Abstract
A simple strategy is proposed to model total accumulation in non-overlapping clusters of extreme values from a stationary series of daily precipitation. Assuming that each cluster contains at least one value above a high threshold, the cluster sum S is expressed as the ratio S=M/P of the cluster maximum M and a random scaling factor P ∈ (0,1]. The joint distribution for the pair (M,P) is then specified by coupling marginal distributions for M and P with a copula. Although the excess distribution of M is well approximated by a generalized Pareto distribution, it is argued that, conditionally on P<1, a scaled beta distribution may already be sufficiently rich to capture the behaviour of P. An appropriate copula for the pair (M,P) can also be selected by standard rank-based techniques. This approach is used to analyse rainfall data from Burlington, Vermont, and to estimate the return period of the spring 2011 precipitation accumulation which was a key factor in that year's devastating flood in the Richelieu Valley Basin in Québec, Canada.
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 831-858 |
| Seitenumfang | 28 |
| Fachzeitschrift | Journal of the Royal Statistical Society. Series C: Applied Statistics |
| Jahrgang | 68 |
| Ausgabenummer | 4 |
| DOIs | |
| Publikationsstatus | Veröffentlicht - Aug. 2019 |
| Extern publiziert | Ja |
Bibliographische Notiz
Funding Information:Thanks are due to the National Centers for Environmental Information of the US National Oceanic and Atmospheric Administration for freely providing the precipitation data that were used in this study. Funding in partial support of this work was provided by the Canada Research Chairs Program, the Natural Sciences and Engineering Research Council (grants RGPIN/2018– 04481, RGPIN/2016–04720 and RGPIN/06801–2015), the Canadian Statistical Sciences Institute and the Fonds de recherche du Québec—Nature et technologies (grant 2015–PR–183236), as well as by the Mitacs Elevate Program.
Funding Information:
Thanks are due to the National Centers for Environmental Information of the US National Oceanic and Atmospheric Administration for freely providing the precipitation data that were used in this study. Funding in partial support of this work was provided by the Canada Research Chairs Program, the Natural Sciences and Engineering Research Council (grants RGPIN/2018–04481, RGPIN/2016–04720 and RGPIN/06801–2015), the Canadian Statistical Sciences Institute and the Fonds de recherche du Québec—Nature et technologies (grant 2015–PR–183236), as well as by the Mitacs Elevate Program.
Publisher Copyright:
© 2019 The Authors Journal of the Royal Statistical Society: Series C (Applied Statistics) Published by John Wiley & Sons Ltd on behalf of the Royal Statistical Society.