OCS Study

BOEM 2024-042

Assessing the Threat from Oil Spills 

in the Southern California Bight

U.S. Department of the Interior

Bureau of Ocean Energy Management

Pacific Regional Office, Camarillo, CA
OCS Study

BOEM 2024-042

Assessing the Threat from Oil Spills 

in the Southern California Bight

August 2024 

Authors: 

Dylan Righi 

Christopher H. Barker

Amy MacFadyen

Prepared under BOEM Interagency Agreement M16PG00008 

By

National Oceanic and Atmospheric Administration

Office of Response and Restoration

7600 Sand Point Way NE 

Seattle, WA 98115 

U.S. Department of the Interior

Bureau of Ocean Energy Management

Pacific Regional Office, Camarillo, CA
DISCLAIMER

This study was funded, in part, by the U.S. Department of the Interior, Bureau of Ocean Energy 

Management (BOEM), Environmental Studies Program, Washington, DC, through Interagency 

Agreement Number M16PG00008 with the National Oceanic and Atmospheric Administration Office of 

Response and Restoration. This report has been technically reviewed by BOEM, and it has been approved 

for publication. The views and conclusions contained in this document are those of the authors and should 

not be interpreted as representing the opinions or policies of BOEM, nor does mention of trade names or 

commercial products constitute endorsement or recommendation for use.

REPORT AVAILABILITY

Download a PDF file of this report at https://espis.boem.gov/Final%20Reports/BOEM_2024-042.pdf. To 

search for other studies funded by BOEM’s Environmental Studies Program, visit

https://www.boem.gov/environment/environmental-studies/environmental-studies-information/. 

CITATION

Righi D, Barker CH, MacFadyen A. 2024. Assessing the threat from oil spills in the Southern California 

Bight. Camarillo (CA): U.S. Department of the Interior, Bureau of Ocean Energy Management. 

46 p. Report No.: OCS Study BOEM 2024-042. Contract No.: M16PG00008. 

ABOUT THE COVER

Map of twenty-three drilling platform locations in the northern Southern California Bight used as source 

sites for the TAP development.
i 

Contents 

List of Figures ............................................................................................................................................. ii

List of Tables .............................................................................................................................................. iii

List of Abbreviations and Acronyms .......................................................................................................iv

1 Introduction ......................................................................................................................................... 1

2 The TAP Approach.............................................................................................................................. 1

3 NOAA GNOME Modeling Suite .......................................................................................................... 3

4 Environmental Model Forcing ........................................................................................................... 3

5 Southern California TAP .................................................................................................................... 5

5.1 Source Sites .................................................................................................................................. 5

5.2 GNOME Model Setup ................................................................................................................... 8

5.3 Seasonality.................................................................................................................................... 9

5.4 Receptor Grid..............................................................................................................................10

6 Visualizing and Interpreting the Results ........................................................................................12

6.1 Impact Analysis Mode .................................................................................................................13

6.2 Oiling Analysis.............................................................................................................................15

6.3 Response Time Analysis.............................................................................................................17

6.4 Threat Analysis............................................................................................................................18

6.5 Level of Concern .........................................................................................................................19

6.5.1 Relating TAP’s Levels of Concern to Amounts of Shoreline Oiling ........................................19

6.5.2 Relating TAP’s Levels of Concern to Amounts of Oil on the Sea Surface..............................20

7 Summary............................................................................................................................................21

8 References.........................................................................................................................................22

Appendix A: Subsurface Source Modeling ............................................................................................24

A.1 Introduction..................................................................................................................................24

A.2 Methods.......................................................................................................................................24

A.3 Results and Discussion...............................................................................................................25

A.3.1 Plume Dynamics Overview .....................................................................................................25

A.3.2 Droplet Size Distribution..........................................................................................................26

A.3.3 Worst-case Discharge Scenario..............................................................................................26

A.3.4 Plume-trapping Scenario.........................................................................................................30

A.3.5 Subsurface Transport Modeling..............................................................................................34

A.4 Conclusions................................................................................................................................. 37

A.5 Appendix References..................................................................................................................38

 
ii

List of Figures

Figure 1. University of California Los Angeles (UCLA) ROMS model domain ............................................. 4

Figure 2. Map of twenty-three drilling platform locations in the northern Southern California Bight used as 

source sites for the TAP development .......................................................................................... 6

Figure 3. Monthly averaged wind roses from NDBC Buoy #46053, located in the Channel, approximately 

12 miles south of Santa Barbara, California................................................................................10

Figure 4. Southern California TAP receptor grid.........................................................................................11

Figure 5. Close-up of receptor grid, showing resolution between Santa Cruz and Santa Rosa Islands ....12

Figure 6. TAP viewer for Southern California .............................................................................................13

Figure 7. Impact Analysis Mode in the WebTAP viewer for the Hondo platform source site for the “All 

Year” season ...............................................................................................................................14

Figure 8. A comparison for the Impact Analysis for releases from the Hondo platform in Summer (top) and 

Winter (bottom)............................................................................................................................15

Figure 9. Oiling Analysis Mode in the WebTAP viewer for the Hondo platform source site and a receptor 

cell on the northern side of Santa Cruz Island ............................................................................16

Figure 10. Oiling Analysis Mode in the WebTAP viewer for the Hondo platform source site and a receptor 

cell on the southern side of Santa Cruz Island............................................................................17

Figure 11. Response Time Analysis mode in the WebTAP viewer for the Hondo platform source site and 

a receptor cell on the northern side of Santa Cruz Island...........................................................18

Figure 12. Threat Analysis Mode in the WebTAP viewer for a receptor cell on the northern side of Santa 

Cruz Island ..................................................................................................................................19

Figure A-1. Water column properties extracted from the UCLA Southern California ROMS model at the 

beginning of the simulation..........................................................................................................27

Figure A-2. Flow velocity throughout the water column at the beginning of the simulation........................27

Figure A-3. Results of the TAMOC simulation for the worst-case discharge .............................................28

Figure A-4. Results of the TAMOC simulation for the worst-case discharge .............................................29

Figure A-5. Results of the TAMOC simulation for the worst-case discharge .............................................29

Figure A-6. Methane flux in the plume for the worst-case discharge .........................................................30

Figure A-7. Results of the TAMOC simulation for a moderate discharge...................................................31

Figure A-8. Results of the TAMOC simulation for a moderate discharge...................................................32

Figure A-9. Results of the TAMOC simulation for a moderate discharge...................................................32

Figure A-10. Methane flux in the plume for a moderate discharge.............................................................33

Figure A-11. Estimated droplet size distribution for a moderate discharge ................................................34

Figure A-12. Trajectory results for the moderate subsurface release ........................................................35

Figure A-13. Trajectory results for the moderate subsurface release ........................................................36

Figure A-14. Maximum concentration of oil as a function of longitude and depth, looking from the south 37

 
iii

List of Tables

Table 1. Platform names, locations and oil types used for running the GNOME spill trajectory model ....... 7

Table 2. Pipeline names, locations and oil types used for running the GNOME spill trajectory model........ 8

