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2.3.3 Application of Mike Flood 1D/2D Engine In Mike Urban. 10. 2.4 Previous Modelling Work In Sukhumvit. 10. 2.5 Intensity Duration Frequency Curve. 11.
IMPACT OF CLIMATE CHANGE ON URBAN FLOODING IN SUKHUMVIT AREA OF BANGKOK

by

Ashish Shrestha

A thesis submitted in partial fulfilment of the requirements for the Degree of Master of Engineering in Urban Water Engineering and Management at the Asian Institute of Technology and the degree of Master of Science at the UNESCO - IHE

Examination Committee: Dr. Sutat Weesakul (Chairperson) Prof. Mukand S. Babel (Co-Chairperson) Dr. Zoran Vojinovic (External Expert) Dr. Thammarat Kottatep

Nationality: Pervious degree:

Scholarship Donor:

Nepalese Bachelor of Technology in Environmental Engineering Kathmandu University, Nepal Gates Foundation / UNESCO - IHE

Asian Institute of Technology School of Engineering and Technology, School of Environment, Resource and Development Thailand May 2013

ACKNOWLEDGEMENTS Being part of Asian Institute of Technology (AIT) and UNESCO-IHE Institute for Water Education family and completion of joint Masters Degree is one of the great opportunity and moment of bliss in my life. It is my pleasure to express my heartfelt gratitude to those who inspired me and helped me realize my goals especially within my Masters thesis. First of all, I would like to express my sincere gratitude to my advisors and committee mentors Dr. Sutat Weesakul, Prof. M.S. Babel, Dr. Zoran Vojinovic and Dr. Thammarat Kottatep. It is an honor for me to have their continuous guidance and support. I am very grateful to Bill and Melinda Gates Foundation / UNESCO-IHE due to their commitments and financial supports during my studies at Thailand and the Netherlands. I am also grateful to DHI group and DHI Thailand for license support for application of the great modelling tools in my study. Finally, my appreciation to the most important people in my life, my beloved parents, those are my motivation at all moments.

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ABSTRACT Several studies have shown the trend of increasing frequency, intensity and volume of extreme precipitation under climate change. Urban flooding or pluvial flooding has become frequent phenomenon in most of the cities. The climate change could have potentially outsized impact in existing ecological, human system and built systems. The effects of climate change can be seen in large or small municipalities of urban and rural nature with distinct implication in various types of municipal infrastructure. The built systems are susceptible to endure greater exposure to extreme events in the future, resulting in increased demand for maintenance and upgrades. Most of the urban drainage systems are designed under stationary climate consideration, as the consequences most of them are running over capacity before their design period. The technologies for the urban drainage have been developed over a long period of time, though the design criteria have been relatively been constant throughout the major urbanization period. The major challenge in urban flood studies is the requirement of high resolution temporal and spatial climate data. Most of the places to not have continuous observed data and to generate such data for future is challenging with uncertainties. The study, specific to Sukhumvit, Bangkok, aims to analyze the performance of urban drainage under the changing rainfall extremes due to climate change. The climate models and rainfall disaggregation method are applied to project future climate in high spatial and temporal resolution. Hence, future Intensity Duration and Frequency curves are projected. The performance and flooding of drainage network is studied with application of 1 dimensional and 2 dimensional modelling approach using tools like Mike Urban (MOUSE) and Mike Flood. Stochastic weather generator LARS WG is used to project future climate under SRA1B and SRA2 scenario using 15 GCMs. Two GCMs projecting maximum 3 h rainfall depth during future extent 2011-2030 and 2046-2065 are selected in each emission scenarios. The IDF generated from selected GCMs are used to derive design storm for urban drainage model simulation. The flood volume, outflow volume, flooded 2D cells or area, flood duration and surcharging nodes increases in 2011-2030 and 2046-2065 compared to baseline condition. The impact of climate change on urban catchment showed augmenting volume of pluvial floods as a consequence of urban drainage capacity becoming inadequate in future. The pluvial flood results in increase of flooded area though the velocity and depth remains within the level of medium hazard category.

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TABLES OF CONTENTS CHAPTER

TITLE

PAGE

Acknowledgements Abstract Tables of Contents List of Figures List of Tables List of Abbreviations

II III IV vi xi XI

1.

Introduction 1.1 Background 1.2 Rationale 1.3 Statement of Problem 1.4 Objectives of Study 1.5 Scope

1 1 2 2 3 3

2.

Literature Review 2.1 One Dimensional Urban Drainage Modelling 2.2 Coupled 1D Pipe Flow and 2D Overland Flow Model 2.3 Numerical Models For Urban Drainage Analysis 2.3.1 Application of Mike Urban For Pipe Flow Model 2.3.2 Numerical Solution In Mouse Networks 2.3.3 Application of Mike Flood 1D/2D Engine In Mike Urban 2.4 Previous Modelling Work In Sukhumvit 2.5 Intensity Duration Frequency Curve 2.6 Temporal Rainfall Disaggregation 2.7 Climate Change and Downscaling of Climate Data From GCMS 2.7.1 Statistical Downscaling Using SDSM 2.7.2 LARS Weather Generator 2.7.3 Climate Change, Urban Drainage and Flooding

4 4 4 7 8 9 10 10 11 15 17 20 20 21

3.

Methodology 3.1 Study Area 3.2 Data Collection 3.3 Flowchart For Methodological Overview 3.4 Downscaling of Climate Data From GCMS 3.4.1 Statistical Downscaling Using SDSM 3.4.2 LARS Weather Generator 3.5 Temporal Rainfall Disaggregation 3.6 Intensity Duration Frequency Curve 3.7 Urban Drainage Model Setup 3.7.1 1D Urban Drainage Model 3.7.2 Calibration and Verification

23 23 24 25 26 26 26 27 27 27 28 28

iv

3.7.3 Coupled 2D Overland Flow Model

29

4.

Results and Discussion 4.1 Analysis of Present Rainfall Scenario 4.2 Downscaling of Climate Data 4.2.1 Application of SDSM 4.2.2 Application of LARS WG 4.3 Temporal Disaggregation of Rainfall 4.4 Generation of IDF Curves 4.5 IDF Curves For Future 4.6 Urban Drainage Model Structure 4.6.1 Boundary Conditions 4.6.2 Pipe Flow Model 4.6.3 Overland Flow Model 4.7 Simulation Results 4.8 Flood Hazards Due to Climate Change

30 30 31 31 33 37 40 44 52 52 53 53 54 63

5.

Conclusion and Recommendations 5.1 Conclusion 5.2 Recommendation

67 67 68

6. 7.

References Appendixes

69 76

v

LIST OF FIGURES FIGURE 2.1 2.2 2.3 2.4 2.5 2.6 3.1 3.2 3.3 3.3 3.4 3.5 3.6 4.1 4.2 4.3 4.4 4.5

4.6 4.7

4.8

4.9

TITLE

PAGE

Structure of the node (Source: mike urban pipe flow reference manual) 8 Computation grid implemented in mouse conduits (Source: MOUSE reference manual) 9 Centred six point Abbott Scheme (Source: MOUSE reference manual) 10 General assumption of Bartlett Lewis Rectangular Pulse model 16 Emission of carbon dioxide under different emission scenarios (Source: IPCC, 2000) 18 General flood hazard categories based on velocities and depth (Adopted from Flood Risk and Social Justice, 2012) 22 Areal map showing Sukhumvit, Bangkok area over Thailand region (Source: Bing map) 23 Districts Wattana (Area 39) and Khlongtoei (Area 33) within the study catchment 23 Map of Sukhumvit with urban drainage network (Map Source: Bing Map) 23 Canal (Khlong San Sabe) also used in navigation 24 Outflow to the canal from the drainage system 24 Pipe flow model over the overland land flow model 28 Digital Elevation Model of the study area with the designated boundary based on the extent of the sewer network 29 Average maximum daily precipitation (a) and maximum daily observed precipitation (b) in each month during 1981-2010 in Bangkok Metropolis Station 30 Present IDF curve for Bangkok metropolis station using annual maximum series from 3 hour rainfall depth from 1981 – 2010 30 Calibration results for the NCEP, HADCM3 GCMs with observed data from 1961-1990 32 Verification results for the NCEP, HADCM3 GCMs with observed data from 1991-2001 33 Comparison of monthly total mean precipitation, standard deviation and maximum between observed and simulated synthetic time series data between 1981-2000 34 Comparison of monthly total mean precipitation, standard deviation and maximum between observed and Generated time series data between 2001-2010 35 Histogram showing average values of maximum daily data in each months during 2011-2030 from 15 GCMs with scenarios SRA1B, SRA2 and SRB1 and error bars showing range of maximum values 36 Histogram showing average values of maximum daily data in each months during 2046-2065 from 15 GCMs with scenarios SRA1B, SRA2 and SRB1 and error bars showing range of maximum values 37 Comparison of mean for synthetic rainfall simulated by BL parameters and historical observed data (1986-2000) 38 vi

4.10 4.11 4.12 4.13

4.14 4.15 4.16 4.17 4.18

4.19

4.20 4.21 4.22 4.23 4.24 4.25 4.26 4.27 4.28 4.29

Comparison of variance for synthetic rainfall simulated by BL parameters and historical observed data (1986-2000) 38 Comparison of lag 1 autocovariance for synthetic rainfall simulated by BL parameters and historical observed data (1986-2000) 39 Comparison of proportion dry for synthetic rainfall simulated by BL parameters and historical observed data (1986-2000) 39 Comparison of IDF curve from gauged 3 hourly historical data (solid lines) are compared with IDF curve generated from disaggregated 3 hourly data from gauged 24 hour data (dash lines) 41 Comparison of IDF curve from gauged 3 hourly historical data with IDF curve generated from disaggregated 3 hourly data from gauged 24 hour data 42 Ratios of intensities between observed and modeled IDF curves for each return period and average values 43 Fitting the average ratios for all return period into the power equation 43 Corrected IDF curve for 1981-2010 44 Intensity Duration Frequency (IDF) in SRA1B scenario for durations from 1 hour to 24 hours with range of maximum, minimum and mean values obtained from BCM2, CGMR, CNCM3, CSMK3, FGOALS, GFCM21, GIAOM, HADCM3, HADGEM, IMCM3, IPCM4, 45 Intensity Duration Frequency (IDF) in SRA2 scenario for durations from 1 hour to 24 hours with range of maximum, minimum and mean values obtained from CNCM3, GFCM21, HADCM3, HADGEM, IMCM3, IPCM4, MPEH5, NCCCSM and NCPCM results of for future time sections 2011-2030 and 20462065 with return periods 2, 5 and 20 years 46 Comparison of IDF curves generated under SRA1B emission scenario for 2 year return period at different present and future time extents 47 Comparison of IDF curves generated under SRA1B emission scenario for 5 year return period at different present and future time extents 48 Comparison of IDF curves generated under SRA1B emission scenario for 20 year return period at different present and future time extents 49 Comparison of IDF curves generated under SRA2 emission scenario for 2 year return period at different present and future time extents 49 Comparison of IDF curves generated under SRA2 emission scenarios for 5 year return period at different present and future time extents 50 Comparison of IDF curves generated under SRA2 emission scenarios for 20 year at different present and future time extents 51 Calculation of time of concentration, figure (a) showing input and outlet points and figure (b) showing the water level showing peak around 2.5 hours 52 Simulation result showing observed water level and model simulated water level at Ekamai and Sukhumvit 26 stations 53 Coupled grid cell (10 x 10 m) of DEM with nodes of sewer network 54 Flood hazard with velocity and depth for each 2D computation cells for 2 year return period in (a) present baseline condition (b) 2011-2030, IPCM4 under vii

4.30

4.31

E.1.1 E.1.2 E.1.3 E.2.1

E.2.2

E.2.3

E.2.4

E.2.5

E.2.6

E.2.7

E.2.8

E.3.1

SRA1B (c) 2046-2065, IPCM4 under SRA1B (d) 2011-2030, HADCM3 under SRA2 (e) 2046-2065, HADCM3 under SRA2 64 Flood hazard with velocity and depth for each 2D computation cells for 5 year return period in (a) present baseline condition (b) 2011-2030, IPCM4 under SRA1B (c) 2046-2065, IPCM4 under SRA1B (d) 2011-2030, HADCM3 under SRA2 (e) 2046-2065, HADCM3 under SRA2 65 Flood hazard with velocity and depth for each 2D computation cells for 20 year return period in (a) present baseline condition (b) 2011-2030, IPCM4 under SRA1B (c) 2046-2065, IPCM4 under SRA1B (d) 2011-2030, HADCM3 under SRA2 (e) 2046-2065, HADCM3 under SRA2 66 Maximum water depth (a) in the event of 2 year return period rainfall (b) at 3 h present rainfall depth and (c) maximum water depth at main street 90 Maximum water depth (a) in the event of 5 year return period rainfall (b) at 3 h present rainfall depth and (c) maximum water depth at main street 90 Maximum present water depth (a) in the event of 20 year return period rainfall (b) at 3 h present rainfall depth and (c) maximum water depth at main street 91 Maximum water depth (a) in the event of 2 year return period rainfall during 2011-2030 (CGMR/SRA1B) (b) 3 h rainfall depth and (c) maximum water depth at main street 92 Maximum water depth (a) in the event of 2 year return period rainfall during 2011-2030 (IPCM4/SRA1B) (b) 3 h rainfall depth and (c) maximum water depth at main street 92 Maximum water depth (a) in the event of 2 year return period rainfall during 2011-2030 (GFCM21/SRA2) (b) 3 h rainfall depth and (c) maximum water depth at main street 93 Maximum water depth (a) in the event of 2 year return period rainfall during 2011-2030 (HADCM3/SRA2) (b) 3 h rainfall depth and (c) maximum water depth at main street 93 Maximum water depth (a) in the event of 2 year return period rainfall during 2046-2065 (CGMR/SRA1B) (b) 3 h rainfall depth and (c) maximum water depth at main street 94 Maximum water depth (a) in the event of 2 year return period rainfall during 2046-2065 (IPCM4/SRA1B) (b) 3h rainfall depth and (c) maximum water depth at main street 94 Maximum water depth (a) in the event of 2 year return period rainfall during 2046-2065 (GFCM21/SRA2) (b) 3 h rainfall depth and (c) maximum water depth at main street 95 Maximum water depth (a) in the event of 2 year return period rainfall during 2046-2065 (HADCM3/SRA2) (b) 3 h rainfall depth and (c) maximum water depth at main street 95 Maximum water level (a) in the event of 5 year return period rainfall during 2011-2030 (CGMR / SRA1B) (b) 3h rainfall depth and maximum water depth at main street 96 viii

E.3.2

E.3.3

E.3.4

E.3.5

E.3.6

E.3.7

E.3.8

E.4.1

E.4.2

E.4.3

E.4.4

E.4.5

E.4.6

E.4.7

Maximum water level (a) in the event of 5 year return period rainfall during 2011-2030 (IPCM4 / SRA1B) (b) 3h rainfall depth and maximum water depth at main street 96 Maximum water level (a) in the event of 5 year return period rainfall during 2011-2030 (GFCM21/SRA2) (b) 3h rainfall depth and maximum water depth at main street 97 Maximum water level (a) in the event of 5 year return period rainfall during 2011-2030 (HADCM3/SRA2) (b) 3h rainfall depth and maximum water depth at main street 97 Maximum water level (a) in the event of 5 year return period rainfall during 2046-2065 (CGMR / SRA1B) (b) 3h rainfall depth and maximum water depth at main street 98 Maximum water level (a) in the event of 5 year return period rainfall during 2046-2065 (IPCM4/SRA1B) (b) 3h rainfall depth and maximum water depth at main street 98 Maximum water level (a) in the event of 5 year return period rainfall during 2046-2065 (GFCM21/SRA2) (b) 3h rainfall depth and maximum water depth at main street 99 Maximum water level (a) in the event of 5 year return period rainfall during 2046-2065 (HADCM3/SRA2) (b) 3h rainfall depth and maximum water depth at main street 99 Maximum water level (a) in the event of 20 year return period rainfall during 2011-2030 (CGMR/SRA1B) (b) 3 h rainfall depth and maximum water depth at main street. 100 Maximum water level (a) in the event of 20 year return period rainfall during 2011-2030 (IPCM4/ SRA1B) (b) 3 h rainfall depth and maximum water depth at main street 100 Maximum water level (a) in the event of 20 year return period rainfall during 2011-2030 (GFCM21/SRA2) (b) 3 h rainfall depth and maximum water depth at main street 101 Maximum water level (a) in the event of 20 year return period rainfall during 2011-2030 (HADCM3/SRA2) (b) 3 h rainfall depth and maximum water depth at main street 101 Maximum water level (a) in the event of 20 year return period rainfall during 2046-2065 (CGMR / SRA1B) (b) 3 h rainfall depth and maximum water depth at main street 102 Maximum water level (a) in the event of 20 year return period rainfall during 2046-2065 (IPCM4 / SRA1B) (b) 3 h rainfall depth and maximum water depth at main street 102 Maximum water level (a) in the event of 20 year return period rainfall during 2046-2065 (GFCM21 / SRA2) (b) at present condition and maximum water depth at main street 103 ix

E.4.6

Maximum water level (a) in the event of 20 year return period rainfall during 2046-2065 (HADCM3 / SRA2) (b) at present condition and maximum water depth at main street 103

LIST OF TABLES TABLE 2.1 2.2 2.3 3.1 3.2 4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9 4.10 4.11 4.12 4.13 4.14 4.15 4.16 4.17

TITLE

PAGE

Some commonly used design storm for urban drainage design 14 Summary of disaggregation models 15 Different methods of downscaling 19 Source of data required 24 Summary of components in pipe flow model 28 Selection of predictor variables 31 Root Mean Square Error and Efficiency Index summary for calibration and verification process 33 RMSE and EI for site analysis or calibration and validation process in LARS WG 35 BLRP parameter estimation 40 Ratio and difference between observed and simulated rainfall intensities 41 Correction Factors for IDF curve 43 Change in percentage of 3 hour rainfall depths from present baseline condition 51 Simulation results during present time period 55 Simulation results for 2 year return period during 2011-2030 and 2046-2065 56 Simulation results for 5 year return period during 2011-2030 and 2046-2065 57 Simulation results for 20 year return period during 2011-2030 and 2046-2065 58 Percentage of future increments in outflow volume from baseline conditions 59 Percentage of future increments in flooded volume from baseline conditions 60 Percentage of future increments in number of 2D flooded cells compared with baseline conditions 60 Percentage of future increments in flood depth at main street compared to baseline 61 Percentage of future increments in surcharging nodes compared to baseline condition 61 Percentage of future increments in flood duration compared to baseline condition 62

x

LIST OF ABBREVIATIONS 1D 2D AOGCM BCR BL CRF CSO DDS DEM DTM GCM GEV GHG GIS HIRHAM IDF IPCC LARSWG LiDAR LID MASL MOUSE NOAA RCM RDII RTC SDSM SSO SWMM TMD TRMM UNFCCC UTM WWTP

One Dimensional Two Dimensional Atmosphere-Ocean Global Circulation Model Building Coverage Ratio Bartlett Lewis Conveyance Reduction Factor Combined Sewer Overflows Department of Drainage and Sewerage Digital Elevation Model Digital Terrain Model General Circulation Model Generalized Extreme Value Greenhouse Gas Geographic Information System High Resolution Limited Area Model Intensity Duration Frequency Intergovernmental Panel on Climate Change Long Ashton Research Station Weather Generator Light Detection And Ranging Low Impact Development Meter Above Mean Sea Level Model for Urban Sewers National Oceanic and Atmospheric Administration Regional Climate Model Rainfall Dependent Inflow/Infiltration Real Time Control Statistical Downscaling Model Sanitary Sewer Overflow Storm Water Management Model Thai Meteorological Department Tropical Rainfall Measuring Mission United Nations Framework Convention on Climate Change Universal Transverse Mercator Waste Water Treatment Plant

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CHAPTER 1 INTRODUCTION 1.1

Background

The increasing concentration of carbon dioxide in atmosphere has been the foremost reason of global warming and climate change. This changing pattern in temperature and precipitation has resulted in transformation of hydrological cycle. Projections from climate models showing increasing frequency, intensity and volume of extreme precipitation have been shown by many studies (Trenberth, 1999; Emori and Brown, 2005; Boo et al, 2006). The slight changes in climate normals could have potentially outsized impact in existing ecological, human system and built systems. The effects of climate change can be seen in municipalities of large and small size of urban or rural nature with distinct implication in various types of municipal infrastructure. The built systems are often susceptible which are likely to endure greater exposure to extreme events in the future, resulting in increased demand for maintenance and upgrades. Water and sewage networks need to accommodate more intense precipitation (Mehdi et.al. 2006). Berggren et al, (2007), the study conducted in the climate change impact on urban drainage have discussed possible impacts on urban drainage which are increased Combined Sewer Overflows (CSO) causing environmental problems and polluting drinking water sources, infiltration of the ground water into sewer system, increased pollutants level in the WWTP affecting their normal treatment process, flooding the pumping stations, increment in sediment volume in the storm water detention pond in the separate system, decreased infiltration capacity in the infiltration basin. The urban drainage infrastructures designed in most of the places have considered stationary climate condition, as the consequences most of them are running over capacity before their design period (Mailhot and Duchesne, 2010). The technologies for the urban drainage have been developed over a long period of time, though the design criteria have been relatively been constant throughout the major urbanization era (Berggren et al, 2007). Storm water management is not only directly related to climate change. Apart from climate change, there is however other factors such as increase in population, new developments (Berggren et al, 2007). However, climate change impacts are considered important and shall be studied into detail for proper analysis of the drainage system. Urban storm water infrastructures in most of the cities are under direct exposure of extreme events. Sukhumvit, in the Central Bangkok is one of the representative urbanized cities where ground surface is mostly impermeable that generates higher percentage of surface runoff and densely established buildings and structures makes flood flow routing even complicated. It also represents the condition of urban cities where terrain is flat and gravity discharge of storm water is difficult. Past studies done in Sukhumvit have shown that, drainage capacity of the existing system is not adequate. Department of Drainage and Sewerage (DDS) had operated secondary drainage in Sukhumvit. There is no retention pond and water levels in both surrounded canals (Klong Phra Khanong, Klong San Sabe 1

and Klong Tan) almost reach embankment level. The drainage system inside Sukhumvit is connected with outside canals as primary drainage systems which connects overall Bangkok. 1.2

