The role and value of distributed precipitation data in hydrological models

This study investigates the role and value of distributed rainfall for the runoff generation of a mesoscale catchment (20 km(2)). We compare four hydrological model setups and show that a distributed model setup driven by distributed rainfall only improves the model performances during certain periods. These periods are dominated by convective summer storms that are typically characterized by higher spatiotemporal variabilities compared to stratiform precipitation events that dominate rainfall generation in winter. Motivated by these findings, we develop a spatially adaptive model that is capable of dynamically adjusting its spatial structure during model execution. This spatially adaptive model allows the varying relevance of distributed rainfall to be represented within a hydrological model without losing predictive performance compared to a fully distributed model. Our results highlight that spatially adaptive modeling has the potential to reduce computational times as well as improve our understanding of the varying role and value of distributed precipitation data for hydrological models.

Automated summary

This study found that distributed rainfall improves model performance during convective summer storms. The use of a spatially adaptive model can represent varying relevance of distributed rainfall without reducing predictive performance, potentially reducing computational times and enhancing understanding of the role and value of distributed precipitation data in hydrological models.