s - Universiti Teknologi Malaysia

DEVELOPMENT OF FLOOD FORECASTING MODEL FOR
SUNGAI KELANTAN
(TANK MODEL)
CHONG CHEW HOONG
A report submitted in partial fulfillment of the
requirements for the award of the degree of
Bachelors Degree in Civil Engineering
Faculty of Civil Engineering
Universiti Teknologi Malaysia
APRIL 2007
iii
To the two men who have made me who I am today.
iv
ACKNOWLEDGEMENT
My deepest gratitude goes to my supervisor, Associate Professor Dr Ahmad
Khairi Abdul Wahab, for being consistently supportive and enthusiastic throughout
the course of this project. His enthusiasm has been a strong motivation to the project
team.
I would also like to express my sincere gratitude to my father, Ir Chong Sun
Fatt, and Mr. Cheok Hou Seng for giving me the inspiration to initiate this project,
providing me with guidance and ideas to further enhance the Sungai Kelantan flood
forecasting model.
My sincere gratitude also goes to Ir. Low Koon Sing and all the personnel of
the Jabatan Pengairan dan Saliran, Malaysia for their assistance in the data
acquisition process, as well as providing me with valuable suggestions that helped to
improve the features of the model.
I would also like to thank Mr. Lee Jin Ming and Mr. Ang Zhili, who have
collaborated enthusiastically in the development of the Sungai Kelantan flood
forecasting model. Lastly I would like to thank my family and friends for their
support and faith in me.
v
ABSTRACT
The state of Kelantan is situated at the east coast of the Peninsular Malaysia,
which is subjected to the north-east monsoon, and major flooding caused by the
overflow of Sungai Kelantan and Sungai Golok has been occurring on a yearly basis
typically during the months of November and December. A reliable flood
forecasting system will be able to provide early warning to local authorities so that
appropriate measures can be taken to protect lives and properties. With this objective,
a flood forecasting model is developed for Sungai Kelantan in this study. The model
is divided into two components: hydrological and hydraulic model. Tank model by
M. Sugawara is adopted as the hydrological model and it is built using Microsoft
Excel with Visual Basic for Application (VBA). After calibration and verification of
model using four sets of storm data, the model is able to provide reliable forecast
with an average model efficiency of 96 % and mean absolute error of 0.33 m for
twelve hours forecasts.
vi
ABSTRAK
Negeri Kelantan terletak di kawasan pantai timur semenanjung Malaysia
yang tertakluk kepada pengaruh monsun timur-laut, dan setiap tahun berlakunya
banjir utama yang disebabkan oleh kelimpahan Sungai Kelantan dan Sungai Golok,
terutamanya semasa bulan November dan Disember. Dengan adanya satu model
ramalan banjir yang dapat memberikan amaran awal, ia akan membantu pihak
berkuasa tempatan dalam mengambil tindakan untuk menyelamatkan nyawa dan
harta benda. Maka dalam kajian ini, satu model ramalan banjir telah dibina untuk
Sungai Kelantan. Model ini terbahagi kepada dua komponen: model hidrologi dan
hidraulik. Model tangki yang dicipta oleh M. Sugawara telah dipilih sebagai model
hidrologi and ia dibina menggunakan MS Excel dan aturcara Visual Basic for
Application (VBA). Selepas menjalani proses kalibrasi and verifikasi model dengan
menggunakan empat set data hujan, adalah didapati bahawa model yang
dibangunkan ini mampu memberikan ramalan paras air dengan keberkesanan model
(ME) sebanyak 96 % dan purata perbezaan mutlak sebanayak 0.33 m untuk ramalan
12 jam.
vii
TABLE OF CONTENTS
CHAPTER
1
2
TITLE
PAGE
DECLARATION
ii
DEDICATION
iii
ACKNOWLEDGEMENT
iv
ABSTRACT
v
ABSTRAK
vi
TABLE OF CONTENT
vii
LIST OF TABLES
x
LIST OF FIGURES
xi
LIST OF SYMBOLS/ABBREVIATIONS
xiii
LIST OF APPENDICES
xiv
INTRODUCTION
1.1
Background of Study
1
1.2
Statement of Problem
3
1.3
Objective of Study
4
1.4
Scope and Limitation of Study
4
1.5
Significance of Study
5
LITERATURE REVIEW
2.1
Introduction
6
2.2
Types of Model Commonly Used in
7
Malaysia
2.2.1
Unit Hydrograph
8
viii
TABLE OF CONTENTS
CHAPTER
TITLE
PAGE
2.2.2 Tank Model
9
2.2.3
10
Sacramento Soil Moisture
Accounting and Routing
2.3
Hydrological Model Selection
11
2.4
Software Programming
12
2.5.1
Microsoft Excel
12
2.5.2
Microsoft Visual Basic for
12
Application
3
HYDROLOGICAL MODEL: TANK MODEL
14
3.1
Introduction
14
3.2
Computation Process within a Tank
17
3.3
Computation of Total Discharge and
18
Water Level
3.4
4
Autoregressive Model
18
RESEARCH METHODOLOGY
20
4.1
Research Design
20
4.2
Background of the Sungai Kelantan River
22
Basin
4.3
Data Acquisition and Analysis
24
4.3.1
27
Missing Rainfall Data
4.4
Structure of Model
28
4.5
Development of Tank Model
32
4.6
Model Calibration and Verification
34
4.6.1
34
Model Calibration Process
ix
TABLE OF CONTENTS
CHAPTER
TITLE
4.7
5
6
PAGE
4.6.2
Model Performance Evaluation
36
4.6.3
Model Verification
37
Model Integration
37
RESULT ANALYSIS
39
5.1
Tank Model Calibration Results
39
5.2
Significance of Autoregressive Model
42
5.3
Tank Model Verification
45
CONCLUSION
47
6.1
Model Performance
47
6.2
Limitations of the Model
48
6.2.1
Model Structure
49
6.2.2
Hydrological Data
49
6.3
Recommendations
REFERENCES
50
52
x
LIST OF TABLES
TABLE NO.
TITLE
PAGE
2.1
Operational Flood Forecasting Models of JPS (2004)
7
3.1
Computation of AR coefficients and adjusted forecast
19
values
4.1
Rainfall stations with its corresponding replacement
27
stations
5.1
Calibrated parameters for the Sungai Kelantan Tank model
40
5.2
Result of model performance evaluation
40
5.3
Model efficiency for three historical storm events used for
43
model calibration
5.4
Mean absolute error for three historical storm events used
43
for model calibration
5.5
Tank model verification results using year 2003 storm
45
event (23/11/2003 – 27/12/2003)
5.6
Tank model verification results using year 2004 storm
event (27/11/2004 – 27/12/2004)
45
xi
LIST OF FIGURES
FIGURE NO.
1.1
TITLE
PAGE
Flooding at Tambatan Diraja, Kelantan on 18 December
2
2005
2.1
Concept of the Sacramento model to simulate the
11
discharge hydrograph
3.1
Schematic Diagram of the Structure of Tank Model
15
4.1
Project Flow of the Development of the Flood
21
Forecasting Model for Sungai Kelantan
4.2
Main Tributaries of Sungai Kelantan
23
4.3
Thiessen Polygon for Sungai Kelantan Catchment
25
4.4
Rating curve for Sungai Kelantan at Guillemard Bridge
26
(1993)
4.5
Schematic diagram of the structure of the flood
28
forecasting model for Sungai Kelantan
4.6
Screen shot showing the Tank Model setup worksheet
30
4.7
Screen shot showing the rainfall data input and
31
processing worksheet
4.8
Screen shot showing the main forecasting worksheet
32
4.9
Procedure within the Tank model
33
4.10
Graph showing the comparison between individual tank
36
discharge and total discharge
xii
LIST OF FIGURES
FIGURE NO.
TITLE
PAGE
5.1
Observed and forecasted discharge for 1988 storm event
40
5.2
Observed and forecasted discharge for 1993 storm event
41
5.3
Observed and forecasted discharge for 2001 storm event
41
5.4
Comparison between observed and forecasted discharge
43
(with and without correction) for 1988 storm event
5.5
Comparison between observed and forecasted discharge
44
(with and without correction) for 1993 storm event
5.6
Comparison between observed and forecasted discharge
44
(with and without correction) for 2001 storm event
5.7
Comparison between observed and forecasted discharge
46
(with and without correction) for 2003 storm event
5.8
Comparison between observed and forecasted discharge
(with and without correction) for 2004 storm event
46
xiii
LIST OF SYMBOLS/ABBREVIATIONS
JPS
-
Jabatan Pengairan dan Saliran, Malaysia
DOS
-
Disk Operating System
PC
-
Personal Computer
FFC
-
Flood Forecasting Center
UH
-
Unit Hydrograph
VBA
-
Visual Basic for Application
RF
-
Rainfall
WL
-
Water Level
EV
-
Evaporation
TS
-
Tank Storage
Q
-
Discharge
AR
-
Autoregressive
ObsQ
-
Observed Discharge
SimQ
-
Simulated Discharge
Res
-
Error between observed and simulated discharge
FCQ
-
Adjusted Forecasted Discharge
GUI
-
Graphical User Interface
TIDEDA
-
Time Dependant Data
MS Excel
-
Microsoft Excel
1-D
-
One Dimensional
ME
-
Model Efficiency
MAE
-
Mean Absolute Error
xiv
LIST OF APPENDICES
APPENDIX
A
TITLE
Graphs showing comparison between individual
PAGE
53
tank discharge and total discharge
B
Detail Calculation of the Tank Model in
Simulating the Discharge and Water Level for the
year 2004 Storm Event
(27/11/2004 – 27/12/2004)
55
CHAPTER I
INTRODUCTION
1.1
Background of Study
Malaysia has a tropical climate and is endowed with abundant rainfall.
