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

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

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

“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”

NOAA Ship – OKEANOS EXPLORER

America’s Ship for Ocean Exploration

Image: NOAA

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

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

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

oceanexplorer.noaa.govoceanexplorer.noaa.gov

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