Modeling agricultural nonpoint source pollution using a geographic information system approach.
Identifieur interne : 000386 ( Ncbi/Merge ); précédent : 000385; suivant : 000387Modeling agricultural nonpoint source pollution using a geographic information system approach.
Auteurs : Lisa A. Emili [États-Unis] ; Richard P. GreeneSource :
- Environmental management [ 1432-1009 ] ; 2013.
English descriptors
- KwdEn :
- MESH :
- analysis : Water Pollution.
- methods : Environmental Monitoring.
- Agriculture, Geographic Information Systems, Models, Theoretical, Water Supply.
Abstract
Agricultural non-point source (NPS) pollution, primarily sediment and nutrients, is the leading source of water-quality impacts to surface waters in North America. The overall goal of this study was to develop geographic information system (GIS) protocols to facilitate the spatial and temporal modeling of changes in soils, hydrology, and land-cover change at the watershed scale. In the first part of this article, we describe the use of GIS to spatially integrate watershed scale data on soil erodibility, land use, and runoff for the assessment of potential source areas within an intensively agricultural watershed. The agricultural non-point source pollution (AGNPS) model was used in the Muddy Creek, Ontario, watershed to evaluate the effectiveness of management strategies in decreasing sediment and nutrient [phosphorus (P)] pollution. This analysis was accompanied by the measurement of water-quality parameters (dissolved oxygen, pH, hardness, alkalinity, and turbidity) as well as sediment and P loadings to the creek. Practices aimed at increasing year-round soil cover would be most effective in decreasing sediment and P losses in this watershed. In the second part of this article, we describe a method for characterizing land-cover change in a dynamic urban fringe watershed. The GIS method we developed for the Blackberry Creek, Illinois, watershed will allow us to better account for temporal changes in land use, specifically corn and soybean cover, on an annual basis and to improve on the modeling of watershed processes shown for the Muddy Creek watershed. Our model can be used at different levels of planning with minimal data preprocessing, easily accessible data, and adjustable output scales.
DOI: 10.1007/s00267-012-9940-4
PubMed: 22983655
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The overall goal of this study was to develop geographic information system (GIS) protocols to facilitate the spatial and temporal modeling of changes in soils, hydrology, and land-cover change at the watershed scale. In the first part of this article, we describe the use of GIS to spatially integrate watershed scale data on soil erodibility, land use, and runoff for the assessment of potential source areas within an intensively agricultural watershed. The agricultural non-point source pollution (AGNPS) model was used in the Muddy Creek, Ontario, watershed to evaluate the effectiveness of management strategies in decreasing sediment and nutrient [phosphorus (P)] pollution. This analysis was accompanied by the measurement of water-quality parameters (dissolved oxygen, pH, hardness, alkalinity, and turbidity) as well as sediment and P loadings to the creek. Practices aimed at increasing year-round soil cover would be most effective in decreasing sediment and P losses in this watershed. In the second part of this article, we describe a method for characterizing land-cover change in a dynamic urban fringe watershed. The GIS method we developed for the Blackberry Creek, Illinois, watershed will allow us to better account for temporal changes in land use, specifically corn and soybean cover, on an annual basis and to improve on the modeling of watershed processes shown for the Muddy Creek watershed. Our model can be used at different levels of planning with minimal data preprocessing, easily accessible data, and adjustable output scales.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">22983655</PMID><DateCreated><Year>2012</Year><Month>12</Month><Day>25</Day></DateCreated><DateCompleted><Year>2013</Year><Month>05</Month><Day>31</Day></DateCompleted><DateRevised><Year>2012</Year><Month>12</Month><Day>25</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1432-1009</ISSN><JournalIssue CitedMedium="Internet"><Volume>51</Volume><Issue>1</Issue><PubDate><Year>2013</Year><Month>Jan</Month></PubDate></JournalIssue><Title>Environmental management</Title><ISOAbbreviation>Environ Manage</ISOAbbreviation></Journal><ArticleTitle>Modeling agricultural nonpoint source pollution using a geographic information system approach.</ArticleTitle><Pagination><MedlinePgn>70-95</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1007/s00267-012-9940-4</ELocationID><Abstract><AbstractText>Agricultural non-point source (NPS) pollution, primarily sediment and nutrients, is the leading source of water-quality impacts to surface waters in North America. The overall goal of this study was to develop geographic information system (GIS) protocols to facilitate the spatial and temporal modeling of changes in soils, hydrology, and land-cover change at the watershed scale. In the first part of this article, we describe the use of GIS to spatially integrate watershed scale data on soil erodibility, land use, and runoff for the assessment of potential source areas within an intensively agricultural watershed. The agricultural non-point source pollution (AGNPS) model was used in the Muddy Creek, Ontario, watershed to evaluate the effectiveness of management strategies in decreasing sediment and nutrient [phosphorus (P)] pollution. This analysis was accompanied by the measurement of water-quality parameters (dissolved oxygen, pH, hardness, alkalinity, and turbidity) as well as sediment and P loadings to the creek. Practices aimed at increasing year-round soil cover would be most effective in decreasing sediment and P losses in this watershed. In the second part of this article, we describe a method for characterizing land-cover change in a dynamic urban fringe watershed. The GIS method we developed for the Blackberry Creek, Illinois, watershed will allow us to better account for temporal changes in land use, specifically corn and soybean cover, on an annual basis and to improve on the modeling of watershed processes shown for the Muddy Creek watershed. Our model can be used at different levels of planning with minimal data preprocessing, easily accessible data, and adjustable output scales.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Emili</LastName><ForeName>Lisa A</ForeName><Initials>LA</Initials><AffiliationInfo><Affiliation>Division of Mathematics and Natural Sciences, Penn State Altoona, 3000 Ivyside Park, Altoona, PA 16601, USA. lae18@psu.edu</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Greene</LastName><ForeName>Richard P</ForeName><Initials>RP</Initials></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2012</Year><Month>09</Month><Day>16</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>Environ Manage</MedlineTA><NlmUniqueID>7703893</NlmUniqueID><ISSNLinking>0364-152X</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000383" MajorTopicYN="Y">Agriculture</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D004784" MajorTopicYN="N">Environmental Monitoring</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D040362" MajorTopicYN="Y">Geographic Information Systems</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008962" MajorTopicYN="N">Models, Theoretical</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D014876" MajorTopicYN="N">Water Pollution</DescriptorName><QualifierName UI="Q000032" MajorTopicYN="Y">analysis</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D014881" MajorTopicYN="N">Water Supply</DescriptorName></MeshHeading></MeshHeadingList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2010</Year><Month>12</Month><Day>15</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2012</Year><Month>08</Month><Day>14</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2012</Year><Month>9</Month><Day>18</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2012</Year><Month>9</Month><Day>18</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2013</Year><Month>6</Month><Day>1</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">22983655</ArticleId><ArticleId IdType="doi">10.1007/s00267-012-9940-4</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>États-Unis</li></country><region><li>Pennsylvanie</li></region></list><tree><noCountry><name sortKey="Greene, Richard P" sort="Greene, Richard P" uniqKey="Greene R" first="Richard P" last="Greene">Richard P. Greene</name></noCountry><country name="États-Unis"><region name="Pennsylvanie"><name sortKey="Emili, Lisa A" sort="Emili, Lisa A" uniqKey="Emili L" first="Lisa A" last="Emili">Lisa A. Emili</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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