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![Page 1: Application of Ocean Observing Systems in Aiding Predictive Water Quality Modeling in Long Bay, South Carolina Emily McDonald, M.S. Knauss Marine Policy.](https://reader035.fdocuments.us/reader035/viewer/2022062305/56649eec5503460f94bfe432/html5/thumbnails/1.jpg)
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
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Introduction & Background Information Study Objectives & Hypothesis Model Development Modeling Results
NOAA’s Office of Ocean Exploration & Research
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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
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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
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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
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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
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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
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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
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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)
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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
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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
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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
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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
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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
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Analysis & Modeling Results
Key Variables What was important in predicting bacterial
levels Misclassifications
Incorrect predictions Comparison with initial studies
Similar Trends Lower Misclassification Rates
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Key Variables
Previous 24-hours rainfall Previous 72-hour rainfall Tidal Range / Water Level Salinity Wind Direction Current Direction
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Misclassification Percent Comparison
0.00
5.00
10.00
15.00
20.00
25.00
30.00
35.00
A B C D E F G H
Model Set
Per
cen
t E
rro
r
IOOS Model
Combination Model
SCDHEC Model
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Comparison with Previous Studies
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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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“To support NOAA and National objectives by exploring the Earth's largely unknown oceans in all their dimensions for the purpose of discovery and the advancement of knowledge, using state-of-the-art technologies in evolutionary and revolutionary ways”
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NOAA Ship – OKEANOS EXPLORER
America’s Ship for Ocean Exploration
Image: NOAA
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OKEANOS EXPLOREROKEANOS EXPLORER Explore Explore
unknown areas unknown areas of the oceanof the ocean Multi-beam Multi-beam
MappingMapping 6000m 6000m RRemotely emotely
OOperated perated VVehicleehicle
Telepresence Telepresence TechnologyTechnology
Image: NOAA
Multi-beam map of Alaskan Seamount
ROV on the back deck of the OKEANOS EXPLORER
Image: Dave Lovalvo, Eastern Oceanics
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What’s What’s Telepresence?Telepresence?
Connects ship Connects ship to shore in to shore in near-real timenear-real time
Allows Allows Scientists Scientists on shore on shore thousands thousands of miles of miles away to away to participate participate in the in the expedition!expedition!
Image: Paul Oberlander, WHOI
Image: NOAA
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Exploration ExpeditionsExploration Expeditions
AUVfest 2008AUVfest 2008 Archaeology in Archaeology in
Narragansett Bay, Narragansett Bay, Rhode IslandRhode Island
Thunder Bay Thunder Bay SinkholesSinkholes Mapping & Mapping &
Biological sampling Biological sampling in Lake Huron, MIin Lake Huron, MI
Lophelia IILophelia II Deep Corals in the Deep Corals in the
Gulf of MexicoGulf of Mexico
AUV Side-Scan-Sonar image of a shipwreck in Narragansett BayScientists 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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oceanexplorer.noaa.govoceanexplorer.noaa.gov
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Questions?
Twenty years from now you will be more disappointed by the things that you didn't do than by the ones you did do. So throw off the bowlines. Sail away from the
safe harbor. Catch the trade winds in your sails. Explore. Dream. Discover.
-Mark Twain