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Classifying Streamflow Duration: The Scientific Basis and an Operational Framework for Method Development.
Fritz, Ken M; Nadeau, Tracie-Lynn; Kelso, Julia E; Beck, Whitney S; Mazor, Raphael D; Harrington, Rachel A; Topping, Brian J.
Afiliação
  • Fritz KM; Center for Environmental Measurement and Modeling, Office of Research and Development, US Environmental Protection Agency, Cincinnati, OH 45268, USA.
  • Nadeau TL; Region 10, US Environmental Protection Agency, Portland, OR 97205, USA.
  • Kelso JE; Office of Wetlands, Oceans, and Watersheds, US Environmental Protection Agency, Washington, DC 20460, USA.
  • Beck WS; Office of Wetlands, Oceans, and Watersheds, US Environmental Protection Agency, Washington, DC 20460, USA.
  • Mazor RD; Oak Ridge Institute for Science and Education Fellow, Oak Ridge, TN 37831, USA.
  • Harrington RA; Office of Wetlands, Oceans, and Watersheds, US Environmental Protection Agency, Washington, DC 20460, USA.
  • Topping BJ; Southern California Coastal Water Research Project, Costa Mesa, CA 92626, USA.
Water (Basel) ; 12(9): 1-2545, 2020 Sep 11.
Article em En | MEDLINE | ID: mdl-33133647
ABSTRACT
Streamflow duration is used to differentiate reaches into discrete classes (e.g., perennial, intermittent, and ephemeral) for water resource management. Because the depiction of the extent and flow duration of streams via existing maps, remote sensing, and gauging is constrained, field-based tools are needed for use by practitioners and to validate hydrography and modeling advances. Streamflow Duration Assessment Methods (SDAMs) are rapid, reach-scale indices or models that use physical and biological indicators to predict flow duration class. We review the scientific basis for indicators and present conceptual and operational frameworks for SDAM development. Indicators can be responses to or controls of flow duration. Aquatic and terrestrial responses can be integrated into SDAMs, reflecting concurrent increases and decreases along the flow duration gradient. The conceptual framework for data-driven SDAM development shows interrelationships among the key components study reaches, hydrologic data, and indicators. We present a generalized operational framework for SDAM development that integrates the data-driven components through five process

steps:

preparation, data collection, data analysis, evaluation, and implementation. We highlight priorities for the advancement of SDAMs, including expansion of gauging of nonperennial reaches, use of citizen science data, adjusting for stressor gradients, and statistical and monitoring advances to improve indicator effectiveness.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2020 Tipo de documento: Article