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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 22, issue 1
Hydrol. Earth Syst. Sci., 22, 655-672, 2018
https://doi.org/10.5194/hess-22-655-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.
Hydrol. Earth Syst. Sci., 22, 655-672, 2018
https://doi.org/10.5194/hess-22-655-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 25 Jan 2018

Research article | 25 Jan 2018

Stochastic generation of multi-site daily precipitation focusing on extreme events

Guillaume Evin, Anne-Catherine Favre, and Benoit Hingray Guillaume Evin et al.
  • Univ. Grenoble Alpes, CNRS, IRD, Grenoble INP, Grenoble, France

Abstract. Many multi-site stochastic models have been proposed for the generation of daily precipitation, but they generally focus on the reproduction of low to high precipitation amounts at the stations concerned. This paper proposes significant extensions to the multi-site daily precipitation model introduced by Wilks, with the aim of reproducing the statistical features of extremely rare events (in terms of frequency and magnitude) at different temporal and spatial scales. In particular, the first extended version integrates heavy-tailed distributions, spatial tail dependence, and temporal dependence in order to obtain a robust and appropriate representation of the most extreme precipitation fields. A second version enhances the first version using a disaggregation method. The performance of these models is compared at different temporal and spatial scales on a large region covering approximately half of Switzerland. While daily extremes are adequately reproduced at the stations by all models, including the benchmark Wilks version, extreme precipitation amounts at larger temporal scales (e.g., 3-day amounts) are clearly underestimated when temporal dependence is ignored.

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This research paper proposes a multi-site daily precipitation model, named GWEX, which aims to reproduce the statistical features of extremely rare events at different temporal and spatial scales. Recent advances and various statistical methods (regionalization, disaggregation) are considered in order to obtain a robust and appropriate representation of the most extreme precipitation fields. Performances are shown with an application to 105 stations, covering a large region in Switzerland.
This research paper proposes a multi-site daily precipitation model, named GWEX, which aims to...
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