4.4 Review

A framework for hydrologic classification with a review of methodologies and applications in ecohydrology

期刊

ECOHYDROLOGY
卷 5, 期 4, 页码 503-518

出版社

WILEY
DOI: 10.1002/eco.251

关键词

dams; flow regime; environmental flow; river regulation; hydrologic metric

资金

  1. USGS National Gap Analysis Program
  2. U.S. Environmental Protection Agency Science to Achieve Results (STAR) program [833834]
  3. Tropical Rivers and Coastal Knowledge (TRaCK) Research Hub through the Australian Rivers Institute, Griffith University
  4. Australian Government's Commonwealth Environment Research Facilities initiative
  5. Australian Government's Raising National Water Standards Program, Land and Water Australia
  6. Fisheries Research and Development Corporation
  7. National Environmental Research Program
  8. Queensland Government's Smart State Innovation Fund

向作者/读者索取更多资源

Hydrologic classification is one of the most widely applied tasks in ecohydrology. During the last two decades, a considerable effort has gone into analysis and development of methodological approaches to hydrologic classification. We reviewed the process of hydrologic classification, differentiating between an approach based on deductive reasoning using environmental regionalization, hydrologic regionalization and environmental classification whereby environmental variables assumed to be key determinants of hydrology are analysed and one based on inductive reasoning using streamflow classification whereby hydrologic data are analysed directly. We explored past applications in ecohydrology, highlighting the utility of classifications in the extrapolation of hydrologic information across sparsely gauged landscapes, the description of spatial patterns in hydrologic variability, aiding water resource management, and in the identification and prioritization of conservation areas. We introduce an overarching methodological framework that depicts critical components of the classification process and summarize important advantages and disadvantages of commonly used statistical approaches to characterize and predict hydrologic classes. Our hope is that researchers and managers will be better informed when having to make decisions regarding the selection and proper implementation of methods for hydrologic classification in the future. Copyright (c) 2011 John Wiley & Sons, Ltd.

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