borang pengesahan status tesis - Universiti Teknologi Malaysia
Transcription
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