TY - JOUR
T1 - Joint distribution and risk of the compound disaster caused by rainfall and storm surge across Chinese coastal region
AU - Xu, Hanqing
AU - Tan, Jinkai
AU - Li, Mengya
AU - Liu, Qing
AU - Wang, Jun
N1 - Publisher Copyright:
© 2022, Editorial office of PROGRESS IN GEOGRAPHY. All rights reserved.
PY - 2022/10/28
Y1 - 2022/10/28
N2 - Coastal regions are extremely vulnerable to compound floods caused by extreme rainfall and strong storm surge. To improve the effectiveness of flood control measures in Chinese coastal cities and reduce the losses caused by urban compound flooding, it is important to analyze the joint probability of occurrence of extreme rainfall and strong storm surge and design the joint distribution function of daily storm surge and cumulative rainfall. In this study, we employed the Copula function to fit the joint probability distribution of storm surge and cumulative rainfall from 1979 to 2014. Then we used the Kolmogorov-Smirnov (K-S), Akaike Information Criteria (AIC), and Bayesian Information Criteria (BIC) statistical methods to obtain the optimal Copula function between storm surge and cumulative rainfall at each gauge alone the coast of China. Finally, based on the Copula method, we assessed the design value of compound scenarios of storm surge and rainfall in coastal China. The results indicate that the northern and southern parts of Chinese eastern coast have high frequency and the middle part has low frequency of compound disasters of strong storm surge and extreme rainfall. Western Guangdong, northern Fujian, southern Zhejiang, Shandong, and Liaoning provinces have a high frequency of compound disasters. Under the 50-year return period, the Beibu Gulf, northern Hainan Island, the coast of Zhejiang Province, and parts of the Bohai Bay have extreme rainfall and high storm surge. This study shows the temporal and spatial distribution of compound disasters of storm surge and rainfall in coastal China, and provides a framework for compound disaster scenario prediction.
AB - Coastal regions are extremely vulnerable to compound floods caused by extreme rainfall and strong storm surge. To improve the effectiveness of flood control measures in Chinese coastal cities and reduce the losses caused by urban compound flooding, it is important to analyze the joint probability of occurrence of extreme rainfall and strong storm surge and design the joint distribution function of daily storm surge and cumulative rainfall. In this study, we employed the Copula function to fit the joint probability distribution of storm surge and cumulative rainfall from 1979 to 2014. Then we used the Kolmogorov-Smirnov (K-S), Akaike Information Criteria (AIC), and Bayesian Information Criteria (BIC) statistical methods to obtain the optimal Copula function between storm surge and cumulative rainfall at each gauge alone the coast of China. Finally, based on the Copula method, we assessed the design value of compound scenarios of storm surge and rainfall in coastal China. The results indicate that the northern and southern parts of Chinese eastern coast have high frequency and the middle part has low frequency of compound disasters of strong storm surge and extreme rainfall. Western Guangdong, northern Fujian, southern Zhejiang, Shandong, and Liaoning provinces have a high frequency of compound disasters. Under the 50-year return period, the Beibu Gulf, northern Hainan Island, the coast of Zhejiang Province, and parts of the Bohai Bay have extreme rainfall and high storm surge. This study shows the temporal and spatial distribution of compound disasters of storm surge and rainfall in coastal China, and provides a framework for compound disaster scenario prediction.
KW - Copula function
KW - coastal region
KW - compound disaster
KW - extreme rainfall
KW - storm surge
UR - https://www.scopus.com/pages/publications/85148628163
U2 - 10.18306/DLKXJZ.2022.10.007
DO - 10.18306/DLKXJZ.2022.10.007
M3 - 文章
AN - SCOPUS:85148628163
SN - 1007-6301
VL - 41
SP - 1859
EP - 1867
JO - Progress in Geography
JF - Progress in Geography
IS - 10
ER -