Peninsular Malaysia receives an average of 2500 mm rainfall annually. With the
heavy rainfall, flood has become the most severe natural disasters that causes great
amount of socio-economic losses. An estimated 9 % of Malaysia’s land area, which
amounts to 29,000 km2, is flood prone. The average annual flood damage was
estimated to be RM 1 billion 1 (year 2000 pricing) and about 22 % of our nation’s
population is affected by flood.
The state of Kelantan covers a total land area of 15,022 km2, which is
equivalent to 4.4 % of Malaysia’s land area. The population, according to the
statistic revealed by the Department of Statistics Malaysia in the year 2000, is close
to 1.313 million people. Kelantan is situated at the northeast corner of Peninsular
Malaysia facing the South China Sea. It is subject to the north-east monsoon, and
flooding has been occurring on a yearly basis typically during the months of
November and December. For the river basin of Sungai Kelantan itself, a total of
1
http://agrolink.moa.my/did/river/r_fcom.html
2
RM 12.1 2 millions was reported as direct flood damage for year 2005, and
three people were killed.
Figure 1.1
Flooding at Tambatan Diraja, Kelantan on 18 December 2005
The severe losses have called for a need to develop an effective flood control
strategy. The control measures suggested by the Flood Commission Committee are
generally divided into categories of structural and non-structural measures. Proposed
structural measures include the implementation of flood mitigation projects, such as
construction of dams, river diversion, construction of river levee and embankment,
widening and deepening of river channel, and flood retention using mining ponds
and lakes. Meanwhile there are two types of non-structural measures being proposed;
the development of planning control and the provision of flood forecasting and
warning system.
Flood forecasting and warning system is a cost-effective way to significantly
reduce the damage of flood occurrence, especially to urban areas with high-density
of population. Early warning will provide adequate lead time for the residents to be
mobilized to safer places, help them to save lives and reduce damages to properties.
2
Laporan Banjir 2005/2006. Bahagian Hidrologi, Jabatan Pengairan dan Saliran Kelantan.
3
In the subject of this research, the river basin of Sungai Kelantan includes a few
major population centres that are threatened by the overspills of Sungai Kelantan,
such as Kota Bharu, Tanah Merah, Machang and Kuala Krai. A total of 3209 people
were evacuated in these areas during the year 2005 floods. It was suggested in the
flood report for year 2005, that there is a need to upgrade the current flood warning
system. By installing more flood warning sirens, the authorities will be able to warn
the public of possible flood threat. But in order for the siren system to provide early
warning, the system will require a flood forecasting system that can accurately
predicts water level in the near future.
1.2
Statement of Problem
The current DOS version of Tank Model for Sungai Kelantan was developed
by Jabatan Pengairan dan Saliran (JPS) for flood forecasting purposes in the early
1980s. After more than 25 years in operation, the performance of the Sg Kelantan
Tank Model has deteriorated primarily because of changes in the catchment land-use
and river characteristics. However its source code is not accessible to users and it
does not allow model re-calibration. This research aims to develop a similar
hydrological model, yet it is more user-friendly and allows re-calibration so that the
model can be updated in order to respond to current developments in the catchment
area.
The current Tank Model can only provide flood forecast of Sg Kelantan at
Guillemard Bridge. It can not provide forecast for important population centre such
as Kota Bahru because it is subject to tidal effect. A hydraulic model is also being
developed to consider the tidal effect so that flood forecast can be provided for tidal
reach of the river. This part of the research is conducted concurrently by Lee J.M.
(2007) and Ang (2007).
4
1.3
Objectives of Study
The main objectives of the project are:
1.
To develop and calibrate a hydrological model to simulate discharge
hydrograph of Sungai Kelantan at Guillemard Bridge.
2.
To develop a PC-based flood forecasting model, combining with the
hydrological and hydraulic models to simulate the water level profile at
Guillemard Bridge and Jeti Kastam (Kota Bahru) by taking into
considerations the tidal effects.
1.4
Scope and Limitation of Study
1. To obtain and process relevant hydrological data for several historical storms
which will be used for calibration and verification purposes of the
hydrological model.
2. To develop, calibrate and verify the performance of the hydrological model
using historical flood events.
3. To develop a flood forecasting model that integrates both the hydrological
model and hydraulic model into a complete model.
4. The main limitation of the study is the availability of reliable data. The
performance of the model is highly dependent upon reliable and timely data,
and the availability of information on catchment and river characteristics.
5
1.5
Significance of Study
The flood forecasting model adopted by JPS currently is a DOS version of
Sugawara’s Tank Model. The model parameters could not be updated as the users
are unable to edit the source code of the model. This has also caused inconvenience
for the users to understand the underlying theory of the model. Therefore it is
proposed in this study to re-develop the flood forecasting model using Microsoft
Excel. Another significant improvement of the integrated model produced at the end
of the study is the hydraulic model, which is a combination of St. Venant’s unsteady
flow and Standard Step model that is able to include tidal effect to the prediction of
water level profile at Jeti Kastam. The integration of both hydrological and hydraulic
model is expected to be able to produce better forecast results.
The end product of this study should be a cost-effective and user friendly
flood forecasting model, that will aid the JPS officers to accurately predict the flood
levels at designated points along the river. Using the predicted water level, the local
authorities will be well informed of the possible time and severity of flood
occurrence.
CHAPTER II
LITERATURE REVIEW
2.1
Introduction
The literature review in this study is focused on two major components. It
will be discussing on the fundamentals of the modeling technique and the
programming technique applied in this study. Modeling technique refers to the
hydrological and hydraulics modeling that will generate forecast of water level
profile. Programming technique will discuss on how one can utilize several software
that are readily available in most personal computers, in order to develop the flood
forecasting system.
This chapter serves two main purposes: one is to summarize the information
gathered throughout the process of reviewing literature, including the various types
of flood forecasting models, and to justify on the selection of modeling and
programming techniques. Selecting and developing a model where its fundamental
theories are popular among the flood forecasters can be done by understanding the
flood modeling trend in Malaysia. During recent years, Japan has been working
towards the uniformity of modeling techniques in order to minimize the effort
needed in operating and maintaining the flood forecasting models.
7
2.2
Types of Model Commonly Used In Malaysia
A flood forecasting model is a type of hydrologic model that is developed
specifically to simulate catchment responses to precipitation, and generate forecasts
of the water level and streamflow. Generally flood forecasting models can be
categorized into three types:
1. Distributed physics-based models
2. Lumped conceptual models (e.g. Tank, Sacramento)
3. Black box models (e.g. Unit Hydrograph, Stage Regression)
The Flood Forecasting Center (FFC) of JPS has developed operational flood
forecasting models for a number of important river systems in the country. The
models developed by FFC were handed over to the State JPS for operation.
Currently operational forecasting models have been developed for 12 river basins
providing water level prediction at 21 forecasting points (see Table 1).
Table 2.1: Operational Flood Forecasting Models of JPS (2004) 3
No.
1
2
3
Muda River
Perak River
Muar River
4
5
6
7
Johor River
Besut River
Golok River
Pahang River
8
9
Kuantan River
Kelantan River
10
11
Sadong River
Kinabatangan
River
Klang River
12
3
River Basin
No. of
Forecasting Model
Forecasting
Point
1
Stage Regression
2
Stage Regression
2
Discharge-Correlation Model
& Tank Model
2
Regression Model
1
Stage Regression
1
Stage Regression
3
Linear Transfer Function and
Stage Regression (back-up)
1
Tank Model
2
Tank Model and
Stage Regression (back-up)
1
Linear Transfer Function
1
Linear Transfer Function
4
Flood Watch
Ir. Chong Sun Fatt, Cheok Hou Seng, “Development of a Digital Database and Information System
for the Six Selected Flood Forecasting Systems of JPS Malaysia (Final Report)”, 2006.
8
The Stage Regression model is being adopted as the flood forecasting model
for Sungai Muda, Sungai Perak, Sungai Besut, and Sungai Golok. It features an
equation that predicts the water level of a downstream point using the current water
level of an upstream station. For example, the Stage Regression model for Sungai
Golok uses the water level at Jenob to predict the water level at Rantau Panjang with
an 8 hours lead time. This model is purely mathematical and statistical, using
historical storm events to obtain the best fit equation.
