Journal cover Journal topic
Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

IF value: 3.102
IF3.102
IF 5-year value: 3.284
IF 5-year
3.284
CiteScore value: 5.1
CiteScore
5.1
SNIP value: 1.37
SNIP1.37
IPP value: 3.21
IPP3.21
SJR value: 1.005
SJR1.005
Scimago H <br class='widget-line-break'>index value: 90
Scimago H
index
90
h5-index value: 42
h5-index42
Preprints
https://doi.org/10.5194/nhess-2020-379
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-2020-379
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  21 Nov 2020

21 Nov 2020

Review status
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.

Interactive discussion

Status: open (until 02 Jan 2021)
Status: open (until 02 Jan 2021)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
[Subscribe to comment alert] Printer-friendly Version - Printer-friendly version Supplement - Supplement

Sang-Guk Yum et al.

Sang-Guk Yum et al.

Viewed

Total article views: 186 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
167 19 0 186 0 0
  • HTML: 167
  • PDF: 19
  • XML: 0
  • Total: 186
  • BibTeX: 0
  • EndNote: 0
Views and downloads (calculated since 21 Nov 2020)
Cumulative views and downloads (calculated since 21 Nov 2020)

Viewed (geographical distribution)

Total article views: 182 (including HTML, PDF, and XML) Thereof 179 with geography defined and 3 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved

No saved metrics found.

Discussed

No discussed metrics found.
Latest update: 02 Dec 2020
Publications Copernicus
Download
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.
Develop statistical model to predict non-exceedance probability of extreme storm surge-induced...
Citation
Altmetrics