Streamflow prediction in ungauged basins: analysis of regionalization methods in a hydrologically heterogeneous region of Mexico

Abstract

The ability of three regionalization methods – multiple linear regression (MLR), spatial proximity (SP) and physical similarity (PS) – to predict streamflow in ungauged catchments in Mexico is estimated. Three hydrological models (GR4J, HMETS and MOHYSE) are calibrated on 30 diverse catchments in Mexico. Implementation of leave-one-out cross-validation enabled the regionalization skill at each of the 30 study sites to be estimated, or the site to be considered as being ungauged. This study shows that regionalization in a hydrologically heterogeneous area, such as the Mexican area under study, poses problems to regionalization approaches that depend on physical catchment descriptors, such as MLR and PS. The transfer of complete parameter sets from a neighbouring catchment provided the most robust method to estimate streamflow in semi-arid and humid ungauged basins. The model performance for arid catchments was worse in the context of regionalization, with GR4J being more robust than the other models due to its simpler structure.