July 2011 - The Institution of Engineers Sri Lanka

Transcription

Vol. XXXXIV, No. 03, July 2011
Printed by Karunaratne & Sons (Pvt) Ltd.
ENGINEER
CONTENTS
Vol.: XXXXIV, No. 03, July 2011
JOURNAL OF THE INSTITUTION OF ENGINEERS, SRI LANKA
EDITORIAL BOARD
Eng. (Prof.) A. K. W. Jayawardane
Eng. Priyal De Silva
Eng. W. T. R. De Silva
Eng. (Prof.) K. P. P. Pathirana
- Editor Transactions
Eng. (Prof.) T. M. Pallewatta
- Editor “ENGINEER”
Eng. (Dr.) D. A. R. Dolage
Eng. (Miss.) Arundathi Wimalasuriya
Eng. M. L. Weerasinghe
- Editor “SLEN”
Eng. (Dr.) K. S. Wanniarachchi
Eng. (Prof.) S. S. L. Hettiarachchi
The Institution of Engineers, Sri Lanka
120/15, Wijerama Mawatha,
Colombo - 00700
Sri Lanka.
Telephone: 94-11-2698426, 2685490, 2699210
Fax: 94-11-2699202
E-mail: [email protected]
E-mail (Publications): [email protected]
Website: http://www.iesl.lk
The statements made or opinions expressed in the
“Engineer” do not necessarily reflect the views of
the Council or a Committee of the Institution of
Engineers Sri Lanka, unless expressly stated.
COVER PAGE
Vol. XXXXIV, No. 03, July 2011
Kokavil Tower
Kokavil tower, the main component of the Multi functional Communication Transmission Center, was
commissioned on the 6th June 2011, stands as the tallest
tower in Sri Lanka. At a total height of 174 m, this tower is
further distinguished as the tallest free standing tower in
South Asia. The new Kokavil Tower, constructed under
the auspices of the Uthuru Wasanthaya Programme, is
located at the site of the previous tower which was
destroyed by the terrorists. The center built under the
direction of the Telecommunications Regulatory
Commission of Sri Lanka was constructed at a cost of Rs.
310 million, by the Central Engineering Consultancy
Bureau on a Design and Built Contract Basis. This
Transmission Tower will be of great help in alleviating
problems associated with telecommunication, television,
radio and ICT, affecting the communities in the Northern
Sri Lanka.
Courtesy of:
Eng. Dharmasiri De Alwis,
Head of Projects of TRCSL
ISSN 1800-1122
From the Editor ...
III
SECTION I
Identification of the Spatial Variability of
Runoff Coefficients of Three Wet Zone
Watersheds of Sri Lanka
by : Eng. (Dr.) (Mrs) K. R. J. Perera and
Eng. (Prof.) N. T. S. Wijesekara
1
The following Paper was placed in the First ‘Over 35
years of age’ Category at the Competition on
“Infrastructure for Sustainable Development of Water
and Other Natural Resources” 2009/2010.
Preparation of the Stormwater Drainage
Management Plan for Matara Municipal
Council
by : Eng. (Prof.) N. T.S. Wijesekera and
Eng. (Dr.) K.M.P.S. Bandara
11
SECTION II
Impact on Existing Transport Systems by
Generated Traffic due to New
Developments
by : Eng. (Prof.) K. S. Weerasekera
31
Comparison of Performance Assessment 39
Indicators for the Evaluation of Irrigation
Development of Sri Lanka
by: Eng. S. M. D. L. K. De Alwis and
Pull-out Behavior of Reinforcing Tendons 51
of Nehemiah Anchored Earth System
by: Eng. K. J. S. Munasinghe and
Eng. R. D. D. Dayawansha
The following Paper was placed in the Second ‘Over 35
years of age’ Category at the Competition on
“Infrastructure for Sustainable Development of Water
and Other Natural Resources” 2009/2010.
Economic Analysis of
Water Infrastructure:
57
Have We Got It Right?
By : Eng. (Dr.) (Mrs.) Bhadranie Thoradeniya,
Eng. (Prof.) Malik Ranasinghe and
Notes:
ENGINEER, established in 1973, is a Quarterly
Journal, published in the months of January,
April, July & October of the year.
All published articles have been refereed in
anonymity by at least two subject specialists.
Section I contains articles based on Engineering
Research while Section II contains articles of
Professional Interest.
FROM THE EDITOR…………..
Tallest self standing tower in South Asia, a remarkable entity indeed, is the newly
commissioned Kokavil multi-functional communication tower. Though Sri Lanka is a
small country, we have had more than our fair share of biggest, longest, etc., of things to
be proud of. Tallest masonry structure – inclusive of foundation, largest sugar factory,
biggest school are some that comes to one’s mind, without much difficulty. So have we
got in to the Guinness book of records in no insubstantial manner through deeds as well
as icons. Whatever is said and done, we are a nation inspired by record breaking
creations and achievements.
Coming back to Kokavil tower, our object of discussion, it is heartening to note that the
design and construction was carried out by a local semi government Engineering
organization – namely Central Engineering Consultancy Bureau. Apart from the record
setting height, the project has set another record in safety by completing the tower
reaching precarious heights, without a single noteworthy accident.
In an era where even the simplest of construction tasks are entrusted to expatriate
consultants and constructors, the initiative by the client, the Telecommunications
Regulatory Commission and the Government of Sri Lanka, to entrust this extraordinary
works to local Engineers is laudable. Further, the pioneering spirit of undertaking such a
challenge by the Central Engineering Consultancy Bureau Engineers as a Designed and
Built project has to be commended. This project would have undoubtedly imparted
them with a wealth of experience and confidence. All in all, the direction indicated by
this successful landmark project is the correct path for sustainable national
infrastructure development while consolidating the confidence in the professional skills
of our Engineers.
Eng. (Prof.) T. M. Pallewatta, Int. PEng (SL), C. Eng, FIE(SL), FIAE(SL)
Editor, ‘ENGINEER’, Journal of The Institution of Engineers.
III
SECTION I
ENGINEER - Vol. XXXXIV, No. 03, pp. [1-10], 2011
© The Institution of Engineers, Sri Lanka
Identification of the Spatial Variability of Runoff
Coefficients of Three Wet Zone Watersheds of Sri
Lanka
K. R. J. Perera and N. T. S. Wijesekera
Abstract:
Runoff estimation from rainfall records in the absence of stream gauge records is
essential in Sri Lanka, because most of the watersheds are ungauged. Since runoff depends on the
catchment characteristics in addition to the rainfall, this study focuses on streamflow determination as
a function of land use, soil and slope from developed GIS model. This study developed a method to
estimate runoff coefficient as a function of land use, soil and slope within the wet zone basins of Sri
Lanka.
Three Wet Zone basins, Kalu Ganga, Kelani Ganga and Attanagalu Oya were selected for the study.
Regression analysis showed that the computed runoff agreed with the observed runoff with R 2 values
of 0.80, 0.78 and 0.83 for Kalu Ganga, Kelani Ganga and Attanagalu Oya basin respectively. Averaged
runoff coefficients, for basins with the spatial variation were calculated as 0.52, 0.49 and 0.51 for Kelani
Ganga, Kalu Ganga and Attanagalu Oya sub basin respectively. Study revealed that credible runoff
coefficient will not be represented simply by the ratio between runoff and rainfall where runoff
depends highly on catchment characteristics.
Keywords:
Spatial variability, GIS (Geographic Information Systems), river basin planning,
runoff, catchment characteristics.
1.
Introduction
function. Kumar and Sathish [8] and Agarwal
and Singh [2] have utilized Artificial Neural
Networks, Recurrent Neural Networks for
runoff modelling and river flow forecasting.
Liu et al. [9,10] also have performed a study on
storm runoff prediction from different land use
classes using GIS-based distributed model.
Estimating runoff from rainfall records in the
absence of stream gauge records is extremely
important in water resources development. It
is more so in Sri Lanka where most of the
watersheds are ungauged. Runoff coefficients
enable the estimation of runoff for practical
applications
such
as
water
resource
management and river basin planning.
Runoff is governed by many factors in addition
to rainfall. It has been known that land use, soil
type and slope are the primary catchment
characteristics that govern runoff and hence
runoff coefficient [6].
Determining runoff
coefficient and its variation with the major
parameters is important for water resources
assessments giving due consideration to the
soil, slope and land use variations.
Catchment specific studies have been carried
out all over the world. Moreover, rainfall runoff
models have been developed over several
decades. Abulohom et al. [1] have developed a
rainfall runoff model based on water balance
equations where inputs to the model include
precipitation and potential evapotranspiration
on monthly basis which in turn give simulated
runoff at watershed outlet. De Smedt et al. [6]
have developed a physically based distributed
hydrological model which simulates the
hydrologic behavior and runoff in a river basin
where the model has been validated on a small
watershed in Belgium. Naden [11] presented
spatially distributed rainfall-runoff model
which included hillslope, network width, and
routing as functions which were finally
combined to find overall catchment response
Eng. (Dr.) (Mrs.) K.R.J.Perera, B.Sc. Eng. (Moratuwa),
M.Phil. (Moratuwa), MS(USA), Ph.D. (USA), AMIE(Sri
Lanka),College Assistant Professor of Civil Engineering,
Department of Civil Engineering, New Mexico State
University, NM, USA.
Eng. (Prof.) N.T.S.Wijesekera, B.Sc. Eng. (Sri Lanka),
C. Eng., FIE(Sri Lanka), MICE(UK), PG Dip Hyd
Structures (Moratuwa), M. Eng. (Tokyo), D. Eng. (Tokyo).
Senior Professor of Civil Engineering, Department of Civil
Engineering, University of Moratuwa, Sri Lanka.
1
ENGINEER
2.
Study Area
In addition, a streamflow gauging station
maintained at Karasnagala by the Irrigation
Department of Sri Lanka is the only available
stream gauging station (non-recording) at
Karasnagala. Stream gauging station had a
recorder at Karasnagala, which is not
functioning at present. The details of this
station were verified by personal consultation
and through field visits.
Attanagalu Oya basin at Karasnagala gauging
station (Attanagalu Oya sub basin), Kelani
Ganga at Glencourse gauging station (Kelani
Ganga sub basin) and Kalu Ganga at Putupaula
gauging station (Kalu Ganga sub basin) were
selected as study areas of this study. Details of
these sub basins are given below.
2.2 Kelani Ganga basin at Glencourse (Kelani
Ganga sub basin)
Kelani Ganga basin is located in the wet zone of
Sri Lanka between the latitudes 6° 45' and 7° 15'
N, Longitudes 79° 50’ and 80° 45'E. Sub basin of
the Kelani Ganga at Glencourse was selected
for this study. Total catchment area is about
1537 km2 at Glencourse (Kelani Ganga sub
basin). Data from six rainfall gauging stations
and one streamflow gauging were available for
the study. Figure 2 shows the stream network
and hydro-meteorology network of the Kelani
Ganga basin at Glencourse which is the selected
catchment outlet.
2.1 Attanagalu Oya basin at Karasnagala
(Attanagalu Oya sub basin)
Attanagalu Oya is located in the wet zone of Sri
Lanka between the latitudes 7° 00' and 7° 17'N,
Longitudes 79° 50’ and 80° 15'E. Total
catchment area at Kotugoda is 539 km2. It
contains eleven secretariat divisions in the
Gampaha and Kegalle administrative districts
of Western and Sabaragamuwa provinces
respectively. Figure 1 shows the stream
network of the sub catchment of Attanagalu
Oya basin at Karasnagala stream gauging point
(Attanagalu Oya sub basin).
Two rainfall gauging stations were selected
(Karasnagala- within the basin boundary and
Vincit- just outside the boundary) to cover the
study area.
#
0$1
Karasnagala
#
0
Vincit
Kotugoda
1
$
Stream Gauge
#
0
Rainfall Gauges
Stream Network
Karasnagala Boundary
1
0.5
0
1
Kilometers
Attanagalu Oya at Kotugoda
Figure 1 - Attanagalu Oya catchment at Karasnagala gauging point
ENGINEER
2
-
#
0
#
0
Pindeniya
Dunedin
#
0
$
1
Ingoya estate
Glencourse
#
0
Maliboda
#
0
#
0
Rainfall Gauges
$
1
Stream Gauge
Luccombe estate
#
0
Stream Network
10
5
Campion
0
Kelani Sub-catchment Boundary
10
Kilometers
Figure 2 - Stream network and hydro-meteorology network of the Kelani Ganga basin
2.3 Kalu Ganga basin at Putupaula (Kalu
Ganga sub basin)
Kalu Ganga basin is located in the wet zone of
Sri Lanka between the latitudes 6° 20' and 6° 55'
N, Longitudes 79° 55’ and 80° 45'E. Sub basin
of the Kalu Ganga at Putupaula was selected
for this study.
-
#
0
Total catchment area is about 2627 km2 at
Putupaula (Kalu Ganga sub basin). Data of
seven rainfall gauging stations and one
streamflow gauging station were available for
the study. Figure 3 shows the stream network
and hydro-meteorology network of the Kalu
Ganga basin at the Putupaula gauging station.
Ehelyagoda
#
0
#
0
Hapugastenna
#
0
Ratnapura
#
0
$
1
Putupaulla
#
0
Pelmadulla
Gonapinuwala
#
0
$
1
#
0
Alupota
Dependene Group
Streamflow_Gauge
Rainfall Gauges
Stream Network
10
Kalu Sub-catchment Boundary
5
0
10
Kilometers
Figure 3 - Stream network and hydro-meteorology network of the Kalu Ganga basin at
Putupaula gauging station
3
ENGINEER
3.
Data and Methodology
Field visits were carried out for data collection
and verification. Tables 1a) and 1b) show the
rainfall and streamflow data availability and
the selected data for the study.
Rainfall, streamflow, land use, soil and slope
data were collected (Table 1a - Table 4).
Collected data were checked for consistency
and compatibility. Visual examination, annual
water balance, Double Mass Curve method and
statistical checks for homogeneity were used.
Table 1a) - Rainfall data availability within basins
Basin Name
Station Name
Kelani ganga basin
at Glencourse*
Kalu ganga basin
at Putupaulla*
Attanagalu Oya basin
at Karasnagala**†
Sources: *
**
†
Campion
Dunedin
Ingoya Estate
Luccombe Estate
Pindeniya
Alupota
Dependene Group
Eheliyagoda
Gonapinuwala
Hapugastenna
Pelmadulla
Ratnapura
Karasnagala
Vincity
Period of
Availability
1943-85
1949-85
1949-85
1943-76
1949-73
1949-85
1949-85
1949-85
1949-84
1949-85
1949-84
1949-85
Continuous
1925/09-to Date
Selected
Data Set
1950-70
1950-70
1950-70
1950-70
1950-70
1950-70
1950-70
1950-70
1950-70
1950-70
1950-70
1950-70
1971-80
1971-81
- Ceylon Electricity Board Master Plan
- Irrigation Department Data
- Meteorological Department Data
Table 1b) - Streamflow data availability within basins
Basin Name
Station Name
Glencourse
Putupaula
Period of
Availability
1948-85
1943-85
Selected
Data Set
1950-70
1950-70
Kelani ganga*
Kalu ganga*
Attanagalu Oya**
Karasnagala
Continuous
1971-80
Table 2 - Land use data for three sub basins.
Land Use Type
Cultivation
Forest
Garden
Grass and
Chena
Rock, Tanks
and Reservoirs
ENGINEER
Attanagalu Oya sub
basin at
Karasnagala
Percentage
Area
of Area (%)
(km2)
38.96
72.82
1.20
2.25
7.76
14.50
5.14
9.61
0.44
Kelani Ganga sub
basin at Glencourse
Kalu Ganga sub basin
at Putupaula
Area
(km2)
944.76
297.96
214.70
59.54
Percentage
of Area (%)
61.46
19.38
13.97
3.87
Area
(km2)
1470.85
616.87
90.82
448.85
Percentage
of Area (%)
55.98
23.48
3.46
17.08
20.12
1.31
0.02
0.00
0.82
4
Table 3 - Soil data for three sub basins
Soil Type
RYP
Alluvial
Rockland & Lithosols
Bog &Half Bog Soils
Attanagalu Oya sub
basin at Karasnagala
Percentage
Area
(km2) of Area (%)
52.79
98.71
0.69
1.29
-
Kelani Ganga sub
basin at Glencourse
Area
Percentage
(km2)
of Area (%)
1387.77
90.29
130.57
8.50
18.74
1.22
-
Kalu Ganga sub basin
at Putupaula
Area
Percentage
(km2)
of Area (%)
2468.92
93.97
117.44
4.47
13.91
0.53
Table 4 - Slope class classification
Major soil type for Attanagalu Oya sub basin
was Red Yellow Podzolic (>98%). Hence Red
Yellow Podzolic soil was considered for the
analysis of Attanagalu Oya sub basin.
Slope (%)
Slope Class
0-2%
Flat
2-7%
Average
>7%
Steep
Source: Andy and Stanley [3].
Twenty years of monthly rainfall data were
used in the analysis of Kelani Ganga and Kalu
Ganga basins while ten years data were used in
Attanagalu Oya basin as indicated in Tables 1
a) and 1 b).
For Kelani and Kalu sub
catchments rainfall runoff data from 1950-1970
and for Attanagalu Oya sub catchment, data
from 1970-1980 were employed. Rainfall data
were used to calculate runoff with the assumed
runoff coefficients. Initial values of Runoff
coefficients were identified from literature
survey [3, 4, 5, 7].
Spatial variability of runoff coefficients were
assessed using a Geographic Information
System (GIS). Land use, soil and slope polygon
layers (vector format) prepared using GIS and
overlay operations, table operations, etc., were
used in the analysis. Table 2, Table 3 and Table
4 show the land use, soil variation and slope
classification respectively for the three study
areas.
Observed streamflow data at Glencource,
Putupaula and Karasnagala were used to
compare computed runoff. Kelani Ganga data
was used first and coefficients were determined
by trial and matching with observed data. The
model calibration and verification used the
Mean Ratio of Absolute Error (MRAE) as the
objective function [12, 13]. Model verification
and calibration for each watershed was carried
out with different datasets.
A similar
procedure was adapted to the Kalu Ganga
basin and the Attanagalu Oya basins to identify
the respective parameters. The overall
methodology adopted for the study is shown in
Figure 4.
Land use, soil and slope soft copy data were
obtained from Survey Department of Sri Lanka
which was digitized from 1:50,000 topo-sheets.
Land use of the catchment was classified into
five groups; soil into two; and slope into three.
There were two major soil types in each
catchment and therefore two classes were
considered. Kalu Ganga sub basin has Red
Yellow Podzolic and Alluvial soils as the main
soil types (>98%) while two other soil types
with less than 2% of the covered area (Table 3).
Similar pattern was observed in Kelani Ganga
sub basin (Table 3).
Literature Survey/Situation Analysis
Conceptual Model Development
Data Collection and Checking
GIS for selected basins
Model Data Preparation
Model calibration and validation for selected basins
Runoff coefficients for wet zone
5
ENGINEER
Table 5 -Land use, slope and soil classification
Spatially varied land use, soil and slope data
which were collected and identified on maps
were digitized using GIS (vector format). Three
catchment characteristics for a given basin was
overlaid using overlay operation in GIS. This
activity creates different land parcels with
different catchment characteristics. Required
land parcel was selected using table operations
for later applications.
Land use (i)
i Class
1 Forest
Slope (j)
j Class
1 Flat
2
3
2 Average
3 Steep
4
5
3.1 Model development
A simple conceptual model was used to
compute runoff from each land parcel. The
model assumed a linear function incorporating
land use, slope and soil as major catchment
parameters contributing to convert rainfall into
surface runoff.
Garden
Grass &
Chena
Cultivation
Rocks,
tanks &
reservoirs
Soil (k)
k Class
1 Red
Yellow
Podzolic
2 Alluvial
Pijk represents the coefficient for ith land use
type, jth slope class and kth soil type where i
varies from 1-5, j varies between 1-3 and k from
1-2. For an instance, P231 represents the
coefficient for steep slope garden areas with red
yellow podzolic soil type. Parameters of the
model assigned by the above criteria are given
in Table 6. Model parameters (P values) were
estimated (optimized) using Mean Ratio of
Absolute Error as the objective function [12, 13].
Parameter optimization was initiated with the
literature values [3, 4, 5, 7]. Parameters at
which minimum MRAE were selected as
finalized parameters of the model.
Surface Runoff = ƒ (Runoff Coefficient, Rainfall)
...(1)
The cumulative runoff contributed from each
land parcel in a catchment was taken as the
surface runoff generated from the catchment.
Runoff Coefficient = ƒ (Land use, Slope, Soil)
...(2)
...(3)
Q = (Pijk *Aijk)*R
where,
R = Rainfall
Aijk = Area of concern with given factors i,j,k
Coefficient Pijk= ƒ (i(1-5), j(1-3), k(1-2))
3.2 Overall runoff coefficient
In the present work the overall runoff
coefficient which is the area weighted runoff
coefficient for a particular watershed was
computed using equation 4.
Table 6 - Classification of model parameters on land use, soil and slope
Land Use (i)
Forest (1)
Garden (2)
Grass & Chena (3)
Cultivation (4)
Rocks, Tanks & Reservoirs (5)
ENGINEER
Slope Class (j)
Flat (0-2 %) (1)
Average Slope (2-7 %) (2)
Steep Slope (over 7 %) (3)
Flat (0-2 %)
Average Slope (2-7 %)
Steep Slope (over 7 %)
Flat (0-2 %)
Flat (0-2 %)
Any Slope
6
Soil (k)
Red
Yellow
Alluvial
Podzolic
(AL) - (2)
(RYP) - (1)
(P111)
(P112)
(P121)
(P122)
(P131)
(P132)
(P211)
(P212)
(P221)
(P222)
(P231)
(P232)
(P311)
(P312)
(P321)
(P322)
(P331)
(P332)
(P411)
(P412)
(P421)
(P422)
(P431)
(P432)
(P5,1-3,1-2)
This runoff coefficient provides the opportunity
to compute an aggregated runoff coefficient for
a basin to compute a single runoff coefficient
incorporating the variation of soil, slope and
land use.
Using this method overall runoff
coefficients were computed for Kelani, Kalu
and Attanagalu Oya basins.
Overall Runoff Coefficient
(P
ijk
* Aijk(
Hence the runoff coefficients with spatial
variation were obtained as 0.51, 0.52, and 0.49
for Attanagalu, Kelani and Kalu sub basins
respectively.
4.2 Model parameters and optimized values
Each basin contributes to different parameters.
Parameters of Kelani Ganga basin were
optimized first. Table 7 shows the parameters
optimized from Kelani Ganga basin.
...(4)
Aijk
where,
Aijk = Area of concern with given factors i,j,k
Coefficient Pijk= ƒ (i(1-5), j(1-3), k(1-2)) and i, j, k are
as explained in Table 5.
4.
The optimized parameters from the Kelani
Ganga basin were fixed for the Kalu Ganga
basin and the rest of the parameters were
optimized for the Kalu Ganga basin which are
given in Table 8. Similar analysis was carried
out for the Attanagalu Oya basin at
Karasnagala and the parameters used and
optimized are shown in Table 9. Table 10 shows
the finalized coefficients from all three basins,
in other words the runoff coefficient matrix.
Results and Discussion
4.1 General results for selected watersheds
Data checks provided agreeable results with
minor error records at Karasnagala streamflow
data in year 1975. Annual rainfall for three
catchments ranged from 2500mm-5000mm.
Typical dry month for the Attanagalu Oya
basin is January while for Kelani and Kalu
Ganga sub basins, January and February are the
dry months.
Cultivation contributes higher percentage of
land use for all basins. Cultivation includes
coconut, rubber, and other cultivation. For the
cultivation group optimized runoff coefficients
are 0.61, 0.57 and 0.20 for steep, average and
flat slopes for red yellow podzolic soils while
that for alluvial soils are 0.55 and 0.50 for steep
and average slope respectively. Higher
amounts of runoff from rainfall was generated
in residential areas which was indicated by
higher runoff coefficients: 0.65, 0.60, and 0.55
(steep, average and flat slope respectively) for
red yellow podzolic soils and 0.56 (steep slope)
and 0.52 (average slope) for alluvial soils.
Lowest runoff coefficients were obtained for
forest areas with alluvial soils (0.10 for steep
slope and 0.05 for average slope) followed by
red yellow podzolic soils (0.25, 0.20, and 0.10
for steep, average and flat slope respectively).
Average rainfall in each basin and runoff data
at stream gauging locations of each basin
(Karasnagala, Putupaulla and Glencourse for
Attanagalu Oya, Kalu Ganga and Kelani Ganga
sub basin respectively) were used and
calculated the ratio of runoff between rainfall
(catchments’ average runoff coefficients). These
numbers were found as 0.40, 0.72, and 0.70 for
Attanagalu, Kelani and Kalu sub basins
respectively. As spatially varied runoff
coefficients were found in this study,
catchments’ average runoff coefficient were
found using the established coefficients as
explained in section 3.2.
Table 7 - Land use, soil, slope factors considered in the Kelani sub basin and model
finalized values
Parameter
Land Use
Forest
Garden
Grass & Chena
Cultivation
Rocks, Tanks, Res.
Slope Class \ Soil
Any Slope
7
RYP
(P131)
(P221)
(P231)
AL
(P222)
(P232)
(P331)
(P421)
(P422)
(P431)
(P432)
(P5,1-3,1-2)
Optimized
Parameter
RYP
AL
0.25
0.60
0.65
0.52
0.56
0.55
0.57
0.61
0.50
0.55
1.00
ENGINEER
Table 8 - Land use, soil, slope factors considered in the Kalu Ganga sub basin and
finalized values with the model
Parameter
Land Use
Slope Class \ Soil
Forest
Garden
Grass & Chena
Cultivation
Rocks, Tanks & Res.
Optimized
Parameter
RYP
AL
RYP
AL
(P112)
(P131)
(P221)
(P122)
(P132)
(P222)
0.20
0.25
0.60
0.05
0.10
0.52
Any Slope
(P231)
(P232)
(P321)
(P322)
(P331)
(P332)
(P421)
(P422)
(P431)
(P432)
(P5,1-3,1-2)
0.65
0.52
0.55
0.57
0.61
0.56
0.35
0.40
0.50
0.55
1.00
Table 9 - Land use, soil, slope factors considered in the Karasnagala sub basin with
optimized values
Parameter
Land Use
Forest
Garden
Grass & Chena
Cultivation
Rocks, Tanks & Res.
Slope Class \ Soil
Flat (0-2 %)
Flat (0-2 %)
Flat (0-2 %)
Flat (0-2 %)
Any Slope
RYP
