High accuracy river discharge estimation using UAS-based hydrometry and SWOT-derived WSE            

Abstract. River discharge remains critically ungauged across much of the globe, limiting the accuracy of flood forecasting and constraining climate adaptation strategies. To address this, we propose a novel framework that integrates occasional Unoccupied Aerial Systems (UAS) with satellite Earth observations. Specifically, we construct a high-resolution hydraulic model of the Torne River in northern Scandinavia by combining a steady gradually varied flow (SGVF) solver with riverbed geometry extracted from UAS-based water-penetrating radar (WPR). The model is calibrated using four in-situ measured discharge–water surface elevation (WSE) snapshots from the Surface Water and Ocean Topography (SWOT) mission to estimate spatially variable, depth-dependent Manning’s roughness coefficients via automated optimization. This calibration enables river discharge to be estimated solely from satellite altimetry data (e.g., SWOT, Sentinel-3, and ICESat-2). Our approach demonstrates high accuracy and operational feasibility, achieving a mean absolute relative error of only 6.15 % when validated against in situ gauge measurements. Remarkably, the model successfully reconstructed an extreme 100-year flood event observed by ICESat-2, with an error of just 2.59 %. This framework provides a scalable and transferable approach for accurately estimating river discharge in virtually any reach observable by SWOT, in combination with one-off or occasional UAS hydrometry surveys.