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Predicting the Hypoxic‐Volume in Chesapeake Bay with the Streeter–Phelps Model: A Bayesian Approach 1

dc.contributor.authorLiu, Yongen_US
dc.contributor.authorArhonditsis, George B.en_US
dc.contributor.authorStow, Craig A.en_US
dc.contributor.authorScavia, Donalden_US
dc.date.accessioned2012-01-05T22:07:05Z
dc.date.available2013-02-01T20:26:14Zen_US
dc.date.issued2011-12en_US
dc.identifier.citationLiu, Yong; Arhonditsis, George B.; Stow, Craig A.; Scavia, Donald (2011). "Predicting the Hypoxic‐Volume in Chesapeake Bay with the Streeter–Phelps Model: A Bayesian Approach 1 ." JAWRA Journal of the American Water Resources Association 47(6). <http://hdl.handle.net/2027.42/89549>en_US
dc.identifier.issn1093-474Xen_US
dc.identifier.issn1752-1688en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/89549
dc.publisherBlackwell Publishing Ltden_US
dc.publisherWiley Periodicals, Inc.en_US
dc.subject.otherHypoxiaen_US
dc.subject.otherChesapeake Bayen_US
dc.subject.otherBayesian Inferenceen_US
dc.subject.otherMarkov Chain Monte Carloen_US
dc.subject.otherStreeter–Phelps Modelen_US
dc.subject.otherUncertainty Analysisen_US
dc.subject.otherEutrophicationen_US
dc.titlePredicting the Hypoxic‐Volume in Chesapeake Bay with the Streeter–Phelps Model: A Bayesian Approach 1en_US
dc.typeArticleen_US
dc.rights.robotsIndexNoFollowen_US
dc.subject.hlbsecondlevelNatural Resources and Environmenten_US
dc.subject.hlbtoplevelScienceen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumProfessor, School of Natural Resources & Environment, University of Michigan, Ann Arbor, Michigan 48109en_US
dc.contributor.affiliationotherRespectively, Research Professor, College of Environmental Science and Engineering, The Key Laboratory of Water and Sediment Sciences Ministry of Education, Peking University, Beijing 100871, Chinaen_US
dc.contributor.affiliationotherAssociate Professor, Department of Physical and Environmental Sciences, University of Toronto, Toronto, Canada M1C 1A4en_US
dc.contributor.affiliationotherSenior Research Scientist, NOAA Great Lakes Environmental Research Laboratory, Ann Arbor, Michigan 48105‐2945en_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/89549/1/j.1752-1688.2011.00588.x.pdf
dc.identifier.doi10.1111/j.1752-1688.2011.00588.xen_US
dc.identifier.sourceJAWRA Journal of the American Water Resources Associationen_US
dc.identifier.citedreferenceArhonditsis, G.B., B.A. Adams‐VanHarn, L. Nielsen, C.A. Stow, and K.H. Reckhow, 2006. Evaluation of the Current State of Mechanistic Aquatic Biogeochemical Modeling: Citation Analysis and Future Perspectives. Environmental Science & Technology 40: 6547 ‐ 6554.en_US
dc.identifier.citedreferenceArhonditsis, G.B. and M.T. Brett, 2004. Evaluation of the Current State of Mechanistic Aquatic Biogeochemical Modelling. Marine Ecology-Progress Series 271: 13 ‐ 26.en_US
dc.identifier.citedreferenceArhonditsis, G.B., D. Papantou, W. Zhang, G. Perhar, E. Massos, and M. Shi, 2008b. Bayesian Calibration of Mechanistic Aquatic Biogeochemical Models and Benefits for Environmental Management. Journal of Marine Systems 73: 8 ‐ 30.en_US
dc.identifier.citedreferenceArhonditsis, G.B., G. Perhar, W. Zhang, E. Massos, M. Shi, and A. Das, 2008a. Addressing Equifinality and Uncertainty in Eutrophication Models. Water Recourses Research 44: W01420.en_US
dc.identifier.citedreferenceArhonditsis, G.B., S.S. Qian, C.A. Stow, C.E. Lamon, and K.H. Reckhow, 2007. Eutrophication Risk Assessment Using Bayesian Calibration of Process‐Based Models: Application to a Mesotrophic Lake. Ecological Modelling 28 ( 2‐4 ): 215 ‐ 229.en_US
dc.identifier.citedreferenceBoesch, D.F., R.B. Brinsfield, and R.E. Magnien, 2001. Chesapeake Bay Eutrophication: Scientific Understanding, Ecosystem Restoration and Challenges for Agriculture. Journal of Environment Quality 30: 303 ‐ 320.en_US
