In a context of climate change and potential intensification of the hydrological cycle, improving representation of water fluxes within river basins is of paramount importance for hydrological sciences and operational forecasts. To leverage multi-sourced observations (in situ, satellite, drones) of the critical zone, innovative approaches integrating hydraulic-hydrological modeling and multi-variate assimilation methods are needed. They should enable ingesting spatially distributed forcings, physiographic descriptors, hydrodynamic signatures from multi-source observables, and tackle calibration/correction problems in integrated models. Water surface observables are valuable to constrain hydraulic models of river reaches ( and references therein) and complex flow zones (confluences, floodplains), forced by spatially distributed inflows (, ). Since hydraulic large scale modeling can be computationally costly, a combination of effective 1D representations and 2D zooms, completed by hydrological modules, may be useful. This contribution presents the development of a complete multi-dimensional hydraulic-hydrological toolchain, based on the 2D hydraulic model and variational data assimilation platform DassFlow [4,5]. A new method for multi-dimensional hydraulic modeling, relying on a single 2D 2nd order solver applied to 1Dlike-2D meshes, is presented. Inferences of large composite control vectors, including hydrological and hydraulic controls, are carried out on academic and real cases in twin experiments. Accurate results are achieved given sufficient observability of parameters signatures, including information feedback from the river network to upstream hydrological catchments models.
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