Constraining Conceptual Hydrological Models With Multiple Information Sources

The calibration of hydrological models without streamflow observations is problematic, and the simultaneous, combined use of remotely sensed products for this purpose has not been exhaustively tested thus far. Our hypothesis is that the combined use of products can (1) reduce the parameter search space and (2) improve the representation of internal model dynamics and hydrological signatures. Five different conceptual hydrological models were applied to 27 catchments across Europe. A parameter selection process, similar to a likelihood weighting procedure, was applied for 1,023 possible combinations of 10 different data sources, ranging from using 1 to all 10 of these products. Distances between the two empirical distributions of model performance metrics with and without using a specific product were determined to assess the added value of a specific product. In a similar way, the performance of the models to reproduce 27 hydrological signatures was evaluated relative to the unconstrained model. Significant reductions in the parameter space were obtained when combinations included Advanced Microwave Scanning Radiometer - Earth Observing System and Advanced Scatterometer soil moisture, Gravity Recovery and Climate Experiment total water storage anomalies, and, in snow-dominated catchments, the Moderate Resolution Imaging Spectroradiometer snow cover products. The evaporation products of Land Surface Analysis - Satellite Application Facility and MOD16 were less effective for deriving meaningful, well-constrained posterior parameter distributions. The hydrological signature analysis indicated that most models profited from constraining with an increasing number of data sources. Concluding, constraining models with multiple data sources simultaneously was shown to be valuable for at least four of the five hydrological models to determine model parameters in absence of streamflow.