Estimating spatial and temporal variations of surface waters is important for water resources management. The upcoming Surface Water and Ocean Topography (SWOT) mission will enhance our understanding on global water cycle by measuring water surface elevations at a high resolution. It will be benificial to combine SWOT observations to hydrodynamic modelling to overcome its limited observation frequency. We performed an observing system simulation experiment (OSSE) for estimating river channel bathymetry from water surface elevation (WSE) measured by SWOT. A Local Ensemble Transform Kalman Filter (LETKF) assimilation algorithm was applied to the CaMa-Flood hydrodynamic model to estimate WSE and bathymetry simultaneously via state-parameter estimation. Synthetic SWOT observations were generated from a “true” CaMa-Flood simulation based on the original bathymetry while the corrupted model used channel bathymetry parameters different from the true model. The LETKF via state-parameter estimation succeeded to estimate the true bathymetry using SWOT observations with 1.36m global average root mean square error (RMSE), which was improved by 68.0% from the corrupted bathymetry. Furthermore, the river discharge was also reasonably estimated. These results indicate the potential of the future SWOT mission to spatially and temporally estimate river bathymetry and river discharge on a global scale.
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