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Precipitation is one of the most important components of the water cycle and its accurate spatial and temporal representation is fundamental for hydrological modeling. In the present study, we investigated the impact of spatial resolution of various precipitation datasets on discharge estimates. First, a new precipitation spatial downscaling procedure was developed and applied to four gridded global precipitation datasets based on (i) solely satellite observations: CMORPH and PERSIANN, (ii) satellite and in situ observations: TRMM and (iii) satellite and in situ observations and reanalysis data: MSWEP. The here presented downscaling methodology blended global precipitation datasets with data on vegetation and topography to improve the representation of precipitation spatial variability. Second, interpolated in situ, non-downscaled (25 km) and downscaled (1 km) precipitation data were used to force a grid-distributed version of the HBV-96 rainfall-runoff model for the Magdalena River basin in Colombia. Results showed that MSWEP and TRMM outperformed CMORPH and PERSIANN precipitation datasets. The downscaling procedure resulted in considerable improvements in coefficient of determination, root mean square error and bias in comparison with in situ precipitation observations. Discharge model estimates were also in better agreement with the observations when the model was forced with the downscaled precipitation. Model performance was improved with Kling Gupta efficiency increases in the order of 0.1 to 0.5. Moreover, better discharge simulations were obtained using downscaled precipitation compared to using only in situ precipitation data when using less than 100 stations.

Spatial Downscaling of Satellite-Based Precipitation and Its Impact on Discharge Simulations in the Magdalena River Basin in Colombia