Urban Flood Simulation by Coupling a Hydrodynamic Model with a Hydrological Model Hongbin Zhang A thesis submitted for the degree of Doctor of Philosophy School of Civil Engineering and Geosciences Faculty of Science, Agriculture and Engineering Newcastle University Newcastle upon Tyne UK July 2014 i Declaration I hereby certify that this work is my own, except where otherwise acknowledged, and that it has not been submitted previously for a degree or other qualification at this, or any other university. Signature: Hongbin Zhang 03/07/2014 ii Abstract This work introduces a new integrated flood modelling tool in urban areas by coupling a hydrodynamic model with a hydrological model in order to overcome the drawbacks of each individual modelling approach, i.e. high computational costs usually associated with hydrodynamic models and less detailed physical representations of the underlying flow processes corresponding to hydrological models. Crucial to the simulation process is to first divide the catchment hydraulic and hydrological zones where the corresponding model is then applied. In the hydrological zones that have more homogeneous land cover and relatively simple topography, a conceptual lumped model is applied to obtain the surface runoff, which is then routed by a group of pre-acquired ‘unit hydrographs’ to the zone border, for high-resolution flood routing in the hydraulic zones with complex topographic features, including roads, buildings, etc. In hydraulic zones, a full 2D hydrodynamic model is applied to provide more detailed flooding information e.g. water depth, flow velocity and arrival time. The new integrated flood modelling tool is validated in Morpeth, the North East of England by reproducing the September 2008 flood event during which the town was severely inundated following an intense rainfall event. Moreover, the coupled model is investigated and evaluated according to the effects from temporal and spatial resolutions, friction, rainfall, infiltration, buildings and coupling methods. In addition, the model is also employed to implement flood damage estimations with different scenarios of the upstream storage and flood defences in the town centre. Whilst producing similar accuracy, the new model is shown to be much more efficient compared with the hydrodynamic model depending on the hydrological zone percentage. These encouraging results indicate that the new modelling tool could be robust and efficient for practitioners to perform flood modelling, damage estimation, risk assessment and flood management in urban areas and large-scale catchments. i Acknowledgements First of all I would like to express my deepest gratitude to my supervisor, Professor Qiuhua Liang, for his guidance throughout. He has supported, encouraged and provided me with constructive criticism right through my PhD study. I have also learnt a rigorous scientific approach and a positive philosophy of life from his example that will be beneficial in my future career and life. At the same time I have appreciated the patient instruction and helpful suggestions of my other supervisors, Professor Chris Kilsby and Professor Lan Li. All three professors have enabled me to complete this PhD thesis. I would like to acknowledge, too, the financial sponsorship of ‘The School of Civil Engineering and Geosciences’ and ‘The Chinese Scholarship Council’ for the first three years. Then for the fourth year of my PhD study ‘The Henry Lester Trust Limited’ and ‘The Great Britain-China Educational Trust’ took over the financial expenditure. Without all their funding, I could not have afforded the four years of study overseas. There are other individuals who have given me their time and effort so that I could complete my research. Dr. Geoff Parkin has given me access to observed data and shared useful information with me about the research site. The Environmental Agency and Morpeth Flood Scheme Team have provided me with a range of useful data, too. These sources are the base for and a prerequisite of my thesis. Additionally, I have to thank for the technical support from my colleagues, Jingming Hou, Luke Smith, Yueling Wang, Rong Zhang, Steve Birkinshaw, Reza Amouzgar, Martin Robertson and Laura Hanson as well as Samantha Mahaffey for proof reading this thesis. Life is always punctuated by ups and downs, which have been ever-present over the past four years, so I have cherished the selfless and unwavering love from my parents, my Aunty Lirong and my best friend, Huiyong Li. When the way forward has been difficult, they have been constantly encouraging me to persevere. So I would like to dedicate this thesis to them as a token of my appreciation of all they have done for me. Besides them, I would also like to thank my dear friends, Miao Wang, Roger