borang pengesahan status tesis - Universiti Teknologi Malaysia

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borang pengesahan status tesis - Universiti Teknologi Malaysia
PSZ 19 : 16 (Pind. 1/97)
UNIVERSITI TEKNOLOGI MALAYSIA
BORANG PENGESAHAN STATUS TESIS♦
JUDUL :
FLASH FLOOD STUDY AT BUNUS RIVER CATCHMENT AREA BY
USING HEC-HMS COMPUTER PROGRAM
SESI PENGAJIAN : 2004 / 2005
WONG XIN EE
Saya
(HURUF BESAR)
mengaku membenarkan tesis (PSM/Sarjana/Doktor Falsafah)* ini disimpan di Perpustakaan
Universiti Teknologi Malaysia dengan syarat-syarat kegunaan seperti berikut:
1.
2.
3.
4.
Tesis adalah hakmilik Universiti Teknologi Malaysia.
Perpustakaan Universiti Teknologi Malaysia dibenarkan membuat salinan untuk tujuan
pengajian sahaja.
Perpustakaan dibenarkan membuat salinan tesis ini sebagai bahan pertukaran antara institusi
pengajian tinggi.
** Sila tanda ( √ )
√
SULIT
(Mengandungi maklumat yang berdarjah keselamatan atau
kepentingan Malaysia seperti yang termaktud di dalam AKTA
RAHSIA RASMI 1972)
TERHAD
(Mengandungi maklumat yang TERHAD yang telah ditentukan
oleh organisasi/badan di mana penyelidikan dijalankan)
TIDAK TERHAD
Disahkan oleh
(TANDATANGAN PENULIS)
Alamat Tetap: 15, JALAN BUDIMAN 3/3,
TAMAN PUTRA BUDIMAN,
43200 BALAKONG,
SELANGOR.
Tarikh:
15 MAC 2005
(TANDATANGAN PENYELIA)
EN. KAMARUL AZLAN BIN MOHD NASIR
Nama Penyelia
Tarikh:
15 MAC 2005
CATATAN: *
Potong yang tidak berkenaan.
** Jika tesis ini SULIT atau TERHAD, sila lampirkan surat daripada pihak berkuasa/organisasi
berkenaan dengan menyatakan sekali sebab dan tempoh tesis ini perlu dikelaskan sebagai
SULIT atau TERHAD.
♦ Tesis dimaksudkan sebagai tesis bagi ijazah Doktor Falsafah dan Sarjana secara penyelidikan,
atau disertai bagi pengajian secara kerja kursus dan penyelidikan, atau Laporan Projek
Sarjana Muda (PSM).
“I/We* hereby declare that I/we* have read this thesis and in my/our*
opinion this thesis is sufficient in terms of scope and quality for the
award of the degree of Bachelor of Engineering(Civil)”
Signature
:
Name of Supervisor :
EN. KAMARUL AZLAN BIN MOHD NASIR
Date
15 Mac 2005
* Delete as necessary
:
FLASH FLOOD STUDY AT BUNUS RIVER CATCHMENT AREA BY USING
HEC-HMS COMPUTER PROGRAM
WONG XIN EE
A thesis submitted in fulfilment of the
requirements for the award of the degree of
Bachelor of Engineering (Civil).
Faculty of Civil Engineering
Universiti Teknologi Malaysia
MARCH 2005
ii
I declare that this thesis entitled
“Flash Flood Study at Bunus River Catchment Area by Using HEC HMS”
is the result of my own research except as cited in the references.
The thesis has not been accepted for any degree and
is not concurrently submitted in candidature of any other degree.
Signature
: ....................................................
Name
: WONG XIN EE
Date
: 15 MARCH 2005
iii
To my beloved father, mother and brother
Thank you for your love
and support all these years.
iv
ACKNOWLEDGEMENT
Throughout the preparation of this thesis, I have been guided by the expertise
of many people along my way. Encik Kamarul Azlan bin Mohd Nasir, who is my
thesis supervisor has been very helpful in my thesis writing process. He has always
been enthusiastic in checking my work for accuracy, assisting me with problems and
offering numerous suggestions for my own improvement. I really appreciate Encik
Kamarul for his patient guidance, advices and giving me such a wonderful
opportunity to gain more knowledge.
In addition, I am indebted to Puan Nor Rozaini bt Abdullah, an engineer from
the Department of Drainage and Irrigation (DID) Wilayah Persekutuan for supplying
relevant literatures and flood statistics.
I would also like to acknowledge my coursemates especially Nurul Aida and
the rest of my friends, for their encouragement, motivation and assistance in
everything.
At last, I thank my wonderful family for continuing to support and understand
my commitment towards this thesis writing.
Without the help of such cooperative efforts, this thesis would not have been
the same as presented here.
v
ABSTRACT
Since a few years back, flooding problem in the Kuala Lumpur city has
worsened as a result of rapid development in the catchment area. Jalan Tun Razak,
Chow Kit and Kampung Baru which are located at the downstream of the Bunus
River catchment area are particularly prone to flash floods. Therefore, this flash
flood study is conducted with the aid of HEC-HMS computer software to regulate
upstream storm water to prevent flooding at downstream area. Using HEC-HMS
gave satisfying results and it is suitable for simulating future urbanisation. 15 April
2004 storm event is selected for calibration process and its Efficiency Index yields a
high percentage of 96.48%. The validation process using 7 April 2004 storm event
also achieved a good percentage of 97.47%. After calibration process, all the
hydrology parameters of the catchment area are used in the rainfall design of 100
years ARI to estimate peak flow with HEC-HMS. The results shows that the present
channel capacity of 37 m3/s is more than enough to accommodate the design rainfall
of 100 years ARI which is only 8.22 m3/s. This proves that flash flood is unlikely to
happen at the upstream area and does not play a part in contributing flood to the
downstream area. However, the Event Mean Concentration (EMC) for Suspended
Solids (SS) is high with a mean of 1329.21 mg/l and median of 1429.6 mg/l.
vi
ABSTRAK
Sejak kebelakangan ini, masalah banjir di Bandaraya Kuala Lumpur menjadi
semakin serius disebabkan pembangunan yang pesat di kawasan tadahan. Jalan Tun
Razak, Chow Kit dan Kampung Baru yang terletak pada bahagian hilir kawasan
tadahan Sungai Bunus adalah di antara kawasan yang sering dibanjiri. Oleh itu,
kajian banjir kilat ini dijalankan dengan bantuan perisian komputer HEC-HMS untuk
mengawal air larian di bahagian hulu sungai untuk mengelakkan daripada berlakunya
banjir di bahagian hilir. Dengan menggunakan HEC-HMS, keputusan yang
dijangkakan adalah memuaskan dan ini membuktikan bahawa HEC-HMS sesuai
digunakan bagi menjalankan simulasi urbanisasi masa depan. Kejadian hujan 15
April 2004 dipilih untuk proses kalibrasi dan Indeks Keberkesanan mencapai peratus
yang tinggi iaitu 96.48%. Proses validasi yang menggunakan kejadian hujan 7 April
2004 juga mencapai peratus yang baik iaitu 94.47%. Selepas proses kalibrasi, semua
parameter hidrologi kawasan tadahan digunakan dalam rekabentuk hujan 100 tahun
ARI untuk menganggar aliran puncak dengan menggunakan HEC-HMS. Keputusan
menunjukkan bahawa kapasiti sungai sedia ada iaitu 37 m3/s adalah lebih daripada
mencukupi untuk menampung rekabentuk hujan 100 tahun ARI iaitu 8.22m3/s sahaja.
Ini menunjukkan bahawa banjir kilat tidak mungkin berlaku di kawasan hulu dan
tidak memainkan peranan dalam mengakibatkan banjir di bahagian hilir Sungai
Bunus. Akan tetapi, "Event Mean Concentration" (EMC) bagi pepejal terampai
adalah tinggi dengan purata 1329.21 mg/l dan median adalah 1429.6 mg/l
vii
TABLE OF CONTENTS
CHAPTER
1
TITLE
INTRODUCTION
1.1 Introduction
1
1.2 Research Problem
2
1.2.1 Bunus River Kuala Lumpur
2
PAGE
3
1.3 Objectives
5
1.4 Scope of Study
5
LITERATURE REVIEW
2.1 Computer Models and Softwares
7
2.2 Applications of Computer Modelling
8
2.3 Software Selection
9
2.4 GIS for Hydrologic and Hydraulic Modelling
9
2.5 Hydrologic and Hydraulic Models and Softwares
10
2.5.1 HEC-1
11
2.5.2 HEC-2 and HEC-RAS
12
2.5.3 Soil Conservation Service (SCS) TR-20
13
2.5.4 Storm Water Management Model (SWMM)
14
2.5.5 QHM
14
2.5.6 HSPF (Hydrologic Simulation Program Fortran)
15
2.5.7 MIKE 11
16
2.5.8 STORM (Storage, Treatment, Overflow, Runoff
16
Model)
2.5.9 Watershed Modelling System (WMS)
17
viii
2.5.10 XP-RAFTS2000
18
2.5.11 HEC-HMS
19
2.6 Choice of Hydrologic Software: HEC-HMS
19
2.7 Water Quality Models and Softwares
20
2.7.1 AQUALM-XP
21
2.7.2 QUAL2EU
22
2.7.3 Surface Water Modelling System (SMS)
22
2.7.3.1 RMA2
23
2.7.3.2 RMA4
23
2.7.3.3 SED2D
24
2.7.4 Storm Water Management Model (SWMM)
24
2.7.5 AQUASEA
25
2.7.6 WASP5/DYNHYD5
25
2.7.7 AquaChem
26
2.8 Alternatives for Water Quality Characteristics
26
Determination
2.8.1 Event Mean Concentration (EMC) Method
27
2.8.2 Pollutant Export Rates Method
29
2.8.3 Build-up and Wash-off Method
30
2.8.4 Universal Soil Loss Equation Method
31
2.9 Water Quality Characteristic Determination – EMC
34
Method
2.10 HEC-HMS Model Calibration and Validation
34
2.10.1 Methods of Calibration and Validation
35
2.10.1.1 Numerical Measures of Fit
35
2.10.1.2 Graphical Measures of Fit
38
2.10.2 Search Methods
2.11 Selection of Calibration and Validation Method
3
40
40
OPERATION FRAMEWORK
3.1 Introduction
41
3.2 Using the HEC-HMS Software
42
ix
3.3 Identify Basin Models
44
3.3.1 Hydrologic Elements
44
3.4 Identify Subbasin Elements
45
3.4.1 Subbasin Loss Methods
45
3.4.1.1 Initial and Constant
46
3.4.1.2 Estimating Initial Loss and Constant
46
Rate
3.4.2 Subbasin Transform Methods
47
3.4.2.1 Clark Unit Hydrograph
47
3.4.2.2 Estimating Time of Concentration and
49
Storage Coefficient
3.5 Meteorologic Models
3.5.1 Precipitation Methods
3.5.1.1 User Hyetograph
4
5
50
51
51
3.6 HEC-HMS Application Steps
52
3.7 Calibration
61
3.8 Annual Pollutant Load Estimate Formula
62
STUDY AREA
4.1 Introduction
63
4.2 Catchment Study
64
RESULTS AND ANALYSIS
5.1 Calibration and Validation Results
69
5.2 Efficiency Index
72
5.2.1 Efficiency Index for Calibration Process
73
5.2.2 Efficiency Index for Validation Process
74
5.3 100 Years ARI Rainfall Estimation
5.3.1 Rainfall Intensity-Duration-Frequency (IDF)
75
75
Relationships
5.3.2 Polynomial Approximation of IDF Curves
76
x
5.3.3 Design Rainfall Temporal Patterns
6
77
5.4 Comparison with Rational Method
80
5.5 Current Channel Capacity
80
5.6 Comments
82
5.7 Determination of Storm Water Runoff Quality
82
5.7.1 Event Mean Concentration (EMC)
83
5.8 Runoff Volume Estimation
85
5.9 Annual Pollutant Load
85
5.10 Comments
86
CONCLUSION AND RECOMMENDATION
6.1 Conclusion
87
6.2 Recommendation
88
REFERENCES
89
APPENDICES A - D
90-103
xi
LIST OF TABLES
TABLE NO.
TITLE
PAGE
1.1
Urban Drainage Projects at Bunus River
4
1.2
Flash Floods at Bunus River Catchment Area in 2003
4
2.1
Typical Event Mean Concentration (EMC) Values in
28
mg/L
2.2
Pollutant Export Equations for Urban Areas
29
2.3
HEC-HMS objective functions for calibration
36
3.1
Simulation Methods for Finding Runoff Volume
41
5.1
Parameters after Calibration and Validation Process
69
5.2
Calculation table for efficiency index of calibration
73
process
5.3
Calculation table for efficiency index of calibration
74
process
5.4
Coefficients of the fitted IDF Equation for Kuala
76
Lumpur
5.5
Rainfall Temporal Patterns
78
5.6
100 ARI Rainfall Data
78
5.7
Calculation example of EMC
83
5.8
EMC values for 8 storm events taken
84
5.9
Typical Event Mean Concentration (EMC) Values in
84
mg/l
5.10
Runoff Volume Calculation for 50% DirectlyConnected Impervious Area
85
xii
LIST OF FIGURES
FIGURE NO.
TITLE
PAGE
1.1
Schematic of Calibration Procedure
6
2.1
How well does the computed hydrograph “fit”?
38
2.2
Scatter plot
39
2.3
Residual plot
39
3.1
Clark Unit Hydrograph Computation of tc
50
3.2
Create a New Project
52
3.3
Project Definition Screen
52
3.4
Create Precipitation Gages
53
3.5
New Precipitation Record Screen
53
3.6
Time Parameters Screen
54
3.7
Data Editor Screen
54
3.8
Create a Basin Model
55
3.9
New Basin Model Screen
55
3.10
Add Hydrologic Elements
56
3.11
Enter Loss Rate Parameters
56
3.12
Enter Transform Parameters
57
3.13
Select Baseflow Method
57
3.14
Create a Meteorologic Model
58
3.15
Subbasin List Screen
58
3.16
Meteorologic Model Screen
59
3.17
Control Specification Screen
59
3.18
Run Configuration Screen
60
3.19
Compute
60
3.20
Optimization Manager Screen
62
xiii
4.1
Location of Bunus River in Kuala Lumpur
64
4.2
Location of Outlet near SMK Wangsa Maju Zon R1
65
4.3
Location of Study Area (Zone 5) in Bunus River
66
Catchment Area
4.4
Bunus River Study Catchment Area (Zone 5)
67
4.5
Present Landuse / Landcover Classification
68
5.1
Calibration Results for 15 April 2004 Storm Event
70
5.2
Calibration Results Summary Table for 15 April 2004
70
Storm Event
5.3
Validation Results for 7 April 2004 Storm Event
71
5.4
Validation Results Summary Table for 7 April 2004
71
Storm Event
5.5
100 years ARI hydrograph
79
5.6
100 years ARI peak discharge result summary table
79
5.7
Cross Section of Channel at the Outlet
81
6.1
Natural wet pond at study area
88
xiv
LIST OF EQUATIONS
EQUATION
TITLE
NO.
