Framework for Dynamic Modelling of Urban Floods at Different Topographical Resolutions: UNESCO-IHE PhD Thesis

1st Edition

Solomon D. Seyoum

CRC Press
Published March 8, 2013
Reference - 160 Pages
ISBN 9781138000483 - CAT# K20817
Series: IHE Delft PhD Thesis Series

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Urban flood risks and their impacts are expected to increase as urban development in flood prone areas continues and rain intensity increases as a result of climate change while aging drainage infrastructures limit the drainage capacity in existing urban areas. Flood mitigation strategies are required as part of sound urban flood management plans to assess flood risk and identify technically feasible and cost-effective options to reduce the risk.

Central to the development of flood mitigation strategies is the efficient prediction of flood propagation characteristics in urban areas. Detailed predictions of flood flows in urban areas require the use of high resolution topographic data. However, due to computational demand topographic data is often generalised to a more manageable resolution and floodplain models are built at much coarser resolutions.

The research presented in this thesis addresses the problem of capturing small-scale features in coarse resolution urban flood models with the aim of improving flood forecasts in geometrically complex urban environments. The approach adopted in the research used a two-dimensional surface flow modelling system that can extract and incorporate useful information available in high resolution topographic data into coarse grid models. The thesis also presents modelling of the complex interaction between surcharged sewer and flows associated with urban flooding and incorporation of infiltration process in surface flow modelling tools.

Case study results showed that the incorporating volume-depth and flow-area-depth relationships extracted from high resolution topographic data significantly improved the results of coarse grid urban flood models while taking the advantage of reduced computational time to ensure efficient prediction of urban flood characteristics.


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