Global 1D river bathymetry estimation from remotely sensed observations

Flooding has major economic, social, and environmental implications. Its modeling provides insights into potential risks and contributes to the protection of lives, natural resources, and infrastructure. In flood hazard assessment, the topography representation is a key factor as it dictates the water extent resulting from the simulations. In particular, for small and medium flood scenarios, it is imperative to have good knowledge of the modeled in-channel water height, especially for the river's bank full discharge. As these constitute the majority of flood events, the risk assessment is severely impacted by the quality of their estimates. However, the determination of the water profile can be a challenging task in data-sparse areas, as the bathymetry of the river channels is not well described in open-access digital elevation models (DEMs). Using the global coverage of remote sensing derived water levels and extents, this study builds towards a global estimation of river bathymetry. The methodology to achieve this can be divided into two parts, the correction of the river topography that can be directly observed by the sensors, above a minimum water level (the dry bathymetry), and the estimate of the part under the minimum observed water line (wet bathymetry). For the improvement of the dry bathymetry, the contours from water masks derived by optical sensors are projected in DEMs and a smooth profile is built from upstream to downstream. The wet bathymetry is calculated using hydraulic simulation and inverse problem methodologies. It requires as inputs the corrected dry bathymetry, observed water surface elevation and slope, and a prior discharge. The algorithm computes the flow using an integrated version of a modified Manning–Strickler’s equation and probability from beta distribution. It computes the roughness and the bottom depth of the section assuming a rectangular shape. Preliminary results are promising; a good agreement with in-situ discharge was achieved for the Po River (NSE > 0.8). It shows the potential and importance of accurate estimates of the river bathymetry for future flood monitoring and forecast.