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Framework for Dynamic Modelling of Urban Floods at Different Topographical Resolutions - UNESCO-IHE PhD Thesis (Hardcover)
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Framework for Dynamic Modelling of Urban Floods at Different Topographical Resolutions - UNESCO-IHE PhD Thesis (Hardcover)
Series: IHE Delft PhD Thesis Series
Expected to ship within 12 - 17 working days
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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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