Development of a multiobjective optimization tool for the selection and placement of best management practices for nonpoint source pollution control
Identifieur interne : 001207 ( Istex/Corpus ); précédent : 001206; suivant : 001208Development of a multiobjective optimization tool for the selection and placement of best management practices for nonpoint source pollution control
Auteurs : Chetan Maringanti ; Indrajeet Chaubey ; Jennie PoppSource :
- Water Resources Research [ 0043-1397 ] ; 2009-06.
Abstract
Best management practices (BMPs) are effective in reducing the transport of agricultural nonpoint source pollutants to receiving water bodies. However, selection of BMPs for placement in a watershed requires optimization of the available resources to obtain maximum possible pollution reduction. In this study, an optimization methodology is developed to select and place BMPs in a watershed to provide solutions that are both economically and ecologically effective. This novel approach develops and utilizes a BMP tool, a database that stores the pollution reduction and cost information of different BMPs under consideration. The BMP tool replaces the dynamic linkage of the distributed parameter watershed model during optimization and therefore reduces the computation time considerably. Total pollutant load from the watershed, and net cost increase from the baseline, were the two objective functions minimized during the optimization process. The optimization model, consisting of a multiobjective genetic algorithm (NSGA‐II) in combination with a watershed simulation tool (Soil Water and Assessment Tool (SWAT)), was developed and tested for nonpoint source pollution control in the L'Anguille River watershed located in eastern Arkansas. The optimized solutions provided a trade‐off between the two objective functions for sediment, phosphorus, and nitrogen reduction. The results indicated that buffer strips were very effective in controlling the nonpoint source pollutants from leaving the croplands. The optimized BMP plans resulted in potential reductions of 33%, 32%, and 13% in sediment, phosphorus, and nitrogen loads, respectively, from the watershed.
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DOI: 10.1029/2008WR007094
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However, selection of BMPs for placement in a watershed requires optimization of the available resources to obtain maximum possible pollution reduction. In this study, an optimization methodology is developed to select and place BMPs in a watershed to provide solutions that are both economically and ecologically effective. This novel approach develops and utilizes a BMP tool, a database that stores the pollution reduction and cost information of different BMPs under consideration. The BMP tool replaces the dynamic linkage of the distributed parameter watershed model during optimization and therefore reduces the computation time considerably. Total pollutant load from the watershed, and net cost increase from the baseline, were the two objective functions minimized during the optimization process. The optimization model, consisting of a multiobjective genetic algorithm (NSGA‐II) in combination with a watershed simulation tool (Soil Water and Assessment Tool (SWAT)), was developed and tested for nonpoint source pollution control in the L'Anguille River watershed located in eastern Arkansas. The optimized solutions provided a trade‐off between the two objective functions for sediment, phosphorus, and nitrogen reduction. The results indicated that buffer strips were very effective in controlling the nonpoint source pollutants from leaving the croplands. The optimized BMP plans resulted in potential reductions of 33%, 32%, and 13% in sediment, phosphorus, and nitrogen loads, respectively, from the watershed.</div></front></TEI><istex><corpusName>wiley</corpusName><author><json:item><name>Chetan Maringanti</name><affiliations><json:string>Department of Agricultural and Biological Engineering, Purdue University, Indiana, West Lafayette, USA</json:string></affiliations></json:item><json:item><name>Indrajeet Chaubey</name><affiliations><json:string>Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, Indiana, USA</json:string><json:string>Also at Department of Earth and Atmospheric Sciences, Purdue University, West Lafayette, Indiana, USA.