Articles | Volume 30, issue 19
https://doi.org/10.5194/hess-30-6207-2026
© Author(s) 2026. 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-30-6207-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 Oct 2026
Research article |
| 06 Oct 2026
| 06 Oct 2026
The ability of LSTM to model snowmelt versus rainfall generated floods
Department of Hydrology, Norwegian Water Resources and Energy Directorate, Oslo, Norway
Danielle Marie Barna
Department of Hydrology, Norwegian Water Resources and Energy Directorate, Oslo, Norway
Kolbjørn Engeland
Department of Hydrology, Norwegian Water Resources and Energy Directorate, Oslo, Norway
Sjur Anders Kolberg
Department of Hydrology, Norwegian Water Resources and Energy Directorate, Oslo, Norway
Sunniva Nordeide
Department of Hydrology, Norwegian Water Resources and Energy Directorate, Oslo, Norway
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Short summary
Hydrological models need to simulate both rainfall and snowmelt generated floods in regions with snow. We evaluated a deep learning model’s ability to capture timing and magnitude of floods generated by snowmelt and rainfall separately. Timing was better simulated for rainfall than snowmelt generated floods, whereas results for flood peak magnitudes were similar. Compared to an operational model, the deep learning model was better at simulating both flood types in the majority of the catchments.
Hydrological models need to simulate both rainfall and snowmelt generated floods in regions with...
