Floods are known as one of the most damaging natural hazards with devastating influence on people and the environment. Accurately estimating flood frequencies is essential for effective design of flood mitigation systems. Estimation of these frequencies is difficult since extreme events are rare and the length of recorded data is often short. In such situations, extreme flow information from a number of similar sites is combined (pooled) to augment the available at-site information. Pooled flood frequency analysis is a well-known approach used to improve the estimation of extreme flow quantiles at sites with short data records. Identification of pooling groups that will effectively transfer extreme flow information is thus essential. The present paper proposes an approach to improve flood quantile estimates through utilizing the concept of super regions integrated with seasonality-based similarity measures to conduct pooled frequency analysis for extreme flow events. To identify homogeneous regions, this study focuses on the region of influence (ROI), or focussed pooling group approach among hydrological neighborhood techniques. To define the hydrologically similar neighborhood of a target site, a single numeric that measures similarity/dissimilarity between sites is usually utilized. This work investigates the effect of employing catchment physiographic-climate characteristics and several flood seasonality statistics as the similarity measures. Moreover, this study explores and establishes a super region technique that in a hierarchical process employs the two types of similarity measures. A large dataset of catchments across Canada was used to compare the proposed method with more traditional approaches. The effectiveness of these techniques both in terms of constructing homogeneous pooling groups and accurately estimating extreme flow quantiles is explored for the catchments under study. The proposed super region approach was shown to form more reliable homogeneous pooling groups. Analyzing confidence intervals of quantile estimates obtained from pooled and at-site estimates revealed promising improvement.

洪水是破坏力最大的自然灾害之一，对人类和环境造成破坏性影响。准确估算洪水频率对于有效设计防洪系统至关重要。这些频率的估计很困难，因为极端事件很少发生，并且记录数据的长度通常很短。在这种情况下，来自（多个）类似站点的极端流量信息将被合并（合并）以增强可用的站点信息。合并洪水频率分析是一种众所周知的方法，用于改进数据记录较短的站点的极端流量分位数的估计。因此，确定有效地传输极端流量信息的汇聚组至关重要。本文提出了一种利用超级区域的概念与基于季节性的相似性度量相结合的方法来提高洪水分位数的方法，以对极端流量事件进行集中频率分析。为了确定同质区域，本研究着重于影响区域（ROI），或者是水文邻域技术中的集中池组方法。为了定义目标站点的水文相似邻域，通常使用单个数字来测量站点之间的相似性/相似性。这项工作调查了利用流域的地貌气候特征和几种洪水季节性统计数据作为相似性度量的效果。此外，本研究探索并建立了一种超级区域技术，该技术在分层过程中采用了两种类型的相似性度量。使用加拿大大范围的流域数据集将建议的方法与更传统的方法进行比较。对于正在研究的流域，探索了这些技术在构造均质池组和准确估算极端流量分位数方面的有效性。所提出的超级区域方法显示出可以形成更可靠的均质池组。分析从汇总和现场估计中获得的分位数估计的置信区间显示出令人鼓舞的改进。对于正在研究的流域，探索了这些技术在构造均质池组和准确估算极端流量分位数方面的有效性。所提出的超级区域方法显示出可以形成更可靠的均质池组。分析从汇总和现场估计中获得的分位数估计的置信区间显示出令人鼓舞的改进。对于正在研究的流域，探索了这些技术在构造均质池组和准确估算极端流量分位数方面的有效性。所提出的超级区域方法显示出可以形成更可靠的均质池组。分析从汇总和现场估计中获得的分位数估计的置信区间显示出令人鼓舞的改进。