(P111)
(P131)
(P211)
(P231)
(P311)
(P331)
(P411)
(P431)
(P5,1-3,1-2)
Optimized
Parameter
0.10
0.25
0.55
0.65
0.45
0.55
0.20
0.61
-
Table 10 - Finalized Runoff Coefficient Matrix
Land Use
Forest
Garden
Grass & Chena
Cultivation
Rocks, Tanks & Res.
ENGINEER
Slope Class \ Soil
Flat (0-2 %)
Flat (0-2 %)
Flat (0-2 %)
Flat (0-2 %)
Any Slope
8
RYP
0.10
0.20
0.25
0.55
0.60
0.65
0.45
0.52
0.55
0.20
0.57
0.61
AL
0.05
0.10
0.52
0.56
0.35
0.40
0.50
0.55
1.00
4.3 Calculated vs. observed results
The predictive ability of this approach is
demonstrated
by
comparing
calculated
streamflows against the observed values.
Linear regression analysis was carried out with
MRAE (objective function) which was used as
the measure of the quality of prediction. MRAE
of 0.9 and R2 0.83 for Attanagalu Oya sub basin
was obtained (Figure 5). For Kelani Ganga sub
basin, MRAE was 0.3 and R2 was 0.78 while
MRAE for Kalu Ganga sub basin was 0.44 with
R2 of 0.80 (Figure 6, Figure 7 respectively).
When compared with the objective function,
Kelani Ganaga sub basin provided the best fit
among three basins.
Ganga sub basin. Data from years 1961-1970
was used for model validation. Even though
the R2 value is lower compared to the other two
basins, best relationship could be observed in
Kelani Ganga sub basin data set with low
MRAE (Figure 6).
Overall runoff coefficients, for basins with the
spatial variation were calculated as 0.52, 0.49
and 0.51 for Kelani, Kalu and Attanagalu sub
basins, respectively.
Data from five year period (1971-1975) was
selected for calibration of Attanagalu Oya sub
basin while data from different five years (19761980) was used for validation of the model.
Figure 5 shows the agreement between
modeled and observed streamflow values.
Figure 6 - Calculated Vs observed streamflows
for Kelani sub basin
The agreement between the model and the
observed streamflow for Kalu Ganga sub basin
is shown in Figure 7. Data from 1950-1960 was
used for calibration while 1961-1970 used for
validation of the model. An underestimation is
observed in Kalu Ganga sub basin. Kalu Ganga
sub basin land use is cultivation and it receives
higher rainfall compared to other two basins of
consideration. Overall quality of fit plotted for
all three sub basins is shown in Figure 8.
for Karasnagala sub basin
The plot in Figure 5 for Attanagalu Oya sub
basin
(at
Karasnagala),
indicates
an
overestimation of the streamflow. As
mentioned in methodology and data section,
gauging station at Karasnagala was not
functioning properly due to clogging by sand
where the observed data are undervalued.
for Kalu Ganga sub basin
Figure 6 shows the quality of fit between
calculated and observed streamflow values
from model calibration (1951-1960) in Kelani
9
ENGINEER
3.
Andy, D.,
Stanley, W., Environmental
Hydrology, 2nd ed., Boca Raton, FL, USA: CRC
Press, 2004.
4.
Bedient, P., Huber, W., Hydrology of Floodplain
Analysis, 2nd ed., New York, USA: AddisonWesley, 1992.
5.
Chow, V. T., Maidment, D., Mays, L., Chow, V.
T., Applied Hydrology, Austin, TX: McGraw-Hill,
1988.
6.
De Smedt, F., Yongbo, L., & Gebremeskel, S.,
Hydrologic Modelling on a Catchment Scale using
GIS and Remote Sensed Land use Information. In
C. A. Brebbia (Ed.), Risk Analysis II.
Southampton, Boston: WIT press, 2000, pp. 295304.
7.
Glenn, O., Frevert, R., Kenneth, K., Edminster,
T., Elementary Soil and Water Engineering, 3rd ed.,
New York: John Wiley, 1985.
8.
Kumar, D., Sathish, S., “River Flow Forecasting
using Recurrent Neural Networks”. Journal of
Water Resources Management , Vol. 18, No. 02,
2004, pp. 143-161.
9.
Liu, Y. B., Gebremeskela, S., De Smedt, F.,
Hoffmannb, L., Pfisterb, L., “A Diffusive
Transport Approach for Flow Routing in GISBased Flood Modeling”. Journal of Hydrology ,
Vol. 283, 2003, pp. 91-106.
10.
Liu, Y. B., Gebremeskela, S., De Smedt, F.,
Hoffmannb, L., Pfisterb, L., “Predicting Storm
Runoff from Different Land use Classes using a
Geographical
Information
System-Based
Distributed Model”. Hydrological Processes, Vol.
20, 2003, pp. 533- 548.
11.
Naden, P., “Spatial Variability in Flood
Estimation for Large Catchments: The
Exploitation of Channel Network Structure”.
Hydrological Sciences , Vol. 37, No. 01, 1992, pp.
53-71.
12.
Wijesekera, N.T.S., “Parameter Estimation in
Watershed Model: A Case Study Using Gin
Ganga Watershed, Transactions”. Annual
Sessions of the Institution of Engineers, Sri Lanka,
October 2000.
13.
Wijesekera,
N.T.S.,
Abeynayake,
J.C.,
“Watershed Similarity Conditions for Peakflow
Transposition – A Study of River Basins in the
Wet Zone of Sri Lanka”. Engineer Journal of the
Institution of Engineers, Sri Lanka, April 2003.
Figure 8 - Overall quality of fit for three sub
basins
5.
Conclusions
A simple conceptual model was developed in
this study to estimate runoff from catchment
characteristics and rainfall data. Model agreed
well with observed data (MRAE values of 0.44,
0.30 and 0.90 for Kalu Ganga sub basin, Kelani
Ganga sub basin and Attanagalu Oya sub basin
respectively) with overall R2 of 0.73.
Overall basin averaged runoff coefficients
calculated in this study are 0.52, 0.49 and 0.51
for Kelani Ganga sub basin, Kalu Ganga sub
basin, and Attanagalu sub basin, respectively.
Garden, cultivation, grass & chena and forests
contribute to runoff in decreasing sequence
with selected slope or soil type.
Acknowledgement
This research was supported by University of
Moratuwa Senate Research Grant Number 202.
Encouragement given by the University of
Moratuwa and the Senate Research Committee
is gratefully acknowledged.
References
1.
2.
Abulohom, M., Shah, S., Ghumman, R.,
“Development of a Rainfall-Runoff Model, its
Calibration and Validation”. Journal of Water
Resources Management, 2001, pp.149-163.
Agarwal, A., Singh, R., “Runoff Modelling
Trough back Propergation Artificial Neural
Network with Variable Rainfall Runoff Data”.
Journal of Water Resources Management, 2004, pp.
285-300.
ENGINEER
10
Preparation of the Stormwater Drainage Management
Plan for Matara Municipal Council
N.T.S. Wijesekera and K.M.P.S. Bandara
Abstract:
Matara Municipal Council area had been experiencing stormwater drainage problems
causing inconvenience to public, interruption to work and damage to property. Though the Matara
Municipal Council (MMC) had carried out a project in 2001 to develop its drainage canals, there were
many cases of flooding within its boundary limits. In order to achieve a suitable plan for stormwater
drainage management, the present work carried out an analysis of the associated stormwater drainage
system. Systematic field data collection activities were done to identify the flood problem of the area,
and to capture sufficient details of terrain and drainages. GPS surveys were conducted to identify the
road and drainage alignments. A main feature of the study was the conduct of a road drainage survey
which among many other details captured drainage directions along and across the roads. This
survey helped to rationally identify the undulations in the terrain to generate the digital terrain model
for the generation of stream network and delineation of watersheds. The 1:10,000 elevation data
supported by the field work information showed the capability to generate a representative
topography for stormwater drainage assessments. Analysis also used a simple Geographic
Information System to prioritize critical flood affected areas and enabled identification of critical
watersheds for engineering interventions. The present canal system was evaluated with that
generated by the model and several sections were identified for early drainage designs these locations
were verified in the field. Present work identified that in the MMC area 42% of roads coincide with the
stream network indicating a loading of street stormwater drains with runoff generation as a result of
terrain changes affected at individual compounds. 164 identified flood locations were analysed with
drainage directions and surrounding elevations supported by detailed engineering inspections at
specific locations to provide short term solutions. The study made recommendations with respect to
development plan approval procedures, preparation of a suitable stormwater drainage database and
the need of guidelines for developers to mitigate stormwater drainage problems as part of the long
term solutions.
Keywords:
Urban, Stormwater, Drainage, Management, Terrain Model, GIS, Flood, Field Survey.
1. Introduction
The Matara Municipality Area (Figure 1) had
been experiencing stormwater drainage
problems causing inconvenience to public,
interruption to work and damage to property.
The reasons cited for poor stormwater drainage
had been given as, the non existence of natural
drainage to the sea because most of the lands
are either below the sea level or at the same
level, many buildings and boundary walls have
come up obstructing the natural drainage
paths, uncontrolled landfills creating obstacles
for flood water flow and storage, lack of proper
drainage of stormwater as a result of
Urban areas often experience drainage
problems causing flooding to disrupt human
activities
and
leading
to
numerous
environmental problems such as creating
mosquito breeding grounds, washing away of
garbage to undesirable places,
creating
stagnant water holes generating unpleasant
odour and deteriorating road surfaces. Urban
area flooding is usually attributed to new
developments blocking the natural waterways,
land filling creating changes to drainage
directions, filling of flood retention and
detention areas, and diversion of natural
streams to road side drains etc. In near coast
urban centres the low-lying lands also cause
drainage problems thereby leading to flood
situations when land use changes are
conducive to higher runoff generation than that
were previously experienced.
Eng. (Prof.) N.T.S. Wijesekera, B.Sc. Eng. Hons, (Sri Lanka),
P.G.Dip(Moratuwa), M.Eng.(Tokyo), D.Eng.(Tokyo), C.Eng,
FIE(Sri Lanka), MICE(London), Senior Professor of Civil
Engineering, Department of Civil Engineering, University of
Moratuwa.
Eng. (Dr.) K.M.P.S.Bandara, B.Sc.Eng. (Sri Lanka), M.Sc
(Geoinformatics-Netherlands), PhD(Netherlands), MBA
(Colombo), C.Eng, FIE(Sri Lanka), MICE(London), Director of
Engineering Service Board, Ministry of Public Administration
& Home Affairs.
11
ENGINEER
Figure 1 - Survey Department Map of Matara Municipal Council Area
construction activities without proper plans or
control and also issues such as land filling
activities without making provision for natural
streams and drainage paths. In order to identify
the stormwater drainage issues and to carryout
necessary mitigatory activities, the present
work was under taken to prepare a stormwater
management plan for the Matara Municipal
Council (MMC) area.
III.
IV.
V.
VI.
The present work describes the preparation of a
stormwater management plan based on field
work and mathematical modelling, the details
of field work carried out, along with
engineering
and
management
options
recommended as short term and long term
solutions.
VII.
3. Methodology
2. Objective and Specific Objectives
Overall methodology used for the plan
preparation is schematically shown in Figure 2.
Data was collected to capture the background
to the problem and also to perform a situation
assessment. Activities carried out to achieve
the specific objectives are as indicated below.
2.1. Objective
The objective of the present work was to
analyse the stormwater drainage system and to
recommend stormwater management options
for the Matara Municipal Council area.
Agency Data Collection, Reviewing Data
and Information of Study Area
Watershed delineation, land use and
physical parameter characterization
Characterization of the drainage, stream
banks and associated environmental
conditions through a detailed survey and
other fieldwork
2.2. Specific Objectives
I.
II.
Characterization and delineation of
Matara Municipal Council (MMC) area
watersheds
Characterization of land use conditions
affecting the runoff generation
ENGINEER
Characterization of Drainage Patterns
Identification of stormwater drainage
canal infrastructure within the MMC area
and the surrounding areas
Identification of existing problems in the
stormwater conveyance system
Identification of the stormwater runoff
discharge problems through the existing
storm drainage system and the associated
canals
Identification of potential alternatives and
a strategy for long and short term
management of stormwater drainage in
the Matara Municipal Council area
12
the upstream watersheds and the other is due
to inadequate drainage as a result of rainfall
that directly falls in to the project area (Mays
2004). During the study, an assessment of the
present flood drainage system of the MMC area
was carried out. In the MMC area two distinct
flood bund sections are in existence to prevent
flood water of the Nilwala river reaching the
MMC area. A pumping station is in operation
to discharge drainage water of the Thudawe Ela
to the Nilwala river. Gravitational flow of
drainage water from the MMC area and close to
the bunds had been restricted due to the
construction of bunds. In this area a fresh path
for the gravitational drainage had been
facilitated through a canal linking the restricted
area to the upstream of pumping station. A
Major drainage canal network had been
designed and developed in 2001 under a project
called “Improvements to Stormwater drainage
in Matara Urban Council Area”. Development
activities are taking place in most of the area.
Many evidence of clearing land cover and earth
filling could be observed. These locations were
even visible in the available satellite imagery.
Major drainage canals in the MMC area that
had been improved recently are included in the
Figure 3.
Identification of the stormwater drainage
system
infrastructure
locations
and
dimensions, and survey of key parameters
Figure 2 - Methodology Flow Chart
4.1. Field Visits and Discussions
Terrain model development, runoff system
generation and associated field verification
Development of a Geographic Information
System to carryout spatial analysis of
stormwater drainage problems
Identification and evaluation of stormwater
management alternatives
Meetings and Coordination to perform
progress
review,
management
and
coordination of activities between the field
staff
and
managers,
inter
agency
discussions etc.
Preparation of outputs and associated
documents such as results of the stream
assessments, maps of drainage patterns,
present locations, problem areas, proposed
structures, model and outputs.
Field visits and discussions with officials and
public were carried out to identify the
functioning of the drainage network and the
associated problems. Initial discussions with
MMC officials enabled the identification of
fifteen locations of reported flood problems.
Visits to each of the sites revealed that some
were significant and others were insignificant.
Significant problem locations were identified as
the locations which inundated the roads and
property causing unbearable inconvenience to
public. Field visits and discussions with public
at various locations revealed that the major
canal network draining surface water
generating from within the city area, did not
function as expected and hence either flooding
was taking place or there were stagnant water
with unpleasant water quality. At places near
the flood bunds, the public indicated that the
natural streams which were initially draining
across the bund alignment are now forced to
drain in a different direction. There were
complaints that the new canals do not function
as expected.
4. Reconnaissance
Floods in an area at close proximity to a major
river and its floodplain can be due to two
reasons. One is due to floods arising from river
overflow as a result of rainfall experienced in
13
ENGINEER
Figure 3 – Major Drainage Canals
stormwater drainage problems are not due to
the Nilwala river water flooding the MMC area
but because of issues that rise in the process of
draining local rain water out of the dwelling
and commuting area.
In many of the canals there was significant
pollution due to stagnant water.
Nearby
residents complained about the foul odour.
The elderly indicated that when they were
children, they had bathed in these canals which
then carried clear water. At the field visits it
could be identified that the drainage canals
were not even a bearable sight. At many
locations, public complained about increased
flood inundations experienced year after year.
City dwellers blamed the new property
constructions and disposal of stormwater from
homesteads on to the roads as the cause for
increased inundation and poor drainage. On
many occasions and at many locations it could
be observed that land filling was taking place.
In almost all houses, stormwater drainage from
compounds were directly connected to the road
drains or to the nearby low lands. Houses
constructed at lower elevations had weirs
constructed at the entrances in order to obstruct
the natural flow of water over the terrain.
Construction of boundary walls blocking
natural drains was a common sight. Public
were of the opinion that the remedy to the
flooding of a land is to raise the elevation of
that particular land. If the land filling is not
practicable then the public would seek
excavating or cleaning drains through other
lands or along roads. Those who are unable to
do any of the above, were suffering and were
resorting to making complaints to the public
bodies and officials.
5. Data Collection, Checking and
Filling
5.1. Field Data Collection
Available map and report data (Table 1) had to
be satisfactorily updated and strengthened in
order to identify stormwater management
issues pertaining to the drainage of water from
the catchments within the MMC area. For this
purpose, updated road and canal layouts and
elevation details were identified as necessary to
support the stormwater modelling.
Field work were undertaken to update the road
network. A GPS survey was carried out to
capture the road alignments, points of drainages
that were intersecting the roads, drainage
alignments, culvert locations, and reported
flooding areas. A separate field survey was
carried out to capture the drainage pattern
along the roads, relative elevation of lands with
respect to roads, drainage structure details,
flooding information along the road network,
and nature of built up area along the roads of
the MMC area.
5.2. GPS Survey of Roads and Culverts
These initial field visits and discussions with
public revealed that the Matara Municipal area
ENGINEER
Magellon Triton 2000 and Magellon 600 hand
held GPS mounted on vehicles were used to
14
Table 1 - Map and Report Data Availability
Item
Data Type
Data Details
1
Boundary Maps
Map of Municipality available with the MMC
Map of MMC in the Greater Matara Development Plan
of Urban Development Authority
Survey plan MTR/2000/131 of 31st March 2000 done
under the directive of Surveyor General.
Boundaries of these
maps did not match
with each other
2
Topographic
Data
Survey Department Data made available by the MMC.
Scale 1:10,000, Buildings, Contours and Spot Heights
of the project area
Consisted of Four Sheets
which required edge
matching.
Survey Department Topographic Maps of 1:50,000
Hard copies scanned
and Georeferenced.
Blue Prints, scanned and
Georeferenced
Georeferenced to the
project area
Mosaic prepared and
Georeferenced
3
4
5
6
Engineering
Survey Sheets
Satellite
Imagery
Internet Map
Extract
Project Report
Development
Plan
Engineering Survey(ES) sheets of Survey Department,
1995, Scale 1:5000, spot heights, building and roads
Satellite imagery of 2001 and 2007, in Picture format,
resolution approximately 0.5 meters, color.
Google internet site imagery, screen capture, JPEG
format
Stormwater System Improvement, Urban Development
and Low Income Housing Project, Funded by Asian
Development Bank, Design rainfall values and runoff
coefficients, design drawings.
Plans for the Urban Development Area of Greater
Matara (Volume 1 and 2), UDA development plan,
2005
Guidelines for future development, development
regulations, zonal information and urban planning
targets
Remarks
were captured from both sides of the roads.
Culvert details, flood inundation depths and
durations, availability of boundary walls,
relative elevation of adjoining lands, percentage
of built up area, drainage directions along the
road and drainage directions across the road
were the data collected at each road section of
approximately 100 feet (~30m). Collected field
survey data were checked for position and the
alignment with the use of satellite images. GPS
survey of roads was also used to check data
compatibility.
Random checking of data
capturing sheets verified the accuracy of field
data collection. Survey data collectors were
given a briefing prior to data collection
missions. Each team of data collectors consisted
of three persons with one for recording on the
sheet and the other two to capture information
pertaining to the road and also to make
necessary measurements. Surveyed data were
mapped on to 1:5,000 scale sheets of base
datasets. Road survey was carried out for
almost all roads except short private roads
leading to specific dwelling units. A total of 165
kilometres of roads were surveyed at the said
resolution
track the road network at a longitudinal
resolution of 20 meter. Capturing of important
points such as road beginning and end; junction
and structure locations was supported with
specific waypoints. The GPS data captured
through this methodology were verified with
plots on satellite imagery and through field
testing of sample areas. Culverts for significant
drainage that cross the roads were captured
during the same field survey.
5.3. Road Drainage Survey
A detailed field survey of road network at
approximately 30 meter longitudinal resolution
captured information pertaining to stormwater
drainage
characteristics
and
flooding
information. A datasheet was prepared for the
survey to enable easy recording of data. Some
data collected were based on measurements
whereas some numerical records were based on
visual approximations. Survey data collectors
were trained at both off and on site locations.
The developed datasheet was tested with pilot
surveys. The data sheet used is shown in the
Annex 1. This datasheet was carefully designed
and field tested using numerous trials, to
capture details on a sketch which required
minimum recording during field work. Details
15
ENGINEER
5.4. Drainage Detail and Status Survey
required. A terrain model for the assessment of
drainage in MMC needs to ensure necessary
terrain features for watershed delineation in a
flat terrain. Also this terrain model needs to be
of sufficiently high resolution to capture the
localized flooding issues reported by the public
and the Municipality officials.
Stormwater
generation is highly dependent upon the built
up and non-built up area. Therefore it is also
necessary to capture the land cover information
with adequate representation of the built up
area. Accordingly terrain model development
was commenced with the use of collected
information from maps and reports.
A field survey was conducted to capture the
locations which had attracted public complaints
about poor stormwater drainage.
These
locations were visited and details were collected
including discussions with residents and
collection of location photographs.
Flood
problems were cited with local name of the
drainage canals and
therefore needed
comparison with map data for clarification. A
detailed drainage line field survey was
conducted to assess the field situation of the
drainage canals and to capture other relevant
details.
6.1.1. Spatial Resolution
5.5. Data Checking and Filling
In order to model the stormwater drainage in
the project area, various spatial resolutions were
taken into consideration (Maidment and Djokic
2000). A spatial resolution of 5m was selected
by considering the adequacy of details for a
management plan, and also considering the
computational time required for high resolution
data processing (Dutta, Herath and Wijesekera,
2002). Modelling work were also carried out to
compare 35m and 50m spatial resolutions and it
was noted that such coarse resolutions
prevented the reflection of finer details required
for stormwater modelling in urban flat terrain.
All maps were scanned and georeferenced to
the Kandawala datum enabling the features to
be extracted and were utilized for GIS based
computations. Data layers were then printed to
a scale of 1:5000 and random field checks of
reported information was performed. Data
were checked for consistency, and accuracy. In
case of elevations when absolute values were
not known, a relative comparison of either the
data from the same dataset or from different
datasets was done to identify disparities.
Data showing inconsistency were taken out and
wherever possible missing data were given
