Nonlinear regression modeling of nutrient loads in streams: A Bayesian approach
A Bayesian nonlinear regression modeling method is introduced and compared with the least squares method for modeling nutrient loads in stream networks. The objective of the study is to better model spatial correlation in river basin hydrology and land use for improving the model as a forecasting to...
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| Veröffentlicht in: | Water resources research 2005-07, Vol.41 (7), p.n/a |
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| container_title | Water resources research |
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| creator | Qian, S.S Reckhow, K.H Zhai, J McMahon, G |
| description | A Bayesian nonlinear regression modeling method is introduced and compared with the least squares method for modeling nutrient loads in stream networks. The objective of the study is to better model spatial correlation in river basin hydrology and land use for improving the model as a forecasting tool. The Bayesian modeling approach is introduced in three steps, each with a more complicated model and data error structure. The approach is illustrated using a data set from three large river basins in eastern North Carolina. Results indicate that the Bayesian model better accounts for model and data uncertainties than does the conventional least squares approach. Applications of the Bayesian models for ambient water quality standards compliance and TMDL assessment are discussed. |
| doi_str_mv | 10.1029/2005WR003986 |
| format | Article |
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The objective of the study is to better model spatial correlation in river basin hydrology and land use for improving the model as a forecasting tool. The Bayesian modeling approach is introduced in three steps, each with a more complicated model and data error structure. The approach is illustrated using a data set from three large river basins in eastern North Carolina. Results indicate that the Bayesian model better accounts for model and data uncertainties than does the conventional least squares approach. Applications of the Bayesian models for ambient water quality standards compliance and TMDL assessment are discussed.</description><identifier>ISSN: 0043-1397</identifier><identifier>EISSN: 1944-7973</identifier><identifier>DOI: 10.1029/2005WR003986</identifier><language>eng</language><publisher>Blackwell Publishing Ltd</publisher><subject>Bayesian statistics ; Bayesian theory ; eutrophication ; hydrologic models ; hydrology ; land use ; least squares ; MCMC ; pollution load ; rivers ; SPARROW ; surface water ; water quality ; watershed modeling</subject><ispartof>Water resources research, 2005-07, Vol.41 (7), p.n/a</ispartof><rights>Copyright 2005 by the American Geophysical Union.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a4029-d939e2bb55518fd1d130ace19edf5945067462a817089f94030a33c0de46aa903</citedby><cites>FETCH-LOGICAL-a4029-d939e2bb55518fd1d130ace19edf5945067462a817089f94030a33c0de46aa903</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1029%2F2005WR003986$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1029%2F2005WR003986$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1416,11513,27923,27924,45573,45574,46467,46891</link.rule.ids></links><search><creatorcontrib>Qian, S.S</creatorcontrib><creatorcontrib>Reckhow, K.H</creatorcontrib><creatorcontrib>Zhai, J</creatorcontrib><creatorcontrib>McMahon, G</creatorcontrib><title>Nonlinear regression modeling of nutrient loads in streams: A Bayesian approach</title><title>Water resources research</title><addtitle>Water Resour. Res</addtitle><description>A Bayesian nonlinear regression modeling method is introduced and compared with the least squares method for modeling nutrient loads in stream networks. The objective of the study is to better model spatial correlation in river basin hydrology and land use for improving the model as a forecasting tool. The Bayesian modeling approach is introduced in three steps, each with a more complicated model and data error structure. The approach is illustrated using a data set from three large river basins in eastern North Carolina. Results indicate that the Bayesian model better accounts for model and data uncertainties than does the conventional least squares approach. Applications of the Bayesian models for ambient water quality standards compliance and TMDL assessment are discussed.</description><subject>Bayesian statistics</subject><subject>Bayesian theory</subject><subject>eutrophication</subject><subject>hydrologic models</subject><subject>hydrology</subject><subject>land use</subject><subject>least squares</subject><subject>MCMC</subject><subject>pollution load</subject><subject>rivers</subject><subject>SPARROW</subject><subject>surface water</subject><subject>water quality</subject><subject>watershed modeling</subject><issn>0043-1397</issn><issn>1944-7973</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2005</creationdate><recordtype>article</recordtype><recordid>eNp9kFFLwzAQx4MoOKdvvpsPYPXSJE3j2yw6hbHBdPQx3Np0Rrt2JBXdt7dSEZ98Orj7_Y67PyHnDK4YxPo6BpD5EoDrNDkgI6aFiJRW_JCMAASPGNfqmJyE8ArAhEzUiCzmbVO7xqKn3m68DcG1Dd22pe27G9pWtHnvvLNNR-sWy0BdQ0PnLW7DDZ3QW9zb4LChuNv5FouXU3JUYR3s2U8dk9X93XP2EM0W08dsMotQ9JdGpebaxuu1lJKlVclKxgELy7QtK6mFhESJJMaUKUh1pQX0Y84LKK1IEDXwMbkc9ha-DcHbyuy826LfGwbmOwzzN4we5wP-4Wq7_5c1-TJbMuCx7q1osFzo7Oevhf7NJIorafL51HAteZbkMyN6_mLgK2wNbrwLZvUUQ_8bAyGZ0PwLF3x4TQ</recordid><startdate>200507</startdate><enddate>200507</enddate><creator>Qian, S.S</creator><creator>Reckhow, K.H</creator><creator>Zhai, J</creator><creator>McMahon, G</creator><general>Blackwell Publishing Ltd</general><scope>FBQ</scope><scope>BSCLL</scope><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>200507</creationdate><title>Nonlinear regression modeling of nutrient loads in streams: A Bayesian approach</title><author>Qian, S.S ; Reckhow, K.H ; Zhai, J ; McMahon, G</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a4029-d939e2bb55518fd1d130ace19edf5945067462a817089f94030a33c0de46aa903</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2005</creationdate><topic>Bayesian statistics</topic><topic>Bayesian theory</topic><topic>eutrophication</topic><topic>hydrologic models</topic><topic>hydrology</topic><topic>land use</topic><topic>least squares</topic><topic>MCMC</topic><topic>pollution load</topic><topic>rivers</topic><topic>SPARROW</topic><topic>surface water</topic><topic>water quality</topic><topic>watershed modeling</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Qian, S.S</creatorcontrib><creatorcontrib>Reckhow, K.H</creatorcontrib><creatorcontrib>Zhai, J</creatorcontrib><creatorcontrib>McMahon, G</creatorcontrib><collection>AGRIS</collection><collection>Istex</collection><collection>CrossRef</collection><jtitle>Water resources research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Qian, S.S</au><au>Reckhow, K.H</au><au>Zhai, J</au><au>McMahon, G</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Nonlinear regression modeling of nutrient loads in streams: A Bayesian approach</atitle><jtitle>Water resources research</jtitle><addtitle>Water Resour. 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| ispartof | Water resources research, 2005-07, Vol.41 (7), p.n/a |
| issn | 0043-1397 1944-7973 |
| language | eng |
| recordid | cdi_crossref_primary_10_1029_2005WR003986 |
| source | Wiley-Blackwell AGU Digital Library; EZB-FREE-00999 freely available EZB journals; Wiley Online Library All Journals |
| subjects | Bayesian statistics Bayesian theory eutrophication hydrologic models hydrology land use least squares MCMC pollution load rivers SPARROW surface water water quality watershed modeling |
| title | Nonlinear regression modeling of nutrient loads in streams: A Bayesian approach |
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