2.2.1
Unit Hydrograph
Unit Hydrograph method (UH) can be defined as the hydrograph resulting
from unit depth of surface runoff generated by a storm of uniform intensity and
specified duration. It is usually being categorized as a type of black box model. Unit
hydrograph is generally derived from streamflow data and estimates of temporal
distribution of rainfall excess, or by synthesis using catchment characteristics data.
There are 2 important assumptions made in the UH model:
(a)
Catchment linearity – the proportion of inflow and outflow of the system
remains constant, regardless of the volume of inflow.
(b)
Catchment as a lump system – the input rainfall excess is assumed to be
uniform over the entire catchment area. The input rainfall excess for the
model is a single rainfall excess hyetograph.
Advantages of the Unit Hydrograph method can be summarized as follow:
(a) The model is simple and easy to understand;
(b) It is considerably accurate especially for storms with high intensities over
short durations;
(c) The assumption of linearity is acceptable for large events in most catchments;
(d) The model is based on the integrated response of the catchment, and it does
not require assumptions regarding the spatial variation of rainfall and losses,
and of storage routing effects;
9
Disadvantages of the Unit Hydrograph method are as follows:
(a)
The model is not suitable for multiple period storms of long duration;
(b)
It is not suitable for catchments with non-linear behaviors, for example
catchments with large reservoirs;
(c)
The model cannot provide accurate flood predictions when spatial
distribution of rainfall excess has very low uniformity;
2.2.2
Tank Model
The Tank model is categorized as one of the lumped conceptual models and
it consists of several tanks that erected vertically on top of one another. Each tank
represents a layer of soil, from the surface soil layer to the bottom soil layer above
the impervious rock layer. Storage volume in each tank represents the moisture
storage in each layer of the soil. The discharge of the tank outlets are equivalent to
the discharge to the river flow contributed by the respective soil layer.
Several advantages of the Tank model are:
(a)
The model is suitable for a great variety of catchment size; however the
model efficiency does increase when the catchment area decreases4 .
(b)
It has a small number of parameters compared to other conceptual
rainfall-runoff models, and the calibration process is relatively easy once
the operator understands how the parameters affect the hydrograph
simulation;
(c)
4
The model calculation only involves simple equations;
Ng, B.C. (2003). Ramalan Banjir Bagi Sungai Kuantan Dengan Menggunakan Model Tangki.
Skudai: Universiti Teknologi Malaysia: Thesis Sarjana Muda.
10
(d)
The model is able to provide accurate forecast results for a considerably
longer lead time (for the current model for Sungai Kelantan, the lead time
ranges from 6 to 24 hours);
(e)
2.2.3
The users can easily understand the structure of the model.
Sacramento Soil Moisture Accounting and Routing
This model was originally adopted for the flood forecasting system of Sungai
Kelantan, but it was being replaced by the Tank model in year 1981. The
Sacramento model can be defined as a spatially-lumped continuous soil moisture
accounting model. It is an ideal model to simulate large catchments that covers an
area of more than 1000 km2. The model takes into account of all water entering,
stored in and leaving the river basin. Though many parameters are used in the water
balance accounting process, precipitation contributes the most impact to the runoff.
The model consists of 3 main components: the rainfall generation component,
the soil water mass balance component, and the kinematic routing component for
channel flow propagation and attenuation. The model calibration can be done by
adjusting the baseflow, tension water capacities and runoff simulation parameters.
The model requires point or areal estimates of historical precipitation, potential
evaporation and temperature as main input. Secondary input data include the
catchment topography, soil characteristics, and location of important features such
as reservoirs and river junctions.
11
Figure 2.1
Concept of the Sacramento model to simulate the discharge h
hydrograph
2.3
Hydrological Model Selection
Choosing the right type of model is essential to meet the objectives of the
study. Therefore it is relevant to outline the criteria to be adopted in the selection
process:
(a)
The model is able to generate discharge and water level forecast with
reasonable accuracy and acceptable lead time;
(b)
The model should be simple and easy to programme;
(c)
The model is suitable for the type of catchment area that the Sungai
Kelantan river basin is having;
(d)
The model can be adjusted and calibrated easily when more observed
flood events are available in the future;
(e)
The operators can easily understand and comprehend the model.
12
The Tank model is chosen to be the hydrological model to simulate
discharge in this study. Both Unit Hydrograph method and Tank model satisfy all
the criteria as listed above. But, considering that the available hydrological data is
not complete and reliable, adopting a conceptual model will be more suitable
because it does not entirely depend on historical data.
2.4
Software Programming
The development of the hydrological model as well as the complete flood
forecasting system requires the utilization of various types of software developing
and programming tools. The following sub-chapters will briefly discuss on the
programming machines involved, its basic features and uniqueness that will
contribute to the effectiveness and user-friendliness of the flood forecasting system.
2.4.1
Microsoft Excel
Microsoft Excel is a spreadsheet program that is bundled with the Microsoft
Office package, which is available in most personal computers that operate on the
Windows Operating System. It features a useful collection of calculating modules as
well as extensive graphing abilities. The advantages of using Microsoft Excel as the
underlying programming tool are:
a) The user interface in Microsoft Excel allows user to understand the logical
progression and calculations involved in the model. Modules can be
separated in different sheets, and data can be transferred from one sheet to
another easily.
13
b) It allows user to re-calibrate the model parameters without the need to edit
source codes. Instead of going through lines of source codes that requires the
user to be well versed in computer programming, the user can perform the
calibration by simply replacing the parameter values in cells. Instantaneous
hydrograph plotting will allow user to compare between the forecasted and
observed values and determine the new parameters’ performance. Thus the
JPS officers will be able to maintain the system without relying on external
IT expertise.
c) Microsoft Excel is available on almost every computer that runs on the
Microsoft Windows operating system. The user will not need to purchase
additional software in order to operate the system.
2.4.2
Microsoft Visual Basic for Application
Visual Basic for Application (VBA), which is also known as macro, is an
event driven programming language that is built into most of the Microsoft Office
applications, and partially implemented into several third party software such as
AutoCAD and ESRI ArcGIS. It can be used to control almost every aspect of the
host application, such as manipulation of user interface, customization of user forms
and dialog boxes. VBA is also able to control one application from another, using
the Object Linking and Embedding (OLE) Automation.
CHAPTER III
HYDROLOGICAL MODEL: TANK MODEL
3.1
Introduction
The Tank model was founded by M. Sugawara in year 1979, and the model
was widely used in Japan, Korea and other Asian countries for purposes of flood
forecasting, reservoir and catchment flow simulation. In Malaysia, the Tank model
has been adopted as the flood forecasting model for Sungai Kelantan (1981) and
Sungai Kuantan (2004).
When rainfall occurs, the precipitation enters directly into the top tank. Most
of its volume will be discharged into the river, causing the water level to rise in
direct respond to the rainfall. Part of the volume in the top tank will then infiltrate to
the second tank, just as how surface runoff would infiltrate through the soil layer in
reality. Another part of it will be evaporated back into the atmosphere. In the second
tank, water will vertically infiltrate into the third tank, and at the same time
horizontally infiltrate and form discharge that goes into the river. The discharge
from the base tank represents base flow.
15
RF
EV
C1
TS1
X1
Tank 1
X2
C2
Q1 = C1 * (TS1 – X1)
Q2 = C2 * (TS1 – X2)
C3
TS2
Tank 2
C4
Q3 = C4 * (TS2 – X3)
C6
Q4 = C6 * (TS3 - X4)
X3
C5
TS3
Tank 3
X4
C7
C8
TS4
Figure 3.1
Tank 4
X5
C9
Q5 = C8 * (TS4 - X5)
Q6 = C9 * (TS4)
Schematic Diagram of the Structure of Tank Model
There are four types of model parameters in a Tank Model, as shown in Figure
2.2. They are:
a) Tank Storage (TS)
Indicates the amount of water stored in the tank in mm. Its initial value for
each tank is obtained from the calibration process, and it changes along the
modeling process. The initial tank storage indicates the moisture content of
the soil layers at the beginning point for the forecast. The value increases
when water enters the tank in the form of rainfall or infiltration, and
16
decreases when water leaves the tank through infiltration to lower tank or
discharge through the outlet.
b) Outlet Coefficient (C)
This parameter indicates the outlet efficiency in letting water to flow through
it. Thus outlets (including discharge and infiltration) with higher coefficient
value will generate more discharge or infiltration, depending on the location
of the outlet. This set of parameter is obtained from calibration, and it will
remain constant throughout the modeling process, until it is being modified
in future re-calibrations.
c) Outlet Height (X)
This is the vertical distance between the outlet and the base of tank. It may
be interpreted as the storage capacity of the soil layers. The higher the outlet
height is, the higher the tank storage is needed to generate discharge.
d) Evaporation (EV)
This parameter represents the amount of water in mm that is being
evaporated from the top tank. In this study, the lower tanks are assumed to
have no evaporation as the evaporation rate in the lower soil layers is
negligible.