PAGE
2.1
Calculation of annual load using EMC Method
27
2.2
General form of the pollutant rate equation
29
2.3
Daily runoff equation
30
2.4
Modified Soil Loss Equation (MSLE)
31
2.5
Rainfall factor equation
32
2.6
Annual erosivity equation
32
2.7
Soil-erodibility factor equation
32
2.8
Length-steepness factor equation
32
2.9
Vegetation Management Factor
33
3.1
Typical time-area relationship used in HEC-HMS
49
3.2
Calculation of annual load using EMC Method
62
5.1
Efficiency index equation
72
5.2
Formula of SSTotal
72
5.3
Formula of SSError
72
5.4
Polynomial expressions in the form of equation
76
5.5
Rainfall depth equation
77
5.6
Rational method formula
80
5.7
Manning’s formula for calculation of channel capacity
80
5.8
EMC formula
83
xv
LIST OF SYMBOLS
A
-
Area
a
-
Fitting constants dependent on ARI
b
-
Fitting constants dependent on ARI
C
-
Concentration
Co
-
Dimensionless runoff coefficient
c
-
Fitting constants dependent on ARI
d
-
Fitting constants dependent on ARI
Fi
-
Simulated discharge at time i
R
It
-
Average rainfall intensity (mm/hr) for ARI R and duration t
N
-
Number of discharge data
n
-
Manning’s roughness coefficient
P
-
Wetted perimeter
Q
-
Discharge
Qav
-
Mean of observed discharge
Qi
-
Observed discharge at time i
QR
-
R year ARI peak flow (m3/s)
R
-
Average return interval (years)
Ra
-
Hydraulic radius
2
R
-
Proportionate reduction in error
So
-
Friction slope
t
-
Duration (minutes)
tc
-
Time of Concentration
Σ
-
Sum
xvi
LIST OF ABBREVIATIONS
ARI
-
Average Recurrence Interval
DID
-
Department of Irrigation and Drainage
EMC
-
Event Mean Concentration
Eq.
-
Equation
HEC-HMS
-
Hydrologic Engineering Center's Hydrologic Modelling System
IDF
-
Intensity-Duration-Frequency
KL
-
Kuala Lumpur
MASMA
-
Manual Mesra Alam
SSError
-
Sum of squared error
SSTotal
-
Total sum of squared error
TSS
-
Total Suspended Solids
xvi
LIST OF APPENDICES
APPENDIX
A1
A2
A3
A4
A5
A6
A7
B1
B2
C
D
TITLE
Newspaper cutting (New Straits Time – Monday, 1
May 2000)
Newspaper cutting (Berita Harian – Thursday, 21
December 2000)
NSTP online News Archive (Malay Mail – 12 June
2002)
NSTP online News Archive (New Straits Time – 12
June 2002)
Newspaper cutting (Harian Metro – Wednesday, 11
June 2003)
Table of flash flood areas in Wilayah Persekutuan in
year 2003
Table of flash flood on 10 June 2003 at Bunus River
Table of incremental precipitation and discharge
data collected on 15 April 2004
Table of incremental precipitation and discharge
data collected on 7 April 2004
Table of Event Mean Concentration (EMC) for Total
Suspended Solids (TSS) for 8 events taken
Figures of equipments used
PAGE
90
91
92
94
95
96
97
98
99
100
103
CHAPTER 1
INTRODUCTION
1.1
Introduction
Kuala Lumpur City Centre is situated at the Klang River Basin which covers
an area of approximately 1290 km2. It consists of Wilayah Persekutuan area, a part of
Gombak district, Petaling Jaya and Ulu Langat in Selangor state [1].
Klang River has a length of about 120 km and it is the main river in Selangor
state [1]. Flash floods often occur in known flood plains in Kuala Lumpur as a result
of an intense rainfall within a brief period. These floods can happen "in a flash" with
little or no warning and the flood waters can reach full peak in only a few minutes.
The areas prone to flooding in Kuala Lumpur are the Klang River, Kerayong River,
Gombak River, Batu River and Bunus River.
Other than the forces of nature, human activities are among the major factors
of contributing flash floods in the city. As land in Kuala Lumpur is converted from
fields or forests to roads, parking lots and buildings, it loses its ability to absorb
rainfall. Its concrete or asphalt surface layer causes the land surface to be less
permeable. When heavy rain falls for days and days, it builds up too much water for
the groundwater system to handle.
Urbanization in Kuala Lumpur increases surface runoff volume two to six
times over what would occur on natural terrain [1]. Low permeability land comes
with increasing surface runoff volume will lead to reduction of carrying capacity of
drainage system. Poor drainage system causes drains, culverts or river to overflow
and flood the surrounding area. During periods of urban flooding, streets such as
Jalan Tun Razak, Jalan Dang Wangi, Jalan Yap Kwan Seng and Jalan Munsyi
Abdullah can become rivers, while basements can become death traps as they are
filled with water. Floods often cause destruction of properties and interruptions such
as traffic, social and economical activities.
Urbanization in Kuala Lumpur also affects the water quality. The filtering
effect of vegetation is lost when runoff from impervious areas is transported directly
to streams via the stormwater conveyance system. The increased rates of runoff
result in fewer particles settling out of the water stream before it reaches a receiving
water body; thus, suspended sediment is a major pollutant of urban runoff. However,
the introduction of paved or otherwise non erodible area that accompanies
urbanization may actually decrease upland sediment loss. The major sources of
pollution in urban areas include litter, dust fall, septic tanks, pet excretions,
chemicals (fertilizers and deicers), and wastes from cars.
1.2
Research Problem
In Peninsular Malaysia, the average annual rainfall is estimated at 2420 mm
and the total annual surface water resources is estimated at 147 billion m3 [1]. Since
a few years back, flooding problem in the Kuala Lumpur city has worsened as a
result of rapid development in the catchment area and obstructions in waterway flow.
Under the conventional method, excess water is discharged into the drains
and rivers as quickly as possible. This is referred to as the rapid disposal approach.
However, this is usually detrimental to downstream areas. The approach may have
3
worked well in the past but due the rapid urbanization, it is no longer possible for the
existing rivers to accommodate a large volume of storm water. When the water
volume exceeds the capacity of the conduits, it overflows and flooding takes place.
Thus, causing damage properties and loss of lives. Continuing deepening and
widening the drains and rivers is obviously not the answer. It costs too much and in
most cases there never is enough land or space for it. The answer lies in using
different approaches to these problems. Instead of regarding excess storm water as
the nuisance and getting rid of it as fast as possible, the new thinking focuses on the
storm water control at source, retention and detention to gain maximum benefits.
This study checks the storm water sources at the upstream of Bunus River
catchment area. This is to determine the suitability of the current channel capacity at
the upstream of Bunus River in order to regulate the upstream storm water. This
study also serves the purpose for the further development of storm water
management in order to cope and cater to the drain flood flows it is now facing.
1.2.1
Bunus River, Kuala Lumpur
The frequency of flash floods happening in Kuala Lumpur City Centre is
increasing by the year. Flood affected area in Kuala Lumpur is 13.18 km2, with
157,302 people and 35,750 urban area houses affected, causing an Annual Average
Damage of RM 99.3 million [2]. One of the rivers that contributes flood in Kuala
Lumpur City Centre is the Bunus River. Chow Kit, Jalan Tun Razak and Kampung
Baru which are located at the downstream of Bunus River catchment area are
particularly prone to flooding. On the 25th May 2002, Hospital Besar Kuala Lumpur
which is located in the catchment area of Bunus River was caught in a flash flood.
The estimated damage was RM 200 million. [3]
Table 1.1 gives the list of Urban Drainage Projects that has been implemented
by Dewan Bandaraya Kuala Lumpur (DBKL) at Bunus River.
4
Table 1.1: Urban Drainage Projects at Bunus River [2]
Name of Projects
Bunus River, Setapak Drainage
Construction Project (Phase II)
Bunus River, Setapak Drainage
Construction Project (Phase III)
Bunus River, Setapak Drainage
Construction Project (Phase IV)
Project Cost
Date of
Date of
(RM Mil)
Commencement
Completion
4.58
19-8-96
20-7-97
2.53
5-8-98
13-4-99
5.49
28-7-99
23-10-00
1.74
3-8-98
14-5-99
1.40
1-7-99
23-2-00
Drainage Enhancement of Bunus River
from Jalan Tun Razak to Kelab Sultan
Sulaiman, Jalan Raja Alang
Drainage Enhancement of Bunus River
from Jalan Raja Abdullah to Jalan Raja
Muda Aziz
Flooding areas at Bunus River have reduced due to the various flood
mitigation programmes carried out. However, it is still insufficient to eradicate floods
completely. Many serious flash floods have happened in year 2000 till 2004 (Refer
Appendix A1-A5). In 2003, Bunus River catchment area had been affected by flash
floods for 3 times. They were on the 10th June, 21st July and 14th August. Areas
flooded are shown in Table 1.2. The worst flood event in 2003 was on the 10th June
(Refer Appendix A5 – A7). The Bunus River water level reached its danger level at
36 m which exceeds the normal water level of 30 m by 6 m. About 180 people from
Kampung Periok were evacuated from their homes and were placed at the Dewan
Kelab Sultan Sulaiman. A great lost of properties was reported in this flood.
Table 1.2: Flash Floods at Bunus River Catchment Area in 2003 [4]
Area
10 June
1. Jalan Tun Razak (Wisma Bernama)
√
2. Kg Periok
√
3. Kg. Boyan
√
21 July
14 August
√
√
4. Kg. Doraisamy
5. Around Bunus River
√
5
1.3
Objectives
The objectives of this Flash Flood Study at the Bunus River Catchment Area are:
•
to determine the suitability of the current channel capacity for 100 years
ARI at the upstream of Bunus River by using HEC-HMS
•
to compare the peak flow rate obtained from the HEC – HMS simulation
software with the observed data
•
to estimate the magnitude of annual pollutant load using Event Mean
Concentration (EMC) Method
1.4
Scope of Study
The scope of study is to determine the accuracy of the results derived from
HEC-HMS computer software. This is done by comparing the simulated runoff with
the measured stream flow data collected by the consultant for the period of 6 months.
Following are the few steps in the scope of study:
(a)
Collect Hydrological Data
Rainfall data and flow rate data of Bunus River are collected by the consultant team
which comprises of Jabatan Hidraul dan Hidrologi (JHH) technicians and graduate
students. These data help in the understanding of past and present conditions within
the catchment.
(b)
Determination of Water Quantity Characteristic
The water quantity characteristics for the existing catchment conditions are
determined with the use of HEC – HMS computer software. Initial/Constant Method
is used for the Loss Rate Method and Clark Method is used as the Transform method.
First, calibration and verification on parameters of models are done to obtain a match
6
between predicted and measured output. Then, the rainfall data will be used as input
data for the HEC – HMS simulation software and the output will be the catchment
area flow rates and total runoff volume. Comparisons will be made between these
output data with the measured data collected from the DID. The flow chart for the
modelling process is described in Figure 1.1.
Figure 1.1: Schematic of Calibration Procedure [5]
(c)
Determination of Storm Water Runoff Quality
Storm water pollutants are transported and accumulated in the runoff. Pollutant
loads are thus estimated according to the total runoff volume obtained from the
modelling earlier. The pollutant loads are estimated using the method of Event Mean
Concentration (EMC).
(d)
Conclusion
Summarise the results of comparisons between predicted output and measured data.
Lastly, make conclusions.
CHAPTER 2
LITERATURE REVIEW
2.1
Computer Models and Softwares
Much new computational software has been developed world-wide based on
the intensive research effort in urban hydrology, hydraulics and storm water quality.
There are four basic groups for computer models:
1) single-even rainfall-runoff and routing models
2) continuous-stream-flow simulation models
3) flood-hydraulics models
4) water-quality models
An engineer with access to computer facilities should normally choose one of
these tools, according to his design objectives and the available resources. However,
proper use of such a new method or tool requires a good knowledge of the detailed
operations which the method or tool can perform. In other words, the engineer
should have knowledge of the hydrological, hydraulic and water quality processes
simulated by the tool he is planning to use.
8
2.2
Applications of Computer Modelling
The US EPA defines models as processes which are "used to increase the
level of understanding of natural or man-made systems and the way in which they
react to varying conditions". By varying the input conditions, the user can examine
the effects of, for example, increased urban development on a drainage system.
Computer models use the computational power of computers to automate the
tedious and time-consuming manual calculations. Most models also include
extensive routines for data management, including input and output procedures, and
possibly including graphics and statistical capabilities.
In addition to the simulation of hydrologic and hydraulic processes, computer
models can have other uses.
They can provide a quantitative means to test
alternatives and controls before implementation of expensive measures in the field.
If a model has been calibrated and verified at a minimum one site, it may be used to
simulate non-monitored conditions and to extrapolate results to similar ungauged
sites.
Models may be used to extend time series of flows, stages and quality
parameters beyond the duration of measurements, from which statistical performance
measures then may be derived. They may also be used for design optimisation and
real-time control.
The analytical power of computer methods gives them major advantages over
manual techniques. This is likely to result in more accurate designs, with cost
savings by avoiding over- or under-sizing. A very important factor is that almost all
computer models can fully account for storage in all stages of the hydrologic/
hydraulic routing.
9
2.3
Software Selection
Hydrological models are simplified representations of the natural hydrologic
system. In each case, the choice of the model to be applied depends mainly on the
objective of the modelling but also on the data availability. For instance, in order to
study flooding frequency and classification of a wetland when discharge information
is missing, the application of a continuous model to obtain long-term discharge series
flowing from upstream basins into the wetland is appropriated.
Many times the lack of data delays the direct application of a model. In this
case, there are also alternatives to overcome this situation. The application of
numerical models as an indirect method to generate needed data for further
modelling is a useful alternative procedure.
The other important factor is familiarity of the potential user with techniques
employed by the software. Inferior techniques applied by a knowledgeable engineer
will often produce much more reliable results than a sophisticated model that the user
does not understand.
The factors mentioned above may help in the selection of modelling software.
However, sometimes the choice is made on much more pragmatic grounds. For
example, a Government agency may specify that certain software be used for reasons
of standardisation or local support may be available for a particular software.
2.4
GIS for Hydrologic and Hydraulic Modelling
Geographic Information Systems (GIS) enable the user to incorporate a wide range
of information about the physical system into a computer database. This can include
10
not only information about the ground surface, but details of the urban infrastructure
(water/wastewater, streets, electric, gas).
Rapid developments are occurring in the GIS field in order to integrate all the
elements described above into a complete mapping and hydrology/hydraulics
analysis and design package that can:
a) Provide watershed physical feature mapping
b) Compute hydrologic model input parameters such as catchment areas.
c) Model the rainfall/runoff process to determine design flows
d) Provide the capability for on-screen design of the system, including
conveyance structures and appurtenances
e) Optimise the final design
f) Map or draw the system as designed, including plan and profile drawings
of all structural components.
These developments can eliminate many of the repetitive calculations in
drainage design. Opportunities for linkage to GIS systems are an important factor in
the selection of computer models.
It should be noted that the requirements for checking and verification of
designs so developed will still be necessary (or perhaps even more important).
2.5
Hydrologic and Hydraulic Models and Softwares
Many computer programs for hydrologic and hydraulic modelling are
available to the engineering community. Most hydrologic models attempt to simulate
the rainfall-runoff process. This ensures that the effects of rainfall, the single most
important hydrologic variable, are properly taken into account.
11
The basic hydraulic and hydrodynamic equations are well known and
different hydraulic models take various approaches to solving these equations within
the bounds of user friendliness, reasonable computing requirements, and stability.
The number of modelling options is very large. Some of these programs have
been developed by the government and are of public domain. The reviews provided
here are representative of the best known operational models, but are not allinclusive.
2.5.1
HEC-1
HEC-1 is a computer model for rainfall-runoff analysis developed by the
Hydrologic Engineering Center of the U.S. Army Corps of Engineers. The program
develops discharge hydrographs for either historical or hypothetical events for one or
more locations in a basin. To account - to a certain extent - for spatial variability of
the system, the basin can be subdivided into subbasins with specific hydrologic
parameters.
The program options include: calibration of unit hydrograph and loss-rate
parameters, calibration of routing parameters, generation of hypothetical storm data,
dam safety applications, multiplan/multiflood analysis, flood damage analysis, and
optimization of flood-control system components. Uncontrolled reservoirs and
diversions can also be accommodated.
