MA9 Hazard Assessments for Near Coastal Ecosystems
D137-140
8:00 AM - 12:00 PM, Monday
() Shellfish harvest area closure prediction using remotely sensed precipitation data and digital forecasting products: pilot study.
Kelsey, R.1, Whitlock, J.1, Scott, G.1, 2, Porter, D.1, 1 University of South Carolina Arnold School of Public Health, Columbia, SC, USA2 NOAA/NOS, CCEHBR, Charleston, SC, USA
ABSTRACT- Closure of some South Carolina shellfish harvesting areas is controlled by empirical relationships between accumulated precipitation at weather stations and fecal coliform densities in the shellfish harvest area surface waters. Resource managers in South Carolina close conditionally approved harvest areas after rainfall events that will likely result in public health hazard, which is indicated by fecal coliform levels above established water quality standards. The location of weather stations and the spatial variability of rainfall may result in discrepancies between rainfall recorded at weather stations and actual precipitation in the harvest area watershed. Additionally, different areas within shellfish harvest area watersheds may experience different precipitation accumulation, and therefore variable resultant water quality effects. This pilot project examines the potential utility of predictive forecasting of shellfish harvest area closure using precipitation estimates derived from forecast data and several remotely sensed rainfall data products available from the National Oceanographic and Atmospheric Administration (NOAA) for several coastal areas of South Carolina. These data are used to develop a model for an improved empirical relationship between precipitation and fecal coliform bacterial densities that require closure of conditionally approved shellfish harvest areas. Data are also used to develop a probability-based model for closures based on precipitation forecast data and the associated probability that a threshold rainfall event will occur. Issues relating to data access, data management, and data accuracy and bias removal are discussed. Additionally, issues related to model development, validation, and utility of model output are also considered.
Key words: shellfish management, coliform bacteria, remote sensing , forecasting