With floods becoming more severe and frequent due to climate change and growing urbanisation, there is a crucial need to improve flood management systems. Flood hazard assessment using hydrodynamic models is more challenging at a large scale, because discretizing an area using a fine mesh is essential for obtaining accurate results, but is found to be highly expensive computationally. The emergence of sub grid models in the past few decades has enabled faster simulations by using coarser cells while preserving small-scale topography variations within one cell. In this context, we propose a modelling framework based on the shallow water 2D model with depth-dependant porosity (SW2D-DDP). We evaluate this approach by setting up a standard 2D shallow water model (SW2D) considered as a benchmark. The 2007 flood event of the River Severn is used as a test case. Our preliminary results demonstrate a high accuracy (~90% during flood peak) and a low computational cost with a ~350 runtime reduction factor of the proposed model compared to a standard model. This opens up new perspectives for large scale applications over areas where bathymetric data is not available.