Rationale

Urban flooding is becoming frequent in recent years in most of the cities. Urbanization and climate change is altering the rainfall pattern usually increasing the extreme precipitation. There has been considerable research done and tools have been developed to analyze the fluvial and coastal flooding, but models for pluvial flooding are less advanced. Therefore, modelling and better understanding of the risk of storm water flooding is needed urgently for flood risk management (Chen et al, 2012). The storm water infrastructures designed under stationary climate condition in the past are facing problem of frequent flooding, surcharging and working over capacity before its design period. The significance of extreme event analysis and its impacts on urban drainage is higher than before with the advancement in Atmosphere – Ocean Global Climate Models (AOGCM) and potential to project future climate in effective manner. The study will focus on developing extreme rainfall for future and study the change in IDF curve in non-stationary climate condition. The urban catchments have usually shorter time of concentration and hence rainfall inputs shall be in fine resolution for accurate model simulation. In most of the part of the World availability of short duration rainfall is the major problem to study urban drainage modelling. There are many methods to disaggregate coarser scale rainfall to higher resolution rainfall. In the study, model based on Bartlett Lewis Rectangular Pulse process theory, Hyetos is applied to study its applicability and obtain high temporal resolution rainfall upto 1 hour interval. The modelling of one dimensional pipe flow model coupled with two dimensional overland flow model provide powerful tool to analyze performance under future change in climate system. The analysis based on water depth and velocity during flood is necessary to understand nature and extent of such events in future. 1.3

Statement of Problem

Urban drainage designed under the stationary climate scenario is flowing over capacity before its design period. The IDF curve in not updated while climate change is modifying the rainfall pattern resulting frequent event of extreme precipitation. The return periods are getting reduced and many studies have shown that return period will halve in next few decades. The urban flooding is getting more frequent and intensity/duration of flooding is also increasing on the urban surfaces. This is resulting into economic loss. The current modelling tool used for assessment of climate change impact on urban drainage can be used to study the past and present condition and predict future performance of the system. Generally, 1D modelling is practiced for urban drainage studies due to fast computation time and lower cost; however, 2D modelling provides better understanding of the problems 2

and helps in effective planning of adaptation measures. The downscaling technique from different climate model from GCMs for future climate scenario gives the daily precipitation as its shortest duration rainfall and in the coarser spatial scale. The downscaling techniques itself have certain uncertainties which therefore shall be assessed prior to application. 1.4

Objectives of study

The main objective of the study is to evaluate the impact of climate change on performance of urban drainage under different rainfall extremes and subsequent generation of flood hazards with urban drainage modelling application. The specific objectives of the study are as follows: 

 

1.5

To study application of different spatial downscaling approaches together with temporal disaggregation method to generate higher resolution future climate using different GCMs. To study future change in IDF curves under different climate scenarios. To evaluate impacts of different rainfall extremes on generation of flood depths with application of one dimensional pipe flow model and two dimensional overland flow modelling. Scope

The scope of this study will be: 

 

 

Downscale GCMs using stochastic weather generator LARS WG and SDSM statistical downscaling method and analyze result to generate future climate scenarios. Apply Hyetos model for the rainfall disaggregation to 1 hour rainfall to develop IDF curves for present and future. Study IDF relationship to the climate change, generate IDF curves for the Sukhumvit region based on downscaled and disaggregated data and develop design storm for urban drainage modeling. Utilize 1D/1D model developed and studied by Chingnawan, (2003), Nguyen (2010), in Mike MOUSE to develop 1D pipe flow model in Mike URBAN. Couple 1D model with 2D overland flow model in Mike Urban (Mike Flood) to study pluvial flood issues related to future climate.

3

CHAPTER 2 LITERATURE REVIEW This section consist review of theoretical current knowledge, findings relevant to this study which are in general modelling of drainage systems, downscaling and disaggregation techniques of climate variable. 2.1

One dimensional urban drainage modelling

The 1D modelling of flow in the conduits or open channels follows the Saint Venant equation. The equation can be represented mathematically as (Vojinovic and Tutulic, 2009): ∂A ∂t ∂Q ∂t

∂Q

+ ∂x = Fs ∂

βQ2

+ ∂x (

A

(2.1) ∂h

Q|Q|

) + gA ∂x + g C2 AR = 0

(2.2)

Here, h is water depth, Q is discharge, β is the velocity distribution coefficient, x is distance between chainages, t is time, Fs is course term, g is gravitational acceleration, C is the Chezy number, A is the area of the cross flow section which is f(h), R is hydraulics radius, P is wetted perimeter. 2.2

Coupled 1D pipe flow and 2D overland flow model

The system of 2D shallow-water equations consists of two equations for conservation of momentum and one continuity equation in Cartesian coordinates. Mathematically this can be expressed as (Vojinovic and Tutulic, 2009): ∂s





+ ∂x Uh + ∂x Vh = Fs ∂t

∂U ∂t ∂V ∂t

(2.3)

∂U

∂U

∂s

g



∂U



∂U

∂V

∂V

∂s

g



∂V



∂V

+ U ∂x + V ∂x + g ∂x + C2 d U√U 2 + V 2 + ∂x (K xx ∂x ) + ∂y (K yy ∂y ) = Fs Us + U ∂x + V ∂y + g ∂y + C2 d V√U 2 + V 2 + ∂x (K xx ∂x ) + ∂y (K yy ∂y ) = Fs Vs

(2.4)

(2.5)

Here, s is the water surface elevation, U and V are depth-averaged velocities, K xx and K yy are eddy viscosities and Us and Vs are the velocities at the source. When a storm event occurs in cities, runoff produced in roofs and terraces is generally directly conveyed to the underground sewer networks, while runoff produced in roadway, parks, squares, etc. circulates over the urban surfaces up to reach the inlet structures of the drainage systems. Therefore, it is neessary to estimate the hydraulics of the surface 4

drainage structures and to model them through a coupled 1D/2D approach (Russo et al, 2011). Physical process like evaporation, infiltration can be considered as they interact with the urban flooding. Apirumanekul, (2001), concluded that evaporation was insignificant during maximum flood. Also, groundwater level, infiltration shall be considered in the model as it plays major role. Infiltration rate depends on land use type, soil type, soil moisture content and infiltration can take place from soil to pipe system and pipe system to soil depending upon system condition and ground water level (Mark et al, 2004). Mark et al, (2004), results showed that the greatest inaccuracy of 1D model is to assume flow as one dimensional. The street curbs, pervious areas change the nature of the flow in the street. The assumptions made in the 1D modelling do not reflect the actual situation where as in real situation it is more complex and it cannot accurately simulate local conditions on small scale, especially when flood occurs. However, for large scale urban flooding the paper showed promising results. Digital Elevation Model (DEM) is required to represent land elevation data which is essential in representing flood volume and produce inundation maps. The data can be obtained from field survey, digitizing contour map or some other techniques. The resolution of the DEM shall be fine enough to represent the important features of the street that affects the water flow. Adeyemo et al, (2008), presented the work of Urban Water Research Group (UWRG) of Imperial College London about research to enhance the potential of 1D/1D type of model by more accurate GIS-application. The paper presents the potential of 1D/1D modelling, study the sensitivity analysis of parameters in GIS based tools. The results showed considerable number of pond loss but only less loss of volume while shifting from high resolution DTM (1m x1m) to coarser resolution DTM (10m x 10m). Combination of pond depth and volume is sensitive parameter while filtering ponds to eliminate noise. The results conclude that DTM size affects the modelling results. Boonya-aroonnet, (2008), explored the potential of pluvial flood modeling beyond the limitations studied by Mark et al, (2004). The concepts are based on GIS-centered analysis of the DTM/DEM so that the features of the catchment crucial for identification of flood vulnerable areas (mainly ponds) are derived and the geometric characteristics of the preferential paths computed. Using high resolution LiDAR (Light Detection and Ranging) dataset, the author presented the study as bridging gap between 1D/2D modelling by increasing the accuracy of 1D modeling. Besides 1D modelling, while considering overland flows from excess rainfall-runoff, surcharged flow from the 1D networks, 2D modelling is necessary (Price and Vojinovic, 2011). Generally, most of the industry as well as researchers use 1D modelling due to its relative easiness in set up and calibration. 1D/1D modelling approach or dual drainage concept considers the urban surface as networks of pipes and ponds. However, flood generated over ground are complex due to various geometry and hence 1D/2D modelling 5

in required while considering both pipe flow and surface flow (Vojinovic and Abbott, 2012). In the study by Seyoum et al. (2012), the 2D model is developed and coupled with SWMM5 to study flow dynamics in sewer networks and overland flow during urban flooding. When the flow path can be identified and as long as overland flow stays on the street profile 1D/1D modelling is sufficient but in the events of extreme flooding 1D/2D model is much preferred. The two important objectives of 1D/2D modelling are to assess performance of the combined sewer system and economic evaluation of flood damage that assist decision makers and second is ability to predict the extent of flooding using equation like Navier- Stokes by depth-average method. Neelz, (2010), while bench marking 2D modelling it was highlighted that 2D modelling can provide abundant information on dynamics of flooding, which will help in the flood risk management. The efficiency of 2D flood modelling has been one of the major challenges to modelers as the performance of existing 2D models varies significantly depending on the choice of time steps and the number of iterations within each time step, the efficiency of numerical algorithms, the use of multi-processing, the hardware specification and other computational overhead costs for modelling. Other studies related to 2D modelling focuses on studying the methods to reduce the computation time while level of accuracy and details are higher. The capturing of urban features in the coarse resolution 2D models and limitations of fine grid resolution of DTM in 2D modelling is studied by Vojinovic et al, (2012). The study based on adjusted conveyance and storage characteristics showed that straight forward 2D modelling approach is inadequate irrespective of the grid resolution. The better simulation of flooded cells and average depth are observed more accurately with lesser computation time. Chen et al, (2012 a), studied parallel computing techniques for increasing the computational speed by grid coarsening as the straight forward way to reduce computing efforts in 2D modelling, by applying building coverage ratio (BCR) and the conveyance reduction factor (CRF) parameters to abstract building features in coarse grid. The application of BCR and CRF to increase accuracy of modelling results with considerably smaller computational time, in other hand also failed to reflect the situation of flow phenomena when a building bisects a coarse cell. To solve this problem another study by Chen et al, (2012 b), showed the application of multilayered approach in grid coarsening and obtaining the accurate results without additional computation cost. The study showed that multilayered modelling of grid resolution 20 m is subsequently better in computation time, root mean square error, accuracy of flow path routing, among averaged DEM and single layer modelling of same grid resolution in comparison to benchmark of 1m grid resolution.

6

2.3

Numerical Models for Urban Drainage Analysis

Danish Hydraulic Institute (DHI), developed various specific tools based on modelling objectives and based on different numerical computation approach. Mike Model for Urban Sewers (MOUSE) is one dimensional pipe flow computing model. MOUSE can be used for analyzing Combined Sewer Overflows (CSOs) and Sanitary Sewer Overflow (SSOs), evaluating RDII, network capacity, predicting local flooding, estimating sediment build-up and transport, optimization and design of Real Time Control (RTC) solutions, analyzing water quality and sediment problems, and real-time modeling embedded in RTC solutions. Mike 11 is the one dimensional tool for analyzing flood, simulating flow and water level, water quality and sediment transport in rivers, flood plains, irrigation canals, reservoirs and other inland water bodies. It is used mostly for studying flood, dam break analysis, optimisation of reservoir and canal gate/structure operations, ecological and water quality assessments in rivers and wetlands, sediment transport and river morphology studies, salinity intrusion in rivers and estuaries. Mike 21 is two dimensional tools used for simulating flows, waves, sediments and ecology in rivers, lakes, estuaries, bays, coastal areas and seas. It is comprised of three simulation engines: Single grid, Multiple grid and Flexible grid. Single grid engine uses full time dependent non-linear equations of continuity and conservation of momentum which are solved by implicit finite difference techniques. Multiple grid engine is similar to single grid but it has the possibility of refining the study area of interest within model area by nesting process. Flexible mesh is based on linear triangular elements which is unstructured mesh and uses a cell centered finite volume solution technique. The application of Mike 21 is done in inland flooding, overland flooding and coastal flooding. Mike Flood is dynamically coupled 1D (Mike 11 or MOUSE) and 2D (Mike 21) tool which are used for simulation of inundation of rivers, flood plains, urban drainage systems. Mike Urban is the fully Geographic Information System (GIS) based urban modeling system for water distribution systems and wastewater collection systems. Mike Urban also supports SWMM5, EPANET files and is useful for urban water system including sewers, water distribution and collection system. Mike Urban is integrated into ArcGIS tool. The 2D overland flow Mike Flood feature is integrated into Graphic User Interface of Mike Urban. Storm Water Management Model (SWMM) developed by United States Environmental Protection Agency (EPA) is dynamic rainfall – runoff – subsurface runoff model used for simulation of different rainfall events. SWMM is the one dimensional model which can be used to model Low Impact Development (LID) and Best Management Practice (BMP) application in sub-catchments to study runoff, infiltration in study catchment.

7

2.3.1 Application of Mike Urban for Pipe flow model Modelling of the unsteady flow is based on implicit finite difference numerical method. Saint Venant Equation is used to represent depth, mean velocity/discharge and which consider the conservation of mass and conservation of momentum. The scheme used to solve saint venant equations in Mike software are based on implicit finite difference developed by Abbott and Ionescu (1967). Mike Urban also uses the same computational MOUSE engine for pipe flow computation. General default specifications and conditions for all the components are stored in dhiapp.ini file. The components of the urban drainage model includes: Nodes, Links, Functional units such as pumps, orifices and weirs. Nodes: Nodes are structures connecting two links which can be volume-free (also known as junction node) or having volume (also known as manholes). In calculation nodes poses additional friction when water passes through it. Nodes can be classified as Junction nodes, Manholes, Outlets and Basins. The input data required for the manhole are: x,y coordinate, diameter, ground level, invert level, critical level and outlet shape. Basins are arbitrarily shaped nodes structures of significant volume which can be Non-circular manholes, tanks, reservoirs, basins or natural ponds. The geometry of the basins is defined by water level, cross sectional flow area and surface areas.

Figure 2.1: Structure of the node (Source: mike urban pipe flow reference manual)

Outlets are type of nodes in which system flow is discharged into external water body. The flows through the manholes are computed by two equations: 𝑣𝑚 = (𝐻

𝑄

(2.6)

𝑚 −𝐻𝐵𝑜𝑡𝑡𝑜𝑚 ).𝐷𝑚

𝜋

𝐷

6.8

𝐴 = 4 . 𝐷𝑖𝑛 2 . (1 + 2. 𝐷𝑚 . 𝑡𝑎𝑛 (360 . 2𝜋)) 2

(2.7)

𝑖𝑛

Where, Hm = Water level in manhole (m), Vm = Velocity (m/s), Dm = Diameter of the manhole (m) Din = Diameter of the inlet pipe (m) Links: Links are pipes or open channels having constant cross section, friction properties and bottom slope along its entire length between two nodes. In Mike Urban digi points or geographical coordinates are automatically added to the nodes connecting each link at upstream and downstream. 8

Pipe flow model can be run in three choices: Dynamic wave approach, Kinematic wave approach and Diffusive wave approach. Dynamic wave approach considers full momentum equation, including acceleration forces and it results in correct simulation of fast transients and backwater profiles. It should be applied where the change in inertia of the water body over time and space is of importance i.e. when the bed slope is small and bed resistance forces are relatively small. Kinematic wave approach considers the balance between the friction and gravity forces which cannot simulate backwater effects. Hence, it is applicable for steep pipes without backwater effects. Diffusive wave approach considers only bed friction, gravity force, and the hydrostatic gradient terms in the momentum equation. This allows the user to take downstream boundary conditions into account, and thus simulate backwater effects. However, it ignores inertia terms and is suitable for backwater analyses in cases where the link bed and wall resistance forces dominate, and for slowly propagating waves where the change in inertia is negligible. Pumps: Pumps can be associated to manholes and storage nodes. Pump operation can be defined by the range of operation: Start level (Hstart), Stop level (Hstop). Pump discharge if the function of water level defined by following condition. Qpump = {

Q(H)if HStop ≤ H or if HStart ≤ H else 0

2.3.2 Numerical Solution in MOUSE networks Implicit finite difference method is applied to solve the flow equations. The computation grids are generated by alternating discharge- Q and water level – h points which are computed in each time step.

Figure 2.2: Computation grid implemented in mouse conduits (Source: MOUSE reference manual)

Both the continuity and momentum equations are solved using Abbott scheme.

9

Figure 2.3: Centred six point Abbott Scheme (Source: MOUSE reference manual)

2.3.3 Application of Mike Flood 1D/2D engine in Mike Urban Surface flooding can be simulated by two approach. 1D/1D approach by defining pipe network as 1D model interlinked with overland 1D model defined by cross section of open channels. 1D/2D approach which represents overland flow condition and interaction with sewer networks more accurately. However, 1D/2D model is computationally time consuming than former. The input file required for 1D/2D model is working pipe flow model, DEM in raster data set and number of defined couplings between 1D and 2D model. Selection of 2D model can be based on different types of solvers which are: single grid using rectangular cll solver, single grid using rectangular multi-cell solver and flexible mesh solver. DEM consists of grid cells of defined size which have different elevations. Elevation threshold for DEM, flooding and drying depth, bed resistance, eddy viscosity shall be specified prior to running the simulation. The flow parameter in the coupled nodes and basins are governed by orifice, weir or exponential function. 2.4

Previous modelling work in Sukhumvit

Boonya Aroonet et al. (2002) studied three different urban drainage modelling approaches in Sukhumvit, Bangkok. The first approach of exchanging surcharged water in virtual reservoir connected to pipe network. The second approach is by applying street network model to the pipe network model. Third approach by using DEM linked with pipe flow model. The third approach showed best results compared to other two. Chingnawan, (2003) studied the applying pipe flow model to the street network and developing inundation maps using MIKE 11 GIS. The representation of the flood is made in DEM layer. Nguyen, (2010) studied the rainfall forecasting and real time hydrologic information system for Bangkok with the application of urban drainage model in Sukhumvit. The work is also the 1D-1D modelling approach of pipe flow and street flow network. The streets are 10

created as channel flow with cross section similar to the open canals that are connected to the pipe flow network for calculation of surcharged or flooded water volume. 2.5

Intensity Duration Frequency Curve

Intensity-Duration-Frequency (IDF) relationship is a mathematical relationship among the rainfall intensity i, the duration d, and the return period T (Koutsoyiannis et al,1998). IDF curve is the most convenient form of rainfall information. Duration and intensity are two inversely proportional elements in IDF curve which varies for different return periods (Butler and Schutze, 2011). Rainfall intensity estimates are necessary for hydrologic analysis, design and planning problems. IDF curve is the important tool for design of urban drainage (Solaiman and Simonovic, 2011). The IDF can be expressed mathematically as i = f(T,d). The generalized IDF relationship can be expressed as a(T)

i = b(d)

(2.8)

Here, a and b are constants, d is arbitrary time duration (typically from a few minutes to several hours or few days), T is the return period. This expression has the advantage of a separable functional dependence of i on T and d. The function b(d) is: b(d) = (d+θ) η

(2.9)

where θ and η are parameters to be estimated (θ > 0, 0 < η < 1) a(T) = λTk = c + λ lnT

(2.10)

This equation is oldest, empirical and most commonly used in computation due to its simplicity otherwise if the maximum rainfall intensity has the Gumbel distribution then k and λ depends on the return period T (Koutsoyiannis et al,1998). Koutsoyiannis et al, (1998) presents other distribution functions for IDF analysis: Gumbel Distribution Function: The maxima of Type I is also known as Gumbel distribution. It is most widely used for IDF analysis. In gumbel distubution a(T) is expressed as: 1

a(T) = λ {ψ − ln [−ln (1 − T)]}

(2.11)

Here, λ and ψ are the scale and location parameter respectively of the distribution function. Generalized extreme value (GEV) distribution: Extreme values are selected maximum and minimum values of the data set. The probability distribution function of GEV which incorporates type I, type II and type III distributions can be written in the form: 11

F(x) = exp [− (1 − k

x−μ 1/k

)

α

]

(2.12)

For k = 0, GEV turns into Gumbel distribution The equation obtained for a(T) is given by: 1

−k

a(T) = λ’{ψ′ + [− ln (1 − T)] }

(2.13)

where, λ’= λ/k and ψ′ = k ψ − 1 Gamma Distribution: In gamma distribution the expression for a(T) is simplified by Koutsoyiannis et al.,(1998) which is given by: a(T) =

λμ α

1 α

(1 − T) +

λv β

1 β

[ξ − (T) ]

(2.14)

where, μ = 0.6 (√k − 1) − (1/√k − 1), V = 0.6 (√k − 1) + 0.01(k − 1) + 1, α =

0.6 √k

+ 0.08

1, 𝑘 < 1 1 ξ={ (k−1)−0.25 −11.6 (k−1)−0.25 , f(x) = { −11.6 31 e 31 e , 𝑘>1 Here, k ≠ 1, For k=1 the distribution function changes into exponential distribution. μ, v, α, β are coefficients dependent on the shape parameter k Log Pearson III distribution: It is very common distribution for the IDF analysis which is logarithmic transformation of the Gamma distribution. The function obtained is given by: a(T)= exp {𝑐 +

𝜆𝜇 𝛼

1 𝛼

(1 − 𝑇) +

𝜆𝜇 𝛽

1 𝛽

[𝜁 − (𝑇) ]} , 𝑘 ≠ 1

(2.15)

where, μ, v, α, β are coefficients dependent on the shape parameter k. If k = 1 then the distribution will be pareto distribution. Lognormal Distribution: This distribution is sometimes used for IDF analysis. The expression for a(T) is given by: 1 𝛼

1 𝛼

a(T) = exp[𝜇𝑧 + 𝑣𝜎𝑧 (1 − 𝑇) − 𝜎𝑧 (𝑇) ]

(2.16)

