A Study of Storm Surge Disasters Based on Extreme Value Distribution Theory
In this study, a statistical model was set up using extreme value distribution theory to estimate the return periods for both the highest surge levels and the adjusted direct economic losses from storm surge disasters based on the historical database. The extreme value distribution theory has been w...
| Main Authors: | , , , , , |
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| Format: | Journal Article |
| Published: |
Coastal Education and Research Foundation
2017
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| Online Access: | http://www.jcronline.org/doi/abs/10.2112/JCOASTRES-D-16-00041.1 http://hdl.handle.net/20.500.11937/59467 |
| _version_ | 1848760488748384256 |
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| author | Yang, S. Liu, Xin Liu, Q. Guan, L. Lee, J. Jung, K. |
| author_facet | Yang, S. Liu, Xin Liu, Q. Guan, L. Lee, J. Jung, K. |
| author_sort | Yang, S. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In this study, a statistical model was set up using extreme value distribution theory to estimate the return periods for both the highest surge levels and the adjusted direct economic losses from storm surge disasters based on the historical database. The extreme value distribution theory has been widely applied in hydrology and coastal engineering, and one well-performing extreme distribution is the Gumbel distribution. Based on the Gumbel distribution, three parameter estimation methods were used to determine the best method for generating the Gumbel distribution functions; subsequently, the expressions for the return periods were derived. The least square method was identified as the best parameter-estimation method for this study. Comparisons were implemented amo ng return periods of the highest surge levels with the adjusted direct economic loss, which showed that the linear functional relationship between these two indicators was not significant. This study also found there was strong spatial autocorrelation for the highest surge levels with the adjusted direct economic loss by employing spatial analysis along the China's coastline. Analysis based on comparisons among the return periods of the highest surge levels and the adjusted direct economic loss in three coastal regions showed different levels of return periods the regions tended to have. Furthermore, analysis of the variation in indicators between the former half and the latter half of the study period reflected the change in climate. The application of the extreme value distribution theory was extended to evaluate economic losses during a storm surge disaster, and the underlying relationships and the deviations between the highest surge levels and the adjusted direct economic loss were analyzed, which indicated the damages caused by storm surges did not completely depend on the surge level. |
| first_indexed | 2025-11-14T10:16:35Z |
| format | Journal Article |
| id | curtin-20.500.11937-59467 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:16:35Z |
| publishDate | 2017 |
| publisher | Coastal Education and Research Foundation |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-594672019-02-19T05:36:18Z A Study of Storm Surge Disasters Based on Extreme Value Distribution Theory Yang, S. Liu, Xin Liu, Q. Guan, L. Lee, J. Jung, K. In this study, a statistical model was set up using extreme value distribution theory to estimate the return periods for both the highest surge levels and the adjusted direct economic losses from storm surge disasters based on the historical database. The extreme value distribution theory has been widely applied in hydrology and coastal engineering, and one well-performing extreme distribution is the Gumbel distribution. Based on the Gumbel distribution, three parameter estimation methods were used to determine the best method for generating the Gumbel distribution functions; subsequently, the expressions for the return periods were derived. The least square method was identified as the best parameter-estimation method for this study. Comparisons were implemented amo ng return periods of the highest surge levels with the adjusted direct economic loss, which showed that the linear functional relationship between these two indicators was not significant. This study also found there was strong spatial autocorrelation for the highest surge levels with the adjusted direct economic loss by employing spatial analysis along the China's coastline. Analysis based on comparisons among the return periods of the highest surge levels and the adjusted direct economic loss in three coastal regions showed different levels of return periods the regions tended to have. Furthermore, analysis of the variation in indicators between the former half and the latter half of the study period reflected the change in climate. The application of the extreme value distribution theory was extended to evaluate economic losses during a storm surge disaster, and the underlying relationships and the deviations between the highest surge levels and the adjusted direct economic loss were analyzed, which indicated the damages caused by storm surges did not completely depend on the surge level. 2017 Journal Article http://hdl.handle.net/20.500.11937/59467 10.2112/JCOASTRES-D-16-00041.1 http://www.jcronline.org/doi/abs/10.2112/JCOASTRES-D-16-00041.1 Coastal Education and Research Foundation fulltext |
| spellingShingle | Yang, S. Liu, Xin Liu, Q. Guan, L. Lee, J. Jung, K. A Study of Storm Surge Disasters Based on Extreme Value Distribution Theory |
| title | A Study of Storm Surge Disasters Based on Extreme Value Distribution Theory |
| title_full | A Study of Storm Surge Disasters Based on Extreme Value Distribution Theory |
| title_fullStr | A Study of Storm Surge Disasters Based on Extreme Value Distribution Theory |
| title_full_unstemmed | A Study of Storm Surge Disasters Based on Extreme Value Distribution Theory |
| title_short | A Study of Storm Surge Disasters Based on Extreme Value Distribution Theory |
| title_sort | study of storm surge disasters based on extreme value distribution theory |
| url | http://www.jcronline.org/doi/abs/10.2112/JCOASTRES-D-16-00041.1 http://hdl.handle.net/20.500.11937/59467 |