Improvements to Shellfish Harvest Area Closure Decision Making Using GIS, Remote Sensing, and Predictive Models
- 166 Downloads
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 r 2 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.
KeywordsRemote sensing Ecological forecasting GIS Fecal pollution modeling Decision support tools
The authors wish to thank Mr. Charles Newel and the South Carolina Department of Health and Environmental Control for their support and guidance in the development of this research. Figure 1 was prepared by Caroline Wicks (www.Eco-Check.org) with symbols prepared by the Integration and Applications Network at the University of Maryland Center for Environmental Science in Cambridge, MD. This paper is a result of research sponsored by the NOAA Center for Sponsored Coastal Ocean Research/Coastal Ocean Program through the S.C. Sea Grant Consortium through the following grants: the NOAA NOS-funded Urbanization and Southeastern Estuarine Systems Project (USES) grant no. NA05NOS4261154, and the NOAA NOS-funded Land Use–Coastal Ecosystem Study (LU-CES) grant no. NA960PO113. This publication does not constitute an endorsement of any commercial product or intend to be an opinion beyond scientific or other results obtained by the National Oceanic and Atmospheric Administration (NOAA). No reference shall be made to NOAA, or this publication furnished by NOAA, to any advertising or sales promotion which would indicate or imply that NOAA recommends or endorses any proprietary product mentioned herein or which has as its purpose an interest to cause the advertised product to be used or purchased because of this publication. The US government is authorized to produce and distribute reprints for governmental purposes notwithstanding copyright notation that may appear hereon. This manuscript is contribution number 1598 of the Belle W. Baruch Institute for Marine and Coastal Sciences, and University of Maryland Center for Environmental Science contribution number 4381.
- Chapra, S.C. 1997. Surface water quality modeling. Boston: McGraw-Hill.Google Scholar
- 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
- Insightful Corporation. 2001. S-PLUS 6 for windows guide to statistics, volume 2. Seattle: Insightful Corporation.Google Scholar
- Interstate Shellfish Sanitation Conference (ISSC). 2004. Analysis classified shellfish water 1985–2003. Columbia, SC, Interstate Shellfish Sanitation Conference, p 13.Google Scholar
- 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
- 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.
- National Digital Forecast Database (NDFD). 2004. About the NDFD GRIB2 Decoder. http://www.nws.noaa.gov/mdl/NDFD_GRIB2Decoder/. Retrieved September 6, 2005.
- 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
- NOAA Fisheries. 2008. http://www.st.nmfs.noaa.gov/pls/webpls/MF_ANNUAL_LANDINGS.RESULTS.
- 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