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Comparative Evaluation of Statistical and Mechanistic Models of Escherichia coli at Beaches in Southern Lake Michigan

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    Comparative Evaluation of Statistical and Mechanistic Models of Escherichia coli at Beaches in Southern Lake Michigan
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    † Department of Civil and Environmental Engineering, Michigan State University, 1449 Engineering Research Court, East Lansing, Michigan 48824, United States
    ‡ U.S. Geological Survey, Great Lakes Science Center, Lake Michigan Ecological Research Station, 1574 N. County Road, 300 E. Chesterton, Indiana 46304, United States
    § U.S. Geological Survey, Wisconsin Water Science Center, 8505 Research Way, Middleton, Wisconsin 53562, United States
    *Phone: 517-432-0851; e-mail: [email protected] (M.S.P.).
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    Environmental Science & Technology

    Cite this: Environ. Sci. Technol. 2016, 50, 5, 2442–2449
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    https://doi.org/10.1021/acs.est.5b05378
    Published January 29, 2016
    Copyright © 2016 American Chemical Society
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    Abstract

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    Statistical and mechanistic models are popular tools for predicting the levels of indicator bacteria at recreational beaches. Researchers tend to use one class of model or the other, and it is difficult to generalize statements about their relative performance due to differences in how the models are developed, tested, and used. We describe a cooperative modeling approach for freshwater beaches impacted by point sources in which insights derived from mechanistic modeling were used to further improve the statistical models and vice versa. The statistical models provided a basis for assessing the mechanistic models which were further improved using probability distributions to generate high-resolution time series data at the source, long-term “tracer” transport modeling based on observed electrical conductivity, better assimilation of meteorological data, and the use of unstructured-grids to better resolve nearshore features. This approach resulted in improved models of comparable performance for both classes including a parsimonious statistical model suitable for real-time predictions based on an easily measurable environmental variable (turbidity). The modeling approach outlined here can be used at other sites impacted by point sources and has the potential to improve water quality predictions resulting in more accurate estimates of beach closures.

    Copyright © 2016 American Chemical Society

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    The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.est.5b05378.

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    Environmental Science & Technology

    Cite this: Environ. Sci. Technol. 2016, 50, 5, 2442–2449
    Click to copy citationCitation copied!
    https://doi.org/10.1021/acs.est.5b05378
    Published January 29, 2016
    Copyright © 2016 American Chemical Society
    Request reuse permissions

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