reasonable values considering the surrounding
values supported with field visit information.
Where the canals had been rehabilitated but the
elevations were not available, design gradients
in the drawings were used to compute
approximate elevations. In cases where canal
bank elevations were not available, the ground
levels from the topographic maps were taken as
the bank elevations. In case elevations of only
one bank was available, then the elevations of
both banks were considered equal.
The
drainage and stream alignment were checked
closely with the satellite imagery and the traces
were adjusted to suit the tracks shown in the
satellite imagery.
Road traces were also
checked, field verified and adjusted in a similar
manner.
The data checking activity was
combined with field visits to ensure data
accuracy suitable for computations.
6.1.2. Elevation Data Adequacy
An attempt was taken to develop the terrain
model using available spot heights and contour
information of the 1:10,000 scale maps in order
to capture terrain and associated changes that
had taken place since 2002. The available major
drainage canal bed and bank elevations from
design
drawings,
identified
railway
embankment and the flood bunds, and Nilwala
river details etc., were incorporated to prepare
the digital terrain model pertaining to the year
2002. MMC area at a data scale pertaining to
1:10,000 reflected only the general flat terrain
outline with a very limited hilly area in the
western side and in the eastern side of the
project area (Figure 4). The only hilly area
within the flood plain and at close proximity to
the Nilwala river is approximately in the North
Eastern side of the MMC area and this patch of
high land had been used to bridge the two flood
bunds presently protecting the city from a
Nilwala river waters. These mapping efforts
revealed that spot heights and contours of
topographic maps were of a resolution
incapable of reflecting the undulations that
would lead to interpret the present stormwater
drainage problems.
6. Stormwater Modelling
6.1. Terrain Model Development
The assessment of stormwater drainage issues
requires identifying the geography of the MMC
area in sufficient detail and hence a satisfactory
digital terrain model of the study area is
ENGINEER
16
Figure 4 – Digital Terrain Model with Major Features, Canals and the 1:10,000 Elevation Data
features, vegetation and with subsequently
collected field data in order to capture the
streams as at present. A common terrain
depression was then imposed on the stream
lines to suit site conditions so that valley lines in
the terrain could be captured through stream
network burning (Saunders 1999). Relative
measurements from drainage field survey data
were plotted to identify drainage directions
along and across the roads (Figure 5). Road
alignments captured by the project specific
work were also overlaid on the available terrain
and then adjusted to suit the drainage
directions.
.
Watershed generation efforts with digital terrain
models indicated flat triangles due to lack of
data (Maidment 2002).
Hence available
elevation data needed enhancement to carryout
modelling to suit the MMC objectives.
6.1.3. Elevation Data Enhancement
Elevation data available from 1:10,000 maps
were improved using the relative information of
adjacent lands which were identified during the
road drainage survey. Initially natural stream
network alignments were captured from the
terrain mapping carried out using the 1:10,000
maps. These data were then compared with the
satellite image information pertaining to land
Figure 5 - Drainage Directions Identified by Field Surveys
17
ENGINEER
Figure 6- Digital Terrain Model with Major Features, Canals and the 1:10,000 Elevation Data
6.2.1. Terrain Model
The terrain model development for the year
2008, utilized a Triangular Irregular Network
(TIN) model developed to a spatial resolution
of 5m with the spot heights of the terrain, roads
and adjoining lands, streams and major
drainage channels, Nilwala river and the sea
coast details. This terrain model was then
filtered for irregularities initially by digital
checking of data and then by visual and manual
smoothening to capture and remove unrealistic
representations due to data overshoots or
undershoots. The terrain model which was
shown in the Figure 6 represented the finer
details adequate for a watershed assessment
purpose. TIN model development was an
iterative process with the incorporation of
various data resolutions and data corrections to
achieve a realistic representation of the terrain.
Terrain verification was done with checks on
the representation of selected and known
locations.
However several irregularities
indicated the need of higher resolution data for
a smoother terrain model.
The finalized road elevation data were used to
establish the near road terrain that had been
captured from the field data collected as part of
road drainage survey. Incorporation of road
and adjacent drainage direction details enabled
the generation of supplementary spot
elevations. This effort made it possible to
enhance the terrain model digital data from the
original 15,900 spot heights to 34,500 spot
heights. The digital terrain model for the
present data set is shown in Figure 6. The
terrain model with enhanced elevation data
reflected the surface details adjacent to the
roads. Since the road network was quite dense,
the road survey provided sufficient details to
enhance the terrain data to a sufficient
geographic coverage
6.2. Watershed Modelling
Watershed modelling for the stormwater
management options in the MMC area
consisted of two parts. They are (i) Modelling
of terrain to capture the stream network and
carryout watershed delineation using the
digital elevation model in order to assess the
drainage areas that drain the stormwater either
to the sea or to the Nilwala river, and (ii)
Modelling of the flooded area to prioritize the
watersheds for management activities through
the identification of critical parameters and
then carrying out a spatial modelling exercise to
determine priority basins and associated
characteristics.
ENGINEER
6.2.2. Stream Network: Flow Accumulation
Modelling
TIN model developed for the enhanced dataset
pertaining to the year 2008 was converted to a
raster format data of 5 meter grid resolution to
capture elevation information of the terrain.
Slope and aspect maps for the terrain enabled
the generation of the flow direction and flow
accumulation maps.
Generation of flow
direction maps were verified manually using a
sample grid developed with the drainage
survey data. Flow direction model filtered by
18
6.2.3. Modelling for Watershed Delineation
filling the sinks was used to capture the flow
accumulation model for the project area.
Stream network was delineated with various
threshold values for flow accumulation. Trial
and error computations indicated that a
threshold value of 100 showed the best detailed
stream network. Considering the available
dataset and while considering the issues
pertaining to generating streams in a flat terrain
without detailed elevation data, this threshold
value presented a highly acceptable stream
network map. Streams thus generated were
checked for the matching with known terrain
and field data.
Subsequently the stream
network data were compared at selected field
locations and was found reliably representative.
Field testing locations for the stream network
were the flood complaint locations. Stream
network generated through the model on most
occasions followed the trace of the major
drains. However in case of secondary and
tertiary drainages, the matching showed a
deviation from the terrain depressions that
were naturally present in the spot height
dataset and also indicated by the vegetation
observed in the satellite imagery. The stream
network also reflected the drainage direction
concerns mentioned by the public. The Nupe
Ela and the Kithulampitiya (Weragampita)
Canals which were captured from the satellite
imagery and rehabilitation plans deviate from
the generated ones indicating a different flow
direction than that had been anticipated.
During field visits and discussion with public
verified that the model results are closer to the
reality.
Watersheds for the generated streams were
delineated to capture the surface area at the
drainage outlets. Watershed draining points
were either at the Nilwala river banks or at the
MMC boundary. Generation of watersheds
(drainage basins) through the model indicated
3088 individual basins draining either to the
river or to the boundary of the MMC area.
These generated basins were grouped into four
classes based on the surface area. The four
classes are, (i) Area less than 5 ha, (ii) 5-10 ha,
(iii) 10-100 ha, and (iv) >100ha. 3044 of the
basins were having less than 5 ha in extent and
these were located either in the riparian zone of
the Nilwala or located at very close proximity
to the sea shore. These were grouped as one
and the rest of the basins were numbered in the
ascending order of surface area (Figure 7).
Significant number of very small basins
indicated the sensitive nature of the extent
represented by those basins which contribute to
stormwater discharge from the MMC area in a
distributed manner.
Though these basins would not significantly
impact the stormwater drainage within MMC
area because of the nature of their location and
the size, these will contribute significantly for
the water quality issues of the draining water
bodies. The project area showed that there are
three major basins, namely the No 1, 2 and 3.
Figure 7- Watersheds Delineated from the Terrain Model with Streams at a Threshold of 100
19
ENGINEER
7. Modelling Mitigation Activity
Prioritisation
The No 1 is draining to the sea, No 2 is draining
to the Thudawe Ela and the No 3 is draining to
the Niwala river.
Management of Stormwater related issues
pertaining to a large spatial extent would
consist of a significant number of parameters
that vary geographically. In case of stormwater
management, it is necessary to carryout
planning, management, implementation and
monitoring of related activities. In case of
managing the activities at a set of locations with
a wide spatial variation pertaining to the
magnitude of the event and also pertaining to
the geography of the locality, there should be a
tool to facilitate the prioritisation of such
activities. This need arises when the finances or
other resources required for the management
actions gets restricted. Considering the above,
a Geographic Information System (GIS) was
developed and modelling was carried out to
identify the priority of stormwater management
locations that require early attention. In this
work as part of prioritisation, GIS modelling
was carried out to prioritise the reported flood
locations for the incorporation of either
mitigatory activities or effecting preventive
action within the concerned area.
6.2.4. Modelling of Drainage Patterns
Major drainage line network was compared
with the rehabilitated canal system for the
capability to drain stormwater from respective
areas. Generated canal network with enhanced
terrain inputs showed a very good match at
most of the locations. The digital terrain model
showed that terrain elevations and boundary
conditions which were input to the model were
satisfactorily represented in the mathematical
interpolations.
A comparison of drainage
patterns was made with the input canal
network and the generated system. As shown
in the Figure 8, there were several locations
which indicated flow of water generated by the
mathematical model deviating from the major
drainages which were on ground. The changed
locations were verified with the source data
which were input for model computations.
Field visits were also conducted to identify the
issues and needs pertaining to the deviation of
the generated stream network from that
physically existed on ground. Other locations
with respect to the major drainages were well
represented in the terrain model.
Figure 8 - Comparison of Generated Canal Network and Major Drainage Network
ENGINEER
20
Field surveys were carried out in the project
area to identify the issues pertaining to the 14
locations cited by the MMC officials. Apart
from these, the project specific flood survey
along the road network identified 577 locations
which were captured as 30 meter stretches of
flooded road sections. This indicated that
approximately 17 kilometres of road length in
the MMC area were experiencing floods.
Surveys also captured the inundation depth
and duration at each location. Each of these
reported flood locations was also studied in
detail to identify engineering options available
to mitigate the flooding impacts at each
location.
Flood Location
Density (m/100
sqm)
Building
Density
(area/unit area)
Inundation
Depth (ft)
Class Value
7.1. Priority Locations for Mitigation
Inundation
Duration (Hr)
Table 2 - Classification of Parameters to Flood
Area Prioritisation GIS Model
GIS modelling was then carried out to perform
a spatial aggregation in order to identify
prominent sections of the MMC area where the
stormwater
drainage
paths
mostly
concentrated.
1
<1
<0.5
<0.2
< 0.001
2
1-6
0.5-1
0.2-0.4
0.001-0.004
3
6-12
1-2
0.4-0.6
0.004-0.007
4
12-24
2-4
0.6-0.8
0.007-0.012
5
>24
>4
>0.8
>0.012
The final result was reclassified to identify the
region according to four zones, namely, Very
High Priority, High Priority, Critical and Less
Critical.
The reported flood points were
classified according to the above classes and
there were 34, 58, 90 and 395 locations in each
of the Very High Priority, High Priority, Critical
and Less Critical classes respectively (Figure 9).
Model output locations were verified with the
MMC identified flood locations and the results
indicated a very good match of the results
corresponding to the complaints received.
There were many other locations that were
identified during the field survey and the
model outputs revealed that some such
locations also require priority attention.
A spatial model was developed to identify the
flooding areas that have most impact on public.
The GIS based spatial decision making tool was
based on the objective function which
considered that the high impact flood locations
are those which are (i) at places mostly used by
the public, (ii) which are in clusters of other
flooding points, which have (iii) significant
inundation depths and (iv) prolonged
inundation durations. Geographic data layers
representing these were then overlayed to
obtain the impact zoning map. In the analysis,
the concentration of public was considered to
be represented by the building density and the
flooding location clusters were computed from
the density of road lengths that were
undergoing floods. Inundation depths and
inundation time were assigned to each road
element. Each layer was classified in to five
classes and each class were assigned numerical
values ranging from 1-5. The classes assigned
were based on modified natural breaks of the
geographic dataset.
Values used for
classification are shown in Table 2.
7.2. Priority Locations for Prevention
A stormwater manager needs to identify
locations which would create the most impact
with regards to stormwater generation. Since
stormwater generation is directly proportional
to the land changes, it is necessary for a
manager to identify the locations that would
most likely influence the critical flooding points
due to changes affected to the land in the
concerned area. Therefore it is important to
identify the critical watersheds and their land
cover parameters in order to assess and
monitor
any
forthcoming
development
activities. This identification would also enable
a manager to affect restoration programmes
depending on the status of each watershed.
The Geographic Information System developed
for the study enabled the extraction of the
watersheds pertaining to the critical flood
localities and to carry out a comparative
analysis enabling a stormwater manager to take
preventive (or restorative) decisions regarding
Model output ranked the spatial extent of the
MMC according to the criticality of a particular
area determined by the above four parameters.
This GIS enables a manager to filter the results
to select a number of sites according to a
priority criterion.
21
ENGINEER
Figure 9 - Priority Locations out of the Reported Flooding Locations
flood points. 29 watersheds were identified as
critical watersheds that were contributing to the
priority flooding area. Landuse values were
extracted in each of the watersheds for the
years 2002 and 2008 in order to assess the
change to built-up area in each watershed.
the development activities on a watershed
basis.
Another common problem in urban area is the
changes to land parcels which result in loading
the road drainage network with stormwater
that otherwise would have either got infiltrated
at individual compounds or would have
drained in another direction. Such property
changes would require a manager to strengthen
the stormwater drainage infrastructure in that
particular area and hence it is important to
identify the locations where most of the
roadways coincide with the waterways.
Critical watersheds showed a marked
difference in the high runoff land use
percentage when compared with the same for
the Matara MMC in general. The high runoff
land use for 2008 in critical basins was 41%
compared to the MMC average High runoff
land use value of 28%. Results points to
another interesting information. The 2002 high
runoff land cover/use percentage in critical
basins is equal to the 2008 high runoff land use
average for the MMC area. This shows that rest
of the area would also become rapidly
urbanised similar to the critical basins.
Therefore the MMC need to effect early action
to establish proper stormwater management
strategies.
The present work also carried out a GIS
modelling effort to identify the geographic
locations where such road sections are mostly
concentrated.
Once such locations are
identified a manager could assess the
infrastructure at those areas and carryout
strengthening where necessary. These two GIS
modelling activities and results are described
below.
7.2.2. Critical Drainage Network
7.2.1. Critical Watersheds
The most common complain encountered
during the field visits was that the stormwater
from individual allotments are drained directly
on to the road or to road drains and as a result,
the roads become waterways.
In such
occasions the road drains have to be expanded
to a sufficient capacity for the disposal of
stormwater. This was very well reflected in the
generated streamline network.
At those
locations the road drains should have a good
The zones which were identified during the
priority flooding areas showed 26 locations
with varying importance and pertaining to
three zones. These points were demarcated on
the terrain model to capture the stormwater
drainage lines contributing to the flooding at
the identified priority points. Then the terrain
model was used to capture the watersheds that
need to be managed to mitigate the critical
ENGINEER
22
connectivity to the nearest drainage line while
ensuring the capacity of the road drain to
dispose the water without overflowing on to
the road. Computations were carried out to
capture road sections where the streamlines fall
on the road alignment. Such locations were
captured through the model and were verified
manually with on screen comparisons
incorporating field observations. The model
results provide valuable information for a
manager to carryout stormwater drainage
management within the MMC area. Model
results have to be filtered to ensure that the
problem causing sections are highlighted.
Therefore from the model outputs it is
necessary that the road sections which are
already in operation with a suitable drainage
canal by the side needs to be filtered from the
rest of the area. Nupe Ela is one such canal.
Model computations filtered the major drainage
canals with roads and the results for roads that
coincide with significant streamlines.
The GIS computations revealed that a total of
75 km long road sections out of an entire road
network of 177km, carry stormwater along the
roads. This is mainly because the public
carryout earth filling to maintain their land
elevations higher than the roads so that the
stormwater could be directly discharged to the
road drain. This type of behaviour in urban
areas changes most of the roadways to
waterways. Coinciding of waterways with
roadways was shown approximately as 42 % of
the total road length for the MMC area. The
diversion of household drainage water directly
to the street drains appears as the greatest
challenge for the stormwater manager. Spatial
variation of the density of such critical road
sections were calculated considering the lengthweighted distribution of such sections in the
vicinity. Since these density maps indicate the
sections common to stream network and the
roads, a manager should combine this
information with the slope of terrain to separate
the stormwater management issue from
drainage and erosion.
The identification of road sections that coincide
with stormwater streamlines provides a
manager with the capability to capture critical
road sections and then to plan and effect
stormwater management actions for critical
areas (Figure 10).
7.3. Runoff Characteristics
Stormwater generation depends on the increase
of impervious areas in a particular land extent.
In an urban area, the runoff generation
increases mainly due to construction of
buildings, roads and due to paving of land
surfaces.
These results from the present work indicate
only the sections that are longer than 30 meters.
However depending on the management
requirements
the
computations
on
a
Geographic Information System (GIS) can
highlight the road sections of a desired length.
Therefore a manager could carry out work
prioritisation in case of resource limitations.
In this study calculations were done to
determine the building and road area
Figure 10 - Density of Road Sections that Coincides with the Drainage Network Generated the DTM
23
ENGINEER
in the two datasets corresponding to 2002 and
2008. Roads which were in the 1:10,000 scale
had subjected to many changes. Roads and
Buildings of each year was overlaid on the
landuse map of 2002 and each land use type
was
extracted
using
the
Geographic
Information System. These land use categories
were then classified into two broad groups as
high runoff generating and low runoff
generating. Watersheds in general showed an
increase in the impervious land cover
indicating a high runoff land use percentage of
approximately 28% as against a value of 17%
for high runoff land cover corresponding to the
year 2002. The land use changes of watersheds
incorporating average runoff coefficients of 0.7
and 0.25 for high runoff generating and low
runoff generating land uses respectively show
that the generation of stormwater has increased
by 14% from the year 2002 to year 2008. The
same computations for a high runoff value of
0.8 and with the same low runoff value of 0.25
would increase the runoff by 16%.
indicated that the functioning of the drainage
canals at certain points need detailed studies in
order to identify the appropriate drainage canal
trace, size, slope and other parameters.
The stream network generation by the model
follows a system which initially identifies the
flow directions and then picks up sinks where
pools of water results in sets of cells with
undetermined flow directions. In the model
such pools are filled and then calculations are
carried out to identify the flow directions to
compute the stream network. An iterative
process fills the terrain sinks to produce the
final stream network.
Stream network
generated by the model reflects the functioning
of the drainage direction once the pools are
filled and hence the direction in which the flood
waters would recede. However in reality,
when the rains are inadequate then the flow
will stop at the sinks creating pools of water.
This means that even if the flood is receding
there will be pools of water at the locations
where the terrain indicate sinks. A close
scrutiny of the major canals identified several
key issues pertaining to the major canals. Their
locations are shown in Figure 8 and
descriptions are given thereafter.
8. Management Recommendations
The MMC stormwater drainage system which
has many similarities to other urban areas
along the coast of Sri Lanka lies on a flat terrain.
The main common characteristic is that the
public directly discharge stormwater from their
properties to the roadway drains. Paved areas
in the city are increasing with time due to
pressure of dwelling needs in the urban
community.
The present work of stormwater system
analysis indicated the options for stormwater
management. The options can be broadly
divided into long term and short term. The
long term ones would be the preventive actions
