An integrated framework for high-resolution urban flood modelling considering multiple information sources and urban features

TLDR

High accuracy models are required for informed decision making in urban flood management. The study develops a holistic framework that integrates multiple information sources to set parameters for a 2D flood model. The framework employs a Cellular Automata model (CADDIES) that incorporates terrain‑derived urban features and parameterizes infiltration and drainage using data from reports, flood photos, and social media. The results show that accurately representing urban micro‑features is essential, and that a constant infiltration approach outperforms rainfall‑reduction methods in capturing flood recession, offering deeper insight into high‑resolution flood modelling.

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.