Estuaries and Coasts

, Volume 33, Issue 3, pp 712–722 | Cite as

Improvements to Shellfish Harvest Area Closure Decision Making Using GIS, Remote Sensing, and Predictive Models

  • Rense Heath Kelsey
  • Geoffrey I. Scott
  • Dwayne E. Porter
  • Thomas C. Siewicki
  • Donald G. Edwards
Article

Abstract

Currently, many states use precipitation information to regulate periodic closures of shellfish harvest areas based on a presumptive relationship between rainfall and bacteria concentration. We evaluate this relationship in four South Carolina estuaries and suggest new predictive models that integrate remote sensing precipitation data with additional environmental and climatic data. Model comparisons using Akaike’s information criterion, tenfold cross-validation, and model r2 values show substantial and consistent improvements using integrated precipitation, salinity, and water temperature data as predictors. These models may be useful for shellfish area closure regulation support. The model development approaches used here may also be useful in estimating bacteria concentration at beaches and can serve as the basis for developing near-real-time estimates and forecast predictions of bacteria levels for closure decision-making tools.

Keywords

Remote sensing Ecological forecasting GIS Fecal pollution modeling Decision support tools 

References

  1. Chapra, S.C. 1997. Surface water quality modeling. Boston: McGraw-Hill.Google Scholar
  2. Hastie, T., R. Tibshirani, and J. Friedman. 2001. The elements of statistical learning. Data mining, inference and prediction, 214–216. New York: Springer.Google Scholar
  3. Insightful Corporation. 2001. S-PLUS 6 for windows guide to statistics, volume 2. Seattle: Insightful Corporation.Google Scholar
  4. Interstate Shellfish Sanitation Conference (ISSC). 2004. Analysis classified shellfish water 1985–2003. Columbia, SC, Interstate Shellfish Sanitation Conference, p 13.Google Scholar
  5. Kelsey, R.H., G.I. Scott, D.E. Porter, B. Thompson, and L. Webster. 2003. Using multiple antibiotic resistance and land use characteristics to determine sources of fecal coliform bacterial pollution. Environmental Monitoring and Assessment 81: 337–348.CrossRefGoogle Scholar
  6. Kelsey, H., D.E. Porter, G.I. Scott, M.J. Neet, and D.L. White. 2004. Using geographic information systems and regression analysis to evaluate relationships between land use and fecal coliform bacterial pollution. Journal of Experimental Marine Biology and Ecology 298(2): 197–209.CrossRefGoogle Scholar
  7. Kim, J.H., and S.B. Grant. 2004. Public mis-notification of coastal water quality: A probabilistic evaluation of posting errors at Huntington Beach, California. Environmental Science & Technology 38(9): 2497–2504.CrossRefGoogle Scholar
  8. Mallin, M.A., S.H. Ensign, M.R. McIver, G.C. Shank, and P.K. Fowler. 2001. Demographic, landscape, and meteorological factors controlling the microbial pollution of coastal waters. Hydrobiologia 460: 185–193.CrossRefGoogle Scholar
  9. National Center for Environmental Prediction (NCEP). 2002. NCEP STAGE II DATA README FILE. http://www.joss.ucar.edu/data/gcip_eop/docs/katz_stageII_readme.txt. Retrieved September 6, 2005.
  10. National Digital Forecast Database (NDFD). 2004. About the NDFD GRIB2 Decoder. http://www.nws.noaa.gov/mdl/NDFD_GRIB2Decoder/. Retrieved September 6, 2005.
  11. National Shellfish Sanitation Program (NSSP). 2003. National Shellfish Sanitation Program guide for the control of molluscan shellfish. Washington: International Shellfish Sanitation Conference, US Department of Health and Human Services.Google Scholar
  12. Olyphant, G.A., and R.L. Whitman. 2004. Elements of a predictive model for determining beach closures on a real time basis: The case of 63rd Street Beach Chicago. Environmental Monitoring and Assessment 98(1–3): 175–190.CrossRefGoogle Scholar
  13. Olyphant, G.A., J. Thomas, R.L. Whitman, and D. Harper. 2003. Characterization and statistical modeling of bacterial (Escherichia coli) outflows from watersheds that discharge into southern Lake Michigan. Environmental Monitoring and Assessment 81(1–3): 289–300.CrossRefGoogle Scholar
  14. Siewicki, T.C., T. Pullaro, W. Pan, S. McDaniel, R. Glenn, and J. Stewart. 2007. Models of total and presumed wildlife sources of fecal coliform bacteria in coastal ponds. Journal of Environmental Management 82: 120–132.CrossRefGoogle Scholar
  15. Wymer, L.J., K.P. Brenner, J.W. Martinson, W.R. Stutts, S.A. Schaub, and A.P. Dufour. 2005. The EMPACT Beaches Project. Results from a study on microbiological monitoring in recreational waters. EPA 600/R-04/023 August 2005. Washington: United States Environmental Protection Agency.Google Scholar

Copyright information

© Coastal and Estuarine Research Federation 2010

Authors and Affiliations

  • Rense Heath Kelsey
    • 1
  • Geoffrey I. Scott
    • 2
  • Dwayne E. Porter
    • 3
  • Thomas C. Siewicki
    • 2
  • Donald G. Edwards
    • 4
  1. 1.EcoCheck: National Oceanic and Atmospheric Administration PartnershipUniversity of Maryland Center for Environmental ScienceOxfordUSA
  2. 2.National Oceanic and Atmospheric AdministrationCenter for Coastal Environmental Health and Biomolecular ResearchCharlestonUSA
  3. 3.Department of Environmental Health Sciences, Belle W. Baruch Institute for Marine and Coastal SciencesUniversity of South CarolinaColumbiaUSA
  4. 4.Department of Statistics, College of Arts and SciencesUniversity of South CarolinaColumbiaUSA

Personalised recommendations