1960) gained wide recognition. The CIF approach
considers the following assumptions: (i) Records of the variable
at each site in a region are identically distributed; (ii) Records at
each site are serially independent; (iii) There is no dependence
between records at different sites; and (iv) Frequency
distribution of the variable is identical across sites in the region,
except for a site-specific scaling factor called index-flood. Of
these assumptions, the first three are generally valid for analysis
of a random variable representing hydro-meteorological extreme
event, but the fourth is specific to only index-flood related
approach. Implementation of the CIF approach involves
normalization of records of the variable for each site by dividing
them by the site‟s scaling factor and combining information
from those normalized records to construct a „dimensionless
distribution function‟ (growth curve) that is assumed to be
unique for all the sites in the region. Required quantiles at the
target site are estimated by multiplying the growth curve by sitespecific scaling factor, which is often chosen as mean of the
variable.
For the index-flood approach to be effective, the aforementioned
assumptions (i)-(iv) should be valid for the records before and
after normalization. Validity of the first three assumptions can
be ensured by considering the scaling factor to be a population
statistic. However, as population statistic is unknown in real
world scenario, modelers chose sample statistic for
normalization. In real world scenario, the scale and shape
parameters of sites in a homogeneous region may not be close
enough to be considered identical, even if the type of frequency
distribution is the same for all the sites in the region. The
shortcomings associated with CIF approach motivated the
authors to develop a newmathematical approach to RFA. The
RFA is deemed to be effective if knowledge of location, scale as
well as shape parameters of all the sites is utilized in the
analysis, to properly characterize the growth curve
(dimensionless distribution function) that represents the region.
The proposed approach involves: (i) identification of an
appropriate frequency distribution to fit the random variable
being analyzed for the homogeneous region, (ii) use of a
proposed transformation mechanism to map observations of the
variable from original space to a dimensionless space where the
form of distribution does not change, and variation in values of
location, scale as well as shape parameters of the distribution is
minimal across sites, thus satisfying all the assumptions of
index-flood approach, (iii) construction of a growth curve in the
dimensionless space, and (iv) mapping the growth curve to the
original space for the target site by applying proposed inverse
transformation to arrive at required quantile(s) for the site.
The reminder of this paper is structured as follows: Methodology
for new mathematicalRFA approach is presented in section 2.1
and that of CIF approach is provided in section 2.2.
Effectiveness of the new mathematical approach is demonstrated
by application to real world data in section 3. Finally, summary
and conclusions are given in section 4.
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2. METHODOLOGY
2.1 Methodology for new mathematical approach to RFA
This section presents methodology of a novel mathematical
approach that was recently proposed by authors (Basu and
Srinivas, 2013). Let N denote the number of sites in a region
that is homogeneous with respect to a random variable
X depicting peak flows. Let x denote an observation (data
point) corresponding to X . Implement the following steps to
arrive at regional quantile function for a target site in the region.
(i) Identify an appropriate regional frequency distribution to fit
X . In real world scenario, the distribution can be identified
using observations (data) corresponding to sites in the
region by an effective regional goodness-of-fit test.
(ii) Map observations corresponding to X from the original
space to those corresponding to a random variable Y in a
dimensionless space, such that frequency distribution of X
and Y remain the same, and variation in at-sites values of
location, scale as well as shape parameters of the
distribution is minimal. Use equation (1) for mapping when
X follows Generalized Logistic (GLO), Generalized
Extreme Value (GEV), Generalized Pareto (GPA) or
Generalized Normal (GNO) distributions, and use equation
(2) for mapping when X followsPearson type-3 (PE3)
distribution.
1  kX  x   X  
ln 1 
 , x  X , y  Y
kX 
X

x X
y
, x  X , y  Y
X
y
Where
X
equation
denotes location parameter,
(1)
denote
parameters, whereas
X
respectively
X
scale
and
k X in
and
shape
in equation (2) represents scale
parameter of the frequency distribution of X . Equation of
cumulative
distribution
function
(CDF)
of
X corresponding toGLO, GEV, GPA, GNO and PE3
distributions can be found in Hosking and Wallis (1997).
The CDF of Y that follows GLO, GEV, GPA or GNO
distributions, and the corresponding values for L-moments
and parameters are given in Table 1, while those for PE3
distribution are provided in Table 2. It may be noted that
the values of location, scale and shape parameters for
GLO, GEV, GPA, and GNO populations are 0, 1, and 0
respectively. Further values of location and scale
parameters for PE3 population are 0 and 1 respectively,
whereas the value of shape parameter is the same as that in
the original space. Details pertaining to derivation of
population parameter values and the corresponding
equations for population growth curves in the
dimensionless space can be found in Basu and Srinivas
(2013, Appendix).
(iii) Compute L-statistics corresponding to each of the sites in
the dimensionless space using values obtained from
mapping of observations and use those as the basis to
estimate regional average L-statistics.
(iv) Estimate location, scale and shape parameters of regional
frequency distribution using the regional average Lstatistics and construct growth curve
ŷ  F  in the
dimensionless space.
(v) To arrive at regional quantile function for the target site, map
the growth curve to the original space by applying
proposed inverse transformation equation. Use equation (3)
if regional frequency distribution is among GLO, GEV,
GPA or GNO, and equation (4) if it is PE3.
x  F    X 
 X
k X
1  exp k  ˆy  F 
X
x  F    X   X  ˆy  F 
Where
 X
denotes location parameter,
 X
and
k X in
equation (3) represent respectively scale and shape
parameters, and
 X
in equation (4) represents scale
parameter corresponding to the target site. The subscript X
indicates that all the parameters are estimated in the original
space. Those parameters can be reliably estimated using
observations at the target site if record length for that site is
large enough. However, if the site is ungauged or has
inadequate data, the required parameters can be estimated
based on regional information by various methods. One
option is to estimate
(1) those parameters using regional
average values of L-statistics. An alternate option is to
estimate those parameters by using regression relationships
developed between each
(2) of them and site-specific attributes
that influence the variable being analyzed. The site-specific
attributes should be those that are readily available even for
ungauged locations. For example, catchment area, slope,
drainage density and soil characteristics could be considered
as attributes in the case of RFA of floods.
