Regional Rainfall Damage Functions to Estimate Direct Economic Losses in Rainstorms:A Case Study of the 2016 Extreme Rainfall Event in Hebei Province of China

作     者：Xiaojuan Chen Yifu Xu Ting Li Jun Wei Jidong Wu Xiaojuan Chen;Yifu Xu;Ting Li;Jun Wei;Jidong Wu

作者机构：China Meteorological Administration Xiong’an Atmospheric Boundary Layer Key LaboratoryXiong’an New Area 071800China Key Laboratory of Meteorology and Ecological Environment of Hebei ProvinceShijiazhuang 050021China Meteorological Disaster Prevention and Environment Meteorological Center of Hebei ProvinceShijiazhuang 050021China Joint International Research Laboratory of Catastrophe Simulation and Systemic Risk GovernanceBeijing Normal University at ZhuhaiZhuhai 519087China 

出 版 物：《International Journal of Disaster Risk Science》 (国际灾害风险科学学报（英文版）)

年 卷 期：2024年第15卷第4期

页      面：508-520页

核心收录：

学科分类：07[理学] 070601[理学-气象学] 0706[理学-大气科学] 

基　　金：funded by the National Key R&D Program of China(Grant No.2022YFC3004404) the Key Research and Development Project of Science and Technology Department of Hebei Province(No.21375410D and No.22375421D) 

主　　题：Regional rainfall damage function Loss attribution Direct economic losses Rainfall intensity Asset value Vulnerability 

摘      要：Developing a regional damage function to quickly estimate direct economic losses(DELs) caused by heavy rain and floods is crucial for providing scientific supports in effective disaster response and risk reduction. This study investigated the factors that influence regional rainfall-induced damage and developed a calibrated regional rainfall damage function(RDF) using data from the 2016 extreme rainfall event in Hebei Province, China. The analysis revealed that total precipitation, asset value exposure, per capita GDP, and historical geological disaster density at both the township and county levels significantly affect regional rainfall-induced damage. The coefficients of the calibrated RDF indicate that doubling the values of these factors leads to varying increases or decreases in rainfall-induced damage. Furthermore, the study demonstrated a spatial scale dependency in the coefficients of the RDF, with increased elasticity values for asset value exposure and per capita GDP at the county level compared to the township level. The findings emphasize the challenges of applying RDFs across multiple scales and highlight the importance of considering socioeconomic factors in assessing rainfall-induced damage. Despite the limitations and uncertainties of the RDF developed, this study contributes to our understanding of the relationship between physical and socioeconomic factors and rainfall-induced damage. Future research should prioritize enhancing exposure estimation and calibrating RDFs for various types of rainfall-induced disasters to improve model accuracy and *** study also acknowledges the variation in RDF performance across different physical environments, especially concerning geological disasters and slope stability.