Application of Ocean Observing Systems in Aiding Predictive Water Quality Modeling in Long Bay, South Carolina Emily McDonald, M.S. Knauss Marine Policy Fellow Office of Ocean Exploration and Research October 30, 2008 Knauss Fellow Lecture Series

Introduction & Background Information Study Objectives & Hypothesis Model Development Modeling Results NOAA’s Office of Ocean Exploration & Research

Introduction National focus on ocean observing systems Implementation & upkeep New technology / concept Providing vast array of data Modeling Applications Public Health Application Beach Water Quality South Carolina Beach Monitoring

Acronyms IOOS – Integrated Ocean Observing System SCDHEC – South Carolina Department of Health & Environmental Control MPN – Most probable number (used for bacterial counts) Caro-COOPS – Carolina’s Coastal Ocean Observing and Prediction System SCDNR – South Carolina Department of Natural Resources NERR – NI-WB – National Estuarine Research Reserve at North Inlet – Winyah Bay

Issues IOOS Applicability Questions of usefulness of observing systems and data they provide Majority of observing system models are physical oceanographic models Water Quality at Swimming Beaches Closing beaches for health risks Most accurate current predictive models require on- site visits

Study Objectives & Hypothesis Longitudinal integration of regional IOOS efforts Consistent with IOOS goals Practical application of IOOS data Models developed with IOOS data will improve upon predictive capability of current SCDHEC models Data availability through IOOS Minimize misclassification rates Science & management connection

Study Location Area known as “Long Bay” extending from the Cape Fear River, NC to Winyah Bay, SC, includes highly-populated tourist destination of Myrtle Beach, SC

Beach Monitoring & Advisories in South Carolina Weekly Sampling - May 15 – Oct. 15 Contamination Advisory Issuance Two successive samples with in 24 hours >= 104 MPN / 100ml Single Sample > 500 MPN /100ml Preemptive Advisories Currently based on rainfall & CART model decision tool Myrtle Beach

Balancing Public Health & Economics Large tourism industry in area 13.8 Million annual visitors 60-70% jobs tourism-based Increasing population & development Linked to bacterial abundance (Mallin, 2000)

Predictive Modeling of SC Beaches CART model decision support tool – Johnson, 2007 Determine MPN / 100ml at Beaches Rainfall variables; preceding dry days; weather; tidal range; moon phase & station Three Levels of Models Level 1 Model – Currently implemented Level 2 & 3 models not currently in use  Data Collection constraints  Level 3 most accurate – additional variables including salinity; wind speed & direction; current speed & direction Enterococcus faecalis

CART Modeling C lassification A nd R egression T ree Clear visual picture No transformation of data Multivariate approach Numerical & Categorical Variables split at ‘nodes’ Recursive Partitioning algorithm Pruning Decrease complexity &/or redundancy

Methodology Study Location Variable Selection Data Assimilation from regional IOOS platforms May 15 – October 15, 2006 & 2007 Application of Modeling Techniques SCDHEC Predictive Model CART Model Construction

Data Assimilation Easily accessible ocean observing system platforms Caro-COOPS Sunset Array EPA – STORET SCDNR Apache Pier NERR – NI-WB Met station SCDHEC Manipulation to fit model parameters

Application of Modeling Techniques Model Groups Replicate of current SCDHEC model with data for Data from regional IOOS Combination using regional IOOS data and DHEC inputs R – Statistical programming CART Model Construction

Analysis & Modeling Results Key Variables What was important in predicting bacterial levels Misclassifications Incorrect predictions Comparison with initial studies Similar Trends Lower Misclassification Rates

Key Variables Previous 24-hours rainfall Previous 72-hour rainfall Tidal Range / Water Level Salinity Wind Direction Current Direction

Misclassification Percent Comparison

Comparison with Previous Studies

Implications of Research Increase accuracy and predictive modeling capabilities New focus for predictive models Improving management decision tools Applicability of IOOS for management needs Near & off-shore observations predicting shoreline parameters Biological Modeling

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Exploration Expeditions AUVfest 2008 AUVfest 2008 Archaeology in Narragansett Bay, Rhode Island Archaeology in Narragansett Bay, Rhode Island Thunder Bay Sinkholes Thunder Bay Sinkholes Mapping & Biological sampling in Lake Huron, MI Mapping & Biological sampling in Lake Huron, MI Lophelia II Lophelia II Deep Corals in the Gulf of Mexico Deep Corals in the Gulf of Mexico AUV Side-Scan-Sonar image of a shipwreck in Narragansett Bay Scientists and crew work to deploy and ROV in Lake Huron Image: AUVfest 2008: Partnership Runs Deep, Navy/NOAA Image: NOAA Thunder Bay Sinkholes 2008 Image: Lophelia II 2008: Deepwater Coral Expedition: Reefs, Rigs, and Wrecks A redeye gaper at 240 m depth seen during an ROV dive

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