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Estuaries and Coasts

, Volume 33, Issue 3, pp 629–639 | Cite as

Analysis of the Chesapeake Bay Hypoxia Regime Shift: Insights from Two Simple Mechanistic Models

  • Yong Liu
  • Donald ScaviaEmail author
Article

Abstract

Recent studies of Chesapeake Bay hypoxia suggest higher susceptibility to hypoxia in years after the 1980s. We used two simple mechanistic models and Bayesian estimation of their parameters and prediction uncertainty to explore the nature of this regime shift. Model estimates show increasing nutrient conversion efficiency since the 1980s, with lower DO concentrations and large hypoxic volumes as a result. In earlier work, we suggested a 35% reduction from the average 1980–1990 total nitrogen load would restore the Bay to hypoxic volumes of the 1950s–1970s. With Bayesian inference, our model indicates that, if the physical and biogeochemical processes prior to the 1980s resume, the 35% reduction would result in hypoxic volume averaging 2.7 km3 in a typical year, below the average hypoxic volume of 1950s–1970s. However, if the post-1980 processes persist the 35% reduction would result in much higher hypoxic volume averaging 6.0 km3. Load reductions recommended in the 2003 agreement will likely meet dissolved oxygen attainment goals if the Bay functions as it did prior to the 1980s; however, it may not reach those goals if current processes prevail.

Keywords

Hypoxia Regime shift Mechanistic model Chesapeake Bay Conversion efficiency 

Notes

Acknowledgements

This work was supported in part by grant NA05NOS4781204 from NOAA’s Center for Sponsored Coastal Ocean Research. It is contribution number CHRP118. The authors gratefully acknowledge Dr. James D. Hagy III for use of the data amassed in his dissertation, Chesapeake Bay Program Office and Dr. Hongguang Ma for the chlorophyll data, the Bayesian insights gained from conversations with George Arhonditsis and Craig Stow, and comments from three anonymous reviewers.