Table 3. Levels of Concern in TAP and depths of a swath of equivalent amounts of beached oil 1 km long 

and 2 m wide, at 50% cover........................................................................................................20

Table 4. TAP’s Levels of Concern in terms of estimated thickness and appearance of the liquid oil if 

evenly distributed on the sea surface at 25% cover....................................................................20

Table 5. TAP’s Levels of Concern in terms of estimated thickness and appearance of the liquid oil if 

evenly distributed on the sea surface at 100% cover..................................................................21

Table 6. TAP’s Levels of Concern in terms of the average number of tarballs per hectare and average 

area per tarball, if all the spilled oil was evenly distributed in the form of 1-cm diameter tarballs

.....................................................................................................................................................21

Table A-1. Oil properties used in the model simulations ............................................................................25

Table A-2. Rise velocities for droplets of varying sizes from GNOME .......................................................34

 
iv

List of Abbreviations and Acronyms

°C degrees Celsius

3D three-dimensional

ADIOS Automated Data Inquiry for Oil Spills

API American Petroleum Institute

bbl barrel(s)

BOEM Bureau of Ocean Energy Management

cm centimeter(s)

cSt centistokes

DOI US Department of the Interior

DSD Droplet Size Distribution

GNOME General NOAA Operational Modeling Environment

g/cm3 gram per cubic centimeter

km kilometer(s)

LOC Level of Concern

m meter(s)

mm millimeter(s)

μm micron(s)

NDBC National Data Buoy Center

N/m Newton meter

NOAA National Oceanic and Atmospheric Administration

ORR Office of Response and Restoration

ppm parts per million

ROMS Regional Ocean Modeling System

scf standard cubic feet

sq km square kilometer(s)

sq m square meter(s)

TAMOC Texas A&M Oilspill Calculator

TAP Trajectory Analysis Planner

UCLA University of California Los Angeles
1 

1 Introduction

Through an Interagency Agreement, the National Oceanic and Atmospheric Administration’s (NOAA) 

Office of Response and Restoration (ORR) and the Bureau of Ocean Energy Management (BOEM) 

partnered to develop an oil spill Trajectory Analysis Planner (TAP) implementation for the Southern 

California Planning Area.

The General NOAA Operational Modeling Environment (GNOME) is an oil fate and transport model 

developed and used operationally by NOAA for emergency spill response. These predictions rely on 

environmental information such as ocean currents and winds, which are highly variable and can typically 

be predicted with reasonable accuracy for only a few days. For applications relevant to BOEM, like 

planning and preparedness work, the user doesn't know when or where a spill might occur. In this case, it 

is more informative to have a tool that incorporates historic weather and ocean currents to explore a wide 

range of possible outcomes in the case of a spill. In order to address this need, NOAA developed TAP.

TAP provides a regional analysis of the most probable impacts for use in spill planning and preparedness 

work when the user doesn’t know when or where a spill might occur. A regional TAP implementation 

involves the development of a database of the results from a large number of simulated spill trajectories. 

These trajectories are computed with the GNOME model with realistic spill scenarios and historical 

environmental information. For this project, the trajectories were run using forcing from a high-resolution 

(1 km) Regional Ocean Modeling System (ROMS) (Shchepetkin and McWilliams 2005) hindcast. This 

extensive model output allows modeling of realistic oil spill scenarios over a range of different regional 

oceanographic regimes (such as upwelling, relaxation, and eddy-driven flow). Modeled spills were started 

at the locations of Federal offshore oil and gas operations in southern California, and at four locations 

representing pipelines servicing a subset of those platforms.

The TAP viewer accesses this database and visualizes the results allowing response planners to answer a 

myriad of questions about possible spill behavior in the region. As part of this project, NOAA’s 

Emergency Response Division has developed a web-based version of the TAP viewer (WebTAP) to 

enable this tool to be more flexible and easily available for oil spill response planners. The interactive 

TAP viewer allows response planners to tailor the output to examine the most relevant regional concerns. 

The web-based TAP viewer (https://tap.orr.noaa.gov) hosts results from not only this present study but 

also from previous and future TAP studies developed by ORR (including the U.S. Arctic, San Francisco 

Bay, San Diego Bay, Lake Sabine and Lake Calcasieu). 

In the report, we begin by describing the TAP methodology (Section 2) and the underlying NOAA 

GNOME model used for the spill fate and transport simulations (Section 3). We then provide a brief 

overview in Section 4 of the models used for environmental fields (surface currents and winds) that are 

inputs to GNOME. The Southern California TAP implementation is described in detail in Section 5. 

Finally, in Section 6, we present some sample results that can be visualized and interpreted in the 

WebTAP viewer. Lastly, Appendix A discusses three-dimensional (3D) oil trajectories and justifies the 

use of surface releases in TAP to represent potential sub-surface blowouts.

2 The TAP Approach

Once oil is released into the sea, it is moved and transformed by the surface winds and ocean currents, 

potentially impacting regions far from the source location. During a spill response, modeling systems 

such as NOAA’s GNOME are used to forecast where the oil might go and how it will be transformed 

while in the environment. This information can be used to direct responders to where they can clean up 

the oil or protect sensitive areas.
2 

Preparing for a potential oil spill requires an assessment both of the spills that might occur and what the 

possible consequences might be. Those consequences and the response resources required to mitigate 

them depend on the transport, or trajectory, of the oil after a release. But when planning for a possible 

spill, there is no way to know exactly what the ocean currents and winds will be in some unknown future 

time. Often planners address this issue by selecting a “worst case” scenario, or using average or “typical” 

conditions. The response planning process is then based around the resultant single-scenario or a small 

number of oil trajectories. Decisions are made about response resource allocation and response times to 

sensitive sites based upon the trajectories of how oil will move and where it will impact during that 

particular set of environmental conditions. Clearly, if all the resources to respond to oil spills are stored at 

locations that expedite response to the chosen scenario, the response community may be set up for failure 

(delayed response time) if the spill that does occur happens from a different start location or under 

different environmental conditions. 

The shortfalls of determining response resource amounts and allocation are evident when focusing 

planning efforts solely on a single-scenario trajectory. An innovative and more appropriate approach is 

statistics-based planning. If planners use statistical information about how an oil spill might behave, they 

can determine where oil spilled from one location is likely to go most of the time, where it can go during 

extreme conditions, and where it will most likely never go. 

The TAP approach generates statistics through ensemble modeling. By examining an entire ensemble of 

oil spill trajectories from a particular location, but under the entire range of possible environmental 

conditions, statistics can be generated that facilitate planning for the full range of possible outcomes, 

rather than an arbitrary small set of scenarios.

With the ability to examine the statistics resulting from this sample of the "population" of all possible 

spill trajectories, planners can quickly build their intuition of how spilled oil behaves in their area. 

Secondly, statistics-based planning helps the response community determine the total amount of response 

resources they should have in their area to respond to most of the possible spills in their area, rather than 

just one spill scenario. Ensemble modeling can show how well prepared the community is for the full 

range of possibilities. 