Table 1. Formulations related to GLO, GEV, GPA and GNO
frequency distributions for the random variable Y . FY  y  is
cumulative distribution function, 1Y , 2Y and 3Y are the first
three L-moments,  Y ,  Y , and kY denote, location, scale and
shape parameters respectively, and y  F  is population growth
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Implementation of CIF approach involves the following steps:
(i) Normalize peak flow values corresponding to each gauged
site in the region by dividing them by the sites‟ scaling
factor, which is considered to be the mean annual peak flow.
(ii) Estimate L-statistics (L-mean; coefficient of L-variation, Lskewness, L-kurtosis) corresponding to each of the sites
using the respective normalized records.
(iii) Compute regional average L-statistics by taking weighted
average of at-site values of those statistics computed in step
(ii), with weights being proportional to sites‟ record length.
(iv) Use the regional average L-statistics as the basis to identity
an appropriate regional frequency distribution by regional
goodness-of-fit test (Hosking and Wallis, 1997).
(v) Let
Table 2. Formulations related to random variable Y in case of
PE3 frequency distribution. FY  y  is cumulative distribution
function, 1Y , and 2Y are the first two L-moments,  Y , Y
and  denote parameters related to distribution of random
variable Y and y  F  is population growth
curve.
q  denote
CDF (quantile function) corresponding to
the fitted distribution. Refer to it as growth curve.
(vi) Determineregional quantile function
ungauged site
k
as,
Qk  F   q  F   k ,
Qk 
for the
F   0,1
where q  F  is ordinate of growth curve corresponding to
non-exceedance probability
F ,and  k
is scaling factor
(index-flood) corresponding to the ungauged site. The factor
is estimated using regression relationship developed
between the scaling factor and catchment attributes
corresponding to gauged sites in the region. Attributes
should be those that influence peak flows in catchments of
the study area and which can be determined even for
ungauged locations. Typical examples of attributes include
variables related to catchment‟s physiography, shape, soil,
drainage,climate, land-use/land-cover, and geographic
location.
3. CASE STUDY
3.1.Description of study area and data
Effectiveness of the new mathematicalRFA approach in
predicting quantiles for ungauged sites is demonstrated through
a case study on watersheds in Godavari river basin, India, using
a jackknife procedure. The river basin extends from 16°16' and
23°43' north latitude and 73°26' and 83°07' east longitude, and
has an area of 3,12,813 km2 (Figure 1). The river originates near
Trayambak in the state of Maharashtra at an elevation of 1067
m, and flows from west to east and confluences with Bay of
Bengal near Rajahmundry in Andhra Pradesh. The river has its
catchment in Maharashtra, Karnataka, Madhya Pradesh,
Chhattisgarh, Orissa and Andhra Pradesh states. Boundary of the
river basin was extracted from watershed atlas (AISLUS, 1990).
2.2. Methodology
approach to RFA
for
conventional
index-flood
(CIF)
Information on annual maximum flows at 50 sites (gauges) in
the Godavari river basin, their location (latitude and longitude)
and contributing drainage areas was collated from Central Water
Commission (CWC) offices in Hyderabad and Nagpur, India.
Watershed corresponding to each of the gauges was delineated
from 90m resolution Shuttle Radar Topography Mission
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(SRTM) digital elevation model (DEM)using ArcHydro tools in
ArcGIS environment. Attributes of the watersheds, namely
average elevation (above mean sea level), perimeter, length of
longest stream, main stream slope, drainage density,
compactness coefficient, circularity ratio, form factor and
elongation ratio were computed using tools in ArcGIS. In
addition, area weighted annual rainfall was computed for each of
the watersheds using one-degree resolution gridded daily rainfall
data available for the period 1951-2004 from India
Meteorological Department (IMD).
Information on nature, areal extent and spatial distribution of
soils in the study region was extracted from soil map obtained
from National Bureau of Soil Survey and Land Use Planning
(NBSS&LUP). Further, information pertaining to land-use/landcover was extracted from Earth Science Data Interface (ESDI) at
the Global Land Cover Facility (GLCF) available at web site:
http://glcfapp.umiacs.umd.edu. The extracted information
includes areas classified as built-up, agricultural, forest, water
bodies and waste lands.
Figure 1.Location of gauges considered for thepresent study in
Godavari river basin
3.2. Results and Discussion
Database of attributes prepared for watersheds corresponding to
50 sites in the Godavari river basin was scrutinized to identify
irredundant attributes that are fairly well correlated with mean of
Annual Maximum Flows (AMFs). The attributes identified
based on this analysis were drainage area, perimeter, main
channel slope and average watershed elevation. Those four
attributes together with two location indicators (latitude and
longitude) were chosen as attributes for regionalization.Among
the six attributes, values corresponding to „drainage area‟ were
quite large and their distribution was highly skewed.
Consequently, those values were transformed using logarithmic
transformation. Subsequently values (or transformed values)
corresponding to each of the six attributeswere standardized by
subtracting by its respective mean and then dividing by their
standard deviation. The resulting values are referred to as scaled
attributes.
Jackknife procedure was implemented to demonstrate
effectiveness of the new mathematicalRFA approach in
predicting quantiles for ungauged sites. It involved considering
one site at a time (from among 50 sites) to be ungauged, and
preparing pooling group (region) for the ungaugedsite based on
„Region of Influence‟ (ROI) (Burn, 1990) approach. The ROI
approach isone of the widely used approaches for
regionalization, though none of the available regionalization
approaches is proven to be universally superior.To prepare
pooling group for the ungauged site using ROI approach, other
gauged sites were ranked in ascending order of their Euclidean
distance to the ungauged site in the six-dimensional space of the
scaled attributes. Following this, those sites were considered one
at a time (in order of their distance), and assigned to the pooling
group until collective record length of all the sites in the group
exceeded 500 station-years. This ensures that pooled information
is adequate to determine quantiles corresponding to return period
T up to 100-years, as per 5T rule (Institute of Hydrology,1999),
and adequate sites are available to develop regression
relationship using information in the group for estimating first Lmoment (index-flood) for the ungauged site. The foregoing
analysis yielded 50 pooling groups, each corresponding to one of
the 50 sites in the study area that was assumed to be ungauged.