Here, μz and σz are shape and scale factors respectively. Exponential Distribution: Exponential distribution function is not so common in IDF analysis. From exponential distribution function expression for a(T) can be obtained as: a(T) = λ(ψ + lnT)

(2.17)

Here, λ and ψ are scale and location parameters respectively 12

Pareto Distribution: Pareto distribution consists of three parameters. The expression for a(T) is obtained as: a(T) = 𝜆 (𝜓 +

𝑇 𝑘 −1 𝑘

)

(2.18)

Here, k, λ and ψ are shape, scale and location parameters respectively. In the study by Mailhot and Duchesne, (2010), for developing IDF under non-stationarity condition, it explains that urban drainage infrastructures which had been designed under stationary climate situation for its particular design period are being affected as the rainfall pattern, intensity and frequency of extreme rainfall modifies under climate change impacts. The design criteria shall be revised as part of the global adaptation strategies. However, there are uncertainties which deter developing of unambiguous guidelines for the design criteria. Solaiman and Siminovic, (2011), developed probability based IDF curve using Gumbel probability distribution for city of London with methodology consisting of 11 AOGCM and disaggregation by algorithm that consider short duration rainfall shapes from historical data to be projected in similar shapes for future. The study showed the rainfall patterns will change in future for London. Liew S.C. et al, (2012), conducted the study for developing IDF curves for present and future, in the situation of data scarce condition by using dynamic downscaling using RCM, Weather Research and Forecasting (WRF) model. The linear adjustment of IDF curve with bias obtained from comparison to present existing IDF curve was carried out in the study. The study was based on Singapore, Kuala Lumpur and Jakarta. The data from ERA-40 (European Centre for Medium range Weather Forecasting) was utilized which provide 6h resolution rainfall data from 1957 to 2002. First the study was conducted for the observed data to calculate the bias of the observed data and WRF-ERA-40 generated data. It was found that WRF-ERA40 generated data underestimated rainfall intensities by 21% on lower bound and 45 % on upper bound. Later, the bias correction was applied to the downscaled future data from 2071-2100. The future IDF curves are thus generated but for the coarse resolution of minimum 6h duration rainfall as in the cities like Jakarta, KualaLumpur, floods are caused by long duration rainfall. Furthermore, the study showed that in Jakarta, Indonesia, for the future period 2071-2100, the rainfall intensity is expected to increase between 49% and 82% for the upper bound and between 71% and 118% for the lower bound for 5, 25 and 50 year return period over the present climate. In the study linear adjustment of IDF is carried out. For the IDF of longer duration above 6 hours the nature of IDF can be expressed as linear. Design storm is the precipitation pattern defined for use in hydrologic system design. Design storm is derived from the IDF curve for different return periods. The most common design storms applied in the engineering practice are summarized in the table below: 13

Table 2.1: Some commonly used design storm for urban drainage design

Type Description Triangular Hyetograph Method

Alternating Block Method

Chicago Design Storm

Source Chow et (1988)

Here, Td is precipitation duration ta is time before the peak r is storm advancement coefficient, r = ta/ Td tb is recession time = Td – ta = (1 - r) Td It is the simple way to develop a design hyetograph Chow from IDF curve. (1988)

The process of developing design storm is includes intensity, depth, incremental depth and arrangement of depth with maximum starting from middle range of time interval to the minimum value far to the starting and ending time. The method is based on the parameters of the IDF Keifer curve given by the relation: (1957) a i = (t +b)c (2.19) b

where, i is rainfall intensity, td is storm duration in min and a,b,c are constant. The equation for this method are: b

t a[(1−b)( b ) +c]

i=

r

(2.20)

2

b

t [( b ) +c] r

(Before peak rainfall) b

t a[(1−b)( b ) +c]

i=

r

b

(2.21)

2

t [( b ) +c] 1−r

(After peak rainfall) where, ta is time after peak and tb is time before peak

14

al,

et

al,

et

al.

2.6

Temporal Rainfall Disaggregation

In most of the places there are number of raingauges which are in operation for few decades but most of them measures daily rainfall and rain gauge measuring hourly or subhourly data are few. The flood studies require short duration rainfall for continuous simulation tools for design and management of hydro-systems (Koutsoytannis, 2003). We need fine scale rainfall in many hydrological applications. The limitation of fine scale (sub hourly) rainfall data in world is largely because of high cost and low reliability of monitoring data. Where there is fine scale data generally short record length undermines its application for water resources projects. Stochastic disaggregation models for rainfall disaggregation can be divided into two categories: First is point process theory based on Bartlett-Lewis and Neyman-Scott rectangular pulses and second is scale invariance theory of cascade, fractal and multifractal approaches (Gyasi-agyei and Mahbub, 2007). Most of the disaggregation techniques developed mainly before 2001, are single site where as the problem of multiple site rainfall disaggregation as a means of simultaneous spatial and temporal disaggregation is of significant interest but also with increased mathematical complexity than in single site (Koutsoytannis, 2003). The development of disaggregation models in the literature are as follows: Table 2.2: Summary of disaggregation models

Methods

Developer

Markov Chain model

Schaake 1972

Based on the non dimentionalised Markov process Dynamic Disaggregation Model (DDM)

Urn Model

Description

Disaggregation of monthly rainfall to daily Woolhiser and Disaggregation of an individual storm’s Osborn, 1985 depth into fractional depths each corresponding to one tenth of the storm’s duration. Koutsoyiannis Generalized stepwise approach to and disaggregation problems allowing for a Xanthopoulos, variety of configuration 1988, 1990 Grace and Single site Disaggregation of Storm Eagleson, 1996 depth into shorter durations. Bardossy, 1997 Disaggregation of daily precipitation into a number of wet sub periods conditioned on the total daily amount. Single site

Polya distribution and a Markov-chain autocorrelation structure maintained by a Monte Carlo technique BLRP process based on Koutsoyiannis Later developed into classical probability and and Onof, 2000, programme known as Hyetos 15

computer

stochastic processes 2001 theory MuDRain (Multivariate Koutsoyiannis, disaggregation of 2003 rainfall)

Single site Multivariate Model

Details of disaggregation method applied for this study: Hyetos: Single variate disaggregation model for fine time scale – This method is based on the Bartlett-Lewis process which is now developed into computer program. Here, the higher and lower level time scale are daily and hourly respectively. The methodology uses repetition to derive a synthetic rainfall series, which resembles the given series at the daily scale. The general assumptions of the model are:     

Storm origins occur following a Poisson process (rate lambda- λ) Origins of cells of each storm arrive following a Poisson process (rate Beta - β) Arrivals of each storm terminate after a time exponentially distributed (parameter gamma - γ) Each cell has a duration exponentially distributed (parameter eta - η) Each cell has a uniform intensity with a specified (exponential or gamma) distribution

The parameter η is randomly varied from storm to storm with a gamma distribution with shape parameter alpha - α and scale parameter v. Subsequently, parameters β and γ also vary in a manner that the ratios k = β / η and phi φ = γ / η be constant. The distribution of the uniform intensity Xij is typically assumed exponential with parameter 1 / μx. Alternatively, it can be assumed two-parameter gamma with mean μx and standard deviation σx.

Figure 2.4: General assumption of Bartlett Lewis Rectangular Pulse model

The relationship for mean, variance, lag 1 autocovariance and proportion dry and Bartlett Lewis parameters are given by following equations (Koutsoyiannis, 2003): Calculation of mean for particular time duration T,

(2.22) Calculation of variance at time duration T, 16

(2.23) Where,

Calculation of proportion dry at time duration T, (2.24) Where,

Calculation of lag one auto covariance,

(2.25) Koutsoyiannis (2003), highlights the validation of the model is performed by Koutsoyiannis and Onof (2001) on case studies at Heathrow airport raingauge (UK) and Walnut Gulch Gauge 13 (USA) and depicts the good model performance of predicting dry/wet probabilities, the coefficient of variance, skewness of the hourly rainfall intensities, the autocorrelation coefficients of the hourly rainfall intensities for lags up to 10, and the hourly maxima. Hanaish et al, (2011), studied the applicability if Hyetos in Peninsular Malaysia where the rainfall is intense for short time duration. The model showed the discrepancies between observed and simulated results as simulated rainfall underestimate the extreme observed values for short duration. 2.7

Climate change and Downscaling of Climate Data from GCMs

IPCC has defined climate change as change in state of climate over time due to natural or human induced reasons which persists for extended period of time from decades to longer.

17

UNFCCC has defined climate change as change of climate that is attributed directly or indirectly to human activity which alters the composition of global atmosphere and which is in addition to natural climate variability observed over comparable time periods. IPCC (2000), in the special report on emission scenarios, SRES scenarios are summarized into four storyline A1, A2, B1 and B2. It explains that future greenhouse gas (GHG) emissions are the consequence of very complex dynamic systems, based on driving forces like demographic development, socio-economic development, and technological change. Scenarios are highly uncertain and are another image of future world and are an appropriate tool with which to analyze how driving forces may influence future emission outcomes and to assess the associated uncertainties. They assist in climate change analysis, including climate modeling and the assessment of impacts, adaptation, and mitigation.

Figure 2.5: Emission of carbon dioxide under different emission scenarios (Source: IPCC, 2000)

A1 storyline and scenario family explains future world of rapid economic and technological growth. The world population peaks around the mid 20th century and declines then after. Further with certain technological priorities, the A1 group can be sub divided into three smaller groups: the A1F1 which is fossil fuel intensive; the A1T which does not have a fossil fuel emphasis and the A1B which represents a balanced use of energy resources. A2 storyline and scenario family describes the future world is very heterogeneous with preservation of local identities. Economic development is primarily regionally oriented and per capita economic growth and technological change are more fragmented and slower than in other storylines while there is continuously increasing global population. B1 storyline and scenario family assumes global integration with an emphasis on sustainable development and equity. The demographic development is similar to A1 but the economic structure will become increasingly service and information oriented. Clean and more efficient technologies are developed and global solutions to economic, equity and environmental issues are proposed. 18

In B2 storyline and scenario family, the future world depends on local solutions for economic, social, and environmental sustainability. Global population continuously increase at a rate lower than A2, intermediate levels of economic development, and less rapid and more diverse technological change. The scenario is further oriented toward environmental protection and social equity, it focuses on local and regional levels. Global Circulation Models (GCMs) represent the fundamental background of physics in the mathematical expressions which simulates the behavior of the climate systems. GCMs are used to project the future climate with the alteration in atmospheric variables under different scenarios defined by Intergovernmental Panel of Climate Change (IPCC). GCMs outputs are defined at 150-300 km which is coarser grid however, Regional Climate Model (RCM) resolution is up-to 12-50 km (Sunyer et al, 2012). There are basically two downscaling techniques: dynamic and statistical downscaling. Wilby et al, (2004), shows that statistical downscaling approach is comparatively inexpensive for computation from GCMs and RCMs. However, drawback in statistical downscaling in the assumption of constant relationship between large and local scale in the future time. Various downscaling techniques being used can be classifies into following types: Table 2.3: Different methods of downscaling

Methods Dynamical Downscaling

Descriptions Physically based method that fits output from GCMs into regional meteorological models by incorporating regional climatic setting like topography, land covers.

Statistical Downscaling

Develops quantitative relationship between large scale predictor variables of GCM and local climatic predictand variables to generate future climate scenarios. Empirical relationships between Ease of application, coupled with local scale predictands and regional their use of observable trans-scale scale predictors. relationships and weakness involves only partial explanation of observed climate variability. It groups local, meteorological It is inadequate basis for simulating

Regression based downscaling

Weather

19

Remarks RCMs are computationally demanding placing constraints on feasible domain size, number of experiments and duration of simulations But, RCMs is able to resolve smaller-scale atmospheric features such as orographic precipitation or low-level jets better than the host GCM (Wiley et al, 2002). Cheap, computationally undemanding, readily transferable and more flexible (Wiley et al, 2002).

typing procedure Stochastic weather generator

variables in relation to different classes of atmospheric circulation (Hay et al, 1991) Modify parameters of conventional weather generators and generate future scenarios stochastically using revised parameter sets scaled in direct proportion to the corresponding variable changes in GCM

rare or extreme events (Wiley et al, 2002). It can exactly reproduce many observed climate scenarios (Wiley et al, 2002). It has wide application from environment management, hydrological application, and agricultural risk assessment (Semenov and Barrow, 1997).

2.7.1 Statistical Downscaling using SDSM Statistical downscaling using SDSM is based on regression equation which develops empirical relationships between local-scale predictands (observed station climatic data) and regional-scale predictors (GCMs). The downscaling process consists of five steps. Screening of the predictor variables is the most critical step in SDSM. There are 26 predictor variables. For calibration 2 to 3 variables should be taken carefully such that the regression equation represents good relationship between predictand and predictor variables. SDSM is a Windows-based decision support tool for the rapid development of single-site, ensemble scenarios of daily weather variables under current and future regional climate forcing. Wiley et al, (2002), presented the general methodology for SDSM downscaling which is includes procedures as Screening of predictor variables, Model calibration, Synthesis of observed data, Generation of climate change scenario and Diagnostic testing and statistical analyses. Screening variables for identifying empirical relationships between gridded predictors (such as mean sea level pressure) and single site predictands (such as station precipitation) is more challenging in the development of any statistical downscaling model since the choice of predictors largely determines the character of the downscaled climate scenario. The decision process is complicated by the fact that the explanatory power of individual predictor variables varies both spatially and temporally. 2.7.2 LARS Weather generator Long Ashton Research Station Weather Generator is the single site numerical model for simulating time series of daily weather. A Weather Generator is a model which, after calibration of site parameters with observed weather at that site, is capable of simulating synthetic time-series of daily weather that are statistically similar to observed weather (Wilks and Wilby, 1999). The procedure for the future time series weather generation includes three major steps: Site Analysis, Q test and Generator. 20

The Site Analysis is model calibration process which is done to determine statistical characteristics of the observed data. The parameter file is generated with this process which the LARS WG use for synthetic weather generation. The information of the statistical properties are stored in the two files which are generated, one of which is parameter file (*.wg) and other is statistics file (*.sta). Q test is then carried out to determine if there is any significant difference in the synthetic generated weather data. This process is the model validation step. The site parameter file produced from the observed weather data is used to generate synthetic weather data which resembles with same statistical properties like the observed weather. Climate data are generated based on site parameters and climate scenarios from GCMs of interest. The scenario file is required for this process. The scenario file can be manually edited and prepared or can be derived automatically from regional GCMs data. The latest version of stochastic weather generator is developed as LARS WG 5 which has capability to generate high resolution climate change scenarios over a region using the direct outputs from GCM. LARS WG incorporates predictions from 15 GCMs used in IPCC AR4 and climate predictions are available for the SRES emissions scenarios SRB1, SRA1B and SRA2 for most of the GCMs (Semenov and Stratonovitch, 2010). The details of the 15 GCMs are provided in the Appendix B.4. Nyugen (2005), studied downscaling methods on evaluating climate change and variability on hydrological regime at basin scale. The study observed that LARS WG results were better for precipitation while regression based SDSM results were better for temperature projection. Semenov (2008), in the study of simulating extreme weather events by stochastic weather generator in 20 climatically diverse locations showed that extreme precipitation are predicted accurately while extreme temperature were predicted less accurately. Irwin et al, (2012), concluded that the both LARS WG and SDSM could reproduce temperature accurately. Although all downscaling models can simulate precipitation amount quite correctly, the LARS WG is much superior in producing precipitation statistics such as mean and standard deviations. 2.7.3 Climate change, urban drainage and flooding The Fourth Assessment Report (AR4) of Intergovernmental Panel on Climate Change (IPCC, 2007) reports the worldwide increase in the frequency of extreme rain storms as a result of global warming for the late 20th century. While based on the climate projections and model simulations with further green house emission scenarios it is likely with 90 % likelihood that this trend will continue in 21th century. The study done by Chinvanno (2009), for projecting future climate scenario in Thailand and Southeast Asia region using dynamic downscaling of global climate change using ECHAM GCM A2 scenario have showed that average annual temperature and minimum 21

temperature will increase in the future. In Thailand at the end of century the precipitation will increase by 25% to 50%. The number of studies dealing with climate change and urban drainage is limited, partly due to the requirement of specific focus on small urban catchment (less than 500 km2) and short duration precipitation (normally less than 1 day). Despite the computational power in recent years climate models still remain relatively coarse in space and time resolution and are unable to resolve significant features at fine scales of urban drainage systems (Willems et al, 2012). Brisette et al, (2006), highlighted the challenges involved in quantifying the flooding due to climate change mainly in the large watershed. The coarser spatial and temporal resolution of the climate data is the major setback. The model that engineer require depends on time step of the basin size for example daily time step is adequate for larger basin while sub daily or even sub hourly time step are required for urban basin depending upon it size. The possible flood hazard categories in relation to the depth and velocities can be defined based on low, medium, high, very high and extreme hazard. While the categories could be used as general for all flood types.

Figure 2.6: General flood hazard categories based on velocities and depth (Adopted from Vojinovic and Abbott, 2012)

22

CHAPTER 3 METHODOLOGY 3.1

Study Area

The study area is the Sukumvit Area which is located at Eastern sub urban part of Bangkok which is one of the parts of Central Business District. The geographical location of the Sukumvit, Bangkok is 13°44'18.01"N lalitude and 100°33'41.31"E longitude. For modelling purpose geographical coordinate system of 47 UTM is used. The catchment has an area of 24 km2 and located at right side of Chao Phra Ya River. The elevation of the study area is around 0.4 to 1 MASL. The primary drainage of Bangkok consists of 10 polders as the secondary drainage system. Its characteristics are to prevent flood water from outside by pumping storm water out of the area to main drainage. The area was able to prevent fluvial floods caused by Chao Phraya River overflow in 1995 and 1996 (Boonya-aroonnet et al, 2001). The rainfall station data used is from Bangkok Metropolis rainfall station of Thai meteorological station. Bangkok Metropolis Station

Figure 3.1: Areal map showing Sukhumvit, Bangkok area over Thailand region (Source: Bing map)

Figure 3.2: Districts Wattana (Area 39) and Khlongtoei (Area 33) within the study catchment

Figure 3.3: Map of Sukhumvit with urban drainage network (Map Source: Bing Map)

23

The study catchment lies within two district of Bangkok namely Wattana and Khlongtoei. The total population in the two districts is 191,328. The Eastern side of the Sukhumvit area is along canal Khlong Tan and Northern side is along canal Khlong San Sabe. The outlets from the Sukhumvit drainage systems are connected to these canals which are further linked as tributaries of Chao Phra Ya river that is located along Western and Southern region.

Figure 3.3: Canal (Khlong San Sabe) also used in navigation

As the Sukumvit area is flat, pump are used to drain water to main drainage. The existing network is the combined sewer system.

Figure 3.4: Outflow to the canal from the drainage system

3.2

Data Collection

The data collected and application has been shown in table 3.1.a. Table 3.1: Source of data required Data type Source 3 hourly data for Thai Meteorological Bangkok metropolis Department (TMD) Station http://www.tmd.go.th/ en/ (24 hourly) Daily Thai Meteorological Rainfall Data for Department (TMD) Bangkok metropolis http://www.tmd.go.th/ Station en/

Application Generation of present IDF curve. Estimation of BLRP parameters Generation of present IDF curve. Data input source for SDSM, LARS WG and HYETOS Digital Elevation Previous study in AIT 5 m resolution 2D overland flow in Model (DEM) and UNESCO-IHE DEM with streets MIKE Urban and MIKE and buildings Flood.