17
3.2
Computation Process within a Tank
The tank starts off with its initial tank storage value which is obtained from
the trial and error calibration process. Computation will start by adding rainfall (RF)
and deducting the evaporation (EV) from the initial tank storage (TS), in order to
obtain a new tank storage value, named as TS(a).
Step 1: TS(a) = Initial TS + RF – EV
The next event to consider will be the infiltration (Infil), followed by the
discharge of the lower outlet (Q2), and lastly the upper outlet (Q1). Water is
assumed to infiltrate first, then only being discharged by the outlets; though in
reality, these movements occur simultaneously.
Step 2: Infil = C3 * TS(a)
Then deduct the amount of water being infiltrated from the tank storage.
Step 3: TS(b) = TS(a) – Infil
Next is to compute the discharge of the lower outlet.
Step 4: Q2 = C2 * (TS(b) – X2)
Deduct the amount of water being discharged from the tank storage.
Step 5: TS(c) = TS(b) – Q2
Then compute the discharge of the upper outlet, if TS(c) is greater than X1.
Step 6: Q1 = C1 * (TS(c) – X1)
Finally calculate the final tank storage. This value will be used as the initial tank
storage for the next time step.
Step 7: TS(d) = TS(c) – Q1
18
3.3
Computation of Total Discharge and Water Level
The summation of all the tank outlet discharges forms the total discharge Q
(in mm), which is a depth value that symbolizes a thin layer of water covering the
entire catchment area. In order to convert this symbolical layer of water into volume
of discharge, the following equation is used:
(
)
Total Discharge m 3 / s =
Q(mm )× Catchment Area(km 2 )
3600(s )
(3.1)
Conversion to water level is done by interpolating the stage-discharge rating
curve to obtain the corresponding water level for the discharge volume. Otherwise
the conversion can also be done using the rating curve equation derived from the
data.
3.4
Autoregressive Model
Autoregressive (AR) is a correctional model that is included in the Sungai
Kelantan Tank model in order to improve the forecasted discharge and water level
simulation results. AR model is a group of linear prediction formulas used to predict
an output of a system which only depends on the previous outputs. The name
autoregressive comes from the fact that predicted value is regressed on the past
value of itself. In this case, the value that is being predicted using the AR model is
the error, not the discharge itself. The predicted error is then used to adjust the
forecasted discharge value. Detailed calculation is demonstrated in Table 3.1, where
the bold numbers in the table are the adjusted forecast values of water level.
Table 3.1
Time
ObsQi
SimQi
Resi
Computation of AR coefficients and adjusted forecast values
Lag1
Lag2
Lag3
Lag4
Step, i
1
9.30
11.06
-1.76
2
9.30
11.14
-1.84
-1.76
3
9.30
11.22
-1.92
-1.84
-1.76
4
9.30
11.30
-2.00
-1.92
-1.84
-1.76
5
9.30
11.30
-2.00
-2.00
-1.92
-1.84
-2.00
-2.00
-2.00
6
11.30
7
11.22
8
11.22
9
11.30
j
FCQ1+i
FCQ2+i
FCQ3+i
FCQ4+i
(AR1)
(AR2)
(AR3)
(AR4)
9.38
Plot graph Res against Lag. The
slope is the ARi coefficient.
9.38
9.47
9.38
9.47
9.55
-1.76
9.30
9.39
9.48
9.57
-1.92
-1.84
9.30
9.31
9.40
9.49
-2.00
-1.92
9.23
9.24
9.33
-2.00
-2.00
9.24
9.25
-2.00
= Number of time steps ahead of i
9.33
AR coefficients used
ObsQi = Observed discharge value for time step i
in this example:
SimQi = Simulated discharge value for time step i
AR1 = 0.9988
Resi
= Error between observed and simulated discharge = ObsQi - SimQi
AR2 = 0.9963
ARj
= Least square correlation (slope) between Res and Lagj (AR Coefficient)
AR3 = 0.9918
FCQi+j = Adjusted forecasted discharge value (refer to blue boxes)
AR4 = 0.9855
= SimQi+j + (Resi * ARj) = SimQi+j + (Lagi+j * ARj)
19
CHAPTER IV
RESEARCH METHODOLOGY
4.1
Research Design
The development of the flood forecasting model for Sungai Kelantan
involves hydrological and hydraulics modeling, as well as software programming
works. In this study, only the hydrological model is covered while the hydraulic
model is jointly developed by Lee J.M. (2007) and Ang (2007). The hydrological
model is built to generate simulation of discharge based on rainfall data for the
upstream area of the interest point, while the hydraulic model simulates the tidal
effect towards the water level profile of the river. The model integration process is a
joint effort to produce a complete flood forecasting model. The process of
developing the system for Sungai Kelantan can be divided into several stages so that
the project progress can be monitored. The general flow for the project is shown in
Figure 3.1, and the proposed work schedule is attached in Appendix A.
The project is generally divided into four major phases: model development,
calibration, model integration and verification. First phase of the project involved
tasks such as hydrological data collection and processing, catchment characteristics
identification, and development of the Tank model. The second phase, which is the
calibration process, was the effort of fine tuning the model parameters for optimum
21
performance. In this phase, three past storm events are identified as the basis of
calibration, and the model’s performance is evaluated using mean absolute error and
model efficiency. In the third phase, both hydrological and hydraulic model are
integrated to form a complete flood forecasting model that will consider effects of
both the rainfall-runoff and tidal effect. Model integration focused mostly on
programming and development of graphical user interface (GUI). The fourth phase,
model verification, involved the evaluation of the Tank model by itself as well as the
complete model. The latest storm event is used for the verification and the model
performance will be evaluated.
Data Collection
Development of Hydrological Model
Development of Hydraulic Model
Model Calibration
and Verification
Model Integration
Test Run
Figure 4.1
Project Flow of the Development of the Flood Forecasting Model for
Sungai Kelantan
22
4.2
Background of the Sungai Kelantan River Basin
Sg Kelantan is the third largest river system in Peninsular Malaysia. It
originates from mountain ranges with elevation exceeding +2,000m. At Guillemard
Bridge, the point of interest for the hydrological model development in this study,
the catchment area is about 12,000 sq km5 .
There are two main tributaries contributing to Sungai Kelantan: Sungai Galas
and Sungai Lebir. Sungai Kelantan itself does not have any major tributary, but
Sungai Galas and Sungai Lebir are fed by many tributaries and their flow volume
has significant impact to the flow of Sungai Kelantan. Among the major tributaries
are Sungai Nenggiri, Sungai Aring, Sungai Chiku and Sungai Pergau, where a
hydroelectric dam is built. The major tributaries as well as the main river of Sungai
Kelantan are as shown in Figure 4.2. As the tributaries are denoted in different
colours, the significance of discharge contribution by every tributary can be
estimated by observing the area covered by the network of river branches.
The river flows past several major towns, such as Pasir Mas, Kuala Krai and
Kota Bharu. Sungai Kelantan overspills on a regular basis, especially during months
of November till February, where the north-east monsoon brings along heavy
downpour. For the year of 2004, JPS has recorded a total of 10476 evacuees and a
reported flood damage of RM 14.3 millions 6 .
5
Sepakat Setia Perunding Sdn Bhd and SMHB Sdn Bhd. (1999). Final Report: Kelantan River Flood
Mitigation Plan Feasibility Study.
6
Laporan Banjir 2004/2005. Bahagian Hidrologi, Jabatan Pengairan dan Saliran Kelantan.
23
Figure 4.2
Main Tributaries of Sungai Kelantan
24
4.3
Data Acquisition and Analysis
The performance of the flood forecasting model is significantly limited by
the availability of hydrological data in terms of its quality as well as quantity. Data
have been acquired from JPS, which included rainfall and water level data for all
stations situated in the state of Kelantan. The data are then filtered, where stations
situated outside of the Sungai Kelantan catchment area will be eliminated. In this
case, most of the stations situated outside of Sungai Kelantan catchment belong to
the Sungai Golok catchment. Stations with unreliable data and significant amount of
missing data are also being discarded.
The water level data used in this model is taken from the Sungai Kelantan at
Guillemard Bridge station (No. 5721442). Several rainfall stations are then selected
to be the contributing rainfall gauges for the Tank model. Selection was based on
data availability for the selected storm events, percentage of missing data, and the
location of stations for accurate representation of the entire catchment area. These
selected stations are all telemetric stations except for JPS Machang, Gob and Blau.
The rainfall stations that are required to provide input to the Sungai Kelantan Tank
model ought to be telemetric stations so that real-time data could be acquired for
forecasting operations. Based on preliminary evaluation on data availability, eight
rainfall stations that are strategically scattered around the catchment area are
selected to represent the entire catchment area. These stations are:
1.
JPS Machang (5722057)
2.
JPS Kuala Krai (5522047)
3.
Dabong (5320038)
4.
Gob (5216001)
5.
Kg Aring (4923001)
6.
Gua Musang (4819027)
7.
Gunung Gagau (4726001)
8.
Blau (4717001)
25
Processed data are stored in an MS Excel file that has a predefined format.