and good management practices whereas short
term ones would be to ease the problems which
are presently being experienced.
No.1: In the generated stream network, the
Nupe canal is shown as two sections flowing in
two directions. A major section is flowing
southwards and a smaller section is draining
towards the northern boundary.
This
behaviour shows that even when the surface
depressions had been incorporated for the
terrain model indicating the presence of a canal,
the functioning in reality is different or in other
words the flow in the canal is not in one
direction. Terrain model has indicated that
canal elevation inputs have not supported
water flow through the canal alignment.
However it is noteworthy that the terrain
model represents the behaviour of the canal as
described by the officials and the public. Site
visits also revealed the presence of a regulator
to raise the parent canal head so that the water
could flow along the Nupe canal in the
direction from North to South. This regulator
does not appear to be in operation as at present.
Also the recent constructions raising the bed
level of the Nupe canal indicates that for the
regulator to be useful then the water level
would have to be raised to significant
elevations.
The present work led to the identification of
problem areas and goes on to recommend how
such areas could be dealt with and also how a
manager could determine the areas of priority.
8.1. Short Term Solutions
8.1.1. Major Drainage Lines
The mathematical modelling and field work
identified that the generated major streams
reflected a different behaviour when compared
with what had been anticipated during the
previous designs and construction.
This
ENGINEER
No 2: In the Kithulampitiya canal too there is a
section which behaves similarly to Nupe Ela.
24
The physical inputs show a canal construction
but the terrain model shows flow in two
separate directions. This indicates the problem
when attempting to drain water through the
Kithulampitiya canal in the direction of
Thudawa.
behaviour and then to execute engineering
solutions.
The generated stream network was categorised
according to stream order of tributaries and this
enables a manager to initially identify the
problem area in the high stream order locations
and then to move towards less order streams.
In case the terrain details that have been fed to
the model are of finer resolution then it is
possible to satisfactorily capture the drainage
issues even in the level 1 stream order canals.
The model has identified locations of
incompatibility in the existing drainage system.
This enables the stormwater manager to
commence action to carryout detailed studies
and identify locality specific engineering
designs for implementation as indicated above.
No 3: Kithulampitiya canal is expected to drain
water from regions inside the flood bunds at
the western edge of the MMC area to the
Thudawe Ela at the Northern end. However
the model indicates that instead of flowing
along the canal trace the drainage flow deviates
to lower terrain at a location close to the
Mathugala Bund (Wella). Water from the
Harischandra properties also drain towards the
Mathugama Wella contradicting the flow
direction expected from the canal.
These
behaviour matches with the observations of the
public and the complaints made by them
indicating that the canal levels need checking
because it appears that the canal has been
constructed to push the flow in the upstream
direction. The terrain model developed for the
study has successfully indicated the geographic
features that support the drainage of
stormwater along this canal system.
8.1.2. Identified Flooding Locations
Each of the flooding locations identified during
field surveys and at discussions with MMC
officials were inspected to identify the best
management options. The observations were
studied with the terrain model and the
generated streamlines to provide options for
easing the stormwater drainage problem as at
present. Engineering field inspections of the
surrounding environment were made at each of
the identified flood complaint locations. At
most occasions it was identified that either the
natural stormwater discharge path had been
obstructed by a land or had been over loaded
due to large stormwater contributions from
surrounding urbanised land. The proposals
whilst using site inspection details and satellite
imagery recommend the most suitable path for
stormwater disposal. However engineering
surveys at the recommended vicinity should be
carried out prior to construction work.
Importantly, long term solutions should be
incorporated for each and every critical location
to ensure the sustainability of these solutions.
Specific solutions were given for the identified
164 locations and a sample is shown in the
Figure 11.
No 4 at Piladuwa Ela shows two section of the
Ela draining in two separate directions while
having a ridge close to the railway line. This
indicates that the elevations do not permit
water to flow along the canal though the ends
are linked to form a single canal. The stream
network generated by the model clearly reflects
the behaviour of the canal system as described
by the public during field surveys.
At Nos 5,6, 7 and 8 too, the flow directions
deviate from the physically excavated canal
alignments. This indicates that the terrain does
not facilitate the flow directly into the canals
which have been rehabilitated. It also means
that at the deviations there will be locations of
flooding and water collecting pools prior to
draining excess water through the stream
network as identified by the model.
This detailed comparison of model outputs
with the physically existing canal system points
to the problem locations which are having
difficulties in draining stormwater to ensure
flood mitigation. Therefore the terrain model
output has indicated its strong potential to
capture critical locations in the main drainages
so that a manager could commence action to
pursue detailed engineering studies in the
model identified locations to identify the exact
8.2. Long Term Solutions
8.2.1. Development Planning and Approval
The study identified that the major issue for
stormwater management in the long run would
be to control the stormwater generation from
the urban lands and also to control the
simultaneous discharge of the stormwater to
the street drainage network.
Blockage of
natural drainage paths were also identified as a
25
ENGINEER
Figure 11- Specific Short Term Solutions Provided through Field Verifications
sufficiently detailed dataset is preferred. In the
present work many rational approximations
were done to overcome a data scarce situation
and time barriers for carrying out detailed data
capturing. MMC needs to commence collecting
requisite data in digital form to support long
term sustainability of stormwater drainage
management.
Present study also demonstrated the methods
and availability of tools to prioritise the
locations that need to be attended in case of
resource limitations.
This included the
prioritisation of the flooding locations using a
geographic information model that considers
four parameters namely, the building density,
flooded area density, flood duration and depth
of inundation. These types of tools should be
available with trained staff capable of
providing necessary outputs to enable
satisfactory stormwater management. MMC
should take necessary steps to develop
management tools to prioritise the stormwater
management activities and also to monitor the
status of the associated watersheds.
Staff
training would be an essential component with
regards to sustainability of the use of tools.
major cause for the change of drainage
directions and this has to be carefully
controlled to ensure that no blockage of
drainage water would take place leading to
enhanced flood situations. Construction of
walls has prevented natural flow of water
across boundaries. It is important that the
MMC identifies a methodology to ensure that
the development plan approval procedure in
future would satisfactorily cater to the above
needs. The best option would be to impose
conditions and to incorporate the needs that
have to be fulfilled when granting the
development approval. Study revealed the
problems caused due to obstruction of natural
drainages. Though the short term solutions
have been proposed, the MMC should take
adequate steps to demarcate and declare
reservations for natural drainages so that the
stormwater could be properly managed in the
years to come.
8.2.2. Detailed Database and Tools
Stormwater management monitoring and
decision making requires a detailed database.
The database should include a high resolution
elevation dataset, land cover details, property
boundaries, stream network, drainage canals
etc. The present work in order to assess the
situation and propose solutions used various
techniques such as field surveys and
interpretation of satellite imagery. However a
ENGINEER
8.2.3. Engineering Solutions and Guidelines
In the country the common practise is to
discharge the stormwater from one’s own
property to the closest road drain. This has
been identified as the primary cause for
26
flooding and especially flash flooding. The
MMC needs to ensure that such discharges are
carried out within engineering limits imposed
by considering the load that can be undertaken
by a reasonable drainage infrastructure system.
Since the increase of stormwater on the
drainage system requires financial inputs to
sustain the infrastructure, it may be appropriate
for the MMC to consider imposing a levy on
those properties that have not constructed
stormwater retention or detention facilities.
In order to ensure that the public are properly
assisted to incorporate stormwater retention
and detention, it is necessary that the MMC
establishes guidelines for the developers as to
what should be constructed, what type and size
should be used etc. It is recommended that a
handbook for the design and building of such
facilities should be made available at the MMC
for public use.
6.
7.
8.
9. Conclusions
1. The present study carried out systematic
field and desk studies and clearly identified
the long term and short term solutions for
rational stormwater management.
2. Watershed delineation efforts for flat terrain
requires high resolution elevation data, but
in the absence of such survey data, it is
possible to use field drainage surveys to
create representative digital elevation
models using 1:10,000 topographic map
elevation
data
with
satellite
map
interpretations.
3. Simple Geographic Information System
incorporating flood inundation level,
inundation
duration,
proximity
to
populated area, and nearby flood location
density enabled the prioritisation of critical
locations for management interventions.
4. The present work compared the terrain, the
generated stream network, the constructed
drainage canals and carried out field
inspection of specific
locations to
successfully propose practically feasible
short term solutions for Stormwater
drainage management
5. The generated stream network developed
for the present situation confirmed that the
road drains have become the major
drainage water disposal route as a result of
landfills and drainage closures. The GIS
9.
10.
enabled the identification of such road
section clusters enabling a manager to
propose adequate interventions.
Eight critical locations in the major drainage
network
were
identified
and
recommendations were made to ensure
proper drainage of stormwater using the
existing drainage canal network.
Individual flooding locations which were
identified during field surveys were
grouped in to 164 localities. These locations
were inspected, analysed for suitable
options, and solutions
for proper
stormwater disposal have been made while
taking efforts to address each individual
case and its surroundings.
The flood locations identified were spatially
modelled using a GIS to identify 34 No of
Very High Priority, 58 No of High Priority,
and 90 No of Critical flooding locations for
implementation of mitigatory action
especially in resource limited situations.
29 Critical watersheds pertaining to the
priority given to flooding locations were
identified with its landuse components
facilitating a stormwater manager to
monitor and control the development
activities in a sustainable manner.
A Total of 75 kilometres of road sections
which is approximately 42% of the MMC
roads, that carry significant stormwater
either in its side drains or on the road
surface were identified, mapped and
prioritised for effecting suitable stormwater
management programs.
References
27
1.
Maidment, David and Djokic, Dean “Hydrologic
and Hydraulic Modelling Support with
Geographic Information Systems”, Maidment,
David
and
Djokic,
Dean
Editors,
Environmenatal Systems Research Institute, Inc.,
Redlands, California, ISBN 1-879102-80-3, 2000.
2.
Dutta D., Herath, S., Wijesekera, N.T.S.,
“Understanding The Impacts of Spatial Data
Resolution
in
Flood
Risk
Modelling”,
Proceedings of the International Symposium on
Geoinformatics for Spatial Infrastructure
Development in Earth and Allied Sciences,
GISIDEAS, Hanoi, Vietnam, September, 2002.
ENGINEER
3.
Saunders, William., “Preparation of DEMs for
Use in Environmental Modelling Analysis”,
ESRI User Conference, July 24-30, San Diego,
California, 1999.
4.
Mays, Larry. W., “Floodplain Management,
Water Resources Engineering”, Replika Press
Pvt. Ltd., India, ISBN 9812-53-116-5, 2004
ENGINEER
5.
28
Maidment David., “Arc Hydro, GIS for Water
Resources”,
David
Maidment
Editor,
Environmenatal Systems Research Institute, Inc.,
Redlands, California, ISBN 1-58948-034-1, 2002.
Annex 1 - Data Sheet for Field Data Collection During the Road Drainage Surveys
29
ENGINEER
SECTION II
Impact on Existing Transport Systems by
Generated Traffic due to New Developments
K. S. Weerasekera
Abstract:
Impact of future traffic generated due to a development activity was forecasted on
available information at the time, for a proposed building complex in Colombo taking it as a study
sample. Through this study a complete pre-assessment of ‘in-coming’ and ‘out-going’ vehicles which
generates due to the proposed development was conducted before hand, and observed how it would
affect the surrounding road network in future. Then it was also observed that, whether the impact was
within, or outside the tolerable limits.
Once the proposed development was completed, and after few years of its operation, a validation was
carried-out to assess the findings of the initial study which was conducted at the proposal stage of the
development. The validation study results confirmed that the traffic generated due to the
development was within tolerable limits of the surrounding road network as indicated in the initial
study; hence the initial predictions were valid. Study also highlights the importance of development
of local norms for traffic generating factors for different types of developments.
Keywords:
1.
Traffic Impact Assessments, TIA, Traffic Generation, Validation of TIA
Introduction
The impact of newly generated traffic due to a
proposed development activity was studied
taking the proposed administrative building
complex
for
Aitken
Spence
Property
Development Ltd. (proposal stage in 2005) at
Vauxhall Street in Colombo 2 as a study
sample. Through the initial study conducted in
2005 it was intended to carry-out a complete
assessment of how ‘in-coming’ and ‘out-going’
vehicles due to the proposed development was
to affect on the surrounding road network, and
see whether the impact was within tolerable
limits or not. Importance of this type of studies
is emphasized in [1], and a validation of the
initial study predictions is further conducted to
confirm the initial study findings.
Proposed
Development
Figure 1 – Site Layout
2.
As indicated in Figure 1, the proposed
administrative building (in 2005) for Aitken
Spence Ltd., if constructed will add
considerable amount of traffic during busy
hours, to the already heavily trafficked union
place, especially towards Hyde Park corner.
Hence before granting approval for the
building an initial traffic study was conducted
in 2005 when the building was at its proposal
stage. Initial study was conducted with the
view to observe, if when constructed how it
was going to affect the surrounding road
network. Now in 2011, having been constructed
and in full operation, a validation was carriedout to assess the initial study findings.
Methodology
During the initial study site surveys were
designed and specified to observe the existing
traffic pattern in the surrounding road network,
and it was expected to look into, how the
Eng. (Prof.) K. S. Weerasekera, BSc Eng
(Moratuwa), MEngSc (UNSW), PhD (UNSW), FIE (Sri
Lanka), CEng, IntPE(SL), MIE (Aust), CPEng, MIHT
(UK), MASCE, Professor in Civil Engineering,
Department of Civil Engineering, The Open University
of Sri Lanka.
31
ENGINEER
proposed development was going to affect the
surrounding road network in the future in
advance, once the development is in full
operation. Through traffic studies conducted
along Union place, Darley road, and Vauxhall
Street, morning, mid-day and evening peaks
were identified. They were 8:00am to 9:00am,
12:45pm to 1:45pm, and 4:45pm to 5:45pm
respectively. Morning peak was not crucial
since, once constructed the offices housed by
the building will only come into operation after
9:30am. Hence the more crucial peaks of
12:45pm to 1:45pm, and 4:45pm to 5:45pm were
studied in detail.
traffic surveys were conducted (i) Manual
Classified Counts (MCC) of 12 hour duration
(7:00am to 7:00 pm) on the surrounding roads
(at mid-blocks D1, D2 and D3, shown in Figure
2) and (ii) Turning Movement Surveys (TMS) at
the intersections A, B and C shown in Figure 2.
3.
Calculation and Analysis
For the traffic surveys vehicles were
categorized into 5 typical types as described in
Table 1 for the calculation of traffic flows, and
subsequently to check with service flow
capacities. Passenger Car Unit (PCU) factors
for each category for different lane conditions
on level terrain are indicated in Table 1.
In view to study the existing traffic situation in
the surrounding road network two types of
Table 1 – Types of Vehicles and Relevant Passenger Car Unit (PCU) Factors
Vehicle Type
PCU
2-way, 2-lane Multi-lane
Type 1 – Passenger cars, jeeps, SUVs, pick-ups, single and double
cabs and small vans.
Type 2 - Three-wheelers
Type 3 – Small trucks, large vans and small buses
Type 4 – Medium trucks and standard buses
Type 5 – Heavy trucks, large buses, tourist coaches and multi axle
vehicles
(Source: [2] Geometric Design Standards, RDA )
Figure 2 – Site Layout and Turning Movements
ENGINEER
32
1.0
1.0
0.5
2.0
2.2
2.8
0.5
1.5
1.7
2.2
3.1
Generated Traffic
Although it is known that traffic generation is
a function of the land use and type of
development, some broad assumptions had to
be made due to lack of local norms [3]. Hence
following method for traffic forecasting was
adopted in the study.
The proposed development will have 100
parking bays (as per UDA guidelines [5]), and
this value was used for traffic generation
computations. Under the broader assumptions
that when the proposed development is in full
operation, that every 15 minutes 25% of bays
gets a new vehicle and the earlier vehicle
leaves the bay. This is probably an estimation
of the higher order, since in practice chances of
vehicles parked for longer durations are more,
with vehicles of the office staff and
customers/visitors spending more than 45
minutes at business is higher. Hence a higher
factor of safety is assumed. Therefore every 15
minutes 25 vehicles arrive and 25 vehicles
depart the development area.
Figure 3 – Vehicular ‘in’ and ‘out’ Movement
3.2
Current Traffic (i.e., in 2005)
Current peak hour traffic movements during
day-time and evening peaks at intersections A,
B and C are shown in Figures 4 and 6.
3.3
Future Traffic
Based on current and generated traffic due to
proposed development, future traffic was
computed. Peak hour movements for future
traffic during day-time and evening at
intersections A, B and C are shown in Figures 5
and 7.
Figure 3 indicates ‘in’ and ‘out’ movements of
vehicles around the development area.
Assuming a 50:50 directional split, Vauxhall
Street will have an additional traffic of 100 vph
(maximum).
Figure 4 – Current Day-time Peak Flows
Figure 5 – Future Day-time Peak Flows
Figure 6 – Current Evening Peak Flows
Figure 7 – Future Evening Peak Flows
33
ENGINEER
Table 2 – Peak Hour Traffic Flow Projections (Turning Movements) –
Intersection at Hyde Park Corner (A)
Peak
Hour
Intersection - A
Intersection
Daytime
Peak
(12:45 to
13:45)
Evening
Peak
(16:45 to
17:45)
Movement
A1
A2
A3
A4
A5
A6
A7
A8
A9
Current Peak
Hour Volume
62
889
10
11
52
467
310
846
265
Share contribution
from development
Added volume
Predicted Peak
Hour Volume
% increase
Current Peak
Hour Volume
Share contribution
from development
Added volume
Predicted Peak
Hour Volume
% increase
0
0
0.005
0.0199
0.1134
0.8667
0.133
0
0
0
62
0
889
0
10
1
12
6
58
43
510
7
317
0
846
0
265
0%
76
0%
886
3%
6
9%
11
11%
102
9%
680
2%
308
0%
826
0%
300
0
0
0.005
0.0199
0.1134
0.8667
0.133
0
0
0
76
0
886
0
6
1
12
6
108
43
723
7
315
0
826
0
300
0%
0%
4%
9%
6%
6%
2%
0%
0%
Table 3 – Peak Hour Traffic Flow Projections (Turning Movements) –
Intersection at Vauxhall Street / Dawson Street (C)
Peak
Hour
Daytime
Peak
(12:45 to
13:45)
Evening
Peak
(16:45 to
17:45)
4.
Intersection - C
Intersection
Movement
C1
C2
C3
C4
C5
C6
Current Peak
Hour Volume
123
413
263
72
125
163
0
0.52
0.833
0.1671
0.1395
0
0
123
26
439
42
305
8
80
7
132
0
163
0%
187
6%
586
16%
270
12%
64
6%
174
0%
126
0
0.52
0.833
0.1671
0.1395
0
0
187
26
592
42
312
8
72
7
181
0
126
0%
5%
15%
13%
4%
0%
Share contribution
from development
Added volume
Predicted Peak
Hour Volume
% increase
Current Peak
Hour Volume
Share contribution
from development
Added volume
Predicted Peak
Hour Volume
% increase
Initial Study Recommendations
on the surrounding road network during the
peak hours is indicated in Figures 5, 7 and
Tables 2 and 3.
The proposed development will have an
impact of additional 100 vph at its peak and
lesser contribution during other times to
adjoining Vauxhall Street and the surrounding
road network. The effect of this contribution
ENGINEER
Table 2 indicates the increased vehicular flows
of different turning movements during
daytime and evening peak hours, at adjoining
34
importance of development of local norms to
indicate traffic generation factors for different
types of developments separately, as discussed
in [3]. Due to lack of local norms to indicate
accurate traffic generation factors for different
types of developments such as office
complexes, business establishments, hotel
developments, hospitals, recreational areas
etc., it is hard to forecast accurate future traffic
figures that will generate due to the proposed
new developments. Hence further studies on
traffic generation due to new developments,
and development of local norms on traffic
generation for different types of developments
are recommended.
intersections
due
to
the
proposed
development. The overall percentage increase
on traffic on Vauxhall Street is around 9%,
which is below the threshold level, for twoway roads that are operating under service
flow capacity [4]. The percentage increase on
traffic on other surrounding roads is much
lesser and impact is minimal, so that the
proposed development could be granted
approval subjected to the satisfaction of other
requirements laid in the UDA guidelines [5].
5.
Validation
A validation study was conducted in March
2011, two years after completion of the
building and when it is in full operation.
Vehicle entry and exit gates were monitored
over a period of 5 days from Monday to Friday
during an average week (Appendices A and
B).
Acknowledgment
Author wishes to acknowledge Mr. S.
Kokulkanth and his team of surveyors who
assisted in field surveys, and also thank Mr.
Sampath Godawitharana DGM Aitken Spence
Property Development Ltd., for his kind
assistance during validation stage.
After recording vehicles entering and exiting
times at both entry and exit gates for 5 days
from 7:00AM to 7:00PM, five week-day
averages of entry and exit times were
computed and figures were listed at 15 minute
intervals (Appendices A and B).
References
It was observed that highest ‘in and out’
movement took place in the morning between
8:00AM to 9:00AM which included 81 entering
vehicles and 39 exiting vehicles from the
premises. That will lead to 120 additional
vehicles on Vauxhall Street during morning
peak, an increase of 20 vehicles than initial
prediction of 100 vehicles in the traffic impact
assessment.
Similarly during the evening, between 4:45PM
to 5:45PM, 84 vehicles exited while 35 vehicles
entered. Comparatively mid-day in and out
movements were not as higher as expected in
the initial study. That will lead to 119
