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A Bayesian Analysis of Return Level for Extreme Precipitation in Korea
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 Title & Authors
A Bayesian Analysis of Return Level for Extreme Precipitation in Korea
Lee, Jeong Jin; Kim, Nam Hee; Kwon, Hye Ji; Kim, Yongku;
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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.
Bayesian analysis;daily precipitation;extremes;generalized extreme value distribution;generalized Pareto distribution;return level;spatial process;
 Cited by
Casson, E. and Coles, S. (1999). Spatial Regression Models for Extremes, Extremes, 1, 449-468. crossref(new window)

Coles, S. G. (2001). An Introduction to Statistical Modeling of Extreme Values, London: Springer-Verlag.

Coles, S., Heffernan, J. and Tawn, J. (1999). Dependence measures for extreme value analysis, Extremes, 2, 339-365. crossref(new window)

Cooley, D., Naveau, P., Jomelli, V, Rabatel, A. and Grancher, D. (2006). A Bayesian hierarchical extreme value model for lichenometry, Environmetrics, 17, 555-574. crossref(new window)

Cooley, D., Nychka, D. and Naveau, P. (2007). Bayesian spatial modeling of extreme precipitation return levels, Journal of the American Statistical Association, 102, 824-840. crossref(new window)

Cunderlik, J. M., Burn, D. H., Posbherg, D., Robinson, B. A. and Zyvoloski, G. A. (2008). Generalized likelihood uncertainty estimation(GLUE) using adaptive Markov Chain, Journal of Hydrology, 276, 210-223.

de Haan, L. (1985). Extremes in Higher Dimensions: The Model and Some Statistics, in proceedings of the 45th session of the International Statistical Institute.

Embrechts, P., Kluppelberg, C. and Mikosch, T. (1997). Modelling Extremal Events for Insurance and Fi-nance, Berlin: Springer-Verlag.

Heffernan, J. E. and Tawn, J. A. (2004). A conditional approach for multivariate extreme values, Journal of the Royal Statistical Society, Series B, 66, 497-546. crossref(new window)

Lee, J. J. (2010). Assessment of nonstationarity in precipitation and development of nonstationary frequency analysis, Ph.D. Thesis, Chonbuk National University.

Jang, S. W., Seo, L., Kim, T. W. and Ahn, J. H. (2011). Non-stationary rainfall frequency analysis based on residual analysis, Journal of the Korean Society of Civil Engineers, 31, 449-457.

Kwak, M. and Kim, Y. (2014). Multi-site stochastic weather generator for daily rainfall in Korea, The Korean Journal of Applied Statistics, 27, 475-485. crossref(new window)

Matern, B. (1986). Spatial Variation, 2nd edition, Springer-Verlag.

Oliveria, O. A., Gomes, M. I. and Alves, M. I. F.(2006). Improvements in the estimation of a heavy tail, REVSTAT - Statistical Journal, 4, 81-109.

Schlather, M. and Tawn, J. (2003). A dependence measure for multivariate and spatial extreme values: Properties and inference, Biometrika, 90, 139-156. crossref(new window)

Strupczewski, W. G., Singh, V. P. and Mitosek, H. T. (2001). Non-stationary approach to at-site flood frequency modeling III : Flood analysis of Polish rivers, Journal of Hydrology, 248, 152-167. crossref(new window)