Precipitation excess is transformed into direct runoff using either unit hydrograph or
kinematic wave techniques. Several unit hydrograph options are available: it may be
supplied directly by the user, or it may be expressed in terms of Clark, Snyder, or
Soil Conservation Service unit hydrograph parameters. The kinematic wave option
permits depiction of subbasin runoff with elements representing one or two overland
flow planes, one or two collector channels, and a main channel.
12
2.5.2
HEC-2 and HEC-RAS
HEC-2 was developed by the Hydrologic Engineering Center of the U.S.
Army Corps of Engineers to compute steady-state water surface elevation profiles in
natural and constructed channels. Its primary use is for natural channels with
complex geometry such as rivers and streams. The program analyzes flow through
bridges, culverts, weirs, and other types of structures.
The encroachment computation option has been widely used in the analysis
of floodplain encroachments for the U.S. Federal Emergency Management (FEMA)
flood insurance program. There are several types of encroachment calculation
procedures, including the specification of encroachments with fixed dimensions and
the designation of target values for water surface increases associated with floodplain
encroachments. The program requires that three flow path distances be used between
cross sections: a channel length and left and right overbank lengths.
HEC-2 uses the standard direct step method for water surface profile
calculations, assuming that flow is one-dimensional, gradually varied, and steady.
The program computes water surfaces as either a subcritical flow profile or a
supercritical profile. Mixed subcritical and supercritical profiles are not computed
simultaneously. If the computations indicate that the profile should cross critical
depth, the water surface elevation used for continuing the computations to the next
cross section is the critical water surface elevation.
HEC-2 computes up to 14 individual water surface elevation profiles in a
given run. Usually a different discharge is used for each profile, although when the
encroachment or channel improvement options are used, the section dimensions are
changed rather than the discharge. The discharge can be changed at each cross
section to reflect tributaries, lateral inflows, or diversions.
In the last years, HEC has developed HEC-RAS, for River Analysis System,
which has the same features as HEC-2 but with a Windows interface. Besides the
user interface, no major differences between the programs have been observed.
13
2.5.3
Soil Conservation Service (SCS) TR-20
The U.S. Soil Conservation Service (SCS) TR-20 computer model is a single-
event rainfall-runoff model that is normally used with a design storm as rainfall input.
The program computes runoff hydrographs, routes flows through channel reaches
and reservoirs, and combines hydrographs at confluences of the watershed stream
system. Runoff hydrographs are computed by using the SCS curve number method,
based on land-use information and soil maps indicating soil type, and the SCS
dimensionless unit hydrograph defined by a single parameter, the watershed lag. TR20 utilizes the SCS methods given in the Hydrology section of the National
Engineering Handbook.
Watersheds are usually divided into subbasins with similar hydrologic
characteristics and which are based on the location of control points through the
watershed. Control points are locations of tributary confluences, a structure, a
reservoir, a diversion point, a damage center, or a flood-gauge location.
Historical or synthetic storm data are used to compute surface runoff from
each subbasin. Excess rainfall is applied to the unit hydrograph to generate the
subbasin runoff hydrograph. Base flow can be treated as a constant flow or as a
triangular hydrograph. A linear routing procedure is used to route flow through
stream channels. The modified Puls method (storage-indication routing) is used for
reservoir routing. As many as 200 channel reaches and 99 reservoirs or waterretarding structures can be used.
TR-20 has been widely used by SCS engineers in the United States for urban
and rural watershed planning, for flood insurance, and flood hazard studies, and for
design of reservoirs and channel projects. The SCS methodology is accepted by
many local agencies also. TR-55 is a simplified version of TR-20 that does rainfallrunoff modeling for a single watershed.
14
2.5.4
Storm Water Management Model (SWMM)
The original version of the Storm Water Management Model (SWMM) was
developed for EPA as single-event model specifically for the analysis of combined
drain overflows (Metcalf and Eddy Inc. 1971). Through continuous maintenance and
support, the software now is well suited to all types of storm water management from
urban drainage to flood routing and floodplain analysis. Version 4 (Huber and
Dickinson 1988; Roesner et al. 1988) performs both continuous and single-event
simulations.
SWMM is segmented into the Runoff, Transport, Extran, Storage/Treatment
and Statistics blocks for rainfall-runoff, routing and statistical computations. The
Runoff block provides five alternative hydrograph methods: the Runoff non-linear
method, kinematic wave, Laurenson routing, SCS Unit Hydrograph, and Time-area
methods. Water quality may be simulated in all blocks except Extran, and metric
units are optional. Ex-proprietary portions have been adapted for various specific
purposes and locales by individual consultants and other federal agencies, e.g.,
FHWA. Mainframe and microcomputer versions are available from EPA in Athens,
Georgia.
2.5.5
QHM
QHM is a Windows-based continuous watershed quantity and quality
simulation model intended for watershed management and stormwater design. The
program can also be used as an effective teaching tool. Most applications have been
watershed analysis usually related to selection and sizing of stormwater BMPs,
particularly, flow control and treatment ponds. QHM offers an important planning
and design tool to watershed managers and designers of stormwater quantity and
quality control systems.
15
QHM is capable of continuous simulation of:
2.5.6
•
Surface runoff and base flow
•
Detention pond routing
•
Reservoir routing
•
Soil freeze-thaw
•
Snowmelt and snow removal/disposal
•
Evaporation and evapotranspiration
•
Pollutant generation and routing
•
Stream erosion potential
HSPF (Hydrologic Simulation Program Fortran)
The HSPF Model (Hydrologic Simulation Program Fortran) is a U.S. EPA
program for simulation of watershed hydrology and water quality for both
conventional and toxic organic pollutants. The HSPF model uses information such as
the time history of rainfall, temperature and solar radiation; land surface
characteristics such as land use patterns; and land management practices to simulate
the processes that occur in a watershed. The result of this simulation is a time history
of the quantity and quality of runoff from an urban or agricultural watershed. Flow
rate, sediment load, and nutrient and pesticide concentrations are predicted. HSPF
includes an internal database management system to process the large amounts of
simulation input and output. HSPF is used frequently for generating models for
TMDL (Total Maximum Daily Load) studies.
16
2.5.7
MIKE11
MIKE11 is a powerful, and comprehensive 1-D dynamic flow model for
simulating hydrodynamic flows, water quality, and sediment transport in estuaries,
rivers, irrigation systems, channels, and other water bodies. MIKE11 has been used
since 1979 by consulting firms and government reviewing agencies worldwide. It
can be used for detailed design, management, and operation of both simple and
complex river and channel systems. In addition, it can be used to simulate
stormwater runoff and progressive inundation from river overflows and coastal storm
surges.
MIKE11 can be directly linked with the DHI MOUSE Stormwater and
Sanitary Sewer Model, allowing unparalleled urban drainage modeling. This allows
runoff, stormwater sewer, sanitary sewer, river, and floodplain modeling to be
performed simultaneously, allowing sophisticated simulations to be performed.
2.5.8
STORM (Storage, Treatment, Overflow, Runoff Model)
The STORM was developed and is maintained by the U.S. Army Corps of
Engineers’ Hydrologic Engineering Center and is intended primarily for use as an
urban model. It is a continuous lumped parameter model, simulating a watershed by
percent of land in each land use type. Runoff is routed first to treatment, then storage,
and then any excess is modeled as overflow. The STORM model uses a modified
rational method to compute runoff in developed areas and the SCS curve number
method to compute runoff in undeveloped areas. Suspended solids are computed
using the USLE and pollutant buildup is estimated with an exponential decay
equation.
17
The model runs on a one-hour time step to match hourly National Weather
Service data. Numerous watersheds have been used to test STORM, including one
reported by Warwick and Wilson (1990) in Dallas, Texas. The users had difficulty
determining the appropriate daily accumulation rates for total suspended solids
(among other water quality components) needed for input to the model. As a result,
the runoff quality computations did not match observed data as well as they expected
(Warwick and Wilson, 1990).
2.5.9
Watershed Modeling System (WMS)
The Watershed Modeling System (WMS) is a comprehensive graphical
modeling environment for all phases of watershed hydrology and hydraulics. WMS
includes powerful tools to automate modeling processes such as automated basin
delineation, geometric parameter calculations; GIS overlay computations (CN,
rainfall depth, roughness coefficients, etc.), cross-section extraction from terrain data,
and many more. With the release of WMS 7, the software now supports hydrologic
modeling with HEC-1 (HEC-HMS), TR-20, TR-55, Rational Method, NFF,
MODRAT, and HSPF. Hydraulic models supported include HEC-RAS and CE
QUAL W2. 2D integrated hydrology (including channel hydraulics and groundwater
interaction) can now be modeled with GSSHA. All of this in a GIS-based data
processing framework will make the task of watershed modeling and mapping easier
than ever before.
The modular design of the program enables the user to select modules in
custom combinations, allowing the user to choose only those hydrologic modeling
capabilities that are required. Additional WMS modules can be purchased and added
at any time. The software will dynamically link to these subsequent modules at run
time—automatically adding additional modeling capability to the software.
18
2.5.10 XP-RAFTS2000
XP-RAFTS2000 is a non-linear runoff routing model used extensively
throughout Australasia and South East Asia. XP-RAFTS2000 has been shown to
work well on catchments ranging in size from a few square metres to 1000’s of
square kilometres of both urban and rural nature. XP-RAFTS2000 can model up to
2000 different nodes and each node can have any size subcatchment attached as well
as a storage basin.
XP-RAFTS2000 uses the Laurensen non-linear runoff routing procedure to
develop a stormwater runoff hydrograph from either an actual event (recorded
rainfall time series) or a design storm utilising Intensity-Frequency-Duration data
together with dimensionless storm temporal patterns as well as standard AR&R 1987
data.
Three loss models may be employed to generate excess rainfall. They are (1)
Initial/Continuing, (2) Initial/Proportional and (3) the ARBM water balance model.
A reservoir (pond) routing model allows routing of inflow hydrographs through a
user-defined storage using the level pool routing procedure and allows modelling of
hydraulically interconnected basins and on-site detention facilities.
Three levels of hydraulic routing are possible including simple Manning’s
based lagging in pipes and channels, the Muskingum-Cunge procedure to route
hydrographs through channel or river reaches or the hydrographs may be transferred
to the XP-SWMM/XP-UDD Hydro- Dynamic simulation model.
19
2.5.11 HEC – HMS
HEC-HMS is designed to simulate the precipitation-runoff processes of
watershed systems. It is the successor to HEC-1 and provides a similar variety of
options but represents a significant advancement in terms of both computer science
and hydrologic engineering. In addition to unit hydrograph, hydrologic and reservoir
routing options, capabilities include a linear quasi-distributed runoff transform (Mod
Clark) for use with gridded precipitation, continuous simulation with either a onelayer or more complex five-layer soil moisture method, and a versatile parameter
estimation option [6]. The software is designed for interactive use in a multi-tasking,
multi-user network environment, and can be used with both X-Windows and
Microsoft Windows.
The Hydrologic Engineering Center's Hydrologic Modeling System (HECHMS) provides a variety of options for simulating precipitation-runoff processes. In
addition to unit hydrograph and hydrologic routing options similar to those in HEC-1,
capabilities currently available include: a linear-distributed runoff transformation that
can be applied with gridded (e.g., radar) rainfall data, a simple "moisture depletion"
option that can be used for simulations over extended time periods, and a versatile
parameter optimization option. The latest version also has capabilities for continuous
soil moisture accounting and reservoir routing operations.
2.6
Choice of Hydrologic Software: HEC-HMS
After reviewing a few computer models, HEC-HMS is chosen for the data
analysis for this flash flood study of Bunus River Catchment Area. The conventional
methods of manual calculations are not chosen because they are tedious and timeconsuming.
20
HEC-HMS would be a good choice as it has been widely used by the
engineering community all over the world other than just the software developer.
This proves its effectiveness, suitability and accuracy in its computation solution.
Furthermore, user feedback has always been an invaluable means for The U.S. Army
Corps of Engineers Hydrologic Engineering Centre in identifying their software
limitations and “bugs”. Therefore, improvements and corrections to the software are
always initiated from time to time.
The HEC-HMS software is easily obtained by just downloading from the
internet for free. This is a great advantage compared to other softwares which
requires high cost of purchase. The HEC-HMS software developer provides user
groups. That means the users can obtain answers from the user groups, by telephone
or written correspondence, to problems that arise during model implementation and
use.
HEC-HMS also has a detail user’s manual that describes input data requirements,
outputs to be expected, and computer requirements. In addition, the theory and
numerical procedures used in the model predictions are also stated.
2.7
Water Quality Models and Softwares
Only the deterministic type of water quality model is really useful for urban
stormwater studies. Because this type of model allows the inputs to be varied, the
effects of various alternative stormwater management actions can be tested (at least
in principle).
All water quality models of the deterministic type have a hydrodynamic
model as their base. It is obviously necessary to have an adequate understanding of
the quantities of stormwater and its movement, before any attempt can be made to
21
investigate its quality. It is equally essential to calibrate the hydrodynamic base as
accurately as possible, before investigating water quality.
Various water quality modules are then applied to the basic hydrodynamic
model. Often there is a facility for the user to select which of the different water
quality modules are required for a particular application.
2.7.1
AQUALM-XP
AQUALM-XP is a relatively simple model for calculating pollutant loads
over long periods. It also includes some types of structural control measures such as
ponds or GPTs.
AQUALM-XP uses the modified Boughton rainfall/ runoff model, which is
especially suitable for long-period simulation. It uses a daily time step. The model
is not intended for flood simulation as it does not accurately represent flood peaks.
Pollutant loads are generated using either export rates or EMC's.
It is strongly recommended that the AQUALM-XP model should only be
used if flow calibration data is available, or if hydrology model parameters can be
transposed from a nearby catchment for which model calibration has been performed.
Use of the model without flow calibration can give very misleading answers.
22
2.7.2
QUAL2EU
QUAL2EU, Enhanced Stream Water Quality Model with Uncertainty
Analysis, is a U.S. EPA steady-state model for conventional pollutants in branching
streams and well mixed lakes. QUAL2EU can be operated as either a steady-state or
dynamic model. It is intended for use as a water-quality planning tool. QUAL2EU
can be used to study the impact of waste loads on instream water quality. It also can
be used to identify the magnitude and quality characteristics of non-point waste loads
as part of a field sampling program.
QUAL2EU is the U.S. EPA Enhanced Stream Water Quality Model with
Uncertainty Analysis. The QUAL2EU package consists of four modules:
z
QUAL2E - Enhanced Stream Water Quality Model
z
QUAL2EU - Enhanced Stream Water Quality Model with Uncertainty Analysis
z
AQUAL2 - Interactive Data Preprocessor Program for QUAL2E and QUAL2EU
z
Q2PLOT - Interactive Graphics Postprocessor Program for QUAL2E and
QUAL2EU
2.7.3
Surface-Water Modeling System (SMS)
SMS is a comprehensive environment for 1D, 2D, and 3D hydrodynamic
modeling. It is a pre- and post-processor for surface water modeling and design. SMS
includes 2D finite-element, 2D finite-difference, 3D finite-element and 1D backwater
modeling tools. Supported models include the USACE-WES supported TABS-MD
(GFGEN, RMA2, RMA4, SED2D-WES), ADCIRC, CGWAVE, STWAVE, M2D,
HIVEL2D, and HEC-RAS models.
The numeric models supported in SMS compute a variety of information
applicable to surface-water modeling. Primary applications of the models include
23
calculation of water surface elevations and flow velocities for shallow water flow
problems for both steady-state or dynamic conditions. Additional applications
include the modeling of contaminant migration, salinity intrusion, sediment transport
(scour and deposition), wave energy dispersion, wave properties (directions,
magnitudes and amplitudes) and others.
2.7.3.1 RMA2
RMA2 is a hydrodynamic modeling code that supports subcritical flow
analysis, including wetting and drying and marsh porosity models. It is part of the
TABS analysis package written by the U.S. Army Corps of Engineers Waterways
Experiment Station (USACE-WES). The methods of analysis used by the TABS
codes along with their file formats and input parameters are described in their own
documents. SMS supports both pre- and post-processing for RMA2.