dc.identifier.citedreferenceBoesch, D.F., V.J. Coles, D.G. Kimmel, and W.D. Miller, 2007. Ramifications of Climate Change for Chesapeake Bay Hypoxia. In: Regional Impacts of Climate Change: Four Case Studies in the United States, Kristie L. Ebi, Gerald A. Meehl, Dominique Bachelet, Robert R. Twilley and Donald F. Boesch (Editors). Pew Center on Global Climate Change, Arlington, Virginia, pp. 54 ‐ 70.en_US
dc.identifier.citedreferenceBorsuk, M.E., D. Higdon, C.A. Stow, and K.H. Reckhow, 2001. A Bayesian Hierarchical Model to Predict Benthic Oxygen Demand From Organic Matter Loading in Estuaries and Coastal Zones. Ecological Modelling 143: 165 ‐ 181.en_US
dc.identifier.citedreferenceBricker, S., B. Longstaff, W. Dennison, A. Jones, K. Boicourt, C. Wicks, and J. Woerner, 2007. Effects of Nutrient Enrichment in the Nation’s Estuaries: A Decade of Change. NOAA Coastal Ocean Program Decision Analysis Series No. 26. National Centers for Coastal Ocean Science, Silver Spring, Maryland, 328 pp.en_US
dc.identifier.citedreferenceCerco, C.F. and T.M. Cole, 1993. Three‐Dimensional Eutrophication Model of Chesapeake Bay. Journal of Environmental Engineering 119: 1006 ‐ 1025.en_US
dc.identifier.citedreferenceCerco, C. and M. Noel, 2004. Process‐Based Primary Production Modeling in Chesapeake Bay. Marine Ecology Progress Series 282: 45 ‐ 58.en_US
dc.identifier.citedreferenceChapra, S.C., 1997. Surface Water‐Quality Modeling. McGraw‐Hill, New York.en_US
dc.identifier.citedreferenceCohn, T.A., L.L. Delong, E.J. Gilroy, R.M. Hirsch, and R.M. Wells, 1989. Estimating Constituent Loads. Water Resources Research 25: 937 ‐ 942.en_US
dc.identifier.citedreferenceCooper, S.R. and G.S. Brush, 1991. Long‐Term History of Chesapeake Bay Anoxia. Science 254: 992 ‐ 996.en_US
dc.identifier.citedreferenceDiaz, R.J. and R. Rosenberg, 2008. Spreading Dead Zones and Consequences for Marine Ecosystems. Science 321: 926 ‐ 929.en_US
dc.identifier.citedreferenceDorazio, R.M. and F.A. Johnson, 2003. Bayesian Inference and Decision Theory ‐ A Framework for Decision Making in Natural Resource Management. Ecological Applications 13: 556 ‐ 563.en_US
dc.identifier.citedreferenceGelfand, A.E. and A.F.M. Smith, 1990. Sampling‐Based Approaches to Calculating Marginal Densities. Journal of the American Statistical Association 85: 398 ‐ 409.en_US
dc.identifier.citedreferenceGelman, A. and J. Hill, 2007. Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press, New York.en_US
dc.identifier.citedreferenceGill, J., 2002. Bayesian Methods: A Social and Behavioral Sciences Approach. Chapman & Hall/CRC, Boca Raton, Florida.en_US
dc.identifier.citedreferenceHagy, J.D., W.R. Boynton, C.W. Keefe, and K.V. Wood, 2004. Hypoxia in Chesapeake Bay, 1950–2001: Long‐Term Change in Relation to Nutrient Loading and River Flow. Estuaries 27: 634 ‐ 658.en_US
dc.identifier.citedreferenceHarding, L.W., and E. Perry, Jr., 1997. Long‐Term Increase of Phytoplankton Biomass in Chesapeake Bay. Marine Ecology Progress Series 157: 39 ‐ 52.en_US
dc.identifier.citedreferenceHastings, W.K., 1970. Monte Carlo Sampling Methods Using Markov Chains and Their Applications. Biometrika 57 ( 1 ): 97 ‐ 109.en_US
dc.identifier.citedreferenceKemp, W.M., W.R. Boynton, J.E. Adolf, D.F. Boesch, W.C. Boicourt, G. Brush, J.C. Cornwell, T.R. Fisher, P.M. Glibert, J.D. Hagy, L.W. Harding, E.D. Houde, D.G. Kimmel, W.D. Miller, R.I.E. Newell, M.R. Roman, E.M. Smith, and J.C. Stevenson, 2005. Eutrophication of Chesapeake Bay: Historical Trends and Ecological Interactions. Marine Ecology Progress Series 303: 1 ‐ 29.en_US
dc.identifier.citedreferenceLiu, Y. and D. Scavia, 2010. Analysis of the Chesapeake Bay Hypoxia Regime Shift: Insights from Two Simple Mechanistic Models. Estuaries and Coasts 33 ( 3 ): 629 ‐ 639.en_US
dc.identifier.citedreferenceLunn, D.J., A. Thomas, N. Best, and D. Spiegelhalter, 2000. WinBUGS ‐ a Bayesian Modelling Framework: Concepts, Structure, and Extensibility. Statistics and Computing 10: 325 ‐ 337.en_US
