中国农业气象 ›› 2025, Vol. 46 ›› Issue (01): 80-88.doi: 10.3969/j.issn.1000-6362.2025.01.008

• 农业气象保险专刊 • 上一篇    下一篇

广东台风灾害风雨灾损分离方法

刘蔚琴,唐力生,谢礼江,何研   

  1. 1.中国气象科学研究院&中再巨灾风险管理股份有限公司·气象风险与保险联合开放实验室,北京100081;2.广东省气候中心,广州510641
  • 收稿日期:2024-03-28 出版日期:2025-01-20 发布日期:2025-01-17
  • 作者简介:刘蔚琴,E-mail:24936687@qq.com
  • 基金资助:
    气象风险与保险联合开放实验室开放基金项目(2023F004;2023F005);中国气象局气象软科学研究课题(2024ZDAXM01);广东省气象局科研项目(GRMC2020M10);中国气象局重点创新团队(CMA2024ZD03);广东省气象局科技创新团队(GRMCTD202005)

Wind and Rain Disaster Loss Separated Methods from Typhoon in Guangdong Province

LIU Wei-qin, TANG Li-sheng, XIE Li-jiang, HE Yan   

  1. 1.Joint Open Lab on Meteorological Risk and Insurance, Chinese Academy of Meteorological Sciences & China Re-catastrophe Risk Management Company Ltd, Beijing 100081, China; 2.Guangdong Climate Center, Guangzhou 510641
  • Received:2024-03-28 Online:2025-01-20 Published:2025-01-17

摘要:

广东台风灾情数据按灾因统计风雨灾损,是影响台风巨灾指数保险基差风险的原因之一。为降低基差风险,本文提出一种分离台风风雨灾损的方法。基于广东沿海936个县(区)20142022台风灾情数据,选取风、雨指标,采用多元回归方法构建台风风雨灾损分离模型,分离各县(区)台风灾情统计数据中因风、雨的灾损,并以江门台山市和潮州湘桥区的台风观测数据和直接经济损失数据为例进行模型验证结果表明:建模之初通过线性相关分析可消除或降低多重共线性对回归方程的影响,但可能导致部分有效信息丢失。伽马分布能很好地拟合广东台风风雨及其灾损的分布规律;在显著性水平为0.05时,36个县(区)中有22个县(区)灾损分离通过检验;构建模型能有效分离台风灾统计数据中的因风、雨灾损,为台风灾情数据的灾因归类分析提供参考。

关键词: 台风, 灾损分离, 多元回归, 广东

Abstract:

 The lack of separate statistics on wind and rain damage by disaster-causing factors in the typhoon disaster data in Guangdong is a contributing factor to the existence of typhoon catastrophe index insurance basis risk. To mitigate this basis risk, this paper proposes a method to separate typhoon wind and rain damage. An analysis was conducted on the typhoon disaster data from 2014 to 2022 for 36 counties (districts) in nine coastal cities in Guangdong, focusing on wind and rain indicators. A multiple regression method was employed to construct a typhoon wind and rain disaster loss separation model, which separated the disaster loss due to wind and rain in the typhoon disaster statistics of each county (district). Furthermore, the typhoon observation data and direct economic loss data from Jiangmen Taishan city and Xiangqiao district of Chaozhou were presented as case studies to illustrate the model's validation. Linear correlation analysis showed that eliminating or reducing the effect of multicollinearity on the regression equation at the beginning of modeling may result in the loss of some valid information. The Gamma distribution was found to appropriately fit the distribution pattern of typhoon wind, rain, and damage in Guangdong. The results indicated that 22 counties (districts) passed the significance test at the 0.05 level. The model successfully separated wind- and rain- related damage in Guangdong's typhoon disaster loss statistics, providing a valuable reference for categorizing and analyzing typhoon disaster data by cause.

Key words: Typhoon disaster, Disaster damage separation, Multiple regression, Guangdong