TAP accomplishes this ensemble approach by building up a database of possible oil spill trajectories, 

based on measured historical data or modeled hindcasts of the atmospheric and oceanographic conditions 

over a long enough period to capture the regional climatology. The most challenging part of this process 

is the development of a hindcast oceanographic model with sufficient accuracy and precision to simulate 

oil spill behavior.

Once a suitable hindcast has been developed, the process involves:

• Selecting locations in the region where spills might occur (source sites). 

• Determining likely types and quantities of potential spills.

• Running an oil spill trajectory model multiple times for each spill location, randomly sampling 

the period of the hindcast. 

• Recording the locations (and timing) of oil impacts on a suitable grid.

All the details of this process are specific to the region of interest.

After the database of spill impacts has been generated, it can be analyzed to answer a variety of questions 

about possible spill behavior in the region. As there are many different questions that could be asked, and 

multiple perspectives about important resources to protect, NOAA provides the WebTAP tool to aid in 

performing custom analysis of this database of spill results, so individual planners can address the specific 

issues that they have. This tool has multiple modes that help answer specific questions:
3 

• Impact Analysis helps answer the question: If oil is spilled at a given spot, what shoreline 

locations are likely to be impacted? 

• Oiling Analysis provides a way to visualize how a particular receptor site is likely to be oiled by a 

spill originating at a particular location.

• Response Time Analysis displays information about how quickly a response must be established 

at a given location in order to precede the arrival of the oil. 

• Threat Analysis provides information on what source locations are most likely to threaten a 

particular region or resource of interest. 

All of these analyses provide statistical assessments: probabilities, not absolute values.

3 NOAA GNOME Modeling Suite

NOAA’s oil spill modeling software, the General NOAA Operational Modeling Environment (GNOME) 

has been under active development in recent years. The original desktop GNOME application (Zelenke et 

al. 2012) was completely refactored to separate the computational code base from the graphical user 

interface. Oil weathering algorithms from the stand-alone NOAA model Automated Data Inquiry for Oil 

Spills (ADIOS2) were also incorporated into GNOME. A new web-based user interface, WebGNOME 

(https://gnome.orr.noaa.gov) was developed and at present supports deterministic (single) model runs for 

simulating surface spills. A Python scripting environment (PyGNOME) can be utilized for more 

computationally expensive or ensemble model runs (e.g., for conducting the multiple spill simulations 

necessary for TAP). Subsurface releases (e.g., well blowouts) can also be modeled using the scripting 

environment and a user interface is currently being designed for incorporation into WebGNOME. The 

GNOME computational code is Open Source and can be downloaded via GitHub 

(https://github.com/NOAA-ORR-ERD/PyGnome).

Similar to most oil spill models, GNOME utilizes a Lagrangian particle tracking approach which 

essentially divides spilled oil into a large number of particles that move under the influence of ocean 

currents, wind drift, and horizontal and vertical mixing. GNOME is purposely designed to be flexible in 

its inputs; currents and winds used in GNOME can be derived from available model output on structured 

or unstructured grids. In the case of a surface spill, GNOME transport algorithms include advection due to 

currents, a user specified wind-drift parameterization, and a random walk to simulate the effect of 

turbulent diffusive processes that spread spills horizontally. 

Individual particles also have weathering algorithms applied to them using algorithms similar to the 

ADIOS2 model (Lehr et al. 2002). For a surface spill, these include spreading, evaporation, vertical 

dispersion, sedimentation, and emulsification. With the exception of emulsification, these algorithms 

essentially result in a loss of mass from the surface slick. The mass lost from the ocean surface due to 

weathering is typically not tracked further in the model, although this could be implemented through the 

scripting environment (i.e., oil droplets permanently dispersed into the water column). 

An updated technical manual detailing the coupled transport/weathering algorithms utilized in GNOME 

for surface and subsurface releases will be released later this year. Meanwhile, the GNOME and ADIOS2 

references cited above provide more detail on individual algorithms.

4 Environmental Model Forcing

To predict oil spill trajectories for TAP, GNOME requires regional ocean currents and wind information. 

For TAP statistics to be significant, it is important that these current and wind data-sets are over a long 
4 

enough period of time that multi-scale (interannual, seasonal, and intra-seasonal scales) variations of 

physical processes (wind, current, and wave) are represented.

An associated study, also funded by BOEM, titled “Expansion of West Coast Oceanographic Modeling 

Capability”, provided long time-series modeled outputs that were utilized for this Southern California 

TAP project (Dong et al. 2017). That project yielded a 10-year high-resolution hindcast product, 

including hourly sea surface wind and sea surface currents. 

The oceanic simulations were performed with ROMS (Regional Ocean Model System) (Shchepetkin and 

McWilliams 2005) and produced a hindcast for 2004–2013 (Dong et al. 2017). The model domain 

extends from just south of Monterey Bay, California south to the border with Mexico (Figure 1). The 

model spatial resolution is 1 km in the horizontal, with 42 terrain-following vertical levels. The temporal 

resolution of the model data is 1 hour. 

The ROMS model used nested boundary conditions from a larger model extending along the entire U.S. 

west coast with a horizontal grid resolution of 4 km. 

Figure 1. University of California Los Angeles (UCLA) ROMS model domain

Color shows ocean bathymetry (meters). 
5 

The meteorological model used in this study was the Weather Research and Forecasting model (WRF) 

(Skamarock et al. 2008), a next-generation mesoscale numerical weather prediction system designed for

both atmospheric research and operational forecasting needs. Output from the 6-km WRF model was used 

both to force the ROMS mode and as a direct forcing for GNOME via the wind-drift parameterization. A 

full description of the WRF/ROMS model implementations and their validation is provided in Dong et al. 

2017. 

5 Southern California TAP

Although the TAP methodology described above is common to every implementation, each regional 

study has unique characteristics. For instance, the relevant risk of spills in the region due to the presence 

of offshore drilling or vessel traffic varies widely and this influences the spill scenario parameters (spill 

sites, types and duration of releases). Some regions also have strong seasonal variability in transport that 

should be included whereas other regions may be dominated by tidal or larger-scale ocean currents. In 

this section, the details specific to the Southern California TAP are described.

5.1 Source Sites

The source sites for the Southern California Planning Area TAP study were chosen based on the 23 

Federal drilling platforms in the region and four pipeline locations. These platforms and pipelines are 

listed in Table 1 below along with summary information (location, the oil field, and the specific oil type 

each produces/carries as selected from the ADIOS database). The majority of the sources are in the Santa 

Barbara Channel, to the north and northeast of the Channel Islands. Five are outside of the Channel, to the 

west of Point Arguello, and five more are found south of Los Angeles, between Catalina Island and Long 

Beach. Figure 2 shows the locations of the platforms. 
6 

Figure 2. Map of twenty-three drilling platform locations in the northern Southern California Bight 

used as source sites for the TAP development

To generate the TAP database, the GNOME model was run 200 times for each designated source site 

within each “season” (described below). This is intended to capture the variability in winds and ocean 

currents; each model run or “scenario” uses a randomly selected start date from the 10-year ROMS and 

WRF model outputs available for currents and winds. Each scenario predicts oil weathering and 

movement over 21 days from a 5-day continuous release of 200 barrels (bbl) per day. 

