EGU25-16887, updated on 15 Mar 2025
https://doi.org/10.5194/egusphere-egu25-16887
EGU General Assembly 2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Deep learning for efficient semi-distributed streamflow modeling
Basil Kraft1, William H. Aeberhard2, and Lukas Gudmundsson1
Basil Kraft et al.
  • 1ETH Zurich, Environmental Systems Science, Zurich, Switzerland (basil.kraft@env.ethz.ch)
  • 2ETH Zurich, Swiss Data Science Center, Zurich, Switzerland

Neural networks are increasingly used in hydrological applications. In streamflow modeling, long short-term memory (LSTM) networks have demonstrated considerable skill in lumped configurations, where hydrological and meteorological properties are averaged at the catchment scale. However, such averaging may mask important sub-catchment dynamics and routing processes. Process-based, semi-distributed models address these limitations by partitioning catchments into smaller hydrological response units (HRUs) for more detailed simulations, albeit at higher computational cost and with added complexity.

This research proposes a semi-distributed deep learning approach, merging the computational efficiency of neural networks with the spatial fidelity of HRU-based models. By explicitly modeling streamflow routing at the sub-catchment level, the framework seeks to provide improved streamflow predictions, whilst providing spatially explicit runoff predictions at sub-catchment scale.

We developed and tested our approach on a fine-grained grid of 20’000 HRU polygons over Switzerland. Despite the fine spatial resolution, a routed forward run for multiple decades is computed within minutes. The proposed framework has the potential to deliver real-time, spatially resolved forecasts to support improved water resource management, risk mitigation, and early warning efforts.

How to cite: Kraft, B., Aeberhard, W. H., and Gudmundsson, L.: Deep learning for efficient semi-distributed streamflow modeling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16887, https://doi.org/10.5194/egusphere-egu25-16887, 2025.