Bayesian spatial modeling of extreme precipitation return levels

被引:355
|
作者
Cooley, Daniel [1 ]
Nychka, Douglas
Naveau, Philippe
机构
[1] Colorado State Univ, Dept Stat, Ft Collins, CO 80523 USA
[2] Natl Ctr Atmospher Res, Geophys Stat Project, Boulder, CO 80307 USA
[3] Natl Ctr Atmospher Res, Inst Math Geosci, Boulder, CO 80307 USA
[4] CNRS, IPSL, Lab Sci Climat & Environm, Gif Sur Yvette, France
[5] Univ Colorado, Dept Appl Math, Boulder, CO 80309 USA
基金
美国国家科学基金会;
关键词
Colorado; extreme value theory; generalized Pareto distribution; hierarchical model; latent process;
D O I
10.1198/016214506000000780
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
Quantification of precipitation extremes is important for flood planning purposes, and a common measure of extreme events is the r-year return level. We present a method for producing maps of precipitation return levels and uncertainty measures and apply it to a region in Colorado. Separate hierarchical models are constructed for the intensity and the frequency of extreme precipitation events. For intensity, we model daily precipitation above a high threshold at 56 weather stations with the generalized Pareto distribution. For frequency, we model the number of exceedances at the stations as binomial random variables. Both models assume that the regional extreme precipitation is driven by a latent spatial process characterized by geographical and climatological covariates. Effects not fully described by the covariates are captured by spatial structure in the hierarchies. Spatial methods were improved by working in a space with climatological coordinates. Inference is provided by a Markov chain Monte Carlo algorithm and spatial interpolation method, which provide a natural method for estimating uncertainty.
引用
收藏
页码:824 / 840
页数:17
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