To arrive at regional quantile function for ungauged site
corresponding to each of the 50 pooling groups, the RFA was
performed on each pooling group using the new mathematical
approach (MA) and the CIF approach described in section 2. The
regional quantile function constructed for an ungauged site using
each of the approaches was compared with the “true” quantile
function (CDF) corresponding to the site for five return periods
(T = 25, 50, 75, 100 and 200 years) in terms of three
performancemeasures (R-bias, AR-bias, and R-RMSE). The
“true” quantile function was constructed by fitting the best-fit
frequency distribution to AMF data available for the
ungaugedsite, following the conventional practice (e.g.,
Cunderlik and Burn, 2006). The best-fit at-site frequency
distribution was found to be GLO for 10 sites, GEV for 4 sites,
GNO for 8 sites, PE3 for 15 sites, and GPA for 13 sites using Lmoment based goodness-of-fit test (Hosking and Wallis, 1997)
with 90% confidence level. Values of the performancemeasures
indicate that errors are significantly lower for the MA when
compared to that for CIF method (Table 3). To gain further
insight, scatter plots between the “true” at-site quantile estimates
and regional quantile estimates based on MA and CIF were
prepared for various return periods. They showed that points
corresponding to PA are less deviated with respect to the solid
1:1 line than those corresponding to CIF approach. Results
corresponding to a typical return period (T = 100 years) are
presented in Figure 2, for brevity. Overall the results indicate
that the proposed approach offers significant improvement over
the CIF method for RFA.
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Table 3.Performance measures R-bias, AR-bias and R-RMSE
computed based on errors in flood quantiles estimated
corresponding to 50ungauged sites.
REFERENCES:
i.
AISLUS, 1990 Watershed atlas of India, All India soil and land use
survey, Ministry of Agriculture, Government of India.
ii.
Basu, B., and V. V. Srinivas (2013), Formulation of a mathematical
approach to regional frequency analysis, Water Resour. Res., 49,
doi:10.1002/wrcr.20540.
iii.
Burn, D.H. (1990), Evaluation of regional flood frequency analysis
with a region of influence approach, Water Resour. Res., 26(10), 2257-2265,
doi:10.1029/WR026i010p02257.
iv.
Cunderlik, J. M., and D. H. Burn (2006), Switching the pooling
similarity distances: Mahalanobis for Euclidean, Water Resour. Res., 42(3),
W03409, doi:10.1029/2005WR004245.
v.
Dalrymple, T. (1960), Flood frequency analysis, U.S. Geol. Surv.
Water Supply Pap., 1543-A, 11 – 51.
vi.
Hosking, J. R. M., and J. R. Wallis (1997), Regional frequency
analysis: An approach based on L-moments, Cambridge University Press, New
York, USA.
vii.
Institute of Hydrology (1999), Flood Estimation Handbook, vol. 3,
Wallingford, UK.
Performance Comparative Of Wavelets And
Savitzky-Golay Filter On Bathymetry Survey Data
Figure 11. Comparison of at-site (true) quantile estimates with
regional quantile estimates for ungauged sites based on new
mathematical approach and CIF methods for 100-year return
period. The solid 1:1 line corresponds to the case where at-site
and regional estimates are equal. A method is considered to be
effective if points corresponding to the method are closer to the
2
solid line. R (coefficient of determination) corresponds to the
dash-dot trend line fitted to points in a plot.
4. SUMMARY AND CONCLUSIONS
The key assumption of the conventional index-flood approach is
that it requires location, scale and shape parameters of frequency
distributions of normalized records to be identical for all the
sites in a homogeneous region. For practical applications, this
assumption is always violated, which leads to ineffective
quantile estimation for ungauged sites using conventional index
flood approach. To overcome the shortcoming of CIF approach,
a novel mathematical approach is proposed for RFA in Lmoment framework. Transformation mechanisms corresponding
to various commonly used frequency distributions are proposed
to facilitate mapping the random variable being analyzed from
original space to a dimensionless space where distribution of the
random variable does not change, and deviations of regional
estimates of all the parameters (location, scale, shape) of the
distribution with respect to their population values as well as atsite estimates are minimal. The location, scale and shape
parameters corresponding to GLO, GEV, GPA and GNO
populations are analytically derived to be 0, 1 and 0 respectively,
in the dimensionless space. Experiments on real world data
showed that the new mathematicalapproach offers significant
improvement over CIF, method in RFA. Further improvement in
results could be possible by considering Mahalanobis distance to
form ROI (Cunderlik and Burn, 2006), instead of Euclidean
distance considered in this study.
M.Selva Balan1
Arnab Das2
Chief Research Officer, Central Water and Power Research
Station, Khadakwasla, Pune 411024, India
2
Commander, Indian Navy, Military Institute of Technology,
Girinagar, Pune-41125. India
1
ABSTRACT : Bathymetry survey is one of the most reliable
and practical way to assess the reservoir capacity as well as to
estimate the sediment volume. Accurate estimation of
reservoirs volume is of crucial importance to make optimum
utilization of stored water and to plan the reservoir operations.
This also will enable the dam authorities to plan the dredging
techniques. The correct knowledge of the volume of dams
facilitate in planning the amount of water discharge and silt
removal. The volume is determined using the area which is
extracted from the satellite imagery and depth collected
through echo sounder by running a boat along survey lines. A
precise, linear indication of the depth of water as well as the
sediment deposit in a specific part of water body is what always
required. Presently there are a wide variety of ways to produce
a signal that tracks the depth of water bodies. The Ultrasonic
signal offers the benefits of shorter wavelength which resolves
smaller details and inaudibility so humans are unaffected,
hence most commonly used for the depth estimation. This
signal is affected by various underwater noises which results in
inaccurate depth estimation. In case of finding the layer width
below the sediment the reflected ultrasound signal gets severely
affected by the underwater noises. The objective of this paper is
to provide noise reduction methods for underwater acoustic
signal. In present work, the signal processing is done on the
data collected using TC2122 dual frequency echo transducer.
There are two signal processing techniques which are applied
on a case study: The first method is denoising algorithm based
on Stationary wavelet transform (SWT) and second method is
Savitzky-Golay filter. The results are evaluated based on the
criteria of peak signal to noise ratio and volume estimation is
done by combining the data related to different locations of the
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reservoir and plotting them inside the boundary extracted from
satellite imagery. However the results obtained with SavitzkyGolay filter matches acceptable level of interpolation and also
matches the depth measured at site. This paper shows the
performance of two newly developed techniques applied on
depth data which was acquired with underwater noise. 3D
Surfer plots of the reservoir whose depth and volume
estimation has to be done are shown with different processing
for the performance comparison.
