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Field Verification of Oil Spill Fate & Transport Modeling and

Linking CODAR Observation System Data with SIMAP Predictions

James R. Payne, Ph.D.Payne Environmental Consultants, Inc.

Deborah French-McCay, Ph.D.Applied Science Associates, Inc.

Eric Terrill, Ph.D.Scripps Institution of Oceanography

Walter Nordhausen, Ph.D.California Office of Spill Prevention and Response

March 13-14, 2006CRRC PI Workshop

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Acknowledgement

Funding for this project was provided by the Coastal Response Research Center

www.crrc.unh.edu

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Objectives

• measure small-scale transport processes and • develop/validate oil-spill model algorithms

for application to subsurface dispersion modeling of naturally-entrained and chemically-dispersed oil.

This CRRC project will utilize the release and surface vessel/aircraft tracking of fluorescein dye and subsurface drogues to:

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Model development and field validation are essential to the evaluation of

environmental trade-offs justified in the decision to use dispersants under certain

circumstances, with direct applicability to:

• spill response/dispersant use decision making, • net environmental benefit analysis, • Natural Resource Damage Assessments after

an oil spill, and • educating the spill community and public.

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The CRRC-funded field experiments and algorithm development are designed to:

• Develop understanding of small scale oil transport processes (i.e., vertical and horizontal dispersion (mixing)) important towater-column transport and impact analysis;

• Provide detailed measurements of small-scale horizontal and vertical diffusivities;

• Evaluate the efficacy and reliability of Coastal Ocean Dynamic Applications Radar (CODAR) for providing surface current input data to NOAA and private-sector oil spill models (e.g., SIMAP);

• Develop algorithms quantifying small scale transport processes and associated uncertainty that can be included in oil fates models;

• Verify CODAR- and model-predicted movement of surface and subsurface oil (simulated by dye) through comparison to drogue movements and measured dye concentrations over three dimensions and time; and

• Provide for publication of the data sets and algorithms for use in other circumstances, locations, and models.

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Project Implementation:Data will be collected from four cruises

(two funded by CRRC and two by CA OSPR)• Plume dispersal vessel: 32’ work boat operated by

Marine Spill Response Corporation (MSRC)• Plume sampling vessel: 22’ or 26’ work boat

operated by Scripps Institution of Oceanography (SIO)• USCG SMART system operated by Strike Team• SIO Scientists with CTD + fluorometer

• GPS tracked drifter array operated by SIO• Surface Current Maps created by HF radar

• Integrated Ocean Observatory

• CA OSPR aircraft overflights for aerial imaging• SIMAP modeling of plume dispersion and advection

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Preliminary Results from November 2005 CA OSPR Cruise

Dye release site:32° 37’117° 17’

Site selected to beover 3 nmi from shore

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200 m diameter plume deployed November 8, 2005.

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Plume dispersed as function of time.

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In plume sampling conductedby SIO and USCG Strike Teamwith SMART system.

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GPS Drifter deployments at the initial edge of the plume.

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DRIFTER TRACKS DURING TEST

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Surface current mapping HF radars operated in real time during test.

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Trajectories of simulated plume computed using surface current maps from HF radar – Good agreement found with drifter trackers.

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Water-column structure and plume development

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SMART and SIO in situ fluorometer measurements

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SIMAP projections of subsurface dye plume using CODAR generated surface current field 30, 60, 90, and 120 minutes after dye release.

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SIMAP projections of subsurface dye plume using CODAR generated surface current field 3, 4.5, 5.5, and 6.5 hours after dye release.

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Additional refinements to dye plume tracking with CRRC funding

• Tethered or autonomous UV/Fluorometers and CTD instrument package on Mini-BAT

• Continuous data readout on board the tracking vessel

• Deployment of multiple drogues at different depths (1, 3, and 10 m) to better track displacement of subsurface plume

• Continued validation and use of SIMAP to provide a broad and rapid dissemination of the knowledge gained

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Mini-BAT planned for subsurface plume mapping in CRRC program. Data from CTD and UF/Fluorometer are fed back to surface vessel for continuous readout. The Mini-BAT can be towed at speed of 2-15 knots facilitating data acquisition, and it can be programmed to “fly” at fixed depths or in an undulating mode extending to 40 m.

