Abstract

The Department of Energy’s (DOE’s) National Energy Technology Laboratory’s (NETL’s) Blowout and Spill Occurrence Model (BLOSOM), and the National Oceanic and Atmospheric Administration’s (NOAA’s) General NOAA Operational Modeling Environment (GNOME) are compared. Increasingly complex simulations are used to assess similarities and differences between the two models’ components. The simulations presented here are forced by ocean currents from a Finite Volume Community Ocean Model (FVCOM) implementation that has excellent skill in representing tidal motion, and with observed wind data that compensates for a coarse vertical ocean model resolution. The comprehensive comparison between GNOME and BLOSOM presented here, should aid modelers in interpreting their results. Beyond many similarities, aspects where both models are distinct are highlighted. Some suggestions for improvement are included, e.g., the inclusion of temporal interpolation of the forcing fields (BLOSOM) or the inclusion of a deflection angle option when parameterizing wind-driven processes (GNOME). Overall, GNOME and BLOSOM perform similarly, and are found to be complementary oil spill models. This paper also sheds light on what drove the historical Point Wells spill, and serves the additional purpose of being a learning resource for those interested in oil spill modeling. The increasingly complex approach used for the comparison is also used, in parallel, to illustrate the approach an oil spill modeler would typically follow when trying to hindcast or forecast an oil spill, including detailed technical information on basic aspects, like choosing a computational time step. We discuss our successful hindcast of the 2003 Point Wells oil spill that, to our knowledge, had remained unexplained. The oil spill models’ solutions are compared to the historical Point Wells’ oil trajectory, in time and space, as determined from overflight information. Our hindcast broadly replicates the correct locations at the correct times, using accurate tide and wind forcing. While the choice of wind coefficient we use is unconventional, a simplified analytic model supported by observations, suggests that it is justified under this study’s circumstances. We highlight some of the key oceanographic findings as they may relate to other oil spills, and to the regional oceanography of the Salish Sea, including recommendations for future studies.

Highlights

  • The National Oceanic and Atmospheric Administration’s (NOAA’s) Office of Response and Restoration’s (OR&R) Emergency Response Division built General NOAA Operational Modeling Environment (GNOME) to predict the potential trajectory of offshore pollutants at the sea surface

  • The Foss Barge—Point Wells Spill was the basis of a comparison between two offshore spill trajectory models: Blowout and Spill Occurrence Model (BLOSOM) and GNOME

  • GNOME includes a refloating algorithm that empirically describes the adhesiveness of the oil to the shoreline; a “half-life” parameter can be set by the user, representing the number of hours over have a much larger refloat half-life, the Doe-Kag-Wats marsh is mostly protected by a part-sand and part-gravel beach, with a small opening into the marsh

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Summary

Oil Spill Models

The Department of Energy’s National Energy Technology Laboratory (NETL) created BLOSOM, a comprehensive modeling suite that follows the fate and transport of both subsurface oil blowouts and surface spills. A comparison comprising many of the well-known models, and focused on a blowout and the use of subsea dispersants, can be found in Socolofsky et al [1]. Both models use ocean currents, typically from ocean models, and wind, from atmospheric models or weather stations, to force the movement of oil at the sea surface. BLOSOM and the latest version of GNOME are able to simulate a fully three-dimensional spill or blowout over time. The primary focus of this study was placed on model trajectories rather than weathering

Blowout and Spill Occurrence Model
General NOAA Operational Modeling Environment
Foss Barge—Point Wells Oil Spill
Data and Methods
Additional Ambient Forcing for Oil Spill Models
Wind Data
Diffusion
Test 1
Test 5
Test 6
Test 7
Test 8
Hindcasting the Historical Foss Point Well Spill
Integration Geometry
Including the Effect of Earth’s Rotation
Interpolation of Wind Forcing
Differences in Beaching Algorithms
Sensitivity to Initial Position
Turbulent Diffusion
Release Period
Findings
Conclusions
Full Text
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