References

  1. Arhonditsis, 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: 215–229.CrossRefGoogle Scholar
  2. Armstrong, R.A. 1994. Grazing limitation and nutrient limitation in marine ecosystems: steady state solutions of an ecosystem model with multiple food chains. Limnology and Oceanography 39(3): 597–608.CrossRefGoogle Scholar
  3. Boesch, D.F., R.B. Brinsfield, and R.E. Magnien. 2001. Chesapeake Bay eutrophication: scientific understanding, ecosystem restoration and challenges for agriculture. Journal of Environmental Quality 30: 303–320.Google Scholar
  4. Borsuk, 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.CrossRefGoogle Scholar
  5. Boynton, W.R., W.M. Kemp, J.M. Barnes, L.L. Matteson, F.M. Rohland, D.A. Jasinski, and H.L. Kimble. 1993. Ecosystem processes component level 1. Interpretive Report No. 10 Solomons, Maryland: Chesapeake Biological Laboratory. University of Maryland System.Google Scholar
  6. Bricker, 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. Silver Spring, Maryland: National Centers for Coastal Ocean Science, NOAA.Google Scholar
  7. Cerco, C.F. 1995a. Response of Chesapeake Bay to nutrient load reductions. Journal of Environmental Engineering 121(8): 549–556.CrossRefGoogle Scholar
  8. Cerco, C.F. 1995b. Simulation of long term trends in Chesapeake Bay eutrophication. Journal of Environmental Engineering 121(4): 298–310.CrossRefGoogle Scholar
  9. Cerco, C.F., and T.M. Cole. 1993. Three-dimensional eutrophication model of Chesapeake Bay. Journal of Environmental Engineering 119: 1006–1025.CrossRefGoogle Scholar
  10. Cerco, C., and M. Noel. 2004. Process-based primary production modeling in Chesapeake Bay. Marine Ecology Progress Series 282: 45–58.CrossRefGoogle Scholar
  11. Chapra, S.C. 1997. Surface water-quality modeling. New York: McGraw-Hill.Google Scholar
  12. Cohn, 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.CrossRefGoogle Scholar
  13. Cooper, S.R., and G.S. Brush. 1991. Long-term history of Chesapeake Bay anoxia. Science 254: 992–996.CrossRefGoogle Scholar
  14. Cranford, P.J., P.M. Strain, M. Dowd, B.T. Hargrave, J. Grant, and M. Archambault. 2007. Influence of mussel aquaculture on nitrogen dynamics in a nutrient enriched coastal embayment. Marine Ecology Progress Series 347: 61–78.CrossRefGoogle Scholar
  15. Diaz, R.J., and R. Rosenberg. 2008. Spreading dead zones and consequences for marine ecosystems. Science 321: 926–929.CrossRefGoogle Scholar
  16. Edwards, A.M., and A. Yool. 2000. The role of higher predation in plankton population models. Journal of Plankton Research 22(6): 1085–1112.CrossRefGoogle Scholar
  17. Fennel, W. 2004. Introduction to the modelling of marine ecosystems, 297. Boston: Elsevier.Google Scholar
  18. Fennel, K., and E. Boss. 2003. Subsurface maxima of phytoplankton and chlorophyll: steady state solutions from a simple model. Limnology and Oceanography 48(4): 1521–1534.CrossRefGoogle Scholar
  19. Gelman, A., and J. Hill. 2007. Data analysis using regression and multilevel/hierarchical models. New York: Cambridge University Press.Google Scholar
  20. Genkai-Kato, M. 2007. Regime shifts: catastrophic responses of ecosystems to human impacts. Ecological Research 22: 214–219.CrossRefGoogle Scholar
  21. Gill, J. 2002. Bayesian methods: a social and behavioral sciences approach. Boca Raton: Chapman & Hall/CRC.Google Scholar
  22. Hagy, 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.CrossRefGoogle Scholar
  23. Hagy, J.D., W.R. Boynton, and D.A. Jasinski. 2005. Modeling phytoplankton deposition to Chesapeake Bay sediments during winter–spring: interannual variability in relation to river flow. Estuarine Coastal and Shelf Science 62: 25–40.CrossRefGoogle Scholar
  24. Harding, L.W., and E. Perry Jr. 1997. Long-term increase of phytoplankton biomass in Chesapeake Bay. Marine Ecology Progress Series 157: 39–52.CrossRefGoogle Scholar
  25. Humborg, K.F., M. Pastuszak, and W. Fennel. 2000. A box model approach for a long-term assessment of estuarine eutrophication, Szczecin Lagoon, southern Baltic. Journal of Marine System 25: 387–403.CrossRefGoogle Scholar
  26. Jørgensen, S.E. 1994. Fundamentals of ecological modeling. New York: Elsevier Science.Google Scholar
  27. Kemp, W.M., and E.B. Goldman. 2008. Thresholds in the Recovery of Eutrophic Coastal Ecosystems—A Synthesis of Research and Implications for Management. Maryland: Sea Grant Publication UM-SG-TS-2008-01.Scientific and Technical Advisory Committee (STAC) Publication 08-002.Google Scholar
  28. Kemp, W.M., P.A. Sampou, J. Garber, J. Tuttle, and W.R. Boynton. 1992. Seasonal depletion of oxygen from bottom waters of Chesapeake Bay: roles of benthic and planktonic respiration and physical exchange processes. Marine Ecology Progress Series 85: 137–152.CrossRefGoogle Scholar
  29. Kemp, W.M., E.M. Smith, M. Marvin-DiPasquale, and W.R. Boynton. 1997. Organic carbon balance and net ecosystem metabolism in Chesapeake Bay. Marine Ecology Progress Series 150: 229–248.Google Scholar
  30. Kemp, W.M., W.R. Boynton, J.E. Adolf, D.F. Boesch, W.C. Boicourt, G. Brush, J.C. Cornwell, T.R. Fisher, P.M. Gilbert, J.D. Hagy, L.W. Harding, 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.CrossRefGoogle Scholar
  31. Kimmerer, W.J., V.S. Smith, and J.T. Hollibaugh. 1993. A simple heuristic model of nutrient cycling in an estuary. Estuarine and Continental Shelf Science 37: 145–159.Google Scholar
  32. Koroncai, R., L. Linker, J. Sweeney, and R. Batiuk. 2003. Setting and allocating the Chesapeake Bay basin nutrient and sediment loads the Collaborative Process, Technical Tools and Innovative Approaches. U.S. Environmental Protection Agency, Chesapeake Bay Program Office, Annapolis, MD.Google Scholar
