Abstract Region of influence is a common approach to estimate runoff information at ungauged locations. To estimate flood quantiles from annual maximum discharges, the Generalized Least Squares (GLS) framework has been recommended to account for unequal sampling variance and intersite correlation, which requires a proper evaluation of the sampling covariance structure. Since some jurisdictions do not have clear guidelines to perform this evaluation, a general procedure using copulas and a nonparametric intersite correlation model is investigated to estimate sampling covariance structure in situations where no common at-site distribution is imposed or when some paired sites do not have common periods of record. The investigated methodology is applied on 771 sites in Canada. The Normal copula is verified to be an adequate model that better fit paired observations than other types of extreme copulas. A sensitivity analysis is carried out to evaluate the impact of either ignoring, or considering a simpler form of, intersite correlation. Additionally, super regions are defined based on drainage area and mean annual precipitation to improve the calibration of pooling groups across large territories and a wide range of climate conditions. Performance criteria based on cross-validation revealed that using super regions and a combination of geographic distance and similarity between catchment descriptors improves the calibration of the pooling groups by providing more accurate estimates.

中文翻译：

---

使用 ROI/GLS 与 copulas 和超级区域在未测量站点的全国区域洪水频率分析

摘要 影响区域是估计未测量位置径流信息的常用方法。为了从年度最大流量估计洪水分位数，建议使用广义最小二乘法 (GLS) 框架来解释不等采样方差和站点间相关性，这需要对采样协方差结构进行适当评估。由于某些司法管辖区没有执行此评估的明确指南，因此研究了使用联结和非参数站点间相关模型的一般程序，以在没有强加共同现场分布的情况下或某些配对站点没有的情况下估计抽样协方差结构常见的记录期。所调查的方法应用于加拿大的 771 个站点。Normal copula 被证实是一个合适的模型，比其他类型的极端 copula 更适合配对观察。执行敏感性分析以评估忽略或考虑更简单形式的站点间相关性的影响。此外，根据流域面积和年平均降水量定义超级区域，以改进跨大区域和广泛气候条件的汇集组的校准。基于交叉验证的性能标准表明，使用超级区域以及流域描述符之间的地理距离和相似性的组合，通过提供更准确的估计来改进汇集组的校准。或考虑一种更简单形式的站点间相关性。此外，根据流域面积和年平均降水量定义超级区域，以改进跨大区域和广泛气候条件的汇集组的校准。基于交叉验证的性能标准表明，使用超级区域以及流域描述符之间的地理距离和相似性的组合，通过提供更准确的估计来改进汇集组的校准。或考虑一种更简单形式的站点间相关性。此外，根据流域面积和年平均降水量定义超级区域，以改进跨大区域和广泛气候条件的汇集组的校准。基于交叉验证的性能标准表明，使用超级区域以及流域描述符之间的地理距离和相似性的组合，通过提供更准确的估计来改进汇集组的校准。