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http://dx.doi.org/10.5351/KJAS.2014.27.6.947

A Bayesian Analysis of Return Level for Extreme Precipitation in Korea  

Lee, Jeong Jin (Department of Statistics, Kyungpook National University)
Kim, Nam Hee (Department of Statistics, Kyungpook National University)
Kwon, Hye Ji (Statistics Korea)
Kim, Yongku (Department of Statistics, Kyungpook National University)
Publication Information
The Korean Journal of Applied Statistics / v.27, no.6, 2014 , pp. 947-958 More about this Journal
Abstract
Understanding extreme precipitation events is very important for flood planning purposes. Especially, the r-year return level is a common measure of extreme events. In this paper, we present a spatial analysis of precipitation return level using hierarchical Bayesian modeling. For intensity, we model annual maximum daily precipitations and daily precipitation above a high threshold at 62 stations in Korea with generalized extreme value(GEV) and generalized Pareto distribution(GPD), respectively. The spatial dependence among return levels is incorporated to the model through a latent Gaussian process of the GEV and GPD model parameters. We apply the proposed model to precipitation data collected at 62 stations in Korea from 1973 to 2011.
Keywords
Bayesian analysis; daily precipitation; extremes; generalized extreme value distribution; generalized Pareto distribution; return level; spatial process;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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