The modeled oil released from each source site is specific to the platform and pipeline at that location. 

Tables 1 and 2 present the names and locations of the platforms and pipelines and the oil type used for 

each, and its ADIOS database identifier. Using a specific oil type for each individual source site is an 

approach that is unique to the Southern California TAP project. 
7 

Table 1. Platform names, locations and oil types used for running the GNOME spill trajectory 

model

Geographic coordinates are given in decimal degrees per the World Geodetic System 1984 (WGS 84) datum. The 

American Petroleum Institute (API) gravity values provide a measure of the density of the oil types compared to 

water.

Platform Latitude (°N) Longitude (°W) Field ADIOS Name ADIOS API

Irene 34.61041944 120.7294278 Point 

Pedernales

Point Arguello 

Heavy 18.2

Hidalgo 34.49501389 120.7022889 Point Arguello Point Arguello 

Heavy 18.2

Harvest 34.46913611 120.6808167 Point Arguello Point Arguello 

Heavy 18.2

Hermosa 34.45550833 120.6463889 Point Arguello Point Arguello 

Heavy 18.2

Heritage 34.39073056 120.2791833 Pescado Hondo Blend 20.8

Harmony 34.35039167 120.167525 Hondo Hondo Blend 20.8

Hondo 34.39073056 120.1205306 Hondo Hondo Blend 20.8

A 34.3318861 119.6124694 Dos Cuadras Dos Cuadras 25.6

B 34.33234167 119.6215361 Dos Cuadras Dos Cuadras 25.6

C 34.332925 119.6307667 Dos Cuadras Dos Cuadras 25.6

Hillhouse 34.33134444 119.6032472 Dos Cuadras Dos Cuadras 25.6

Habitat 34.28661667 119.5880944 Pitas Point Pitas Point 38

Henry 34.33325556 119.5603972 Carpinteria Carpinteria 22.9

Houchin 34.33499167 119.5521167 Carpinteria Carpinteria 22.9

Hogan 34.337675 119.5414861 Carpinteria Carpinteria 22.9

Grace 34.17959667 119.4693917 Santa Clara Santa Clara 22.1

Gilda 34.18234167 119.4185639 Santa Clara Santa Clara 22.1

Gail 34.12510833 119.4002167 Sockeye Platform Gail 20.6

Gina 34.11749722 119.2762583 Hueneme Port Hueneme 14.8

Edith 33.59578611 118.1406861 Beta Beta Production 15.1

Ellen 33.58236667 118.1282222 Beta Beta Production 15.1

Elly 33.58340278 118.1270889 Beta Beta Production 15.1

Eureka 33.56378056 118.1164944 Beta Beta Production 15.1

 
8 

Table 2. Pipeline names, locations and oil types used for running the GNOME spill trajectory 

model

Geographic coordinates are given in decimal degrees per the World Geodetic System 1984 (WGS 84) datum. The 

API gravity values provide a measure of the density of the oil types compared to water.

Pipeline Latitude (°N) Longitude (°W) Field ADIOS Name ADIOS API

Irene 34.6412 120.6910 Point 

Pedernales

Point Arguello 

Heavy 18.2

Hondo/Harmony/Heritage 34.4103 120.1030 Hondo Hondo Blend 20.8

Hillhouse/A/B/C 34.3267 119.503 Dos 

Cuadras Dos Cuadras 25.6

Edith/Ellen/Eureka 33.67 118.1022 Beta Beta 

Production 15.1

Another aspect of the oil trajectory modeling that is unique to this project is that oil released underwent 

modeled weathering processes along its trajectory simulated with the GNOME model. Previous TAP 

projects applied weathering in a post-processing approach as a simple half-life decay dependent on oil 

type. In this application, weathering (spreading, evaporation of the lighter components of the oil into the 

atmosphere, vertical dispersion of some of the oil into the water column by the action of wind and waves, 

sedimentation, and emulsification) was done using wind, wave, and temperature data from the forcing 

models along each oil particle’s trajectory at each time step in the model integration. This approach 

provides a more exact and localized result for the oil mass loss due to weathering.

5.2 GNOME Model Setup

To run a GNOME trajectory model, certain spill parameters must be defined. For this project the oil type 

was determined by what a likely spill from one of the platforms or pipelines would look like. A release 

duration of 5 days was chosen based on estimates from BOEM of how long a worst-case platform release

from Southern California Platforms could occur. The total GNOME model run time of 21 days was set 

based on transport distances over that time period being similar to the scale of the TAP grid domain. Each 

GNOME spill was modeled with 10,000 Lagrangian elements (LEs). 

In spill trajectory models, it is common to combine a number of physical processes related to wind 

forcing (e.g., Stokes drift, surface drift, Langmuir circulation) into a wind-drift factor (Galt 1994). This 

has been determined experimentally to be approximately 3–4% of the wind speed for fresh oil in light 

winds without breaking waves (Reed et al. 1994). As the oil weathers and/or if wind speed increases, the 

oil may spend a significant portion of time away from the surface and out of the influence of many of the 

processes associated with the wind forcing, and the average drift factor may be much lower. In general, 

this parameterization is a very useful approach but requires observational feedback during spill events 

(Galt 1994). GNOME allows the user to specify a range of values for the wind drift along with a 

persistence time scale, simulating the time-varying windage as the wind and wave conditions are not 

generally spatially or temporally constant. In this simulation, wind drift parameter values were specified 

as 2-4% with a persistence time scale of 15 minutes.

Turbulent diffusive processes that spread spills horizontally are simulated in GNOME by a random walk. 

A diffusion coefficient of 1 m2

s

-1 was used to calculate random step lengths in the x- and y-directions 

from a uniform distribution. The current version of GNOME does not allow for spatial variability in the 

horizontal diffusion, so this results in a uniform spreading of the particles over time.
9 

Trajectory data was output every 12 hours and archived. The TAP analysis for the Southern California 

project processes this data at 1, 2, 3, 5, 7, 14, and 21 days from release, allowing spill statistics to be 

viewed at intermediate time intervals within the entire 21 day run.

5.3 Seasonality 

To study the variability of oil spill impacts over different seasons, it is useful to look at TAP results for 

those specific seasons. TAP is set up to do this by computing statistics for only GNOME runs that are 

started within specified time intervals. Previous TAP projects have designated seasons as defined 

traditionally (e.g., summer as June/July/August), or due to other forcing; for instance, the Arctic TAP 

project defined seasons as “Ice” or “No Ice” based on data showing when ice was present in the majority 

of the TAP domain being considered. 

For the Southern California domain, the wind is the strongest factor in determining current and transport 

patterns. Wind data from a set of National Data Buoy Center (NDBC) buoys in the region, and from the 

Santa Barbara pier were studied to test if there is strong seasonality present. Figure 3 shows the monthly 

averaged winds at the buoy in the Santa Barbara Channel (NDBC #46053) as wind roses (where the 

frequency of wind direction is represented by the length of the bars and the magnitude by the bar colors). 

