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. The understanding of physical dynamics is crucial toprovide scientifically credible information on lake ecosystem management.We show how the combination of in situ observations, remote sensing data, andthree-dimensional hydrodynamic (3D) numerical simulations is capable ofresolving various spatiotemporal scales involved in lake dynamics. Thiscombination is achieved through data assimilation (DA) and uncertaintyquantification. In this study, we develop a flexible framework byincorporating DA into 3D hydrodynamic lake models. Using an ensemble Kalmanfilter, our approach accounts for model and observational uncertainties. Wedemonstrate the framework by assimilating in situ and satellite remotesensing temperature data into a 3D hydrodynamic model of Lake Geneva.Results show that DA effectively improves model performance over a broadrange of spatiotemporal scales and physical processes. Overall, temperatureerrors have been reduced by 54 %. With a localization scheme, an ensemblesize of 20 members is found to be sufficient to derive covariance matricesleading to satisfactory results. The entire framework has been developedwith the goal of near-real-time operational systems (e.g., integration intometeolakes.ch).

Data assimilation of in situ and satellite remote sensing data to 3D hydrodynamic lake models: a case study using Delft3D-FLOW v4.03 and OpenDA v2.4