This is to ensure that the data extraction macro will be able to identify the start and
end of the data correctly. Users are required to do some preliminary data editing,
such as to convert the TIDEDA data from Notepad file into an MS Excel workbook,
and also to copy and paste the data for the storm duration needed into a formatted
sheet with a designated starting cell.
Both rainfall and water level data used in this model are hourly data. It is
crucial that the users are aware that the storm data used in the model must share the
same start and end time. Once the complete storm data is ready, an average rainfall
data set is calculated using the Thiessen Polygon method. The polygon is as shown
in Figure 3.2.
Figure 4.3
Thiessen Polygon for Sungai Kelantan Catchment
26
Evaporation rate is a necessary parameter for model calibration and
operation. The daily evaporation value was obtained from JPS Kelantan. In the
Sungai Kelantan Tank model, an average value of 4.8 mm/day for non-rainy day has
been adopted. Evaporation rate is assumed to be half when the hourly rainfall is
greater than 0.5 mm.
Besides that, the stage-discharge rating curve for Sungai Kelantan at
Guillemard Bridge is another necessary input for the model. Conversion between
water level and discharge occurred in the model in both ways.
Rating Curve for Sg Kelantan at Guillemard Bridge
25.0
Water Level (m)
20.0
15.0
10.0
4
3
2
y = -4E-14x + 5E-10x - 2E-06x + 0.0049x + 7.6842
5.0
0.0
0.0
1000.0
2000.0
3000.0
4000.0
5000.0
6000.0
3
Discharge (m /s)
Figure 4.4
Rating curve for Sungai Kelantan at Guillemard Bridge (1993)
27
4.3.1
Missing Rainfall Data
The occurrence of missing data during flood is very common, as
telecommunication system used to transmit the data or even the recording station
itself was being affected by the flood or the stormy weather. Therefore a sound
logical system must be used in order to fill in the missing data with the best
representation of what may be the actual situation.
For the Sungai Kelantan Tank model, users have the freedom to choose
either to fill in the missing data manually, or allow the model to do it for them. The
missing data module contained in this model will first try to replace the data with the
ones recorded by the nearest station. The replacement stations corresponding to each
station are as shown in Table 3.1. For example if Station A has a missing data for a
certain time step, Station B is assigned to provide replacement data. In the case
where the Station B also missed its data for that time step, Station A will be ignored
in the calculation of average rainfall, meaning that the catchment area does not
include the area covered by Station A. It is not advisable to carry on the forecast if
less than 6 stations contain data.
Table 4.1
Rainfall stations with its corresponding replacement stations.
Rainfall Station
Replacement Station(s)
Blau (4717001)
Gua Musang (4819027)
Gua Musang (4819027)
Blau (4717001)
Kg Aring (4923001)
Kg Aring (4923001)
Gua Musang (4819027)
Gunung Gagau (4726001)
Kg Aring (4923001)
JPS Machang (5722057)
JPS Kuala Krai (5522047)
JPS Kuala Krai (5522047)
JPS Machang (5722057)
Dabong (5320038)
Dabong (5320038)
JPS Kuala Krai (5522047)
Gob (5216001)
Gob (5216001)
Dabong (5320038)
28
4.4
Structure of Model
The complete flood forecasting model for Sungai Kelantan is a combination
of the hydrological Tank model, 1-D hydrodynamic (hydraulic) model and also the
tidal model. Tank model requires input of rainfall and water level data to simulate
the discharge at Guillemard Bridge; while the 1-D hydrodynamic model consists of
the Standard Step 7 module and St. Venant’s unsteady flow 8 module. The 1-D
hydrodynamic model requires river channel cross section data during the model
setup stage; it uses the discharge value at Guillemard Bridge generated by Tank
model as the upstream boundary, and the tidal levels as the downstream boundary to
simulate the water profile at Jeti Kastam. In this study, only the Tank model is
covered. A schematic diagram of the model structure is shown in Figure 4.4.
Input: Rainfall, water level, rating curve, model parameters
Tank Model
Input:
River cross
section data
Output: Simulated Discharge and
Water Level at Guillemard Bridge
Tide Model
1-D Hydrodynamic Model
Output: Tide
Level
Standard Step
Standard Step & St. Venant’s
Output: Simulated Discharge
and Water Level at Jeti Kastam
Figure 4.5
Schematic diagram of the structure of the flood forecasting model for
Sungai Kelantan
7
Ang, Zhili (2007). Development of Flood Forecasting Hydraulic Model for Sungai Johor.
Lee, J.M. (2007). Development of Flood Forecasting Model for Sungai Kelantan (1-D
Hydrodynamic Model).
8
29
Most of the worksheets (or modules) are hidden from user’s access and only
seven worksheets are accessible; and they can generally be categorized into three
types:
(a)
Model setup
These worksheets require extensive information input at the initial
setup stage of the model. Once all the required information has been
inserted, update is only required when new information is available
(e.g. new river survey data, new set of harmonic equation constituents
for tidal model) or when re-calibration of Tank model is required.
The worksheets placed under this category are Tank model, hydraulic
model (1-D Hydrodynamic model), tidal model8 and also the rating
curve for Sungai Kelantan.
(b)
Data input
The worksheets grouped under this category are the rainfall and water
level input sheets and they need to be updated for every storm event.
It is recommended that at least fifteen days worth of data should be
provided prior to the start of forecasting and the data will be kept for
use for the entire storm event. For Sungai Kelantan’s case, most
probably these data will be only need to be updated once a year.
(c)
Forecasting
This is the main forecasting worksheet which user will be dealing
with every time they wish to generate the water level forecast. This
worksheet requires user to provide the latest rainfall and water level
data for every time step, which for this model the time step used is
one hour. Forecasted hourly rainfall values for the next twelve hours
are also required for the water level simulation. Results of the
simulation are displayed within this worksheet in both tabular and
graphical forms.
30
Some of the main worksheets are shown below along with brief explanation
of its functions. The Tank model setup worksheet (Figure 4.6) is designed for the
purpose of Tank model setup and calibration. The model parameters are to be
modified here. In order to aid the users in the calibration process, this page also
shows the model performance evaluation results (ME and MAE), as well as the
following graphs:
(d)
Observed and forecasted discharge (Figure 5.2)
(e)
Individual tank discharge and total discharge (Figure 3.4)
Model Parameters
Model Performance
Evaluation Results
Figure 4.6
Screen shot showing the Tank Model setup worksheet.
31
Thiessen
Polygon
Weights
Figure 4.7
Screen shot showing the rainfall data input and processing worksheet
The rainfall data input and processing worksheet (Figure 4.7) is where the
user determines the rainfall stations to be used in the model and the Thiessen
polygon weights for each station; it also contains a macro that calculates the average
rainfall. Currently the model is capable of handling a maximum of eight rainfall
stations and it can be customized to suit other rivers by changing the Thiessen
weights.
The main forecasting worksheet (Figure 4.8) is the main worksheet that the
user will be working on for flood forecasting purposes. The users need to insert the
current water level data as well as the forecasted rainfall data for the next twelve
hours, and these data will become the input to the Tank model to generate the water
level forecasts. Forecasted water level will be displayed here along with a graph that
shows the projected water level as well as the water level profile for the immediate
past three days.
32
Rainfall and
Water Level
Data Input
Result of
Water Level
Simulation
Figure 4.8
4.5
Screen shot showing the main forecasting worksheet
Development of Tank Model
The model has been built using MS Excel, while Visual Basic for
Application (VBA) macros are embedded to execute the operations of the model. All
the operations such as computation and data ingestion are executed by the macros.
The MS Excel spreadsheets are only used to display the data as well as the outcome
of the computation, which means that the cells only contain numbers without any
formulation. This is to minimize the risk of any unintended changes being made by
users when they are using the model. Another advantage of building the model as
such is that the model will be able to work with any amount of data as required by
the user, which also means that the model is capable of handling various storm
durations as well as various combinations of outlets. Figure 4.9 shows the sequence
of procedures within the model.
33
Data Ingest
Tank Discharge Calculation
(Refer to Section 3.2)
Total Discharge in mm and m3/s
Convert to water level using rating curve
-
Autoregressive model
(refer to Section 3.4)
correction on the simulated water level
conversion to corrected discharge
Model Performance Evaluation
Figure 4.9
Procedure within the Tank model
The data ingestion macro is the first step in the Tank model. This macro
serves two purposes: to extract rainfall and water level data from its respective
worksheet, and to convert the water level data into discharge through interpolation
of the rating curve. The model then proceeds on with the determination of
evaporation rate for every time step. With this, all the inputs for the model are ready
and the next steps are the computation of discharge and the Autoregressive (AR)
model. After running the model, the forecast results simulated are then used to
evaluate the model performance and both the results of simulation and evaluation
are displayed in its respective worksheets.
34
4.6
Model Calibration and Verification
The Tank model calibration is a process of trial and error to obtain the best
fitting set of model parameters. It is done by comparing the simulated outcome to
the actual observed water level through visual inspection with both hydrographs
plotted together, and the process involves all selected storm events simultaneously.