It was determined that the majority of the variability could be covered by defining two seasons: 

“Summer” as May through October, and “Winter” as November through April. Dorman and Winant 

(2000) describe the summer winds to be westerly and consistently strong south of Point Conception into 

the Santa Barbara Channel and decreasing in magnitude to further east into the Channel. Winter winds are 

driven by travelling cyclones and accompanying fronts, bringing strong southeast and then northwest 

winds. 

Two hundred GNOME run start dates were randomly selected within each of these periods over the 10-

year ROMS forcing data set. Also, if more general planning is required, the “All Year” season is included 

in the viewer. This is constructed with another 200 GNOME runs started at times randomly chosen over 

the entire 10-year record. 
10

Figure 3. Monthly averaged wind roses from NDBC Buoy #46053, located in the Channel, 

approximately 12 miles south of Santa Barbara, California

The length of the bar represents the frequency of occurrence of wind direction, while the color shows the wind 

magnitudes. Note the direction of the “spokes” indicates the direction the wind is coming from.

5.4 Receptor Grid 

Results of the GNOME trajectory analyses were compiled into data files containing statistics for where, 

when, and how much oil would be predicted to impact receptor cells in the Southern California Bight. 

The receptor sites were defined by an approximately 2.3-kilometer (km) by 2.3-km grid as seen in 

Figure 4. The grid extends from 32° to 32.5°N, and from 116.26° to 121.5°W, covering from San Luis 

Obispo in the north, to beyond the Mexican border in the south. The grid is 176 × 211 (37,136 cells), 

which is reduced to 20,220 receptor cells after filtering out cells that are completely on land. 
11

Figure 4. Southern California TAP receptor grid

The grid covers from 32° to 32.5°N, and from 116.26° to 121.5°W, with an approximate resolution of 2.3 km. 

The definition of the receptor grid is a trade-off between obtaining a high-resolution grid sufficient to 

show variation of oil impacts along the coast and limiting the data files to reasonable sizes for serving 

over a web application. Also of importance is comparing the receptor cell resolution to the forcing grid 

resolution: having receptor cells smaller than the forcing grid (here, the 1 km grid ROMS) is redundant 

and will not produce “higher resolution” maps. Lastly, smaller grid cells require many more Lagrangian 

elements in the GNOME model runs in order to ensure cells get enough hits to build statistically 

significant results. Figure 5 shows the receptor cell resolution in the 9 km pass between Santa Rosa and 

Santa Cruz Islands, which was one determinant of the grid resolution.
12

Figure 5. Close-up of receptor grid, showing resolution between Santa Cruz and Santa Rosa 

Islands

6 Visualizing and Interpreting the Results

This project resulted in the development of a new web-based interactive viewer. The “WebTAP” viewer 

replaces the TAP desktop application which required downloading and installing the relevant regional 

databases. The WebTAP viewer is faster and more flexible than the desktop viewer, and allows view 

customizations and an improved user experience. In this section, we demonstrate the utility of the 

WebTAP viewer by presenting results from the Southern California TAP for one platform release site. 

The TAP results are viewed in the different analysis modes and the results are summarized.

The initial WebTAP landing page, found at https://tap.orr.noaa.gov presents an introduction to the TAP 

approach, a link to the TAP user manual, and a list of the available TAP projects hosted in the viewer. 

NOAA ORR is currently hosting all the TAP projects that have been developed. A full list of TAP 

projects can be found at https://response.restoration.noaa.gov/oil-and-chemical-spills/oil-spills/responsetools/trajectory-analysis-planner.html. (Also available at that link are downloadable packages that contain 

the TAP data-structures and the desktop viewer.)

Once you start by choosing the TAP region of interest from the landing page the viewer will load. The 

user will see a few components:

• a menu bar for selecting among visualization “modes” and customizing other parameters 

• an interactive map window in which results are displayed, and which initially shows the TAP 

receptor grid 

• a right-side panel listing the available spill sites with their platform names, locations, and oil 

types (if used for that particular TAP project) 

Figure 6 shows the viewer after selecting the Southern California TAP. The TAP User Manual, which is 

linked from the landing page and from the Help Menu, describes how to use the TAP menu and set 

parameters for each analysis mode. Selecting a source site, either by clicking on the map or selecting from 
13

the Spill Sources panel, will display results of the Impact Analysis Mode (default initial view) for that site 

as color-coding on the map.

Figure 6. TAP viewer for Southern California

6.1 Impact Analysis Mode 

To examine the threat of oiling to specific areas from a worst-case discharge at the Hondo platform select 

the Hondo spill source either by clicking on the platform location on the map, or by selecting it from the 

spill source list on the right panel. The resulting plot is shown in Figure 7 (after turning off the Show Grid 

option in the View menu). 
14

Figure 7. Impact Analysis Mode in the WebTAP viewer for the Hondo platform source site for the 

“All Year” season

This image is a statistical composite of the 200 individual GNOME scenarios. Each scenario predicts oil 

weathering and movement over 21 days from a 5 day continuous release of 200 bbl per day. Note that this 

is the “All Year” season, meaning that the start times for those 200 GNOME scenarios are randomly 

distributed over the entire 10 year ROMS forcing record. The colors indicate the percentage of the spill 

scenarios that resulted in a volume of oil greater than the Level of Concern (LOC) in a particular grid cell, 

at least once within the 21 days since the start of the spill. Here the LOC is specified as 1 bbl/cell. The 

threat map suggests that the strongest threat from this platform is to the south and southeast, towards the 

shorelines of Santa Rosa and Santa Cruz Islands. For more precise estimates of the likelihood of oiling, 

the threat levels in certain receptor cells can be displayed by hovering the mouse over individual cells in 

the map panel of the viewer; doing so shows that over 40% of the scenarios result in oil over the LOC 

moving between Santa Rosa and Santa Cruz, and then being transported further southward. The TAP 

results here suggest that beaches to the east of Santa Barbara are at a lower risk from this source site, with 

estimates that 10% or less of the 200 releases result in oil on the shorelines over the specified LOC. 

Hovering the mouse over a cell also displays the cells identification number, if it is needed for later 

comparisons.

The variation of oil impact with seasons can be displayed by selecting the Summer or Winter season from 

the Season pull-down menu. A comparison of those seasons is shown in Figure 8. These modes display, 

again, a composite of 200 GNOME scenarios, but with start times that fall within May–October for 

summer, and November–April for winter. The winter vs. summer results are similar in this scenario, with 

summer results showing a tendency for oil to be transported further to the west of the Channel Islands, 

while winter winds and currents tend to keep transport within the Southern California Bight. 
15

Figure 8. A comparison for the Impact Analysis for releases from the Hondo platform in Summer 

(top) and Winter (bottom) 

Lastly, a very important note is that the colors on the map in Impact Analysis mode do not provide any 

information about the amounts of oil that could reach cells, beyond indicating that the LOC is predicted to 

be exceeded at least once during one or more of the modeled spills. 

6.2 Oiling Analysis 

The Impact Analysis mode generates a picture of how the whole Southern California region could be 

affected by an oil spill. In contrast, the Oiling Analysis mode focuses on specific locations of concern, 

such as specific environmentally sensitive shoreline areas or marinas, to explore this question: How much 

oil from a release site could reach a particular location of concern?

