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
Classification And Regression Tree 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 2006-2007 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
0.00
5.00
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25.00
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35.00
A B C D E F G H
Model Set
Per
cen
t E
rro
r
IOOS Model
Combination Model
SCDHEC Model
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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