A mesh for use with RMA2 is created and edited in SMS using the Mesh Module.
The modeling parameters required by RMA2 are generated and applied to the mesh
using commands grouped in the RMA2 menu. Post processing of solution data
generated by RMA2 is done using the generic visualization tools of SMS.
2.7.3.2 RMA4
RMA4 is part of the TABS-MD suite of programs and is used for tracking
constituent flow in 2D models. RMA4 can be applied to represent the transport of a
contaminant, salinity intrusion, or tracking DO and BOD in a system. RMA4 can
only be run after having initially run a hydrodynamic solution. Currently, SMS only
interfaces RMA4 with a solution file from RMA2. Because RMA4 is tracking the
24
transport or a constituent, it is by its nature a dynamic model. RMA4 uses the flow
solutions to compute the constituent concentration as it flows through the mesh. The
initial hydrodynamic solution computed by RMA2 can be either steady state or
dynamic. RMA4 will utilize a steady state solution repetitively, or can loop through a
portion of a dynamic solution repeatedly to simulate the transport over time.
2.7.3.3 SED2D
SED2D is part of the TABS modeling system developed by the U.S.
Army Corp
of
Engineers
Waterways
Experiment
Station.
It
had
been
distributed previously under the name of STUDH. It has the ability to compute
sediment loading and bed elevation changes when supplied with a hydrodynamic
solution computed by RMA2. The model supports both clay and sand beds
individually, but the two bed types cannot be contained within the same model.
2.7.4
Storm Water Management Model (SWMM)
SWMM is an example of a general-purpose model capable of being used in a
wide variety of water quality studies. Processes, which can optionally be simulated
within the software, include pollutant build-up, wash-off during rainfall, transport,
advection, sedimentation, and bio-chemical processes. In all cases the user will need
to choose suitable values for the process parameters. Limited guidance is available
within the program or from documentation. However, in the current state of
knowledge these models are best used only by those with adequate expertise and in
situations where calibration to local conditions is possible.
25
2.7.5
AQUASEA
AQUASEA for Windows software is a powerful and easy to use finite
element program to solve flow in rivers tidal flow problems in estuaries and coastal
areas, lake circulation and problems involving transport of mass heat and suspended
sediments. First developed in 1985 it has been continuously upgraded since then. It
has been used worldwide on the most difficult modeling problems.
AQUASEA contains both a hydrodynamic flow model and a transportdispersion model. The AQUASEA flow model can simulate water level variations
and flows in response to various forcing functions in lakes, estuaries, bays and
coastal areas. The transport-dispersion model simulates the spreading of a substance
in the environment under the influence of the fluid flow and the existing dispersion
processes. The substance may be a pollutant of any kind, conservative or nonconservative, inorganic or organic salt, heat suspended sediment, dissolved oxygen,
inorganic phosphorus, nitrogen and other water quality parameters.
2.7.6 WASP5/DYNHYD5
WASP5, Water Quality Analysis Simulation Program, is a U.S. EPA
generalized modeling framework that simulates contaminant fate in surface waters.
Based on the flexible compartment modeling approach, WASP5 can be applied in
one, two, or three dimensions. WASP5 is designed to permit easy substitution of
user-written routines into the program structure. Problems that have been studied
include biochemical oxygen demand, dissolved oxygen dynamics, nutrients, bacterial
contamination and toxic chemical movement.
26
The DYNHYD5 model is a simple hydrodynamic model that simulates
variable tidal cycles, wind, and unsteady inflows. It produces an output file that can
be linked with WASP5 to supply the flows and volumes to the water quality model.
2.7.7
AquaChem
AquaChem is an advanced software package for graphical and numerical
analysis and modeling of water quality data. It features a fully customizable database
of physical and chemical parameters and it provides a comprehensive selection of
analysis tools, calculations and graphs for interpreting, plotting and modeling water
quality data. The analysis tools in AquaChem range from simple unit transformations,
mixing calculations, statistics and charge balances to more complex calculations
involving correlation matrices and geothermometrics.
AquaChem's graphical plotting techniques include Piper, Stiff, Durov,
Langelier-Ludwig, Schoeller and ternary diagrams, radial plots, scatter graphs,
frequency histograms, pie charts, geothermometer plots and time series graphs. In
addition, AquaChem features a graphical interface to the popular geochemical
modeling program PHREEQC for calculating equilibrium concentrations (or
activities) of chemical species in solution and saturation indices of solid phases in
equilibrium with a solution.
2.8
Alternatives for Water Quality Characteristics Determination
One of the steps in water quality characteristics determination is to estimate
the magnitude of the pollutant loads. A number of empirical approaches have been
27
proposed by MASMA (Manual Mesra Alam) as a basis for calculating pollutant
loads. The best-known and widely applicable approaches are:
•
Event Mean Concentration (EMC) Method
•
Pollutant Export Rate Method
•
Build-up and Washoff Method
•
USLE Method (for sediments)
It is the responsibility of the user to select a suitable method for each application.
2.8.1
Event Mean Concentration (EMC) Method
In this method the load is approximated by the simple relation:
L = 10-4 .
.
VR . A
[Eq. 2.1]
where,
L
=
Annual load (kg)
=
Long term EMC (mg/l)
VR
=
Runoff volume (mm)
A
=
Catchment area (ha)
Table 2.1 gives suggested guideline values of pollutant EMCs for Malaysia.
The values in Table 2.1 indicate that sediment yield will decrease if a catchment
changes from agriculture to urban, as a result of improved land surface and improved
channel stabilisation.
adverse.
However, the other impacts of urbanisation are generally
28
Table 2.1: Typical Event Mean Concentration (EMC) Values in mg/L [7]
Land use/vegetation categories
Pollutant
Native
vegetation/
forest
Sediment 1
Rural
grazing
Industry
Urban
85 3
500
50 to 200
50 to
200
Suspended solids
6
30
60
85
Total Nitrogen 2
0.2
0.8
1.0
1.2
Total Phosphorus
0.03
0.09
0.12
0.13
0.01 to 0.03
0.01 to 0.26
0.01 to
9.8
Faecal coliforms 260 to 4,000
700 – 3,000
4,000 –
200,00
0
2
2
Ammonia 5
5
Copper
0.03 to
0.09
Lead
0.2 to
0.5
Zinc
0.27 to
1.1
Constructio
n
4,000
Sources:
1.
Auckland Regional Council (1992)
2.
Willing & Partners (1999)
3.
Sungai Kinta Dam Project, Flood Hydrology Report, Angkasa GHD
Engineers (1998).
4.
NURP (USA) National Average, from Schueler (1987)
5.
EPA, NSW (1997a)
29
2.8.2
Pollutant Export Rates Method
An alternative to the use of the simple EMC is to represent pollutant loads as
a function of runoff. The form of the function should be derived by regression
analysis of real data. If locally-collected data is to be used, the statistical effects of a
small sample size and sampling errors should be taken into account.
The general form of the pollutant rate equation is:
L = a . Re
[Eq. 2.2]
where,
L
=
daily load in kg/km2/ day,
R
=
daily stormwater runoff (mm/day),
a
=
an empirical coefficient, and
e
=
an empirical exponent
The EMC method is a particular form of the Pollutant Export Rate method with
e = 1.0.
The sub-tropical export rate equations used in Brisbane, Australia (WP, 1999)
may be suitable for application in Malaysia. The suggested pollutant load equations
for urban landuse are given in Table 2.2.
Table 2.2: Pollutant Export Equations for Urban Areas [7]
Pollutant
Equation
Sediment
L = 1000 R 1.4
Suspended solids (SS)
L = 166 R 0.75
Total Phosphorus (TP)
L = 0.15 R 0.90
Total Nitrogen (TN)
L = 1.45 R 0.86
30
The daily runoff can be calculated using the following equation.
VR = P.Cv
[Eq. 2.3]
where,
VR
=
runoff depth (mm)
P
=
rainfall depth (mm)
Cv
=
average annual runoff coefficient
It will be seen by comparison with the EMC equation that these relationships
are non-linear, unlike the EMC which implies a linear response to runoff. Also, the
equations are intended only for use in calculating daily loads. The analysis can be
extended to longer time periods by summing the daily loads.
2.8.3 Build-up and Wash-off Method
Build-up refers to the processes whereby pollutants accumulate in an urban
area, as a function of time. For example, a long dry period is likely to lead to a larger
accumulation of pollutants in the urban catchment due to deposition and littering.
Build-up is modified by management practices such as street sweeping. Washoff is
the process whereby accumulated pollutants are washed into the stormwater system.
The pollutant build-up and washoff approach offers potentially a more
accurate way of characterising pollutant loads, as it attempts to represent the physical
processes rather than merely providing a statistical correlation. It is used, as an
option, in several stormwater quality computer models such as SWMM.
The build-up and washoff approach is not recommended for general
application in Malaysia at this time, because no local data is available to characterise
31
the processes. In general, satisfactory results can be obtained using the simpler
methods described above. However the buildup and washoff approach may be
applied in particular situations where detailed analyses, supported by local data, are
being undertaken.
2.8.4
Universal Soil Loss Equation Method
Sediment load in watercourses typically does not follow a build-up / washoff
process. Rather, it is more likely to be influenced by erosion processes in the
catchment area. Sediment load on exposed areas, such as construction sites, is also
largely dominated by erosion.
In the search for a model for planning erosion measures at the construction
sites, the Universal Soil Loss Equation (USLE) developed by Wischmeier and other
(1965, 1971a) for the U.S. Department of Agriculture stands out as the most widely
used predictive method [8].
The USLE method has been modified by FRIM for Malaysian conditions. In
this form, the Modified Soil Loss Equation (MSLE) is written as:
qc = R . K . LS . VM
[Eq. 2.4]
It expresses the annual rate of soil erosion, qc, from a site as the product of
factors for rainfall erosivity, R, soil erodibility K, length-slope factor, LS, and
vegetation management factor, VM. Detailed guidance on the use of the MSLE in
forest areas is given in FRIM (1999). With suitable adaptations, the MSLE can also
be used for other types of land use. qc is expressed in tonnes of soil loss per hectare
per year.
The rainfall factor, R, is a measure of the erosive energy of the rainfall. It is
expressed in units of cumulative value of storm rainfall erosivity index, EI, for a
32
fixed period of time. The following relationships between R, EI and annual rainfall
are given in FRIM (1999):
R = (E . I30) / 170.2
[Eq. 2.5]
E = 9.28 P – 88838.15
[Eq. 2.6]
where,
I30 =
the maximum 30-minute rainfall intensity (mm/hr) for the storm of required
ARI
E =
annual erosivity (units of J/m2)
P =
annual rainfall (mm)
The soil-erodibility factor, K, is a measure of the intrinsic susceptibility of a
given soil to detachment and transport by rainfall and runoff, on the basis of five soil
parameters: percent silt, percent sand, organic matter content, soil structure and
permeability of the soil profile. K is defined by as below. The K values can also be
estimated from the nomograph in FRIM (1999) developed by Warrington et al. (1980)
K = 2.1 x 10-6 (12 – OM) M1.14 + 0.0325 (S – 2) + 0.025 (P – 3)
[Eq. 2.7]
Definitions of OM, M, S and P are given in FRIM (1999).
The length-steepness factor, LS, combines the effects of slope and length of
eroding surface. It is the ratio of soil loss per unit area from a slope land to that from
a standardised measured plot. Wischmeier (1975) gives the following equation for
LS = ( λ /22.13)m (0.065 + 0.046 S + 0.0065 S2)
[Eq. 2.8]
where λ is the slope length (m) and S is the slope in percent. The exponent m has
values of 0.2 for S <1, 0.3 for 1<S <3, 0.4 for 3<S <5, 0.5 for 5<S <12 and 0.6 for
S>12%. Alternatively, the nomograph in FRIM (1999) can be used.
The vegetation management factor, VM, is defined as the ratio of soil loss
from a field subject to a system of control measures to that from the same site
33
without any control provision. It combines two factors C and P used in the original
USLE.
The expression for VM given in FRIM (1999) is intended mainly for forest
cover. It incorporates three sub-factors for forest canopy cover, mulch or ground
vegetation cover, and bare ground with fine roots. A VM factor of unity can be
assigned to a recently stripped surface at a construction site since the condition
essentially resembles a continuous fallow condition.
In urban stormwater practice, the factor C accounts for the effect of various
control practices related to surface stabilising treatment, runoff-reduction measures,
sediment-trapping, scheduling in time and space of exposed areas, and other
conventional or unconventional control practices.
By definition, the overall
VM factor of a system of control practices can be evaluated as the product of the
control factors associated with each of the individual control measures.
At present there is insufficient data to give detailed guidance on suitable
values of VM or C for Malaysia. Goldman et al (1986) suggest that C ranges from
1.0 for bare soil, to 0.45 for established grass cover. This should be adjusted for the
amount of exposed land surface in the urban area, assuming that impervious areas
would not produce any sediment).
It is recommended that equation below be
adopted for all urban drainage calculations.
VM = C . (1 – IA)
[Eq. 2.9]
where IA is the fraction of impervious area in the catchment, and C ranges from 1.0
for bare soil, to 0.45 for established grass cover.
34
2.9
Water Quality Characteristic Determination – EMC Method
From the review, the EMC method is chosen as it is recommended for
general application in Malaysia, unless sufficient local data is available to justify use
of an alternative method.
There is very little measured data available on stormwater pollutant loads in
Malaysia. Therefore, in this flash flood study, modelling using sophisticated water
quality softwares is not necessary. Detailed long-term studies are required in order to
derive reliable estimates of pollutant exports. Published data from other countries,
climatic zones, and land uses may vary greatly from local conditions.
In the absence of any local data on pollutant loads, published data from references
may be used for preliminary studies only. Preliminary studies based on such data
can only give an approximate estimate of pollutant loads.
2.10
HEC -HMS Model Calibration and Validation
Model calibration is the process of adjusting model parameter values until
model results match historical data. The process can be completed manually using
engineering judgement by repeatedly adjusting parameters, computing and inspecting
the goodness-of-fit between computed and observed hydrographs. Significant
efficiencies can be realized with an automated procedure.
The quantitative measure of the goodness-of-fit is the objective function [7].
An objective function measures the degree of variation between computed and
observed hydrographs. It is equal to zero if the hydrographs are exactly identical. The
key to automated model calibration is a search method for adjusting parameters to
minimise the objective function value and find optimal parameter values. A
minimum objective function is obtained when the parameter values best able to
35
reproduce the observed hydrographs are found. Constraints are set to ensure that
unreasonable parameter values are not used.
Model validation is a similar process with the model calibration. Model
validation is done after the model calibration to verify the calibrated model
parameter values until model results match historical data.
2.10.1 Methods of Calibration and Validation
Calibration and validation processes are to compare a computed hydrograph
to an observed hydrograph, HEC-HMS computes an index of the goodness-of-fit.
Algorithms included in HEC-HMS search for the model parameters that yield the
best value of an index, also known as objective function.
2.10.1.1 Numerical Measures of Fit
In HEC-HMS, one of four objective functions can be used, depending upon
the needs of the analysis. The goal of all four calibration schemes is to find
reasonable parameters that yield the minimum value of the objective function. The
objective function choices are:
•
Sum of Absolute Errors
•
Sum of Squared Residuals
•
Percent Error in Peak
•
Peak Weighted Root Mean Square Error
36
Table 2.3: HEC-HMS objective functions for calibration [5]
•
Sum of absolute errors
This objective function compares each ordinate of the computed hydrograph
with the observed, weighting each equally. The index of comparison, in this
case, is the difference in the ordinates. However, as differences may be
positive or negative, a simple sum would allow positive and negative
differences to offset each other. In hydrologic modeling, both positive and
negative differences are undesirable, as overestimates and underestimates as
equally undesirable. To reflect this, the function sums the absolute differences.