Selecting the Oiling Analysis mode for the Hondo source site, and then selecting a receptor cell on the 

northern side of Santa Cruz Island results in the view shown in Figure 9. The inset window displays the 

selected cell’s ID number, its center latitude and longitude, and information about the selected spill source 

location. The plot shows the amount of oil predicted to be transported to the receptor cell as a function of 

the percent of the spills that reach that amount. Hovering the mouse over the graph shows a vertical red 

line on the graph and the corresponding predicted values. For instance, in the presented scenario, 10% of 

the spills are expected to bring 3 bbl or more of oil to that cell.
16

Figure 9. Oiling Analysis Mode in the WebTAP viewer for the Hondo platform source site and a 

receptor cell on the northern side of Santa Cruz Island

As a contrast, Figure 10 shows the same result for a receptor cell on the southern shore of Santa Cruz 

Island. From the Impact Analysis mode discussion, we know that this site should have less oil impact. 

This is reflected in the Oiling Analysis graph, that shows less than 10% of the spills will bring any oil at 

all to this cell.
17

Figure 10. Oiling Analysis Mode in the WebTAP viewer for the Hondo platform source site and a 

receptor cell on the southern side of Santa Cruz Island

6.3 Response Time Analysis

If oil were released from the Hondo platform, the amount of time it would take that oil to transit to a 

threatened shoreline has implications both for the potential impacts and the response. Figure 11 shows the 

Response Time Analysis mode for the Hondo platform source. The color levels on the map represent the 

shortest time (in days) for oil to impact each receptor cell, over the LOC. Note that the time is defined as 

the time since the start of the spill; in the Southern California TAP scenarios the oil release occurs over 

5 days, which has implications for arrival times. 

The “Response Threshold” default of 95% limits the displayed data by excluding the fastest 5% of the 

spill transports in order to attempt to filter out outlier cases. The WebTAP user guide provides more 

information on choosing an appropriate Response Threshold. 

If there is an area of particular concern, the inset window displays predictions for a specific receptor cell. 

The header in the inset displays the selected cell’s ID number, its center latitude and longitude, and 

information about the selected spill source location. The plot shows the arrival time estimates for that cell 

in days, as a function of the percentage of spill scenarios that reach the cell in that time frame. By 

hovering the mouse over the plot, we can see that 20% of the spills from the Hondo platform are predicted 

to impact that cell above the specified LOC within 5 days. 
18

Figure 11. Response Time Analysis mode in the WebTAP viewer for the Hondo platform source 

site and a receptor cell on the northern side of Santa Cruz Island

6.4 Threat Analysis 

Threat Analysis mode is useful when you have an area of particular concern and need to know which spill 

sites have the most potential for impacting that area. After selecting the mode, a receptor cell of concern 

is chosen. Figure 12 shows the Threat Analysis for the same receptor cell on the northern side of Santa 

Cruz Island as the previous section. The colors of circles at each platform source represent the percentage 

of spills from that site that will impact the selected receptor cell over the chosen LOC. This provides an 

easy and quick way to understand where a threat could come from, and to scale the risk of damage at an 

important or sensitive location. In the map the largest threat comes from spills that originate at the 

Heritage, Hondo and Harmony platforms, with about 25% of the spill scenarios starting at these sites 

impacting the Santa Cruz Island shoreline. The platforms to the northeast of Santa Cruz island, while 

closer, have lower threat values due to the nature of the currents and winds in the region. If exact values 

are needed, instead of just the color scale information, hovering the mouse pointer over a source site will 

show the exact threat percentage from that source. 
19

Figure 12. Threat Analysis Mode in the WebTAP viewer for a receptor cell on the northern side of 

Santa Cruz Island

The color of the circles at the source locations predicts what percentage of spills from each site will impact the 

selected receptor cell.

6.5 Level of Concern 

For most of TAP’s display modes, a LOC must be specified and this value strongly impacts the results. In 

TAP, this is the volume of spilled oil on the water surface within one map cell, above which a responder 

would plan to take particular response actions in order to mitigate the expected impacts of that oil. 

Choosing a LOC that reflects the potential impacts of the oil is one part of determining the response 

actions taken. The other part has to do with the question of resource constraints in terms of how much oil 

can realistically be responded to. To assist in selecting a LOC for planning work, the tables below 

(Tables 3 through 6) are provided. When setting up a scenario in TAP, the default LOC can be selected, 

or a different LOC can be chosen. In either case, the LOCs chosen in TAP should be meaningful for the 

scenario being investigated. 

6.5.1 Relating TAP’s Levels of Concern to Amounts of Shoreline Oiling

One way to relate TAP’s LOCs to the “real world” is to visualize how the amount of oil equivalent to a 

LOC might appear if it were to beach on a shoreline. For example, if the 5 bbl of oil in one 2.5 by 2.5-km 

cell were driven straight to shore by an onshore wind, forming a swath of oil 10 km long and 2 meters (m) 

wide, then that swath of oil would be about 0.08 centimeters (cm) deep at 50% cover. When a swath of oil 

on a shoreline is patchy or broken so that it covers about 50% of the area under it, the percent cover is 

considered to be 50%. Table 3 shows how deep the swath of beached oil would be for amounts of oil 

equivalent to selected TAP LOCs.
20

Table 3. Levels of Concern in TAP and depths of a swath of equivalent amounts of beached oil 

1 km long and 2 m wide, at 50% cover

TAP Level of 

Concern

(bbl/cell)

TAP Level of 

Concern

(bbl/sq km) 

TAP Level of 

Concern

(liter/sq km) 

Average thickness of a swath of an 

equivalent amount of beached oil 1 km long 

and 2 m wide at 50% cover

(cm) 

1 0.16 0.25 0.016

2 0.32 0.50 0.032

5 0.8 0.99 0.08

10 1.6 2.48 0.16

20 3.2 4.95 0.32

6.5.2 Relating TAP’s Levels of Concern to Amounts of Oil on the Sea Surface

Another way to relate TAP’s LOCs to real oil spills is to visualize how the oil could appear on the sea 

surface. For simplicity’s sake, consider a 2.5 by 2.5-km cell, representative of the Southern California 

TAP receptor cells. As Table 3 shows, if 10 bbl of oil were spread out in an even layer at 25% cover 

across the sea surface within each 6.25-square kilometer (sq km) cell, that layer of oil would be about 

1 micron (μm) thick (1 μm = 4 × 10-5 inches). This is the approximate thickness of rainbow sheen (0.30–

5.0 μm), as defined in the Bonn Agreement Oil Appearance Code (BAOAC, 

http://www.bonnagreement.org/eng/html/welcome.html).

