Abstract. The understanding of physical dynamics is crucial to
provide scientifically credible information on lake ecosystem management.
We show how the combination of in situ observations, remote sensing data, and
three-dimensional hydrodynamic (3D) numerical simulations is capable of
resolving various spatiotemporal scales involved in lake dynamics. This
combination is achieved through data assimilation (DA) and uncertainty
quantification. In this study, we develop a flexible framework by
incorporating DA into 3D hydrodynamic lake models. Using an ensemble Kalman
filter, our approach accounts for model and observational uncertainties. We
demonstrate the framework by assimilating in situ and satellite remote
sensing temperature data into a 3D hydrodynamic model of Lake Geneva.
Results show that DA effectively improves model performance over a broad
range of spatiotemporal scales and physical processes. Overall, temperature
errors have been reduced by 54 %. With a localization scheme, an ensemble
size of 20 members is found to be sufficient to derive covariance matrices
leading to satisfactory results. The entire framework has been developed
with the goal of near-real-time operational systems (e.g., integration into
meteolakes.ch).