Apart from that, the model performance is also evaluated through its mean absolute
error (MAE) and model efficiency (ME). Then finally the model will be verified
using the latest recorded storms (year 2003 and 2004).
4.6.1
Model Calibration Process
The model calibration involves the repetitive trial and error process in order
to obtain a set of parameters which gives a best fit by comparing between the graphs
of simulated and observed discharge for Sungai Kelantan at Guillemard Bridge, as
shown in Figure 5.1. For a large catchment such as the one for Sungai Kelantan, its
hydrograph are typically very smooth with gradual rising and falling limb, and it
generally reacts slower to the rainfall hyetograph compared to smaller catchments.
From the graph, we can observe that there is a 2 days gap between the peak rainfall
and the peak discharge. We can also observe that the current set of parameters is
able to behave as the actual catchment (shape of the hydrograph is similar), but it is
yet to be able to provide accurate prediction of the peak flow in terms of the timing
as well as the magnitude of flow.
In order to assist the calibration process, another graph showing the
contribution of each tank towards the total discharge was plotted, as shown in Figure
4.10. From this graph, the main contributing tank at a certain point of time can be
identified. And thus we can make suitable adjustments to the relevant parameters to
generate better forecast results. For the Sungai Kelantan Tank model, the main
35
contributing tanks are tank No.3 (Q4) and tank No.4 (Q5). Any tank can be selected
as the main contributing tank by adjusting the outlet coefficient for the discharge
outlet of that particular tank. In this case, Q4 is selected as the main contributing
discharge as it generates a discharge profile that is the most similar to the observed
hydrograph of the river. Q4 represents the discharge from lower layer of soils while
Q5 represents the baseflow.
The influence caused by each type of the tank parameters are as explained
here:
(a)
Evaporation (EV)
The rate of evaporation will affect on the amount of discharge
generated by the first tank as the evaporation rate for lower layers of
soil is assumed to be negligible.
(b)
Tank Storage (TS)
The initial values of tank storage represent the moisture content of
each layer of soil at the starting of forecast. This value will influence
the tank’s discharge at the early stage, as in the later stage, the
discharge will be governed by the amount of rainfall (for top tank) or
infiltration (other tanks apart of the top tank) entering the tank.
(c)
Outlet Height (X)
This parameter will determine when will the tank start or stop
generating discharge. The tank will only generate discharge when the
tank storage exceeded the outlet height. When the tank storage falls
below the outlet height, the remaining water in the tank will only be
leaving the tank through infiltration, until the tank becomes empty.
Therefore a higher outlet height will cause a tank to generate more
infiltration compared to discharge, while a lower outlet height will
cause the tank to react fast to flow entering the tank.
36
(d)
Outlet Coefficient (C)
This coefficient will determine which tanks contribute more to the
flow while other tanks contribute less. In this model, the coefficient
for Q4 is much bigger compared to other outlets.
Individual Tank Discharge and Total Discharge for 1993 Storm Event
(17/12/1993 - 6/1/1994)
1.80
6000.0
Q1
1.60
Q2
Q3
5000.0
Q4
Q5
1.20
Total Q
1.00
4000.0
Q6
Obs Q
0.80
3000.0
0.60
2000.0
0.40
0.20
1000.0
0.00
12/16/1993 0:00
-0.20
12/21/1993 0:00
12/26/1993 0:00
12/31/1993 0:00
1/5/1994 0:00
0.0
Date Time
Figure 4.10
Graph showing the comparison between individual tank discharge
and total discharge.
4.6.2
Model Performance Evaluation
The Tank model’s performance is evaluated quantitatively using two
approaches: mean absolute error (MAE) and model efficiency (ME). During the
process of calibration, these performance indicators are able to signify whether the
calibration is progressing towards the right direction or the other way round. The
MAE indicates the average of absolute differences between each point of the
simulated and observed hydrograph, while the ME compares the deviation between
simulated and observed discharge to the average discharge for the storm event.
Total Discharge (m3/s)
Tank Discharge (mm)
1.40
37
MAE =
1 1
∑ QObs − QSim
n n
∑ (Q
ME = 1 −
∑ (Q
Obs
− QSim )
Obs
4.6.3
(4.1)
−Q
2
)
2
(4.2)
Model Verification
Model verification is divided into two stages: the Tank model verification is
done using the observed water level at Guillemard Bridge; while the the complete
model is tested using data recorded at Jeti Kastam. The most recent storm events
(year 2003 and 2004) will be used for verification, and the model efficiency and
mean absolute error obtained from this process will be concluded as the result of the
study. In this report only the result of the Tank model verification will be covered.
4.7
Model Integration
The completed Tank model will be integrated with the 1-D hydrodynamic
model, which consists of the Standard Step model and the St. Venant’s unsteady
flow model, to form a complete flood forecasting model for Sungai Kelantan that
will generate water level forecasts for Guillemard Bridge and Jeti Kastam. The
model integration focused mostly on efforts to establish the structure of the entire
model and the sequence of procedures, debugging the macros as all the worksheets
from the four independent models (Tank model, Standard Step, St. Venant’s, Tide)
are being combined into one workbook, and developing the user interface.
38
For the simulation of water level at Jeti Kastam, the user has the freedom to
choose either to run the simulation using only the Standard Step model, by using the
combination of Standard Step and St. Venant’s unsteady flow model. In the latter’s
case, the Standard Step model generates the initial routing condition by using the
discharge and water level generated by Tank model as the upstream boundary, and
the tidal level generated by the tide model as the downstream boundary; discharge is
assumed to be constant along the channel. The St. Venant’s model then extracts the
output of Standard Step model as its initial input. Therefore another major task in the
model integration stage is to establish the connections between the models, so that
the output of a model can be transferred as the input of another model (refer to
Figure 4.5).
CHAPTER V
RESULT ANALYSIS
5.1
Tank Model Calibration Results
The Sungai Kelantan Tank model is calibrated using three storm events
(years 1988, 1993 and 2001). The year 1993 storm event was used for the initial
calibration, while further adjustment of the parameters was done using the three
storms simultaneously. After going through numerous trial and errors, an optimum
set of parameter has been obtained and it is shown in Table 5.1. This set of
parameter will be used to simulate the water level profile in the future. But the user
should be aware that the model requires re-calibration after several years of use or
when the river experiences changes or developments.
The observed and forecasted discharge generated using the set of model
parameters as listed in Table 5.1 for all three storm events (years 1988, 1993 and
2001) are shown in Figure 5.1, Figure 5.2 and Figure 5.3. The model performance is
evaluated and the results are shown in Table 5.2. The average model efficiency is
0.83 while the average mean absolute error is 0.97 m for twelve hours forecast. In
order to generate better simulation results, the Autoregressive model is used and its
significance in improving the model’s performance will be discussed in the
following section.
40
Table 5.1
Calibrated parameters for the Sungai Kelantan Tank model
Parameter
Value
Parameter
Value (mm)
EV
0.2 mm/hr
TS1
15
C1
0.001
TS2
20
C2
0.001
TS3
30
C3
0.08
TS4
20
C4
0.002
X1
10
C5
0.09
X2
0
C6
0.0065
X3
0
C7
0.01
X4
0
C8
0.002
X5
20
C9
0.0008
X6
0
Table 5.2
Result of model performance evaluation
Storm Event
Model Efficiency
Mean Absolute Error (m)
1. 17/1/1988 – 15/12/1988
0.86
0.98
2. 17/12/1993 – 6/1/1994
0.91
0.82
3. 10/12/2001 – 10/1/2002
0.72
1.12
Observed and Forecasted Discharge for 1988 Storm Event
(17/11/1988 - 15/12/1988)
6000.0
25.00
Observed Q
5000.0
Forecasted Q
20.00
4000.0
15.00
3000.0
10.00
2000.0
5.00
1000.0
0.00
0.0
11/15/1988 0:00 11/20/1988 0:00 11/25/1988 0:00 11/30/1988 0:00 12/5/1988 0:00 12/10/1988 0:00 12/15/1988 0:00
-5.00
Date Tim e
Figure 5.1
Observed and forecasted discharge for 1988 storm event
Rainfall (mm)
Discharge (m 3/s)
Rainfall
41
Observed and Forecasted Discharge for 1993 Storm Event
(17/12/1993 - 6/1/1994)
6000.0
16.00
Observed Q
5000.0
12.00
Rainfall
4000.0
10.00
8.00
3000.0
6.00
2000.0
Rainfall (mm)
Discharge (m 3/s)
14.00
Forecasted Q
4.00
2.00
1000.0
0.00
0.0
12/15/1993 0:00
12/20/1993 0:00
12/25/1993 0:00
12/30/1993 0:00
-2.00
1/9/1994 0:00
1/4/1994 0:00
Date Tim e
Figure 5.2
Observed and forecasted discharge for 1993 storm event
Observed and Forecasted Discharge for 2001 Storm Event
(10/12/2001 - 10/1/2002)
16.00
4000.0
Observed Q
3500.0
Forecasted Q
12.00
Rainfall
10.00
2500.0
8.00
2000.0
6.00
1500.0
Rainfall (mm)
Discharge (m 3/s)
3000.0
14.00
4.00
1000.0
2.00
500.0
0.0
12/7/2001
0:00
0.00
-2.00
12/12/2001
0:00
12/17/2001
0:00
12/22/2001
0:00
12/27/2001
0:00
1/1/2002 0:00 1/6/2002 0:00
1/11/2002
0:00
Date Tim e
Figure 5.3
Observed and forecasted discharge for 2001 storm event
The graphs show that the model has limited ability of imitating the actual
catchment behavior. The overall pattern for both simulated and observed
hydrographs shows certain similarity; hence the model is able to react to rainfall in a
similar manner as the actual catchment in terms of timing. But the magnitude of
discharge values simulated by the model has a significant difference from the
observed values.