Darsley, Norma Darsley, Lili Zhang, Jingchun Wang, Alexander Nicolson, Xiaohao Shi, Xilin Xia, Guang Gao, Ciprian Spatar, Mengfei Yang, Dan McBride, Pan Chen, Yu Huang, You Luo, Lei Cui, Xueguan Song, Wenbin Liu, Jia Jia, Huibo Liu, not forgetting the ii care shown by Dr. Sophie Weatherhead and the nurses in the Royal Victoria Infirmary, Newcastle. My final acknowledgement is for the services offered by the computing technicians, administrative staff and librarians at Newcastle University, especially Graham Patterson, Melissa Ware, Hannah Lynn, Lynn Patterson and Ruth Vater. iii Abbreviations 1D One-dimensional 2D Two-dimensional 3D Three-dimensional ADI Alternating Direction Implicit ADO above Ordnance datum AMR Adaptive Mesh Refinement ANN Artificial Neural Network API Application Processing Interface ArcGIS A GIS for working with maps and geographic information ARMA Auto-Regressive Moving Average AUD Australia dollar CEH Centre for Ecology and Hydrology CFL Courant-Friedrichs-Lewy CNY Chinese Yuan CPU Central Processing Unit CUDA Compute Unified Device Architecture DDF Depth-Duration-Frequency DEFRA Department for Environment, Food and Rural Affairs DEM Digital Elevation Model DIVAST Diffuse Source Vertical Analytical Solute Transport Model DSM Digital Surface Model DTM Digital Terrain Model DWM Diffusion Wave Model EA Environment Agency EPSRC Engineering and Physical Sciences Research Council ESRI Environmental Systems Research Institute FDM Finite Difference Method FEH Flood Estimation Handbook FEM Finite Element Method FHRC Flood Hazard Research Centre FRMRC Flood Risk Management Research Consortium FSR Flood Studies Report iv FVM Finite Volume Method GBP Great Britain Pound GIS Geographic Information System GNSS Global Navigation Satellite System GPGPU General-Purpose Graphics Processing Units GPS Global Positioning System GPU Graphics Processing Unit HBV Hydrologiska Byråns Vattenbalansavdelning (Swedish) HEC-RAS Hydrologic Engineering Center - River Analysis System HLLC Harten Lax and van Leer-Contact HOST Hydrology of Soil Type HYMAS Hydrological Model Application System ID Identification IDF Intensity-Duration-Frequency IHDM Institute of Hydrology Distributed Model InterpOSe A software to process Ordnance Survey MasterMap files IPCC Intergovernmental Panel on Climate Change ISIS A commercial model by CH2M HILL engineering company IUDM Integrated Urban Drainage Management JBA Jeremy Benn Associates JFLOW A 2D flood model by JBA consulting company LiDAR Light Detection and Ranging MIKE A series of commercial models by the Danish Hydraulics Institute MPI Message Passing Interface NVIDIA An American global technology company ODE Ordinary Differential Equation OpenCL Open Computing Language OpenMP Open Multiple Processing OS Ordnance Survey PVM Parallel Virtual Machine RAF Royal Air Force ReFH Revitalised Flood Hydrograph RMA2 Resource Management Association-2 (a hydraulic model) RNLI Royal National Lifeboat Institution v SAR Synthetic Aperture Radar SHE Système Hydrologique Européen (French) SWAT Soil Water Assessment Tool SWM Stanford Watershed Model SWMPs Surface Water Management Plans TBR Tipping Bucket Rain gauge TELEMAC A commercial model by the Division for Research and Development of the French Electricity Board TOPMODEL A topography-based hydrological model TUFLOW Two-dimensional Unsteady FLOW TVD Total variation diminishing TWI Topographic Wetness Index UH Unit Hydrograph USDA United States Department of Agriculture WBM Water Balance Model ZIM Zero Inertial Model vi Notations A cross-sectional area AREA catchment area (km) B benchmark dataset 3 BF0 initial base flow (m /s) BFIHOST base flow index from ‘Hydrology of Soil Types’ classification BL base flow recession constant BR ratio of base flow recharge to runoff C DDF curve parameter C f bed roughness coefficient Cini initial soil moisture content (mm) Cmax maximum soil moisture content (mm) Cr Courant number D design rainfall duration (hours) D1 DDF curve parameter D2 DDF curve parameter D3 DDF curve parameter dis distance between rain gauges and the town centre DPLBAR mean drainage path length (km) DPSBAR mean drainage path slope (m km-1) E east interface of a cell E0 DDF curve parameter EE evapotranspiration f flux vector in x-direction in shallow water equations f infiltration F DDF curve parameter F1 first fit statistic F2 second fit statistic FF cumulative depth of infiltration g fluxes vector in y-direction in shallow water equations g gravitational acceleration GE global error of water depth vii h water depth h water depth before non-negativity depth reconstruction i cell index in x-direction I position at inner boundary ii index of rain gauges j cell index in y-direction k time level Ks saturated hydraulic conductivity Ks1 saturated