additional vehicles on Vauxhall Street during
evening peak. This is an increase of 19 vehicles
than initial prediction of 100 vehicles in the
traffic impact assessment.
6.
Conclusion
The outcome of the validation study of the
initial traffic impact assessment emphasise the
35
1.
Weerasekera, K. S., An Introduction to
Traffic Engineering, Incolour (Pvt) Ltd,
Colombo, 2009.
2.
Geometric Design Standards of Roads:
Road Design Manual, Road Development
Authority of Sri Lanka, 1998.
3.
Weerasekera, K. S., ‘Some Problems
Associated with Development of Traffic
Impact Assessments in Developing
Countries’, Proceedings of the First Brunei
International Conference on Engineering and
Technology, Institute of Technology Brunei,
Bandar Seri Begawan, Brunei Darussalam,
9-11 October 2001.
4.
Salter, R. J and Hounsell, N. B, Highway
Traffic Analysis and Design, MACMILLAN
Press Ltd, London, 1996.
5.
UDA Planning and Building Regulations,
Vol. 2, City of Colombo Development Plan,
Published
by
Urban
Development
Authority, Ministry of Housing and Urban
Development Authority, Sethsiripaya,
Battaramulla, March 1999.
ENGINEER
Appendix - A
Entry Gate - (Gate No: 1)
Time
7:00 - 7:15
7:15 - 7:30
7:30 - 7:45
7:45 - 8:00
8:00 - 8:15
8:15 - 8:30
8:30 - 8:45
8:45 - 9:00
9:00 - 9:15
9:15 - 9:30
9:30 - 9:45
9:45 - 10:00
10:00 - 10:15
10:15 - 10:30
10:30 - 10:45
10:45 - 11:00
11:00 - 11:15
11:15 - 11:30
11:30 - 11:45
11:45 - 12:00
12:00 - 12:15
12:15 - 12:30
12:30 - 12:45
12:45 - 13:00
13:00 - 13:15
13:15 - 13:30
13:30 - 13:45
13:45 - 14:00
14:00 - 14:15
14:15 - 14:30
14:30 - 14:45
14:45 - 15:00
15:00 - 15:15
15:15 - 15:30
15:30 - 15:45
15:45 - 16:00
16:00 - 16:15
16:15 - 16:30
16:30 - 16:45
16:45 - 17:00
17:00 - 17:15
17:15 - 17:30
17:30 - 17:45
17:45 - 18:00
18:00 - 18:15
18:15 - 18:30
18:30 - 18:45
18:45 - 19:00
ENGINEER
14th Mar
Mon
0
16
5
15
37
23
22
21
12
8
17
9
4
11
13
17
14
4
16
9
12
13
14
10
10
6
11
2
11
11
13
4
19
10
11
16
11
10
6
4
13
12
6
2
6
4
4
0
15th
Mar
Tue
6
8
18
3
18
22
7
17
22
3
13
13
5
21
4
18
10
14
5
10
2
4
21
9
11
12
7
6
13
12
12
10
6
8
8
16
12
8
13
12
11
23
7
3
1
1
1
2
16th
Mar
Wed
1
9
14
29
16
29
15
9
19
20
8
6
10
13
6
9
8
11
8
16
7
17
12
7
11
11
7
10
11
8
9
13
8
6
6
10
10
12
8
9
9
13
7
4
3
1
4
4
36
17th
Mar
Thu
4
10
8
11
29
10
24
23
17
8
10
15
11
11
8
9
7
14
7
19
7
7
10
6
3
20
9
10
5
6
11
0
11
10
5
11
8
1
6
6
5
5
1
2
4
3
2
2
18th
Mar
Fri
0
9
14
8
23
20
17
21
16
17
6
11
11
9
15
7
8
15
11
12
11
10
18
8
16
5
11
5
5
2
10
8
10
2
11
8
7
6
10
6
9
12
3
6
3
1
5
5
Average
Hourly
Total
2
10
12
13
25
21
17
18
17
11
11
11
8
13
9
12
9
12
9
13
8
10
15
8
10
11
9
7
9
8
11
7
11
7
8
12
10
7
9
7
9
13
5
3
3
2
3
3
38
60
70
76
81
73
64
57
50
41
43
41
42
44
42
42
44
42
41
46
41
43
44
38
37
35
32
34
35
37
36
33
38
37
37
38
33
33
38
35
31
25
14
12
11
Appendix - B
Exit Gate - (Gate No: 2)
Time
7:00 - 7:15
7:15 - 7:30
7:30 - 7:45
7:45 - 8:00
8:00 - 8:15
8:15 - 8:30
8:30 - 8:45
8:45 - 9:00
9:00 - 9:15
9:15 - 9:30
9:30 - 9:45
9:45 - 10:00
10:00 - 10:15
10:15 - 10:30
10:30 - 10:45
10:45 - 11:00
11:00 - 11:15
11:15 - 11:30
11:30 - 11:45
11:45 - 12:00
12:00 - 12:15
12:15 - 12:30
12:30 - 12:45
12:45 - 13:00
13:00 - 13:15
13:15 - 13:30
13:30 - 13:45
13:45 - 14:00
14:00 - 14:15
14:15 - 14:30
14:30 - 14:45
14:45 - 15:00
15:00 - 15:15
15:15 - 15:30
15:30 - 15:45
15:45 - 16:00
16:00 - 16:15
16:15 - 16:30
16:30 - 16:45
16:45 - 17:00
17:00 - 17:15
17:15 - 17:30
17:30 - 17:45
17:45 - 18:00
18:00 - 18:15
18:15 - 18:30
18:30 - 18:45
18:45 - 19:00
14th
Mar
Mon
0
5
18
2
12
12
10
12
4
17
8
13
9
9
6
15
17
21
15
11
12
15
12
1
1
0
4
13
3
19
11
3
4
8
17
1
20
15
5
10
9
22
26
26
17
14
17
2
15th
Mar
Tue
2
5
4
7
3
8
10
9
10
9
13
17
8
14
10
8
11
10
14
14
9
12
11
15
11
10
9
15
7
8
8
9
20
16
12
14
12
11
6
12
24
25
19
14
9
4
11
7
16th
Mar
Wed
4
3
2
8
12
10
10
13
9
5
8
9
14
10
12
16
14
6
15
13
10
18
22
14
5
16
9
16
13
7
12
9
8
10
10
12
11
12
9
14
22
24
25
19
17
10
7
6
17th
Mar
Thu
0
1
4
2
13
8
6
6
18
6
5
8
9
16
3
3
12
13
10
9
5
12
17
11
6
10
17
14
5
9
11
11
3
7
11
13
7
9
6
6
23
20
19
7
16
10
18
7
37
18th
Mar
Fri
2
4
6
8
14
8
11
7
8
11
7
9
3
20
6
9
11
13
20
3
14
10
10
12
11
19
5
16
6
7
9
15
6
7
10
15
11
13
13
12
25
34
16
22
17
8
13
5
Average
Hourly
Total
2
4
7
5
11
9
9
9
10
10
8
11
9
14
7
10
13
13
15
10
10
13
14
11
7
11
9
15
7
10
10
9
8
10
12
11
12
12
8
11
21
25
21
18
15
9
13
5
17
27
32
35
39
38
38
37
39
38
42
41
40
44
43
51
50
47
48
48
48
45
43
37
41
41
40
42
36
38
37
39
41
45
47
43
43
51
64
77
84
79
63
55
43
ENGINEER
Comparison of Performance Assessment Indicators for
Evaluation of Irrigation Scheme Performances in Sri
Lanka
S.M.D.L.K. De Alwis and N.T.S. Wijesekara
Abstract:
Managing resources in a major irrigation scheme needs more attention on system
performance in order to get optimum production out of available resources. In Sri Lanka most major
irrigation schemes are managed using conventional management strategies together with the traditional
experiences of farmers and managers. In most instances a systematic approach for observation or resource
use and management are not adhered to either by scheme managers or by farmers. It is often observed that
this results in low productivity. As such there is a need to evaluate irrigation scheme performance using
suitable performance indicators in order to identify shortcomings and to find out solutions for increasing the
productivity of such schemes. Since there are many factors affecting productivity of an irrigation scheme,
most relevant factors should be identified in order to ascertain the most relevant data, minimum time,
money, and expert services are spent. It is commonly believed that the present way of data collection by the
majority for scheme evaluations do not serve the purpose since they are not designed for Sri Lanka’s
national needs. The present work is towards the development of a suitable performance assessment program
for irrigation schemes in Sri Lanka considering water use efficiency, irrigation practices and land
productivity. A critical comparison and review of available indicators were done considering the adequacy
to monitor service delivery, productivity and agricultural economics and financing on irrigation system
sustainability. One new indicator for water service delivery reflecting the effect of actual rainfall received
was identified in this study along with two new indicators as Government Involvement and Beneficiary
Involvement. This is proposed to monitor system sustenance which is a very important issue in the light of
recent state policy of handing over of irrigation schemes to farmers. The present work after a systematic
evaluation identified eleven suitable indicators for system performance measurement that would require
minimum efforts on additional data collection and mobilising of fresh resources.
Key words:
1.
Water, Irrigation, Performance Indicators, Sri Lanka, Policy, Farmer, Beneficiary,
Introduction
The actual yield values for the year 2005 had been
observed as 3.96 MT/Ha while in year 2008 this
value had reached 4.18MT/Ha [4] (internet
http://www.statistics.gov.lk) In this context it is
very important for irrigation stakeholders to
carryout evaluation of irrigation scheme
performances so that there is potential to effect
timely and appropriate measures. Presently
performance
indicators
in
use
measure
productivity of water corresponding to the
irrigated land area by computing the water duty in
Acft/Ac, seasonal grain yield in MT/Ha and the
contribution of district level paddy production or
the average yield in MT/Ha [1] (ID 2003).
In Sri Lanka, irrigated agriculture with a 28% share
is the major user of surface water resources [1] (ID
2003). Irrigated agriculture accounts for about 96%
of withdrawals of water from the surface. Out of a
total irrigated area of 642,000 ha in 1991, the dry
zone lands had been given irrigation water to the
area of approximately 85% of the total annual
irrigated area [2] (Amarasinghe, Mutuwatte and
Sakthivadivel 1999).
Since the livelihood of
majority of dry zone community is agriculture, it is
very important to efficiently and effectively use
water for improving their living standards. Proper
management of land, water, and agricultural
inputs along with efficient operation and
maintenance of irrigation systems are prerequisites
for achieving optimum yield from the agricultural
lands.
Eng. S. M. D. L. K. De Alwis, BSc Eng., C. Eng., MIE(Sri
Lanka), MEng, Environmental Water Resources Engineering &
Management (Moratuwa), Deputy Director, Irrigation
Department, Sri Lanka
Eng. (Prof.) N.T.S.Wijesekera, B.Sc. Eng. (Sri Lanka),
C. Eng., FIE(Sri Lanka), MICE(UK), PG Dip Hyd Structures
(Moratuwa), M. Eng. (Tokyo), D. Eng. (Tokyo), Senior
Professor of Civil
Engineering, Department of Civil
Engineering, University of Moratuwa.
In the year 2000, national annual average yield had
been reported as 3.7 MT/Ha while the target had
been to reach a potential national average yield of
4.1MT/Ha for the year 2005 [3] (Dhanapala 2000).
39
ENGINEER
It has been noted that water duty values at the end
of the season provide the overall water
adequate information about the adequacy,
reliability and timeliness of operations. Average
yield which provides average productivity per unit
area does not reflect the reasons for such yields.
Literature indicated that most agriculture
production indicators enable assessment of the
performance of a season in terms of a selected
criterion with respect to benchmark values.
However scheme level benchmark values were not
available at the Department of Agriculture except
district level yield forecasts. In order to assess the
Agriculture Economics & Financing, status of the
irrigated agriculture sector, the most common
indicator used in Sri Lanka is ‘Profit’ which usually
presents the national level performance using
district level values [5] DOA(2005).
consumption
the
area,
do
not
provide
pertaining to three major categories as (i) service
delivery, (ii) production (iii) economics &
financing.
In this backdrop, the objective of the present work
is to carryout a comparison of most common
irrigation performance indicators to identify the
best for Sri Lanka in order to address water use
efficiency, irrigation practices, land productivity
and system sustenance etc., with respect to water,
yield and income consideration of Irrigation
Department and the Farmer Organisations who are
the two major stakeholder groups in the irrigation
sector.
In the present work extensive discussions were
held with irrigation managers and farmer
organisation
representatives
to
capture
performance assessment needs especially in a
turnout area basis for productivity enhancement.
They are water utilisation and service delivery,
land utilization, effective use of rainfall for
production, provision of engineering and
agricultural inputs, government support, and
strength of farmer organizations and the
enhancement of farmer status. The literature
survey for the present work identified eleven major
documents describing and discussing performance
indicators that are relevant to the study objectives
and number of indicators were captured with a
listing of an associated categorisation (Table 1).
There are a large number of indicators presented in
the literature together with those already in use
enabling an irrigation manager to identify the
effectiveness and efficiency of land and water
resource usage ([1] ID 2003, [6] Bos Burton and
Molden 2005, [7] Malano and Burton 2001). [8]
Murray-Rust and Snellen (1993) carrying out an
irrigation system case study including four schemes
from Sri Lanka has indicated that lack of systematic
measurement of performance, minimum concern
about long term sustainability and poor
consideration of institutional and resource
condition are key factors that need to be addressed
in the performance assessment of irrigated
agriculture. [6] BOS, Burton, and Molden (2005) in
their guideline very clearly indicate that the
assessment of performance assessment procedure
would vary depending on the purpose of the
assessment and the type of scheme. In almost all
irrigation schemes there are several indicators
computed resulting from a vast data collection
effort. However there are no supporting literatures
or case studies to guide a scheme manager about
the best indicators that could be used with either
already collected data or with a minimum
additional data collection effort.
Therefore it is
very important to carefully evaluate these
indicators for the identification of the optimum
effective management of the spatial units starting
from irrigation scheme as operational units.
2.0 Methodology and Analysis
2.1
Literature Survey
Performance assessment is the systematic
observation, documentation and interpretation of
the management of an irrigation and drainage
system to ensure intended outputs and proper
functioning ([6] Bos, Burton and Molden 2005).
The three key stakeholder interactions that have
been identified in the irrigated agricultural sector
are between, i) Water Institutions and Farmer
Organizations, ii) Farmer Organizations and the
Government and iii) Water Institutions and the
Government ([9] Bandara 2006).
Performance
optimization of irrigation schemes is commonly
applied either through planning, management, or
structural interventions to improve application,
conveyance or economic efficiency ([10] Jayatillake
2000).
Informal discussions had with irrigation managers and
farmers revealed that some of the important questions
that require answers are the identification of
operational performance of irrigation scheme tracts,
productivity of turnout areas, performance of
irrigation practices and assessment of profitability.
Accordingly it is necessary to identify information
ENGINEER
of
40
2.2
Selection of Indicators
officers of the Irrigation Department (ID),
officers of line agencies and personnel from
associated private sector agencies and chaired
by the District Secretary, discussions are held
and decisions are taken regarding the proposed
cropping pattern, availability of water, water
delivery time schedule, schedule of schememaintenance
works,
maintenance
work
responsibilities of ID and FO, availability of
other inputs such as seeds, fertilizer, credit
facilities targeting optimum productivity. At
these scheme level meetings, water duty and
yield are the only performance indicators which
are normally discussed. Only a minimum
attention is given to irrigation management
activities while in a majority of cases, no effort is
made to identify problems or to find short and
long term solutions for prevailing issues.
Therefore it is important that suitable indicators
are selected through a critical evaluation in
order to surface most important issues.
Indicators identified from the selected major
references were then classified according to the (i)
Service Delivery in three sub categories as Water
Delivery, Maintenance, and Duty, (ii) Agriculture
Production and (iii) Agricultural Economics and
Financing (Table 2) since the interest group
discussions and the literature survey identified
these classes as the most important.
In a
comprehensive study for two irrigation schemes in
Sri Lanka to develop and introduce cost effective
performance assessment using remote sensing data,
[9] Bandara (2006) listed 16 most relevant
performance indicators based on the operational
activities and based on the resource utilization.
An initial screening of literature was done to
capture suitable indicators considering importance
of Turnout level monitoring, capability to compute
either with presently collected data or with
acceptable estimations from scheme level or
guideline information. Special attention was given
to minimize the cost of data collection and to avoid
carrying out special measurements unless the
stakeholders
expressed
a
pressing
need.
Subsequent to a comparison of similarity and
effectiveness of outputs when compared with other
identified indicators, a total of 18 indicators as
indicated were listed (Table 2) under three main
categories.
2.3.1
2.3.1.1 Water Delivery
A summary of key aspects pertaining to the
selected water delivery indicators are in the
Annex 1. Water delivery indicators attempt to
reflect the effectiveness of delivering optimum
water expected for crop growth. Each indicator
was assessed through a study of the definition,
description, nature of intervention and
comments were made. Indicator comparison
also considered the requirement of temporal
and spatial monitoring requirements to achieve
optimum outputs.
All selected indicators
provide similar results through a measure of
supply relative to the demand. Hence delivery
can be either by rainfall, irrigation or other
inflows, indicators have attempted to ensure the
accounting of effective rainfall during the
identification of water expected for optimum
crop growth.
Eight service delivery indicators contained 4 of
Water Delivery, 2 each of maintenance and duty.
Five indicators related to Agriculture Production
represent the measurement of productivity.
Another five indicators which quantify the crop
resource utilisation in financial terms and economic
considerations are chosen to represent the
Economics and Financing group.
2.3
Service Delivery Performance
Comparison of Indicators
A comparison of the selected indicators was
initially done with respect to a water delivery
system, and then a broader evaluation was carried
out to select appropriate indicators for the entire
system. Descriptions and definitions pertaining to
each indicator were studied in detail to capture the
objective, the evaluation criteria, and other
important features for a critical assessment.
2.3.1.2 System Sustenance
In the literature only two performance
indicators which directly address system
sustenance could be identified. A brief outline
interpretation of the two indicators is given in
the Annex 1.
Estimation, availability and
utilization of funds for irrigation system
sustainability activities and their periodical
achievements reflect the way an irrigation
system had performed and would enable
forecasting to assess performance during its
service delivery period. Financial involvement
In major irrigation schemes of Sri Lanka, the
common practice is to finalize the Seasonal
Operation Plan (SOP) at a gathering called ‘Kanna’
meeting. This is a very special meeting in terms of
irrigated agriculture in the scheme. Attended by
the representatives of Farmer Organizations(FO),
41
ENGINEER
for Maintenance, Operation and Management
(MOM) in an irrigation scheme is generally taken to
reflect the strength of Irrigation System sustenance.
In major and medium irrigation schemes, the
Government of Sri Lanka (GOSL) shoulders a large
component of MOM fund requirements while
Farmer Organizations too support several selected
activities. Department of Irrigation of Sri Lanka as
the implementing agency appointed by the
government presently estimates the MOM costs at a
fixed rate based only on the irrigable area. This
often creates a mismatch between the budget
provision and the actual requirements because
market price of labour, material and transport
significantly vary with the spatial location work
even within a province.
In Sri Lanka when
identifying
the
Maintenance
Budget
Implementation Efficiency (MBIE) the assessments
normally measure government expenses incurred
for main and sub system maintenance against
government fund allocation. Comparison of the two
indicators show that while expressing the total
MOM role without a separation between the
involved parties, one indicates the per ha funding
involvement while the other makes a comparison of
allocation and the actual on an annual basis.
selected crop, volume of water and the fund
generation per cultivation area.
In order to
measure Agricultural Production performance,
relative yield and the output per unit command
are the indicators which reflects the input
output status in the specialized fields of
agriculture and marketing but they are posing
difficulties in collecting reliable data because of
farmer reluctance to indicate actual income.
The Yield provides a good measure by
cultivated area only, cropping intensity reflects
the efficient use of the total irrigable area, and
Water use efficiency assesses the adequacy,
equity and efficiency of water utilization.
2.3.3
Performance of Agriculture Economics
and Financing
Selected indicators in this area attempt to
capture the financial gains over the expenditure
through various means. Out of the identified
indicators (Table 1), the price ratio which
provides details of price difference at two
locations reflecting a farmer’s profits and
income, indicate difficulties in collecting reliable
data. There is a difficulty in collecting data
from reliable resources and therefore common
practice is to perform periodical sample data
collections which provide difficulties in spatial
management. Cost recovery ratio and the O &
M Fraction which provide details of cost
recovery and the proportion of MOM costs also
indicate a significant increase of fresh data
collection efforts since it is difficult to separate
these components from the already available
data.
On the other hand the indicators,
Resource Utilization which reflects the
efficiency, and the Profit which reflects
dependability of a scheme shows that unit level
indications could be obtained with only a
marginal effort enhancement.
2.3.1.3 Water Use
Two indicators of water use could be identified
from the literature and practice. Annex 1 provides
a summary of the indicators detailed in the Table 1
and 2. In order to assess the water use efficiency on
a per hectare of command area, Department of
Irrigation, Sri Lanka use two indicators namely the
irrigation duty and water duty respectively.
Irrigation duty indicates the utilization of irrigation
releases from head sluice to the command area.
Water duty points to the water that was given to a
soil plant system including effect of effective
rainfall along with the irrigation water. Since the
water use would be expected to assess efficiency in
relation to irrigation efforts it is important to
capture the irrigation duty of a particular scheme
for comparison of irrigation efforts. The total water
use with the inclusion of rainfall would also be an
important factor but in an irrigation scheme there
would be more value if the total water use could be
related to the plants than simply to the land area.
Therefore it is noted to capture this total water use
by plant through an indicator within the water
delivery system.
2.3.2
Discussion and Results
3.1
It could be noted that the identified water
delivery indicators attempt to capture the
probable contribution of effective rainfall
towards the crop water requirement when
comparisons are made with the irrigation water
delivered. However, it could be identified that
none provide the opportunity to reflect the
actual attempts taken by the technical inputs to
account for actual rainfall and to reflect efforts
taken at operation and planning. This can be
achieved with a modification to the Delivery
Performance Ratio ([6] Bos, Burton and Molden
Agricultural Production Performance
The selected indicators (Table 1) attempt to capture
the efficiency of production either in terms of land
cultivated or command area, potential of the
ENGINEER
3.0
42
2005), which is incorporating the system concept
and accounting for both irrigation and rain as a
delivery service.
Though the delivery of rain
cannot be treated as part of service delivery, the
attempts taken to account for rain during the
operation of irrigation delivery can be grouped as a
service. Therefore as shown below System Water
Delivery Service is defined as the ratio of water
actually received by the soil, plant system to
volume of water targeted for the system for
optimum crop growth i.e. the theoretically
computed volume of water to be delivered to the
system.
System Water
Delivery Service =
(SWDS)
shoulder system support activities. As this is a
recent and important implementation, it is
necessary to have close monitoring and
meaningful evaluations. It is also necessary to
identify a suitable performance indicator to
capture the status of system sustenance because
service delivery and agriculture production
cannot be met without proper functioning of a
particular irrigation system.
(Irrigation Water Delivered + Effective Rainfall + Other Inflow)
Intended Water Delivery to the System
Accordingly two indicators were developed to
assess the Beneficiary Involvement (BI) and also
the Government Involvement (GI). Beneficiary
involvement is defined as the ratio of farmer
organization contribution and the Government
contribution to the FO as explained in the annex
1. This enables a comparison of the contributory
components by the two major stakeholders
spatially and with time enabling the
identification of spatio-temporal consistency,
attitudes, infrastructure status and sustenance
in the light of system handing over.
The two indicators of system sustenance were
