基于C-Vine Copula函数的台风灾害链“风-雨-潮”联合概率分布研究

Joint Probability Distribution of Typhoon Disaster Chain"Strong Wind-Rainstorm-Storm Surge"Based on C-Vine Copula Function

现有台风灾害链研究大多采用高维对称Copula模型建立多个致灾因子的联合分布,对致灾因子之间非线性、非对称的复杂关联结构探究不足。文章以浙江岛屿城市舟山为例,通过C-VineCopula函数刻画当地台风灾害链“风-雨-潮”之间的复杂依赖关系,利用1979—2018年逐日的最大持续风速、累积降雨量以及最大风暴增水数据估算三者的联合概率分布以及重现期。研究表明:1)风速与降雨量在常规数值区间(非极端情况)具有较强的相关性,最佳联合分布为Frank Copula;风速与风暴增水具有上尾依赖的特征,最佳联合分布为Gumbel Copula;2)降雨量分布在风速条件下显示2处峰值,风暴增水分布在风速条件下近似于均匀,两者之间的最佳联合分布为Gumbel Copula;3)以单变量100 a重现期为例,风速-降雨量与风速-风暴增水组合事件的二维联合重现期分别缩短至29和30 a,而风速-降雨量-风暴增水组合事件的三维联合重现期缩短至17 a。综上,C-VineCopula函数能准确有效地刻画台风灾害链“风-雨-潮”之间的复杂依赖关系,深化对于台风灾害链内在作用机制的理解,为台风灾害风险管理和工程设计提供科学支持。

Typhoons and their associated disaster chains pose serious threats to the lives and property of coastal residents,and they remain a focal point for research and response.Previous studies on typhoon disaster chains often employed high-dimensional symmetric Copula models to establish the joint distribution of multiple hazard factors,however they failed to explore the complex nonlinear and asymmetric dependencies among them.This study aimed to depict these complex relationships more comprehensively and efficiently to provide a more accurate typhoon hazard assessment.Focusing on Zhoushan,a city comprising numerous islands in Zhejiang Province that faces multiple typhoon threats,this study employed the C-Vine Copula function to model the complex dependencies among"strong wind-rainstorm-storm surge"in the typhoon disaster chain.Utilizing observational data from 1979 to 2018,this study involves three main steps:first,fitting the marginal distribution of each hazard factor and identifying the best one from Lognormal,Gamma,GEV(Generalized Extreme Value),and Burr functions based on the K-S test;second,fitting the bivariate joint distributions of wind speed-rainfall and wind speed-storm surge using Gaussian,Clayton,Gumbel,Frank,and Joe Copula functions,and determining the best fit based on the AIC(Akaike Information Criterion);and finally,estimating the trivariate joint probability distribution and corresponding return periods for wind speed-rainfall-storm surge using the C-Vine Copula function.This revealed(1)a strong correlation between wind speed and rainfall observed within regular value ranges(non-extreme conditions),were best represented by the Frank Copula,In addition,wind speed and storm surge exhibit an upper-tail dependence,best captured by the Gumbel Copula.(2)The rainfall distribution under certain wind speed conditions revealed dual peaks,whereas the storm surge distribution maintained a uniform pattern,with the best joint distribution fitting the Gumbel Copula.(3)Considering a 100-year return period for individual variables,the bivariate return periods for wind speed-rainfall and wind speed-storm surge events were significantly reduced to 29 and 30 years,respectively,while the trivariate return period for the wind speedrainfall-storm surge combination was further reduced to 17 years.Overall,the C-Vine Copula function effectively characterizes the complex nonlinear and asymmetric dependencies among the typhoon disaster chain"strong wind-rainstorm-storm surge",reducing high-dimensional parameter estimation complexity.This method provides new insights for constructing joint probability and return period models for multiple hazard factors and offers a scientific basis for disaster risk assessment and management strategies.Therefore,this enhances the accuracy of disaster prevention and mitigation efforts.Additionally,the application of the C-Vine Copula assists to deeply understand the mechanisms and development processes of natural disasters,providing new tools for on-site emergency response and decision-making.