Table 4. TAP’s Levels of Concern in terms of estimated thickness and appearance of the liquid oil 

if evenly distributed on the sea surface at 25% cover

TAP Level 

of Concern

(bbl/cell)

TAP Level of 

Concern

(bbl/sq km) 

Average 

thickness of 

liquid oil at 25% 

cover

(μm)) 

Average 

thickness of 

liquid oil at 25% 

cover

(inches)

Expected appearance of liquid 

oil at 25% cover if evenly 

distributed on surface and not 

emulsified

(μm)

1 0.16 0.1 4 x 10-6 Silver/gray sheen (0.04 – 0.30 μm)

2 0.32 0.2 8*10-6 Silver/gray sheen (0.04 – 0.30 μm)

5 0.8 0.5 2 x 10-5 Rainbow (0.30 – 5.0 μm)

10 1.6 1 4 x 10-5 Rainbow (0.30 – 5.0 μm)

20 3.2 2 8 x 10-5 Rainbow (0.30 – 5.0 μm)

If the same 10 bbl were spread out in an even layer at 100% cover across the sea surface within each 6.25-

sq km cell, the oil layer would be four times thinner, about 1⁄4 μm in this example (Table 4).
21

Table 5. TAP’s Levels of Concern in terms of estimated thickness and appearance of the liquid oil 

if evenly distributed on the sea surface at 100% cover

TAP Level of 

Concern

(bbl/cell)

TAP Level of 

Concern

(bbl/sq km)

Average 

thickness of 

liquid oil at 

100% cover

(μm)) 

Average 

thickness of 

liquid oil at 

100% cover

(inches)

Expected appearance of liquid oil 

at 100% cover if evenly distributed 

on surface and not emulsified

(μm)

1 0.16 0.06 2.4 x 10-6 Silver/gray sheen (0.04 – 0.30 μm)

2 0.32 0.13 5 x 10-6 Silver/gray sheen (0.04 – 0.30 μm)

5 0.8 0.25 1.0 x 10-5 Silver/gray sheen (0.04 – 0.30 μm)

10 1.6 0.6 2.4 x 10-5 Silver/gray sheen (0.04 – 0.30 μm)

20 3.2 1.25 4.9 x 10-5 Silver/gray sheen (0.04 – 0.30 μm)

In reality spilled oil is never distributed uniformly on the water surface. It will be in variable thickness 

slicks, bands, tarmats, or tarballs with clean water in between. Table 6 shows amounts of oil equivalent to 

each of TAP’s LOCs if that oil is distributed as tarballs.

Table 6. TAP’s Levels of Concern in terms of the average number of tarballs per hectare and 

average area per tarball, if all the spilled oil was evenly distributed in the form of 1-cm diameter 

tarballs

TAP Level of Concern

(bbl/cell)

TAP Level of Concern

(bbl/sq km) 

Average area per tarball

(sq m)

Average number of 

tarballs per hectare

(#) 

1 0.16 20.6 486

2 0.32 10.3 972

5 0.8 4 2430

10 1.6 2 4860

20 3.2 1 9720

7 Summary 

The work presented will be important for BOEM, and other planners, to better understand the risk from 

an oil spill in the Southern California Planning Area. By using realistic weather and ocean current forcing 

to develop oil spill trajectory statistics for potential spills, this TAP project allows a user to predict what 

areas could be impacted by a potential spill from one of the region’s platforms or pipelines. How much oil 

could be present at threatened locations? How long do responders have to respond? And what sources are 

the most likely threats to sensitive locations and resources? These questions can be explored using the 

TAP results.

As part of this project, the WebTAP viewer was developed to make the TAP statistics more available to 

emergency response planners. All the TAP results images in this report are from the developed WebTAP 

viewer. The previous TAP viewer was only available through a desktop application. As a web application 

this new version of the viewer is much more accessible and will make it easier to explore the TAP 

statistics for the Southern California region. ORR is also hosting data from other TAP projects on the web 

viewer, making the TAP data and results available to the larger emergency response community. And 

lastly, while the trajectories and TAP analysis here were developed explicitly for spills from oil platforms, 
22

it is possible that the ROMS data and the TAP approach could be applied to other areas of interest: such 

as marine debris transport (Righi et al. 2018) or possible spills from vessels impacting otter populations 

(T. Tinker, email communication, Dec. 2020). 

The WebTAP viewer is still in development, and is being designed such that features can be added in the 

future, such as allowing the user to define a “season” for the TAP statistics to be computed from, or, 

ultimately, for the user to define a spill type and location and build the TAP statistics from GNOME runs 

carried out on demand. 

It is important to understand that the Southern California TAP can’t provide all the information needed 

for planning efforts, but provides a solid starting point for further investigations. Some caveats to keep in 

mind when working with TAP: 

• This TAP analysis does not address the subsurface transport of oil.

• This analysis does not take into account any response actions (skimming, dispersant application, 

burning, booming of oil). 

• The statistics presented by TAP are derived from 10 years of data from 2004 to 2013. We do not 

know how valid these statistics are for future years. Long-term variations in climate and forcing 

could change the statistics of the transport in the area.

• The Southern California TAP results (or any TAP project results) should not be used in the 

event of an actual spill. It is a planning tool that can be used to investigate “what if?” situations

based on the region’s long-term variability in currents and winds. TAP will not predict the 

trajectory of oil under specific conditions of winds and ocean currents. When modeling support is 

needed during an actual spill response, contact a NOAA ORR Scientific Support Coordinator. 

They can ask NOAA’s oil spill scientists to run GNOME to narrow the possibilities of where the 

oil will go under the specific conditions of a real-time oil spill.

Two factors that are more difficult to obtain from the TAP analysis, but pertinent for developing plans, 

are the duration of shoreline impacts and the type of impacts expected. As pointed out earlier, onshore 

winds are required for oil to come ashore. This fact implies that shoreline impacts will be episodic. In an 

example extreme case, a stretch of beach may receive 100 bbl of oil over a span of 2 days, but no oil 

before or after. It is unlikely that any stretch of beach would receive oil for more than a week or two at a 

time because of the episodic nature of the winds.

The type of beach impacts one can expect will depend upon the amount of oil and the environmental 

conditions to which the oil was exposed. If oil were to come ashore in a short duration of time, an 

outcome that would require strong winds and high seas, we would expect that oil to be broken into widely 

scattered tarballs of varying sizes. If oil were to come ashore under moderate and sustained wind 

conditions, shoreline impacts would be more in the form of large tarmats and slicks.

8 References

Dong C, Renault L, Zhang Y, Ma J, Cao Y. 2017. Expansion of West Coast oceanographic modeling 

capability. US Department of the Interior, Bureau of Ocean Energy Management. 83 p. OCS Study 

BOEM 2017-055. https://espis.boem.gov/final%20reports/5636.pdf

Dorman CE, Winant CD. 2000. The structure and variability of the marine atmosphere around the Santa 

Barbara Channel. Mon. Weather Rev. 128:261-282. 

Galt JA. 1994. Trajectory analysis for oil spills. Journal of Advanced Marine Technology Conference 

11:91-126. 
23

Lehr WJ, Jones R, Evans M, Simecek-Beatty D, Overstreet R. 2002. Revisions of the ADIOS oil spill 

model. Environ. Model. Softw. 17:191-199. 

Reed M, Turner C, Odulo A. 1994. The role of wind and emulsification in modelling oil spill and surface 

drifter trajectories. Spill Science & Technology 1(2):143-157. 