picked due to their representation of very important
characteristics. Since water duty in the group of
water use indicators reflected a similarity in the
above defined SWDS, only irrigation duty was
selected.
3.2
Agricultural Production, Economics and
Financing
In case of assessing agricultural
cultivation land and command
were
considered
very
important together with the
efficient
use
of
water.
Considering the data collection
difficulties and also the objectives
of scheme level monitoring, two
indicators were chosen for capturing
enhancing the financial status.
3.3
production both
area assessment
Beneficiary
Involvement =
(BI)
Total Contribution by Farmer Organisation
Government Contribution to Farmer Organization
the productivity of
The Government Involvement (GI) is to obtain
an indication of contribution by the state
towards the total MOM cost. There are four
main components that constitute the total MOM
cost. They are, the labour, management and
sense of ownership input by Farmer
Organizations, Expenditure borne by the
Farmer Organization Funds, the Payment made
to the Farmer Organizations by the Government
through the irrigation Department and the
Management Cost borne by the Irrigation
Department.
Institutional Development
A macro look at the selections made from the
indicators taken for comparison, it was noted that
system sustenance aspects with respect to the
Institutional
Development
require
better
representations. Farmer organizations (FOO) play a
vital role in the irrigation scheme management
because they are given the responsibility to manage
all operations and maintenance activities
of field canals and distributory canals in
Total Contribution by the Government
Government
a Turnout area. FOO receive a financial
Involvement =
allocation from the state for this work
Total MOM Cost
(GI)
through an agreement signed with the
Department of Irrigation. Previously the
state was solely responsible for the system
The first two are the FO contribution, while the
maintenance. According to the present policy the
latter two are the state contribution. This
system component delivery is handed over to
indicator would enable total system valuation
stakeholders in 1984, thereby requiring farmers to
concepts.
43
ENGINEER
3.4
The selected indicators did not contain the
capability to capture the accounting of rainfall
in irrigation water delivery, or the assessment of
the provision of satisfactory engineering
services, through a single indicator. This was
not considered as a hindrance because these two
aspects can be monitored reasonably through
the others and also since they would have to be
looked after when achieving other objectives.
This was also due to the fact that the study
attempts were to keep the number of indicators
at a minimum.
Indicator Coverage
Based on the reasoning for acceptability to carryout
performance monitoring in Sri Lankan irrigations
schemes and especially with the author experience
in the North Central Province, 11 indicators were
selected as appropriate for irrigation scheme
performance assessment. Adequacy of coverage of
land and water resources, the aspect of resource
that would be monitored and the data requirements
to compute each indicator were listed and
presented in the Table 3. Data needs to be indicated
so that indicator values could be computed to a
reasonable accuracy with the already available data
and guidelines, though it may be necessary to
strengthen the assessments at a Turnout level and at
finer temporal resolutions. Assessment of indicator
coverage on land and water as shown in the Table 3
shows that there are two and three indicators
specifically on water and land respectively while
the other six revealed and emphasised on cross
linkages.
4.0
1.
2.
An evaluation of the assessment capability of the
selected indicators with respect to irrigation scheme
performance assessment objectives is shown in the
Table 4. In this evaluation each performance
indicator was first assessed in relation to each
identified objective using a three class qualitative
ranking representing Very High, High and
Moderate. In order to obtain a numerical indication
of the coverage, these qualitative classes were
assigned values of 3, 2 and 1 for very high, high and
moderate representations. Table 4 shows that
except for two objectives all the other indicators
were emphasising a single objective. It could also
be noted that there were a sufficient cross
connection representation enabling a mix of
indicators providing details to easily identify focus
regions for early action. The higher total numerical
values for either the indicators or objectives
indicated a representation of overall aspects and
thereby hinting on the system healthiness. The
lower values indicated more emphasis on specific
target areas such as stakeholder involvement,
engineering interventions, accounting for effective
rainfall etc.
3.
4.
A new indicator by the name of System
Water Delivery Service was defined to
capture the accounting of actual rainfall
received for optimum utilisation of water
resources, ensuring a better delivery service.
A gap was identified in the available
indicators to assess the institutional strength
in irrigation schemes and this was filled by
defining two new performance assessment
indicators as Government Involvement and
Beneficiary
involvement
reflecting
government, irrigation department and
framer organisation contributions.
Eleven indicators were identified as suitable
for performance evaluation of irrigation
schemes under four main sectors as Service
delivery,
Agricultural
Production,
Agriculture Economics and financing, and
System Sustenance.
Four indicators for (i) service delivery, three
for (ii) Production, two each for (iii)
Economics and Financing and (iv)
Institutional development were recognised
as satisfactory for carrying out irrigation
scheme performance assessment.
References
Selected indicators marks percentages that were
computed by considering a possible maximum
value that could be assigned. These percentages
enable not only the identification of key parameters
or the objectives with more emphasis, but also
reflect that the specific objectives are reasonably
represented in an overall manner having
percentage values not less than 15%.
ENGINEER
Conclusions
44
1.
ID 2003 Irrigation Department, Water statistics
handbook, Department of Irrigation, Colombo, Sri
Lanka, 2003.
2.
Amarasinghe, U.A., Mutuwatte, L., and
Sakthivadivel, R., “Water Scarcity Variations
within a Country: A Case Study of Sri Lanka”,
Research
Report
32,
International
Water
Management Institute, ISBN 92-9090-383-X, ISSN
1026-0862, Colombo, Sri Lanka,1999.
3.
Dhanapala, M.P., “Bridging the Rice Yield Gap
in Sri Lanka”, Food and Agriculture Organization of
the United Nations. Regional office for Asia and the
Pacific, RAP publication 2000/16.
4.
Agriculture Statistics of Sri Lanka, Average Yield of
2008,
http://www.statistic.gov.lk/agriculture,
visited 16th January 2011.
5.
DOA (2005), “Cost of Cultivation of Agricultural
Crops Maha 2004/2005”, Publications of Socio
Economics
and
Planning
Centre
(www.agridept.gov.lk), Department of Agriculture,
Peradeniya, Sri Lanka.
6.
17. Ponrajah, A.J.P., Revised Edition, “Technical
Guide Line for Irrigation Works, Irrigation
Department, Sri Lanka”. 1998, pp. 236 – 249.
18. Siriwardane, S.M.P., “Operational Performance
Monitoring in System C of the Mahaweli
Development Programme in Sri Lanka”, 2001.
BOS, M.G., Burton, M.A., Molden, D.J., “Irrigation
and Drainage Performance Assessment”, practical
guide line. (ICTAD, CID, IWMT). Ch 3, 2005, pp. 26 –
86 and pp. 144 – 151.
7.
Malano, H., and Burton, M., “Guideline for Bench
Marking Performance in the Irrigation and Drainage
Sector”, 2001.
8.
Murray-Rust, D.H. and Snellen, W.B., “Irrigation
System Performance Assessment and Diagnosis”.
Joint
IIMI/ILRI/IHEE
publication.
International
Irrigation Management Institute, Colombo, Sri
Lanka,1993.
9.
QASMEH RAS – EL – AIN Irrigation scheme
(Lebanon)”, Technical Paper on Irrigation System
Performance.
19. Ariyarathna, D.M., “Towards a New Agriculture
Volume II Ministry of Agriculture and Land, Sri
Lanka”,1998.
20. Molden, D., “Accounting for Water Use and
Productivity”. SWIM paper I, International
Irrigation Management Institute, Colombo, Sri
Lanka,1997.
21. Strzepek, K., Molden, D., Galbraith, H.,
“Comprehensive Globle Assessment of Costs,
Benefits and Future Directions of Irrigated
Agriculture”, 2001, Working paper 03.
Bandara,
K.M.P.S.,
“Assessing
Irrigation
Performance by Using Remote Sensing”. ch 3, pp. 27
– 41, ch 7, 2006, pp. 109 – 122.
10. Jayathilake, H.M., “Rice in Major Irrigation Schemes
Potential for Increased Cropping Intensities with
Limiting Water Resources: Working paper Rice
Congress”. Audio Visual center of the Department of
Agriculture, Peradeniya, Sri Lanka. ch 2, 2000, pp. 14 –
63.
11. Bos, M.G. and Nugteren, J., On Irrigation Efficiencies,
2nd edn. ILRI publication No. 19. International
Institute for Land Reclamation and Improvement
(ILRI), Wageningen, The Netherlands,1974.
12. Molden, D.J., Sakthivadivel, R., Perry, C.J., de
Fraiture, C. and Kloezen, W., “Indicators for
Comparing Performance of Irrigated Agricultural
Systems”. Research Report 20. International Water
Management Institute, Colombo, Sri Lanka,1999.
13. Rao, P.S., “Review of Selected Literature on
Indicators of Irrigation Performance”. IIMI Research
paper. International Irrigation Management Institute,
Colombo, Sri Lanka. ch 2, pp. 5 -10, ch 3, pp. 55 – 62
and ch 4, 1993, pp. 63 – 64.
14. Diskin Patrick, “Agriculture Productivity Indicator
Measurement Guide”. Food and Nutrition Technical
Assistance, 1997, pp 01 – 41.
15. Wim, H., Klozenand & Carlas Garces – Restrepo,
“Assessing Irrigation Performance with comparative
Indicators; The Case of Alto Rio Lerma Irrigation
District, Mexico”, Research Report 22 IWMI, 1998.
16. Karaa, K., Karam, F., Tarabey. N., “Attempt to
Determine some Performance Indicators in the
45
ENGINEER
Table 1 – Selected Literature for Identifying Performance Indicators
Reference
Category
Water
Delivery
and
Utilization
01. Bos M.G., Burton M.A., Molden D.J., 2005, Agriculture Production
Irrigation & Drainage Performance Assessment
Agricultural Economics and
Practical Guideline, 2005. (ICID-CIID, IWMI), pp. 143
financing
– 151.
Socio Economics
Environment
No. of
Indicators
33
17
13
10
5
Financial
Productive Efficiency
Environmental Performance
Irrigated
Agricultural
Outputs
Water Delivery
Financial Indicators
3
6
04. Rao, P.S., Review of Selected Literature on
Indicators of Irrigation Performance, 1993. (Research
Paper IIMI)
Water Delivery
Production
Agriculture Economics
5
3
1
05. Patrick Diskin, Agriculture Productivity Indicator
Measurement Guide, 1997.
Agriculture Productivity
8
06. Wim, H. Klozenand & Carlas Garces – Restrepo,
Assessing Irrigation Performance with comparative
Indicators; The case of Alto Rio Lerma Irrigation
District, Mexico, 1998. (Research Report IWMI)
Water Delivery
Production
Financial
Agriculture
Environmental
9
4
2
4
2
07. Attempt to Determine some Performance
Indicators in the QASMEH RAS – EL – AIN Irrigation
Scheme (Lebanon)
Water Delivery Performance
Financial
Sustainability
02. Malano H., Burton M., Guideline for
Benchmarking Performance in the Irrigation &
Drainage Sector, 2001,
03. Molden,D.J., Sakthivadivel, R., Perry, C.J., de
Fraiture, C. & Kloezen, W., Indicators for Comparing
Performance of Irrigated Agriculture System, 1999,
08. Muray-Rust, D.H. & Snellen, W.B., Irrigation
System Performance Assessment and diagnosis
(IIMI/ILRI/IHEE Publication), 1993
9. Bandara, K.M.P.S., 2006 Assessing Irrigation
Performance by using Remote Sensing (Doctoral
Thesis)
10. Ponrajah, A.J.P., Revised Edition, Technical Guide
Line for Irrigation Works, Irrigation Department, Sri
Lanka, 1988,
11.
Siriwardane,
S.M.P.,
2001
Operational
Performance Monitoring in system C of the Mahaweli
Development programme in Sri Lanka, 2001
ENGINEER
46
Operational Performance
8
7
6
6
4
5
6
1
An overall
view
Strategic Performance
Operational Performance
10
Resource Utilization
6
Water Delivery
2
Land Productivity
1
Water Delivery
Financial
Productivity
5
9
8
Table 2 - Indicator Classification – Service Delivery, Agriculture Production and Agricultural
Economics and Financing
Performance
Indicators
Definition
Evaluated Target
Criteria
Service Delivery
Service Delivery - Water Delivery
1. Relative Water
Supply (RWS)
2. Delivery
Performance Ratio
3. Overall Consumed
Ratio (OCR)
4. Water Use Efficiency
(WUE)
Total Water Supply
Crop Water Demand
Actual Volume of Water
Intended Volume of Water
(Potential Evpotranspiration – Effective Rainfall)
Water Volume Supplied to Command Area
Crop Water Demand
Total Water Supply
Service Delivery - System Maintenance
5. Total MOM Cost per
Total MOM Cost
Unit Area (Rs/ha)
Command Area Serviced by System
6. Maintenance Budget
Implementation
Efficiency
Annual Expenditure on Maintenance
Adequacy and
Efficiency
Efficiency
Adequacy, Equity
and Efficiency
Operational Viability,
Sustainability
Efficiency
Annual Money Allocated for Maintenance
Service Delivery - Water Duty
7. Irrigation Duty
8. Water Duty
(acft/acre)
Adequacy and
Equity
Volume Water Issued to Farms
Area Cultivated
Volume Water Issued to Farms+ Effective Rainfall
Utility
Area Cultivated
Equity and
Efficiency
1. Yield (Mt/ha)
Production
Crop Production
Productivity
2. Relative yield
Cropped Area
Actual Crop Yield
Production
Potential Crop Yield
Area cultivated During the Year
Production
3. Cropping Intensity
4. Water use efficiency
(Kg/m3)
5. Out put per unit
command
1. Resource utilization
Command Area
Crop Production
Volume of Water Supplied in Season
Value of Production
Adequacy, equity
and efficiency
Productivity
Command Area
Economics and Financing
Value of Production
Efficiency
Cost of Production
Gross Revenue Collected
Dependability
3. Profit (Rs/ha)
Total MOM Cost
(Income – Expenditure)
profitability
4. O & M fraction
Cost of O & M
2. Cost recovery ratio
5. Price ratio
Total Agency Budget
Cost of O & M
Total Agency Budget
47
Operational
viability
Profitability,
Farmer Economy
ENGINEER
Resource utilization
Profit
Beneficiary Involvement
Government. Involvement
PI_9
PI_10
PI_11
Yield
PI_5
PI_8
Maintenance budget
implementation efficiency
PI_4
Water use efficiency
Total MOM cost per unit area
PI_3
PI_7
Irrigation duty
PI_2
Cropping intensity
System Water Delivery
Service
PI_1
PI_6
Performance Indicator
Identity
Sector
Institutional
Development
Institutional
Development
Land
Land
Land /
Water
Land
/Water
Rs./ha
Rs./ha
Rs./ha
Rs./ha
Farmer Income
Status
Water
kg/m3
Resource use
efficiency
Agriculture
Productivity per Unit
of Water
Land /
Water
Area Ratio
kg
MOM cost incurred by the ID in FO Area
Annual Payment for FO MOM works by ID
Duration
Yield
Selling Price
Cultivated Extent
Total Expenses of Crop Cultivation
Average Yield
Selling Price
Cultivated Extent
Total Expenses of Crop Cultivation
Labour and Money for MOM work by Farmer Organization
Annual MOM Payment to FO by ID
MOM cost incurred by the ID in FO Area
Labour and Money for MOM work by Farmer Organization
Rs.
Rs.
Rs.
Acres
Acres
kg
ft3/sec
hrs
kg
Rs.
Acres
Rs.
kg
Rs.
Acres
Rs.
Rs.
Rs.
Rs.
Land
Mt/ha
Total Area cultivated during the year
Command Area
Seasonal Yield
Daily Canal Discharge
Agriculture
Productivity per Unit
of Land
Land Usage
Productivity
Land /
Water
Finances
Ratio
Rs.
Rs.
Acres
Rs.
Rs.
Acres
Operation &
Maintenance
Land /
Water
Rs./ha
Units
ft3/sec
hrs
mm
Acres
ft3/sec
hrs
Acres
Rs.
Cultivated Area
Seasonal Yield
Efficiency of Water
usage per land unit
Land /
Water
Acft/acre
Operation &
Maintenance
Expenditure Incurred by ID for MOM
Expenditure Incurred by Farmer Organization on MOM
Command Area
Annual Allocation for Maintenance
Annual Expenditure for Maintenance
Water Delivery
Efficiency
Water
Volume
Ratio
Associated Data
Duration
Rainfall
Cultivated Area
Duration
Cultivated Area
Management cost of Operational Staff
Resource
Unit
Aspect
Table 3 - Selected Indicators, assessment parameters and domain of assessment
Service Delivery
Production
Economics and
Financing
48
Institutional
Development
ENGINEER
Table 4 - Assessment of Indicator Capability
Selected Indicator
1
2
3
4
5
6
7
8
9
Assessment
Objective
Irrigation Water
Utilisation
Provision of Water
Delivery Service
PI_1
PI_2
1
3
3
1
2
Utilisation of Land
Government Fund
Disbursement
PI_3 PI_4 PI_5
PI_6
PI_7
PI_8
2
2
1
2
2
2
2
3
PI_9 PI_10 PI_11 Total
1
10
30%
1
11
33%
8
24%
5
15%
8
24%
8
24%
1
3
FO Contribution
Government
Contribution
Provision of
Agricultural Input
Farmer Economic and
Financial status
System Infrastructure
Maintenance
10 Utilisation of Rainfall
Provision of
11
Engineering Services
Total (Assigned
Marks)
%
1
1
2
1
1
2
3
3
3
1
2
1
3
2
2
3
2
2
9
27%
3
3
9
27%
11
33%
1
7
21%
1
1
9
27%
1
1
1
2
1
2
1
2
9
9
8
7
10
11
7
11
10
7
6
27%
27%
24%
21%
30%
33%
21%
33%
30%
21%
18%
1
2
2
49
%
1
2
ENGINEER
Annex 1 - Outline Description of Selected Indicators from the Literature
Performance Indicator Description
1. Relative water supply is the measure of total volume of water delivered at turnout per unit of crop
water demand. In this the total volume is the sum of surface diversion, net groundwater draft and the
rainfall. Crop Water Demand is the total of potential crop evapotranspiration, deep percolation and
seepage. RWS represents a combined effect on delivery performance and indicates whether the
deliveries have met the crop water demand.
2. The Delivery Performance Ratio is the ratio of Actual Flow of water Delivered and the Field Irrigation
Requirement which is the Intended flow to be delivered in this flow can be determined in two ways as
flow rates and volume during a given period.
3. The Overall Consumed Ratio is computed as the ratio of irrigation requirement of a plant with respect
to actual deliveries and other inflows to the soil plant system. OCR measures the water delivery
efficiency indicating the adequacy of water for optimum growth of a crop.
4. Water Use Efficiency measures the Crop Water Demand of an area per unit of water supply to the
crops.
5. Total MOM Cost per Unit Area is the total management, operation and maintenance cost per unit
command area serviced by the system considering the costs that are borne by both Irrigation
Department and Farmer Organisation involving the entire cost of providing irrigation and drainage
service including capital expenditure, depreciation and renewal.
6. Maintenance Budget Implementation Efficiency is a relative measure of the actual and annual
allocation of funds for system maintenance.
7. Irrigation Duty is defined as the volume of water issued to a unit farm area cultivated up to the point a
crop reaches maturity.
8. Water duty relating the area irrigated and the quantity of water available for a crop to reach maturity is
the sum of the volume of water issued to farms from the sluice of a tank and the effective rainfall in a
particular season per unit cultivated area.
9. Yield defined as the crop production per unit crop area and usually measures the average yield in a
command area in Mt/ha.
10. Cropping Intensity is the ratio of total area cultivated under both Yala and Maha season against the
available total area of command.
11. Water use Efficiency is the ratio of weight of harvested yield and total volume of irrigation water
supplied during a season and computed on an average basis for the command area in kg/m3.
12. Output per unit command is the Ratio of value of production computed based on the farm gate price
and the average harvested yield of actually cultivated command area measured in Rs./ha.
13. Resource utilization is defined as the ratio of value of production to cost of production. Value of
production is the average yield at the local market price and cost of production is based on average
expenses incurred for crop cultivation at scheme level and does not take the farmer family labour into
account.
14. Cost Recovery Ratio is ratio of total revenue collected from water uses and total MOM cost incurred in
the irrigation system.
15. Profit of a particular scheme is defined as the net earnings from a unit area of land and is calculated per
season basis by subtracting the total expenditure incurred for the cultivation from the income from the
crop cultivation.
16. Operation & Maintenance Fraction is computed by dividing the sum cost of operation and maintenance
by the total agency budget allocated for the scheme.
17. Price Ratio is the ratio of farm gate price and the nearest market price of crops.
18. Beneficiary Involvement is defined as the ratio of the value of farmer organization contribution for
operation & maintenance works and the value of government contribution to farmer organization to
meet the operation & maintenance expenses.
19. Government Involvement is the ratio of government contribution which is the total expenditure
incurred by the government for management, operation and maintenance activities and total MOM cost
which is the cost borne by both FO and government for the management, operation and maintenance
activities. .
ENGINEER
50
Pull-out Behavior of Reinforcing tendons of Nehemiah
Anchored Earth System
K. J. S. Munasinghe and R.D.D. Dayawansha
Abstract:
The Nehemiah anchored earth wall system is a type of mechanically stabilized backfill
structure where the mode of stress transfer from the backfill to the reinforcement is by passive
resistance in addition to the friction. This paper presents the findings of pull out resistance of the
reinforcing tendon together with the anchor block of the anchored earth wall system. Nine
experimental tests were carried out to demonstrate the factor of safety of the pull out resistance of the
anchored earth wall. In addition, a summarized historical background, design concepts, construction
procedures and performance of the Nehemiah anchored earth wall system is provided.
Keywords:
Nehemiah Anchored Earth Wall System, Design, Construction, Pull out Behavior
1. Introduction
A schematic representation of the embodiment
of the anchored earth system is shown in
Figure 1.
The Nehemiah anchored earth wall system
was first developed and introduced in
Malaysia in 1993. The system has been used all
over Malaysia and it is now being
implemented in countries like Singapore,
India, Bangladesh and Sri Lanka. This system
has been used as part of the bridge abutment
that retains embankment soil on the Southern
Transport Development Project.
2.1 Facing Panels
The facing panels are hexagonal shaped and
are made of precast concrete (grade 30/20) as
shown in Figure 2. They are interlocked with
dowel bars with tolerance for horizontal
moments. The horizontal joint between the
panels are inserted with compressible material
to allow for vertical moments. As such the
facing is flexible and can tolerate large
differential settlement.
The Nehemiah anchored earth wall is a type of
reinforced soil wall system, which is reinforced
by galvanized steel bars and anchored by
precast concrete blocks. The facing is vertical
consisting of modular hexagonal shaped
concrete panels interlocked together. The
mode of stress transfer from the backfill to the
reinforcement is by passive resistance in
addition to friction.
2.2 Reinforcing Tendons
The reinforcing tendons are made of carbon
steel rods in compliance with BS 8006: 1994
code of practice for strengthened/reinforced
soil and other fills. The tendons are hot-dipped
galvanized to prevent corrosion. The
advantage of using round bars instead of strips
is the greater durability against corrosion in
view of the reduced surface area exposed. The
tendons are connected to the facing panels by
nuts with the threaded end coated with epoxy.
The system is ideal for urban highway
interchanges, railway embankments, bridge
abutments, housing retaining walls, marine
walls, river walls, secondary containment
dykes and military walls.
The advantages of such a system are cost
effectiveness, technical feasibility, rapid and
easy installation, minimum supervision
requirements,
aesthetically
pleasing
appearance,
environmental
friendliness,
flexibility and durability (Life span extending
up to 120 years).
2.3 Anchor Blocks
The anchor blocks are discrete precast concrete
blocks which act as a deadman.
Eng. K.J.S. Munasinghe, B.Sc. Eng. (Hons)
(Ruhuna),
AMIE(Sri
Lanka),
Engineering
Consultant Ltd., Sri Lanka.
Eng. R.D.D. Dayawansha, B.Sc. Eng. (Hons)
(Ruhuna),
AMIE(Sri
Lanka),
Engineering
Consultant Ltd., Sri Lanka.
2. The Anchored Earth System
The anchored earth system consists of three
major components namely the facing panels,
the reinforcing tendons and the anchor blocks.
51
ENGINEER
A hole is preformed in the centre of the block
to enable the tendon to pass through, thereby
connected with a nut and washer.
3. Design of Anchored Earth Wall
System
A typical arrangement is shown in Figure 3.
The advantage of using anchor blocks is that it
