A Super Region Approach to Improve Pooled Flood Frequency Analysis

Abstract

Abstract 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.