基于Copula函数的深圳河流域降雨潮位组合风险分析

在全球气候变化背景下，降水和潮位变化是滨海城市洪涝灾害的主要原因. 定量评估滨海城市降雨潮位组合风险率，对于滨海城市洪涝灾害的防治具有十分重要的现实意义. 本文基于深圳站53 a最大日降水量与赤湾站相应最高潮位数据，通过K-S检验、C-vM检验、AIC准则和BIC准则进行边缘分布函数优选，采用Archimedean Copula函数，定量评估了深圳河流域不同重现期下降水和潮位的双阈值组合风险率和单域值组合风险率. 结果表明:深圳河流域降水序列和潮位序列的最优边缘分布函数分别为GEV和Lognormal分布；降水和潮位之间呈现较弱的正相关性； Clayton Copula函数对于深圳河流域雨潮遭遇联合分布特征拟合效果最好；随着降水和潮位重现期的增大，深圳河流域的双阈值组合风险率和单域值组合风险率均呈减小趋势，但二者之间的差距逐渐增大；对于降水和潮位重现期不同时的特定组合风险率，若降水重现期较大，则潮位重现期较小，反之亦然. 

Combination risk of precipitation and tide in Shenzhen River Basin as assessed by Copula function

Due to global climate changes, increased precipitation and tidal level are main causes of flood disasters in coastal cities. Quantitative assessment of combination risk of precipitation and tide is of great significance for the prevention and control of flood disasters. Maximum daily precipitation in a period of 53 years at Shenzhen Station and corresponding maximum tide data at Chiwan Station were studied here. Optimal marginal distribution function was selected by KS test, CvM test, with AIC and BIC criteria. Risk rates of double threshold combination and single domain value combination in precipitation water and tide level in different return periods in Shenzhen River Basin were quantitatively assessed by Archimedean Copula function. Optimal marginal distribution functions of precipitation series and tidal level series were found to follow GEV and Lognormal distributions respectively. A weak positive correlation between precipitation and tide level was identified. Clayton Copula function showed best fitting of joint distribution characteristics of rain and tide encounters. Increased return period of precipitation and tide level led to decreased risk of double threshold combination and single domain value combination, with the gap in between showing gradual increase. When return period of precipitation and tide level was different, for specific combination risk, great precipitation return period led to small tidal level return period, and vice versa.