IDENTIFIABILITY AND ASSIMILATION OF SPARSE ALTIMETRIC DATA IN 1D SAINT-VENANT RIVER MODELS
Résumé
Various altimetry satellite missions provide water level elevation measurements of wide rivers; in particular the future Surface Water and Ocean Topography (SWOT) mission which will provide measurements at unprecedented accuracies for 90% of the globe. The capabilities of these data to infer the inflow discharge and roughness parameters in the 1D Saint-Venant equations are investigated. As a first step, identifiability maps representing the observations and the model waves in the (x, t) plan are analysed. These maps provide a comprehensive overview of the inverse problem diculties. Next the inflow discharge, and potentially the roughness coecients, are inferred by variational data assimilation. The inferences are analysed for various observation sparsity degrees. Sensitivity analyses with respect to the observation errors and the first guess values demonstrate the robustness of the VDA inferences. The identifiability maps may make it possible to recover the space-time windows where the inference obtained by VDA are potentially unreliable. Finally this study demonstrates the great potential of these forthcoming measurements but also their limitations in 1D river flow dynamic models if not combined to other data sources.
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