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Digital Hydrologic Networks Supporting Applications Related to Spatially Referenced Regression Modeling1

ABSTRACT: Digital hydrologic networks depicting surface-water pathways and their associated drainage catchments provide a key component to hydrologic analysis and modeling. Collectively, they form common spatial units that can be used to frame the descriptions of aquatic and watershed processes. In...

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Detalles Bibliográficos
Autores principales: Brakebill, JW, Wolock, DM, Terziotti, SE
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Blackwell Publishing Ltd 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3307631/
https://www.ncbi.nlm.nih.gov/pubmed/22457575
http://dx.doi.org/10.1111/j.1752-1688.2011.00578.x
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author Brakebill, JW
Wolock, DM
Terziotti, SE
author_facet Brakebill, JW
Wolock, DM
Terziotti, SE
author_sort Brakebill, JW
collection PubMed
description ABSTRACT: Digital hydrologic networks depicting surface-water pathways and their associated drainage catchments provide a key component to hydrologic analysis and modeling. Collectively, they form common spatial units that can be used to frame the descriptions of aquatic and watershed processes. In addition, they provide the ability to simulate and route the movement of water and associated constituents throughout the landscape. Digital hydrologic networks have evolved from derivatives of mapping products to detailed, interconnected, spatially referenced networks of water pathways, drainage areas, and stream and watershed characteristics. These properties are important because they enhance the ability to spatially evaluate factors that affect the sources and transport of water-quality constituents at various scales. SPAtially Referenced Regressions On Watershed attributes (SPARROW), a process-based/statistical model, relies on a digital hydrologic network in order to establish relations between quantities of monitored contaminant flux, contaminant sources, and the associated physical characteristics affecting contaminant transport. Digital hydrologic networks modified from the River Reach File (RF1) and National Hydrography Dataset (NHD) geospatial datasets provided frameworks for SPARROW in six regions of the conterminous United States. In addition, characteristics of the modified RF1 were used to update estimates of mean-annual streamflow. This produced more current flow estimates for use in SPARROW modeling.
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spelling pubmed-33076312012-03-26 Digital Hydrologic Networks Supporting Applications Related to Spatially Referenced Regression Modeling1 Brakebill, JW Wolock, DM Terziotti, SE J Am Water Resour Assoc Technical Papers ABSTRACT: Digital hydrologic networks depicting surface-water pathways and their associated drainage catchments provide a key component to hydrologic analysis and modeling. Collectively, they form common spatial units that can be used to frame the descriptions of aquatic and watershed processes. In addition, they provide the ability to simulate and route the movement of water and associated constituents throughout the landscape. Digital hydrologic networks have evolved from derivatives of mapping products to detailed, interconnected, spatially referenced networks of water pathways, drainage areas, and stream and watershed characteristics. These properties are important because they enhance the ability to spatially evaluate factors that affect the sources and transport of water-quality constituents at various scales. SPAtially Referenced Regressions On Watershed attributes (SPARROW), a process-based/statistical model, relies on a digital hydrologic network in order to establish relations between quantities of monitored contaminant flux, contaminant sources, and the associated physical characteristics affecting contaminant transport. Digital hydrologic networks modified from the River Reach File (RF1) and National Hydrography Dataset (NHD) geospatial datasets provided frameworks for SPARROW in six regions of the conterminous United States. In addition, characteristics of the modified RF1 were used to update estimates of mean-annual streamflow. This produced more current flow estimates for use in SPARROW modeling. Blackwell Publishing Ltd 2011-10 /pmc/articles/PMC3307631/ /pubmed/22457575 http://dx.doi.org/10.1111/j.1752-1688.2011.00578.x Text en © 2011 American Water Resources Association http://creativecommons.org/licenses/by/2.5/ Re-use of this article is permitted in accordance with the Creative Commons Deed, Attribution 2.5, which does not permit commercial exploitation.
spellingShingle Technical Papers
Brakebill, JW
Wolock, DM
Terziotti, SE
Digital Hydrologic Networks Supporting Applications Related to Spatially Referenced Regression Modeling1
title Digital Hydrologic Networks Supporting Applications Related to Spatially Referenced Regression Modeling1
title_full Digital Hydrologic Networks Supporting Applications Related to Spatially Referenced Regression Modeling1
title_fullStr Digital Hydrologic Networks Supporting Applications Related to Spatially Referenced Regression Modeling1
title_full_unstemmed Digital Hydrologic Networks Supporting Applications Related to Spatially Referenced Regression Modeling1
title_short Digital Hydrologic Networks Supporting Applications Related to Spatially Referenced Regression Modeling1
title_sort digital hydrologic networks supporting applications related to spatially referenced regression modeling1
topic Technical Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3307631/
https://www.ncbi.nlm.nih.gov/pubmed/22457575
http://dx.doi.org/10.1111/j.1752-1688.2011.00578.x
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