dc.identifier.citedreferenceMalve, O. and S.S. Qian, 2006. Estimating Nutrients and Chlorophyll a Relationships in Finnish Lakes. Environmental Science and Technology 40 ( 24 ): 7848 ‐ 7853.en_US
dc.identifier.citedreferenceMetropolis, N., A.W. Rosenbluth, M.N. Rosenbluth, A.H. Teller, and E. Teller, 1953. Equation of State Calculations by Fast Computing Machines. Journal of Chemical Physics 21 ( 6 ): 1087 ‐ 1092.en_US
dc.identifier.citedreferenceNewcombe, C.L. and W.A. Horne, 1938. Oxygen‐Poor Waters of the Chesapeake Bay. Science 88: 80 ‐ 81.en_US
dc.identifier.citedreferenceNRC, 2000. Clean Coastal Waters: Understanding and Reducing the Effects of Nutrient Pollution. National Research Council, National Academy Press, Washington, D.C.en_US
dc.identifier.citedreferenceNRC, 2001. Assessing the TMDL Approach to Water Quality Management. National Research Council, National Academy Press, Washington, D.C.en_US
dc.identifier.citedreferenceOfficer, C.B., R.B. Biggs, J.L. Taft, L.E. Cronin, M.A. Tyler, and W.R. Boynton, 1984. Chesapeake Bay Anoxia: Origin, Development, and Significance. Science 223: 22 ‐ 27.en_US
dc.identifier.citedreferenceQian, S.S. and K.H. Reckhow, 2007. Combining Model Results and Monitoring Data for Water Quality Assessment. Environmental Science and Technology 41: 5008 ‐ 5013.en_US
dc.identifier.citedreferenceQian, S.S., C.A. Stow, and M.E. Borsuk, 2003. On Monte Carlo Methods for Bayesian Inference. Ecological Modelling 159 ( 2‐3 ): 269 ‐ 277.en_US
dc.identifier.citedreferenceReckhow, K.H., 1994. Importance of Scientific Uncertainty in Decision‐Making. Environmental Management 18: 161 ‐ 166.en_US
dc.identifier.citedreferenceReichert, P. and M. Omlin, 1997. On the Usefulness of Over Parameterized Ecological Models. Ecological Modelling 95: 289 ‐ 299.en_US
dc.identifier.citedreferenceScavia, D. and K.A. Donnelly, 2007. Reassessing Hypoxia Forecasts for the Gulf of Mexico. Environmental Science and Technology 41: 8111 ‐ 8117.en_US
dc.identifier.citedreferenceScavia, D., D. Justic, and V.J. Bierman, Jr., 2004. Reducing Hypoxia in the Gulf of Mexico: Advice From Three Models. Estuaries 27: 419 ‐ 425.en_US
dc.identifier.citedreferenceScavia, D., E.L.A. Kelly, and J.D. Hagy, 2006. A Simple Model for Forecasting the Effects of Nitrogen Loads on Chesapeake Bay Hypoxia. Estuaries and Coasts 29: 674 ‐ 684.en_US
dc.identifier.citedreferenceScavia, D., N.N. Rabalais, R.E. Turner, D. Justic, and W. Wiseman, Jr., 2003. Predicting the Response of Gulf of Mexico Hypoxia to Variations in Mississippi River Nitrogen Load. Limnology and Oceanography 48: 951 ‐ 956.en_US
dc.identifier.citedreferenceScheffer, M. and S.R. Carpenter, 2003. Catastrophic Regime Shifts in Ecosystems: Linking Theory to Observation. Trends in Ecology & Evolution 18: 648 ‐ 656.en_US
dc.identifier.citedreferenceSpiegelhalter, D.J., N.G. Best, B.P. Carlin, and A. van der Linde, 2002. Bayesian Measures of Model Complexity and Fit. Journal of the Royal Statistical Society: Series B 64: 583 ‐ 640.en_US
dc.identifier.citedreferenceStow, C.A., K.H. Reckhow, and S.S. Qian, 2006. A Bayesian Approach to Retransformation Bias in Transformed Regression. Ecology 87: 1472 ‐ 1477.en_US
dc.identifier.citedreferenceStow, C.A. and D. Scavia, 2009. Modeling Hypoxia in the Chesapeake Bay: Ensemble Estimation Using a Bayesian Hierarchical Model. Journal of Marine Systems 76: 244 ‐ 250.en_US
dc.identifier.citedreferenceStreeter, H.W. and E.B. Phelps, 1925. A Study in the Pollution and Natural Purification of the Ohio River, III Factors Concerning the Phenomena of Oxidation and Reaeration. US Public Health Service, Public Health Bulletin No. 146, Feb 1925 Reprinted by US PHEW, PHA 1958.en_US
dc.identifier.citedreferenceZhang, W. and G.B. Arhonditsis, 2008. Predicting the Frequency of Water Quality Standard Violations Using Bayesian Calibration of Eutrophication Models. Journal of Great Lakes Research 34: 698 ‐ 720.en_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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