Articles | Volume 29, issue 4
https://doi.org/10.5194/hess-29-1061-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-1061-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
27 Feb 2025
Research article | Highlight paper |
| 27 Feb 2025
| 27 Feb 2025
CH-RUN: a deep-learning-based spatially contiguous runoff reconstruction for Switzerland
Department of Environmental Systems Science, Institute for Atmospheric and Climate Science (IAC), ETH, Zurich, Switzerland
Michael Schirmer
Swiss Federal Research Institute (WSL), Birmensdorf, Switzerland
William H. Aeberhard
Swiss Data Science Center, ETH, Zurich, Switzerland
Massimiliano Zappa
Swiss Federal Research Institute (WSL), Birmensdorf, Switzerland
Sonia I. Seneviratne
Department of Environmental Systems Science, Institute for Atmospheric and Climate Science (IAC), ETH, Zurich, Switzerland
Lukas Gudmundsson
Department of Environmental Systems Science, Institute for Atmospheric and Climate Science (IAC), ETH, Zurich, Switzerland
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Cited
15 citations as recorded by crossref.
- Technical note: High Nash–Sutcliffe Efficiencies conceal poor simulations of interannual variance in seasonal regimes S. Ruzzante et al. https://doi.org/10.5194/hess-30-2337-2026
- Which strategy to improve the performances of an LSTM-based model for extreme stream temperature values? M. Saadi et al. https://doi.org/10.5194/hess-30-3623-2026
- Extended-range forecasting of stream water temperature with deep-learning models R. Padrón et al. https://doi.org/10.5194/hess-29-1685-2025
- Hybrid models generalize better to warmer climate conditions than process-based and purely data-driven models J. Bohl et al. https://doi.org/10.5194/hess-30-4667-2026
- Impact of bias adjustment strategy on ensemble projections of hydrological extremes P. Astagneau et al. https://doi.org/10.5194/hess-29-5695-2025
- CAMELS-FI: hydrometeorological time series and landscape properties for 320 catchments in Finland I. Seppä et al. https://doi.org/10.5194/essd-18-4745-2026
- Multiscale decomposition and fuzzy-rule attention: A transferable cross-basin framework for long-term water quality forecasting J. Hu et al. https://doi.org/10.1016/j.watres.2026.125593
- EARLS: a runoff reconstruction dataset for Europe D. Klotz et al. https://doi.org/10.5194/essd-18-5485-2026
- A grid-informed physics-guided graph deep learning framework for interpretable distributed daily streamflow prediction Z. Guo et al. https://doi.org/10.1016/j.jhydrol.2026.136474
- Strategies for incorporating static features into global deep learning models T. Liesch & M. Ohmer https://doi.org/10.5194/hess-30-1877-2026
- Testing machine learning algorithms as post-processing tools for hydro-meteorological modelling over a small river basin C. Xu et al. https://doi.org/10.1016/j.envsoft.2025.106592
- AIFL: A global daily streamflow forecasting model using deterministic an LSTM pre-trained on ERA5-Land and fine-tuned on IFS M. Taccari et al. https://doi.org/10.1016/j.jhydrol.2026.136064
- Assessing the stability of LSTM runoff projections in Switzerland under climate scenarios F. Courvoisier et al. https://doi.org/10.5194/hess-30-5873-2026
- On the added value of sequential deep learning for the upscaling of evapotranspiration B. Kraft et al. https://doi.org/10.5194/bg-22-3965-2025
- Recent climate impacts on run-of-river hydropower and electricity systems planning in Switzerland Y. Haddad et al. https://doi.org/10.1088/1748-9326/ade4df
15 citations as recorded by crossref.
- Technical note: High Nash–Sutcliffe Efficiencies conceal poor simulations of interannual variance in seasonal regimes S. Ruzzante et al. https://doi.org/10.5194/hess-30-2337-2026
- Which strategy to improve the performances of an LSTM-based model for extreme stream temperature values? M. Saadi et al. https://doi.org/10.5194/hess-30-3623-2026
- Extended-range forecasting of stream water temperature with deep-learning models R. Padrón et al. https://doi.org/10.5194/hess-29-1685-2025
- Hybrid models generalize better to warmer climate conditions than process-based and purely data-driven models J. Bohl et al. https://doi.org/10.5194/hess-30-4667-2026
- Impact of bias adjustment strategy on ensemble projections of hydrological extremes P. Astagneau et al. https://doi.org/10.5194/hess-29-5695-2025
- CAMELS-FI: hydrometeorological time series and landscape properties for 320 catchments in Finland I. Seppä et al. https://doi.org/10.5194/essd-18-4745-2026
- Multiscale decomposition and fuzzy-rule attention: A transferable cross-basin framework for long-term water quality forecasting J. Hu et al. https://doi.org/10.1016/j.watres.2026.125593
- EARLS: a runoff reconstruction dataset for Europe D. Klotz et al. https://doi.org/10.5194/essd-18-5485-2026
- A grid-informed physics-guided graph deep learning framework for interpretable distributed daily streamflow prediction Z. Guo et al. https://doi.org/10.1016/j.jhydrol.2026.136474
- Strategies for incorporating static features into global deep learning models T. Liesch & M. Ohmer https://doi.org/10.5194/hess-30-1877-2026
- Testing machine learning algorithms as post-processing tools for hydro-meteorological modelling over a small river basin C. Xu et al. https://doi.org/10.1016/j.envsoft.2025.106592
- AIFL: A global daily streamflow forecasting model using deterministic an LSTM pre-trained on ERA5-Land and fine-tuned on IFS M. Taccari et al. https://doi.org/10.1016/j.jhydrol.2026.136064
- Assessing the stability of LSTM runoff projections in Switzerland under climate scenarios F. Courvoisier et al. https://doi.org/10.5194/hess-30-5873-2026
- On the added value of sequential deep learning for the upscaling of evapotranspiration B. Kraft et al. https://doi.org/10.5194/bg-22-3965-2025
- Recent climate impacts on run-of-river hydropower and electricity systems planning in Switzerland Y. Haddad et al. https://doi.org/10.1088/1748-9326/ade4df
Saved (final revised paper)
Latest update: 07 Oct 2026
Editorial statement
This study integrates deep learning techniques into hydrological modelling to reconstruct runoff data. The extended reconstruction of runoff spanning over six decades (1962-2023) provides an unprecedented data basis to study long-term runoff patterns and trends in Switzerland. The findings spotlight a shift towards less frequent wet years and more frequent dry conditions in Switzerland. This insight is also relevant given the current situation of extreme droughts and floods in Europe.
This study integrates deep learning techniques into hydrological modelling to reconstruct runoff...
Short summary
This study reconstructs daily runoff in Switzerland (1962–2023) using a deep-learning model, providing a spatially contiguous dataset on a medium-sized catchment grid. The model outperforms traditional hydrological methods, revealing shifts in Swiss water resources, including more frequent dry years and declining summer runoff. The reconstruction is publicly available.
This study reconstructs daily runoff in Switzerland (1962–2023) using a deep-learning model,...