Keywords: Reservoir Sedimentation, Bathymetry Survey,
Savitzky-Golay filter, Wavelet transform
1. INTRODUCTION:
Irrigation and Agriculture are the main occupations of the people
of India for thousands of years. Amongst the natural resources of
a country, fresh water reservoirs i.e. dams, lakes etc are of
utmost significance. The water stored by the dams can also be
used to prevent floods and facilitate forestation in the catchments
areas of the reservoirs. The measurement of capacity of reservoir
is of crucial importance to regulate the water discharge from the
reservoir for meeting the demands of irrigation and drinking
water supply. The volume measurement is done using area and
depth of the reservoir. Hence area and depth of the reservoir are
to be calculated very precisely. Depth measurement of water
bodies has developed remarkably in the last few decades with
the adaptation of new ultrasonic techniques, which is proven
successful among other methods based on image processing,
airborne laser and mechanical systems.
Photo bathymetry method, discussed by M. Selva Balan, et all
(2013) based on image processing, digitally processes the aerial
pictures to correlate light intensity with depth. This method is
fast depth below the water cannot be measured with it. So it
remains a tool for assessing the present area and approximate
volume. An airborne laser system utilizes method of estimating
the time delay between the surface and bottom reflections of the
transmitted laser light. These systems are efficient, high speed
and have good coverage but water clarity is the primary
constraints as well as initial and operational cost are higher.
Depth measurement methods based on acoustic uses ultrasonic
signal and are classified as single beam and multiple beam eco
sounding. The ultrasonic signal is transmitted towards the
bottom of the reservoir and time interval required for the signal
to reflect and travel back to the transducer is measured. Prior
knowledge of velocity of ultrasonic signal in water and the time
taken gives the distance travelled which is the depth of the
reservoir. Multibeam eco sounding comprises of multiple narrow
single beam transducers mounted near to each other and
focussed at equally spaced angles for covering a large space
beneath the boat. In this paper single beam eco sounding is
utilized as it is simple and inexpensive.
Celsius in temperature, salinity which is a measure of the
quantity of dissolved salts and other minerals in water and the
total amount of dissolved solids in water. As shown in
International hydrographic Bureau, (2005) the pressure also has
a significant impact on the sound velocity variation and has a
major influence on the sound velocity in deep water.
When an ultrasonic wave is transmitted through water, it is
expected to reach the bottom and then reflect back, but instead
of this, it changes the characteristics (i.e. picks up noise) due to
the medium as well as the reflective surface. However
submerged trees and rocks create large spikes, which are mainly
due to multipath effect. This gives a false bottom anticipation
which doesn‟t provide the accurate results. The reflected signal
when graphically plotted clearly indicates the unwanted sharp
peaks, which are normally interpolated with standard
mathematical techniques as given in Surfer manual ver. 8. The
focus of this study is to analyse the reflected signal received
through the sediment particles, which are corrupted badly than
the surface reflections.
The raw depth signal is denoised by applying signal processing
techniques, which is then processed on Surfer ver.8 software to
plot the 3D images of the reservoir bed. These sharp peaks could
be the reflections from the suspended obstacles which come in
the path of the transmitted ultrasonic signal.
The data was collected using sensor Reason‟s TC2122 dual
frequency survey echo sounder transducer which works on two
resonant frequencies 33 kHz and 200 kHz and Reson's
Navisound 415 hydrographic single beam echo sounder. General
assumption is that the noise present is white Gaussian noise but
the underwater noise does not full fill the classical white noise
assumption [3] and hence Non-white noise is assumed. To
reduce noise from the given data and to estimate approximate
depth, two techniques are applied- denoising based on Stationary
Wavelet Transform and Savitzky-Golay filter.
This paper is organized as follows:-Section 2 deals with
methods, limitations, wavelet transforms, Savitzky-Golay filter,
section 3 & Section 4 deal with results & conclusion
respectively.
Volume of the reservoir measurement requires two important
aspects namely; getting the position coordinates accurate and the
third dimension (i.e. depth). The advent of latest GPS
technology allows us to get the position to accuracy in the range
of centimeters. However the depth estimation depends on the
method and the various nonlinear properties it encounters.
1.1 Measurement of reservoir volume:
Ultrasound wave is basically cyclic sound pressure whose
frequency ranges from 15 kHz to 200 kHz as discussed by Sabuj
Das Gupta (2012). The depth measurement is quite sensitive to
variations of the sound velocity profile. The sound velocity
profile is affected by factors such as, variation of one degree
2.1 Limitations of Existing techniques
Echo-sounders are basically designed to operate in standard
frequency. However the medium characteristics it is used is not
same always. Also the characteristics of the bottom surface are
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not of same characteristics. This results in errors in terms of
unacceptable depth readings. This could not be corrected beyond
a limit as the echo reflection differs from different objects found
below the water. CWPRS and many states are using a particular
type of Echo sounder provided under hydrology project.
However the data is collected it comes with various spiky non
Gaussian noises, which could not been eliminated fully by the
filters and the interpretation techniques provided in the software
supports these system. As explained by M. Selva Balan, et all
(2013) for large reservoir the preplanning is essential, which is
possible with the image processing techniques applied on an
satellite imagery as the one shown in fig 1 below.
signals, orthogonality and biorthogonality as per Michel Misiti et
al (2000).
Fig 1. Contour extraction of the reservoir for pre survey planning
As per Meyer M. Kreidl et al (2002) there are a number of
wavelets that can be used for noise removal: Haar, Daubechies,
Symlet, Coiflet, Biorthogonal, Reverse Biorthogonal to name
few. All of them are wavelets with filter having either
orthogonality or biorthogonality. The HARR wavelets are
performs the mathematical operations of averaging and finding
difference on the decomposed values of signal. Daubechies
wavelet are defined same as HARR, has balanced frequency
responses but nonlinear phase responses. Symlet wavelet
comprises of a symmetrical wavelet. Coiflet is the member of a
family of wavelets having zero moments in the support of the
functions and also in the scaling function. Biorthogonal wavelets
are extension of orthogonal wavelet families to resolve the
problem of incompatibility between the symmetry and perfect
reconstruction. As per Michel Misiti et.al (2000) Meyer wavelet
is an infinitely derivable orthogonal wavelet without compact
support. In order to use the wavelet transform effectively the
details of the particular application should be taken into account
and the appropriate wavelet should be chosen. S.Kumari et. Al
(2012) explained that they are chosen based on their shape and
their ability to analyze signal in particular application. The
performance of wavelet based denoising depends on wavelet
decomposition structure.