enhances the pull out resistance of the
reinforcing tendons. As a result, the use of
cohesive frictional material for backfill is
possible since the system does not rely so
much on friction for the stress transfer.
The Nehemiah wall is a type of reinforced
earth system. It is governed by the
combination of earth reinforcement and
deadman anchorage technology. It uses locally
available material such as steel bars and
concrete. The design is based on the BS 8006:
1995 code of practice or AASHTO LRDF
bridge
design
specifications
for
strengthened/reinforced soil and other fills.
The typical design cross section of the wall is
shown in Figures 4 and Figure 5. The design of
the Nehemiah wall involves the external
stability analysis and internal stability
analysis.
Well Compacted Granular Fill for
Nehemiah Reinforced Soil
Figure 1 -- Schematic Representation of
Nehemiah Anchored Earth Wall System
TUBE
Ø32 mm
DOWEL BAR
Ø20 mm
Figure 4 -Typical Transverse Section of
Anchored Earth Wall System
Anchorage
Lug
Figure 2 - Hexagonal Shaped Facing Panel of
Nehemiah Anchored Earth Wall System
Anchorage Lug
Precast Conc. Block
Galvanised Reinforcing
Tendon
Figure 5 - Typical longitudinal Section of
Anchored Earth Wall System for Bridge
Abutment
End Plate
With Nut
Anchored Earth Precast
Conc. panel
Nut & Washer
3.1 External Stability
Conc. Leveling Pad
For the external stability analysis, the
Nehemiah wall is analyzed as a gravity block.
The factors of safety against sliding,
Figure 3 - Typical Arrangement of Facing
Panel, Reinforcing Tendon and Anchor Block
ENGINEER
52
overturning and bearing are checked; designed
to ensure that they are adequate. The global
analysis and the geotechnical analysis are also
external stability analysis. They are not
described in this paper.
The shaft resistance is computed as follows:
FS
Where,
Fs = Shaft resistance
P = Coefficient of friction
Ø = Angle of Internal friction of the backfill
d = Diameter of the tendon
Le = Effective shaft length
3.2 Internal Stability
The internal stability analysis involves checks
to ensure that the factors of safety against
tensile strength and pull out failure of all the
reinforcing tendons are adequate.
The anchor capacity [7] is computed as
follows:
3.2.1 Tensile Failure
Pa 4 K p h w V v .......... .......... .......... .......... 3
In the design, it is important that the number
and size of the reinforcing tendons are
adequately provided so that the tension
developed in the tendons is always less than
the allowable tensile strength of all the
tendons.
Where,
Pa = Passive resistance of the backfill in front of
the anchor block
Kp = Coefficient of passive earth pressure
h = Height of the anchor block
w = Width of the anchor block
The tensions in the reinforcing tendons [4] are
computed as follows:
Ti
Hence, the total pull out resistance is given as
follows:
K SV V V .......... .......... .......... .......... .... 1
Pr
Where,
Ti = Tension developed in the reinforcing
tendon at ith levels
K = Coefficient of earth pressure within the
reinforced block
Sv = Vertical spacing of the tendons
Vv = Vertical stress acting on the ith level of the
tendons according to the Meyerhof pressure
distribution
FOS for Pull out Resis tan ce
T Pr
Tdesign
..5
Where,
Pr = Ultimate pull out resistance
Ø = Resistance factor for pull out resistance,
which can be take as 0.9
Tdesign = Design working load
4.
The ultimate pull out resistance of the
reinforcing tendons is the sum of the shaft
frictional resistance and the anchor capacity of
the anchor block. The shaft resistance is
determined by the friction developed between
the backfill and the effective length of the
tendon [7] Which is shown in Figure 6.
Construction
of
Nehemiah
Anchored Earth Wall System
The construction sequence shall start with site
preparation before wall construction. It is
explained under the following headings.
4.1 Site Preparation
Rankine Failure Plane
Le
FS Pa .......... .......... .......... .......... ...... 4
The factor of safety for pull out resistance must
be larger than or equal to unity. It is computed
as follows.
3.2.2 Pull out Failure
Resistant
Zone
P tan T d Le V V .......... .......... .......... 2
The first step in the construction was to
remove the unsuitable subsoil material or any
weak material with debris and replace it with
compacted granular material. Once the sub
soil was strengthened, the leveling pad was
cast at base level. Unreinforced grade 20/20
concrete was used. After the site preparation
was completed, the wall erection can be
commenced.
Active
Zone
(45 - Ø/2)
Figure 6 - Effective Length of Reinforcing
Tendons – Le
53
ENGINEER
4.2 Wall Erection
The backfill material is compacted to 95% of
the maximum dry density as determined in
accordance with BS 1377: 1975. During the
backfilling operation and compaction, heavy
compaction vehicles should be kept back at
least 1.5m away from the back face of the
facing panel. This 1.5 m zone was compacted
with a 1.0 ton vibratory plate compactor.
The facing panels are hoisted with the aid of a
lifting device and placed on the leveling pad.
The panels are supported with temporary
props and wooden clamps. The granular
material is then backfilled, spread, leveled and
compacted to the first tendon level. The
reinforcing tendons are then connected to the
facing panels and the anchor blocks. This
process of installing panels, backfill, tendons
and anchor blocks is repeated until the full
height of the wall is reached. Sponge was used
as joint material in all the joints between the
panels when placing the panels.
5. Experimental Tests
5.1 Experiment Pull out Test
The experiment was carried out in order to
determine the pull out resistance of the
reinforcing tendon together with the anchor
block of the anchored earth wall. The vertical
facing consisted of modular hexagonal shaped
precast concrete panels interlocked to each
other. Before erection of each panel, a hole of
relevant tendon diameter was constructed in
the center of the anchored earth facing panel.
During the erection of the anchored earth wall,
an extra reinforcing tendon with anchor block
was installed with the free end jutting out by
about 50 mm through the preformed hole in
the panel to be installed at the desired location.
The pull out test was carried out after the wall
erection was completed. Figure 7 shows a view
of the experimental wall with the reinforcing
tendon diameter and the anchor block size.
4.3 Backfilling
The backfilling operation is carried out
immediately following the completion of the
installation of each row of the panels. 0-40 mm
mixed granular fill materials are compacted in
layer thickness not exceeding 375 mm so that
each reinforcing bar can be fixed at the
required level on top of the compacted fill
material without any voids forming directly
underneath the reinforcing bars. The direction
of travel of the construction vehicle for the
placement, spreading and compaction of the
fill is parallel to the alignment of the wall at all
times. Sharp turns of the vehicle or moment
perpendicular to the wall causing centrifugal
forces exerting toward the rear face of the
panel shall be strictly avoided. Heavy vehicles
weighing more than 1000 Kg shall not be
allowed within the 1.5 m zone from the rear
face of the panel.
22000 mm
450x450x9000 mm Concrete Beam-06 Nos (26.2 Ton)
1000x1000x1000 mm Concrete Block Surcharge-48 Nos (115.2 Ton)
Zone Ø 20 mm x 6 m
P4
H2
P4
B2
P4
B2
P4
Ø 12 mm x 7.9 m With
400 mm x 200 mm x 100 mm
Anchor Block
400 mm x 200 mm x 100 mm
Anchor Block
400 mm x 200 mm x 100 mm
Anchor Block
Tendon
Ø 16 mm x 5.9 m (M)
Tendon
Ø 16 mm x 5.9 m (T)
Tendon
Ø 16 mm x 5.9 m (B)
32000 mm
Figure 7 - View of the Experimental Wall
ENGINEER
54
Tendon
Ø 20 mm x 5.9 m (M)
Tendon
Ø 20 mm x 5.9 m (T)
Tendon
Ø 20 mm x 5.9 m (B)
3000 mm 1900 mm
A pull out cage was placed at the level of the
test specimen tendon. The steel bracket and
cage was then connected to the threaded end
of the tendon test specimen.
Table 1 - Factors of safety for ultimate pull out resistance
Reinforcing Bars
Ø 12 mm x 5.9 m
Ø 12 mm x 6.9 m
Ø 12 mm x 7.9 m
Ø 16 mm x 5.9 m T
Ø 16 mm x 5.9 m M
Ø 16 mm x 5.9 m B
Ø 20 mm x 5.9 m T
Ø 20 mm x 5.9 m M
Ø 20 mm x 5.9 m B
Design Working
Load, Tdesign (KN)
ULS
16.19
16.17
16.16
30.64
33.87
36.35
30.64
33.87
36.35
SLS
11.99
11.98
11.97
22.69
25.09
26.93
22.69
25.09
26.93
The pressure value of the hydraulic jack was
then completely released before being fixed to
the steel cage. The pressure gauge meter was
set to indicate a zero reading. The hand pump
was then gradually applied to tighten the gaps
between the connections. The preliminary
displacement was recorded as initial close up
gaps.
Factor of Safety for
Pullout Resistance
Pullout
Force, Pr
(KN)
Estimated
Pullout Force
(KN)
19.62
20.22
23.14
44.39
54.97
65.02
47.08
58.23
68.71
ULS
2.57
2.40
2.55
2.23
2.10
1.93
2.04
2.74
2.46
46.31
43.15
45.78
75.78
78.94
77.89
69.47
103.15
99.46
SLS
3.48
3.24
3.44
3.01
2.83
2.60
2.76
3.70
3.32
length at different levels, anchors at deeper
elevations show higher pullout resistance.
Similarly comparing equal diameter anchors at
same level with different lengths, the longer
anchors show higher estimated pullout force.
Since experimental setup has been carried out
only with 1 m difference of lengths these
aspects can not be clearly distinguished with
results achieved.
The pressure in the hydraulic jack was
gradually increased by manually working on
the hand pump. As the pressure increased, the
tendon was tensioned. The reading on the
pressure gauge was recorded for each tendon
displacement of 0.5 mm as measured by the
dial gauge. The tendon was normally
tensioned until one and a half times the
designed tension capacity of the tendons.
The field pull out test was carried out to
measure the apparent pull out resistance of the
anchored reinforcing tendons. The factors of
safety for pull out resistance under ultimate
and serviceability limit state for all the tested
reinforcing bars are tabulated as shown on
Table 1 and 2. The tables demonstrate the
factors of safety for the pull out resistance
under ultimate and serviceability limit state for
all the tested reinforcing bars and the result
was greater than one.
5.2 Results of the Analysis and Discussion
The pull out force vs displacement of the
anchored reinforcing bars are shown in
Figures 8, 9 and 10 for various sizes of anchor
bars with different lengths at various positions
of the experimental wall. It was observed that
the higher the diameter of the tendons the
higher will be the pull out resistance, and the
lesser the diameter of the tendon the lesser will
be the pull out resistance. As well as the larger
the diameter of the tendon the lesser the
displacement, and the smaller the diameter of
the tendon higher the displacement for a given
force. The reason for this is larger the diameter
the higher the shaft resistance, and smaller the
diameter the lesser the shaft resistance of the
reinforcing tendons. However comparing
graphs the displacement at tested reinforcing
bars were recorded and observed to be very
small.
Further, comparing load against displacement
behavior of same diameter anchors with same
Pull out Force vs Displacement Curve
Pull out Force (KN)
50
40
30
20
10
0
0
1
2
3
4
5
6
7
8
9
10
Displacement (mm)
Dia. 12 mm x 5.9 m
Dia. 12 mm x 6.9 m
Dia. 12 mm x 7.9 m
Figure 8 - Pull out Force vs Displacement
Curve for 12 mm Tendons
55
ENGINEER
Pull out Force (KN)
Acknowledgement
90
80
70
60
50
40
30
20
10
0
0
1
2
3
4
5
6
7
8
Displacement (mm)
Dia. 16 mm x 5.9 m T
9
The authors would like to convey their
heartfelt gratitude to Road Development
Authority for providing background to
carryout investigation in this important
technical area. Further special mention is
made of Roughton International and Kumagai
Gumi Company Limited for their involvement
in this work.
10
Dia. 16 mm x 5.9 m M
Dia. 16 mm x 5.9 m B
Our special gratitude is extended to Eng.
Graham Fary (Senior Resident Engineer) who
gave valuable suggestions while preparing this
article and to Mrs. Lasanthi Wickramasinghe
who supported in the word processing.
Pull out Force (KN)
110
100
90
80
70
60
50
40
30
20
10
0
References
0
1
2
3
4
5
Displacement (mm)
Dia. 20 mm x 5.9 m T
Dia. 20 mm x 5.9 m B
6
7
3.
4.
5.
Nehemiah anchored earth system can
be
effectively
used
in
road
embankments with higher factors of
safety for pull out resistance under
ultimate and serviceability limit state.
The larger the diameter of the tendons
the higher the pull out force and
smaller the diameter of the tendons
the lesser the pull out force.
The larger the diameter of the tendons
the lesser the displacement and the
smaller the diameter of the tendon the
higher the displacement.
The
tested
reinforcing
bar
displacements were recorded and
were observed to be very small.
Factors of safety values given in the
Table 1 may not be the maximum,
because anchors were not loaded upto
ultimate failure , specially in the case
of the larger diameter anchors. Hence,
it can be concluded that FOS is more
than or equal to values given in Table
1.
ENGINEER
2.
BS
8006,
Code
of
practice
for
Strengthened/Reinforced Soils and Other
Fills’, British Standard Institution, London,
1994.
3.
BS 1377, “Soils for Civil Engineering
Purposes”, British Standard Institution, London.
4.
Lee, C. H.
and Oh, Y. C.,
“Design,
Construction and Performance of an
Anchored
Earth
Wall
in
Malaysia”,
Mechanically Stabilized Backfill, Wu(ed)
Balkema, Rotterdam, ISBN 9054109025, 1997.
5.
Faisal, Hi Ali, Bujang, B. K., Huat and Lee
Chee Hai, “Influence of Boundary Conditions
on the Behavior of an Anchored Reinforced
Earth Wall” American Journal of Environmental
Sciences, 2008, PP 289-296, ISSN 1553-345X.
6.
Faisal, Hi Ali, Bujang, B. K., Huat and Lee
Chee Hai, “Field Behavior of High Anchored
Reinforced Earth Wall” American Journal of
Environmental Sciences, , 2008, PP 297-302,
ISSN 1553-345X.
7.
Lee Chee hai, Nilaweera, Nimal S. , “Design
and Construction of a 20.5 m High Innovative
Nehemiah Wall Near Cameron Highland,
Pahang” Nehemiah Reinforced Soil Sdn Bhd,
Malaysia.
8.
Chin Tat Hing and Jason Khor Lee Chong,
“Repair of Road Embankment Failure using
Reinforced Soil Wall” Nehemiah Reinforced
Soil Sdn Bhd, Malaysia.
9.
Joel
Lim,
“Overcoming
Construction
Challenges of Time Constraint – A Case Study
of Kuantan Interchange Project” Nehemiah
Reinforced Soil Sdn Bhd, Malaysia.
Dia. 20 mm x 5.9 m M
6. Conclusion
2.
BS 5400 part 2, Steel, “Concrete and
Composite Bridge”, British Standard Institution,
London, 1990.
8
1.
1.
56
Economic Analysis of Water Infrastructure: Have We
Got It Right?
Bhadranie Thoradeniya, Malik Ranasinghe, N T S Wijesekara
Abstract:
The paper describes shortcomings of the general economic analysis procedure
adopted in water infrastructure development projects in Sri Lanka. As a case study an application of
the ‘Educated Trade-off’ framework in the Ma Oya river basin is used to illustrate the shortcomings of
general economic analysis procedure. This framework facilitates the systematic identification of
resource uses and the possible range of environmental and social impacts by the water infrastructure
project, through the involvement (consultation and participation) of key stakeholders. The study
revealed two types of shortcomings that result in erroneous economic indicators: first, the lack of a
competent process to establish the baseline situation leading to non-inclusion of some important
social and environmental impacts, both positive and negative, by the project and, second, deviations
from reasonable practices either due to negligence or on purposes that give decision makers
optimistic data which could result in questionable decisions.
Keywords:
economic analysis, water infrastructure development projects, educated trade-off,
stakeholder consultation, natural resources
1.
Introduction
project aggravate the situation. This practice
could be due to lack of knowledge, negligence
or on purpose.
Water infrastructure development can be
considered as a production process as the
purpose of production is to convert a set of
inputs (e.g. river flow, concrete, steel and other
building materials) to a set of outputs (e.g.
irrigation, water supply, and hydropower
generation projects). This justifies the
application of production functions, cost
benefit analysis and other economic analysis to
water resource infrastructure development [20].
The objective of this paper is to present a case
study on proposed Yatimahana multi-purpose
balancing reservoir which is a water
infrastructure project on Ma Oya in order to:
a) highlight deviations in economic analysis
from reasonable practices either due to
negligence or on purpose, and
b) address the above deficiencies through the
application of an ‘Educated Trade-offs’
framework [16], [17].
In the economic analysis of an infrastructure
project, the total value of the resource has to be
considered to maximise the efficiency. The total
value of a resource consists of its use and nonuse values. The basic measures of use and nonuse values are maximum willingness to pay
(WTP) to prevent environmental damage or
realise an economic-environmental benefit;
and/or minimum willingness to accept (WTA)
compensation for accepting a specific
degradation in environmental quality [1].
The
‘Educated
Trade-offs’
framework
developed by Thoradeniya [17] is a decisionmaking tool to facilitate trade-offs between
different resource uses by educating the
stakeholders on the combined economic value
(economic estimates and environmental and
social costs) of each resource use sector.
Non-availability of a systematic approach to
establish the baseline situation of the full range
of use and non-use values of the resources is a
key deficiency associated with the procedure
generally adopted for economic analysis of
water infrastructure projects especially in river
development work. Inadequate efforts to
include the values of full range of social and
environmental impacts due to the development
Eng. Dr. (Mrs.) Bhadranie Thoradeniya, AMIE(Sri Lanka),
PhD, Head, Division of Civil EngineeringTechnology,
Institute of Technology, University of Moratuwa
Eng. (Prof.) Malik Ranasinghe, B.Sc. Eng. Hons, Int. PEng.,
C.Eng., FIE(Sri Lanka), M.A.Sc., PhD, Professor in Civil
Engineering and the Vice-Chancellor of the University of
Moratuwa
Eng. (Prof.) N.T.S. Wijesekera, B.Sc. Eng. (Hons), C.Eng.,
FIE(Sri Lanka), MICE(UK), PG. Dip., M.Eng., D.Eng.,
Senior Professor in Civil Engineering, University of
Moratuwa.
57
ENGINEER
The framework consists of five steps. The first
step identifies the stakeholders and the
uses/issues of the natural resources in the total
area impacted by the project, through the
systematic consultation of stakeholder groups.
The critical bounds of the technical
requirements of the resource uses and issues
identified in step one are then estimated in step
two.
The
economic
value
and
the
environmental (including social) costs of the
respective critical bound of the technical
requirements are estimated in the third and the
fourth steps. The fifth step combines the
economic estimates and the environmental and
social costs of critical bounds, to form the basis
for ‘Educated Trade-offs’ for stakeholder
consultations [16], [17].
discount rate and, throughout the assumed life
of a development project [11]. Then, the
fundamental relationship to determine the
Economic Net Present Value (EcoNPV) of a
resource use can be expressed as;
n
EcoNPV
i 0
Economic Internal Rate of Return (EIRR) is the
most often cited method for comparing
alternatives in development projects. The EIRR
analysis calculates the return from the
development project as a non-dimensional
measure. Present value formulations are the
foundations for EIRR calculation as the EIRR is
calculated by equating EcoNPV given in
equation 2.1 to zero and solving for the
discount rate that allows the equality [11].
Therefore, Internal Rate of Return (IRR) is
generally defined as the rate at which PV of
costs is equal to the PV of benefits, or the rate at
which the NPV is equal to zero.
The IRR preference (ranking) for an alternative
always agrees with that of the NPV preference
for
projects,
which
are
economically
independent of one another (i.e. the selection of
a particular project does not preclude the
choice of the other). When the alternatives are
mutually exclusive, there can be reversal of
rankings.
The next section presents an overview of the
economic theories that are used for these
analyses. The third section describes the case
study (the proposed Yatimahana reservoir) and
the baseline scenario. The fourth section
presents estimates of combined economic
values of the water infrastructure project. The
conclusions are given in the fifth section.
Overview
Theories
of
the
2.1
Economic Indicators
Alternatives are mutually exclusive when the
selection of one alternative eliminates the
opportunity to invest in any of the others. Most
problems in development projects normally fit
into this category because a single course of
action is sought to solve a particular, often
urgent, problem. When the best alternative is
determined, the problem is theoretically
resolved by implementing the indicated course
of action [13].
Economic
Economic Net Present Value (EcoNPV) and
Economic Internal Rate of Return (EIRR)
analyses are frequently used to determine the
difference in economic benefits and economic
costs of a water infrastructure project.
In choosing between alternatives (i.e. different
resource uses), the criterion is to select the one
that maximizes EcoNPV. For instance, an
EcoNPV of Rs. Z means that the PV of the
alternative (resource use) is Rs. Z greater than
on an investment of similar size that produces a
rate of return equal to the discount rate or the
Minimum Acceptable Rate of Return (MARR).
The Present Value (PV) of net benefits and costs
over time is its value today, usually
represented as time zero in a cash flow
diagram. In other words, it is the value
obtained by discounting the benefits and costs
separately for each year over time at a constant
ENGINEER
[2.1]
Where Bi is the annual economic benefits from
the use of resource at the ith year and Ci is the
economic cost of the resource use at the ith year.
‘n’ is the duration of the study period and ‘r’ is
the discount rate [17].
This paper describes the application of the step
1 of the above framework briefly and steps 3
and 4 in detail, which are relevant to the
objectives of this paper.
The case study
application highlighted two main factors; a) the
erroneous approaches in the methodologies
employed by the project analysts in performing
economic analysis of the water infrastructure
project b) the inability of general approaches to
capture ground realities in the economic
analysis. Both the above factors resulted in
obtaining wrong economic indicators that
would mislead the decision makers.
2.
Bi C i
(1 r ) i
58
A negative PV means that the alternative does
not satisfy the rate of return requirement, as
MARR reflects the opportunity cost of capital.
In other words, the possible return the
economy would obtain is lower on the same
amount of capital than if invested elsewhere at
MARR, assuming that the risks are similar for
both investment alternatives [11, 12].
flow techniques to measure benefits and costs
as in the case of the financial analysis, economic
valuation requires the use of economic
techniques of measurement.
Then, the merit of a resource use is assessed
with regard to the impact that use has on the
efficiency of the economy as a whole [7]. The
market prices are adjusted to reflect the
opportunity cost (or shadow price) of goods
and services. Then, the cost to society of the
project is measured in terms of forgone
marginal products of inputs, had they been
used in the next best alternative to the resource
use. The outputs of the resource use are valued
based on their demand price in the absence of
market distortions [7].
2.2 Financial Analysis
The starting point of an economic analysis of a
resource use is the financial analysis of that
resource use. The financial analysis measures
the receipts (benefits) and payments (costs)
relevant to the investors or owners of the
resource/project. It is a tool that provides
investors with the information required to
decide whether to undertake an investment.
Hence, the objectives of the financial analysis
are to determine, analyse and interpret all
financial consequences that may be relevant to
and significant for investment and financing
decisions [11].
Ideally, the price of every input and output
should be adjusted so that “shadow prices” can
be approximated. Once the shadow (economic)
price is known, a conversion factor of the ratio
of the economic price to the market price, is
used to facilitate this adjustment. When the
conversion factors are approximated, computer
spreadsheet programs easily facilitate the
transformation of the financial analysis to the
economic analysis. Instead of replacing all
