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Classifying Streamflow Duration: The Scientific Basis and an Operational Framework for Method Development

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 a...

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Autores principales: Fritz, Ken M., Nadeau, Tracie-Lynn, Kelso, Julia E., Beck, Whitney S., Mazor, Raphael D., Harrington, Rachel A., Topping, Brian J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7592706/
https://www.ncbi.nlm.nih.gov/pubmed/33133647
http://dx.doi.org/10.3390/w12092545
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author Fritz, Ken M.
Nadeau, Tracie-Lynn
Kelso, Julia E.
Beck, Whitney S.
Mazor, Raphael D.
Harrington, Rachel A.
Topping, Brian J.
author_facet Fritz, Ken M.
Nadeau, Tracie-Lynn
Kelso, Julia E.
Beck, Whitney S.
Mazor, Raphael D.
Harrington, Rachel A.
Topping, Brian J.
author_sort Fritz, Ken M.
collection PubMed
description 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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spelling pubmed-75927062021-09-11 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. Water (Basel) Article 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. 2020-09-11 /pmc/articles/PMC7592706/ /pubmed/33133647 http://dx.doi.org/10.3390/w12092545 Text en This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Fritz, Ken M.
Nadeau, Tracie-Lynn
Kelso, Julia E.
Beck, Whitney S.
Mazor, Raphael D.
Harrington, Rachel A.
Topping, Brian J.
Classifying Streamflow Duration: The Scientific Basis and an Operational Framework for Method Development
title Classifying Streamflow Duration: The Scientific Basis and an Operational Framework for Method Development
title_full Classifying Streamflow Duration: The Scientific Basis and an Operational Framework for Method Development
title_fullStr Classifying Streamflow Duration: The Scientific Basis and an Operational Framework for Method Development
title_full_unstemmed Classifying Streamflow Duration: The Scientific Basis and an Operational Framework for Method Development
title_short Classifying Streamflow Duration: The Scientific Basis and an Operational Framework for Method Development
title_sort classifying streamflow duration: the scientific basis and an operational framework for method development
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7592706/
https://www.ncbi.nlm.nih.gov/pubmed/33133647
http://dx.doi.org/10.3390/w12092545
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