Regional flood frequency analysis using kernel‐based fuzzy clustering approach. Issue 4 (16th April 2014)

• Record Type: Journal Article
• Authors: Basu, Bidroha; Srinivas, V. V.
• Is Part Of: Water resources research. Volume 50:Issue 4(2014:Apr.)
• Page Start: 3295
• Page End: 3316
• Publication Date: 2014-04-16
• Subjects: Hydrology -- Periodicals; 333.91
• DOI: 10.1002/2012WR012828
• Languages: English
• ISSNs: 0043-1397

Abstract

<abstract abstract-type="main"> <title>Abstract</title> <p>Regionalization approaches are widely used in water resources engineering to identify hydrologically homogeneous groups of watersheds that are referred to as regions. Pooled information from sites (depicting watersheds) in a region forms the basis to estimate quantiles associated with hydrological extreme events at ungauged/sparsely gauged sites in the region. Conventional regionalization approaches can be effective when watersheds (data points) corresponding to different regions can be separated using straight lines or linear planes in the space of watershed related attributes. In this paper, a kernel‐based Fuzzy <italic>c</italic>‐means (KFCM) clustering approach is presented for use in situations where such linear separation of regions cannot be accomplished. The approach uses kernel‐based functions to map the data points from the attribute space to a higher‐dimensional space where they can be separated into regions by linear planes. A procedure to determine optimal number of regions with the KFCM approach is suggested. Further, formulations to estimate flood quantiles at ungauged sites with the approach are developed. Effectiveness of the approach is demonstrated through Monte‐Carlo simulation experiments and a case study on watersheds in United States. Comparison of results with those based on conventional Fuzzy <italic>c</italic>‐means clustering, Region‐of‐influence approach and a prior study indicate that KFCM approach outperforms the other approaches in forming regions that are closer to being statistically homogeneous and in estimating flood quantiles at ungauged sites.</p> </abstract>