Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 29, issue 4
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.
https://doi.org/10.5194/hess-29-1061-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 29, issue 4
Research article
 | Highlight paper
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27 Feb 2025
Research article | Highlight paper |  | 27 Feb 2025

CH-RUN: a deep-learning-based spatially contiguous runoff reconstruction for Switzerland

Basil Kraft, Michael Schirmer, William H. Aeberhard, Massimiliano Zappa, Sonia I. Seneviratne, and Lukas Gudmundsson

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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.
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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.
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