24

Details Range of gauged data from 19812010 for two stations. Daily gauged rainfall data from 1961-2010

Flowchart for methodological overview Data Sources

Bangkok Metropolitan Station, TMD 3 Hourly Data 1981-2010

24 Hourly Data 1961-2010

Downscaling

GCMs

NCEP & HADCM3

IDF Curve Generation

Disaggregation to hourly data

Annual Maximum Precipitation & Gumbel Distribution

Present IDF based on 3hr rainfall data

Future IDF based on 1hr rainfall data

BL Parameters λ, k, φ, α, v

15 GCMs

SDSM Lars WG

Daily Data 1961- 2099 Scenario A2,B2

Future Scenarios

3.3

Daily Data 2046-2065, Scenario SRA1B,A2, B2

HYETOS Daily Data 2011-2030, Scenario SRA1B, A2, B2

1 Hourly Rainfall

Design Storms

Daily Data 2080- 2099 Scenario SRA1B,A2, B2

Urban Drainage Modelling 1D Pipe Flow Model

Digital Elevation Model (DEM)

Mike Urban (MOUSE)

ArcGIS

Mike Urban (2D Overland) Water Depth

Process/Tools

Data/Inputs

25

Water Velocity

Flood Maps

Outputs/Results from each process

The methodology for the study is divided into four major processes:     3.4

Downscaling of the climate data Temporal disaggregation to hourly resolution Generation of IDF curves for present and future time periods Modelling of Urban Drainage Downscaling of Climate Data from GCMs

There are many available downscaling techniques practiced in research and applications. The statistical downscaling technique is the most common and is used herein for the study. Two downscaling methods using SDSM and LARS WG for the future climate data have been accessed for application in this study. SDSM methods include application of HADCM3 and NCEP GCM for IPCC AR4 emission scenario of A2 and B2. LARS WG can be used with application of 15 GCMs namely BCM2, CGMR, CNCM3, CSMK3, FGOALS, GFCM21, GIAOM, HADCM3, HADGEM, INCM3, IPCM4, MIHR, MPEH5, NCCCSM and NCPCM. Each of these GCMs can be used for downscaling under IPCC scenarios of SRA1B, SRA2 and SRB1. The selection of more than one GCM is essential as there are many uncertainties between applications of GCMs and results vary from each GCMs. The features of these GCMs are summarized in the Appendix B.4. 3.4.1 Statistical Downscaling using SDSM The application of statistical downscaling using SDSM 4.2 is assessed. This method is based on the regression method. The procedure for downscaling using SDSM is followed in same steps as described in above figure. In general the steps include handling the missing data or outliers, screening the appropriate predictor variables from NCEP data, establishing regression based relationship between predictand (historical station data) with predictor variables (GCM), calibration and validation of the relationship and finally generation of future data based on different emission scenarios (A2 and B2). 3.4.2 LARS Weather generator LARS WG, the stochastic weather generator is used for the simulation of the weather data at single site using daily rainfall data from Bangkok Metropolis station from 1981-2010. The weather generation process follows two steps: first analysis of the observed weather parameters such as temperature, precipitation and solar radiation, then second step is generation of synthetic daily weather data from the site specific parameter from observed data. The generated weather data resemble statistically with the observed weather. The downscaling is done using different GCMs under IPCC emission scenarios SRA1B and SRA2. The details of maximum precipitation outputs are compared for different time scales 2011-2030, 2046-2065 and 2080-2099. The details of the comparison are given in Appendix B. 26

3.5

Temporal Rainfall Disaggregation

Estimation of the BLRP parameters are done by calculating mean, variance, autocovariance and proportion dry from the observed 3 hourly, 24 hourly and 48 hourly data from 1986 to 2000 for each month separately. The duration 1986 to 2000 represent the period where maximum peak daily rainfall has occurred in past. The parameters λ, k, φ, α, v, μX and σX are solved in excel using Excel solver to obtain the values for of synthetic rainfall with mean, variance, lag 1 autocovariance and proportion dry as similar to historical data. 3.6

Intensity Duration Frequency Curve

The IDF curves are developed by using annual maximum series and gumbel distribution from 3 hourly and 1 hourly rainfalls for present IDF curves and 1 hourly rainfall for future IDF curves. Gumbel probability distribution can be expressed as: (3.1) Where,

The frequency factor for Extreme value type I distribution can be expressed as:

(3.2) The magnitude of the rainfall intensity is derived from: 3.7

(3.3)

Urban Drainage Model Setup

The Mike Urban uses the MOUSE engine for pipe flow computation. The pipe flow model developed earlier in MOUSE is imported to the Mike Urban and which is then setup as Mike Urban Project file. The boundary conditions for urban drainage modelling include catchment boundary loads and network boundary loads. The dry weather flow and rainfall are entered as the boundary condition in the form of time series, pump operation as controlled operation during the model simulations. The model developed in Mike MOUSE was previously done by 1D-1D approach with street and sewer links connected to each other. In this study, the street links and components are removed for coupling the network nodes to DEM which would represent the overland flow more accurately. Mike Urban is integrated with ArcGIS and which can use MOUSE engine or Mike 1D engine. For this study MOUSE engine is used for the simulation. Mike Flood engine which is combined with Mike 21 and MOUSE is integrated into Mike Urban and 2D overland flow modelling is made in Mike Urban. 27

3.7.1 1D Urban Drainage Model The urban drainage network in the study catchment extends up-to 2,048 hectares. The summary of the drainage networks are as follows: Table 3.2: Summary of components in pipe flow model Items Total No. of Nodes: Manholes & basins 3,487 No. of Links 3,858 No. of Outlets 22 No. of Pumps 26 No. of Catchments 2,019 Catchment Area (Ha) 2,049

Figure 3.5: Pipe flow model over the overland land flow model

The rainfall time series are entered as the boundary item which can be distributed to some nodes or all the nodes in the list. The same rainfall is used to be distributed to all the nodes. The pumps are used for discharge of water to the primary canals. The details of the pumps are given in Appendix A. Simulations of the pipe flow are done in two steps: First the simulation of the runoff is carried for the duration defined by boundary conditions. The runoff simulation generates Catchment Runoff File (*.CRF). Second step is network simulation for which inputs from runoff file is taken and computation is carried out for same simulation periods generating Precipitation Runoff File (*.PRF). The result of the MOUSE simulation can be viewed in Mike Urban itself in the form of different layers or in Mike View in time series files or longitudinal and horizontal results can be viewed. 3.7.2 Calibration and Verification The calibration of the model was carried out by Chingnawan, (2003) with observed data for three stations on 5th October 2002 and verification of the model was done on 7th October 2002. Nguyen (2010), have conducted the calibration of the model using rainfall and three stations’ water level data from 16th November 2004 and 20th November 2004. The three stations were used for the calibration of the models are namely Aree Station, Thonglo Station and Ekamai Station. The calibration done on 16th November 2004 showed the 28

efficiency index of 78, 82, 86 and root mean square error of 0.06, 0.04 and 0.03 for three stations respectively. For 24th November 2004 efficiency index of 78, 82, 86 and root mean square error of 0.06, 0.04 and 0.03 were calculated for three stations respectively. The analysis of the coupled 1D/2D model is carried under the boundary condition of 45% imperviousness with measured station water level and rainfall data of 15th October 2003. The results are discussed in section 4.6.2. 3.7.3 Coupled 2D overland flow model The combination of 1D pipe flow model and 2D overland model gives the better accuracy and dynamic result for the flood calculation. The data needed for the 2D model is raster data set of Digital Elevation Model (DEM). The DEM of 5 m resolution was used and following figure shows the extent of DEM used for the study. Buildings are shown by grey color and green are the streets and ground level. The selection of 2D model is done using Mike 21 single grid using rectangular cell solver. The model area for 2D modelling is whole extent of data set of 1D model. The flooding depth parameter is selected as 0.003 m and drying depth is 0.002 m. Bed resistance for the DEM layer is selected same for the whole area as 32. Finally the simulation is done using time step of 2 sec for all scenarios for present and future rainfall events.

Figure 3.6: Digital Elevation Model of the study area with the designated boundary based on the extent of the sewer network

29

CHAPTER 4 RESULTS AND DISCUSSION This section consists of descriptive information of the results obtained and analysis of downscaling and disaggregation approach, present and future IDF curves, model simulations. These are presented in the following sub-sections. 4.1

Analysis of present rainfall scenario

90

450

80

400

70

350

Precipitation (mm)

Precipitation (mm)

The observed daily maximum rainfall and average of maximums in the Bangkok metropolis station during 1981-2010 is shown in figure.

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300 250 200 150 100 50

0

0 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

Observed daily maximum rainfall in Bangkok Metropolis Station during 1981-2010

Average maximum daily precipitation observed during 1981-2010

(a)

(b)

Figure 4.1: Average maximum daily precipitation (a) and maximum daily observed precipitation (b) in each month during 1981-2010 in Bangkok Metropolis Station

The collected, 3 hourly data from 1981 to 2010 in Bangkok Metropolis, Thai Meteorological station was used to generate present IDF curve using annual maximum series with Gumbel distribution. 60

Intensity (mm/hr)

50 2 YEAR

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5 YEAR 30

10 YEAR 20 YEAR

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100 YEAR

0 0

3

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9

12 15 Duration (Hours)

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24

27

Figure 4.2: Present IDF curve for Bangkok metropolis station using annual maximum series from 3 hour rainfall depth from 1981 – 2010 30

The reason for using Bangkok Metropolis station for the study is due to close proximity to study area among the stations of TMD, Thailand. In addition long range of daily and 3 hourly data is available which is fundamental for the hydrological analysis. 4.2

Downscaling of climate data

The downscaling of climate data from Global Circulation Models (GCMs) are explored using SDSM and LARS WG. The results from SDSM showed higher bias towards the extreme rainfall while LARS WG showed the good projection of extreme rainfalls. 4.2.1 Application of SDSM Screening of variables was carried out using annual correlation, monthly correlation, partial correlation and P value. Usually for precipitation the correlation of predictand and predictor variables are not good and hence optimum best variables shall be carefully selected. Table 4.1: Selection of predictor variables Predictor Variables 500 hpa vorticity (ncepp5_zas) Surface divergence (ncepp_zas) Surface relative humidity (nceprhumas)

Correlation 0.144 0.127 0.038

Partial r 0.083 0.066 0.035

P value 0.0000 0.0003 0.0699

Predictor variable ncepp5_zas have the highest correlation of 0.144 among all the variables with predictand variable and ncepp_zas has correlation of 0.127 with predictant variable. ncepp5_zas and ncepp_zas further have correlation of 0.428. Hence, these two predictors shall be selected for calibration. Again, near surface relative humidity (nceprhumas) seems to have good monthly correlation in all the months among other variables. Therefore, ncepp_zas, ncepp5_zas and nceprhumas are selected for the calibration using NCEP data. The results from the SDSM are shown in the figures below:

Precipitation (mm)

Monthly Mean

Monthly Variance

18 16 14 12 10 8 6 4 2 0

600 500 400 300 200 100 0 1

2

3

4

5

6 7 8 Months

1

9 10 11 12

(a)

2

3

4

5

6 7 8 Months (b)

31

9 10 11 12

Maximum Daily in each month

Precipitation (mm/month)

Precipitation (mm)

Average Monthly Sum 400 350 300 250 200 150 100 50 0 1

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3

4

5

6 7 8 Months

9 10 11 12

400 350 300 250 200 150 100 50 0 1

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5

6

7

8

9 10 11 12

Months

(c)

(d) Legends

Figure 4.3: Calibration results for the NCEP, HADCM3 GCMs with observed data from 1961-1990

From the observation it can be seen that the calibration is good in term of mean and monthly sum but in term of maximum it is not acceptable. Further, the verification for the time interval 1991-2001 is shown in the figure below. Monthly Variance 1200

20

1000

Precipitation (mm)

Precipitation (mm)

Monthly Mean 25

15 10 5 0

800 600 400 200 0

1

2

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4

5

6 7 8 Months (a)

9 10 11 12

1

32

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9 10 11 12

Average Monthly Sum

400

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Precipitation (mm)

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Maximum Daily at each month

180 160 140 120 100 80 60 40 20 0 1

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9 10 11 12

Legends

Figure 4.4: Verification results for the NCEP, HADCM3 GCMs with observed data from 19912001

From the results observed in calibration and verification process, the regression model was not able to capture the extreme events. But for generation of IDF curves only the extreme values are important. Thus, the bias correction shall be done in order to project extreme events more precisely. Table 4.2: Root mean square error and efficiency index summary for calibration and verification process RMSE EI RMSE EI RMSE EI NCEP HADCM3 A2 HADCM3 B2 Calibration Mean 0.740 0.938 1.132 0.832 1.131 0.832 Maximum 110.633 0.398 109.912 0.406 110.024 0.405 Variance 180.614 -0.514 177.757 -0.466 177.786 -0.467 Sum 0.023 0.991 0.109 0.795 0.110 0.792 Verification Mean 2.412 0.600 3.705 0.131 3.341 0.293 Maximum 48.986 -0.938 53.844 -1.341 54.135 -1.366 Variance 154.976 -0.419 161.723 -0.545 169.238 -0.692 Sum 31.056 0.938 73.171 0.656 69.358 0.691

4.2.2 Application of LARS WG The site analysis from the observed daily rainfall data from 1981-2000 gave the site parameters which is then used as calibration. The information of site parameters and statistics are produced in site analysis process. The synthetic weather data based on site parameters are then generated. Climate scenarios derived from the GCMs are used directly 33

which are then developed in to the relationship with observed data to generate future time series. The generation of baseline time series for observed climate can also be carried out without changing the climate scenario. The statistical characteristics established in these processes shall be preserved in order to accurately project the future weather. The statistics of concern chosen here are total monthly mean precipitation, standard deviation of total monthly precipitation and maximum daily precipitation in each month. The results from stochastic weather generator will be used for generating future IDF curves based on annual maximum series and hence maximum values are more significant than other statistics.

400

160

350

140

300

120

Standard Deviation

Monthly Mean Precipitation Totals (mm/month)

The comparison of the mean of monthly sum, standard deviation and maximum during calibration in site analysis process are presented in the following figures below:

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9 10 11 12

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9 10 11 12

Maximum Daily Precipitation (mm)

400 350

Legends

300 250 200 150 100 50 0 1

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3

4

5 6 7 Months (c)

8

9 10 11 12

Figure 4.5: Comparison of monthly total mean precipitation, standard deviation and maximum between observed and simulated synthetic time series data between 1981-2000

After the model calibration where all the necessary site parameters are established the weather generator is applied for 10 years to generate daily precipitation time series which is compared with observed rainfall series from 2001-2010. This validation process proves strong ability to predict the extreme range. The following figures presents comparison of the mean, standard deviation and maximum during validation process:

34

140

100

120

Standard Deviation

Monthly Mean Precipitation Totals (mm/month)

120

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100 80 60 40 20 0

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Maximum Daily Precipitation (mm)

160 140

Legends

120 100 80 60 40 20 0 1

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5 6 7 Months (c)

8

9 10 11 12

Figure 4.6: Comparison of monthly total mean precipitation, standard deviation and maximum between observed and Generated time series data between 2001-2010

The RMSE and EI during the analysis process are highlighted in the table below: Table 4.3: RMSE and EI for site analysis or calibration and validation process in LARS WG RMSE EI Model Calibration Monthly mean precipitation totals (mm/month) 16.45 0.97 Standard Deviation 18.45 0.83 Maximum daily precipitation (mm) 35.34 0.84 Model Validation Monthly mean precipitation totals (mm/month) 10.76 0.9 Standard Deviation 25.42 0.45 Maximum daily precipitation (mm) 29.05 0.62

With comparison of LARS WG performance with SDSM it is evident that LARS WG has better capability to project extreme range as well as monthly average more accurately while SDSM results showed under estimation for extreme range. While calibration results from SDSM showed better results in term of monthly mean precipitation on the basis of RMSE and EI, it also showed inaccurate results for maximum and variance. Hence, the further analysis including temporal disaggregation and IDF generation are done using the results from LARS WG. Furthermore, LARS WG uses 15 GCMs which could 35

cover the range of uncertain results that are commonly observed when only one GCM is used. The site analysis is then carried out again using data from 1981-2010 to generate future weather. The more the observed length of climate variable data is used the more accurate the result could be obtained. The 15 GCMs which are BCM2, CGMR, CNCM3, CSMK3, FGOALS, GFCM21, GIAOM, HADCM3, HADGEM, INCM3, IPCM4, MIHR, MPEH5, NCCCSM and NCPCM was used for climate projections for 2011-2030 and 2046-2065. The result from different GCMs varies from each other and within different emission scenarios. The following figures present the histogram for average maximum values for each month with error bars showing range of maximum values from the GCMs outputs. 450 400

Precipitation (mm)

350 300 250 200 150 100 50 0 JAN

FEB MAR APR MAY JUN JUL AUG SEP OCT NOV Average maximum daily data from 15 GCMs in each month, 2011 - 2030

DEC

Figure 4.7: Histogram showing average values of maximum daily data in each months during 20112030 from 15 GCMs with scenarios SRA1B, SRA2 and SRB1 and error bars showing range of maximum values

During 2011- 2030 the maximum daily value is projected as 420.3 mm from simulation using INCM3 GCM under SRB1 scenario and lowest range of maximum value is projected as 309.3 mm from simulation using IPCM4 GCM under SRA1B scenario. The mean during each month is shown by the histogram in above figure and mean during maximum month is 362.8 mm.

36

450 400

Precipitation (mm)

350 300 250 200 150 100 50 0 JAN

FEB

MAR

APR

MAY

JUN

JUL

AUG

SEP

OCT

NOV

DEC

Average maximum daily data from 15 GCMs in each month, 2046 - 2065

Figure 4.8: Histogram showing average values of maximum daily data in each months during 20462065 from 15 GCMs with scenarios SRA1B, SRA2 and SRB1 and error bars showing range of maximum values

During 2046- 2065 the maximum daily value is projected as 438 mm from simulation using MPEH5 GCM under SRA1B scenario and lowest range of maximum value is projected as 301.4 mm from simulation using NCPCM GCM under SRA2 scenario. The mean during each month is shown by the histogram in above figure and mean during maximum month is 368.3 mm. 4.3

Temporal disaggregation of rainfall

Disaggregation of rainfall is done for each month separately by using respective estimated BL parameters. The hourly rainfall data generated by the SDSM and LARS WG are disaggregated into hourly rainfall based on Bartlett Lewis point process rainfall model (BLRP). The estimated BL parameters are compared with the outputs from simulated rainfall statistics to the historical observed statistics. The synthetic rainfall shall simulate the rainfall with identical values that from observed data. Then, error is calculated giving weighted value of 10 for mean and 1 for variance, Lag 1 auto covariance, Proportion dry each for 0.125 days, 1 day and 2 day. The following figures provided the result of comparison for optimization of the parameters.

37

Mean

25

2 Historical 3 Hr

20 1.5

Simulated 3 Hr Historical 12 Hr

mm

15 1

Simulated 12 Hr

10 Historical 24 Hr 0.5 5

Simulated 24 Hr

0

0

Weighted Square Error

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

Figure 4.9: Comparison of mean for synthetic rainfall simulated by BL parameters and historical observed data (1986-2000)

1200

Variance

2

1000 1.5

Historical 3 Hr

800

mm 2

Simulated 3 Hr 600

1

Historical 12 Hr Simulated 12 Hr

400 0.5 200

Historical 24 Hr Simulated 24 Hr

0

0 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

Weighted Square Error

Figure 4.10: Comparison of variance for synthetic rainfall simulated by BL parameters and historical observed data (1986-2000)

38

250

Lag 1 autocovariance

2

200

Historical 3 Hr 1.5 Simulated 3 Hr

150

mm 2

Historical 12 Hr 1

100

Simulated 12 Hr Historical 24 Hr

0.5

Simulated 24 Hr

50 Weighted Square Error 0

0 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

Figure 4.11: Comparison of lag 1 autocovariance for synthetic rainfall simulated by BL parameters and historical observed data (1986-2000) 1.2

Proportion Dry

1

2.5

2

Historical 3 Hr Simulated 3 Hr

0.8 1.5 0.6

Historical 12 Hr Simulated 12 Hr

1 0.4

Historical 24 Hr Simulated 24 Hr

0.5

0.2 0

Weighted Square Error

0 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

Figure 4.12: Comparison of proportion dry for synthetic rainfall simulated by BL parameters and historical observed data (1986-2000)

The statistical properties of the rainfall series is preserved with estimating BLRP parameters with the already established formulas for mean, variance, proportion dry and lag 1 auto covariance. The process is similar to calibration and thus the disaggregated rainfall series generated have the same statistical properties. The estimation of the parameters can be considered same for the every month or could be done discretely for each month like in this study. The same parameters have been used for all the disaggregation of daily data from GCMs outputs.

39

The parameters as estimated in table below is used to disaggregate the present rainfall and future rainfall. Table 4.4: BLRP parameter estimation Month Lamda, λ Kappa, κ= β/η Phi, φ= γ/η d-1 (-) (-) JAN 0.1190 0.0260 0.0227 FEB 0.1200 0.2400 0.1500 MAR 0.1150 0.1700 0.0900 APR 0.1900 0.1750 0.0910 MAY 0.3000 0.2500 0.0910 JUN 0.4100 0.4500 0.1250 JUL 0.2900 0.1430 0.0330 AUG 0.2350 0.1360 0.0150 SEP 0.9000 0.6640 0.1150 OCT 0.1620 0.3150 0.0150 NOV 0.1150 0.1900 0.0900 DEC 0.0240 0.1826 0.1590

4.4

Alpha,α (-) 69 69 86 65 40 69 69 69 90 69 95 100

ni, ν d 1.500 2.300 3.200 3.690 3.770 2.600 2.450 2.300 2.500 2.560 4.387 1.960

μX mm d-1 70 70 85 90 90 70 90 90 70 70 95 69

σX mm d-1 70 70 85 90 90 70 90 90 70 70 95 69

Generation of IDF Curves

The IDF curve used for urban drainage design should be of short duration rainfall and 3 hourly duration is also not adequate for small catchments as time of concentration is less than 3 hour. Therefore, the temporal disaggregation of daily data using Hyetos model was used in the study. The model generates synthetic rainfall which has statistical properties similarity with the historical observed data which is define by Bartlett Lewis parameters. The disaggregated data is made consistent with the daily data in the methodology of the model. The present IDF curve from gauged 3 hourly historical data are compared with IDF curve generated from disaggregated 3 hourly data from gauged 24 hour data. It was observed from the study that disaggregated data showed significant underestimation in rainfall intensity for shorter duration of 1 hour and lesser towards higher durations. The differences as well as ratios between observed and simulated is higher for shorter duration and less for higher durations. Within all duration the difference and ratio increase with the return period. However, the ratio remains almost near to same value with only fractional difference within return periods.

