UMCES is working with the University of South Carolina Center for Oceans and Human Health to develop a work plan to help lead community engagement and research synthesis activities for the Community Engagement Core. The plan is envisioned to include stakeholder and researcher workshops to enhance two-way communication between community interests and the research communities.

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In partnership with the University of South Carolina and SECOORA, we first developed a mobile app that displays predictions of water quality at 31 locations along the beach at Myrtle Beach, SC. Daily predictions are created using statistical models that incorporate data from remote sensing and ocean observing system sensors.

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In partnership with the Southeast Coast Ocean Observing Regional Association (SECOORA) and the University of South Carolina, IAN automated and improved the accuracy of beach advisory decision making for Myrtle Beach South Carolina. The improvements resulted from integrating information from Ocean Observing Systems and radar-based rainfall data from the National Weather Service to improve bacteria concentration predictions.

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Empirical models were developed at South Carolina beaches and estuaries to create daily forecasts of bacterial water quality for use as decision support tools. These tools predict exceedance of bacteria criteria using integrated monitoring data, remote sensing, and meteorology information. The models developed for beach areas used precipitation data from a rain gauge network, tide data, and qualitative weather information to predict criterion exceedance.

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