Extensive uncertainties exist in hydroclimatic risk analysis. Especially in multivariate hydrologic risk inferences, uncertainties in individual hydroclimatic extremes such as floods and their dependence structure may lead to bias and uncertainty in future hydrologic risk predictions. In this study, a parameter uncertainty and sensitivity evaluation (PUSE) framework is proposed to quantify parameter uncertainties and then reveal their contributions to the multivariate hydroclimatic risk predictions. The predictive risks are finally generated by “integrating” the values over the posterior distributions of the parameters. The proposed approach was applied for bivariate risk analysis of compound floods at the Xiangxi River to characterize the concurrence probabilities of flood peaks and volumes. The results demonstrate that the proposed approach can quantify uncertainties in a copula-based multivariate risk analysis and characterize effects and contributions of parameters in marginal and dependence structures on the multivariate hydroclimatic risk predictions. In terms of the bivariate risk for flood peak and volume at the Xiangxi River, uncertainties in model parameters would lead to noticeable uncertainties even for moderate floods. The performances of the copula model for flood peak-volume at Xiangxi River are mainly affected by the uncertainties in location parameters of the two individual flood variables. Also, parameter uncertainty in the dependence structure (i.e., copula) would also poses explicit impacts on performance of the copula-based risk analyses model. These uncertainties would result into higher bivariate predictive risks than the values obtained by “optimal/deterministic” predictions. This indicates that uncertain- ties are required to be considered to provide reliable multivariate hydroclimatic risk predictions.

中文翻译：

---

基于 Copula 的多元水文气候风险评估的参数不确定性和敏感性评估

水文气候风险分析存在广泛的不确定性。特别是在多变量水文风险推断中，洪水等个别极端水文气候及其依赖结构的不确定性可能导致未来水文风险预测的偏差和不确定性。在这项研究中，提出了一个参数不确定性和敏感性评估 (PUSE) 框架来量化参数不确定性，然后揭示它们对多变量水文气候风险预测的贡献。预测风险最终是通过在参数的后验分布上“整合”这些值而产生的。将所提出的方法应用于湘西河复合洪水的双变量风险分析，以表征洪水峰值和洪水量的并发概率。结果表明，所提出的方法可以量化基于 copula 的多元风险分析中的不确定性，并表征边缘和依赖结构中参数对多元水文气候风险预测的影响和贡献。对于湘西河洪峰和洪量的双变量风险，模型参数的不确定性会导致即使是中度洪水也存在明显的不确定性。湘西河洪水峰流量copula模型的性能主要受两个单独洪水变量位置参数的不确定性影响。此外，依赖结构（即 copula）中的参数不确定性也会对基于 copula 的风险分析模型的性能产生明确的影响。这些不确定性将导致比“最佳/确定性”预测获得的值更高的双变量预测风险。这表明需要考虑不确定性以提供可靠的多变量水文气候风险预测。