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 1023 possible combinations of ten 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 AMSR‐E and ASCAT soil moisture, GRACE total water storage anomalies, as well as, in snow dominated catchments, the MODIS snow cover products. The evaporation products of LSA‐SAF 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.