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Figure 4.13: Comparison of IDF curve from gauged 3 hourly historical data (solid lines) are compared with IDF curve generated from disaggregated 3 hourly data from gauged 24 hour data (dash lines)

The difference and ratio between historical and simulated intensity are summarized in the table below: Table: 4.5: Ratio and difference between observed and simulated rainfall intensities Duration Return Period 2 Yr 5 Yr 10 Yr 20 Yr 50 Yr 3 Hours Ratio= O/M 1.39 1.39 1.39 1.389 1.388 Difference (mm/hr)=O-M 7.79 9.68 10.93 12.13 13.69 6 hours Ratio= O/M 1.111 1.156 1.177 1.192 1.208 Difference (mm/hr)=O-M 1.56 2.77 3.58 4.35 5.34 12 hours Ratio= O/M 1.025 1.033 1.036 1.039 1.041 Difference (mm/hr)=O-M 0.20 0.37 0.49 0.60 0.74 24 hours Ratio= O/M 1.022 1.027 1.029 1.030 1.031 Difference (mm/hr)=O-M 0.10 0.18 0.23 0.28 0.34 *O = Observed and M = Modelled or from disaggregation

Further comparison at separate return periods are shown in figure:

41

100 Yr 1.387 14.85 1.218 6.09 1.042 0.85 1.032 0.39

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X-Coordinate: Duration (Hours), Y-Coordinate: Rainfall Intensity (mm/hr)

Figure 4.14: Comparison of IDF curve from gauged 3 hourly historical data with IDF curve generated from disaggregated 3 hourly data from gauged 24 hour data

The generated IDF curve from observed 3 hourly station data and disaggregated hourly data from 24 hour observed data shows significant difference in lesser time duration than 42

higher time duration. The difference is always positive between observed and modeled rainfall intensities. The correction by finding suitable factor is necessary for under estimated modeled data. This correction shall be used for future modeled IDF curves with the assumption that similar under estimation prevails in all disaggregation. 1.5 1.4

Ratio

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Figure 4.15: Ratios of intensities between observed and modeled IDF curves for each return period and average values

Fitting the average values into regression equation is obtained: Table 4.6: Correction Factors for IDF curve Duration (hours) Correction Factor 1 1.580 3 1.355 6 1.229 12 1.116 24 1.013

1.5

Correction Factors

1.4 1.3 y = 1.5802x-0.149 R² = 0.889

1.2 1.1 1 0.9 0

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6 Average

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Hours Power (Average)

Figure 4.16: Fitting the average ratios for all return period into the power equation

Using the equation, y = 1.58 x −0.14

(4.1)

the correction factor for each durations could be determined while these correction factors are selected for the correction of future modeled IDF curves. The IDF curve for shorter duration cannot be corrected using linear equation and higher order power equation is used. Using the correction factor for each durations 1, 3, 6, 12 and 24 hours, the following IDF curves are obtained. The application of this correction for all IDF curves derived in present or future is the assumption that when disaggregation of daily to sub daily scale is done, the extreme values are underestimated in the results. Therefore, this correction factor is necessary for obtaining the more accurate result. 43

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Duration (Hours) 2 YEAR 10 YEAR 50 YEAR 2 YEAR CORRECTED 10 year corrected 50 Year Corrected

5 YEAR 20 YEAR 100 YEAR 5 YEAR CORRECTED 20 YEAR CORRECTED 100 Year Corrected

Figure 4.17: Corrected IDF curve for 1981-2010

4.5

IDF curves for future

The IDF curves for future are developed using annual maximum series and gumbel distribution for disaggregated hourly rainfall from outputs of different Global Circulation Models (GCMs) using LARS WG. Altogether 15 GCMs were used under IPCC emission scenarios of SRA1B, SRA2 and SRB1. The GCMs used are namely BCM2, CGMR, CNCM3, CSMK3, FGOALS, GFCM21, GIAOM, HADCM3, HADGEM, INCM3, IPCM4, MIHR, MPEH5, NCCCSM and NCPCM. The same correction factor derived in above section was used to obtain future IDF curves for 2011-2030 and 2046-2065. The IDF obtained from all 15 GCMs under SRA1B and SRA2 scenarios are summarized in the figures below. The IDF curves are separately shown for return periods of 2 year, 5 year, and 20 year for time extent of 2011-2030 and 2046-2065. The return period of 2, 5 and 20 year are selected for urban drainage simulation as it characterizes the general engineering practice as design criteria. This will shows the level of hazard associated with such events which is discussed in the later section. The bands for uncertainty in the IDF are shown for maximum and minimum obtained values. It could be manifested from following figures, that the uncertainty is higher for higher return periods in all time intervals. The IDF curves are categorized with SRA1B and SRA2 emissions scenarios which are presented in following figures.

44

The IDF according to emission scenarios SRA1B are as follows: 100

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Figure 4.18: Intensity Duration Frequency (IDF) in SRA1B scenario for durations from 1 hour to 24 hours with range of maximum, minimum and mean values obtained from BCM2, CGMR, CNCM3, CSMK3, FGOALS, GFCM21, GIAOM, HADCM3, HADGEM, IMCM3, IPCM4, MIHR, MPEH5, NCCCSM and NCPCM results of for future time sections 2011-2030 and 20462065 with return periods 2, 5 and 20 years

Similarly, IDF according to emission scenario SRA2 are as follows:

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Figure 4.19: Intensity Duration Frequency (IDF) in SRA2 scenario for durations from 1 hour to 24 hours with range of maximum, minimum and mean values obtained from CNCM3, GFCM21, HADCM3, HADGEM, IMCM3, IPCM4, MPEH5, NCCCSM and NCPCM results of for future time sections 2011-2030 and 2046-2065 with return periods 2, 5 and 20 years

The bandwidth of extreme values is pertinent for determining extreme rainfall scenarios. The bandwidth provided by the error bars gives the general portrait of how the intensities in future might unveil. The details of the IDFs in each scenario are provided in the Appendixes C. The future extents of 2011-2030 and 2046-2065 are considered for IDF generation and urban drainage modeling. The emission scenarios of SRA1B and SRA2 are considered as these scenarios signify maximum carbon dioxide emission during future time extents. The selection of GCMs is based on the maximum 3 hour depth rainfall among all the GCMs results. The maximum depth obtained from one GCM during 2011-2030 might not be the same during 2046-2065. Hence two GCMs are selected for two future extents 46

which have maximum at each particular future extent. The details of the GCMs and IDFs are presented in Appendix C. It was observed that the same GCMs provided the maximum at each return period within same future extent. For SRA1B scenario CGMR and IPCM4 GCMs were used and for SRA2 scenario HADCM3 and GFCM21 GCMs were used as these projects the maximum 3 h rainfall depth. The comparisons of present IDF curve and future IDF curves with consideration of emission scenario SRA1B are presented below. 100

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1981-2010 [2 Year Return Period] 2011-2030 (CGMR-SRA1B)

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h

Figure 4.20: Comparison of IDFs curves generated under SRA1B emission scenario for 2 year return period at different present and future time extents

In the event of 2 year return period, the intensity of 1 hour duration rainfall at present is 39.9 mm/hr and 3 hour rainfall depth is 80 mm. From the results of different GCMs run, ranges of extremes are obtained for future time extent. During 2011-2030, the CGMR GCM showed the maximum 3 hour rainfall depth of 96.7 mm which is +20.9 percent increase from baseline condition and IPCM4 showed 80.9 mm which is only +1.13 percent increments from baseline. During 2046-2065, CGMR GCM showed the maximum 3 hour rainfall depth of 87 mm which is +8.75 percent increment from baseline and IPCM4 showed maximum 3 hour rainfall depth among other as 95 mm which is +18.75 percent increment from baseline.

47

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Figure 4.21: Comparison of IDF curves generated under SRA1B emission scenario for 5 year return period at different present and future time extents

In the events of 5 year return period during the period of 1981-2010, the 3 hour rainfall depth as seen from above figure with dark line is 100 mm. During 2011-2030 and 20462065, two GCMs give the maximum 3 hour rainfall depth. During 2011-2030, CGMR projected the maximum 3 hour rainfall depth of 118.1 mm which is +18.1 percent increments from baseline condition. While, IPCM4 projected maximum 3 hour depth of 103.4 mm which is +3.4 percent increase from the baseline. During 2046 -2065, CGMR projected 3 hour rainfall depth of 112 mm which is +12 percent higher than baseline and IPCM4 projected rainfall depth of 128 mm which is +28 percent higher than the baseline condition of 1981-2010. In the events of 20 years return period as presented in the figure below, during baseline period of 1981-2010 the 3 hour rainfall depth is 126.67 mm. During 2011-2030, the CGMR projected the 3 hour rainfall depth of 145.81 mm which is +15.11 percent increase from baseline while IPCM4 projected 3 hour rainfall depth of 130.66 mm which is +3.15 percent increment from baseline. Similarly, during 2046-2065 the CGMR projected 3 hour rainfall depth of 143.65 mm which is +13.4 percent increase from baseline while IPCM4 projected 3 hour rainfall depth of 174.7 mm which is +37.92 increase from baseline.

48

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Figure 4.22: Comparison of IDF curves generated under SRA1B emission scenario for 20 year return period at different present and future time extents

Similarly, the comparisons of present IDF curve and future IDF curves with consideration of emission scenario SRA2 are presented below. 100 90 80

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Figure 4.23: Comparison of IDF curves generated under SRA2 emission scenario for 2 year return period at different present and future time extents

49

When emission scenario SRA2 is considered similar to the SRA1B scenario described above the two GFMs that project the maximum 3 hour rainfall depths are GFCM21 and HADCM3. At 2 year return period in during 2011-2030, GFCM21 projects 80.9 mm of 3 hour rainfall with only +0.9 percent increment than baseline and HADCM3 projects 92.3 mm 3 hour rainfall depth with +12.3 percent increment than baseline. In the future time extent of 2046-2065, GFCM21 projects 88.2 mm of 3 hour rainfall which is +8.2 percent more than baseline case. HADCM3 projects 86.14 mm which is +6.14 percent more than baseline. 100 90 80

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Figure 4.24: Comparison of IDF curves generated under SRA2 emission scenarios for 5 year return period at different present and future time extents

At 5 year return period in during 2011-2030, GFCM21 projects 104.47 mm of 3 hour rainfall which is +4.5 percent increment than baseline (which is 100 mm for 3 hour rainfall depth) and HADCM3 projects 119.5 mm 3 hour rainfall depth with +19.5 percent increment than baseline. In the future time extent of 2046-2065, GFCM21 projects 118.3 mm of 3 hour rainfall which is +18.3 percent more than baseline case. HADCM3 projects 108.45 mm which is +8.45 percent more than baseline. In the event of 20 year return period in during 2011-2030, GFCM21 projects 131.09 mm of 3 hour rainfall which is +3.5 percent increment than baseline (which is 126.7 mm for 3 hour rainfall depth) and HADCM3 projects 154.76 mm 3 hour rainfall depth with +22.15 percent increment than baseline. In the future time extent of 2046-2065, GFCM21 projects 157.23 mm of 3 hour rainfall which is +24.09 percent more than baseline case. HADCM3 projects 140.34 mm which is +10.76 percent more than baseline. 50

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Figure 4.25: Comparison of IDF curves generated under SRA2 emission scenarios for 20 year at different present and future time extents

The drainage system in practice considers usually 2 and 5 years of return period. 20 year of return period is also considered here for the study purpose. In all the cases it is observed that the rainfall intensities for the future increases than in the present baseline condition. The table below presents the increments in rainfall intensities at emission scenarios of SRA1B and SRA2. Table 4.7: Change in percentage of 3 hour rainfall depths from present baseline condition

Retur n Period s

Retur n Period s

GCMs under SRA1B 2 Year 5 Year 20 Year GCMs under SRA2 2 Year 5 Year 20 Year

2011-2030 IPCM4 +22.25 +2.28 +17.75 +3.09 +15.07 +3.16 GFCM21 HADCM3 +2.28 +16.69 +4.19 +19.14 +3.47 +22.10 CGMR

51

2046-2065 IPCM4 +9.99 +19.85 +11.37 +27.52 +13.65 +37.88 GFCM21 HADCM3 +11.50 +8.85 +17.95 +8.08 +24.07 +10.81 CGMR

4.6

Urban Drainage Model Structure

The previous studies done in the Sukhumvit, Nyugen, (2009), Chingnawan, (2003) have shown that dry weather flow varies from 0.3 to 0.6 that were measured in two stations on 20-22 October, 2003. The drainage system in Sukhumvit area is composed of five major sub-catchments but for modelling purpose each nodes are assigned a small different catchment to generated runoff for the particular node. The time of concentration is the time when the runoff from farthest catchment reaches the outlet. Time of Concentration: The time of concentration is the time taken for the rainfall from far region of basin to reach the outlet. At the time of concentration the outlet will show the peak water level. The constant rainfall is provided only to one catchment which is one at the far location from outlet node shown in the figure below. The water level time series observed in the simulation is shown in the below figure which showed that peak water level is observed at 2.5 hours.

Input Catchment

Outlet Node

(b)

(a)

Figure 4.26: Calculation of time of concentration, figure (a) showing input and outlet points and figure (b) showing the water level showing peak around 2.5 hours

4.6.1 Boundary Conditions Rainfall: The rainfalls for each simulation are provided as a time series with different design storms. The design storm is used instead of continuous simulation because of the faster computational time. The rainfall is specified as the catchments and meteorological boundary item which is applied to all the catchments. The alternating block methods are used for design storm for each IDF curves. The three hour design storm was used as the rainfall inputs for each simulation in Mike Urban. The details of design storm used are included in Appendix E. Dry Weather Flow (DWF): The dry weather flow is the waste water conveyed in the combined sewers. The DWF are specified as catchment boundary items which is calculated based on person equivalent and catchment area. The total population in the study catchment that lies in district Wattana and Khlongtoei is 191,328. The population density of Klong Toei is 8,502 person/km2 and Wattana is 6,434 person/km2 (BMA, 2011). The 52

average population density in each hectare is 75 persons/ha. The average per capita domestic water consumption in Bangkok during 2008 was 210 lpcd (Babel et al., 2010). Assuming that 95% of water consumed is drained onto waste water colleting systems, the generation of waste water is 200 lpcd. The DWF load for each catchments are generated based on the number of inhabitants is then applied directly to the connected nodes. 4.6.2 Pipe Flow model The model developed by Chinawan, (2003) is analyzed based on the rainfall on 5th October 2002. Further calibration was improved by Nyugen, (2010) as discussed in section 3.7.2. The simulation is run under the same condition of 15th October, 2003 date on two stations (Nodes E20304 and B20204). The RMSE and EI in station Sukhumvit 26 are respectively 0.04 m and 82 %. In station Ekamai, RMSE and EI are respectively 0.24 and 60 % respectively. This shows that the model can be used with relatively good accuracy for study purpose. -0.8

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Observed Water Level, Station Ekamai (B20204) Computed Water Level (a)

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Figure 4.27: Simulation result showing observed water level and model simulated water level at Ekamai and Sukhumvit 26 stations

4.6.3 Overland Flow Model The overland flow model is represented in the Digital Elevation Model (DEM), where flow paths are defined by the elevation variation in grid cells. The computation grid sizes are selected as 10 m resolution and computation time step of 2 sec for stability of the model. All the nodes expect outlets are coupled to 2D cells at the ground elevation.

53

Figure 4.28: Coupled grid cell (10 x 10 m) of DEM with nodes of sewer network

4.7

Simulation results

The results obtained from the 1D and 2D are compared and summarized with the following criteria that reflect the performance of the drainage system. The criteria used for comparison of 1D pipe flow model results are:     

Total outflow volume (m3), which is the total volume of water discharged from the outlets. Maximum link velocity (m/s), represents the flow measured in links which have highest velocity recorded during all the time step. Average maximum link velocity (m/s), is the mean maximum velocity of the links recorded during different time steps. Link with ratio Qmax/Qfull>1, which reflects the performance of the drainage system which runs over its capacity during the extreme events. Nodes with water level (WL) above ground level (GL), shows that in the event of flooding or surcharges, the hydraulic gradient is higher than the ground level which results in overland flooding.

The criteria used for comparison of 2D overland flow model results are:  Total number of 2D cells flooded, which is the DEM grid cell each of 10 m × 10 m size.  Total area flooded (km2), is calculated from the number of 2D grid cell that are flooded.  Flood Volume (m3), is the total volume of water flowing from nodes to the 2D overland cells or water causing flooding in the streets.

54





 

Flood duration (Hours), is the extent of time duration where the water level in street is above the ground elevation which is calculated from the water level in the street and time duration. Maximum flood depth in the main street (m), the main street of consideration was taken as Sukhumvit road where the area having lowest ground elevation are flooded in higher extent. Standard deviation for flood depths, shows the variation of maximum water level computed in each computation cells. Average maximum flood velocity U and V (m/s), is the average of maximum velocities observed in different time steps in X and Y direction.

The simulation results are presented in summary in tables 4.8, 4.9, 4.10 and 4.11 below. The results are based on the simulation from 5 hours durations for events of 3 hour rainfall. The detail figures showing flood maps with maximum flood depth in every simulation are included in Appendix E. For the proper illustration three future time extents are denoted as 2020s for 2011-2030 time periods and 2050s for 2046-2065 time periods. Table 4.8: Simulation results during present time period Criterion

Return Periods (Years) 5 20 79.1 100.3 126.7 275,617.3 347,166.2 375936.2 42,900 61,979.9 83,127 26,912 37,542 45,801 2.69 3.75 4.58 3.5 4.25 5 0.38 0.47 0.64 0.056 0.066 0.083 0.0724 0.0809 0.094 0.023/0.028 0.026/0.03 0.032/0.035 4.06 4.42 4.69 0.493 0.52 0.544 1,344 1,526 1,626 280 379 455 2

Total 3 hour Rainfall Depth (mm) Total Outflows (m3) Flooded volume (m3) Number of 2D cells flooded Area Flooded (km2) Flood Duration (Hours) Maximum Flood Depth in main Street (m) Mean value for flood depth (m) Standard Deviation for flood depths Average Maximum Flood Velocity:U/V (m/s) Maximum link velocity (m/s) Average Maximum Link Velocity (m/s) Link with ratio (Qmax/Qfull) > 1 (Nos.) Nodes with WL above GL (Nos.)

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Table 4.9: Simulation results for 2 year return period during 2011-2030 and 2046-2065 Criterion 2011-2030 2046-2065 GCMs/Scenarios CGMR/ IPCM4/ GFCM21/ HADCM3/ CGMR/ IPCM4/ GFCM21/ HADCM3/ SRA1B SRA1B SRA2 SRA2 SRA1B SRA1B SRA2 SRA2 Total 3 hour Rainfall 96.7 80.9 80.9 92.3 87.0 94.8 88.2 86.1 Depth (mm) Total Outflows (m3) 344,179.20 332,105.00 331,704.10 339,945.60 338,483.80 340,516.50 339,510.40 338,459.40 Flooded volume (m3) 56,840.80 51,183.70 50,879.00 54,499.70 53188.80 56,552.30 53,326.10 52,975.50 Number of 2D cells 37,434 28,339 30,121 35,490 34,745 30,121 32,160 34,660 flooded Area Flooded (km2) 3.74 2.83 3.01 3.55 3.47 3.01 3.22 3.47 Flood Duration 4.5 4 4 4.5 4.5 4.5 4.5 4.5 (Hours) Maximum Flood 0.50 0.41 0.42 0.49 0.49 0.42 0.44 0.44 Depth in main Street (m) Mean value for flood 0.06 0.06 0.056 0.106 0.098 0.06 0.1 0.067 depth (m) Standard Deviation for 0.079 0.079 0.077 0.15 0.139 0.08 0.14 0.083 flood depths Average Maximum 0.04/0.04 0.025/0.028 0.025/0.028 0.03/0.029 0.03/0.03 0.026/0.03 0.027/0.029 0.025/0.023 Flood Velocity:U/V(m/s) Maximum link 4.21 4.2 4.16 4.18 4.12 4.07 4.12 4.1 velocity (m/s) Average Maximum 0.502 0.502 0.489 0.502 0.496 0.493 0.497 0.49 Link Velocity (m/s) Link with ratio 1,438 1,355 1,356 1,436 1,397 1,375 1,406 1,375 (Qmax/Qfull) > 1 (Nos.) Nodes with WL above 339 290 283 330 320 300 322 315 GL (Nos.) 56

Table 4.10: Simulation results for 5 year return period during 2011-2030 and 2046-2065 Criterion 2011-2030 2046-2065 GCMs/Scenarios CGMR/ IPCM4/ GFCM21/ HADCM3/ CGMR/ IPCM4/ GFCM21/ HADCM3/ SRA1B SRA1B SRA2 SRA2 SRA1B SRA1B SRA2 SRA2 Total 3 hour Rainfall 118.1 103.4 104.5 119.5 111.7 127.9 118.3 108.4 Depth (mm) Total Outflows (m3) 374,589.20 350,752.20 352,952.90 379,179.30 361,704.10 383,275.30 375,826.00 360,084.10 Flooded Volume (m3) 74,007.80 63,406.70 64,610.50 76,521.70 65,183.70 77,875.60 74,973.90 66,433.80 Number of 2D cells 42,658 37670 38,203 44,265 41,611 45,354 43,354 41906 flooded Area Flooded (km2) 4.26 3.77 3.82 4.43 4.16 4.13 4.33 4.19 Flood Duration 5.75 5 5 6 5.5 5.5 5.25 5.5 (Hours) Maximum Flood 0.6 0.5 0.5 0.65 0.6 0.6 0.5 0.6 Depth in main Street (m) Mean value for flood 0.072 0.063 0.064 0.0764 0.073 0.074 0.069 0.065 depth (m) Standard Deviation for 0.084 0.079 0.08 0.092 0.089 0.086 0.082 0.077 flood depths Average Maximum 0.04/0.04 0.025/0.028 0.035/0.4 0.04/0.04 0.03/0.03 0.04/0.03 0.045/0.04 0.03/0.036 Flood Velocity:U/V(m/s) Maximum link 4.44 4.31 4.31 4.45 4.43 4.43 4.42 4.40 velocity (m/s) Average Maximum 0.52 0.508 0.509 0.525 0.496 0.523 0.521 0.520 Link Velocity (m/s) Link with ratio 1,530 1,460 1,647 1,547 1,497 1,539 1,537 1,526 (Qmax/Qfull) > 1 (Nos.) Nodes with WL above 431 363 360 446 320 405 435 409 GL (Nos.) 57

Table 4.11: Simulation results for 20 year return period during 2011-2030 and 2046-2065 Criterion 2011-2030 2046-2065 GCMs/Scenarios CGMR/ IPCM4/ GFCM21/ HADCM3/ CGMR/ IPCM4/ GFCM21/ HADCM3/ SRA1B SRA1B SRA2 SRA2 SRA1B SRA1B SRA2 SRA2 Total 3 hour Rainfall 145.8 130.7 131.1 154.7 144 174.7 157.2 140.4 Depth (mm) Total Outflows (m3) 415,714.70 385,385.50 387,589.60 429,193.70 410,297.80 464,700.20 430,374.20 404,846.10 Flooded Volume (m3) 91,030.10 77,838.60 77,914.60 95,008.60 89,600.80 97,507.00 96,070.30 84,443.40 Number of 2D cells 54,902 45,869 51,157 58,698 53,796 57,716 56,731 52,122 flooded Area Flooded (km2) 5.49 4.59 5.11 5.87 5.38 5.77 5.67 5.21 Flood Duration 6 5.75 5.85 6 6 6 6 5.75 (Hours) Maximum Flood 0.7 0.64 0.66 0.75 0.7 0.72 0.7 0.65 Depth in main Street (m) Mean value for flood 0.09 0.073 0.077 0.095 0.0878 0.09 0.095 0.0813 depth (m) Standard Deviation for 0.097 0.086 0.084 0.103 0.0954 0.095 0.102 0.0899 flood depths Average Maximum 0.04/0.035 0.025/0.028 0.026/0.028 0.04/0.036 0.034/0.03 0.04/0.043 0.04/0.04 0.039/0.04 Flood Velocity: U/V(m/s) Maximum link 4.68 4.53 4.7 4.76 4.73 4.73 4.68 4.7 velocity (m/s) Average Maximum 0.54 0.528 0.54 0.544 0.53 0.544 0.543 0.542 Link Velocity (m/s) Link with ratio 1,625 1,669 1,621 1,679 1,699 1,703 1,629 1,618 (Qmax/Qfull) > 1 (Nos.) Nodes with WL above 524 447 486 557 563 567 552 518 GL (Nos.) 58

Total Outflow Volume – The total outflow volume is the outflow from sewer system through outlets and pumps. The percentage change in the total outflow volume during 2020s and 2050s under emission scenarios of SRA1B and SRA2 are presented in the table below. The table further illustrates the changes at particular return periods with respect to present return period of baseline condition 1981-2010. In synopsis the outflow volume eventually increases with the increasing amount of rainfall which increases in future extents for every return period. The model is simulated for the 3 hour rainfall design storm derived from CGMR and IPCM4 for SRA1B scenario and from GFCM21 and HADCM3 under SRA2 scenario. The difference between the results from two GCMs also shows the possible uncertainty between the models. Under SRA1B scenario, result from CGMR showed the greater percentage of outflow volume during 2020s in the events of 2, 5 and 20 years return period. While result from IPCM4 showed higher increments during 2050s during all events of 2, 5 and 20 years return period. Under SRA2 scenario, HADCM3 showed greater increments in outflow volume during 2020s and GFCM3 showed greater increments in outflow volume during 2050s in the events of 2, 5 and 20 years return period.

