Articles | Volume 26, issue 10
https://doi.org/10.5194/hess-26-2797-2022
© Author(s) 2022. 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-26-2797-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Jun 2022
Research article |
| 01 Jun 2022
| 01 Jun 2022
Performance-based comparison of regionalization methods to improve the at-site estimates of daily precipitation
Abubakar Haruna
CORRESPONDING AUTHOR
Univ. Grenoble Alpes, Grenoble INP, CNRS, IRD, IGE, 38000 Grenoble, France
Juliette Blanchet
Univ. Grenoble Alpes, CNRS, IRD, Grenoble INP, IGE, 38000 Grenoble, France
Anne-Catherine Favre
Univ. Grenoble Alpes, Grenoble INP, CNRS, IRD, IGE, 38000 Grenoble, France
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Short summary
Reliable prediction of floods depends on the quality of the input data such as precipitation. However, estimation of precipitation from the local measurements is known to be difficult, especially for extremes. Regionalization improves the estimates by increasing the quantity of data available for estimation. Here, we compare three regionalization methods based on their robustness and reliability. We apply the comparison to a dense network of daily stations within and outside Switzerland.
Reliable prediction of floods depends on the quality of the input data such as precipitation....