42
5.2
Significance of Autoregressive Model
Autoregressive (AR) model is used to improve the forecast results generated
by the Tank model (refer to Section 3.4 for details of AR model). The adjusted
hydrograph for all three storm events (years 1988, 1993 and 2001) are shown in
Figure 5.4, Figure 5.5 and Figure 5.6. Forecasted Q represents the original
simulation results before any adjustment, while the AR12 represents the adjusted
simulation results for twelve hours forecast.
The model performance is re-evaluated based on the adjusted forecast of
discharge and water level and the model evaluation results are as shown in Table 5.3
and Table 5.4. The average model efficiency has increased by 0.13, from 0.83 to
0.96 for twelve hours forecast; while the average mean absolute error has been
improved by 60.8 %, from 0.97 m to 0.38 m for twelve hours forecast.
It can also be observed from the graphs that the peak flow simulation has
improved in terms of timing, where the simulated peak occurred almost at the same
time as the observed peak, but the magnitude of the simulated peak seems to be
always lower than the observed peak flow.
43
Table 5.3
Model efficiency for three historical storm events used for model
calibration.
No.
Storm
Event
Model Efficiency
With AR Correction
Without
Correction
3 hours
6 hours
9 hours
12 hours
1.
1988
0.86
1.00
0.99
0.98
0.97
2.
1993
0.91
1.00
0.99
0.98
0.97
3.
2001
0.72
0.99
0.98
0.96
0.93
Table 5.4
Mean absolute error for three historical storm events used for model
calibration.
No.
Storm
Event
Mean Absolute Error (m)
With AR Correction
Without
Correction
3 hours
6 hours
9 hours
12 hours
1.
1988
0.98
0.12
0.21
0.30
0.38
2.
1993
0.82
0.12
0.20
0.28
0.35
3.
2001
1.12
0.13
0.23
0.32
0.41
Observed and Forecasted Discharge for 1988 Storm Event
(17/11/1988 - 15/12/1988)
6000.0
Observed Q
5000.0
Forecasted Q
Discharge (m 3/s)
AR12
4000.0
3000.0
2000.0
1000.0
0.0
11/14/1988 0:00 11/19/1988 0:00 11/24/1988 0:00 11/29/1988 0:00 12/4/1988 0:00
12/9/1988 0:00 12/14/1988 0:00 12/19/1988 0:00
Date Tim e
Figure 5.4
Comparison between observed and forecasted discharge (with and
without correction) for 1988 storm event
44
Observed and Simulated Discharge for 1993 Storm Event
(17/12/1993 - 6/1/1994)
6000.0
Observed Q
5000.0
Simulated Q
Discharge (m 3/s)
AR12
4000.0
3000.0
2000.0
1000.0
0.0
12/16/1993 0:00
Figure 5.5
12/21/1993 0:00
12/26/1993 0:00
12/31/1993 0:00
Date Tim e
1/5/1994 0:00
Comparison between observed and forecasted discharge (with and
without correction) for 1993 storm event
Observed and Forecasted Discharge for 2001 Storm Event
(10/12/2001 - 10/1/2002)
4000.0
Observed Q
3500.0
Forecasted Q
Discharge (m 3/s)
3000.0
AR12
2500.0
2000.0
1500.0
1000.0
500.0
0.0
12/7/2001 0:00
Figure 5.6
12/12/2001
0:00
12/17/2001
0:00
12/22/2001
0:00
12/27/2001
0:00
Date Tim e
1/1/2002 0:00
1/6/2002 0:00 1/11/2002 0:00
Comparison between observed and forecasted discharge (with and
without correction) for 2001 storm event
45
5.3
Tank Model Verification
The Tank model is verified using the year 2003 (23/11/2003 – 27/12/2003)
and 2004 (27/11/2004 – 27/12/2004) storm events. The model verification results
are as shown in Figure 5.7 and Figure 5.8 (AR12 indicates the twelve hours forecast
results), and the model evaluation results are shown in Table 5.5 and Table 5.6. For
twelve hours forecast, the average model efficiency is 0.97 and the average mean
absolute error is 0.26 m. This result shows that the Sungai Kelantan Tank model is
able to generate reliable forecast for up to twelve hours ahead.
Table 5.5
Tank model verification results using year 2003 storm event
(23/11/2003 – 27/12/2003)
Without
With AR Correction
Correction
3 hours
6 hours
9 hours
12 hours
Model Efficiency
0.78
1.00
0.99
0.97
0.96
Mean Absolute Error (m)
0.88
0.09
0.16
0.22
0.28
Table 5.6
Tank model verification results using year 2004 storm event
(27/11/2004 – 27/12/2004)
Without
With AR Correction
Correction
3 hours
6 hours
9 hours
12 hours
Model Efficiency
0.76
1.00
0.99
0.99
0.98
Mean Absolute Error (m)
1.38
0.08
0.14
0.19
0.24
46
Comparison Between Observed and Forecasted Discharge
4000.0
Observed Q
3500.0
Forecasted Q
Discharge (m 3/s)
3000.0
AR12
2500.0
2000.0
1500.0
1000.0
500.0
0.0
11/20/2003
0:00
11/25/2003
0:00
11/30/2003
0:00
12/5/2003
0:00
12/10/2003
0:00
12/15/2003
0:00
12/20/2003
0:00
12/25/2003
0:00
12/30/2003
0:00
1/4/2004
0:00
Date Tim e
Figure 5.7
Comparison between observed and forecasted discharge (with and
without correction) for 2003 storm event
Observed and Forecasted Discharge for 2004 Storm Event
(27/11/2004 - 27/12/2004)
6000.0
Observed Q
5000.0
Forecasted Q
Discharge (m 3/s)
AR12
4000.0
3000.0
2000.0
1000.0
0.0
11/24/2004
0:00
11/29/2004
0:00
12/4/2004
0:00
12/9/2004
0:00
12/14/2004
0:00
12/19/2004
0:00
12/24/2004
0:00
12/29/2004
0:00
1/3/2005 0:00
Date Tim e
Figure 5.8
Comparison between observed and forecasted discharge (with and
without correction) for 2004 storm event
CHAPTER VI
CONCLUSIONS
6.1
Model Performance
Conclusions that can be drawn from this project are as follows:
1.
After the calibration and verification processes for the Sungai Kelantan Tank
model, it is clearly demonstrated by the evaluation results that the model is
capable of generating reliable water level forecasts for Sungai Kelantan at
Guillemard Bridge, with an average model efficiency of 96 % and mean
absolute error of 0.35 m for twelve hours forecast.
2.
The Sungai Kelantan Tank model is very user-friendly as it is built within
MS Excel with minimal user interfaces, with the hidden operation code that
safeguards the system from unintended alteration by the users. Most of the
operations are executed under the control of a minimal number of buttons.
Display of results such as the tabulated forecast values and the hydrographs
are all programmed to be updated automatically. The model is also costeffective as it only requires presence of MS Excel, which is available in most
computers that are using MS Windows as the operating system.
48
3.
The Sungai Kelantan Tank model has high flexibility as it is capable of
handling any storm duration as defined by user, and it can be calibrated to
simulate water level profile for other rivers as well.
4.
The model integration that combines the Tank model, 1-D Hydrodynamic
model (Standard Step and St. Venant’s) and the tide model has been
accomplished successfully. Currently the complete flood forecasting model
is able to generate water level forecast at Jeti Kastam with a lead time of five
hours, and the simulated hydrograph pattern is very similar to the observed
hydrograph.
6.2
Limitations of the Model
The actual hydrological processes that occur within a catchment involve a
series of complex interaction among various components such as precipitation,
evaporation, infiltration, surface detention and runoff, and many others. These
processes take place across the entire catchment simultaneously and it is impossible
to estimate the amount of water involved accurately. Modeling techniques that are
available currently, such as the Tank model that is being adopted in this study, are
mostly based on simplified representative hydrological processes in order to imitate
the actual catchment behavior and provide simulation with the slightest possible
error. The limitations of the model as recognized in this study are:
1.
Though the model is able to simulate the peak magnitude accurately, but the
simulated peak usually occurs around six to twelve hours before the actual
peak. The model’s performance is limited by the model’s structure itself.
