Wavelet Spectrum and Self-Organizing Maps-Based Approach for Hydrologic Regionalization -a Case Study in the Western United States

A. Agarwal, R. Maheswaran, J. Kurths, R. Khosa

Research output: Contribution to journalArticlepeer-review

24 Scopus citations

Abstract

Hydrologic regionalization deals with the investigation of homogeneity in watersheds and provides a classification of watersheds for regional analysis. The classification thus obtained can be used as a basis for mapping data from gauged to ungauged sites and can improve extreme event prediction. This paper proposes a wavelet power spectrum (WPS) coupled with the self-organizing map method for clustering hydrologic catchments. The application of this technique is implemented for gauged catchments. As a test case study, monthly streamflow records observed at 117 selected catchments throughout the western United States from 1951 through 2002. Further, based on WPS of each station, catchments are classified into homogeneous clusters, which provides a representative WPS pattern for the streamflow stations in each cluster.

Original languageEnglish (US)
Pages (from-to)4399-4413
Number of pages15
JournalWater Resources Management
Volume30
Issue number12
DOIs
StatePublished - Sep 1 2016

Bibliographical note

Publisher Copyright:
© 2016, Springer Science+Business Media Dordrecht.

Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.

Keywords

  • K-means technique
  • Regionalization
  • Self-organizing map
  • Ungauged catchments
  • Wavelet power spectrum

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