Estimation of the non-exceedance probability of extreme storm surges in South Korea using tidal-gauge data

Abstract. Global warming, one of the most serious aspects climate change, can be expected to cause rising sea levels. These have in turn been linked unprecedentedly large typhoons that flooding low-lying land, coastal invasion, seawater flows into rivers and groundwater, river levels, aberrant tides. To prevent typhoon-related loss life property damage, it is crucial accurately estimate storm-surge risk. This study therefore develops a statistical model for estimating such surges' probability based on surge data pertaining Typhoon Maemi, which struck South Korea 2003. Specifically, estimation non-exceedance models storm was achieved via clustered separated peaks-over-threshold simulation, while various distribution were fitted empirical investigating risk surges reaching particular heights. explore extreme caused by typhoons, threshold algorithm with clustering methodology applied. enhance accuracy probability, three different components: predicted water level, observed surge. Sea-level from when Maemi collected tidal-gauge station city Busan, vulnerable disasters due its geographical characteristics. Fréchet, gamma, log-normal, generalized Pareto, Weibull distributions data, researchers compared each one's performance at explaining probability. established better than any other modelling Maemi's peak total level. Although this research limited Korean Peninsula weather event, approach could used reliably probabilities regions where are available. In practical terms, findings future ones adopting will provide useful reference designers infrastructure.