</json:string><json:string>E-mail: ichaubey@purdue.edu</json:string></affiliations></json:item><json:item><name>Jennie Popp</name><affiliations><json:string>Department of Agricultural Economics and Agribusiness, University of Arkansas, Arkansas, Fayetteville, USA</json:string></affiliations></json:item></author><subject><json:item><lang><json:string>eng</json:string></lang><value>SWAT model</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>agricultural water quality</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>optimization</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>BMP tool</value></json:item></subject><articleId><json:string>2008WR007094</json:string></articleId><language><json:string>eng</json:string></language><originalGenre><json:string>article</json:string></originalGenre><abstract>Best management practices (BMPs) are effective in reducing the transport of agricultural nonpoint source pollutants to receiving water bodies. However, selection of BMPs for placement in a watershed requires optimization of the available resources to obtain maximum possible pollution reduction. In this study, an optimization methodology is developed to select and place BMPs in a watershed to provide solutions that are both economically and ecologically effective. This novel approach develops and utilizes a BMP tool, a database that stores the pollution reduction and cost information of different BMPs under consideration. The BMP tool replaces the dynamic linkage of the distributed parameter watershed model during optimization and therefore reduces the computation time considerably. Total pollutant load from the watershed, and net cost increase from the baseline, were the two objective functions minimized during the optimization process. The optimization model, consisting of a multiobjective genetic algorithm (NSGA‐II) in combination with a watershed simulation tool (Soil Water and Assessment Tool (SWAT)), was developed and tested for nonpoint source pollution control in the L'Anguille River watershed located in eastern Arkansas. The optimized solutions provided a trade‐off between the two objective functions for sediment, phosphorus, and nitrogen reduction. The results indicated that buffer strips were very effective in controlling the nonpoint source pollutants from leaving the croplands. 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The optimization model, consisting of a multiobjective genetic algorithm (NSGA‐II) in combination with a watershed simulation tool (Soil Water and Assessment Tool (SWAT)), was developed and tested for nonpoint source pollution control in the L'Anguille River watershed located in eastern Arkansas. The optimized solutions provided a trade‐off between the two objective functions for sediment, phosphorus, and nitrogen reduction. The results indicated that buffer strips were very effective in controlling the nonpoint source pollutants from leaving the croplands. 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However, selection of BMPs for placement in a watershed requires optimization of the available resources to obtain maximum possible pollution reduction. In this study, an optimization methodology is developed to select and place BMPs in a watershed to provide solutions that are both economically and ecologically effective. This novel approach develops and utilizes a BMP tool, a database that stores the pollution reduction and cost information of different BMPs under consideration. The BMP tool replaces the dynamic linkage of the distributed parameter watershed model during optimization and therefore reduces the computation time considerably. Total pollutant load from the watershed, and net cost increase from the baseline, were the two objective functions minimized during the optimization process. The optimization model, consisting of a multiobjective genetic algorithm (NSGA‐II) in combination with a watershed simulation tool (Soil Water and Assessment Tool (SWAT)), was developed and tested for nonpoint source pollution control in the L'Anguille River watershed located in eastern Arkansas. The optimized solutions provided a trade‐off between the two objective functions for sediment, phosphorus, and nitrogen reduction. The results indicated that buffer strips were very effective in controlling the nonpoint source pollutants from leaving the croplands. The optimized BMP plans resulted in potential reductions of 33%, 32%, and 13% in sediment, phosphorus, and nitrogen loads, respectively, from the watershed.</p></abstract></abstractGroup></contentMeta></header></component></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Development of a multiobjective optimization tool for the selection and placement of best management practices for nonpoint source pollution control</title></titleInfo><titleInfo type="abbreviated" lang="en"><title>MULTIOBJECTIVE OPTIMIZATION TOOL</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Development of a multiobjective optimization tool for the selection and placement of best management practices for nonpoint source pollution control</title></titleInfo><name type="personal"><namePart type="given">Chetan</namePart><namePart type="family">Maringanti</namePart><affiliation>Department of Agricultural and Biological Engineering, Purdue University, Indiana, West Lafayette, USA</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Indrajeet</namePart><namePart type="family">Chaubey</namePart><affiliation>Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, Indiana, USA</affiliation><affiliation>Also at Department of Earth and Atmospheric Sciences, Purdue University, West Lafayette, Indiana, USA.