As detailed by M.Selva Balan et al (2013), in normal conditions,
the raw data collected by a survey boat generates lots of noise,
which is very difficult to be removed by any manual methods.
And hence two new filters were developed namely Wavelet and
Savitzky-Golay.
For selecting particular type of wavelet, performance
comparison of some known wavelet families was done and their
effect on the given signal was observed. In present case, as
explained earlier smoothness of the surface is the basic criteria
for depth estimation, so accordingly one wavelet from each
wavelet family was selected. These are shown in Table 1.
Table 1.Wavelet selected from respective wavelet family.
2.2 WAVELET TRANSFORM
Wavelet transforms have become one of the most important and
powerful tool for signal denoising as shown by SJS Tsai, (2002).
Discrete Stationary Wavelet Transform is undecimated versions
of discrete wavelet transform which is used for signal denoising
and pattern recognition as shown by Chu-Kueitu et al, (2004).
The main idea is to average several detailed coefficients which
are obtained by decomposition of the input signal as explained
by V. Matz et al, (2005).Signal denoising using wavelet consists
of three steps of decomposition, thresholding of the coefficients
and reconstruction. Decomposition of signal is done over an
orthogonal wavelet basis using the discrete transform.
Thresholding is used to select a part of the coefficients and using
the threshold coefficients the signal is reconstructed. The
reproduced signal is the denoised signal.
Wavelet transforms make use of different basis functions to
decompose the signal. These basis functions can be
differentiated by scaling and shifting parameters. The properties
of wavelet play a key role in the selection of a wavelet for a
particular application. The main properties of wavelet include
speed of convergence which quantifies the localization of the
wavelet in time and frequency, symmetry for avoiding
dephasing, regularity to obtain reconstructed smooth and regular
Wavelet Family
Haar
Daubechies
Symlet
Coiflet
Meyer
Biorthogonal
Reverse biorthogonal
Selected wavelet
Haar
db8
sym5
coif5
Dmey
bior2.2
rbior2.2
The detailed and approximation coefficients are obtained using
signal decomposition. Further decomposition of approximation
coefficients up to specified level is done. The maximum
decomposition level depends on number of data points contained
in a data set. Present depth analysis 5 decomposition levels were
found to be appropriate.
K.Mathan Raj et. al (2011) shown a thresholding of data in
wavelet domain to smooth out or to remove some of the
coefficients of wavelet transform of measured sub-signal
introduced due to noise or obstacles in water bodies. Two
commonly used types of thresholding are hard and soft
thresholding. In hard thresholding if any coefficient (x) less than
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threshold value(t) then it is set to zero otherwise it remains
unchanged.
(1)
Soft thresholding [10][11] is similar to hard thresholding with a
little difference i.e. no coefficient remains unchanged instead it
is shrunken by threshold value(t). The present analysis is done
using soft thresholding technique.
(2)
2.3. SAVITZKY-GOLAY FILTER
The Savitzky-Golay filter is a particular type of low-pass filter.
Sophocles J. Orfanidis (2012) shows that it is well-adapted for
data smoothing. It is also referred to as least-squares or
Polynomial Smoothing filter. Rather than having their properties
defined in the Fourier domain, and then translated to the time
domain, Savitzky-Golay filters derive directly from a particular
formulation of the data smoothing problem in the time domain
as shown by Filip Wasilewski. Ronald W. Schafer (2011) shows
that these filters are of type-I FIR low pass filters with nominal
pass band gain of unity. Savitzky and Golay proposed the
method of data smoothing based on local least-squares
polynomial approximation. Polynomial smoothing is the process
which replaces the noisy samples by the values that lie on the
smooth polynomial curves drawn between the noisy samples.
Sophocles J. Orfanidis (2012) has shown that for every
polynomial order, the coefficients must be determined optimally
such that the corresponding polynomial curve best fits the given
data. Instead of applying averaging filter it is better to perform
least squares fit of a small set of consecutive data points to a
polynomial. Savitzky A., and Golay, M.J.E. (1964) proved that
Least-squares fit technique is used to choose the polynomial
coefficients such that they give minimum mean square error.
The output smoothed value is taken at the center of the window
to replace the original data. Fig 2 below shows the plots of raw
data as well as S-Golay filter processed data.
Figure 2. Plots of Raw depth and data interpolated by S Golay
Filter
In Savitzky-Golay filter, the odd-indexed coefficients of the
impulse response design polynomial are all zero. The nominal
normalized cut off (3 dB down) frequency depends on both the
implicit polynomial order and the length of the impulse
response. The impulse response of filter is symmetric, so the
frequency response is purely real. These filters have very flat
frequency response in their pass bands and fair attenuation
characteristics in their stop band regions.
As per Ronald W. Schafer, (July 2011) following are the
constraints on polynomial fitting;
- The number of data points must be strictly greater than the
number of undetermined coefficients to achieve smoothing by
the Savitzky-Golay process.
- If the order of the polynomial is too large, the solution will be
of no value.
Generalize algorithm is as follows:
Consider
frame
size
odd,
and
polynomial.
or
filter
length
N
whered is order
is
of
Ifx is noisy signal with noisy samples , n = 0,1,.......,L-1 and it
is supposed to be replaced by its smoothed output version y
which contains , n = 0,1,.......,L-1 then input vector hasn =L
input points and x =
is replaced byN
dimensional one, havingM points on each side ofx.
(3)
There are 3 cases, for calculating the output result. These cases
are explained in [16]. Smoothed output y is calculated as
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(4)
The Savitzky-Golay
elements of matrixB.
filter
coefficients
are
the
(5)
(6)
Where
,
,
whe
re-d
[12][13].
From Table 3 it can be seen that as the order of polynomial
increases, PSNR value also increases. So PSNR is directly
proportional to order of polynomial for Savitzky-Golay filter.
