Preprints
https://doi.org/10.5194/nhess-2020-379
https://doi.org/10.5194/nhess-2020-379

  21 Nov 2020

21 Nov 2020

Review status: a revised version of this preprint is currently under review for the journal NHESS.

Identifying the non-exceedance probability of extreme storm surges as a component of natural-disaster management using tidal-gauge data from Typhoon Maemi in South Korea

Sang-Guk Yum1, Hsi-Hsien Wei2, and Sung-Hwan Jang3,4 Sang-Guk Yum et al.
  • 1Department of Civil Engineering and Engineering Mechanics, Columbia University, New York, NY 10027, USA
  • 2Department of Building and Real Estate, The Hong Kong Polytechnic University, Kwoloon, Hong Kong, PR China
  • 3Department of Civil and Environmental Engineering, Hanyang University ERICA, Ansan, Gyeonggi-do 15588, South Korea
  • 4Department of Smart City Engineering, Hanyang University ERICA, Ansan, Gyeonggi-do 15588, South Korea

Abstract. Global warming, one of the most serious aspects of climate change, can be expected to cause rising sea levels. These, in turn, have been linked to unprecedentedly large typhoons that can cause flooding of low-lying land, coastal invasion, seawater flows into rivers and groundwater, rising river levels, and aberrant tides. To prevent loss of life and property damage caused by typhoons, it is crucial to accurately estimate storm surge related risk. This study therefore develops a statistical model for estimating probability model, based on surge data pertaining to Typhoon Maemi, which struck South Korea in 2003. Specifically, estimation of non-exceedance probability models of the typhoon-related storm surge was achieved via clustered separated peaks-over-threshold simulation, while various distribution models were fitted to the empirical data for investigating the risk of storm surge height. The result of this process found that the result of Weibull distribution was better than other distribution model for Typhoon Maemi's peak total water level.

Sang-Guk Yum et al.

 
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Sang-Guk Yum et al.

Sang-Guk Yum et al.

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Short summary
Develop statistical model to predict non-exceedance probability of extreme storm surge-induced typhoon. Various distribution models were applied to find the best fitting to empirical storm surge data.
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