Return Periods

Return Periods

Table 4.12: Percentage of future increments in outflow volume from baseline conditions 2011-2030 2046-2065 GCMs under SRA1B CGMR IPCM4 CGMR IPCM4 2 Year 24.35 3.00 10.00 23.55 5 Year 14.66 3.05 12.83 25.00 20 Year 15.10 3.00 20.00 35.98 GCMs under SRA2 GFCM21 HADCM3 GFCM21 HADCM3 2 Year 3.38 17.17 11.93 10.00 5 Year 4.55 18.73 16.98 8.01 20 Year 3.10 24.15 24.06 9.82

Total Flooded Volume- The amount of water diverted from pipe flow model to the overland flow model is computed in each simulation. The results below present the increase in percentage of the flood volume from baseline condition. Under scenario SRA1B, result from CGMR projected greater increments in flood volume during 2020s and IPCM4 showed comparatively greater increments during 2050s in the events of 2, 5 and 20 years return period. CGMR projected more than 10 percent increments in flood volume in overall future period. IPCM4 projected considerable increments only in 2050s. Under scenario SRA2, result from HADCM3 projected greater increments in flood volume during 2020s while GFCM21 projected greater increments in flood volume. Also HADCM3 projected greater percentage increments during 2 year return period. HADCM3 projected higher increments in both future time extents and GFCM21 projected comparatively maximum increments only during 2050s than in 2020s.

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Return Periods

Return Periods

Table 4.13: Percentage of future increments in flooded volume from baseline conditions 2011-2030 2046-2065 GCMs under SRA1B CGMR IPCM4 CGMR IPCM4 2 Year 32.50 5.32 10.00 20.17 5 Year 19.41 2.30 10.82 25.65 20 Year 15.04 3.01 13.80 19.34 GCMs under SRA2 GFCM21 HADCM3 GFCM21 HADCM3 2 Year 6.71 27.71 10.32 12.01 5 Year 4.24 23.46 20.96 12.19 20 Year 3.35 18.62 16.29 11.21

Total number of flooded 2D cells – The total number of flooded computational cells provided the information of flooded area. The each cell is 10 square meter resolution. The results showed that under SRA1B scenario there is higher percentage of flooded area increments while considering CGMR during 2020s and 2050s. While IPCM4 result presented relative maximum increments only during 2050s. Under scenario SRA2 there is a higher percent increment from GFCM21 and HADCM3 during 2020s and 2050s as well i.e. increments more than 10 percent than baseline condition. However, HADCM3 predicted the intense flooded area during 2020s and 2050s. The flooded area is spread out during future time period compared to the baseline case. The expanded flooded area means higher numbers of people are affected.

Return Periods

Return Periods

Table 4.14: Percentage of future increments in number of 2D flooded cells compared with baseline conditions 2011-2030 2046-2065 GCMs under SRA1B CGMR IPCM4 CGMR IPCM4 2 Year 39.10 5.30 29.11 11.92 5 Year 13.63 1.34 10.84 20.81 20 Year 19.87 2.15 17.46 26.01 GCMs under SRA2 GFCM21 HADCM3 GFCM21 HADCM3 2 Year 11.92 31.87 19.50 28.79 5 Year 10.76 17.91 15.48 11.62 20 Year 11.69 28.16 23.86 13.80

Maximum and Average Flood Depth – The maximum flood depth increases in all return period in future compare with the present flood depth. The maximum flood depth is considered in the main street (Sukhumvit Road) where there is lower ground level. The main street is linked to all the other area and is primary route of transportation. The extended flooding in this region could affect socio-economic environment in the overall region. Nevertheless, due to flat terrain the increase in flood depth is not very intense and rather the water is spread into larger area for maximum flood depth at all the time being less than 1 m. The maximum flood depth in the main street is simulated as 0.5 m during 2 year return period, 0.65 m during 5 year return period and 0.75 m during 20 year return period during 2020s to 2050s. 60

During all future extents there is maximum increment of 31.58 % observed from baseline condition when CGMR GCM is considered under SRA1B scenario and 2 year return period while under SRA2 scenario maximum increment of 38.3 % increment from baseline condition is observed during 5 year return period.

Return Periods

Return Periods

Table 4.15: Percentage of future increments in flood depth at main street compared to baseline 2011-2030 2046-2065 GCMs under SRA1B CGMR IPCM4 CGMR IPCM4 2 Year 31.58 7.89 28.95 10.53 5 Year 27.66 6.38 27.66 27.66 20 Year 9.37 0.00 9.37 12.50 GCMs under SRA2 GFCM21 HADCM3 GFCM21 HADCM3 2 Year 10.53 28.95 15.79 15.79 5 Year 6.38 38.30 6.38 27.66 20 Year 3.13 17.19 9.37 1.56

Number of surcharging nodes – The nodes or manhole is surcharged when the pressure inside the conduit becomes larger shifting hydraulic grade line above the ground level (GL). The surcharging nodes cause the flooding in overland surface during pluvial floods. The number of surcharging nodes increases as rainfall events gets extreme or it increases with return period in general. From the simulation results higher percentage of increments are observed from CGMR GCM during 2020s in 2 year events under SRA1B scenario. While for 2050s same GCM project relatively lesser increments at 2 year event. There is maximum increment of 24.62 percent observed only in 2050s in an event of 20 year rainfall. Similarly under SRA2 scenario, HADCM3 showed higher increments in surcharging nodes during 2020s during events of all return periods and during 2050s GFCM21 projected comparatively higher increments than HADCM3 but relatively less intense than in 2020s. The increasing number of surcharging nodes confirm that the link capacity will get exceeded in future hence, proper upgrade to the system is necessary.

Return Periods

Return Periods

Table 4.16: Percentage of future increments in surcharging nodes compared to baseline condition 2011-2030 2046-2065 GCMs under SRA1B CGMR IPCM4 CGMR IPCM4 2 Year 21.07 3.57 14.29 7.14 5 Year 13.72 0.26 5.54 6.86 20 Year 15.16 2.64 23.74 24.62 GCMs under SRA2 GFCM21 HADCM3 GFCM21 HADCM3 2 Year 1.07 17.86 15.00 12.50 5 Year 1.32 17.68 14.78 5.54 20 Year 6.81 22.42 21.32 13.85

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Surface water velocity- The water velocity is one of the major hazards in the events of flood resulting from fluvial or coastal origin. In case of pluvial flood the simulated results showed very low hazard as the velocity on the surface during flood events is very less. This is due to the flat terrain that water flowing over the surface is accumulated over the long duration. The simulated result of surface water velocity is presented on above table 4.8 to 4.11 for present and future extents and all return periods. The velocities are computed in two directions X and Y as (U and V). The maximum velocity computed is 0.04 m/s. Flood Duration- The flood durations are computed on each simulation from time series results for water depth. The water depth in all the catchments will not be uniform. For the consistent comparison the main street is considered. The duration is obtained from same time series results which present the maximum flood depth. The figures for time series water depth are shown in Appendix E. The time taken for all the flooded water to drain into drainage system provides the duration of flood. The flooding condition is computed for water depth up to 1 cm.

Return Periods

Return Periods

Table 4.17: Percentage of future increments in flood duration compared to baseline condition 2011-2030 2046-2065 GCMs under SRA1B CGMR IPCM4 CGMR IPCM4 2 Year 28.57 14.29 28.57 28.57 5 Year 35.29 17.65 29.41 29.41 20 Year 20.00 15.00 20.00 20.00 GCMs under SRA2 GFCM21 HADCM3 GFCM21 HADCM3 2 Year 14.29 28.57 28.57 28.57 5 Year 17.65 41.18 23.53 29.41 20 Year 17.00 20.00 20.00 15.00

The increment in flood duration is evident for the future time extents in all return periods and emission scenarios. However the maximum flood duration for 3 hour rainfall events is 6 hours. Under SRA1B scenario, the maximum increment of 35.39 percent is observed from CGMR in 5 year event during 2020s. During 2050s both CGMR and IPCM4 showed maximum increments of 29.41 percent in 5 year return period. Under SRA2 scenario, the maximum increment of 41.18 % is observed from HADCM3 also in 5 year return period during 2011-2020s. During 2050s, HADCM3 showed maximum increment in 5 year return period. The maximum increments are observed for 2 and 5 year return periods than in 20 years return period. The criteria such as links flowing over capacity increases during the future time extent and with the return periods as can be observed in above Tables 4.7 to 4.10. In the same way link velocity also increases during future time extent and return periods. These results show that urban drainage system capacity will become insufficient as a result of climate change. This problem due to inadequate capacity could be mitigated by several adaptation measures. In general the tentative required adaptation measures could further be assessed with these results. Since, the urban flood or only pluvial flood is considered in the study the 62

serious future flood portrait is not projected however urban flood could be the serious problem in urban cities. 4.8

Flood hazards due to climate change

The above results present the performance of the drainage system as well as quantitative evaluation of criteria that represent the hazards associate with the events of floods. In general hazards are categorized based on the velocity and depth. The implication of climate change through increasing rainfall intensities in urban basin could create the hazard as a consequence of flood in socio-economic environment. The increasing extents of flood duration connote significant disturbances in socio-economic activities. The following scatter plot diagrams show the hazard in term of velocity and depth. Each point is the 2D computation cell where velocity and depth are computed. The scatter plots are considered for each return period to compare flood hazard from present to future extents in two emission scenarios. Since, it is already evident that IPCM4 GCM projected maximum rainfall depth under SRA1B scenario and HADCM3 projected maximum rainfall depth under SRA2 scenario. The following results are based on results simulated from IPCM4 under SRA1B and HADCM3 under SRA2. The flood hazard for the 2 year return period is presented as follows:

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Figure 4.29: Flood hazard with velocity and depth for each 2D computation cells for 2 year return period in (a) present baseline condition (b) 2011-2030, IPCM4 under SRA1B (c) 2046-2065, IPCM4 under SRA1B (d) 2011-2030, HADCM3 under SRA2 (e) 2046-2065, HADCM3 under SRA2

The flood hazard from 5 year return period is presented as follows:

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Figure 4.30: Flood hazard with velocity and depth for each 2D computation cells for 5 year return period in (a) present baseline condition (b) 2011-2030, IPCM4 under SRA1B (c) 2046-2065, IPCM4 under SRA1B (d) 2011-2030, HADCM3 under SRA2 (e) 2046-2065, HADCM3 under SRA2

The flood hazard for 20 years return period is presented as follows:

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Figure 4.31: Flood hazard with velocity and depth for each 2D computation cells for 20 year return period in (a) present baseline condition (b) 2011-2030, IPCM4 under SRA1B (c) 2046-2065, IPCM4 under SRA1B (d) 2011-2030, HADCM3 under SRA2 (e) 2046-2065, HADCM3 under SRA2

The study results showed that there is no significant increase in the velocity and depth that could be related with high or extreme hazard. In the baseline conditions the for all return period the flood velocity is below 0.8 m/s and depth is below 1 m, which according to figure 2.6 would be within medium hazard. During 2011-2030, it can be observed the slight increment in velocity and depth in all return periods while during 2046-2065 few cells have been observed with slight increments in velocity but less than 1.2 m/s and small depth increments in number of cells toward depth between 0.6 to 1m. The number of cells flooded increases with return period and with future extents. But most of the cells are concentrated in low velocity and low depth region or low hazard region. The pluvial flood in urban areas only as a result of low capacity drainage system does not pose high hazard in term of velocity and depth. However, the impact of climate change does increase the flood volume and area in future. Such floods in urban areas could affect daily lives of people and socio-economic environments. The mitigation or adaptation plans could prevent the serious consequence in socio-economic environment.

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CHAPTER 5 CONCLUSION AND RECOMMENDATIONS 5.1

Conclusion

The study highlighted on one of the imperative challenge in urban water system. The study focused on the need for high resolution spatial and temporal data for urban catchments. The change in IDF curve was studied with respect to un-stationary climate condition. The application of modeling approach was made to project the consequence of climate change quantitatively as well as qualitatively on the urban drainage systems. The first part of study included the study of downscaling method through regression based SDSM and stochastic weather generator using LARS WG. SDSM results showed that there is significant bias while predicting extreme climate variable specifically precipitation. The results from SDSM showed accuracy in projecting mean and sum but against maximum it showed less accuracy. LARS WG however is capable for projecting extreme climate variables. The 15 GCMs are analyzed and two GCMs are selected for SRA1B and SRA2 emission scenarios to develop future IDF curves. The overall result showed that there is increase in precipitation over the time period from 2020s to 2050s. The application of the Hyetos model based on Bartlett Lewis Rectangular Pulse process theory has been useful in disaggregating daily rainfall to sub daily. The model can be applied to other data deficient region to generate high resolution temporal precipitation time series. However, there are other many methods which need further study. The comparative results between observed rainfall at 3 hour duration and disaggregated rainfall by Hyetos with the generation of IDF curves showed that intensity at smaller duration or 3 hour is underestimated. The difference is less for 24 hours duration and difference increases for higher return period. The power equation was obtained to determine correction factor. Higher order equation was used as the nature of short duration IDF curves cannot be defined by linear equation. The correction factor is the function of duration only and not the return period. The derived correction factors are used for correcting IDF curve derived from disaggregated process. The urban drainage simulations are done based on GCMs projecting maximum 3 h rainfall depth from the 15 GCMs during each return periods and future time extent of 2011-2030 and 2046-2065. Under the scenario SRA1B, CGMR and IPCM4 projected maximum 3 h rainfall depth and under scenario SRA2, GFCM21 and HADCM3 projected maximum 3 h rainfall depth. The total outflow volume, flood volume, flooded 2D cells or area, flood depth, surcharging nodes and flood durations increases comparatively to the baseline condition in future extents under both emission scenarios. As the results of climate change urban drainage system will become inadequate in capacity thus more upgrades and maintenance is necessary in future. The future change in IDF curve under un-stationary climate shall be taken into consideration while selecting the design criteria of rainfall intensity in future. 67

The flood hazards are generally described in relation to the velocity and depth. The climate change impact on flood hazard arising from pluvial floods showed the resulting hazard as medium hazard in all cases. 5.2

Recommendation

This study highlighted range of rainfall extremes derived from climate models on urban drainage with the modelling tool. When considering the performance of urban drainage pluvial floods are relevant for which the urban drainage are designed to function. However, there could be threat of outside boundary conditions such as inflow from rivers or sea level rise to the urban basins as a consequence of climate change. The further study in this area can cover overall flood hazard related to climate change. The adaptation measures for such flooding shall be studied with further application of modelling tools in order to define required resilience for future climates. The future climate are generated based on GCMs outputs from IPCC (2007), the recent updates in the GCM data set could result in slight variation or more better accurate results. The urban drainage modeling using 2D approach takes considerable computation time and hence application of real time simulation is the problem. The further study could focus on developing approach for fast computation using 2D approach.

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APPENDICES

Appendix A: Pump stations in Sukhumvit Table of Pump Stations No. 1

Station A

Pump Station Soi Sukhumvit 1

2

B

Soi Sukhumvit 3

3 4

C D

Soi Sukhumvit 14 Soi Sukhumvit 21

5 6

E F

Soi Sukhumvit 26 Soi Sukhumvit 31

7

G

Soi Sawasdee

8 9

H I

Soi Sukhumvit 36 Soi Sukhumvit 40

10 11

J K

Soi Sukhumvit 42 Soi Sukhumvit 49

12

L

Soi Sukhumvit 55

13

M

Soi Torsak

14

N

Soi Promsri 2

15

O

Soi Ekamai(Suan-yoom)

16

P

Soi Ekamai(Klong Pang)

17/1

Q

Prakanong Bridge (outbound)

17/2 18

R S

Prakanong Brigde (inbound) Soi Sukhumvit 71

Ф (inch) 14 20 48 12 20 28 14 20 24 8 24 8 14 40 40 48 40 12 14 14 28 40 14 20 12 14 20 32 40 14 28 12 16 20 28

76

Discharge(l/s) 300 500 3000 200 500 1000 300 500 1000 130 1000 80 300 2000 2000 3000 2000 250 300 300 1000 2000 300 500 200 250 500 1000 2000 300 1000 200 350 500 1000

Head(m) 8.0 3.5 3.5 2.5 3.5 3.0 8.0 4.5 15.0 4.0 3.5 8.0 8.0 3.0 3.0 3.0 3.0 9.0 8.0 8.0 3.0 3.0 8.0 3.5 2.5 2.5 3.0 3.0 3.5 8.0 3.0 4.0 3.5 3.5 3.0

Quantity 1 1 1 1 1 2 1 2 4 1 1 1 2 5 1 1 4 1 1 2 1 2 1 1 1 1 2 1 1 1 3 1 2 2 4

Appendix B.1: Maximum projected daily rainfall (mm) from 15 GCMs in each month during 2011-2030 GCMs BCM2 CGMR CNCM3 CSMK3 FGOALS GFCM21

GIAOM HADCM3

HADGEM IMCM3

IPCM4

MIHR MPEH5

SCENARIOS SRA1B SRB1 SRA1B SRA1B SRA2 SRA1B SRB1 SRA1B SRB1 SRA1B SRA2 SRB1 SRA1B SRB1 SRA1B SRA2 SRB1 SRA1B SRA2 SRA1B SRA2 SRB1 SRA1B SRA2 SRB1 SRA1B SRB1 SRA1B SRA2

JAN 52.4 42.7 44.9 48.0 39.8 44.7 43.3 52.3 53.1 48.6 68.2 42.8 40.1 46.2 62.7 65.4 46.2 48.0 57.7 52.6 43.5 52.0 46.9 47.5 51.4 42.2 35.7 64.2 61.7

FEB 57.8 43.8 54.1 47.0 37.0 59.6 50.8 54.6 55.5 36.0 56.3 36.8 40.8 43.6 57.4 57.3 44.2 46.8 69.1 55.1 51.0 62.3 53.2 46.1 61.8 47.2 40.2 58.1 49.7

MAR 94.6 86.0 100.7 84.7 75.6 110.4 85.3 91.5 94.2 61.6 62.0 68.7 85.0 81.2 88.4 70.5 81.2 77.9 99.1 97.8 93.1 109.3 93.3 76.1 109.2 81.4 75.8 75.2 80.2

APR 134.2 127.5 143.8 124.1 115.0 144.3 119.6 128.2 129.2 96.0 102.9 109.6 128.2 123.0 130.7 122.1 119.3 117.3 124.4 132.9 133.5 147 120.9 111.8 139.1 118.7 115.9 122.3 124.3

MAY 388.4 371.2 403.6 364.9 345.3 352.2 372.9 376.6 368.2 321.6 320.1 346.4 371.8 370.3 387.1 384.6 352.5 345.6 356.3 379.5 384.4 420.3 309.3 351.3 332.7 372.4 359.2 369.5 369.4

77

JUN 247 236 255.7 113.2 109.5 247.8 240.2 115.1 112.9 110.3 104.9 113.1 110.9 112.4 246.5 246.7 108.4 105.6 103.6 244.2 121.0 267.2 108.8 110.8 115.3 242.0 115.9 235.4 235.1

JUL 75.4 72.3 79.2 86.4 85.4 76.8 76.4 88 87.2 87.4 86.1 88.3 85.8 85.8 75.9 76.5 83.6 81.4 79.9 79.4 93.4 79.5 85.6 85.3 88.5 81.0 90.4 72.8 73.2

AUG 94.8 90.6 98.3 95.6 96.3 96.2 95.7 97.8 98.2 95.2 95.6 96.7 96.4 95.9 91.2 93.8 92.5 92.2 90.6 94.2 97.2 97.9 99.4 95.0 95.0 96.1 98.8 91.5 89.3

SEP 144.8 134.6 146.8 141.6 145.6 145.7 141.4 144.5 148.3 139.5 141.4 141.9 144.8 144.6 133.7 140.9 136.6 140.1 135.7 139.2 137.6 143.4 149.9 140.2 137.4 142.7 144.5 134.1 131.2

OCT 152.6 135.4 151.8 142.6 149.8 148.8 137.4 142.3 152.0 141.1 139.8 137.1 148.4 152.6 125.2 143.8 130.5 143.4 133.1 145.0 130.5 136.9 144.1 130.3 134.9 145.8 141.1 128.2 131.3

NOV 129.7 119.1 131.8 124.9 129.4 120.0 116.8 125.6 134.6 134.9 132.9 115.7 129.4 137.1 107.7 126.1 113.0 120.5 111.0 138.2 114.0 121.9 121.2 112.2 114.1 126.6 118.1 111.8 119.4

DEC 22.2 22.3 22.2 24 22.9 17.7 19.9 25.2 26.5 30.3 36.2 21.9 22.7 25.8 25.8 29.8 23.6 21.6 18.9 29.4 21.3 24.9 22.5 23.1 21.5 21.8 19.0 27.5 28.9

