Trend analysis of dependence between rainfall and storm tides in coastal cities

Abstract

Compound flooding from rainfall and storm tides is prone to occur in coastal cities. The identification of them is essential for controlling urban flooding. First, the dependence between rainfall and storm tides is quantified by Kendall's τ, Spearman's ρ, and tail dependence coefficient. Then, a bivariate copula-based probability distribution model is built to calculate the joint and conditional probability of rainfall and storm tides. Finally, MK and SQMK methods are employed to detect the trends of the dependence and joint probability. The results show that: (1) The dependence between strong rainfall and corresponding storm tides is much higher than that of small rainfall and storm tides, and the effect of tropical cyclones may be one of the reasons. (2) The dependence between rainfall and storm tides is the largest in October and the smallest in July. More attention should be paid to the compound flooding caused by rainfall and storm tides in October for Haikou. (3) The upper tail dependence coefficient of the rainfall and storm tides is significantly greater than the lower tail dependence coefficient and exhibits a significant positive trend. The results can provide additional insights into the effect of rainfall and storm tides for coastal flood management. HIGHLIGHTS The compound effects of rainfall and storm tides were investigated from different perspectives.; Trends of the compound risk were analyzed by the copula and Mann–Kendall method.; Significant increasing trends of joint probability were detected after 1988–1992.; Compound event of rainfall and storm tides is more likely to occur when a high storm tide event has occurred.;

Keywords

• compound flooding
• copula model
• dependence
• joint probability