Identifying crop vulnerability to groundwater abstraction: modelling and expert knowledge in a GIS.
Identifieur interne : 000362 ( PubMed/Checkpoint ); précédent : 000361; suivant : 000363Identifying crop vulnerability to groundwater abstraction: modelling and expert knowledge in a GIS.
Auteurs : Chris Procter [Royaume-Uni] ; Lex Comber ; Mark Betson ; Dennis Buckley ; Andy Frost ; Hester Lyons ; Alison Riding ; Kevin VoyceSource :
- Journal of environmental management [ 0301-4797 ] ; 2006.
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Abstract
Water use is expected to increase and climate change scenarios indicate the need for more frequent water abstraction. Abstracting groundwater may have a detrimental effect on soil moisture availability for crop growth and yields. This work presents an elegant and robust method for identifying zones of crop vulnerability to abstraction. Archive groundwater level datasets were used to generate a composite groundwater surface that was subtracted from a digital terrain model. The result was the depth from surface to groundwater and identified areas underlain by shallow groundwater. Knowledge from an expert agronomist was used to define classes of risk in terms of their depth below ground level. Combining information on the permeability of geological drift types further refined the assessment of the risk of crop growth vulnerability. The nature of the mapped output is one that is easy to communicate to the intended farming audience because of the general familiarity of mapped information. Such Geographic Information System (GIS)-based products can play a significant role in the characterisation of catchments under the EU Water Framework Directive especially in the process of public liaison that is fundamental to the setting of priorities for management change. The creation of a baseline allows the impact of future increased water abstraction rates to be modelled and the vulnerability maps are in a format that can be readily understood by the various stakeholders. This methodology can readily be extended to encompass additional data layers and for a range of groundwater vulnerability issues including water resources, ecological impacts, nitrate and phosphorus.
DOI: 10.1016/j.jenvman.2006.01.016
PubMed: 16963176
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uniqKey="Buckley D" first="Dennis" last="Buckley">Dennis Buckley</name></author><author><name sortKey="Frost, Andy" sort="Frost, Andy" uniqKey="Frost A" first="Andy" last="Frost">Andy Frost</name></author><author><name sortKey="Lyons, Hester" sort="Lyons, Hester" uniqKey="Lyons H" first="Hester" last="Lyons">Hester Lyons</name></author><author><name sortKey="Riding, Alison" sort="Riding, Alison" uniqKey="Riding A" first="Alison" last="Riding">Alison Riding</name></author><author><name sortKey="Voyce, Kevin" sort="Voyce, Kevin" uniqKey="Voyce K" first="Kevin" last="Voyce">Kevin Voyce</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2006">2006</date><idno type="RBID">pubmed:16963176</idno><idno type="pmid">16963176</idno><idno type="doi">10.1016/j.jenvman.2006.01.016</idno><idno type="wicri:Area/PubMed/Corpus">000377</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000377</idno><idno 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Abstracting groundwater may have a detrimental effect on soil moisture availability for crop growth and yields. This work presents an elegant and robust method for identifying zones of crop vulnerability to abstraction. Archive groundwater level datasets were used to generate a composite groundwater surface that was subtracted from a digital terrain model. The result was the depth from surface to groundwater and identified areas underlain by shallow groundwater. Knowledge from an expert agronomist was used to define classes of risk in terms of their depth below ground level. Combining information on the permeability of geological drift types further refined the assessment of the risk of crop growth vulnerability. The nature of the mapped output is one that is easy to communicate to the intended farming audience because of the general familiarity of mapped information. Such Geographic Information System (GIS)-based products can play a significant role in the characterisation of catchments under the EU Water Framework Directive especially in the process of public liaison that is fundamental to the setting of priorities for management change. The creation of a baseline allows the impact of future increased water abstraction rates to be modelled and the vulnerability maps are in a format that can be readily understood by the various stakeholders. This methodology can readily be extended to encompass additional data layers and for a range of groundwater vulnerability issues including water resources, ecological impacts, nitrate and phosphorus.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">16963176</PMID><DateCreated><Year>2006</Year><Month>10</Month><Day>09</Day></DateCreated><DateCompleted><Year>2007</Year><Month>01</Month><Day>26</Day></DateCompleted><DateRevised><Year>2013</Year><Month>11</Month><Day>21</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Print">0301-4797</ISSN><JournalIssue CitedMedium="Print"><Volume>81</Volume><Issue>3</Issue><PubDate><Year>2006</Year><Month>Nov</Month></PubDate></JournalIssue><Title>Journal of environmental management</Title><ISOAbbreviation>J. Environ. Manage.