Evaluating precipitation datasets for large-scale distributed hydrological modelling

Our understanding of the advantages and limitations of satellite derived precipitation datasets as a forcing to hydrological models has made tremendous progress over the past decade. However, most studies have analysed only the performance of one or few datasets, were limited to selected small-scale case studies or used lumped models when investigating large-scale basins

In our recent publication on Journal of Hydrology we evaluate 18 precipitations datasets used as input in a distributed hydrological model to simulate river flow in 8 large-scale basins. The findings of our study underlined the importance of the proper selection of precipitation products and demonstrated that there is not a unique best performing precipitation dataset for all basins and results are very sensitive to the basin characteristics.

The outcomes of our research are valuable to support the selection of precipitation dataset to achieve reliable model results for global and large-scale applications. This is the first research that attempts to model large-scale basins using multiple global datasets of precipitations within a distributed hydrological model. Our research offers promising results that might be key in assessing flow values in data scarce river basins.

By Maurizio Mazzoleni


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