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Assessment of typhoon storm surge disaster scale based on expansion model
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作者 Guilin LIU Xiuxiu NONG +3 位作者 Yi KOU Fang WU Daniel ZHAO Zongbing YU 《Journal of Oceanology and Limnology》 SCIE CAS CSCD 2023年第2期518-531,共14页
The South China Sea suffers strongly from the typhoon storm surge disasters in China,and its northern coastal areas are facing severe risks.Therefore,it is necessary and urgent to establish an assessment system for ra... The South China Sea suffers strongly from the typhoon storm surge disasters in China,and its northern coastal areas are facing severe risks.Therefore,it is necessary and urgent to establish an assessment system for rating typhoon storm surge disaster.We constructed an effective and reliable rating assessment system for typhoon storm surge disaster based on the theories of over-threshold,distribution function family,and composite extreme value.The over-threshold sample was used as the basis of data analysis,the composite extreme value expansion model was used to derive the design water increment,and then the disaster level was delineated based on the return period level.The results of the extreme value model comparison show that the Weibull-Pareto distribution is more suitable than the classical extreme value distribution for fitting the over-threshold samples.The results of the return period projection are relatively stable based on different analysis samples.Taking the 10 typhoon storm surges as examples,they caused landfall in the Guangdong area in the past 10 years.The results of the assessment ranking indicate that the risk levels based on the return period levels obtained from different distributions are generally consistent.When classifying low-risk areas,the classification criteria of the State Oceanic Administration,China(SOA,2012)are more conservative.In the high-risk areas,the results of the assessment ranking based on return period are more consistent with those of the SOA. 展开更多
关键词 risk classification South China Sea typhoon storm surge extreme value expansion over threshold sampling
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