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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
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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. |
format | Online Article Text |
id | pubmed-7592706 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
record_format | MEDLINE/PubMed |
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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