The availability of rainfall information with high spatial resolution is of fundamental importance in many the spatial variation of rainfall effectively. Although satellite data have been widely used around the world, when applied to local regions the spatial resolution of these products is too coarse. In this paper, an approach to identify a downscaling method through geostatistical regionalization to improve water resources models with short spatial and temporal scales and with limited rainfall data is presented. Three different models were applied: Cokriging, Inverse Distance Weight (IDW) and Kriging. Statistical parameters such as mean absolute error (MAE) and root using the Cokriging method. The regionalization results were quite comparable with the rain gauge stations data. Although the model outcomes did not improve remarkably, the contribution of this approach may have the potential to provide useful rainfall data at spatial scales shorter than the present resolution.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.