Accuracy of mixing models in predicting sediment source contributions.
Identifieur interne : 001D21 ( Ncbi/Merge ); précédent : 001D20; suivant : 001D22Accuracy of mixing models in predicting sediment source contributions.
Auteurs : Arman Haddadchi [Australie] ; Jon Olley [Australie] ; Patrick Laceby [France]Source :
- The Science of the total environment [ 1879-1026 ] ; 2014.
Abstract
Determining the source of sediment using geochemical properties is now a widely used approach in catchment management. However the outcome of these studies often depends on the type of model used to determine the relative contribution from difference sources. Here we test the accuracy and robustness of four widely used sediment mixing models using artificial mixtures of three well-distinguished geologic sources. Sub-samples from these three sources were mixed to create four groups of samples, each consisting of five samples, with known source contributions, 20 samples in total. The source contributions to the individual and groups of artificial sediment mixtures were calculated using each of the four mixing models: Modified Hughes, Modified Collins, Landwehr and Distribution models. Unlike Modified Collins and Landwehr models which use calculated values from each tracer property of individual sources (e.g. mean and standard deviation), Hughes model uses the measured fingerprint property of replicated samples from each source and Distribution model incorporate distribution of tracers and correlation between tracer properties for sediment samples and sources. For the 20 individual sample mixtures the Distribution model provided the closest estimates to the known sediment source contribution values (Mean Absolute Error (MAE)=10.8%, and standard error (SE)=0.9%). The Modified Hughes (MAE=13.5%, SE=1.1%), Landwehr (MAE=19%, SE=1.7) and Collins models (MAE=29%, SE=2.1%) were the next accurate models, respectively. For the groups of the samples the Modified Hughes was the most robust source contribution predictor with 5.4% error. The Distribution model (MAE=6.1%) and Landwehr model (MAE=7.8%) were the second and third accurate models. Collins model with MAE of 28.3% was a significantly weaker source contribution predictor than the three other models. This study demonstrates the dependence of source attribution on model selection. The study highlight the need to test mixing model using known source and mixture samples prior to applying them to field samples. The results indicate that the Distribution and Modified Hughes models provided the most accurate source attributions using geochemical fingerprint properties.
DOI: 10.1016/j.scitotenv.2014.07.105
PubMed: 25128884
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Electronic address: placeby@lsce.ipsl.fr.</nlm:affiliation><country xml:lang="fr">France</country><wicri:regionArea>Laboratoire des Sciences du Climat et de l'Environnement (LSCE)</wicri:regionArea></affiliation></author></analytic><series><title level="j">The Science of the total environment</title><idno type="eISSN">1879-1026</idno><imprint><date when="2014" type="published">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Determining the source of sediment using geochemical properties is now a widely used approach in catchment management. However the outcome of these studies often depends on the type of model used to determine the relative contribution from difference sources. Here we test the accuracy and robustness of four widely used sediment mixing models using artificial mixtures of three well-distinguished geologic sources. Sub-samples from these three sources were mixed to create four groups of samples, each consisting of five samples, with known source contributions, 20 samples in total. The source contributions to the individual and groups of artificial sediment mixtures were calculated using each of the four mixing models: Modified Hughes, Modified Collins, Landwehr and Distribution models. Unlike Modified Collins and Landwehr models which use calculated values from each tracer property of individual sources (e.g. mean and standard deviation), Hughes model uses the measured fingerprint property of replicated samples from each source and Distribution model incorporate distribution of tracers and correlation between tracer properties for sediment samples and sources. For the 20 individual sample mixtures the Distribution model provided the closest estimates to the known sediment source contribution values (Mean Absolute Error (MAE)=10.8%, and standard error (SE)=0.9%). The Modified Hughes (MAE=13.5%, SE=1.1%), Landwehr (MAE=19%, SE=1.7) and Collins models (MAE=29%, SE=2.1%) were the next accurate models, respectively. For the groups of the samples the Modified Hughes was the most robust source contribution predictor with 5.4% error. The Distribution model (MAE=6.1%) and Landwehr model (MAE=7.8%) were the second and third accurate models. Collins model with MAE of 28.3% was a significantly weaker source contribution predictor than the three other models. This study demonstrates the dependence of source attribution on model selection. The study highlight the need to test mixing model using known source and mixture samples prior to applying them to field samples. The results indicate that the Distribution and Modified Hughes models provided the most accurate source attributions using geochemical fingerprint properties.