Computational complexity is less for higher order. (Processer
used-Intel core i5)
Table 4. Values of PSNR by varying frame size and with fixed
order for Savitzky-Golay filter
As shown by S.Kumari et. al. (2012) the peak signal to noise
ratio represents the measure of peak error. It is given as,
File
Or4_31
Or4_33
Or4_41
Or4_49
File1
44.0280
43.8143
43.0032
42.6637
File2
49.4808
49.1640
48.2272
47.8468
(7)
File3
44.4367
44.0604
43.4561
43.1529
Where
File4
44.7824
44.6722
44.1377
43.7461
File5
40.8566
40.6501
40.0252
39.9840
File6
37.2930
37.0813
37.0102
36.8819
File7
Avg.Tim
e (sec)
41.1404
41.1594
40.9853
40.5424
2.41
2.55
2.53
2.56
(8)
MSE is Mean Square Error with I = original value O= output
value and R= maximum input value
Generally PSNR should be greater than 30dB in order to reduce
noise effectively.
For comparing results of Savitzky-Golay filter, another
parameter used is Time Constraints which is time required for
execution of program.
Table 2. Values of PSNR for different types of wavelets.
From Table 4 it can be seen that as the lesser the frame size,
more is the PSNR. So PSNR is inversely proportional to frame
size for Savitzky-Golay filter. Computational complexity is less
for smaller frame size. (Processer used-Intel core i5)
The volume of reservoir is determined using the area and depth
at different locations in the bed. All the data related to these
locations is collected to provide the complete profile of the
reservoir and then boundary is applied for determining the
volume in Surfer11 software.
Actual volume of the reservoir calculated by design equation:
15475058 cubic meter
Table 5. Values of Volume of reservoir without denoising and
with denoising of signal.
Without denoising
The results presented in Table 2 show PSNR values for different
wavelets. It can be seen that Haar wavelet is giving better result
than other wavelets in this case.
Volume in cubic meter
Error
Percentage error
15448266
26792
0.173
Denoised with
Haar wavelet
15472741
2317
0.015
Denoised with
Savitzky Golay
15475539
481
0.003
3D plots of depth data are obtained using surfer11 are shown in
figures 3to 8 below on two different data sets collected from
reservoirs:
Table 3. Values of PSNR by varying order and with fixed frame
size for Savitzky-Golay filter.
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Figure 3 : Original
signal for right arm
of lake
Figure 4 : Signal
processed using
Haar wavelet
18-19, Dec. 2014
Figure 5 : Signal
processed using
Savitzky-Golay
Filter
denoised echo yields a smooth profile (ie less peaks) the
reservoir volume become more realistic. The error percentage is
reduced to 0 .003 for the signal denoised with Savitzy-Golay.
The analysis has been done on a large volume data where
percentage error for the signal without denoising and with
denoising is small. However for a reservoir with less volume this
much error will be a considerable amount that will affect the
planning for the water discharge. In case of low frequency
reflections (which represent depth with sediment) the variations
due to noise are huge which will give erroneous sediment
volume, which in turn affects the reservoir planning the dredging
process. With this filters the accuracy of sediment volume will
be considerably reduced.
REFERENCES
Figure 6 : Reservoir
bed plotted from
RAW data
Figure 7 :
Reservoir bed
denoised with
Haar wavelet
Figure 8 :
Reservoir bed
denoised with
Savitzky-Golay
The 3D profiles shows that wavelet and Savitzky-Golay filters
have smoothened the noisy data and hence improves the
accuracy of sedimentation volume calculations. Fig 7 shows the
capacity loss calculated with one survey using two frequencies.
Li ve Vol ume Pl ot
(Ch 1 i n Mcum)
Ori gi nal Vol ume pl ot Area
(Ch 2 i n Mcum)
Volume Plot
350
300
Volume (Mcum)
250
200
150
100
50
0
-10
-5
0
5
10
15
20
-50
Water Level (meters)
25
30
Fig7. Final plot
showing the loss
in capacity based
on single survey
done with two
different
frequencies
35
40
4. CONCLUSION
The analysis of ultrasonic depth data received through sediment
and water using two techniques: HARR wavelet Transform and
Savitzky-Golay filter. It is found that out of all wavelet
transforms, HARR wavelet is most suitable for noise reduction
in ultrasonic signal based on high PSNR value. In SavitzkyGolay Filter analysis, higher order of polynomial with lesser
frame size increases the PSNR.
The results from surfer plots show that the HARR wavelet with
decomposition level up to 5 and Savitzky-Golay filter with order
4 and frame size 31 can be effectively used for smoothing the
data obtained which can lead to estimation of depth with
minimum error using empirical formula designed for a particular
application.
i.
Arnaud Jarrot, Cornel Ioana, André Quinquis, (2005)"Denoising
Underwater Signals Propagating Through Multi–path Channels", Oceans Europe (Volume:1) pp.501-506.
ii.Bernhard Wieland, (October 2009) "Speech Signal Noise Reduction with
Wavelets", pp.55-56.
iii.
Chu-Kueitu, Yan-Yao Jang, (2004)"Development of Noise Reduction
Algorithm for Underwater Signals", Underwater Technology, International
Symposium on, pp.175-179.
iv.
Golden Software, Surfer Manual online ver 12.
v.
International
hydrographic
Bureau,
(2005)"Manual
on
hydrography", M-13, pp.126.
vi.
K.Mathan Raj, S.Sakthivel Murugan, V. Natarajan, S.Radha,
(2011)"Denoising Algorithm using Wavelet for Underwater Signal Affected by
Wind Driven Ambient Noise", Recent Trends in Information Technology
(ICRTIT), pp.943-946.
vii.
Md. Abdul Awal, Sheikh Shanawaz Mostafa and Mohiuddin Ahmad,
(2011)"Performance Analysis of Savitzky-GolaySmoothing Filter Using ECG
Signal", IJCIT, VOLUME 01 ISSUE 02, pp.24-29.
viii.
M. Kreidl, P. Houfek, (2002)"Reducing Ultrasounic Signal Noise by
Algorithms based on Wavelet Thresholding", Acts Polytechnica Vol. 42, pp.6065.
ix.
Michel Misiti, Yves Misiti, Georges Oppenheim, Jean-Michel Poggi,
"Wavelets and their Applications", ISTE 2000.
x.
M. Selva Balan, Dr. Arnab Das, Madhur Khandelwal, Piyush
Chaoudhari, ―A Review of Various Technologies for Depth Measurement in
Estimating Reservoir Sedimentaion‖, IJERT, Vol. 2, Issue 10, Oct 2013,
pp.223-228.
xi.
