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.