Plain Language Summary
Hydrological models are important tools for many applications in water resources, such as natural hazards management, quantification of impacts of climate change or anthropogenic effects on the water cycle. However, there are uncertainties in these models, which might lead to inaccurate predictions. In many cases, they are related to calibrating parameters of the model by comparing in-situ streamflow observations with modeled streamflow estimates. Therefore, internal processes in the model might be misrepresented, i.e., the model might be getting the “right results for the wrong reasons”, which compromises model reliability and its estimates. An alternative is to calibrate the model parameters with remote sensing (RS) observations of the water cycle. In this study, we analyzed the contribution of five RS-derived variables (water level, flood extent, terrestrial water storage, evapotranspiration, and soil moisture) to calibrate model parameters. We found that RS-based calibration was able to improve water cycle representation (e.g., calibration with water level was able to improve estimates of water level, flood extent, terrestrial water storage and evapotranspiration). Moreover, by looking at multiple RS observations of the water cycle, we were able to found inconsistencies in model structure and parameterization, which would remain unknown if only discharge observations were considered.