M. Selva Balan, Sedimentation survey using dual frequency echo
sounder, Two days work shop on ―Reservoir Sedimentation‖ by Beuro of
Indian Standards (BIS) , January 2013.
xii.
Ronald W. Schafer, (July 2011)"What is a Savitzky-Golay filter?",
IEEE SIGNAL PROCESSING MAGAZINE, pp.111-115.
xiii.
Savitzky A., and Golay, M.J.E. (1964)"Analytical Chemistry", Volume
36, pp.1627-1639.
xiv.
Sabuj Das Gupta, Islam Md. Shahinur, Akond Anisul Haque, Amin
Ruhul, Sudip Majumder,(October 2012)"Design and Implementation of Water
Depth Measurement and Object Detection Model Using Ultrasonic Signal
System",International Journal of Engineering Research and Development,
Volume 4, Issue 3, pp.62-69.
xv.
SJS Tsai, (2002)"Chapter 4 Wavelet Transform and Denoising".
xvi.
Sophocles J. Orfanidis, (2010)"Introduction To Signal Processing",
Pearson Education, Inc., pp.427-451.
xvii.
S.Kumari, R.Vijay, (January 2012)"Effect of Symlet Filter Order on
Denoising of Still Images", Advanced Computing :An International
Journal(ACIJ).Vol.3.No.1, pp.137-143.
xviii.
V. Matz and J. Kerka, "DIGITAL SIGNAL PROCESSING OF
ULTRASONIC SIGNALS" 2005, pp.3
xix.
wavelets.pybytes.com by Filip Wasilewski.
xx.
William H. Press, Brian P. Flannery, Saul A. Teukolsky, William T.
Vetterling, (1988-1992)"Numerical Recipes in C:The Art of Scientific
Computing", Cambridge University Press, pp.650-651
The reflected echo of sensor without denoising when plotted
yields a profile consisting of a number of peaks. Since the
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Simulation Study On Performance Of Household
Rainwater Harvesting Systems
overview of the materials and methods and the results of the
study are discussed in the subsequent sections.
P.G. Jairaj1
P. Athulya2
Professor in Civil Engineering, College of Engineering,
Trivandrum-695016, Kerala, India
2
Former M.Tech Student, College of Engineering, Trivandrum695016, Kerala, India
[email protected]
2. MATERIAL AND METHODS:
1
ABSTRACT
Water shortage has become a serious problem all over the world
due to rapid urbanization and climatic changes. To cope with
such situation small onsite Rainwater Harvesting (RWH)
Systems can act as alternate water supply source in rural as well
as urban areas. But the efficiency of these RWH systems is
largely affected by the distribution pattern of rainfall as well
water demands. This paper investigates the performance of
Rooftop household rainwater collection systems located at
various geographic regions in Kerala state, India considering the
variation in demand and rainfall. The operation of Rooftop
household rainwater collection systems was simulated using
Standard Operating Policy (SOP), and the performance was
evaluated by three indicators namely; Reliability, Resilience and
Vulnerability. From the simulation study, it is revealed that
while designing the rainwater collection systems, sufficient care
is to be given to the spatial and temporal distribution pattern of
rainfall.
Keywords: Rainwater Harvesting System, Standard Operating
Policy, Demand, Capacity, Performance evaluation
In the present study the performance of household roof top
rainwater collection systems was described by its ability to
satisfy the demand without failure. Using the actual rainfall data
at the locations, the runoff from the catch surface was worked
out on a daily basis. This runoff collected in the rainwater
collection tank was used for satisfying the various household
demands. A simulation model using Standard Operating Policy
(SOP) was made use of for the computation of yield from the
system and the evaluation of the system performance was carried
out using the indicators: Reliability, Resilience and Vulnerability
as follows.
2.1 Simulation Model:
A typical flat rooftop household rainwater harvesting system
having a collection tank capacity of
was considered for
carrying out the simulation study. The yield from the rainwater
harvesting system was drawn according to the water demand.
Simulation of the operation of the system was carried out using
Standard Operating Policy (SOP) given by Equations (1) to (3).
In simulation whenever the demand is not satisfied associated
failure occurs, computed in terms of deficit volume defined by
Equation (4).
(1)
1. INTRODUCTION:
Due to anthropogenic activities the surface water systems are
getting dried up, ground water is depleting and water bodies are
getting polluted. Moreover the water resources are being
depleted faster than it can be replenished. The need of rainwater
harvesting (RWH) has been felt to meet the ever increasing
demand for water and reduce the large volume of surface runoff.
Among the RWH procedures the roof top harvesting using
collection tanks is a widely used one. For a given roof top area
the efficiency of the RWH system greatly depends on the
variability in the rainfall and the demand and in turn is
associated with the capacity of collection tank.
An efficient rainwater harvesting system shall be able to
accommodate the runoff coming from the catchment surface area
so as to satisfy the demand with maximum reliability. This
requires proper sizing of rainwater harvesting systems, so as to
have the maximum efficiency. This paper focuses on the
performance analysis of household rooftop rainwater collection
systems located at various geographical areas of Kerala state,
India, by analysing the performance indices: Reliability,
Resilience and Vulnerability of the system subject to the
restrictions imposed by capacity of the collection tank, demand
to be met and the magnitude of available rainfall. A brief
(2)
(3)
(4)
where Yt is the yield from the collection system at period t (m3);
Qt inflow to the collection tank in period t (m3); Dt is the demand
during the period t (m3); St is the storage in the time period t and
Spillt the spill occurring (m3) if any when the collection tank is
full and Smax the maximum design capacity of the collection
tank. Det represents the deficit occurring (m3) in period t.
Performance of the system was evaluated in terms of Periodbased Reliability (R), Resilience (Res) and Vulnerability (Vul).
Period based reliability estimation evaluates the system
reliability on the basis of the number of time periods of non-
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failure of system to meet the water demand to the total number
of periods in the study. The term Resilience is used as a measure
of how fast the system is likely to return to satisfactory state,
once the system has entered an unsatisfactory state. This
definition of resilience (Res) is equal to the inverse of the mean
value of time the system spends in an unsatisfactory state and
computed using Equation (6) (Kjeldesn et al., 2005).
Vulnerability was calculated as the mean deficit incurred during
the period of study indicated by Equation 7 (Kjeldesn et al.,
2005).
applied in Equations (5), (6) and (7) to obtain the period-based
reliability, resilience and vulnerability of the system.