  33. Linker, L., G. Shenk, P. Wang, C. Cerco, A. Butt, P. Tango, and R. Savidge. 2002. A Comparison of Chesapeake Bay Estuary Model Calibration With 1985–1994 Observed Data and Method of Application to Water Quality Criteria. Chesapeake Bay Program Modeling Subcommittee Report. Chesapeake Bay Program Office, Annnapolis, Maryland.Google Scholar
  34. Liu, Y., G. B. Arhonditsis, G. Stow, and D. Scaiva. Comparing Chesapeake Bay hypoxic-volume and dissolved-oxygen profile predictions with a Bayesian Streeter-Phelps Model. Ecological Modelling (in review).Google Scholar
  35. Lunn, 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.CrossRefGoogle Scholar
  36. Malone, T.C. 1992. Effects of water column processes on dissolved oxygen, nutrients, phytoplankton and zooplankton. In Oxygen dynamics in the Chesapeake Bay, a synthesis of recent research, ed. D.E. Smith, M. Leffler, and G. Mackiernan, 61–112. Maryland: Sea Grant Publication UM-SG-TS-92-01.Google Scholar
  37. Malve, O., and S.S. Qian. 2006. Estimating nutrients and chlorophyll a relationships in Finnish lakes. Environmental Science and Technology 40(24): 7848–7853.CrossRefGoogle Scholar
  38. McManus, G.B., and M. Ederington-Cantrell. 1992. Phytoplankton pigments and growth rates, and microzooplankton grazing in a large temperate estuary. Marine Ecology Progress Series 87: 77–85.CrossRefGoogle Scholar
  39. Murray, A.G., and J.S. Parslow. 1999. The analysis of alternative formulations in a simple model of a coastal ecosystem. Ecological Modelling 119(2–3): 149–166.Google Scholar
  40. Newcombe, C.L., and W.A. Horne. 1938. Oxygen-poor waters of the Chesapeake Bay. Science 88: 80–81.CrossRefGoogle Scholar
  41. NRC (National Research Council), NAP (National Academy Press). 2000. Clean coastal waters: understanding and reducing the effects of nutrient pollution. Washington, DC: National Research Council. National Academy Press.Google Scholar
  42. Officer, 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.CrossRefGoogle Scholar
  43. Painting, S.J., and M.J. Devlin. 2007. Assessing the impact of nutrient enrichment in estuaries: susceptibility to eutrophication. Marine Pollution Bulletin 55(1–6): 74–90.CrossRefGoogle Scholar
  44. Petersen, J.K., J.W. Hansen, M.B. Laursen, P. Clausen, J. Carstensen, and D.J. Conley. 2008. Regime shift in a coastal marine ecosystem. Ecological Applications 18(2): 497–510.CrossRefGoogle Scholar
  45. Qian, S.S., C.A. Stow, and M.E. Borsuk. 2003. On Monte Carlo methods for Bayesian inference. Ecological Modelling 159(2–3): 269–277.CrossRefGoogle Scholar
  46. Reckhow, K.H. 1994. Importance of scientific uncertainty in decision-making. Environmental Management 18: 161–166.CrossRefGoogle Scholar
  47. Savenkoff, C., M. Castonguay, D. Chabot, M.O. Hammill, H. Bourdages, and L. Morissette. 2007. Changes in the northern Gulf of St. Lawrence ecosystem estimated by inverse modelling: evidence of a fishery-induced regime shift? Estuar Coast Shelf Sci 73: 711–724.CrossRefGoogle Scholar
  48. Scavia, D. 1980. An ecological model of Lake Ontario. Ecological Modelling 8: 49–78.CrossRefGoogle Scholar
  49. Scavia, D., and K.A. Donnelly. 2007. Reassessing hypoxia forecasts for the Gulf of Mexico. Environmental Science and Technology 41: 8111–8117.CrossRefGoogle Scholar
  50. Scavia, D., and Y. Liu. 2009. Exploring estuarine nutrient susceptibility. Environmental Science and Technology 43(10): 3474–3479.CrossRefGoogle Scholar
  51. Scavia, D., N.N. Rabalais, R.E. Turner, D. Justic, and Jr W. Wiseman. 2003. Predicting the response of Gulf of Mexico hypoxia to variations in Mississippi River Nitrogen Load. Limnology and Oceanography 48: 951–956.CrossRefGoogle Scholar
  52. Scavia, D., D. Justic Jr., and V.J. Bierman. 2004. Reducing hypoxia in the Gulf of Mexico: advice from three models. Estuaries 27: 419–425.CrossRefGoogle Scholar
  53. Scavia, 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.Google Scholar
  54. Scheffer, M., and S.R. Carpenter. 2003. Catastrophic regime shifts in ecosystems: linking theory to observation. Trends in Ecology and Evolution 18: 648–656.CrossRefGoogle Scholar
  55. Solow, A.R., and A.R. Beet. 2005. A test for a regime shift. Fisheries Oceanography 14(3): 236–240.CrossRefGoogle Scholar
  56. Stow, C.A., and D. Scavia. 2008. Modeling hypoxia in the Chesapeake Bay: ensemble estimation using a Bayesian hierarchical model. Journal of Marine Systems 76: 244–250.CrossRefGoogle Scholar
  57. Swaney, D.P., D. Scavia, R.W. Howarth, and R.M. Marino. 2008. Estuarine classification and response to nitrogen loading: insights from simple ecological models. Estuarine and Continental Shelf Science 77(2): 253–263.CrossRefGoogle Scholar
  58. Tett, P., L. Gilpin, H. Svendsen, C.P. Erlandsson, U. Larsson, S. Kratzer, E. Fouilland, C. Janzen, J.-Y. Lee, C. Grenz, A. Newton, J.G. Ferreira, T. Fernandes, and S. Scory. 2003. Eutrophication and some European waters of restricted exchange. Continental Shelf Research 23: 1635–1671.CrossRefGoogle Scholar
  59. Turner, R.E., N.N. Rabalais, and D. Justic. 2008. Gulf of Mexico hypoxia: alternative states and a legacy. Environmental Science and Technology 42: 2323–2327.CrossRefGoogle Scholar
  60. Zou, R., W.S. Lung, and J. Wu. 2007. An adaptive neural network embedded genetic algorithm approach for inverse water quality modeling. Water Resources Research 43: W08427.CrossRefGoogle Scholar

Copyright information

© Coastal and Estuarine Research Federation 2009

Authors and Affiliations

  1. 1.School of Natural Resources and EnvironmentUniversity of MichiganAnn ArborUSA
  2. 2.College of Environmental Science and EngineeringThe Key Laboratory of Water and Sediment Sciences, Ministry of Education, Peking UniversityBeijingChina

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