NCCCSM

NCPCM

SRB1 SRA1B SRA2 SRB1 SRA1B SRA2 Mean SD MAX MIN POSITIVE NEGATIVE

50.5 45.4 50.6 49.8 45 43.2

48.2 49.2 56.9 52.9 54.6 53.1

88.3 84.6 89.6 86.4 98.7 103

127.6 122.8 121.8 124.6 131.2 143

366.5 368.2 362.6 370.3 328.3 354.6

233.8 113 231.5 110.7 111.3 112.6

74.5 86.6 73.2 85.2 86.7 84.8

94.2 97.3 93.4 97.2 95.3 91.9

137.5 146.5 141.2 148.7 142.1 135.8

133.1 151.7 145.8 155.8 144 139.8

119.3 133.2 127.6 134 123.5 122.4

25.4 23.7 22.8 23.8 21.3 21.6

49.41 7.69 68.20 35.70 18.79 13.71

51.09 7.77 69.10 36.00 18.01 15.09

86.87 12.41 110.40 61.60 23.53 25.27

125.05 11.13 147.00 96.00 21.95 29.05

362.80 23.06 420.30 309.30 57.50 53.50

164.24 66.01 267.20 103.60 102.96 60.64

82.23 5.75 93.40 72.30 11.17 9.93

95.07 2.57 99.40 89.30 4.33 5.77

141.27 4.63 149.90 131.20 8.63 10.07

141.31 8.07 155.80 125.20 14.49 16.11

123.36 8.30 138.20 107.70 14.84 15.66

23.94 3.72 36.20 17.70 12.26 6.24

Appendix B.2: Maximum Projected daily rainfall (mm) from 15 GCMs in each month during 2046-2065 GCMs BCM2 CGMR CNCM3 CSMK3 FGOALS GFCM21

GIAOM

SCENARIO SRA1B SRB1 SRA1B SRA1B SRA2 SRA1B SRB1 SRA1B SRB1 SRA1B SRA2 SRB1 SRA1B SRB1

JAN 48.9 50.0 52.6 42.4 46.1 33.5 36.7 48.7 49.0 37.3 31.7 104.7 38.4 43.6

FEB 53.1 58.0 59.4 34.9 46.8 34.8 36.0 55.4 59.5 25.7 27.6 87.0 38.3 52.1

MAR 91 101.1 105.4 77.7 90.2 67.3 79.1 90.3 95.1 53.6 65.6 75.4 82.2 96.6

APR 133 143.4 146.1 120.2 132.8 113.2 128.5 124.6 125.4 94.1 106.1 114.3 131.7 141.2

MAY 394.2 404.8 372.2 366.6 382.2 381.2 401.3 376.6 363.7 323.1 332.7 347.2 413.3 411.3

78

JUN 251.9 255.4 118.0 117.3 114.0 243.7 255.1 117.3 113.1 109.9 106.3 110.0 264.0 261.3

JUL 76.9 76.2 97.3 92.1 90.9 74.9 77.1 90.2 90.0 88.5 85.3 86.8 82.0 80.5

AUG 98.4 96.3 104.8 100.4 99.7 94.3 98.6 102.5 100.7 99.1 95.7 97.6 98.6 97.7

SEP 147.9 141.0 147.2 144.3 146.1 139.7 147.3 154.6 148.2 149.1 143.9 154.3 145.9 145

OCT 146.7 145.3 134.8 136.5 146.1 125.2 140.4 156.7 142.6 161.1 153.0 191.5 149.4 148.6

NOV 123.2 131.1 118.8 128.7 128.4 94.2 110.8 132.3 118.7 149.6 138.2 195.3 130.8 128.4

DEC 21.8 23.1 24.3 28.0 24.4 15.4 18.0 22.1 19.3 27.0 23.3 52.5 22.5 21.5

HADCM3

HADGEM IMCM3

IPCM4

MIHR MPEH5

NCCCSM

NCPCM

SRA1B SRA2 SRB1 SRA1B SRA2 SRA1B SRA2 SRB1 SRA1B SRA2 SRB1 SRA1B SRB1 SRA1B SRA2 SRB1 SRA1B SRA2 SRB1 SRA1B SRA2 Mean SD MAX MIN PLUS MINUS

68.5 38.2 61.2 56.4 54.1 51.0 46.0 49.0 39.5 30.0 32.8 40.2 35.8 85.4 42.7 80.4 45.2 51.7 52.6 42.6 39.9

48.77 15.67 104.70 30.00 55.93 18.77

62.3 34.8 55.1 58.1 43.5 52.6 57.3 53.9 43.2 37.1 34.3 48.2 36.2 77.9 38.6 71.8 49.8 59.7 56.3 52.5 51.7

79.7 63.6 69.2 87.9 71.8 87.8 97.9 92.5 76.5 74.4 68.0 82.6 76.5 101.4 75.5 74.2 89.6 92.4 86.3 95.3 98.2

125.2 105.4 121.1 119.9 113.4 122.3 125.9 129.9 116.5 116.6 108.8 112.7 118.4 155.5 121.4 122.5 130.9 122.9 121.2 126.9 129.3

377.7 362.7 363.5 344 338.3 389.3 355.4 393.1 364.5 360 353.5 333.5 370.0 438.0 387.2 373.2 372.1 357.3 369.1 314.9 301.4

241.8 236.1 107.4 103.3 99.6 252.8 117.0 121.5 118.9 110.4 111.6 109.3 116 276.8 123.6 114.7 109.7 105.5 112.1 109.4 103

76.2 76.1 82.8 80.4 79.5 80.0 90.9 95.3 93.0 90.9 90.7 88.1 90.8 81.9 92.6 89.6 85.3 83.4 85.7 88.3 85.5

93.7 89.7 94 96.2 92.4 98.9 98.8 103 106 103.7 107.3 97.5 97.2 99.5 95.8 96.5 97.4 98.2 96.1 94.5 94.1

142.4 134.6 141.5 155.2 141.8 149.4 144.7 147.4 158.8 151.8 162.1 148.0 141.2 150.8 130.3 139.8 146.9 151.1 144.0 138.1 139.9

146.7 139.5 139.3 179.6 153.4 149.6 141.3 141.4 153.8 135.0 151.2 165.0 144.1 156.3 121.3 138.7 147.9 153.2 146.0 139.6 143.0

127.6 118.2 113 156.4 140.8 137.3 120.7 129.3 126.2 103.2 122.7 146.4 129.0 135.0 109.1 115.4 129.2 129.5 127.8 121.4 121.9

30 20.1 24.3 27.1 28.8 28.3 20.8 26.7 19.3 15.0 18.0 22.3 22.8 34.3 22.6 28.9 23.5 21.8 23.6 20.5 19.6

49.81 83.20 123.47 368.26 152.51 85.59 98.14 146.12 147.54 128.25 24.04 13.63 12.47 12.07 28.83 65.53 6.15 3.82 6.51 13.13 17.01 6.46 87.00 105.40 155.50 438.00 276.80 97.30 107.30 162.10 191.50 195.30 52.50 25.70 53.60 94.10 301.40 99.60 74.90 89.70 130.30 121.30 94.20 15.00 37.19 22.20 32.03 69.74 124.29 11.71 9.16 15.98 43.96 67.05 28.46 24.11 29.60 29.37 66.86 52.91 10.69 8.44 15.82 26.24 34.05 9.04

79

Appendix B.3: Details of 15 GCMs used in analysis using LARS WG AOGCMs

CGMR

CSMK3

FGOALS CNCM3 IPCM4

Centre

Canadian Center for Climate Modelling and Analysis, Canada Australia's Commonwealth Scientific and Industrial Research Organisation Institute of Atmospheric Physics Centre National de RecherchesMeteorologiques Institute Pierre Simon Laplace

MPEH5 (ECHAM5) MIHR

Max-Planck Institute for Meteorology Meteorological Research Institute,Japan

BCM2

Bjerknes Centre for Climate Research Institute for Numerical Mathematics UK Met. Office

INCM3 HadCM3 HadGEM1 GFCM21 GIAOM NCPCM NCCCSM

Geophysical Fluid Dynamics Laboratory Goddard Institute for Space Studies National Centre for Atmospheric Research

Available SRES Scenarios SRA1B

Canada

Grid resolution of GCM (1° ≈ 111km) 1.9° x 1.9°

SRA1B, SRB1

Australia

1.9° x 1.9°

SRA1B, SRB1

China

2.8° x 2.8°

SRA1B, SRA2

France

1.9° x 1.9°

SRA1B, SRB1, SRA2 SRA1B, SRB1, SRA2 SRA1B, SRB1

France

2.5° x 3.75°

Germany

1.9° x 1.9°

Japan

2.8° x 2.8°

SRA1B, SRB1

Norway

1.9° x 1.9°

SRA1B, SRB1, SRA2 SRA1B, SRB1, SRA2 SRA1B, SRA2 SRA1B, SRB1, SRA2

Russia

4° x 5°

UK

2.5° x 3.75°

USA

1.3° x 1.9° 2.0° x 2.5°

SRA1B, SRB1

USA

3° x 4°

SRA1B, SRA2 SRA1B, SRB1, SRA2

USA

2.8° x 2.8° 1.4° x 1.4°

80

Location of Developer

Appendix C.1: IDF curve calculation for 2 year return period during 2011-2030 in SRA1B scenario 2011-2030 BCM2 SRA1B CGMR SRA1B CNCM3 SRA1B CSMK3 SRA1B FGOALS SRA1B GFCM21 SRA1B GIAOM SRA1B HADCM3 SRA1B HADGEM SRA1B IMCM3 SRA1B IPCM4 SRA1B MIHR SRA1B MPEH5 SRA1B NCCCSM SRA1B NCPCM SRA1B Mean SD MAX MIN POSITIVE NEGATIVE

1 38.56 43.73 40.36 41.94 38.49 39.08 42.87 40.61 40.52 40.61 40.37 42.76 41.02 43.12 39.41 40.73 1.68 43.12 38.37 2.39 2.36

3 29.77 32.24 27.89 29.04 27.75 28.42 30.27 29.21 26.94 29.21 26.98 30.16 28.76 28.74 28.63 28.93 1.36 32.24 26.94 3.31 2.00

6 20.27 21.24 18.81 19.66 19.01 18.80 19.00 19.94 17.74 19.94 17.40 19.37 18.78 19.05 18.27 19.13 1.02 21.24 17.10 2.10 2.03

12 11.49 11.83 10.78 10.85 10.82 10.31 10.45 11.31 10.43 11.31 10.00 11.12 10.24 10.77 10.51 10.81 0.52 11.83 10.00 1.01 0.82

24 5.61 5.75 5.17 5.47 5.26 5.00 5.30 5.53 5.06 5.53 5.06 5.47 5.15 5.35 5.07 5.32 0.23 5.75 5.00 0.43 0.32

3hrdepth 89.32 96.72 83.68 87.12 83.24 85.26 90.80 87.63 80.81 87.63 80.94 90.47 86.27 86.22 85.88 86.80 96.72 80.81

Appendix C.2: IDF curve calculation for 2 year return period during 2046-2065 in SRA1B scenario 2046-2065 BCM2 SRA1B CGMR SRA1B CNCM3 SRA1B CSMK3 SRA1B FGOALS SRA1B GFCM21 SRA1B GIAOM SRA1B HADCM3 SRA1B HADGEM SRA1B IMCM3 SRA1B IPCM4 SRA1B MIHR SRA1B MPEH5 SRA1B NCCCSM SRA1B NCPCM SRA1B Mean SD MAX MIN POSITIVE NEGATIVE

1 43.46 41.10 40.26 39.98 40.76 42.30 39.26 41.30 41.26 41.30 39.36 40.87 41.63 44.50 42.16 41.30 1.41 44.50 39.26 3.20 2.04

3 30.97 29.00 27.83 28.47 28.87 29.88 29.26 30.06 31.95 30.06 31.61 28.42 28.98 31.56 29.23 29.74 1.28 31.95 27.83 2.20 1.91

6 20.52 19.56 18.68 18.96 19.24 19.83 20.16 20.31 21.05 20.31 21.38 19.50 20.73 20.12 18.61 19.93 0.84 21.38 18.61 1.45 1.32

81

12 11.54 10.81 10.42 10.44 11.65 10.97 11.49 11.64 11.80 11.64 11.67 10.96 12.13 11.21 10.18 11.24 0.58 12.13 10.18 0.89 1.05

24 5.66 5.30 5.20 5.19 5.56 5.37 5.73 5.70 5.68 5.70 5.54 5.42 6.05 5.34 4.98 5.49 0.27 6.05 4.98 0.55 0.52

3hrdepth 92.91 87.01 83.50 85.40 86.62 89.65 87.77 90.17 95.84 90.17 94.84 85.27 86.94 94.69 87.69 89.23 95.84 83.50

Appendix C.3: IDF curve calculation for 5 year return period during 2011-2030 in SRA1B scenario 2011-2030 BCM2 SRA1B CGMR SRA1B CNCM3 SRA1B CSMK3 SRA1B FGOALS SRA1B GFCM21 SRA1B GIAOM SRA1B HADCM3 SRA1B HADGEM SRA1B IMCM3 SRA1B IPCM4 SRA1B MIHR SRA1B MPEH5 SRA1B NCCCSM SRA1B NCPCM SRA1B Mean SD MAX MIN POSITIVE NEGATIVE

1 45.85 52.90 49.24 49.05 46.56 49.40 51.65 49.23 48.28 49.23 51.49 48.73 49.86 51.40 46.88 48.98 2.01 52.90 45.85 3.91 3.14

3 35.82 39.36 35.26 36.24 36.02 37.38 36.82 38.07 35.15 38.07 34.45 36.49 37.60 35.90 36.13 36.45 1.61 39.36 32.45 2.91 4.00

6 26.56 28.57 25.77 26.64 27.00 26.24 24.91 28.70 26.26 28.70 22.36 26.10 27.51 26.23 24.02 26.30 1.90 28.70 21.36 2.39 4.95

12 17.45 18.59 17.19 15.61 16.70 15.16 15.61 17.57 16.22 17.57 13.54 17.24 16.22 16.25 15.50 16.43 1.25 18.59 13.54 2.16 2.88

24 8.75 9.02 7.99 8.01 8.18 7.40 8.17 8.57 7.71 8.57 7.12 8.45 8.17 8.18 7.27 8.10 0.55 9.02 7.12 0.92 0.98

3hrdepth 107.46 118.09 105.79 108.72 108.07 112.13 110.45 114.21 105.44 114.21 103.36 109.47 112.79 107.70 108.38 109.35 118.09 97.36

Appendix C.4: IDF curve calculation for 5 year return period during 2046-2065 in SRA1B scenario 2046-2065 BCM2 SRA1B CGMR SRA1B CNCM3 SRA1B CSMK3 SRA1B FGOALS SRA1B GFCM21 SRA1B GIAOM SRA1B HADCM3 SRA1B HADGEM SRA1B IMCM3 SRA1B IPCM4 SRA1B MIHR SRA1B MPEH5 SRA1B NCCCSM SRA1B NCPCM SRA1B Mean SD MAX MIN POSITIVE NEGATIVE

1 53.05 52.45 50.51 53.35 49.51 49.30 48.97 47.63 49.55 47.63 52.19 50.66 49.32 53.61 51.63 50.62 1.99 53.61 47.63 2.98 2.99

3 40.27 37.22 36.18 36.93 36.77 37.31 35.26 36.55 39.17 36.55 42.63 36.65 37.12 40.32 36.80 37.72 1.98 42.63 35.26 4.92 2.46

6 29.15 27.57 25.57 26.38 26.54 27.24 26.48 27.44 27.28 27.44 30.04 26.35 28.49 28.19 25.32 27.30 1.29 30.04 25.32 2.74 1.98

82

12 18.70 16.57 16.67 16.20 17.86 16.17 16.86 18.58 17.05 18.58 17.80 16.55 18.73 17.41 14.74 17.23 1.15 18.73 14.74 1.50 2.49

24 8.93 7.90 8.09 8.43 8.51 7.83 9.22 8.91 8.36 8.91 8.36 7.98 9.74 8.29 7.12 8.44 0.64 9.74 7.12 1.30 1.32

3hrdepth 120.82 111.66 108.55 110.78 110.31 111.92 105.78 109.64 117.51 109.64 127.90 109.95 111.35 120.97 110.41 113.15 127.90 105.78

Appendix C.5: IDF curve calculation for 10 year return period during 2011-2030 in SRA1B scenario 2011-2030 BCM2 SRA1B CGMR SRA1B CNCM3 SRA1B CSMK3 SRA1B FGOALS SRA1B GFCM21 SRA1B GIAOM SRA1B HADCM3 SRA1B HADGEM SRA1B IMCM3 SRA1B IPCM4 SRA1B MIHR SRA1B MPEH5 SRA1B NCCCSM SRA1B NCPCM SRA1B Mean SD MAX MIN POSITIVE NEGATIVE

1 50.67 58.97 55.12 53.76 51.90 56.23 57.46 54.94 53.42 54.94 51.87 52.68 55.71 56.88 51.83 54.43 2.39 58.97 50.67 4.54 3.76

3 39.82 44.08 40.15 41.01 41.50 43.31 41.15 43.94 40.58 43.94 36.08 40.69 43.45 40.64 41.09 41.43 2.11 44.08 36.08 2.65 5.35

6 30.73 33.43 30.38 31.26 32.29 31.16 28.82 34.49 31.90 34.49 24.18 30.56 33.29 30.99 27.82 31.05 2.67 34.49 24.18 3.44 6.88

12 21.39 23.07 21.43 18.76 20.58 18.37 19.03 21.71 20.05 21.71 15.89 21.28 20.17 19.88 18.81 20.14 1.78 23.07 15.89 2.93 4.25

24 10.87 11.22 9.89 9.73 10.14 9.02 10.11 10.62 9.49 10.62 8.52 10.46 10.20 10.08 8.76 9.98 0.77 11.22 8.52 1.24 1.46

3hrdepth 119.47 132.24 120.44 123.02 124.51 129.92 123.46 131.81 121.75 131.81 108.23 122.06 130.34 121.92 123.28 124.28 132.24 108.23

Appendix C.6: IDF curve calculation for 10 year return period during 2046-2065 in SRA1B scenario 2046-2065 BCM2 SRA1B CGMR SRA1B CNCM3 SRA1B CSMK3 SRA1B FGOALS SRA1B GFCM21 SRA1B GIAOM SRA1B HADCM3 SRA1B HADGEM SRA1B IMCM3 SRA1B IPCM4 SRA1B MIHR SRA1B MPEH5 SRA1B NCCCSM SRA1B NCPCM SRA1B Mean SD MAX MIN POSITIVE NEGATIVE

1 59.393 59.956 57.301 62.208 55.299 53.939 55.392 51.826 55.045 51.826 60.684 57.146 54.407 59.638 57.905 56.798 3.178 62.208 51.826 5.411 4.972

3 46.436 42.663 41.714 42.526 41.999 42.225 39.234 40.843 43.952 40.843 50.592 42.096 42.502 46.120 41.815 43.037 2.798 50.592 39.234 7.554 3.803

6 34.871 32.878 30.124 31.299 31.375 32.141 30.661 32.157 31.414 32.157 38.000 30.879 33.620 33.533 29.766 32.325 2.092 38.000 29.766 5.675 2.559

83

12 23.441 20.384 20.810 20.016 21.967 19.617 20.419 23.170 20.533 23.170 21.858 20.246 23.100 21.518 17.754 21.200 1.605 23.441 17.754 2.240 3.446

24 11.095 9.612 10.001 10.570 10.465 9.459 11.527 11.043 10.142 11.043 10.230 9.684 12.181 10.236 8.537 10.388 0.908 12.181 8.537 1.793 1.851

3hrdepth 139.308 127.990 125.142 127.580 125.998 126.674 117.703 122.529 131.855 122.529 151.775 126.287 127.505 138.361 125.444 129.112 151.775 117.703

Appendix C.7: IDF curve calculation for 20 year return period during 2011-2030 in SRA1B scenario 2011-2030 BCM2 SRA1B CGMR SRA1B CNCM3 SRA1B CSMK3 SRA1B FGOALS SRA1B GFCM21 SRA1B GIAOM SRA1B HADCM3 SRA1B HADGEM SRA1B IMCM3 SRA1B IPCM4 SRA1B MIHR SRA1B MPEH5 SRA1B NCCCSM SRA1B NCPCM SRA1B Mean SD MAX MIN POSITIVE NEGATIVE

1 55.30 64.79 60.75 58.27 57.02 62.78 63.04 60.42 58.35 60.42 65.03 56.47 61.33 62.13 56.58 59.65 2.86 64.79 55.30 5.15 4.35

3 43.66 48.60 44.83 45.58 46.76 48.99 45.31 49.57 45.80 49.57 43.55 44.71 49.06 45.19 45.86 46.20 2.70 49.57 39.55 3.36 6.65

6 34.73 38.08 34.80 35.70 37.36 35.88 32.57 40.05 37.31 40.05 28.00 34.83 38.83 35.55 31.47 35.61 3.45 40.05 26.88 4.45 8.73

12 25.17 27.36 25.50 21.78 24.31 21.45 22.31 25.68 23.73 25.68 18.14 25.17 23.97 23.36 21.98 23.70 2.30 27.36 18.14 3.66 5.56

24 12.90 13.34 11.71 11.38 12.03 10.57 11.97 12.59 11.20 12.59 9.87 12.39 12.15 11.91 10.20 11.79 0.99 13.34 9.87 1.55 1.92

3hrdepth 130.99 145.81 134.48 136.73 140.28 146.98 135.94 148.70 137.39 148.70 130.66 134.12 147.18 135.56 137.57 138.61 148.70 118.66

Appendix C.8: IDF curve calculation for 20 year return period during 2046-2065 in SRA1B scenario 2046-2065 BCM2 SRA1B CGMR SRA1B CNCM3 SRA1B CSMK3 SRA1B FGOALS SRA1B GFCM21 SRA1B GIAOM SRA1B HADCM3 SRA1B HADGEM SRA1B IMCM3 SRA1B IPCM4 SRA1B MIHR SRA1B MPEH5 SRA1B NCCCSM SRA1B NCPCM SRA1B Mean SD MAX MIN POSITIVE NEGATIVE

