Abstract
High accuracy models are required for informed decision making in urban flood management. This paper develops a new holistic framework for using information collected from multiple sources for setting parameters of a 2D flood model. This illustrates the importance of identifying key urban features from the terrain data for capturing high resolution flood processes. A Cellular Automata based model CADDIES was used to simulate surface water flood inundation. Existing reports and flood photos obtained via social media were used to set model parameters and investigate different approaches for representing infiltration and drainage system capacity in urban flood modelling. The results of different approaches to processing terrain datasets indicate that the representation of urban micro-features is critical to the accuracy of modelling results. The constant infiltration approach is better than the rainfall reduction approach in representing soil infiltration and drainage capacity, as it describes the flood recession process better. This study provides an in-depth insight into high resolution flood modelling.
Highlights
Urban flooding has become one of the most significant natural hazards due to climate change and rapid urbanization (Di Paola et al, 2014; Fu et al, 2011; Vacondio et al, 2016; Yang et al, 2016; Yin et al, 2016b)
Modelling accuracy is still affected by four main issues: 1) the level of details available in the topographic representations of terrain and urban key features (Haile and Rientjes, 2005; Horritt and Bates, 2001; Leandro et al, 2016; Rafieeinasab et al, 2015); 2) the lack of calibration and validation data (Fu et al, 2011; Hall et al, 2005; Horritt, 2000; Leandro et al, 2011); 3) the approach used to consider the effects of underground urban drainage infrastructure (Chen et al, 2009; Environment Agency, 2013b); and 4) the uncertainty of accelerated land use changes (De MOEL and Aerts, 2011; Du et al, 2015; Shi et al, 2007)
We adopted the same approaches of building footprint treatment, rainfall reduction and roughness setting to the ones used in the Environment Agency (EA)’s surface water mapping for comparing our modelling results
Summary
Urban flooding has become one of the most significant natural hazards due to climate change and rapid urbanization (Di Paola et al, 2014; Fu et al, 2011; Vacondio et al, 2016; Yang et al, 2016; Yin et al, 2016b). Modelling accuracy is still affected by four main issues: 1) the level of details available in the topographic representations of terrain and urban key features (Haile and Rientjes, 2005; Horritt and Bates, 2001; Leandro et al, 2016; Rafieeinasab et al, 2015); 2) the lack of calibration and validation data (Fu et al, 2011; Hall et al, 2005; Horritt, 2000; Leandro et al, 2011); 3) the approach used to consider the effects of underground urban drainage infrastructure (drainage capacity) (Chen et al, 2009; Environment Agency, 2013b); and 4) the uncertainty of accelerated land use changes (De MOEL and Aerts, 2011; Du et al, 2015; Shi et al, 2007). Most of the applications can only underpin the locations and timing of flooding, and require human labour to extract flood depth or extent information (Fohringer et al, 2015)
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