Abstract
Abstract Understanding extreme precipitation events is very important for ood planning purposes. Especially, the r -year return level is a common measure of extreme events. In this paper, we present a spatial analysis ofprecipitation return level using hierarchical Bayesian modeling. For intensity, we model annual maximumdaily precipitations and daily precipitation above a high threshold at 62 stations in Korea with generalizedextreme value(GEV) and generalized Pareto distribution(GPD), respectively. The spatial dependence amongreturn levels is incorporated to the model through a latent Gaussian process of the GEV and GPD modelparameters. We apply the proposed model to precipitation data collected at 62 stations in Korea from 1973to 2011.Keywords: Bayesian analysis, daily precipitation, extremes, generalized extreme value distribution, gener-alized Pareto distribution, return level, spatial process. 1. 서론 최근 지구온난화와 같은전 지구적인기상현상으로 인한 강수의패턴 분석과 예측에 관한 연구가 활발하게 이루어지고 있다. 예를 들어, 대기 중 이산화탄소(CO
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