Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1002/2017WR020758 |
Partitioning into hazard subregions for regional peaks-over-threshold modeling of heavy precipitation | |
Carreau, J.1; Naveau, P.2; Neppel, L.1 | |
2017-05-01 | |
发表期刊 | WATER RESOURCES RESEARCH
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ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2017 |
卷号 | 53期号:5 |
文章类型 | Article |
语种 | 英语 |
国家 | France |
英文摘要 | The French Mediterranean is subject to intense precipitation events occurring mostly in autumn. These can potentially cause flash floods, the main natural danger in the area. The distribution of these events follows specific spatial patterns, i.e., some sites are more likely to be affected than others. The peaks-over-threshold approach consists in modeling extremes, such as heavy precipitation, by the generalized Pareto (GP) distribution. The shape parameter of the GP controls the probability of extreme events and can be related to the hazard level of a given site. When interpolating across a region, the shape parameter should reproduce the observed spatial patterns of the probability of heavy precipitation. However, the shape parameter estimators have high uncertainty which might hide the underlying spatial variability. As a compromise, we choose to let the shape parameter vary in a moderate fashion. More precisely, we assume that the region of interest can be partitioned into subregions with constant hazard level. We formalize the model as a conditional mixture of GP distributions. We develop a two-step inference strategy based on probability weighted moments and put forward a cross-validation procedure to select the number of subregions. A synthetic data study reveals that the inference strategy is consistent and not very sensitive to the selected number of subregions. An application on daily precipitation data from the French Mediterranean shows that the conditional mixture of GPs outperforms two interpolation approaches (with constant or smoothly varying shape parameter). |
英文关键词 | generalized Pareto distribution probability weighted moment spatial interpolation regional frequency analysis French Mediterranean |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000403712100052 |
WOS关键词 | GENERALIZED LEAST-SQUARES ; FLOOD FREQUENCY-ANALYSIS ; RETURN LEVELS ; SERIES ; REGRESSION ; STATISTICS |
WOS类目 | Environmental Sciences ; Limnology ; Water Resources |
WOS研究方向 | Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/21145 |
专题 | 资源环境科学 |
作者单位 | 1.Univ Montpellier, CNRS IRD UM, HSM, Montpellier, France; 2.IPSL CNRS, LSCE, Gif Sur Yvette, France |
推荐引用方式 GB/T 7714 | Carreau, J.,Naveau, P.,Neppel, L.. Partitioning into hazard subregions for regional peaks-over-threshold modeling of heavy precipitation[J]. WATER RESOURCES RESEARCH,2017,53(5). |
APA | Carreau, J.,Naveau, P.,&Neppel, L..(2017).Partitioning into hazard subregions for regional peaks-over-threshold modeling of heavy precipitation.WATER RESOURCES RESEARCH,53(5). |
MLA | Carreau, J.,et al."Partitioning into hazard subregions for regional peaks-over-threshold modeling of heavy precipitation".WATER RESOURCES RESEARCH 53.5(2017). |
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