The role and value of distributed precipitation data for hydrological models

Abstract

Abstract. This study investigates the role and value of distributed rainfall for the runoff generation of a mesoscale catchment (20 km2). We compare the performance of three hydrological models at different periods and show that a distributed model driven by distributed rainfall yields only to improved performances during certain periods. These periods are dominated by convective storms that are typically characterized by higher spatial and temporal variabilities compared to stratiform precipitation events that dominate the rainfall generation in winter. Motivated by these findings we develop a spatially adaptive model that is capable to dynamically adjust its spatial structure during runtime to represent the varying importance of distributed rainfall within a hydrological model without losing predictive performance compared to a spatially distributed model. Our results highlight that adaptive modeling might be a promising way to better understand the varying relevance of distributed rainfall in hydrological models as well as reiterate that it might be one way to reduce computational times. They furthermore show that hydrological similarity concerning the runoff generation does not necessarily mean similarity for other dynamic variables such as the distribution of soil moisture.