Thus, this function implicitly is a measure of fit of the magnitudes of the
peaks, volumes, and times of peak of the two hydrographs. If the value of this
function equals zero, the fit is perfect: all computed hydrograph ordinates
equal exactly the observed values. Of course, this is seldom the case.
37
•
Sum of squared residuals
This is a commonly-used objective function for model calibration. It too
compares all ordinates, but uses the squared differences as the measure of fit.
Thus a difference of 10 m3/sec “scores” 100 times worse than a difference of
1 m3/sec. Squaring the differences also treats overestimates and
underestimates as undesirable. This function too is implicitly a measure of the
comparison of the magnitudes of the peaks, volumes, and times of peak of the
two hydrographs.
•
Percent error in peak
This measures only the goodness-of-fit of the computed-hydrograph peak to
the observed peak. It quantifies the fit as the absolute value of the difference,
expressed as a percentage, thus treating overestimates and underestimates as
equally undesirable. It does not reflect errors in volume or peak timing. This
objective function is a logical choice if the information needed for designing
or planning is limited to peak flow or peak stages. This might be the case for
a floodplain management study that seeks to limit development in areas
subject to inundation, with flow and stage uniquely related.
•
Peak-weighted root mean square error
This function is identical to the calibration objective function included in
computer program HEC-1 (USACE, 1998). It compares all ordinates,
squaring differences, and it weights the squared differences. The weight
assigned to each ordinate is proportional to the magnitude of the ordinate.
Ordinates greater than the mean of the observed hydrograph are assigned a
weight greater than 1.00, and those smaller, a weight less than 1.00. The peak
observed ordinate is assigned the maximum weight. The sum of the weighted,
squared differences is divided by the number of computed hydrograph
ordinates; thus, yielding the mean squared error. Taking the square root yields
the root mean squared error. This function is an implicit measure of
comparison of the magnitudes of the peaks, volumes, and times of peak of the
two hydrographs.
38
2.10.1.2 Graphical Measures of Fit
HEC-HMS also provides graphical comparisons that permit visualization of
the fit of the model to the observations of the hydrologic system. HEC-HMS displays
a comparison of computed hydrographs, much like that shown in Figure 2-1.
Figure 2.1: How well does the computed hydrograph “fit”? [5]
In addition, it displays a scatter plot, as shown in Figure 2.2. This is a plot of
the calculated value for each time step against the observed flow for the same step.
Inspection of this plot can assist in identifying model bias as a consequence of the
parameters selected. The straight line on the plot represents equality of calculated
and observed flows: If plotted points fall on the line, this indicates that the model
with specified parameters has predicted exactly the observed ordinate. Points plotted
above the line represent ordinates that are over-predicted by the model. Points below
represent under-predictions. If all of the plotted values fall above the equality line,
the model is biased; it always over-predicts. Similarly, if all points fall below the line,
the model has consistently under-predicted. If points fall in equal numbers above and
below the line, this indicates that the calibrated model is no more likely to overpredict than to under-predict.
39
The spread of points about the equality line also provides an indication of the
fit of the model. If the spread is great, the model does not match well with the
observations -- random errors in the prediction are large relative to the magnitude of
the flows. If the spread is small, the model and parameters fit better.
Figure 2.2: Scatter plot. [5]
HEC-HMS also computes and plots a time series of residuals—differences
between computed and observed flows. Figure 2.3 is an example of this. This plot
indicates how prediction errors are distributed throughout the duration of the
simulation. Inspection of the plot may help focus attention on parameters that require
additional effort for estimation. For example, if the greatest residuals are grouped at
the start of a runoff event, the initial loss parameter may have been poorly chosen.
Figure 2.3: Residual Plot [5]
40
2.10.2 Search Methods
Two search methods are available for minimising an objective function and
finding optimal parameter values. The univariate gradient method evaluates and
adjusts one parameter at a time while holding other parameters constant. The Nelder
and Mead method uses a downhill simplex to evaluate all parameters simultaneously
and determine which parameter to adjust. The default method is the univariate
gradient method. The univariate gradient method is automatically used when only
one parameter is estimated.
2.11
Selection of Calibration and Validation Method
This study only requires a comparison of peak flow. Therefore, to fulfill the
need of this flash flood study analysis, the “Percent Error in Peak” objective function
will be selected for both calibration and validation processes. This is a simple and
commonly-used objective function for model calibration and validation. It measures
only the goodness-of-fit of the computed-hydrograph peak to the observed peak. It
quantifies the fit as the absolute value of the difference, expressed as a percentage,
thus treating overestimates and underestimates as equally undesirable.
CHAPTER 3
OPERATION FRAMEWORK
3.1
Introduction
HEC-HMS is designed to simulate the precipitation-runoff processes of
watershed systems. The HEC-HMS program can be obtained from the Hydrologic
Engineering Center's home page at: http://www.wrc-hec.usace.army.mil/. The
program runs on Microsoft Windows, NT and Unix platforms.
There are several simulation methods for finding runoff volume:
Table 3.1: Simulation Methods for Finding Runoff Volume
Categories
Methods
Hydrologic Losses
- Initial and constant-rate
- SCS curve number (CN)
- Gridded SCS CN
- Green and Ampt
- Deficit and constant rate
- Soil moisture accounting (SMA)
- Gridded SMA
Direct Runoff
- User-specified unit hydrograph (UH)
- Clark’s UH
- Snyder’s UH
- SCS UH
- ModClark
- Kinematic wave
- User-specified s-graph
42
3.2
Using the HEC-HMS software
To use HEC-HMS to develop information required for planning, designing,
operating, permitting and regulating decision making, the following steps should be
taken:
1.
Identify the decisions required.
In this flash flood study, the objective is to simulate the Bunus River
Catchment Area in order to estimate the peak flow rates and runoff volume.
2.
Determine what information is required to make a decision.
After the decision that is to be made has been identified, the information
required to make that decision must be determined. This subsequently will
guide selection and application of the methods used. In this case, the rainfall
data is used as the input.
3.
Identify the decisions required.
In this flash flood study, the objective is to simulate the Bunus River
Catchment Area in order to estimate the peak flow rates and runoff volume.
4.
Determine what information is required to make a decision.
After the decision that is to be made has been identified, the information
required to make that decision must be determined. This subsequently will
guide selection and application of the methods used. In this case, the rainfall
data is used as the input.
5.
Identify methods that can provide the information, identify criteria for
selecting one of the methods, and select a method.
In some cases, more than one of the alternative methods included in HECHMS will provide the information required. For example, to estimate runoff
peaks for this urban flooding study, any of the direct runoff methods shown in
Table 3.1 will provide the information required. However, the degree of
complexity of those methods varies, as does the amount of data required to
43
estimate method parameters. This should be considered when selecting a
method. If the necessary data or other resources are not available to calibrate or
apply the method, then it should not be selected.
6.
Fit model and verify the fit.
Each method that is included in HEC-HMS has parameters. The value of each
parameter must be specified to fit the model to the particular watershed before
the model can be used for estimating runoff. Some parameters may be
estimated from observation of physical properties of a watershed, while others
must be estimated by calibration–trial and error fitting.
7.
Collect / develop boundary conditions and initial conditions appropriate
for the application.
Boundary conditions are the values of the system input — the forces that act on
the hydrologic system and cause it to change.
8.
Apply the model.
Here is where HEC-HMS shines as a tool for analysis. The program is easy to
apply and the results are easy to visualize.
9.
Do a reality check and analyze sensitivity.
After HEC-HMS is applied, the results must be checked to confirm that they
are reasonable and consistent with what might be expected.
10.
Process results to derive required information
The results from HEC-HMS must be processed and further analyzed to provide
the information required for decision making.
44
3.3
Identify Basin Models
The basin model is one of the components required for a run, along with
meteorologic model and control specifications. The system connectivity and physical
data describing the watershed are stored in the basin model.
3.3.1
Hydrologic Elements
Hydrologic elements are the basic building blocks of a basin model. An
element represents a physical process such as watershed catchment, stream reach or
confluence. Each element represents part of the total response of the watershed to
precipitation. Seven different elements types have been included in the program:
i)
Subbasin
A subbasin is an element that usually has no inflow and only one outflow. It is one of
only two ways to produce flow in the basin model. Outflow is computed from
meteorologic data by subtracting losses, transforming excess precipitation and
adding baseflow. The subbasin can be used to model a wide range of watershed
catchment sizes.
ii)
Reach
A reach is an element with one or more inflow and only one outflow. Inflow comes
from other elements in the basin model. If there is more than one inflow, all inflow is
added together before computing the outflow. Outflow is computed using one of the
several available methods for simulating open channel flow. The reach can be used
to model rivers and streams.
45
iii)
Junction
A junction is an element with one or more inflow and only one outflow. All inflow is
added together to produce the outflow by assuming zero storage at the junction. It is
usually used to represent a river or stream confluence.
3.4
Identify Subbasin Elements
The subbasin element represents a complete watershed. Assumptions are
made that reduce the watershed to three separate processes: loss, transform and
baseflow. Part of the precipitation falling on the land surface infiltrates, ultimately
into the groundwater to become baseflow or deep percolation. All infiltration
processes are represented with a loss method. Rainfall that does not infiltrate
becomes direct runoff and moves across the watershed surface or through the upper
soil horizons to streams and eventually reaches the watershed outlet. All runoff
processes are represents as pure surface routing using a transform method.
Groundwater contributions to channel flow are called baseflow and are represented
with a baseflow method.
3.4.1
Subbasin Loss Methods
All land and water in a watershed can be categorized as either directlyconnected
impervious
surface
or
pervious
surface.
Directly-connected
imperviousness surface in a watershed is that portion of the watershed for which all
precipitation runs off, with no infiltration, interception, evaporation or other losses.
Precipitation on the pervious surfaces is subject to losses. The amount of directly-
46
connected imperviousness surface in a subbasin is specified as the percent
imperviousness. A loss method is used to compute losses from precipitation.
A total of seven methods for estimating losses are included in the program.
There are Deficit / Constant, Green and Ampt, Gridded SCS Curve Number, Gridded
Soil Moisture Accounting, SCS Curve Number, Soil Moisture Accounting and
Initial/ Constant. In this study, Initial / Constant is chosen.
3.4.1.1 Initial and Constant
The initial and constant method represents interception and depression
storage with an initial loss. All other losses are represented with a constant loss rate.
No excess precipitation occurs until the initial loss is satisfied. Required parameters
are the initial loss and the constant loss.
3.4.1.2 Estimating Initial Loss and Constant Rate
The initial and constant-rate model, in fact, includes one parameter (the
constant rate) and one initial condition (the initial loss). Respectively, these represent
physical properties of the watershed soils and land use and the antecedent condition.
If the watershed is in a saturated condition, initial loss will approach zero. If
the watershed is dry, then initial loss will increase to represent the maximum
precipitation depth that can fall on the watershed with no runoff; this will depend on
the watershed terrain, land use, soil types, and soil treatment.
47
The constant loss rate can be viewed as the ultimate infiltration capacity of
the soils. Because the model parameter is not a measured parameter, it and the initial
condition are best determined by calibration.
3.4.2
Subbasin Transform Methods
Precipitation that does not infiltrate or falls on directly-connected
imperviousness surface becomes excess precipitation. While excess precipitation can
remain on the watershed in depressions or ponds, it typically moves down-gradient
on the watershed land surface and becomes direct runoff. A transform method is used
to compute direct runoff from excess precipitation. Direct runoff for each subbasin
can be modeled with one of six different methods. The methods include Clark Unit
Hydrograph, Kinematic Wave, Mod Clark, Snyder Unit Hydrograph, SCS Unit
Hydrograph and User-Specified S-Graph. This study uses Clark Unit Hydrograph.
3.4.2.1 Clark Unit Hydrograph
The Clark unit hydrograph method explicitly represents translation and
attenuation of excess precipitation as it moves across the subbasin to the outlet.
Translation is based on a synthetic time-area and the time of concentration.
Attenuation is modeled with linear reservoir.
The duration of a synthetic unit hydrograph generally is dependent upon the
parameters used in the equations specific to the method. This differs from a derived
unit hydrograph, as unit hydrographs derived directly from gaged data have a
48
duration equal to the duration of excess precipitation from which they were derived.
In this respect Clark’s (1943) unit hydrograph is slightly different from other
synthetic unit hydrograph methods in that it has no duration; it is an instantaneous
unit hydrograph.
Clark’s unit hydrograph theory maintains the fundamental properties of a unit
hydrograph in that the sequence of runoff is the result of one inch of uniformly
generated excess precipitation. However, the duration of the excess precipitation is
considered to be infinitesimally small. Thus the Clark unit hydrograph generally is
referred to as the Clark “Instantaneous Unit Hydrograph,” or IUH. This excess
precipitation is applied uniformly over a watershed which is broken into time-area
increments.
This method is unique in its derivation, which draws from the parameters and
theories of Muskingum hydrograph routing, and its application. The method was
developed for gaged sites only, although later work has produced some suggestions
for transfer of Clark parameters to ungaged sites.
Clark (1943) pointed out several advantages to his method, including the
following:
1. The procedure is highly objective, using mathematically defined parameters based
on observed hydrographs (except in the derivation of the time-area curve, which does
require individual judgment). Thus, the procedure is repeatable by two individuals
using the same data set.
2. The method does not require knowledge of spatial runoff distribution.
3. The ability to account for shape of drainage area and the capacity to produce large
peak flows from concentrated runoff are included, subject to the accuracy of the
developed time-area relationship.
4. The unit hydrograph is the result of an instantaneous rainfall with the time of
concentration defined as the time between the end of rainfall and a mathematically
49
defined point on the falling limb of hydrograph. Therefore, personal judgments on
effective rainfall duration and time of concentration, which inversely affects peak
flow, are eliminated.
3.4.2.2 Estimating Time of Concentration and Storage Coefficient
Application of the Clark model requires:
•
Properties of the time-area histogram; and
•
The storage coefficient, R.
As noted, the linear routing model properties are defined implicitly by a time
area histogram. Studies at HEC have shown that, even though a watershed specific
relationship can be developed, a smooth function fitted to a typical time area
relationship represents the temporal distribution adequately for UH derivation for
most watersheds. That typical time-area relationship which is included in HEC-HMS
is:
1 .5
⎧
⎛ t ⎞
⎪ 1 .414 ⎜⎜ ⎟⎟
At ⎪
⎝ tc ⎠
=⎨
1 .5
A ⎪
⎛
t ⎞
⎪1 − 1 .414 ⎜⎜ 1 − t ⎟⎟
c ⎠
⎝
⎩
tc ⎫
⎪
2⎪
⎬
tc ⎪
for t ≥ ⎪
2⎭
for t ≤
where,
At = cumulative watershed area contributing at time t
A = total watershed area
tc = time of concentration of watershed.
[Eq. 3.1]
50
For application in HEC-HMS, the parameter tc, the time of concentration, is
necessary. This can be estimated by getting the time between the centroid of the
storm and the inflection point of the hydrograph or via calibration as shown in Figure
3.1.
Figure 3.1: Clark Unit Hydrograph Computation of tc
The basin storage coefficient, R, is an index of the temporary storage of
precipitation excess in the watershed as it drains to the outlet point. It, too, can be
estimated via calibration if gaged precipitation and stream flow data are available.
Though R has units of time, there is only a qualitative meaning for it in the physical
sense. Clark (1945) indicated that R can be computed as the flow at the inflection
point on the falling limb of the hydrograph divided by the time derivative of flow.
3.5
Meteorologic Models
The meteorologic model is one of the components required for a run, along
with
a
basin
model
and
control
specifications.
The
precipitation
and
51
evapotranspiration data necessary to simulate watershed processes are stored in the
meteorologic model.
