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A Reduced Complexity, Hybrid Empirical-Mechanistic Model of Eutrophication and Hypoxia in Shallow Marine Ecosystems

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Modeling Coastal Hypoxia

Abstract

Numerical simulation models have a long history as research tools for the study of coastal marine ecosystems, and are increasingly being used to inform management, particularly related to nutrient-fueled eutrophication. Demand for modeling assessments is rapidly increasing, and managers need generally applicable tools that can be rapidly applied with limited resources. Additionally, a variety of calls have been made for the development of reduced complexity models for use in parallel with more complex models. We propose a simplified, empirically constrained modeling approach that simulates the first-order processes involved in estuarine eutrophication, contains a small number of aggregated state variables and a reduced set of parameters, and combines traditional mechanistic formulations with robust, data-driven, empirical functions shown to apply across multiple systems. The model was applied to Greenwich Bay, RI (USA), a subestuary of Narragansett Bay, and reproduced the annual cycles of phytoplankton biomass, dissolved inorganic nutrients, and dissolved oxygen, events including phytoplankton blooms and development of hypoxia, and the rate of annual primary production. While the model was relatively robust to changes in parameter values and initial conditions, sensitivity analysis revealed the need for better constraint of the phytoplankton carbon-to-chlorophyll ratio, temperature dependence of phytoplankton production, and parameters associated with our formulations for water column respiration and the flux of phytoplankton carbon to the sediments. This reduced complexity, hybrid empirical-mechanistic approach provides a rapidly deployable modeling tool applicable to a wide variety of shallow estuarine systems.

Dr. Nixon is deceased.

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Acknowledgements

The authors wish to thank J.N. Kremer and J.W. Brawley, with whom this approach and formulations were developed while working on parallel models for Greenwich Bay and Waquoit Bay, MA. The work further benefited from frequent discussions with C.A. Oviatt, A.A. Keller, S.L. Granger, A.J. Gold, D.E. Campbell, P.V. August, L. Erikson, and M.L. Spaulding. S.L. Granger, M. Traber, M. Richardson, and B. Buckley were responsible for much of the data collection in Greenwich Bay. Funding for this research was provided by the Rhode Island Sea Grant College Program (NOAA), Narragansett Electric, and the NOAA Center for Sponsored Coastal Ocean Research (Coastal Hypoxia Research Program, grant no. NA05NOS4781201). This is VIMS’ contribution no. 3620 and NOAA Coastal Hypoxia Research Program contribution no. 213.

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Authors and Affiliations

  1. Virginia Institute of Marine Science, College of William and Mary, Gloucester Point, VA, 23062, USA

    Mark J. Brush

  2. Graduate School of Oceanography, University of Rhode Island, Narragansett, RI, 02882, USA

    Scott W. Nixon

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  1. Mark J. Brush
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Correspondence to Mark J. Brush .

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Editors and Affiliations

  1. Department of Oceanography and Coastal Sciences, Louisiana State University, Baton Rouge, Louisiana, USA

    Dubravko Justic

  2. Louisiana State University , Baton Rouge, Louisiana, USA

    Kenneth A. Rose

  3. Department of Oceanography, Texas A&M University Department of Oceanography, College Station, Texas, USA

    Robert D. Hetland

  4. Department of Oceanography, Dalhousie University, Halifax, Nova Scotia, Canada

    Katja Fennel

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Brush, M.J., Nixon, S.W. (2017). A Reduced Complexity, Hybrid Empirical-Mechanistic Model of Eutrophication and Hypoxia in Shallow Marine Ecosystems. In: Justic, D., Rose, K., Hetland, R., Fennel, K. (eds) Modeling Coastal Hypoxia. Springer, Cham. https://doi.org/10.1007/978-3-319-54571-4_4

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