A scalable, spatially distributed approach to runoff simulation and river routing

We introduce DROP (Deep Runoff Prediction and Propagation), a scalable deep learning framework for spatially distributed runoff simulation and river routing across large hydrological networks. Reliable representation of runoff generation and streamflow propagation is critical for hydrological forecasting and water resources management, yet remains computationally challenging at high spatial resolution. DROP addresses this challenge by jointly learning runoff dynamics and downstream flow propagation within a single, spatially explicit modeling framework.

The model is trained on daily discharge observations from 273 gauged catchments in Switzerland, covering more than 22,000 drainage units at approximately 2 km² resolution. Using static drainage unit attributes and meteorological forcings, DROP predicts local runoff and routes flow through the river network. The architecture is designed for computational efficiency and generalization across very diverse hydrological regimes, enabling domain-wide simulations without basin-specific calibration.

Evaluation across multiple spatial experiments shows that DROP substantially outperforms baseline deep learning models (lumped LSTMs), achieving relative improvements of up to 60 % in discharge performance metrics (Kling–Gupta Efficiency; KGE) for catchments not seen during training. The model enables rapid inference, allowing simulation of daily discharge over the full domain within seconds on a single GPU. These results demonstrate that spatially explicit deep learning models can provide accurate, efficient, and scalable alternatives to traditional hydrological models for large-scale runoff simulation and river routing, with strong potential for integration into operational forecasting and Earth system modeling frameworks.