</ISOAbbreviation></Journal><ArticleTitle>Identifying crop vulnerability to groundwater abstraction: modelling and expert knowledge in a GIS.</ArticleTitle><Pagination><MedlinePgn>296-306</MedlinePgn></Pagination><Abstract><AbstractText>Water use is expected to increase and climate change scenarios indicate the need for more frequent water abstraction. Abstracting groundwater may have a detrimental effect on soil moisture availability for crop growth and yields. This work presents an elegant and robust method for identifying zones of crop vulnerability to abstraction. Archive groundwater level datasets were used to generate a composite groundwater surface that was subtracted from a digital terrain model. The result was the depth from surface to groundwater and identified areas underlain by shallow groundwater. Knowledge from an expert agronomist was used to define classes of risk in terms of their depth below ground level. Combining information on the permeability of geological drift types further refined the assessment of the risk of crop growth vulnerability. The nature of the mapped output is one that is easy to communicate to the intended farming audience because of the general familiarity of mapped information. Such Geographic Information System (GIS)-based products can play a significant role in the characterisation of catchments under the EU Water Framework Directive especially in the process of public liaison that is fundamental to the setting of priorities for management change. The creation of a baseline allows the impact of future increased water abstraction rates to be modelled and the vulnerability maps are in a format that can be readily understood by the various stakeholders. 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ValidYN="Y"><LastName>Lyons</LastName><ForeName>Hester</ForeName><Initials>H</Initials></Author><Author ValidYN="Y"><LastName>Riding</LastName><ForeName>Alison</ForeName><Initials>A</Initials></Author><Author ValidYN="Y"><LastName>Voyce</LastName><ForeName>Kevin</ForeName><Initials>K</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>2006</Year><Month>09</Month><Day>11</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>J Environ Manage</MedlineTA><NlmUniqueID>0401664</NlmUniqueID><ISSNLinking>0301-4797</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D009566">Nitrates</NameOfSubstance></Chemical><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D014874">Water Pollutants, Chemical</NameOfSubstance></Chemical><Chemical><RegistryNumber>27YLU75U4W</RegistryNumber><NameOfSubstance UI="D010758">Phosphorus</NameOfSubstance></Chemical></ChemicalList><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D018556" MajorTopicYN="Y">Crops, Agricultural</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D017753" MajorTopicYN="N">Ecosystem</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D004784" MajorTopicYN="Y">Environmental Monitoring</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D040362" MajorTopicYN="Y">Geographic Information Systems</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D055593" MajorTopicYN="N">Geological Phenomena</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D005844" MajorTopicYN="N">Geology</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008954" MajorTopicYN="N">Models, Biological</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D009566" MajorTopicYN="N">Nitrates</DescriptorName><QualifierName UI="Q000032" MajorTopicYN="N">analysis</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D010758" MajorTopicYN="N">Phosphorus</DescriptorName><QualifierName UI="Q000032" MajorTopicYN="N">analysis</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D018570" MajorTopicYN="N">Risk Assessment</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D014874" MajorTopicYN="N">Water Pollutants, Chemical</DescriptorName><QualifierName UI="Q000032" MajorTopicYN="Y">analysis</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D014881" MajorTopicYN="Y">Water Supply</DescriptorName></MeshHeading></MeshHeadingList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2005</Year><Month>01</Month><Day>31</Day></PubMedPubDate><PubMedPubDate 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first="Mark" last="Betson">Mark Betson</name><name sortKey="Buckley, Dennis" sort="Buckley, Dennis" uniqKey="Buckley D" first="Dennis" last="Buckley">Dennis Buckley</name><name sortKey="Comber, Lex" sort="Comber, Lex" uniqKey="Comber L" first="Lex" last="Comber">Lex Comber</name><name sortKey="Frost, Andy" sort="Frost, Andy" uniqKey="Frost A" first="Andy" last="Frost">Andy Frost</name><name sortKey="Lyons, Hester" sort="Lyons, Hester" uniqKey="Lyons H" first="Hester" last="Lyons">Hester Lyons</name><name sortKey="Riding, Alison" sort="Riding, Alison" uniqKey="Riding A" first="Alison" last="Riding">Alison Riding</name><name sortKey="Voyce, Kevin" sort="Voyce, Kevin" uniqKey="Voyce K" first="Kevin" last="Voyce">Kevin Voyce</name></noCountry><country name="Royaume-Uni"><noRegion><name sortKey="Procter, Chris" sort="Procter, Chris" uniqKey="Procter C" first="Chris" last="Procter">Chris Procter</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix 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