Bayesian spatial modeling of extreme precipitation return levels

• 成果类型: Article
• 署名作者: Cooley, Daniel; Nychka, Douglas; Naveau, Philippe
• 署名单位: Colorado State University System; Colorado State University Fort Collins; National Center Atmospheric Research (NCAR) - USA; National Center Atmospheric Research (NCAR) - USA; Universite Paris Saclay; Centre National de la Recherche Scientifique (CNRS); Universite Paris Cite; University of Colorado System; University of Colorado Boulder
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214506000000780
• 发表日期: 2007
• 页码: 824-840
• 关键词: regional frequency-analysis; rainfall; climate; MULTIVARIATE; dependence; Flood

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.