Kim, T.-J.; Hwang, K.-N., and Kwon, H.-H., 2018. Stochastic analysis of typhoon-induced storm surge in the coastal area of the Korean peninsula: inference from a nonstationary, Bayesian Poisson generalized Pareto distribution. In: Shim, J.-S.; Chun, I., and Lim, H.S. (eds.), Proceedings from the International Coastal Symposium (ICS) 2018 (Busan, Republic of Korea). Journal of Coastal Research, Special Issue No. 85, pp. 896–900. Coconut Creek (Florida), ISSN 0749-0208.

Climate-related disasters in East Asia have been recently increasing due to enhanced climate variability and climate change. Moreover, the frequency and intensity of typhoons and associated disasters in the East Asia region have been increasing steadily over the past few decades. In fact, the Korean Peninsula is considered among the most disasterprone areas, largely due to the incidence of typhoons. In particular, it is expected that the potential risk of flooding in coastal areas would be greater in the presence of a simultaneous storm surge and heavy rainfall. In this context, understanding the mechanism of interaction between the two factors and estimating the risk associated with their concurrent occurrence are of particular interest because of their impact on in low-lying areas. In this study, we developed a Poisson-Generalized Pareto Distribution (Poisson-GPD)-based nonstationary storm surge frequency model to combine the occurrence of an exceedance of a high threshold and a peak over threshold (POT) within a Bayesian framework. Here, the GPD is employed to describe the maximum storm surge distribution for each typhoon with a storm surge exceeding a certain level using a time-varying scale and shape parameter. On the other hand, the number of typhoons in each year exceeding the storm surge threshold follows a Poisson distribution with a time-varying lambda parameter.