DOI: 10.5194/hess-22-655-2018
Scopus记录号: 2-s2.0-85041189443
论文题名: Stochastic generation of multi-site daily precipitation focusing on extreme events
作者: Evin G ; , Favre A ; -C ; , Hingray B
刊名: Hydrology and Earth System Sciences
ISSN: 10275606
出版年: 2018
卷: 22, 期: 1 起始页码: 655
结束页码: 672
语种: 英语
Scopus关键词: Precipitation (meteorology)
; Stochastic systems
; Daily precipitations
; Extended versions
; Extreme precipitation
; Heavy-tailed distribution
; Statistical features
; Stochastic generation
; Temporal and spatial scale
; Temporal dependence
; Stochastic models
; benchmarking
; extreme event
; performance assessment
; precipitation (climatology)
; precipitation assessment
; spatiotemporal analysis
; stochasticity
; Switzerland
英文摘要: 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. © Author(s) 2018. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79443
Appears in Collections: 气候变化事实与影响
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作者单位: Univ. Grenoble Alpes, CNRS, IRD, Grenoble INP, Grenoble, France
Recommended Citation:
Evin G,, Favre A,-C,et al. Stochastic generation of multi-site daily precipitation focusing on extreme events[J]. Hydrology and Earth System Sciences,2018-01-01,22(1)