3.5.1
Precipitation Methods
A variety of precipitation methods are available but only one can be used for
each meteorological model. There are seven methods, namely User Hyetograph, User
Gage Weighting, Inversed-Distance Gage Weights, Gridded Precipitation, Frequency
Storm, SCS Hypothetical Strom and Standard Project Storm. The method chosen is
the User Hyetograph.
3.5.1.1 User Hyetograph
The user hyetograph method can be used to import a subbasin hyetograph from
outside the program.
52
3.6
HEC-HMS Application Steps
1.
Create a New Project
Create a new project by selecting the File => New Project menu item on the Project
Definition screen. Enter a project name and optionally enter a description and change
the storage location. Long descriptions can be easily entered using a text editor
accessed by pressing the ... button next to the description field.
Figure 3.2: Create a New Project
Press the OK button to create the project or the Cancel button to abort the process.
When the OK button is pressed, the project is created and the Project Definition
screen automatically opens the new project.
Figure 3.3: Project Definition Screen
53
2.
Enter Shared Data
Create precipitation gages that will be required for the meteorologic model. Select
the Data => Precipitation Gages menu item to open the gage manager.
Figure 3.4: Create Precipitation Gages
The New Precipitation Record screen will automatically open because no gages have
been created yet. Select Incremental Precipitation and change Units to Millimeters.
Figure 3.5: New Precipitation Record Screen
54
Enter the start date, start time, end date, end time and time interval for the gage in the
Time Parameters screen. Press the OK button to continue.
Figure 3.6: Time Parameters Screen
Enter the data values for the gage in the Data Editor screen. Press the OK button to
save the data, close the screen and return to the Gage Manager screen. The time
window of the data shown in the editor can be changed by pressing the Reset Time
Parameters button on the right side of the screen. Data values currently shown in the
editor can be graphed by pressing the Plot button.
Figure 3.7: Data Editor Screen
55
The similar steps are followed by selecting Discharge Gages that will be required
for observed flow in the basin model. Finish creating the gage and then create
additional discharge gages.
3.
Create a Basin Model
Create a new basin model by selecting the Component => Basin Model => New
menu item on the Project Definition screen.
Figure 3.8: Create a Basin Model
Enter a name, optional description, and then press that OK button, Set the basin
model attributes to the desired default methods and locate any files.
Figure 3.9: New Basin Model Screen
56
Add hydrologic elements to the schematic and connect them into a dendritic network.
Use the element or global editors to enter data. Save the basin model.
Figure 3.10: Add Hydrologic Elements
Double click the left mouse button on the Subbasin element. The Subbasin Editor
Screen will appear. Choose Initial/ Constant Method for Loss Rate Method and
enter the estimated Loss Rate parameters.
Figure 3.11: Enter Loss Rate Parameters
57
Select Clark Method as Transform Method and input its parameters.
Figure 3.12: Enter Transform Parameters
Select No Baseflow for Baseflow Method.
Figure 3.13: Select Baseflow Method
58
4.
Create a Meteorologic Model
Create a new meteorologic model by selecting the Component => Meteorologic
Model => New menu item on the Project Definition Screen. Enter a name, optional
description, and then press the OK button.
Figure 3.14: Create a Meteorologic Model
The Subbasin List Screen automatically opens whenever the list is empty. Select the
name of the basin model and press the Add button to load the subbasin names from
the basin model into the subbasin list. Press the OK button to continue to the
Meteorologic Model Screen.
Figure 3.15: Subbasin List Screen
59
Select a precipitation method and enter the required data. When the data for the
model is completed, press the OK button to save.
Figure 3.16: Meteorologic Model Screen
5.
Create Control Specifications
Create new control specifications by selecting the Components => Control
Specifications => New menu item on the Project Definition screen. Enter a name,
optional description, and then press the OK button. Enter the start date, start time,
end date, and end time. Select a time interval from the list. Press the OK button to
save the control specifications.
Figure 3.17: Control Specification Screen
60
6.
Simulate and View Results
Create a new run by selecting the Tools => Run Configuration menu item on the
Project Definition Screen. Select one basin model, one meteorologic model, and one
control specifications. Enter a name, optional description, and press the OK button to
create the run.
Figure 3.18: Run Configuration Screen
Select the Tools => Run Manager menu item on the Project Definition screen.
Select a run in the table by clicking it with the left mouse button. Press the Compute
button at the bottom on the screen to execute the run. Press the Close button to exit
the run manager.
Figure 3.19: Compute
61
7.
Exiting the program
Save the project by selecting the File => Save project menu item on the Project
Definition screen. After the project is saved, exit the program by selecting the File
=> Exit menu item. All program screens will automatically close and the program
will exit.
3.7
Calibration and Validation
Calibration and validation can be done with the aid of Optimization Manager.
Optimization results are obtained through a three-step process.
1.
Create an Optimization Run by selecting Tools => Optimization Run
Configuration. A run is configured by selecting a basin model, meteorologic
model and control specifications. Components must be compatible according
to the compatibility rules for runs.
2.
Select Tools => Optimization Manager. The Optimization Manager screen
will appear. Create and Configure trial. An optimization location, objective
function, search method and parameters can be defined for each trial. The
optimization location, an element in the basin model must have an observed
hydrograph. Each run can have more than one trial. Trials within a run are
independent and can use different objective functions or parameters.
3.
Execute a trial. Results can be viewed after the trial is executed.
4.
After calibration process obtained a match between simulated and observed
hydrograph, select another storm event for validation process by using the
same parameters. Repeat the same process as above using Optimization
Manager.
62
Figure 3.20: Optimization Manager Screen
3.8
Annual Pollutant Load Estimate Formula
In the absence of any local data on pollutant loads, published data from
references will be used. Event Mean Concentration (EMC) Method is used in the
calculation of annual pollutant load.
In this EMC method the load is approximated by the simple relation:
L = 10-4 .
where,
L
=
Annual load (kg)
=
Long term EMC (mg/l)
VR
=
Runoff volume (mm)
A
=
Catchment area (ha)
. VR . A
[Eq. 3.2]
CHAPTER 4
STUDY AREA
4.1
Introduction
Bunus River Catchment Area is approximately 18 km2 consisting mainly
Wardieburn, Wangsa Maju, Sri Rampai, Setapak Jaya and Taman Setiawangsa.
The general terrain of the area is quite hilly and rugged with steep stream
longitudinal profiles particularly in the eastern and northern sides. The average
minimum slope is 0.5% and the maximum slope is 11.5% with elevation range from
40m to 290m above mean sea-level.
The Bunus River Catchment Area is bisected by Bunus River from North to
South through a distance of about 2.5 km long and Peran River from east to west
through a distance of about 3.0 km long. The main stream tributary of Bunus River is
Peran River. The subcatchment area of Peran River is about 3.3 km2 with stream
length approximately 3.0 km. Taman Setiawangsa and Setapak Jaya are located
within Peran River subcatchment. About 6.16 km2 of the drainage system tributaries
are within the Upper Bunus River. The boundary catchment of Upper Bunus River is
parallel with Jalan Genting Klang up to Kem Wardieburn in the west then extends to
Taman Bunga Raya and up to hilly area of Taman Setiawangsa in the East. The
average slope of drainage system in upper Bunus River catchment is about 0.2 %
64
with stream length of 2.5 km. Bunus River, Peran River and the drainage system in
urban areas generally have been straightened and lined. Figure 4.1 shows the location
of Bunus River with regards to Klang River Basin.
Figure 4.1: Location of Bunus River in Kuala Lumpur
4.2
Catchment Study Area
The catchment study was carried out at the most upstream of the Bunus River
which covers an area of 0.55 km2. This area consists of commercial area such as
Alpha Angle, residential area which includes a part of Taman Desa Setapak and
Wangsa Maju Seksyen 2 and Surau Al-Ihsan.
65
The average slope of the drainage system study catchment area is about 0.2 %
with an approximate stream length of 0.5 km. Bunus River and the drainage system
in urban areas generally have been straightened and lined. The Manning’s roughness
coefficient of the channel is 0.014 for lined channel. The outlet of this study area is
situated near a secondary school which is SMK Wangsa Maju Zon R1. The location
of the outlet where flow meter was installed is shown in Figure 4.2.
Figure 4.2: Location of Outlet near SMK Wangsa Maju Zon R1
The study catchment (Zone 5) location plan in Bunus River Catchment Area
is shown in Figure 4.3 whereas Figure 4.4 focuses on the plan of the study catchment
area (Zone 5) in detail. In Figure 4.5, the study catchment area shows a land use of
high density residential area, commercial/ industrial area, public facilities, education,
institutional, open space and recreational. The land use has made up an average of
45% impervious area.
66
Figure 4.3: Location of Study Area (Zone 5) in Bunus River Catchment Area
67
WANGSA MAJU
SEKSYEN 2
TAMAN DESA
SETAPAK
ALPHA
ANGLE
LEGEND:
Catchment Boundary
Drain / River
Figure 4.4: Bunus River Study Catchment Area (Zone 5)
68
LEGEND:
Catchment Boundary
Low Density Residential
High Density Residential
Commercial / Industrial
Public Facilities
Recreation & Open Space
Institutional
Education
Open Water / Pond
Construction
Reserve (River / Drain / Road)
Figure 4.5: Present Landuse / Landcover Classification
CHAPTER 5
RESULTS AND ANALYSIS
5.1
Calibration and Validation Results
Storm water event on the 15th April 2004 is selected for the calibration
process while the 7th April 2004 storm event is selected for the validation process
(Refer Appendix B1 – B2 for data used). The following are the calibrated and
validated parameters used to achieve a good fit between the simulated and observed
hydrograph.
Table 5.1: Parameters after Calibration and Validation Process
Initial Loss
Constant Rate
(mm)
(mm/hr)
15
5
Time of
Concentration
Storage Coefficient
(hr)
0.26
(hr)
0.1
The parameters are calibrated and validated based on the Objective Function
Type of Percent Error in Peak Flow. Figure 5.1 and Figure 5.2 show the calibration
results whereas Figure 5.3 and Figure 5.4 show the validation results.
70
Figure 5.1: Calibration Results for 15 April 2004 Storm Event
Figure 5.2: Calibration Results Summary Table for 15 April 2004 Storm Event
71
Figure 5.3: Validation Results for 7 April 2004 Storm Event
Figure 5.4: Validation Results Summary Table for 7 April 2004 Storm Event
72
5.2
Efficiency Index
The accuracy of results computed by HEC-HMS is determined by using one
of the statistics methods which is the Efficiency Index. Efficiency Index [9] can be
defined as below:
Efficiency Index =
SSTotal − SSError
SSTotal
[Eq. 5.1]
SSTotal, the total sum of squared error is the sum of squared error when
predicting using the mean; the sum of the squared products of all the actual values
minus the mean. The formula of SSTotal is as follows:
SSTotal =
∑ (Q
i
− Qav ) 2
[Eq. 5.2]
SSError, the sum of squared error is the sum of the squared error when using
a prediction model; the sum of squared products of all the actual values minus their
predicted values. The formula of SSError is as follows:
SSError =
where,
∑ (Q
i
− Fi ) 2
Qi
=
Observed Discharge at Time i
Qav
=
Mean of Observed Discharge, Qav =
N
=
Number of Discharge Data
Fi
=
Simulated Discharge at Time i
[Eq. 5.3]
∑Q
i
N
With a good prediction, the SSError should be less then the SSTotal. This
comparison is an indicator of the accuracy of this prediction model and is called the
proportionate reduction in error, (R2). This proportion is the percentage of variance.
With a higher percentage, a higher accuracy of the model can be achieved.
73
5.2.1
Efficiency Index for Calibration Process
Calculation of Efficiency Index for 15 April 2004 Calibration Process is as follows:
Table 5.2: Calculation table for efficiency index of calibration process
Time
1610
1612
1614
1616
1618
1620
1622
1624
1626
1628
1630
1632
1634
1636
1638
1640
1642
1644
1646
1648
1650
1652
1654
1656
1658
1700
1702
1704
1706
1708
1710
Qi
0.0010
0.0010
0.0010
0.0010
0.0010
0.0010
0.5468
1.0926
1.6384
2.1842
2.7300
2.5132
2.2964
2.0796
1.8628
1.6460
1.5356
1.4252
1.3148
1.2044
1.0940
0.9242
0.7544
0.5846
0.4148
0.2450
0.2040
0.1630
0.1220
0.0810
0.0400
28.7030
Fi
0.0000
0.0000
0.0038
0.0347
0.1564
0.4276
0.8395
1.3296
1.8099
2.1840
2.3910
2.4220
2.3131
2.1411
1.9750
1.8352
1.7040
1.5542
1.3655
1.1413
0.9115
0.7059
0.5348
0.3966
0.2867
0.1995
0.1364
0.0940
0.0653
0.0455
0.0320
(Qi - Fi)
0.0010
0.0010
-0.0028
-0.0337
-0.1554
-0.4266
-0.2927
-0.2370
-0.1715
0.0002
0.3390
0.0912
-0.0167
-0.0615
-0.1122
-0.1892
-0.1684
-0.1290
-0.0507
0.0631
0.1825
0.2183
0.2196
0.1880
0.1281
0.0455
0.0676
0.0690
0.0567
0.0355
0.0080
Mean of Observed Discharge, Qav
=
=
(Qi - Fi)2
0.0000
0.0000
0.0000
0.0011
0.0241
0.1820
0.0857
0.0562
0.0294
0.0000
0.1149
0.0083
0.0003
0.0038
0.0126
0.0358
0.0284
0.0166
0.0026
0.0040
0.0333
0.0477
0.0482
0.0353
0.0164
0.0021
0.0046
0.0048
0.0032
0.0013
0.0001
0.8027
∑Q
i
N
28.703
31
= 0.9259 m3/s
Qav
0.9259
0.9259
0.9259
0.9259
0.9259
0.9259
0.9259
0.9259
0.9259
0.9259
0.9259
0.9259
0.9259
0.9259
0.9259
0.9259
0.9259
0.9259
0.9259
0.9259
0.9259
0.9259
0.9259
0.9259
0.9259
0.9259
0.9259
0.9259
0.9259
0.9259
0.9259
(Qi - Qav)
-0.9249
-0.9249
-0.9249
-0.9249
-0.9249
-0.9249
-0.3791
0.1667
0.7125
1.2583
1.8041
1.5873
1.3705
1.1537
0.9369
0.7201
0.6097
0.4993
0.3889
0.2785
0.1681
-0.0017
-0.1715
-0.3413
-0.5111
-0.6809
-0.7219
-0.7629
-0.8039
-0.8449
-0.8859
(Qi -Qav)2
0.8554
0.8554
0.8554
0.8554
0.8554
0.8554
0.1437
0.0278
0.5077
1.5833
3.2548
2.5195
1.8783
1.3310
0.8778
0.5185
0.3717
0.2493
0.1512
0.0776
0.0283
0.0000
0.0294
0.1165
0.2612
0.4636
0.5211
0.5820
0.6463
0.7139
0.7848
22.7720
74
Efficiency Index for Calibration
=
SSTotal − SSError
SSTotal
=
22.772 − 0.8027
22.772
= 96.48 %
5.2.2
Efficiency Index for Validation Process
Calculation of Efficiency Index for 7 April 2004 Validation Process is as
follows:
Table 5.3: Calculation table for Efficiency Index of Validation Process
Time
1550
1555
1600
1605
1610
1615
1620
1625
1630
1635
1640
1645
1650
1655
1700
1705
Qi
0.0262
0.1572
0.4008
1.0948
1.9034
3.1704
4.1063
3.7178
3.1477
1.8512
0.7523
0.4438
0.1773
0.0788
0.0000
0.0000
21.0280
Fi
0.0000
0.0000
0.2377
1.0989
2.3732
3.3423
3.7348
3.6854
3.1494
2.2024
1.2340
0.5628
0.2241
0.0810
0.0236
0.0044
(Qi - Fi)
0.0262
0.1572
0.1631
-0.0041
-0.4698
-0.1719
0.3715
0.0324
-0.0017
-0.3512
-0.4817
-0.1190
-0.0468
-0.0022
-0.0236
-0.0044
Mean of Observed Discharge, Qav
=
=
(Qi - Fi)2
0.0007
0.0247
0.0266
0.0000
0.2207
0.0295
0.1380
0.0010
0.0000
0.1233
0.2320
0.0142
0.0022
0.0000
0.0006
0.0000
0.8137
∑Q
i
N
21.028
16
= 1.3143 m3/s
Qav
1.3143
1.3143
1.3143
1.3143
1.3143
1.3143
1.3143
1.3143
1.3143
1.3143
1.3143
1.3143
1.3143
1.3143
1.3143
1.3143
(Qi - Qav)
-1.2881
-1.1571
-0.9135
-0.2195
0.5891
1.8561
2.7920
2.4035
1.8334
0.5369
-0.5620
-0.8705
-1.1370
-1.2355
-1.3143
-1.3143
(Qi -Qav)2
1.6592
1.3389
0.8345
0.0482
0.3470
3.4451
7.7953
5.7768
3.3614
0.2883
0.3158
0.7578
1.2928
1.5265
1.7274
1.7274
32.2422
75
Efficiency Index for Calibration
=
SSTotal − SSError
SSTotal
=
32.2422 − 0.8137
32.2422
= 97.47%
Both the Efficiency Index for calibration and validation yields a high
percentage of 96.48 % and 97.47 %. This proves that the accuracy of the HEC-HMS
simulation is high. Therefore, HEC-HMS is suitable to use for discharge estimation
and future urbanization simulation.