2.
The performance of the model is greatly limited by the completeness and
reliability of the hydrological data (rainfall, water level and rating curve) that
serves as the input for the model.
49
3.
Forecasted rainfall for the next twelve hours is needed as the model input in
order to generate forecast of discharge and water level. Thus the accuracy of
the forecasted rainfall also becomes a limitation to the accuracy of forecasted
water level.
Further explanations on how the model structure and hydrological data are
limiting the Tank model’s performance are discussed in Section 6.2.1 and Section
6.2.2 respectively; while recommendations on how to overcome these limitations
and improve the model’s performance are discussed in Section 6.3.
6.2.1
Model Structure
In this model, there is only one series of tank with a minimal number of
outlets. Each of the outlets is able to generate a unique hydrograph and the
summation of these outlet hydrographs yields the total simulated discharge. By
referring to Figure 4.8 we can observe that the hydrograph patterns generated by the
tanks are very limited, which makes the model have less ability to imitate the actual
behavior of the catchment. Therefore the performance of the model can be enhanced
by adding more outlets or by adding another series of tank which is parallel to the
existing tank.
6.2.2
Hydrological Data
The performance of the Tank Model is significantly limited by the
availability, completeness and reliability of hydrological data such as rainfall and
water level data. Generally Kelantan’s data collection is considerably complete and
50
reliable, and the eight rainfall stations chosen to be used in this model are the main
stations in the state that provide good data.
The accuracy of rating curve is also another issue to be concerned of. River
survey is a costly process and it is not being conducted on a frequent basis. The
rating curve used in this model was developed at the year 2002. The channel
geometry may have experienced changes in this past five years, and this is one of the
major limitations of the model if we consider its performance without the
Autoregressive adjustments.
Water level data is crucial for the model calibration process. Currently this
model does not include the feature of filling in the missing water level data. If the
gap of data is considerably short, for example a few hours, interpolation may be
done to fill in the gap. But if the recorded data has a gap which exceeds twelve hours,
interpolation may not be able to provide reliable data substitution.
6.3
Recommendations
The Tank model’s performance can be further improved in terms of the
accuracy of its simulation results and its user friendliness. The recommended
solutions are suggested as follows:
1.
An additional parallel series tank can be added to the existing tank model
structure to retain water in tanks for longer. This will create a lag effect
which will help to resolve the problem of the early arrival of simulated peak.
2.
By using longer storm duration, the effect of missing data will be reduced as
the large amount of data will be able to stabilize the tank storage. For this
project it is recommended to use at least one month of data prior to the start
of forecast.
51
3.
Forecasted rainfall values are usually generated through educated guesses
with the aid of weather forecast reports provided by the Malaysia
Meteorological Department. However better estimation can be made by
using the radar to forecast rainfall.
4.
The model should be re-calibrated after several years of use, or when the
river experiences significant changes or developments. Re-calibration will
allow the model to take into account the changes of discharge that may be
caused by sedimentation along river which would make the river bed
shallower, or developments along the river side
5.
Currently there are three rainfall stations used in the model that are automatic
recording stations, they are: JPS Machang, Gob and Blau. These stations
need to be upgraded to telemetric stations so that real-time data can be
acquired for forecasting purposes.
6.
Automatic web query can add value to the model as it will be able to obtain
data automatically from the JPS’s online hydrological database Infobanjir for
real-time flood forecasting.
52
REFERENCES
1.
Ng, B.C. (2003). Ramalan Banjir Bagi Sungai Kuantan Dengan
Menggunakan Model Tangki. Skudai: Universiti Teknologi Malaysia: Thesis
Sarjana Muda.
2.
Cheok, H.S. (2005). Development of a PC-Based Tank Model Real-time
Flood Forecasting System. Skudai: Universiti Teknologi Malaysia: Thesis
Sarjana.
3.
Dr. Nik & Associates. (2005). Development of a Rainfall-Flood Correlation
Model cum Flood Message Dissemination System for a Pilot Area in the
Klang River Basin. Kuala Lumpur: Final Report.
4.
Chong, S.F. and Cheok, H.S. (2006). Development of a Digital Database and
Information System for the Six Selected Flood Forecasting Systems of JPS
Malaysia. Kuala Lumpur: Final Report.
5.
Kraijenhoff, D.A. and Moll, J.R. (1986). River Flow Modelling and
Forecasting. Netherlands: D. Reidel Publishing Company.
6.
Sugawara, M. (1979). The Flood Forecasting by a Series Storage Type
Model. Tokyo, Japan: National Research Center for Disaster Prevention.
7.
Viessman, W., Lewis, G. L. (2003). Introduction to Hydrology. New Jersey:
Prentice Hall, Pearson Education, Inc.
8.
Sepakat Setia Perunding Sdn Bhd and SMHB Sdn Bhd. (1999). Final Report:
Kelantan River Flood Mitigation Plan Feasibility Study.
9.
Lee, J.M. (2007). Development of Flood Forecasting Model for Sungai
Kelantan (1-D Hydrodynamic Model). Skudai: Universiti Teknologi
Malaysia: Thesis Sarjana Muda.
10.
Ang, Zhili (2007). Development of Flood Forecasting Hydraulic Model for
Sungai Johor. Skudai: Universiti Teknologi Malaysia: Thesis Sarjana Muda.
53
APPENDIX A
Graphs showing comparison between individual tank discharge and total discharge
Individual Tank Discharge and Total Discharge for 1988 Storm Event
(17/11/1988 - 15/12/1988)
1.80
6000.0
Q1
1.60
Q2
Q3
1.40
5000.0
Q5
4000.0
Total Q
1.00
Q6
Obs Q
0.80
3000.0
0.60
2000.0
0.40
0.20
Total discharge (m3/s)
Tank Discharge (mm)
Q4
1.20
1000.0
0.00
11/15/1988 0:00 11/20/1988 0:00 11/25/1988 0:00 11/30/1988 0:00
-0.20
12/5/1988 0:00 12/10/1988 0:00 12/15/1988 0:00
0.0
Date Time
Individual Tank Discharge and Total Discharge for 2001 Storm Event
(10/12/2001 - 10/1/2002)
4000.0
1.20
Q1
Q2
1.00
3500.0
Q3
3000.0
Q5
Total Q
0.60
2500.0
Q6
Obs Q
2000.0
0.40
1500.0
0.20
0.00
12/7/2001
0:00
-0.20
1000.0
500.0
12/12/2001
0:00
12/17/2001
0:00
12/22/2001
0:00
12/27/2001
0:00
1/1/2002 0:00 1/6/2002 0:00
1/11/2002
0:00
0.0
Date Time
Total Discharge (m3/s)
Tank Discharge (mm)
Q4
0.80
54
Individual Tank Discharge and Total Discharge for 2003 Storm Event
(23/11/2003 - 27/12/2003)
4000.0
1.00
Q1
0.90
Q3
Q4
3000.0
Q5
0.60
Total Q
2500.0
Q6
0.50
Obs Q
2000.0
0.40
1500.0
0.30
0.20
Total Discharge (m 3/s)
0.70
Tank Discharge (mm)
3500.0
Q2
0.80
1000.0
0.10
0.00
11/20/2003
-0.100:00
500.0
11/25/2003
0:00
11/30/2003
0:00
12/5/2003
0:00
12/10/2003
12/15/2003
0:00
0:00
Date Tim e
12/20/2003
0:00
12/25/2003
0:00
12/30/2003
0:000.0
Individual Tank Discharge and Total Discharge for 2004 Storm Event
(27/11/2004 - 27/12/2004)
6000.0
1.60
Q1
1.40
Q2
Q4
Q5
1.00
4000.0
Total Q
Q6
0.80
Obs Q
3000.0
0.60
0.40
2000.0
0.20
1000.0
0.00
11/24/2004
-0.200:00
11/29/2004
0:00
12/4/2004
0:00
12/9/2004
0:00
12/14/2004
0:00
Date Tim e
12/19/2004
0:00
12/24/2004
0:00
12/29/2004
0:00
1/3/2005 0:00
0.0
Total Discharge (m 3/s)
Tank Discharge (mm)
5000.0
Q3
1.20
APPENDIX B
Detail Calculation of the Tank Model in Simulating the Discharge and Water Level for the year 2004 Storm Event
(27/11/2004 – 27/12/2004)
55
APPENDIX B - continue
Detail Calculation of the Tank Model in Simulating the Discharge and Water Level for the year 2004 Storm Event
(27/11/2004 – 27/12/2004)
56
APPENDIX B - continue
Detail Calculation of the Tank Model in Simulating the Discharge and Water Level for the year 2004 Storm Event
(27/11/2004 – 27/12/2004)
57
APPENDIX B - continue
Detail Calculation of the Tank Model in Simulating the Discharge and Water Level for the year 2004 Storm Event
(27/11/2004 – 27/12/2004)
58
APPENDIX B - continue
Detail Calculation of the Tank Model in Simulating the Discharge and Water Level for the year 2004 Storm Event
(27/11/2004 – 27/12/2004)
59