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.

Droughts are a natural phenomenon of water deficit and represent one of the most dangerous natural hazards to human activities; accordingly, its understanding and monitoring are vital. For doing this, long historical series of precipitation and evapotranspiration are considered; however, the sources of this observed information on land are usually limited spatially and temporally. Consequently, the use of complementary sources of information, such as reanalysis, is appropriate in areas with scarce information. Thus, we have evaluated the use of the reanalysis databases of the eartH2Observe project (WFDEI & MSWEP) in the Magdalena-Cauca river basin in Colombia, through the calculation of three drought indicators (SPI, SPEI & WCI). The indices calculated with the in-situ data identified ten drought events of great affectation in the basin. Applying statistical and a Bootstrap uncertainty analysis, we evaluate the performance of the reanalysis, finding that the use of the MSWEP precipitation product has a good potential for the analysis of droughts in Colombia

Los diseños de alcantarillados pluviales y combinados normalmente se realizan bajo el supuesto de que la precipitación es constante en tiempo y espacio para áreas inferiores a 1 km2. Con el fin de aportar al conocimiento de la variabilidad espacio-temporal de la precipitación en cuencas urbanas de este tipo, y de determinar su impacto en el diseño de alcantarillados y, más aún, en los caudales de escorrentía obtenidos a partir de la aplicación de modelos  matemáticos rigurosamente calibrados y validados, se ha instrumentado densamente la microcuenca urbana del campus de la Universidad Nacional de Colombia, sede Bogotá. En este artículo se describen los análisis detallados del evento más importante en términos de intensidad y precipitación total.