financial values with economic values, financial
values can be multiplied by the conversion
factors to yield economic values [11], [4]. The
complexity of finding the economic price
depends on the nature of the good or service
being considered.
The financial analysis of a resource use/project
is typically carried out at market prices
prevailing at the time of the analysis [7]. The
estimates for costs and benefits (receipts) are
therefore in terms of prevailing market prices.
Taxes and subsidies (transfer payments),
foreign exchange distortions, monopoly rents,
and externalities influence market prices.
Market distortions cause market prices to
diverge from economic prices [7].
The third step of the ‘Educated Trade-off’
framework uses these reasoning to estimate the
economic value of the water infrastructure
project.
2.3 Economic Analysis
According to Jenkins and Harberger [7], for
inputs, market prices would reveal the
productive value of an item in its next best use.
The market prices of an output would signal
the level at which the consumer's marginal
WTP for an item just equals the cost of
producing that item (marginal cost). Instead,
market distortions cause market prices to
diverge from "economic prices." For example,
taxes increase prices and reduce demand. The
effect of subsidies is opposite [7], [2].
2.4 Extended Economic Analysis
A negative outcome often linked with the water
resources development is the environmental
degradation that occurs due to the economic
activities [10]. Inclusion of such costs (and
benefits) in the water infrastructure project is
the basis for the ‘Extended Economic Analysis.’
It is imperative to value such impacts to the
environment in related decision making [8], [9],
[6]. Birol et al. [3] defines the role of economic
valuation techniques in the design of efficient,
equitable and sustainable policies for water
resources management in the face of
environmental problems.
The financial analysis relies on cash flow
techniques to compare and analyse the
estimated receipts (benefits) and costs. An
economic analysis is of exactly the same nature
as a financial analysis, except in the case of an
economic analysis, the benefits and costs are
measured from the point of view of the
economy [7]. Instead of relying solely on cash
59
ENGINEER
the environmental and social costs of the water
infrastructure project.
The net environmental cost of a project is the
difference
between
the
environmental
(including social) benefits and the social and
environmental costs of the other uses due to
that project. It must be noted that net social and
environmental costs referred to here are those,
which
are
either
not
quantified
or
underestimated, in the economic analysis. The
fourth step uses these reasonings to estimate
The fifth step of the ‘Educated Trade-off’
framework estimates the combined value of the
water infrastructure project. This in fact is the
net present value of a scenario obtained by
performing the extended economic analysis.
MA OYA BASIN
Figure 1 – Ma Oya river basin
3.
Case Study: Yatimahana Multipurpose Balancing Reservoir
Project
3.1
River Basin
Highly stressed surface water resource
situations are experienced during the 6-8 weeks
of the dry season [5]. Thus during the low flow
periods the two major uses (water supply and
pollutant carrier) are conflicting with each
other and results in critical water stressed
situation both due to inadequate quantity and
poor quality [5], [19].
Ma Oya river commences in the central hilly
regions and flows to the Indian Ocean through
north western Sri Lanka. The river drains a
catchment area of 1528 km2 along its total
length of 130 km [5]. (See Figure 1).
The NWSDB, a key stakeholder of the river
basin has proposed a multi-purpose balancing
reservoir in the upper catchment at Yatimahana
(See Figure 1) as the best option in an attempt
to mitigate the expected severe water shortages
in the near future due to the increasing
demands, [15].
The river flows are mainly used for supplying
drinking water to 17 major population centers,
two major industrial zones and also some
private water supply schemes. The next major
use of the river flow is as a pollutant carrier
(absorber) from a number of cities as well as
private dwellings located on the riverbanks and
from a number of industries located in the river
valley.
ENGINEER
The objective of this reservoir project is to store
the excess flows of the river during rainy
seasons and then to release the required flows,
under control, during the dry weather periods.
The proposal acknowledges the importance of
Integrated Water Resources Management
60
(IWRM) and has considered irrigation, industry
and hydropower sectors in addition to the
water
supply
and
sanitation
sector.
Hydropower is proposed mainly as a strategy
for achieving the economic viability of the
reservoir project [15].
sectors. Second are the other use sectors, which
are more localised in nature; such as recreation
and tourism.
Use sectors like hydropower, tourism, sand and
clay mining have created localised adverse
impacts to the environment and local
populations.
Already
documented
environmental and social impacts by the
different use sectors range from drying up of
springs used for drinking and household needs
of the villagers at upstream locations to dried
up well and abandoned paddy lands [12], [19].
3.2 Baseline Scenario
The resource uses and issues of a river show a
significant variation spatially and temporally. It
is therefore vital to establish the baseline
situation and the possible impacts by the water
infrastructure project prior to engaging in
economic analyses. The baseline situation
should be established by carrying out
systematic surveys covering the entire area that
is expected to be impacted by the project.
An interesting finding of the baseline scenario
was the current situation of the irrigation
sector. The project
economic analysis
considered three irrigation projects down
stream of the Yatimahana reservoir as
contributing economically. However, Yaka
Bendi Ela scheme is still a proposal while the
other two, Pannala and Makandura lift
irrigation schemes are abandoned. The
economic analysis assumed an annual income
of Rs. 100,000,000 from these three irrigation
schemes.
A main feature of the ‘Educated Trade-off’
framework is the establishment of the baseline
scenario with regard to total value of the
resource against the current practice of limiting
the analyses mostly to direct use values.
The first step of the ‘Educated Trade-off’
framework is developed on the premise that
the establishment of the most accurate baseline
situation for identifying the resource uses is
through the bottom level (grass-root)
stakeholder consultations. The methodology
used for the identification of stakeholders and
issues was to conduct sample surveys along the
river banks at the smallest administrative unit
(Grama Niladhari Division – GND) level.
The stakeholder consultations at grass-root
level revealed that the real reason for
abandoning the two lift irrigation schemes was
not the inadequacy of water but the inability of
farmers to meet the cost of energy for lifting
water. More interestingly it was found that
parts of land under these schemes have been
reallocated under the political visions of the
area for other purposes such as housing
making it difficult to rehabilitate the schemes.
Therefore, the assumed irrigable area under the
three schemes of 453 ha., is in reality a fantasy.
In this case study, the sample constituted of 427
stakeholders from 145 GNDs along both river
banks from river estuary to Aranayake (a
location upstream of the proposed reservoir),
representing all sectors having a stake in the
river including public administration, public
and private institutions in river resource use
sectors. It also included representatives of
social, political, religious, and ethnic groups of
public at grass-root level. A detailed analysis of
the data collected through the sample survey is
presented
by
Thoradeniya
[17],
and
Thoradeniya and Ranasinghe [18], with regard
to the resource uses and stakeholders.
Another finding was the inadequacy of the
present head works, which are in a dilapidated
condition, for lift operations as the river water
level has dropped by about 5 - 6 m since the
time these schemes were abandoned.
The major resource use sectors, which could be
significantly impacted by the proposed water
infrastructure project, were identified in two
groups. First are the sectors which are spatially
widespread such as, water supply schemes,
industries using river water, dug-wells, rainfed agriculture and industrial waste disposal
4.
Economic analysis
Yatimahana project
4.1
Feasibility Study
of
the
The project feasibility study [15] identified
economic benefits of the project from power
generation, increased water sales, crop
production, land value increases and
generations of new business activities (Table 1).
61
ENGINEER
Crop prod.
(Irrigation)
Business
sector
230
114
101
146
192
103
75
149
141
145
163
228
196
187
182
88
22
98
134
80
0
4
28
42
2
43
73
22
17
18
3
0
1
0.5
3
13
37
21
15
15
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
130
130
130
130
130
130
130
130
130
130
130
130
130
130
130
130
130
130
130
130
reasonable economic analysis for such water
infrastructure projects:
Land value
Water
supply
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Hydro
power
Year
Table 1 – Project Benefits (in Rs. Millions)
a) Placement of costs and benefits on time axis
of cash flow diagram.
120
-
The reasonable approach to perform
economic analysis for an infrastructure
development project is to consider the
operation and maintenance costs (Ci-1, Ci,
Ci+1) that would incur during a specified
time unit (usually 1 year) at the beginning
of the period and the benefits (Ri-1, Ri, Ri+1)
at end of the period (Figure 2).
Ri-2
i-1
Ci-1
Ri
i
Ci
i+1
Ci+1
Time
(Years)
Figure 2 – Reasonable approach for costs and
benefits in the cash flow diagram
(Source: SWECO GRØNER, 2004)
The economic costs of the project were due to
capital costs, which include construction costs
of the dam and the powerhouse, refurbishment
cost of electrical and mechanical components
and land acquisition costs and recurrent costs
(Table 2).
Ri-1
Ri
i-1
Table 2 – Project Costs (in Rs. Millions)
Year
Item
Estimated
Cost
-2
Capital costs,
1, 352
operation and
maintenance costs,
-1
2, 028
resettlement costs and
compensation costs.
10
Rehabilitation cost
33
1 - 20
Annual operation and
9
maintenance
Ci-1
Ri+1
i
Ci
i+1
Ci+1
Time
(Years)
Figure 3 – Approach used by the project
consultants for costs and benefits in the cash
flow diagram
Even though, reality would be to consider
costs and benefits as they occur in time, the
above process provides a reasonable
approximation. However, in the economic
analysis by the project consultants [15],
both costs and benefits for a single duration
have been considered at the end of the
discounting period (Figure 3). This is a
common mistake that happens when
computer packages are used to estimate the
IRR, which yields an optimistic estimate.
(Source: SWECO GRØNER, 2004)
(Note: In Table 2, Year - 2 and - 1 indicates the time
before the project implementation or the
construction period).
The economic analysis of the benefits and costs
yielded an EIRR of 15.2% considering a project
life of 20 years [15].
The above analysis consisted of following three
deviations from the normal practice (a, b and c)
and two key deficiencies (d and e) from the
ENGINEER
Ri-1
62
b) Use of annual tariff increments
The economic analysis has not captured
important environmental (including social)
costs (or benefits) due to the project impacts. A
key cost that has been missed was from
recreation sector while key benefits from
tourism and industrial sectors have also been
missed. In addition, a key benefit that is missed
is the avoided social and environmental costs
by the beneficiaries of the future water supply
schemes as a result of Yatimahana reservoir.
The economic analysis should be on
constant values of the year on which the
analysis is done. This is to overcome any
over/under estimation of benefits for one
alternative against another. Then, the
comparison between alternatives or “go or
no-go” decision is based on constant
values.
In the study by the consultants [15], water
supply sector annual financial income was
estimated on the assumption that 80% of
the water is sold for domestic purposes and
the balance 20% is sold for commercial
purposes.
e) Lack of a systematic approach to establish
the baseline situation
In estimating the economic benefits from
the irrigation sector, the economic analysis
for example does not provide for the cost of
new infrastructure required for the
rehabilitation of existing schemes and the
construction of the new schemes as
discussed in section 4. Thus, lack of a
systematic approach to establish the
baseline situation has led to the total
income from crop production to be
considered as a benefit for the Yatimahana
reservoir project when in reality there was
no reported water deficiency for the
irrigation sector.
The tariffs for the first year had been taken
as the constant (2004) tariffs, which were
Rs. 2.90 and Rs. 42.00 for the domestic and
commercial
sectors,
respectively.
Thereafter annual tariff increases of 20%
and 16% had been used for the domestic
and commercial sectors claiming that these
assumptions are based on past experience.
This yielded a significant overestimation of
the benefits.
The tariff used for electricity sales is based
on the 2004 rates used by the Ceylon
Electricity Board in purchasing bulk
supplies from private sector. The rate
applied during the lean period of the year
(February, March and April) is Rs. 5.70 per
kwh while a rate of Rs. 4.95 is applied
during the rest of the period considering it
to be the wet period. In the analysis these
basic rates were then increased by 10% per
year for annual price escalations, which
again resulted in overestimation of benefits.
There is a dilemma on the acceptable value of
EIRR. One school of thought is that EIRR for
public utility projects could be very low (even
negative). The underlying argument is that
such projects have an immeasurable social
value (benefit) to the public over time [14]. An
attempt to value the cost of alternative source
of water for project beneficiaries (e.g. use of
bottled water for drinking) could have
captured at least part of this benefit.
Project life
The project life of civil structures such as
dams is usually taken as 50 years for
economic analysis. The cost incurred by
such massive structures cannot be
recovered during 20 years, which is a
relatively short period. Since the selection
of project life has been organisation
dependent rather than the project and its
components, there is an underestimation of
benefits.
The application of the third, fourth and fifth
steps of the ‘Educated Trade-off’ framework
developed by Thoradeniya [17] to the case
study rectified all of the above deviations and
deficiencies except one: the avoided social and
environmental costs by the beneficiaries of the
future water supply schemes as a result of
Yatimahana reservoir. This was because of nonavailability of data of all the water supply
schemes that would be enhanced by
‘Yatimahana’ project and lack of time and
resources to carry out such a study.
d) Inadequate effort to include the values of
full range of social and environmental
impacts.
The normal approach that should be used for
costs and benefits in cash flow diagram as
explained in Figures 2 and 3, brought the EIRR
c)
4.2 Improved Analysis
63
ENGINEER
down by 2% from 15.2% to 13.2%. Once the
corrections were applied for undue annual
tariff escalations, the EIRR became 8.29% for a
project life of 20 years (see Table 3).
(floods) do not permit the elephants to
use the river for bathing due to the
possibility of small elephants being
washed away. In this instance the
impacted sector are the business
enterprises which cater to the tourists
and situated along the route taken by the
elephants from the orphanage to the river
and not the orphanage itself. The benefit
by the proposed balancing reservoir due
to reduced flood days to the tourism
sector is estimated at Rs. 2.59 million per
annum.
Table 3 – Economic Indicators
I%
EcoNPV
B/C
10
8.34
8.29
-412,627
- 15,003
0
0.871
0.995
1.0
PV
Envio
Benefit
+ 12,946
+15,003
+17,695
Comb
NPV
-399,680
0
+ 17 695
B/C
0.875
1.0
1.004
An attempt was made to use longer durations
for project life considering the fact that the
major cost component of the project was for
civil constructions such as the dam, which has a
life span more than 20 years. A forty year life
span with additional refurbishment costs for
electrical and mechanical components at 10
year interval increased the EIRR by 2.34%.
The total Environment (including social)
benefits of the project is therefore Rs. 1.84
millions per annum which raise the EIRR by
0.05%.
The fifth step of the ‘Educated Trade-off’
framework facilitated the estimation of a
combined value for the project scenario by
adding the economic value and the
environmental costs (e.g. Combined NPV for
the project after corrections discussed under
(a), (b) and (d) was Rs. - 399,681 with an EIRR
of 8.34%. See Table 1). Further, analysis
indicated that an additional annual benefit of
Rs. 48.3 million over 20 years would bring the
project EIRR to the MARR of 10%.
Stakeholder consultation survey carried out in
step-1 of the ‘Educated trade-off framework’,
contributed in two ways: First, the stakeholder
consultations revealed the ground reality
regarding the irrigation sector as discussed in
section 4. Accordingly, the removal of irrigation
benefits brought the EIRR down by 3.79% to an
estimated value of 4.5% for the EIRR.
Second, it identified stakeholder concerns in
five use sectors which were expected to be
impacted by the project; Recreation sector, Rain
fed agriculture, Dug-well sector, Industry uses
and Tourism sector. Detailed studies on these
sectors concluded as follows [17]:
i. In the recreation sector the annual loss by
the inundation of a water fall by the
proposed reservoir is estimated as Rs. 1.2
million per annum.
ii. The impacts to the rain-fed agriculture
and the dug-wells depend on the
variation of river water levels during the
dry season which in turn affects the
ground water level in the vicinity. The
analysis in the step-2 of the framework
indicated the variations to the river water
levels in the down stream areas in the dry
season due to the project has a marginal
positive impact which is insignificant to
be estimated in economic terms.
iii. The industries are expected to benefit by
the flood mitigation effect of the
proposed reservoir due to the reduction
of turbid water. This positive impact is
valued at Rs. 0.45 million per annum.
iv. The upper river bank houses a tourist
attraction where an elephant orphanage
is operated at Pinnawala. The high flows
ENGINEER
An important benefit that could have
contributed to the proposed project at the
fourth step of the framework is the avoided
social and environmental cost to the people
who would be supplied with water under the
proposed reservoir project. The estimate of
these benefits were not possible at the time of
the study as the project reports of all the water
supply schemes that would be enhanced by
‘Yatimahana’ project were not available.
However, this is an important benefit that
water infrastructure projects especially in the
water supply sector should estimate.
5.
Conclusions
The economic analysis of the proposed
Yatimahana project on the Ma Oya basin
highlighted deviations and deficiencies from
the reasonable procedures for economic
analysis due to lack of knowledge, negligence
or on purpose.
The application of the ‘Educated Trade-off’
framework enabled identifying the baseline
situation and performing the extended
economic analysis with the inclusion of values
64
of a range of possible environmental (including
social) impacts from the Yatimahana project.
From the experience gained in the case study
the following conclusions with regard to the
economic analysis of water infrastructure
projects can be drawn.
a) The timing of costs and benefits should
be properly used for the economic
analysis when computer packages are
used to estimate the IRR.
b) Economic analysis should be on
constant
values.
Unreasonable
escalations of annual tariff over
estimates the decision parameters, and
gives optimistic results for decision
makers.
c) Use of the ‘Educated Trade-off’
framework [17] in water infrastructure
development projects is recommended
for its multiple advantages. In an
economic analysis it facilitates the
establishment of baseline situation and
identifies the use sectors that could be
impacted by the proposed project.
d) The economic analyses should include
the avoided social and environmental
costs to people who would be
benefitted by the water infrastructure
development project.
5.
DHI, “Working Document A: Maha Oya
River Basin, Detailed Basin Assessment”,
Western River Basin Sector Project TA 3030 – SR
,I Danish Hydraulic Institute, 1999.
6.
Emerton, Lucy and Kekulandala, L. D. C. B.,
“Assessment of the Economic Value of 3.1
River Basin Muthurajawela Wetland”,
Occasional Papers of IUCN, Sri Lanka, 2003.
7.
Jenkins, G. P. and Harberger, A. C., “Manual
Cost-Benefit
Analysis
of
Investment
Decisions”, Harvard Institute for International
Development, 1992.
8.
Markandya, A. and Richardson, R.,
“Environmental
Economics”,
London,
Earthscan Publications Ltd., 1992.
9.
Munasinghe, Mohan and Ernst Lutz.
“Environmental-Economic Evaluation of
Projects and Policies for Sustainable
Development”, Environment Working Paper
No. 42, World Bank, 1991.
10.
Panayotou Theodore., “Green Markets, The
Economics of Sustainable Development”, A co
publication of International Center for Economic
Growth and The Harvard Institute for
International Development, USA, Institute of
Contemporary Studies Press, 1993.
11.
Ranasinghe, M., “Risk and Uncertainty
Analysis of Natural Environmental Assets
Threatened by Hydropower Projects: Case
Study from Sri Lanka,” Energy for Sustainable
Development, 2002, Vol. VI(1), p 56-63.
12.
Ranasinghe,
M.,
“Reconciling
Private
Profitability and Social Costs: The Case of
Clay Mining in Sri Lanka”, Project Appraisal,
Beech Tree Publishing, U.K., Vol. 12, No.1,
pp.31-42. 1997.
13.
Riggs, J. L., Rentz, W. F. and Khal, A. I.,
“Essentials of Engineering Economics”,
Toronto, McGraw-Hill Ryerson Ltd., 1983.
14.
Siyambalapitiya, T., “Solace for Land
Transportation in Sri Lanka - Railway
Electrification and Its Economics”, John
Diandas Memorial Lecture, Institute of
Engineers, Sri Lanka, 2009.
15.
SWECO GRØNER, “Improvement of Water
Availability for Water Supply Schemes from
Maha Oya”, final report, Volume I – Main
report, 2004.
16.
Thoradeniya, B. and Ranasinghe, M., “A
Methodology for Effective Stakeholder
Participation through Educated Trade-Offs”,
Transactions of Institution of Engineers Sri
Lanka, 2006.
Acknowledgement
This research study was carried out with a
grant from the International Development
Research Centre, Ottawa, Canada which is
gratefully acknowledged.
References
1.
ADB, Economic Evaluation of Environmental
Impacts. A Workbook, Asian Development
Bank, Manila, Philippines, 1996.
2.
Baneth Jean, “Selecting Development Projects
for the World Bank,” Discussion Paper, 322,
World Bank, 1996.
3.
Birol Ekin, Katia Ka, Rousakis and Phoebe
Koundouri. “Using Economic Valuation
Techniques to inform Water Resources
Management: A Survey and Critical
Appraisal of Available Techniques and an
Application,” Science of the Total Environment,
2006, Vol. 365, p 105-122.
4.
Curry, S. and Lucking, R., “Shadow Prices for
Sri Lanka, Sri Lanka”, National Planning
Department, 1992.
65
ENGINEER
17.
Thoradeniya, B, “Stakeholder Involvement in
the Decision Making for Development
Projects Using ‘Educated Trade-Offs’”, Ph D
thesis, 2009.
18.
Thoradeniya, B. and Ranasinghe, M., Key
note address, International Conference in
Building Education and Research, Kandalama,
Sri Lanka., 10-15 February, 2008.
19.
Thoradeniya, W. B. M., “Evaluation Report
on the longest serving Area Water
Partnership in Sri Lanka”, Lanka Jalani,
Colombo, 2005.
20.
Vedula, S. and Mujumdar, P. P., “Water
Resources Systems, Modeling Techniques and
Analysis”, New Delhi, Tata McGraw-Hill
Publishing Company Limited, 2005.
ENGINEER
66

July 2011 - The Institution of Engineers Sri Lanka

Transcription

Similar documents

FINA confirms Daniel, Mayumi for Olympics Billionaire

community - DailyNews Archives

Improving Standards of Care - Global Social Service Workforce

Panel 1 - Public Space

The Blue of the Blue Planet

Wins 10 of SL`s 23 swimming medals

Bankless Channel

Lockwood Folly River Restoration Plan

Dr Hussain Gasim Abdelgadir Emergency Medicine specialist from

2014-10-22: Some Characteristics of Taaffeite from Myanmar