Leveraging SWOT Surface Data for River Bathymetry Estimation and Compound Flood Model Calibration in Data-Sparse Regions

Coastlines are increasingly vulnerable to the compound effects of high sea levels, intense rainfall, and extreme river discharge from tropical cyclones. Accurate compound flood modelling is critical for assessing flood risks and informing forecasts under current and future climate scenarios. However, in data-sparse regions like southeastern Africa, such modelling faces significant challenges due to the lack of river bathymetry data, which cannot be obtained remotely, and the limited or absent in situ gauge data required for model calibration. The recently launched Surface Water and Ocean Topography (SWOT) satellite mission offers a transformative solution, as it can observe compound water surface profiles with centimetre-scale vertical accuracy. This study explores the potential of SWOT water elevations to estimate river bathymetry for the Pungwe and Buzi Rivers in Mozambique. This bathymetry data is then integrated with FABDEM for the simulation of compound flooding caused by Tropical Cyclone Idai near Beira, Mozambique, using the LISFLOOD-FP hydrodynamic model. This simulation incorporates coastal water levels from the ADCIRC model as downstream boundary conditions, river discharge data from the ERA5-driven ECLand model as upstream boundary conditions, and precipitation data from ERA5 to drive the LISFLOOD-FP model. A unique aspect of this study is the calibration of the LISFLOOD-FP model using SWOT surface water elevations. This integrated approach enables accurate compound flood simulation in data-sparse regions. By integrating diverse data sources, this research enhances understanding of flood risks from tropical cyclones and provides a framework for enhanced early warning systems and mitigation strategies in data-sparse coastal regions.