1 65.48 67.16 63.81 70.70 60.86 58.39 61.56 55.85 60.31 55.85 68.83 63.37 59.29 65.42 63.92 62.72 4.43 70.70 55.85 7.98 6.87

3 52.35 47.88 47.02 47.90 47.01 46.94 43.05 44.96 48.54 44.96 58.23 47.32 47.67 51.68 46.62 48.14 3.64 58.23 43.05 10.08 5.10

84

6 40.36 37.97 34.50 36.01 36.01 36.84 34.67 36.68 35.38 36.68 48.17 35.23 38.55 38.66 34.03 37.32 3.48 48.17 34.03 10.86 3.29

12 27.99 24.04 24.78 23.68 25.91 22.92 23.83 27.58 23.87 27.58 25.75 23.79 27.29 25.45 20.65 25.01 2.06 27.99 20.65 2.98 4.36

24 13.17 11.26 11.84 12.62 12.34 11.02 13.74 13.08 11.85 13.08 12.02 11.31 14.52 12.11 9.90 12.26 1.17 14.52 9.90 2.27 2.36

3hrdepth 157.04 143.65 141.05 143.70 141.04 140.82 129.14 134.89 145.62 134.89 174.68 141.96 143.01 155.05 139.87 144.43 174.68 129.14

Appendix C.9: IDF curve calculation for 2 year return period during 2011-2030 in SRA2 scenario 2011-2030 CNCM3 SRA2 GFCM21 SRA2 HADCM3 SRA2 HADGEM SRA2 IMCM3 SRA2 IPCM4 SRA2 MPEH5 SRA2 NCCCSM SRA2 NCPCM SRA2 Mean SD MAX MIN POSITIVE NEGATIVE

1 40.27 38.81 42.91 38.97 45.25 39.86 40.77 43.86 44.53 41.87 2.58 45.25 38.81 3.38 3.06

3 28.36 26.98 30.76 27.24 30.94 28.05 29.00 28.56 31.95 29.09 1.74 31.95 26.98 2.86 2.11

6 18.74 18.58 19.31 17.87 19.97 17.84 18.52 18.88 19.95 18.85 0.78 19.97 17.84 1.12 1.02

12 10.44 10.37 11.10 9.58 10.88 9.91 10.08 11.02 10.66 10.45 0.52 11.10 9.58 0.65 0.87

24 5.23 5.01 5.46 4.91 5.11 4.95 5.14 5.38 5.09 5.14 0.18 5.46 4.91 0.32 0.23

3hrdepth 85.07 80.94 92.29 81.72 92.82 84.15 87.01 85.68 95.85 87.28 95.85 80.94

Appendix C.10: IDF curve calculation for 2 year return period during 2046-2065 in SRA2 scenario 2046-2065 CNCM3 SRA2 GFCM21 SRA2 HADCM3 SRA2 HADGEM SRA2 IMCM3 SRA2 IPCM4 SRA2 MPEH5 SRA2 NCCCSM SRA2 NCPCM SRA2 Mean SD MAX MIN POSITIVE NEGATIVE

1 40.51 40.89 40.96 36.48 42.19 39.92 42.06 37.60 38.67 39.92 1.96 42.19 36.48 2.27 3.44

3 30.10 29.41 28.71 26.66 29.07 29.26 29.37 26.57 29.45 28.73 1.26 30.10 26.57 1.37 2.16

6 20.56 20.08 18.86 18.51 18.69 18.82 18.60 18.01 19.12 19.03 0.80 20.56 18.01 1.53 1.02

85

12 11.21 10.79 10.79 10.46 10.71 10.61 10.16 10.83 10.24 10.64 0.32 11.21 10.16 0.57 0.49

24 5.37 5.20 5.20 5.18 5.25 5.16 4.92 5.38 4.99 5.18 0.15 5.38 4.92 0.20 0.26

3hrdepth 90.30 88.22 86.14 79.99 87.20 87.79 88.12 79.71 88.36 86.20 90.30 79.71

Appendix C.11: IDF curve calculation for 5 year return period during 2011-2030 in SRA2 scenario 2011-2030 CNCM3 SRA2 GFCM21 SRA2 HADCM3 SRA2 HADGEM SRA2 IMCM3 SRA2 IPCM4 SRA2 MPEH5 SRA2 NCCCSM SRA2 NCPCM SRA2 Mean SD MAX MIN POSITIVE NEGATIVE

1

3

6

12

51.35 46.59 54.33 49.51 55.95 50.13 49.98 57.65 52.31 51.98 3.47 57.65 46.59 5.68 5.39

36.98 33.82 39.83 36.92 39.35 34.62 37.25 37.80 38.84 37.27 2.02 39.83 33.82 2.56 3.45

26.39 25.10 27.55 25.95 27.61 23.53 26.14 27.03 26.58 26.21 1.28 27.61 23.53 1.40 2.68

15.81 15.66 18.09 14.69 17.65 15.38 15.95 17.47 15.62 16.26 1.18 18.09 14.69 1.83 1.57

24 3hrdepth 7.84 7.39 8.59 7.70 8.12 7.66 8.17 8.27 7.50 7.91 0.40 8.59 7.39 0.67 0.53

110.94 101.47 119.48 110.77 118.04 103.85 111.76 113.40 116.53 111.80 119.48 101.47

Appendix C.12: IDF curve calculation for 5 year return period during 2046-2065 in SRA2 scenario 2046-2065 CNCM3 SRA2 GFCM21 SRA2 HADCM3 SRA2 HADGEM SRA2 IMCM3 SRA2 IPCM4 SRA2 MPEH5 SRA2 NCCCSM SRA2 NCPCM SRA2 Mean SD MAX MIN POSITIVE NEGATIVE

1 49.67 51.40 49.61 43.85 48.06 46.55 52.89 46.66 46.00 48.30 2.85 52.89 43.85 4.59 4.45

3 38.07 39.42 36.15 32.53 34.11 35.57 37.66 33.41 34.40 35.70 2.32 39.42 32.53 3.72 3.17

6 28.57 28.39 25.98 24.34 24.58 25.67 26.90 24.50 24.31 25.92 1.70 28.57 24.31 2.65 1.61

86

12 17.72 16.55 16.83 15.80 16.49 16.92 17.10 16.62 14.41 16.49 0.94 17.72 14.41 1.22 2.09

24 8.41 7.78 8.23 7.83 8.00 7.99 8.08 8.18 7.02 7.95 0.40 8.41 7.02 0.46 0.93

3hrdepth 114.20 118.26 108.45 97.58 102.32 106.70 112.99 100.22 103.19 107.10 118.26 97.58

Appendix C.13: IDF curve calculation for 10 year return period during 2011-2030 in SRA2 scenario 2011-2030 CNCM3 SRA2 GFCM21 SRA2 HADCM3 SRA2 HADGEM SRA2 IMCM3 SRA2 IPCM4 SRA2 MPEH5 SRA2 NCCCSM SRA2 NCPCM SRA2 Mean SD MAX MIN POSITIVE NEGATIVE

1 58.68 51.74 61.90 56.49 63.04 56.93 56.08 66.79 57.46 58.79 4.46 66.79 51.74 8.00 7.05

3 42.69 38.35 45.83 43.34 44.91 38.96 42.72 43.91 43.41 42.68 2.50 45.83 38.35 3.15 4.33

6 31.46 29.42 33.00 31.29 32.67 27.30 31.19 32.43 30.97 31.08 1.78 33.00 27.30 1.92 3.78

12 19.37 19.17 22.72 18.07 22.14 19.01 19.84 21.74 18.90 20.11 1.66 22.72 18.07 2.62 2.04

24 9.60 8.99 10.70 9.57 10.15 9.48 10.21 10.22 9.13 9.78 0.56 10.70 8.99 0.91 0.79

3hrdepth 128.07 115.05 137.49 130.01 134.73 116.89 128.15 131.74 130.22 128.04 137.49 115.05

Appendix C.14: IDF curve calculation for 10 year return period during 2046-2065 in SRA2 scenario 2046-2065 CNCM3 SRA2 GFCM21 SRA2 HADCM3 SRA2 HADGEM SRA2 IMCM3 SRA2 IPCM4 SRA2 MPEH5 SRA2 NCCCSM SRA2 NCPCM SRA2 Mean SD MAX MIN POSITIVE NEGATIVE

1 55.73 58.35 55.34 48.73 51.94 50.94 60.05 52.65 50.86 53.84 3.76 60.05 48.73 6.21 5.11

3 43.34 46.05 41.07 36.41 37.44 39.74 43.15 37.93 37.67 40.31 3.30 46.05 36.41 5.74 3.90

6 33.88 33.90 30.70 28.20 28.48 30.20 32.39 28.80 27.75 30.48 2.41 33.90 27.75 3.42 2.73

87

12 22.02 20.37 20.83 19.34 20.31 21.10 21.70 20.46 17.17 20.37 1.44 22.02 17.17 1.65 3.20

24 10.41 9.48 10.23 9.59 9.82 9.87 10.17 10.03 8.36 9.77 0.61 10.41 8.36 0.64 1.42

3hrdepth 130.02 138.15 123.22 109.23 112.33 119.22 129.46 113.80 113.00 120.94 138.15 109.23

Appendix C.15: IDF curve calculation for 20 year return period during 2011-2030 in SRA2 scenario 2011-2030 CNCM3 SRA2 GFCM21 SRA2 HADCM3 SRA2 HADGEM SRA2 IMCM3 SRA2 IPCM4 SRA2 MPEH5 SRA2 NCCCSM SRA2 NCPCM SRA2 Mean SD MAX MIN POSITIVE NEGATIVE

1 65.72 56.68 69.15 63.19 69.84 63.45 61.93 75.55 62.40 65.32 5.51 75.55 56.68 10.23 8.64

3 48.16 42.70 51.59 49.49 50.25 43.14 47.96 49.78 47.79 47.87 3.06 51.59 42.70 3.71 5.18

6 36.32 33.56 38.24 36.42 37.53 30.91 36.03 37.61 35.17 35.75 2.30 38.24 30.91 2.48 4.84

12 22.78 22.53 27.16 21.31 26.44 22.48 23.58 25.84 22.05 23.80 2.13 27.16 21.31 3.37 2.48

24 11.29 10.54 12.72 11.37 12.09 11.23 12.16 12.09 10.70 11.58 0.73 12.72 10.54 1.14 1.04

3hrdepth 144.49 128.09 154.76 148.47 150.74 129.41 143.87 149.34 143.36 143.61 154.76 128.09

Appendix C.16: IDF curve calculation for 20 year return period during 2046-2065 in SRA2 scenario 2046-2065 CNCM3 SRA2 GFCM21 SRA2 HADCM3 SRA2 HADGEM SRA2 IMCM3 SRA2 IPCM4 SRA2 MPEH5 SRA2 NCCCSM SRA2 NCPCM SRA2 Mean SD MAX MIN POSITIVE NEGATIVE

1 61.55 65.02 60.84 53.41 55.67 55.16 66.92 58.40 55.51 59.16 4.72 66.92 53.41 7.76 5.75

3 48.40 52.41 45.80 40.13 40.64 43.74 48.42 42.28 40.81 44.74 4.29 52.41 40.13 7.67 4.60

6 38.96 39.18 35.22 31.91 32.22 34.55 37.66 32.92 31.05 34.85 3.11 39.18 31.05 4.33 3.80

88

12 26.15 24.02 24.66 22.73 23.98 25.11 26.11 24.14 19.82 24.08 1.93 26.15 19.82 2.07 4.27

24 12.34 11.12 12.15 11.28 11.57 11.67 12.18 11.80 9.65 11.53 0.82 12.34 9.65 0.81 1.88

3hrdepth 145.19 157.23 137.39 120.40 121.93 131.23 145.26 126.83 122.42 134.21 157.23 120.40

Appendix D: Temporal Disaggregation using HYETOS

The input and output file for Hyetos program are presented below with an example: Input file:

The first line contain information such as D = Daily precipitation, 2 = Number of additional numeric fields (which is 1 and 1 in 5 and 6 column from third rows), second line contain information of rainfall parameters of month days (31 for instance) followed by BRLP parameters. From third row, first three columns contain information of days, month and year. The last column contains rainfall depths which are to be disaggregated into hourly rainfall. Output file:

The output file contains disaggregated information with 24 hourly data for only wet days that are provided in input file. 89

Appendix E.1: Simulation results for present time period at return periods of 2, 5 and 20 years Rainfall Depth (mm)

100 80 60 40 20 0

39.00 24.00 16.07

12

1

2

3 h

4

5

6

(b) Water Depth (m)

0.8 0.6 0.4 0.2 0 12:00 AM

2:30 AM Hours

5:00 AM

(c)

(a)

Figure E.1.1: Maximum water depth (a) in the event of 2 year return period rainfall (b) at 3 h present rainfall event and (c) maximum water depth at main street Rainfall Depth (mm)

100 80 60 40 20 0

50.88 31.12 18.35

12

1

2

3 h

4

5

6

(b) Water Depth (m)

0.8 0.6 0.4 0.2 0 12:00 AM

2:30 AM

5:00 AM

Hours

(a)

(c)

Figure E.1.2: Maximum water depth (a) in the event of 5 year return period rainfall (b) at 3 h present rainfall depth and (c) maximum water depth at main street

90

Rainfall Depth (mm)

100 80 60 40 20 0

65.15 39.85

21.69

12

1

2

3 h

4

5

6

(b) Water Depth (m)

0.8 0.6 0.4 0.2 0 12:00 AM

2:30 AM

5:00 AM

Hours

(c)

(a)

Figure E.1.3: Maximum water depth (a) in the event of 20 year return period rainfall (b) at 3h present rainfall depth and (c) maximum water depth at main street

91

Appendix E.2: Simulation results for 2 year return periods during 2011-2030 and 2046-2065 Rainfall Depth (mm)

100 80 60 40 20 0

43.73 30.27

22.72

12

1

2

3 h

4

5

6

(b)

(c)

(a)

Figure E.2.1: Maximum water depth (a) in the event of 2 year return period rainfall during 2011-2030 (CGMR/SRA1B) (b) 3 h rainfall depth and (c) maximum water depth at main street Rainfall Depth (mm)

100 80 60 40 20 0

38.37 25.63

16.94

12

1

2

3 h

4

5

6

(b)

(c)

(a)

Figure E.2.2: Maximum water depth (a) in the event of 2 year return period rainfall during 2011-2030 (IPCM4/SRA1B) (b) 3 h rainfall depth and (c) maximum water depth at main street

92

Rainfall Depth (mm)

100 80 60 40 20 0

39.91 24.09 16.94

12

1

2

3 h

4

5

6

(b)

(c)

(a)

Figure E.2.3: Maximum water depth (a) in the event of 2 year return period rainfall during 2011-2030 (GFCM21/SRA2) (b) 3 h rainfall depth and (c) maximum water depth at main street Rainfall Depth (mm)

100 80 60 40 20 0

42.91 29.09

20.29

12

1

2

3 h

4

5

6

(b)

(c)

(a)

Figure E.2.4: Maximum water depth (a) in the event of 2 year return period rainfall during 2011-2030 (HADCM3/SRA2) (b) 3 h rainfall depth and (c) maximum water depth at main street

93

Rainfall Depth (mm)

100 80 60 40 20 0

41.10 27.90

18.01

12

1

2

3 h

4

5

6

(b)

(a)

(c)

Figure E.2.5: Maximum water depth (a) in the event of 2 year return period rainfall during 2046-2065 (CGMR/SRA1B) (b) 3 h rainfall depth and (c) maximum water depth at main street Rainfall Depth (mm)

100 80 60 40 20 0

39.36 30.64 21.84

12

1

2

3 h

4

5

6

(b)

(c)

(a)

Figure E.2.6: Maximum water depth (a) in the event of 2 year return period rainfall during 2046-2065 (IPCM4/SRA1B) (b) 3h rainfall depth and (c) maximum water depth at main street

94

Rainfall Depth (mm)

100 80 60 40 20 0

40.89 29.11

18.22

12

1

2

3 h

4

5

6

(b)

(a)

(c)

Figure E.2.7: Maximum water depth (a) in the event of 2 year return period rainfall during 2046-2065 (GFCM21/SRA2) (b) 3 h rainfall depth and (c) maximum water depth at main street

Rainfall Depth (mm)

100 80 60 40 20 0

40.96 27.04 18.14

12

1

2

3 h

4

5

6

(b)

(a)

(c)

Figure E.2.8: Maximum water depth (a) in the event of 2 year return period rainfall during 2046-2065 (HADCM3/SRA2) (b) 3h rainfall depth and (c) maximum water depth at main street

95

Appendix E.3: Simulation results for 5 year return periods during 2011-2030 and 2046-2065

(b)

(a)

(c)

Figure E.3.1: Maximum water level (a) in the event of 5 year return period rainfall during 2011-2030 (CGMR / SRA1B) (b) 3h rainfall depth and maximum water depth at main street

(b)

(c)

(a)

Figure E.3.2: Maximum water level (a) in the event of 5 year return period rainfall during 2011-2030 (IPCM4 / SRA1B) (b) 3h rainfall depth and maximum water depth at main street

96

Water Depth (m)

(b) 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 12:00 AM

3:00 AM h

6:00 AM

(c)

(a)

Figure E.3.3: Maximum water level (a) in the event of 5 year return period rainfall during 2011-2030 (GFCM21/SRA2) (b) 3h rainfall depth and maximum water depth at main street

Water Depth (m)

(b) 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 12:00 AM

(a)

3:00 AM h

6:00 AM

(c)

Figure E.3.4: Maximum water level (a) in the event of 5 year return period rainfall during 2011-2030 (HADCM3/SRA2) (b) 3h rainfall depth and maximum water depth at main street

97

Water Depth (m)

(b) 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 12:00 AM

3:00 AM h

6:00 AM

(c)

(a)

Figure E.3.5: Maximum water level (a) in the event of 5 year return period rainfall during 2046-2065 (CGMR / SRA1B) (b) 3h rainfall depth and maximum water depth at main street

Water Depth (m)

(b) 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 12:00 AM

3:00 AM h

6:00 AM

(c) (a)

Figure E.3.6: Maximum water level (a) in the event of 5 year return period rainfall during 2046-2065 (IPCM4/SRA1B) (b) 3h rainfall depth and maximum water depth at main street

98

Water Depth (m)

(b) 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 12:00 AM

3:00 AM h

6:00 AM

(c) (a)

Figure E.3.7: Maximum water level (a) in the event of 5 year return period rainfall during 2046-2065 (GFCM21/SRA2) (b) 3h rainfall depth and maximum water depth at main street

Water Depth (m)

(b) 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 12:00 AM

3:00 AM h

6:00 AM

(c)

(a)

Figure E.3.8: Maximum water level (a) in the event of 5 year return period rainfall during 2046-2065 (HADCM3/SRA2) (b) 3h rainfall depth and maximum water depth at main street

99

Appendix E.4: Simulation results for 20 year return periods during 2011-2030, 2046-2065 and 2080-2099

Water Depth (m)

(b) 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 12:00 AM

3:00 AM h

6:00 AM

(c)

(a)

Figure E.4.1: Maximum water level (a) in the event of 20 year return period rainfall during 2011-2030 (CGMR/SRA1B) (b) 3 h rainfall depth and maximum water depth at main street.

Water Depth (m)

(b) 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 12:00 AM

3:00 AM h

6:00 AM

(c)

(a)

Figure E.4.2: Maximum water level (a) in the event of 20 year return period rainfall during 2011-2030 (IPCM4/ SRA1B) (b) 3 h rainfall depth and maximum water depth at main street

100

Water Depth (m)

(b) 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 12:00 AM

3:00 AM h

6:00 AM

(c)

(a)

Figure E.4.3: Maximum water level (a) in the event of 20 year return period rainfall during 2011-2030 (GFCM21/SRA2) (b) 3 h rainfall depth and maximum water depth at main street

Rainfall Depth (mm)

100 80 60 40 20 0

69.15 48.85

36.76

12

1

2

3 h

4

5

6

Water Depth (m)

(b) 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 12:00 AM

3:00 AM h

6:00 AM

(c)

(a)

Figure E.4.4: Maximum water level (a) in the event of 20 year return period rainfall during 2011-2030 (HADCM3/SRA2) (b) 3 h rainfall depth and maximum water depth at main street

101

Rainfall Depth (mm)

100 80 60 40 20 0

67.16 44.84 31.65

12

1

2

3 h

4

5

6

Water Depth (m)

(b) 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 12:00 AM

3:00 AM h

6:00 AM

(c)

(a)

Figure E.4.5: Maximum water level (a) in the event of 20 year return period rainfall during 2046-2065 (CGMR / SRA1B) (b) 3 h rainfall depth and maximum water depth at main street

Rainfall Depth (mm)

100 80 60 40 20 0

68.83 57.17 48.68

12

1

2

3 h

4

5

6

Wa ter Depth (m)

(b) 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 12:00 AM

3:00 AM h

6:00 AM

(c)

(a)

Figure E.4.6: Maximum water level (a) in the event of 20 year return period rainfall during 2046-2065 (IPCM4 / SRA1B) (b) 3 h rainfall depth and maximum water depth at main street

102

Rainfall Depth (mm)

100 80 60 40 20 0

65.02 52.98 39.23

12

1

2

3 h

4

5

6

Wa ter Depth (m)

(b) 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 12:00 AM

3:00 AM h

6:00 AM

(c)

(a)

Figure E.4.7: Maximum water level (a) in the event of 20 year return period rainfall during 2046-2065 (GFCM21 / SRA2) (b) at present condition and maximum water depth at main street

Rainfall Depth (mm)

100 80 60 40 20 0

65.84 36.39 38.16

12

1

2

3 h

4

5

6

Wa ter Depth (m)

(b) 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 12:00 AM

3:00 AM h

6:00 AM

(c)

(a)

Figure E.4.8: Maximum water level (a) in the event of 20 year return period rainfall during 2046-2065 (HADCM3 / SRA2) (b) at present condition and maximum water depth at main street

103