Daily streamflow prediction in ungauged basins: an analysis of common regionalization methods over the African continent

Abstract

ABSTRACT Predicting streamflow in ungauged basins is a critical task in many projects. This study investigates the applicability and performance of three common regionalization methods over 314 catchments distributed throughout Africa, using the HMETS hydrological model. This study finds that regionalization methods that work well in other regions of the world also do so in the African catchments, except for multiple linear regression which performs worse than expected. Furthermore, skill-based donor filtering is shown to be problematic in some circumstances, notably when the filter rejects mediocre but still useful donors. This study shows that regionalization performance depends more on the hydrological regime of the ungauged catchment and the capacity of hydrological models to adapt to that regime, rather than on any physiographic or climatological metric. In the context of ungauged basins, this indicates that there is no easy recipe to predict the skill of regionalization methods on any given catchment.