5.3
100 years ARI Rainfall Estimation
Variables such as rainfall intensity, duration and frequency are all related to
each other. 100 years ARI rainfall estimation can be derived from the variables and
these data are then used as the input in the HEC-HMS simulation process.
5.3.1
Rainfall Intensity-Duration-Frequency (IDF) Relationships
The total storm rainfall depth at a point, for a given rainfall duration and ARI,
is a function of the local climate. Rainfall depths can be further processed and
converted into rainfall intensities (intensity = depth/duration), which are then
presented in IDF curves. Such curves are particularly useful in stormwater drainage
design because many computational procedures require rainfall input in the form of
average rainfall intensity.
76
5.3.2
Polynomial Approximation of IDF Curves
Polynomial expressions in the form of equation below have been fitted to the
published IDF curves for the 26 main cities in Malaysia [7].
ln (RIt) = a + b ln (t) + c (ln (t))2 + d(ln(t))3
[Eq. 5.4]
where,
R
It
= the average rainfall intensity (mm/hr) for ARI R and duration t
R
= average return interval (years)
t
= duration (minutes)
a to d = fitting constants dependent on ARI.
Table 5.4 gives values of the fitted coefficients in equation above for Kuala Lumpur,
for storm ARI of between 2 years and 100 years.
Table 5.4: Coefficients of the fitted IDF Equation for Kuala Lumpur [7]
ARI (yrs)
2
5
10
20
50
100
a
5.3255
5.1086
4.9696
4.9871
4.8047
5.0064
b
0.1806
0.5037
0.6796
0.7533
0.9399
0.8709
c
-0.1322
-0.2155
-0.2584
-0.2796
-0.3218
-0.3070
d
0.0047
0.0112
0.0147
0.0166
0.0197
0.0186
77
The observed tc is about 24 minutes, therefore the duration of the storm is
rounded up to a figure of 30 minutes. The average rainfall intensity (mm/hr) for ARI
100 years and duration for 30 minutes can be obtained by using Equation 5.4. The
calculation is shown below:
ln 100I30
= 5.0064 + 0.8709 ln (30) – 0.3070 (ln 30)2 + 0.0186 (ln 30)3
= 5.1489
100
= e5.1489
I30
= 172.24 mm/hr
Then, the rainfall intensity is multiplied by the duration to obtain the rainfall depth
just as the following.
Rainfall Depth = Rainfall Intensity x Duration
[Eq. 5.5]
= 172.24 mm/hr x 0.5 hr
= 86.12 mm
5.3.3
Design Rainfall Temporal Patterns
The temporal distribution of rainfall within the design storm is an important
factor that affects the runoff volume, and the magnitude and timing of the peak
discharge.
Design rainfall temporal patterns are used to represent the typical
78
variation of rainfall intensities during a typical storm burst. The annual rainfall depth
of 86.12 mm is multiplied with the fraction of rainfall in each time period to get the
100 years ARI rainfall data as shown in Table 5.6. These data will be entered in
HEC-HMS to produce 100 years ARI hydrograph as shown in Figure 5.5.
Figure 5.6 shows the peak discharge of the 100 years ARI hydrograph which is
approximately 8.22 m3/s.
Table 5.5: Rainfall Temporal Patterns [7]
Temporal Patterns - West Coast of Peninsular Malaysia
Duration (min)
30
60
10
15
2
3
6
12
No of Time Periods
0.570
0.320
0.160
0.039
1
0.430
0.500
0.250
0.070
2
0.180
0.330
0.168
3
0.090
0.120
4
0.110
0.232
5
0.060
0.101
6
0.089
7
0.057
8
0.048
9
0.031
10
0.028
11
0.017
12
120
8
0.030
0.119
0.310
0.208
0.090
0.119
0.094
0.030
180
6
0.060
0.220
0.340
0.220
0.120
0.040
Table 5.6: 100 ARI Rainfall Data
Fraction of Rainfall
in Each Time Period
100 ARI Rainfall Data
0.160
13.78
0.250
21.53
0.330
28.42
0.090
7.75
0.110
9.47
0.060
5.17
1.000
86.12
79
Figure 5.5: 100 years ARI hydrograph
Figure 5.6: 100 years ARI peak discharge result summary table
80
5.4
Comparison with Rational Method
The Rational Formula is one of the most frequently used urban hydrology
methods in Malaysia. The Rational Formula is shown in Equation 5.6 and the
calculation is as follows. The average runoff coefficient used for the calculation is
0.49.
QR
=
Co .R I t .A
360
=
0.49 x172.24 x55
360
[Eq. 5.6]
= 12.89 m3/s
where,
QR
= R year ARI peak flow (m3/s)
Co
= dimensionless runoff coefficient
R
= R year ARI average rainfall intensity over time of concentration, tc, (mm/hr)
It
A
= drainage area (ha)
5.5
Current Channel Capacity
The current channel capacity is calculated using Manning’s Formula.
2
Q=
1
1
x A x Ra 3 x So 2
n
where,
n
=
Manning’s roughness coefficient
A
=
channel cross section area
Ra
=
hydraulic radius
So
=
friction slope
[Eq. 5.7]
81
n = 0.014
So = 0.002
2.6 m
0.5
0.2 m
2.6 m
Figure 5.7: Cross Section of Channel at the Outlet
A
=
2.6(5.2 + 2.6)
0.2(1 + 0.8)
+
2
2
= 10.32 m2
P
= 2.91 + 2.6 + 2.91
= 8.42 m
Ra
=
A
P
=
10.32
8.42
= 1.23 m
2
Q
1
1
= x A x Ra 3 x So 2
n
2
=
1
1.0 m
1
1
x 10.32 x 1.2 3 x 0.002 2
0.014
= 37.22 m3/s
≈ 37 m3/s
82
5.6
Discussion
From the calibration and validation process of HEC-HMS, HEC-HMS shows
a high efficiency index of 96.48% and 97.47%. This indicates that this software is
suitable to use for future urbanization simulation. However, the results generated by
HEC-HMS has shown a low peak discharge of 8.22 m3/s for 100 years ARI
hydrograph compared to the calculation obtained from the Rational Method which is
12.89 m3/s.
This is because the rainfall data collection was only done in a 6 months
period time (Refer Appendix D for the figure of rainfall data collection equipments
used). All the storms were only considered as small storms and were not big storms.
These small storms only show a return period of approximately 1 month. Therefore,
it is an extreme projection from a one month return period to a 100 years return
period. The magnification of a micro return period to a macro return period might
have caused the differences in prediction as the margin is much too wide in
comparison.
Nevertheless, both the peak discharges obtained from HEC-HMS and
Rational Method are within the current channel capacity of 37 m3/s. This proves that
the current channel capacity is more than adequate and flood would not happen at the
study catchment area.
5.7
Determination of Storm Water Runoff Quality
Storm water pollutants are transported and accumulated in the runoff. Total
Suspended Solids (TSS), Biochemical Oxygen Demand (BOD), Chemical Oxygen
Demand (COD), Total Phosphorus (TP), Total Copper (Cu) are among the
constituents that are recommended as ‘standard’ pollutants characterizing urban
83
runoff for Malaysia. In this study, TSS is taken in consideration of the determination
of storm water quality.
5.7.1
Event Mean Concentration (EMC)
The storm water runoff quality is determined by using the Event Mean
Concentration (EMC) Method. The EMC is the flow-weighted mean concentration of
a pollutant. The EMC is computed as the total multiplication between discharge and
concentration divided by the total discharge. EMC estimates are obtained from a
flow-weighted composite of concentration samples taken during a storm. The
following is the EMC formula:
EMC =
∑ (QxC)
∑Q
[Eq. 5.8]
where,
Q
= Discharge
C
= Concentration
Table 5.7 shows a calculation example of EMC on 16 December 2003 event. (Refer
Appendix C for all the EMC data of 8 events taking in consideration.)
Table 5.7: Calculation example of EMC
EVENT
DATE
TIME
TSS
(mg/l)
Flowrate,
Q (m3/s)
1
16/12/2003
15:56
5290.00
0.5071
Total
Flowrate,
Qtotal
4.1615
16:05
1581.48
0.1792
4.1615
68.10
16:15
1894.77
0.0657
4.1615
29.91
16:16
1601.73
0.0617
4.1615
23.75
Total:
EMC (mg/l)
644.61
766.38
84
Taking 8 storm events in consideration, the mean and the median of EMC for TSS
are as follows:
Table 5.8: EMC values for 8 storm events taken
No.
1
2
3
4
5
6
7
8
Date
28-1-2004
4-6-2004
4-7-2004
17-4-2004
16-12-2003
17-12-2003
19-1-2004
15-4-2004
EMC (mg/l)
190.30
422.56
437.73
663.22
766.38
1014.45
2475.76
4663.27
10633.67
Total:
Mean = 10633.67 / 8
= 1329.21 mg/l
Median = (663.22 + 766.38) / 2
= 1429.6 mg/l
Table 5.9: Typical Event Mean Concentration (EMC) Values in mg/l
Landuse/vegetation categories
Pollutant
Suspended
Solids
Native
Rural
Vegetation/Forest
Grazing
6
30
Industry
Urban
Construction
60
85
4,000
85
5.8
Runoff Volume Estimation
It is necessary to estimate runoff volumes before any assessment can be made
of pollutant loads. The annual runoff volume estimates can be done by referring to
MASMA, Appendix 15.C.
Table 5.10: Runoff Volume Calculation for 50% Directly-Connected Impervious
Area
Average
Size of
Event
(ARI)
0.0625
no. of
Events
Runoff (m3)
Runoff
Rainfall
mm per
Intensity
in 10
years
Event
(mm/hr)
160
9.0
18.0
Coefficient
C
0.49
mm per
Event
4.4
Total
per
Total in
Event
10
years
4,410
705,600
From Table 5.9, for a size of event of 23 days ARI, the 10 years total runoff
volume is 705,600 m3. Therefore, it can be estimated that the runoff volume annually
for 23 days ARI is 70,560 m3.
5.9
Annual Pollutant Load
Annual Pollutant Load
=
EMC x Annual Runoff Volume
=
1329.21 mg/l x 70560 m3
=
93.79 tonnes
86
5.10
Discussions
The average Event Mean Concentration (EMC) value for Total Suspended
Solids (TSS) of 1329.21 mg/l is very high compared to the typical EMC at urban
area which suggested only 85 mg/l. This is due to an on-going construction while the
water samples were taken.
A design of sedimentation trap or silt trap at the construction site may be
introduced to reduce the total suspended solids flowing into the drainage system and
rivers. This will help in alleviating the reduction of drainage carrying capacity.
CHAPTER 6
CONCLUSION AND RECOMMENDATION
6.1
Conclusion
The current channel capacity is 37 m3/s and the results generated by HECHMS for 100 years ARI has only shown a peak flow of 8.22m3/s. Therefore, the
capacity of existing river is more than adequate. Flood is unlikely to happen at the
study area. However, the flow from this area may cause flooding at the down stream
area.
The calibration process yields a high percentage of 96.48% for it efficiency
index whereas the validation process achieves a good percentage of 97.47%. This
indicates that the HEC-HMS has a dependable accuracy and it is suitable in
simulating future urbanization.
Lastly, the Event Mean Concentration (EMC) value for Total Suspended
Solids (TSS) is very high at the study area compared to the typical urban EMC value
of only 85 mg/l. The study area EMC value reaches a high level of 1329.21 mg/l in
average as there was a construction on going while the water samples were taken.
This EMC value gives an annual pollutant load of 93.79 tonnes.
88
6.2
Recommendation
With the rapid development of Bunus River Catchment Area, floods can be
effectively controlled by the storm water quantity control facilities with either
retention or detention facilities. Retention refers to the holding of storm water and
preventing storm water runoff. Detention refers to the temporary storage of storm
water runoff.
Detention and retention facilities can reduce the peak and volume of runoff
from a given catchment, which can reduce the frequency and extent of downstream
flooding, soil erosion, sedimentation, and water pollution. Detention and retention
facilities at the upstream area may also be able to reduce the costs of large
stormwater drainage systems construction as there are also no longer space for
further deepening and widening of rivers and drainage systems.
One of the detention facilities which are becoming more commonly used are
‘wet ponds’, which incorporate a permanent pool of stored water for water quality
control as well as provision for the temporary storage and release of runoff for flood
control. There are possible sites in the downstream area that can yet be used as
detention pond in the near future. Figure 6.1 shows a natural wet pond at the
downstream area. The purpose of this pond is to regulate flow from the upstream
area. However, detention facilities should be provided only where they are shown to
be beneficial by hydrologic, hydraulic, and cost analyses.
Figure 6.1: Natural wet pond at downstream area
89
REFERENCES
1.
Department of Irrigation and Drainage, Wilayah Persekutuan. Laporan
Banjir Semasa 2000 di Wilayah Persekutuan. 2000
2.
Dewan Bandaraya Kuala Lumpur. Urban Drainage Projects in Kuala
Lumpur. RBMU No. 15. 2002
3.
NSTP Online. Downpour Causes Flood at Hospital Besar Kuala Lumpur.
New Straits Time, 25 May 2002.
4.
Department of Irrigation and Drainage, Wilayah Persekutuan. Laporan
Banjir Semasa 2003 di Wilayah Persekutuan. 2003
5.
US Army Corps of Engineers, Hydrologic Engineering Center.
Hydrologic Modelling System HEC-HMS, Technical Reference Manual.
California, March 2000.
6.
US Army Corps of Engineers, Hydrologic Engineering Center.
Hydrologic Modelling System HEC-HMS, User’s Manual Version 2.1.
California, January 2001.
7.
Department of Irrigation and Drainage Malaysia. Urban Stormwater
Management Manual for Malaysia (Manual Saliran Mesra Alam
Malaysia). Kuala Lumpur, MASMA. 2000
8.
US Army Corps of Engineers, Hydrologic Engineering Center.
Hydrologic Modelling System HEC-HMS, Applications Guide. California,
December 2002.
9.
Md Ghazali bin Bagimin Kajian Banjir di Daerah Seremban dengan
Menggunakan Permodelan Perisian HEC-HMS. Bachelor Degree Thesis.
