Regional Flood Frequency Analysis Using Entropy-Based Clustering Approach
Publication: Journal of Hydrologic Engineering
Volume 21, Issue 8
Abstract
Hydrologists widely use regional flood frequency analysis to predict flood quantiles for ungauged and sparsely gauged target locations. The analysis involves: (1) use of a regionalization approach to identify a group of watersheds (region) resembling the watershed of target location by searching in watershed-related attribute space, and (2) use of information pooled from the region to perform regional frequency analysis (RFA) for flood quantile estimation. Conventional regionalization approaches prove ineffective for identification of regions in situations where there are outliers in the attribute space. This paper proposes an entropy-based clustering approach (EBCA) to identify regions by accounting for outliers and recommends applying a recently proposed RFA approach on the regions for quantile estimation. The EBCA yielded seven new homogeneous regions in four major river basins (Mahanadi, Godavari, Krishna, and Cauvery) of India. The regions are shown to be effective compared to six existing regions (used by an Indian government organization) and those delineated using global K-means clustering and region-of-influence approaches, in terms of regional homogeneity and utility in arriving at reliable flood quantile estimates for ungauged sites.
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Acknowledgments
We express our appreciation to reviewers and the editors for their constructive reviews that resulted in improving quality of presentation.
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Published In
Journal of Hydrologic Engineering
Volume 21 • Issue 8 • August 2016
Copyright
© 2016 American Society of Civil Engineers.
History
Received: Nov 27, 2014
Accepted: Nov 25, 2015
Published online: Mar 23, 2016
Published in print: Aug 1, 2016
Discussion open until: Aug 23, 2016
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