Articles | Volume 30, issue 21
https://doi.org/10.5194/hess-30-6907-2026
© 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-30-6907-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 Nov 2026
Research article |
| 09 Nov 2026
| 09 Nov 2026
Simple Box-Cox probabilistic models for hourly streamflow predictions
IHCantabria – Instituto de Hidráulica Ambiental de la Universidad de Cantabria, Santander, Spain
Department of Environmental Systems Science, ETH Zürich, Zürich, Switzerland
Eawag, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland
Dmitri Kavetski
School of Civil Engineering and Construction Management, Adelaide University, Adelaide, SA, Australia
IHCantabria – Instituto de Hidráulica Ambiental de la Universidad de Cantabria, Santander, Spain
Fabrizio Fenicia
Eawag, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland
James W. Kirchner
Department of Environmental Systems Science, ETH Zürich, Zürich, Switzerland
Swiss Federal Research Institute WSL, Birmensdorf, Switzerland
Swedish University of Agricultural Sciences (SLU), Umeå, Sweden
David McInerney
School of Civil Engineering and Construction Management, Adelaide University, Adelaide, SA, Australia
Mark Thyer
School of Civil Engineering and Construction Management, Adelaide University, Adelaide, SA, Australia
César Álvarez
IHCantabria – Instituto de Hidráulica Ambiental de la Universidad de Cantabria, Santander, Spain
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Marvin Höge, Andreas Scheidegger, Marco Baity-Jesi, Carlo Albert, and Fabrizio Fenicia
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Hongkai Gao, Chuntan Han, Rensheng Chen, Zijing Feng, Kang Wang, Fabrizio Fenicia, and Hubert Savenije
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Nikos Theodoratos and James W. Kirchner
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Short summary
Hourly streamflow data are increasingly available and can improve streamflow predictions. We tested simple ways to describe uncertainty by transforming flow values and by accounting for how errors persist from hour to hour, using seven catchments in Spain, Switzerland and the United States. Simple transformations and short-term error memory improve the reliability of probabilistic predictions and help combine hourly results into longer time scales for practical operational contexts.
Hourly streamflow data are increasingly available and can improve streamflow predictions. We...
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