New Approaches to At site and Regional Frequency Analysis of Hydrologic Extremes in Peaks Over Threshold Framework

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Frequency analysis procedures are widely used to quantify the risk associated with floods that have devastating consequences worldwide. Conventionally, the frequency analysis is performed based on the annual maximum series (AMS) of peak flows extracted from the available streamflow records. Peaks-over-threshold or Partial duration series (PDS) framework is deemed more efficient than AMS in depicting information on extremes. Despite its advantages, the use of PDS is less prevalent than AMS. It is due to the lack of a universally established systematic approach to select an appropriate threshold for PDS extraction. Various issues affect the performance of different methods available for threshold selection. A novel Mahalanobis distance-based automatic threshold selection method is proposed to address those issues, and its potential is demonstrated over four automatic threshold selection methods. Another issue in flood risk assessment at target locations is sparsity or lack of data. In such situations, practitioners opt for regional frequency analysis (RFA) approaches that involve regionalization (locating groups/regions comprising resembling watersheds) and pooling of flood-related information from outlets of the watersheds to estimate desired flood quantile(s) at the target sites. Most RFA approaches are focused on using AMS rather than PDS. This thesis addresses regionalization-related issues and focuses on leveraging the advantages of using PDS in RFA. Regionalization approaches are many, and their choice is ambiguous as none is established to be universally superior. They differ in their underlying assumptions and strategies, and thus yield regions that vary in composition. A new entropy-based fuzzy ensemble clustering approach is proposed to address the uncertainty in regionalization. It forms effective fuzzy meta-regions by ameliorating information from regions derived using different procedures. Error in flood quantile estimates at ungauged sites based on those meta-regions was the least in Monte-Carlo...

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