</affiliation><affiliation>E-mail: ichaubey@purdue.edu</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Jennie</namePart><namePart type="family">Popp</namePart><affiliation>Department of Agricultural Economics and Agribusiness, University of Arkansas, Arkansas, Fayetteville, USA</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="article" displayLabel="article"></genre><originInfo><publisher>Blackwell Publishing Ltd</publisher><dateIssued encoding="w3cdtf">2009-06</dateIssued><dateCaptured encoding="w3cdtf">2008-04-15</dateCaptured><dateValid encoding="w3cdtf">2009-03-26</dateValid><edition>Maringanti, C., I. Chaubey, and J. Popp (2009), Development of a multiobjective optimization tool for the selection and placement of best management practices for nonpoint source pollution control, Water Resour. Res., 45, W06406, doi:10.1029/2008WR007094.</edition><copyrightDate encoding="w3cdtf">2009</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType><extent unit="figures">12</extent><extent unit="tables">4</extent></physicalDescription><abstract>Best management practices (BMPs) are effective in reducing the transport of agricultural nonpoint source pollutants to receiving water bodies. However, selection of BMPs for placement in a watershed requires optimization of the available resources to obtain maximum possible pollution reduction. In this study, an optimization methodology is developed to select and place BMPs in a watershed to provide solutions that are both economically and ecologically effective. This novel approach develops and utilizes a BMP tool, a database that stores the pollution reduction and cost information of different BMPs under consideration. The BMP tool replaces the dynamic linkage of the distributed parameter watershed model during optimization and therefore reduces the computation time considerably. Total pollutant load from the watershed, and net cost increase from the baseline, were the two objective functions minimized during the optimization process. The optimization model, consisting of a multiobjective genetic algorithm (NSGA‐II) in combination with a watershed simulation tool (Soil Water and Assessment Tool (SWAT)), was developed and tested for nonpoint source pollution control in the L'Anguille River watershed located in eastern Arkansas. The optimized solutions provided a trade‐off between the two objective functions for sediment, phosphorus, and nitrogen reduction. The results indicated that buffer strips were very effective in controlling the nonpoint source pollutants from leaving the croplands. The optimized BMP plans resulted in potential reductions of 33%, 32%, and 13% in sediment, phosphorus, and nitrogen loads, respectively, from the watershed.</abstract><note type="additional physical form">Tab‐delimited Table 1.Tab‐delimited Table 2.Tab‐delimited Table 3.Tab‐delimited Table 4.</note><subject><genre>keywords</genre><topic>SWAT model</topic><topic>agricultural water quality</topic><topic>optimization</topic><topic>BMP tool</topic></subject><relatedItem type="host"><titleInfo><title>Water Resources Research</title></titleInfo><titleInfo type="abbreviated"><title>Water Resour. Res.</title></titleInfo><genre type="journal">journal</genre><subject><genre>index-terms</genre><topic authorityURI="http://psi.agu.org/taxonomy5/1800">HYDROLOGY</topic><topic authorityURI="http://psi.agu.org/taxonomy5/1879">Watershed</topic><topic authorityURI="http://psi.agu.org/taxonomy5/1847">Modeling</topic><topic authorityURI="http://psi.agu.org/taxonomy5/1871">Surface water quality</topic><topic authorityURI="http://psi.agu.org/taxonomy5/1880">Water management</topic><topic authorityURI="http://psi.agu.org/taxonomy5/1900">INFORMATICS</topic><topic authorityURI="http://psi.agu.org/taxonomy5/1952">Modeling</topic><topic authorityURI="http://psi.agu.org/taxonomy5/4300">NATURAL HAZARDS</topic><topic authorityURI="http://psi.agu.org/taxonomy5/4316">Physical modeling</topic><topic authorityURI="http://psi.agu.org/taxonomy5/6300">POLICY SCIENCES</topic><topic authorityURI="http://psi.agu.org/taxonomy5/6334">Regional planning</topic></subject><subject><genre>article-category</genre><topic>Regular Article</topic></subject><identifier type="ISSN">0043-1397</identifier><identifier type="eISSN">1944-7973</identifier><identifier type="DOI">10.1002/(ISSN)1944-7973</identifier><identifier type="CODEN">WRERAQ</identifier><identifier type="PublisherID">WRCR</identifier><part><date>2009</date><detail type="volume"><caption>vol.</caption><number>45</number></detail><detail type="issue"><caption>no.</caption><number>6</number></detail><extent unit="pages"><start>n/a</start><end>n/a</end><total>15</total></extent></part></relatedItem><identifier type="istex">D56E6A04D6646EE2DE2279E26AACD28FCCD143C9</identifier><identifier type="DOI">10.1029/2008WR007094</identifier><identifier type="ArticleID">2008WR007094</identifier><accessCondition type="use and reproduction" contentType="copyright">Copyright 2009 by the American Geophysical Union.</accessCondition><recordInfo><recordContentSource>WILEY</recordContentSource></recordInfo></mods></metadata><serie></serie></istex></record>Pour manipuler ce document sous Unix (Dilib)
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