Abstract/Summary

Floods are among the most devastating natural hazards in the world. With climate change and growing urbanization, floods are expected to become more frequent and severe in the future. Hydrodynamic models are powerful tools for flood hazard assessment but face numerous challenges, especially at a large scale. The downside of discretizing an area using a fine meshing for yielding more accurate results is the expensive computational cost of simulations. Moreover, critical input information such as bathymetry (i.e, riverbed geometry) are required but cannot be easily collected by field or remote sensing derived data. During the past few years, the development of sub grid models has gained a growing interest as these allow combining accuracy with high computational efficiency. They indeed enable faster simulations as they use coarser cells and preserve small-scale topography variations within the cell. In this study, we propose a modelling framework based on the shallow water 2D model with depth-dependant porosity (SW2D-DDP) and assess its ability to represent topography and bathymetry through porosity functions. To enable a careful and meaningful evaluation of the model, we set up a 2D fine model and use it as a reference. We also use ground truth and remote sensing derived flood inundation maps to evaluate the proposed modelling framework. We use as test cases the 2007 and 2012 flood events of the Severn river. Our empirical results holds promising results for fast flood simulation at a large scale, with satisfying performance levels and reduced computational efforts.