</div></front></TEI><pubmed><MedlineCitation Status="PubMed-not-MEDLINE" Owner="NLM"><PMID Version="1">25128884</PMID><DateCreated><Year>2014</Year><Month>09</Month><Day>26</Day></DateCreated><DateCompleted><Year>2015</Year><Month>05</Month><Day>12</Day></DateCompleted><DateRevised><Year>2017</Year><Month>10</Month><Day>08</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1879-1026</ISSN><JournalIssue CitedMedium="Internet"><Volume>497-498</Volume><PubDate><Year>2014</Year><Month>Nov</Month><Day>01</Day></PubDate></JournalIssue><Title>The Science of the total environment</Title><ISOAbbreviation>Sci. Total Environ.</ISOAbbreviation></Journal><ArticleTitle>Accuracy of mixing models in predicting sediment source contributions.</ArticleTitle><Pagination><MedlinePgn>139-152</MedlinePgn></Pagination><ELocationID EIdType="pii" ValidYN="Y">S0048-9697(14)01142-5</ELocationID><ELocationID EIdType="doi" ValidYN="Y">10.1016/j.scitotenv.2014.07.105</ELocationID><Abstract><AbstractText>Determining the source of sediment using geochemical properties is now a widely used approach in catchment management. However the outcome of these studies often depends on the type of model used to determine the relative contribution from difference sources. Here we test the accuracy and robustness of four widely used sediment mixing models using artificial mixtures of three well-distinguished geologic sources. Sub-samples from these three sources were mixed to create four groups of samples, each consisting of five samples, with known source contributions, 20 samples in total. The source contributions to the individual and groups of artificial sediment mixtures were calculated using each of the four mixing models: Modified Hughes, Modified Collins, Landwehr and Distribution models. Unlike Modified Collins and Landwehr models which use calculated values from each tracer property of individual sources (e.g. mean and standard deviation), Hughes model uses the measured fingerprint property of replicated samples from each source and Distribution model incorporate distribution of tracers and correlation between tracer properties for sediment samples and sources. For the 20 individual sample mixtures the Distribution model provided the closest estimates to the known sediment source contribution values (Mean Absolute Error (MAE)=10.8%, and standard error (SE)=0.9%). The Modified Hughes (MAE=13.5%, SE=1.1%), Landwehr (MAE=19%, SE=1.7) and Collins models (MAE=29%, SE=2.1%) were the next accurate models, respectively. For the groups of the samples the Modified Hughes was the most robust source contribution predictor with 5.4% error. The Distribution model (MAE=6.1%) and Landwehr model (MAE=7.8%) were the second and third accurate models. Collins model with MAE of 28.3% was a significantly weaker source contribution predictor than the three other models. This study demonstrates the dependence of source attribution on model selection. The study highlight the need to test mixing model using known source and mixture samples prior to applying them to field samples. The results indicate that the Distribution and Modified Hughes models provided the most accurate source attributions using geochemical fingerprint properties.</AbstractText><CopyrightInformation>Copyright © 2014 Elsevier B.V. All rights reserved.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Haddadchi</LastName><ForeName>Arman</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>Australian Rivers Institute, Griffith University, Nathan, QLD 4111, Australia. Electronic address: arman.haddadchi@griffithuni.edu.au.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Olley</LastName><ForeName>Jon</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>Australian Rivers Institute, Griffith University, Nathan, QLD 4111, Australia. Electronic address: j.olley@griffith.edu.au.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Laceby</LastName><ForeName>Patrick</ForeName><Initials>P</Initials><AffiliationInfo><Affiliation>Laboratoire des Sciences du Climat et de l'Environnement (LSCE), France. Electronic address: placeby@lsce.ipsl.fr.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2014</Year><Month>08</Month><Day>14</Day></ArticleDate></Article><MedlineJournalInfo><Country>Netherlands</Country><MedlineTA>Sci Total Environ</MedlineTA><NlmUniqueID>0330500</NlmUniqueID><ISSNLinking>0048-9697</ISSNLinking></MedlineJournalInfo><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Mixing models</Keyword><Keyword MajorTopicYN="N">Sediment tracing</Keyword><Keyword MajorTopicYN="N">Source contribution</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2014</Year><Month>05</Month><Day>06</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2014</Year><Month>07</Month><Day>25</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2014</Year><Month>07</Month><Day>27</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2014</Year><Month>8</Month><Day>17</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2014</Year><Month>8</Month><Day>17</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2014</Year><Month>8</Month><Day>17</Day><Hour>6</Hour><Minute>1</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">25128884</ArticleId><ArticleId IdType="pii">S0048-9697(14)01142-5</ArticleId><ArticleId IdType="doi">10.1016/j.scitotenv.2014.07.105</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>Australie</li><li>France</li></country></list><tree><country name="Australie"><noRegion><name sortKey="Haddadchi, Arman" sort="Haddadchi, Arman" uniqKey="Haddadchi A" first="Arman" last="Haddadchi">Arman Haddadchi</name></noRegion><name sortKey="Olley, Jon" sort="Olley, Jon" uniqKey="Olley J" first="Jon" last="Olley">Jon Olley</name></country><country name="France"><noRegion><name sortKey="Laceby, Patrick" sort="Laceby, Patrick" uniqKey="Laceby P" first="Patrick" last="Laceby">Patrick Laceby</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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