Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 29, issue 6
Articles | Volume 29, issue 6
https://doi.org/10.5194/hess-29-1685-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-1685-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 29, issue 6
Research article
 | 
26 Mar 2025
Research article |  | 26 Mar 2025

Extended-range forecasting of stream water temperature with deep-learning models

Ryan S. Padrón, Massimiliano Zappa, Luzi Bernhard, and Konrad Bogner

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Short summary
We generate operational forecasts of daily maximum stream water temperature for 32 consecutive days at 54 stations in Switzerland with our best-performing data-driven model. The average forecast error is 0.38 °C for 1 d ahead and increases to 0.90 °C for 32 d ahead given the uncertainty in the meteorological variables influencing water temperature. Here we compare the skill of several models, how well they can forecast at new and ungauged stations, and the importance of different model inputs.
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