Parameter identification in hydrological models using groundwater-level measurements and satellite-based soil moisture

The main objective was to identify representative parameter sets for two mesoscale catchments. Discharge, groundwater level and satellite-based soil moisture were included in the calibration process to increase the robustness and the representativeness of the parameters. The integration of these data led to an innovative, detailed soil water description, which was implemented in a hydrological model with its corresponding parameters as an appropriate interface to optimization algorithms. A new lexicographic calibration strategy was applied. A comparison with results obtained by the SCE-UA (shuffled complex evolution algorithm) optimization showed that the lexicographic approach delivered more similar parameter sets, had more similar effects on the hydrological system and was much faster. The similarity of the parameter values and the cross-validation scores indicates the good representativeness of the model and furthermore emphasizes the application of a lexicographic calibration procedure. The consideration of groundwater level was a clear-cut benefit for the calibration, while the consideration of satellite-based soil moisture showed no significant effects in the two catchments.