Universiti Teknologi Malaysia; 2003
APPENDICES
90
APPENDIX A1
New Straits Time – Monday, 1 May 2000
91
APPENDIX A2
Berita Harian – Thursday, 21 December 2000
92
APPENDIX A3
News Full Document Display
News Archive (1991 - ) Article Full Document Display
Publication :
Edition :
MALAY MAIL
Date :
12/06/2002
Page Number : 17
Headline :
Words :
Chaos in city
under water
502
Byline :
By Azlan Ramli
Text :
HEAVY rain around the Klang Valley yesterday created a host of problems
for everyone, especially motorists and residents in flood-prone areas.
Even The Malay Mail team had trouble going around the city to check the
damage of the three-hour downpour which started about 5.30pm.
There were the usual fender benders - traffic congestion, flaring
tempers, damaged household goods, uprooted trees and fallen branches,
broken-down vehicles and submerged cars.
Many roads were so badly flooded that even lorries and four-wheel-drive
vehicles could not pass. Some motorists had to make U-turns while others
just gave up and waited for the flood to subside.
A few who tried to drive through the flood had engine problems.
Among the roads affected were Lebuhraya Mahameru, Jalan Ampang, Jalan
Tun Razak, Jalan Kampung Pandan, Jalan Genting Kelang, Jalan Dang Wangi
and Jalan Pahang.
The city's Fire and Rescue Department, City Hall's emergency unit and
Civil Defence Unit personnel could be seen around town helping flood
victims, including stranded motorists.
Several areas in Setapak were also flooded, including Kampung Boyan,
Kampung Pandan and Taman Ibu Kota.
However, there were no casualties reported.
Some parts of Kampung Baru, one of the traditional flood-prone areas,
were under water when Sungai Klang swelled up and overflowed. Other
affected areas included Jalan Gurney, Kampung Siam and Jalan Sentul.
There was an unconfirmed report that the roof of a penthouse at a
condominium in Jalan Ipoh caved in during the rain, damaging many items in
the house and also injuring its tenant.
At the Jalan Ipoh-Jalan Tun Razak traffic lights junction, some
motorists and passers-by had a good time catching small tilapia fish
"pushed" out of roadside weepholes when the nearby Sungai Gombak swelled
up.
Some of the motorists and passers-by were seen gleefully walking away
with plastic bags full of the fish that they caught with their bare hands.
Those without any plastic bags or containers even used their umbrellas
93
to scoop the fish.
"Buat gulai untuk makan malam ni! (They will be tonight"s curry for
dinner)," said one of them.
"I'll deep fry them all and then eat with some chili sauce," said
another, who must have easily caught about 40 fish, each of reasonable
size.
Over at the Villa Angsana building at Taman Rainbow, 4th Mile, Jalan
Ipoh, several residents were in for a big clean-up and bracing the
possibility of a big hole in their wallets when their cars, parked at the
lower ground floor parking lot, were flooded.
One of them, identified only as John, said his Proton Iswara was among
the 40 to 50 cars affected.
John said the flood was worse in either September or October last year
when his family's Mercedes-Benz was badly damaged in the same parking lot.
"We had to spend about RM6,000 in repairs as many of the electronic
parts were damaged," he said, adding that he is not sure how badly damaged
his Proton Iswara is.
(END)
94
APPENDIX A4
News Archive (1991 - ) Article Full Document Display
Publication :
NST
Edition :
2*
Date :
12/06/2002
Page Number : 08
Headline :
Downpour causes flood, jams
Words :
327
Byline :
By Lee Shi-Ian
Text :
KUALA LUMPUR, Tues. - Parts of the city were in chaos for several hours
today after a downpour caused flash floods and uprooted trees, causing
massive traffic jams.
Traffic came to a standstill along Jalan Ampang, Jalan Tun Razak, Jalan
Kampung Pandan, Jalan Dang Wangi and Jalan Pahang where water rose to
about a metre.
Numerous vehicles parked in low-lying areas in Setapak such as Kampung
Boyan, Kampung Pandan, Taman Ibu Kota were submerged as flood waters
rose
shortly after rain began about 5.30pm.
Also badly hit were some areas along Jalan Tun Razak near the Vistana
Hotel and Jalan Genting Kelang where flood water rose above many cars.
A Fire and Rescue Department spokesman said the floods were caused by
the Sungai Gombak overflowing its banks.
Low-lying areas in Kampung Baru and Kampung Padang were also flooded
when the Sungai Klang spilled over to Jalan Raja Muda and the surrounding
areas.
Federal Territory Fire and Rescue Department Director Roslan Wahab said
that as at 11pm, almost 40 families from Kampung Periuk had been evacuated
to Dewan Sultan Sulaiman, Kampung Baru, after their houses were flooded.
He said there were also two cases of uprooted trees and fallen branches
reported in Jalan Harmoni 1, Taman Harmoni, Batu 6 1/2, Gombak and Jalan
K5 in Melawati. Other affected areas include Kampung Siam, Jalan Gurney
and Jalan Sentul.
By 8.30pm, the flood waters had receded in most areas with the exception
of Jalan Ampang which saw traffic in a gridlock.
Fire and Rescue Department staff were immediately deployed to help
residents with the assistance of civil defence unit personnel.
Following the two-hour downpour, hundreds of motorists were stranded
along flooded roads.
In March last year, the city was brought to a standstill when a downpour
caused flash floods, traffic jams and short-circuits, killing two and
injuring two others.
Losses ran into millions of ringgit after businesses came to a
standstill.
(END)
95
APPENDIX A5
Harian Metro – Wednesday, 11 June 2003
KUALA LUMPUR, Selasa – Banjir kilat
melanda lagi ibu kota berikutan hujan lebat
selama tiga jam menyebabkan beberapa
kawasan rendah ditenggelami air.
Antara kawasan yang terjejas teruk
adalah Jalan Tun Razak berdekatan Stesen
Star-LRT Masjid Jamek, Jalan Ipoh, Jalan Tun
Ismail, Kampung Baru dan Kampung
Abdullah Hukum.
Hujan lebat bermula kira-kira jam 4
petang ketika warga kota bersiap keluar
pejabat.
Beratus-ratus kenderaan tersadai di
jalan raya berikutan kesesakan teruk.
Sejumlah kenderaan juga ditenggelami
air kerana diletakkan di kawasan rendah.
Sementara itu, Jabatan Pertahanan
Awam (JPA3), Jabatan Bomba dan
Penyelamat dan Skuad Penyelamat Dewan
Bandaraya Kuala Lumpur (DBKL) bertungkus
lumus menyelamatkan warga kota yang
terperangkap dalam banjir berkenaan.
Banjir itu juga menyebabkan 180
penduduk Kampung Periuk, Kampung Baru
dipindahkan ke Dewan Kelab Sultan Sulaiman
untuk sementara waktu.
Pesakit Pusat Rawatan Islam (Pusrawi)
di Jalan Ipoh juga terpaksa dipindahkan ke
Hospital Kuala Lumpur (HKL) kerana
masalah gangguan elektrik.
96
APPENDIX A6
Adapted from the Department of Irrigation and Drainage, Wilayah Persekutuan.
Laporan Banjir Semasa 2003 di Wilayah Persekutuan. 2003
97
APPENDIX A7
Adapted from the Department of Irrigation and Drainage, Wilayah Persekutuan.
Laporan Banjir Semasa 2003 di Wilayah Persekutuan. 2003
98
APPENDIX B1
Incremental precipitation and discharge data collected on 15 April 2004 at the
outlet of the study area of Bunus River Catchment Area. This storm event is used for
the calibration process.
Time
16:14
16:15
16:16
16:17
16:18
16:19
16:20
16:21
16:22
16:23
16:24
16:25
16:26
16:27
16:28
16:29
16:30
16:31
16:32
16:33
16:34
16:35
16:36
16:37
16:38
16:39
16:40
16:41
16:42
16:43
16:44
Incremental
Precipitation
(mm)
0.2
0.4
0.7
1.4
1.5
1.5
1.1
0.8
0.7
0.5
0.3
0.4
0.1
0.1
0.1
0.4
0.5
0.4
0.8
0.7
0.4
0.2
0.2
0.2
0.1
0.1
0.1
0.1
0.1
0.1
0.1
Time
Discharge
(m3/s)
16:09
16:19
16:29
16:39
16:49
16:59
17:09
0.001
0.001
3.730
1.646
1.094
0.245
0.040
99
APPENDIX B2
Incremental precipitation and discharge data collected on 7 April 2004 at the
outlet of the study area of Bunus River Catchment Area. This storm event is used for
the validation process.
Time
15:55
15:56
15:57
15:58
15:59
16:00
16:01
16:02
16:03
16:04
16:05
16:06
16:07
16:08
16:09
16:10
16:11
16:12
16:13
16:14
16:15
16:16
16:17
16:18
16:19
16:20
16:21
16:22
16:23
16:24
16:25
16:26
16:27
16:28
16:29
16:30
Incremental
Precipitation
(mm)
0.0
0.1
0.4
0.8
1.2
1.3
1.5
1.4
1.1
1.0
0.7
0.8
0.6
0.7
1.0
1.3
0.9
1.0
1.0
0.9
1.3
1.4
1.3
0.6
0.5
0.6
0.4
0.4
0.6
0.4
0.2
0.2
0.2
0.1
0.1
0.0
Time
Discharge
(m3/s)
15:49
15:59
16:09
16:19
16:29
16:39
16:49
16:59
0.000
0.262
1.650
4.184
3.407
0.814
0.197
0.000
100
APPENDIX C
Event Mean Concentration (EMC) for Total Suspended Solids (TSS) for 8
events taken.
EVENT
DATE
TIME
TSS
(mg/l)
1
16/12/2003
15:56
16:05
16:15
16:16
5290.00
1581.48
1894.77
1601.73
0.5071
0.1792
0.0657
0.0617
Flowrate,
Q (m3/s)
0.5521
0.8454
1.1541
1.3853
1.5294
1.8460
2.0185
2.1714
2.3242
2.4400
2.5559
2.6215
2.6872
2.7922
2.8973
2.9922
3.0871
3.1310
3.1749
3.1354
EVENT
DATE
TIME
TSS
(mg/l)
2
17/12/2003
11:58
12:01
12:04
12:06
12:07
12:09
12:10
12:11
12:12
12:13
12:14
12:15
12:16
12:17
12:18
12:19
12:20
12:21
12:22
12:23
1179.00
1734.00
936.00
1055.44
567.00
1073.00
1049.00
1189.00
1286.00
2930.00
1076.00
1145.00
1156.00
2379.00
2916.00
821.00
1010.00
1199.00
662.00
447.00
Flowrate,
Q (m3/s)
Total
Flowrate,
Qtotal
4.1615
4.1615
4.1615
4.1615
Total
Total
Flowrate,
Qtotal
58.2836
58.2836
58.2836
58.2836
58.2836
58.2836
58.2836
58.2836
58.2836
58.2836
58.2836
58.2836
58.2836
58.2836
58.2836
58.2836
58.2836
58.2836
58.2836
58.2836
Total
EMC
(mg/l)
644.61
68.10
29.91
23.75
766.38
EMC
(mg/l)
11.17
25.15
18.53
25.09
14.88
33.98
36.33
44.30
51.28
122.66
47.19
51.50
53.30
113.97
144.96
42.15
53.50
64.41
36.06
24.05
1014.45
101
EVENT
DATE
TIME
TSS
(mg/l)
Flowrate,
Q (m3/s)
3
19/1/2004
17:19
17:22
17:23
17:24
17:25
17:26
17:27
17:29
17:30
17:32
17:33
17:35
17:37
17:38
17:40
17:42
17:43
17:45
17:47
17:48
17:50
17:52
17:53
17:54
2207.30
1152.00
2541.60
1966.60
2320.00
2278.00
1470.40
2583.00
2088.00
2826.00
2144.00
1938.20
3551.80
3698.20
1950.00
2771.20
1970.00
2734.20
4577.00
2728.40
2233.00
2887.00
4535.00
2051.20
0.3086
1.1662
2.1529
3.1396
4.1885
5.2372
5.8862
6.6350
6.7348
6.5118
6.2650
5.8561
5.6116
5.5292
5.1838
4.8369
4.6782
4.3897
4.1572
4.0545
3.9006
3.6648
3.5680
3.4711
Flowrate,
Q (m3/s)
0.6857
0.5779
0.4076
0.1757
0.2189
0.0928
0.0600
0.8450
0.3177
1.4052
1.2567
EVENT
DATE
TIME
TSS
(mg/l)
4
28/1/2004
17:33
17:37
17:43
17:54
18:07
18:25
18:29
18:51
18:53
18:55
18:57
563.40
567.20
708.60
782.00
331.70
199.40
144.10
325.30
461.40
732.40
762.90
Total
Flowrate,
Qtotal
111.5413
111.5413
111.5413
111.5413
111.5413
111.5413
111.5413
111.5413
111.5413
111.5413
111.5413
111.5413
111.5413
111.5413
111.5413
111.5413
111.5413
111.5413
111.5413
111.5413
111.5413
111.5413
111.5413
111.5413
Total
Total
Flowrate,
Qtotal
19.1770
19.1770
19.1770
19.1770
19.1770
19.1770
19.1770
19.1770
19.1770
19.1770
19.1770
Total
EMC
(mg/l)
6.11
12.04
49.06
55.35
87.12
106.96
77.60
153.65
126.07
164.98
120.42
101.76
178.69
183.32
90.62
120.17
82.62
107.60
170.59
99.18
78.09
94.86
145.07
63.83
2475.76
EMC
(mg/l)
20.15
17.09
15.06
7.16
3.79
0.96
0.45
14.33
7.64
53.67
49.99
190.30
102
EVENT
DATE
TIME
TSS
(mg/l)
Flowrate,
Q (m3/s)
5
4-6-2004
15:21
15:31
15:39
15:49
15:59
16:09
16:19
947.40
563.20
363.80
445.60
456.60
231.80
498.00
0.0248
1.8574
3.6835
1.3789
0.2796
0.1817
0.0578
EVENT
DATE
TIME
TSS
(mg/l)
Flowrate,
Q (m3/s)
6
4-7-2004
16:09
16:19
16:39
968.80
834.20
961.00
0.2622
5.1838
0.8139
EVENT
DATE
TIME
TSS
(mg/l)
Flowrate,
Q (m3/s)
7
15/4/2004
16:22
16:25
16:29
16:33
16:39
16:45
16:50
16:59
1947.20
3168.00
2614.00
2390.60
1462.80
1352.00
1041.60
1346.00
1.1200
2.2387
3.7303
2.8964
1.6458
1.3149
1.0094
0.2452
EVENT
DATE
TIME
TSS
(mg/l)
Flowrate,
Q (m3/s)
8
17/4/2004
16:49
17:10
17:25
17:40
17:50
18:00
1775.60
1647.40
1331.20
1226.00
1480.40
1460.40
0.0247
7.1586
13.2531
6.6406
1.8574
0.7616
Total
Flowrate,
Qtotal
7.6265
7.6265
7.6265
7.6265
7.6265
7.6265
7.6265
Total
Total
Flowrate,
Qtotal
12.2462
12.2462
12.2462
Total
Total
Flowrate,
Qtotal
6.7581
6.7581
6.7581
6.7581
6.7581
6.7581
6.7581
6.7581
Total
Total
Flowrate,
Qtotal
62.5474
62.5474
62.5474
62.5474
62.5474
62.5474
Total
EMC
(mg/l)
3.08
137.16
175.71
80.57
16.74
5.52
3.77
422.56
EMC
(mg/l)
20.74
353.12
63.87
437.73
EMC
(mg/l)
322.70
1049.44
1442.86
1024.57
356.24
263.05
155.57
48.84
4663.27
EMC
(mg/l)
0.70
188.55
282.07
130.16
43.96
17.78
663.22
103
APPENDIX D
Rainfall gage installed at the outlet of study area
Location of rainfall gage at study area