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Example of fluorescein dye plume mapped by Dale et al. (2004) using a towed Mini-BAT off the coast of Oregon in 2003. The upper trace shows the undulating flight path in the vertical, which provided nearly synoptic coverage of plume dimensions during each transect.

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Modeling Subsurface Concentrations• Oil spill models use Lagrangian element approach• Movement of mass vector sum of:

• Advection = tidal and oceanic currents, measured by • CODAR-generated surface current field• Drifters

• Wind-driven wave transport = Stokes Drift• Leeway drift factor (2-4% of wind speed) and angle to right (N)• Model (Youssef and Spaulding, 1993, 1994) includes vertical

shear• Small scale mixing = diffusion

• Subscale movements not measured in advective field• Eddies• Langmuir cells• Convection caused by cooling at surface

• 3d: horizontal and vertical • Most influential to concentration estimates, yet often

most uncertain input to model

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Estimation of Diffusion Rates/Coefficients

• Empirically measure, theory still developing• Horizontal

• Dye fluorescence dilution in space and time• Aerial photos (measurements over time)• Separation of Lagrangian drifters over time• Variance in CODAR estimates = horizontal

diffusion + error• Vertical

• Dye fluorescence dilution in space and time• Models based on wind speed (drag on water

surface)

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Modeling products will include:• A fitting algorithm for estimating diffusion coefficients from

conservative (dye) tracer concentrations (3d least squares fit to Gaussian shaped model).

• Algorithms for incorporating into oil transport models the magnitudes and uncertainty of: • non-wind-drift currents from water surface observational

current data in rectilinear grid format, • wind (Stokes) drift, and• diffusion rates.

• Quantitative techniques for uncertainty analysis based on uncertainty in input data: • describe the range and uncertainty of each input parameter

as a probability distribution (even, Gaussian, or skewed) • repeatedly sample the distribution for multiple model runs

(Monte Carlo)• provide uncertainty estimates of predicted concentrations

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Conclusions from Initial OSPR-Funded Dye Deployment on 8 November 2005

• Completed 1 of 4 cruises – will continue to build upon experience and approach

• Excellent agreement was obtained between CODAR, surface drifters, and SIMAP predictions in first experiment

• Aircraft support and multi-spectral imaging allowed plume dimensions (advection and diffusion) to be tracked in the horizontal over time

• CTD and in situ UV/Fluorescence data from orthogonal transects allow plume delineation in vertical as well as horizontal dimensions

• More rapid data acquisition with the proposed Mini-BAT with in situ instrumentation will provide better temporal resolution of plume advection and diffusion in three dimensions and time.

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Revised ScheduleProposed Project Timeline

2006 2007Activity J F M A M J J A S O N D J F M A M J J A S O N D

Cruise Planning & CRRC WorkshopsOSPR Cruise Data Analysis & ModelingDrouge Procurement & Equip. StagingMiniBAT Modification (UV/F Instrumentation)1st dye deployment MiniBAT cruiseJune Cruise Data Analysis & Modeling2nd dye deployment MiniBAT cruiseOct. Cruise Data Analysis & ModelingDraft Rpt & Manuscript Prep.Presentations at CRRC WorkshopsFinal ReportPresentations at AMOP

KEY: = California OSPR Funded Activities= CRRC Funded Activities

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Conclusions:Goals and Usefulness of the Study• Develop field measurement and sampling tools for

oil spill assessments• Integrate Regional Observing Systems (like CODAR)

and hydrodynamic model forecasts into 3d oil spill models (including uncertainty)

• Measurement of small-scale ocean surface diffusivities (typically, the most uncertain model inputs)

• Useful for• Trajectory modeling for response• Evaluations of dispersant effectiveness • Modeling water column concentrations and impacts (for

NRDA and Risk Based Decision Making Analyses of dispersant use options)

• Estimate model uncertainty