3. RESULTS AND DISCUSSION:
The study focuses on the analysis of performance of the
rainwater collection systems located in various geographical
locations of Kerala state. Simulation models were developed to
analyse the performance of the system. Performance analysis
was carried out using the indicators Reliability, Resilience and
Vulnerability subjected to the restrictions imposed by available
rainfall, water demand and storage capacity of the collection
tank.
(5)
3.1 Performance of RWH for Average Rainfall:
(6)
(7)
where NT and Nfailure are the total number of periods in the study
and number of periods in which failure occurs, d(j) represents
the duration of jth failure event, v(j) is the deficit occurred during
jth failure event and M is the number of failure events.
2.2 Analysis of problem:
The performance analysis of rainwater collection systems in the
areal extent of Kerala state located at: Trivandrum, Kollam in
Southern region; Kottayam, Chittur, Cochin in Central region;
Calicut, Kannur in the Northern region. The analysis was carried
out on a yearly basis (June to May). The daily rainfall data for
the period 1982 to 2011 for the IMD stations at Trivandrum,
Kollam, Kottayam, Cochin, Chittur, Calicut and Kannur were
made use of in the study. The details pertaining to the study are
given in Athulya (2013).
The daily yield from the rainwater collection system depends on
water demand to be met, and was computed on the basis of daily
per capita demand. As per IS 1172, the per-capita demand for
the household systems in India is 135 lpcd. In the study, the
variation in daily percapaita water demand was considered in the
interval 30 lpcd to 135 lpcd, to incorporate the variation in
demand values. A five user flat roof terrace house of effective
catchment area of 100 m2 with coefficient of runoff of 0.75 was
adopted for computation of runoff that can be harvested in the
study.
The temporal variation of rainfall was also incorporated by
evaluating the system performance for average rainfall situation
as well as rainfall values taken at different probability levels. For
the cases studied, the total deficit and the number of period for
which the system failed to satisfy the demand were worked out
from the simulation results for different combinations of
collection tank capacities and daily demand. This in turn is
The simulation study of the operation of the roof top rainwater
collection system at the different locations for average rainfall
were carried out; yielded the reliability, resilience and
vulnerability values for the specific demand and capacity of the
system considered. The variation in reliability against capacity
for specific demand values are tabulated to obtain the tradeoff as
in Table 1. From the table it can be seen that for the RWH
located in Southern Kerala the magnitude of rainfall limits the
reliability of the system, while in the case of Northern Kerala the
capacity of the collection tank limits the reliability of the system.
For average rainfall situation resilience and vulnerability indices
were also calculated for the proposed rainwater harvesting
stations located in the study area; and the set of representative
values obtained for station Kannur are given in Table 2. From
the table, it can be observed that, resilience of the system
increases with increase in capacity showing that the duration of
time in which system spends in unsatisfactory state decreases in
general. But the increase in resilience is found to be not uniform
as in the case of reliability with capacity. Similarly even though
vulnerability of the system decreases with increase in capacity it
is found to be not directly related to the capacity of the
collection tank. The vulnerability and resilience estimates
generally exhibit a non-monotonic behavior, i.e. the estimates,
for a specified demand, do not vary monotonically as the
capacity increases. So it can be inferred that vulnerability and
resilience indices describe the system performance once the
failure has occurred, whereas the reliability index describes the
overall efficiency of the system. So for further analysis in the
present study the only reliability index was taken into account.
3.2 Performance of RWH system for variation in rainfall
The system performance indicator reliability of household
rooftop rainwater collection system with capacity of the
collection tank was analyzed for probability levels of rainfall for
the stations. The tradeoffs were generated between the reliability
of the system and capacity of the collection tank for different
demands and rainfall values taken at different probability levels
and are tabulated in Table 3. From the table it can be seen that
the performance RWH systems of the stations located in
Southern region is poor even for 50 % probability level of
rainfall, when compared to the
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Table 1: Reliability of the rainwater collection system for
average daily rainfall
Table 2: Resilience and vulnerability values obtained for station
Kannur
Table 3: Reliability of the RWH system for different probability
levels of rainfall
stations in Northern Kerala. It can be observed that the
rainwater collection systems located at Kannur is found to be
most reliable at all probability levels of rainfall. Also rainwater
collection systems located at Trivandrum is found to be least
reliable compared to the other stations, since the reliability
obtained even at 50% probability of rainfall is less than 50%
except for 30 lpcd demand, and for higher probability levels of
rainfall, the system reliability obtained is less than 50% for all
cases considered. From the study it can be seen that the
uncertainty associated with rainfall values affects the
performance of the system.
4. CONCLUSIONS
The focus of the present study was to analyse the spatial and
temporal variation in the performance of household rainwater
collection systems incorporating the variability in rainfall and
demand values. The performance analysis was carried out for the
RWH systems located in different regions of Kerala state. The
specific conclusions from the study are as follows:
On analysing the performance of RWH for average rainfall
situation it seen that RWH collection systems located in
Northern region of Kerala are found to be more reliable
compared to the Southern and Central regions since they are able
to satisfy the complete household demands with
MANIT Bhopal
Page 221
Issue Special3
18-19, Dec. 2014
the need of the hour. In India, the Right to water has been
protected as a fundamental human right by the Indian Supreme
Court as part of the Right to Life guaranteed under Article 21of
the Indian constitution. India with majority of population
dwelling in rural areas faces the problem of acute shortage of
potable water in some rural area. The present paper addresses
such issues in one such rural area called Nawli village in the
Mewat district of Haryana with community participation. The
area has the problem of saline water which is unfit for drinking
as well as other domestic uses. So on-ground water recharge
measures were taken up with community participation.
Rainwater harvesting is the oldest technology to provide water
for human needs. It has been observed through our desktop
research that small communities are increasingly accepting
rainwater harvesting and its augmentation as a possible solution
to meet their water needs. So the community-based water
resource management practices can be the most suitable option
which not only will help the community develop and meet their
essentials but also give them a sense of accomplishment. Also
ArcGIS tool came handy in dealing with the diverse geomorphic
features of the area and demarcating streams and watersheds,
which further helped in augmenting the possibility of maximum
recharge of water.
Keywords: ArcGIS, community participation, w

- International Journal of Engineering Research

Transcription

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