Flood frequency analysis (FFA) considering the confluence of interconnected rivers is important for hydraulic structures (such as dams or diversions) design, but it has received little attention. This study develops a copula-based method for FFA and quantile estimation considering the confluence of two interconnected rivers, along with the uncertainty estimation by a nonparametric bootstrapping algorithm. Flood probability distribution and return periods are estimated for the two rivers by mapping from bivariate to univariate peak flow quantile estimation. The methodology is applied to the case study of Qezel Ozan and Shahrud Rivers which merge to one of the largest reservoir dams in Iran: Sefidrud (Manjil) dam. According to the results from Peak flow records from Gilvan station (GPF) at Qezel Ozan River and from Loshan station (LCF) at Shahrud River, Gaussian copula with Weibull and gamma margins fits best. Also, it shows that some peak flow quantiles with the same magnitudes have a different probability of occurrences at the confluence of the rivers, and the bivariate estimation uncertainty usually plays an important role in FFA. These findings suggest the use of bivariate instead of univariate distributions to the peak flows at the confluence of interconnected rivers, in which the sampling uncertainty should be considered.

考虑到相互连接的河流汇合的洪水频率分析（FFA）对于水工结构（例如大坝或改道）很重要设计，但很少受到关注。这项研究开发了一种基于copula的FFA和分位数估计方法，该方法考虑了两条相互连接的河流的汇合以及非参数自举算法的不确定性估计。通过从双变量到单变量峰值流量分位数估算映射，可以估算两条河流的洪水概率分布和返回期。该方法适用于Qezel Ozan和Shahrud河流的案例研究，这些河流与伊朗最大的水库大坝之一：Sefidrud（Manjil）大坝合并。根据Qezel Ozan河的Gilvan站（GPF）和Shahrud河的Loshan站（LCF）的峰值流量记录的结果，具有Weibull和γ边际的高斯copula最合适。也，结果表明，一些相同大小的峰值流量分位数在河流汇合处出现的可能性不同，而双变量估计不确定性通常在FFA中起重要作用。这些发现表明，在相互连接的河流汇合处，对峰值流量使用二变量而不是单变量分布，在这种情况下应考虑采样不确定性。