Scaling Up Buckets – Using large sample data to build a regional hydrological model

Conceptual bucket-type models have been a mainstay in hydrology for decades due to their simplicity and flexibility. With the emergence of large-sample datasets regional applications of these models has become increasingly feasible and relevant. However, challenges remain: how can we ensure consistent modeling across catchment boundaries, and do typical model setups capture the dominant processes shaping regional water cycles?

To address these challenges, we first leverage large datasets, including CAMELS-DE and thousands of groundwater level time series across Germany, to build and validate a conceptual, fully distributed hydrological model at the national scale. Using the SALTO model as an example, we demonstrate how the Parameter Set Shuffling (PASS) approach enables regional calibration while accounting for spatial variability.

We discuss strategies to incorporate anthropogenic impacts into regional water cycle modeling, including reservoirs, dams, drinking water abstraction, and wastewater return flows. By integrating these human influences, our approach provides a more realistic representation of Germany’s hydrology.

Additionally, we introduce an event-based model diagnostic framework that identifies which hydrological conditions are reliably represented by the model structure and highlights the potential of large-sample data to improve regional hydrological modeling.