Righi D, Lippiatt S, Murphy P, Watabayashi G. 2018. Use of NOAA’s Trajectory Analysis Planner 

(TAP) for Marine Debris Transport Studies. Paper presented at: International Marine Debris 

Conference; 2018 Mar 12-16; San Diego, California, United States. 

Shchepetkin AF, McWilliams JC. 2005. The Regional Oceanic Modeling System (ROMS): A splitexplicit, free-surface, topography-following-coordinate ocean model. Ocean Modelling. 9(4):347-

404. 

Skamarock WC, Klemp JB, Dudhia J, Gill DO, Barker D, Duda MG, Powers JG. 2008. A Description of 

the Advanced Research WRF Version 3 (No. NCAR/TN-475+STR). University Corporation for 

Atmospheric Research. doi:10.5065/D68S4MVH. 

Zelenke B, O’Connor C, Barker C, Beegle-Krause CJ, Eclipse LE (Eds). 2012. General NOAA 

Operational Modeling Environment (GNOME) Technical Documentation. US Department of 

Commerce, NOAA Technical Memorandum NOS OR&R 40. Seattle (WA): Emergency Response 

Division, NOAA. 105 p. http://response.restoration.noaa.gov/gnome_manual

 
24

Appendix A: Subsurface Source Modeling

A.1 Introduction 

The main report has focused on the surface transport from spills originating at platform and pipeline 

locations in BOEM’s Southern California planning area. In the model simulations for the Southern 

California TAP project, the oil was initialized at the ocean surface where it moves under the influence of 

winds and surface ocean currents. These results illustrate which shorelines are at highest risk of being 

impacted along with the response times for implementing protection strategies. While informative for 

developing response plans, these results do not provide information on the scale of subsurface water 

column impacts in the event of a well blowout or pipeline release. 

Much of the recent literature examining well blowout dynamics stems from the 2010 Macondo oil well 

blowout in the Gulf of Mexico (the Deepwater Horizon oil spill). This incident demonstrated the wide 

range of processes that affect oil droplets and gas bubbles released from a subsea blowout (Socolofsky et 

al. 2016). These processes begin with the breakup of the blowout jet into small droplets and bubbles and 

their vertical transport as a buoyant plume. These dynamics are modeled with specialized “near-field” 

models, which can simulate the rising buoyant mixture of oil, gas, entrained seawater and formation of 

gas hydrate, and determine the plume evolution including formation of “intrusion layers” due to ocean 

stratification. Once the liquid hydrocarbon leaves the plume mixture in the form of individual droplets, 

advection by ocean currents controls their distribution and particle tracking models like GNOME are well 

suited to track the individual movement and fate of droplet size classes.

A key difference between the location of the 2010 Deepwater Horizon incident and the platform locations 

in the Southern California Planning area are the relatively shallow water depths which range from 

approximately 30 m (Platform Gina) to 370 m (Platform Harmony). In a shallow water blowout, the 

entrained oil and water in the vertical buoyant plume may interact with the ocean surface. At the surface 

the upward flow of water and oil turns and moves in a horizontal layer away from the center of the plume. 

The surface oil slick then spreads and moves with the prevailing currents and winds. The gas exits from 

the center of the plume and causes a surface disturbance or “boil zone”. In the shallow water case, there is 

less potential for large volumes of the oil to travel subsurface in intrusion layers and subsurface impacts 

may be more constrained to the vicinity of the blowout. 

In addition to the water depth, key factors in determining the partitioning of oil between the surface and 

subsurface include the nature of the release (i.e., how turbulent), the water depth, the gas to oil ratio, and 

the droplet size distribution. In Appendix A, we use results from a near field well blowout (plume) model 

coupled with a 3D particle tracking simulation to examine how far subsurface oil might travel before 

concentrations are reduced below a biologically significant LOC. We simulate scenarios to examine the 

likelihood for significant volumes of oil to remain subsurface and to estimate the extent of potential 

subsurface impacts.

A.2 Methods 

The Texas A&M Oil spill Calculator (TAMOC) modeling suite was used for predicting the nearfield fate 

and transport of oil and gas released (Socolofsky et al. 2015). The model contains modules for handling 

ambient water column information, hydrocarbon equations of state, and bubble and droplet dynamics, 

including particle rise velocity, shape, surface area, and heat and mass transfer rates. It uses these modules 

to estimate the dynamics of the buoyant plume resulting from a subsurface release of oil and gas. 

The TAMOC results are used to initialize subsurface spills in PyGNOME, a 3D particle tracking model 

that is the computational core of NOAA’s GNOME model used for predicting oil spill trajectories 

(Zelenke et al. 2012). Once the liquid hydrocarbon leaves the plume mixture in the form of individual 
25

droplets, rise velocities and advection by ocean currents control their distribution and PyGNOME is used 

to track the individual movement and fate of the droplet size classes.

The California ROMS model developed for BOEM (Dong et al. 2017) and previously described in this 

report is used to drive subsurface trajectories in TAMOC and PyGNOME. A 2-week subset of 3D current 

velocities was selected by searching the model current data for the fastest subsurface velocities in order to 

maximize the possible horizontal transport of oil from the subsurface release.

For these simulations, an idealized well blowout from the Heritage Platform (located in the Santa Barbara 

Channel approximately 13 km from the coast) was considered. At approximately 330 m, this site is one of 

the deeper well locations in the region. The oil type used for the release is a Hondo Blend, which is 

described in the online ADIOS Oil Database (https://adios.orr.noaa.gov/oils/AD02174). The Hondo 

Blend is a result of the blending of a few wells, including the Heritage well, that feed into the same 

pipeline to shore. There is no similar data available for the Heritage platform alone. The oil properties of 

nearby wells are similar and the results of the modeling done here are not highly sensitive to oil 

properties. The oil properties for the Hondo Blend used in these simulations are shown in Table A-1. 

Table A-1. Oil properties used in the model simulations

Property Value 

API Gravity 20.8

Density 0.929 g/cm3 at 15 °C

Kinematic Viscosity 511 cSt at 16 °C

Interfacial Tension 0.0241 N/m at 15 °C

Gas to Oil Ratio (Heritage) 1545 scf/bbl

We present results from two scenarios: a worst-case discharge scenario in which the plume rises to the 

surface quickly and a reduced release rate scenario in which the plume is trapped subsurface. A release 

rate of 33,986 bbl of oil per day was chosen in order to characterize a worst case discharge scenario

(Susan Zaleski, personal communication). A smaller release rate of magnitude 2000 bbl per day was also 

simulated to examine a case in which the buoyant plume might not interact directly with the surface. In 

the second case the near-field model results are used to initialize the GNOME particle tracking model.

A.3 Results and Discussion 

A.3.1 Plume Dynamics Overview

When oil and gas are released below the surface of the ocean, a buoyant plume is formed. The oil and gas 

are less dense than the surrounding water, and will rise in the water column. As a plume rises away from 

its source, it entrains water from the surrounding ocean, and widens as the plume rises. In addition, gas 

bubbles are dissolving into the water in the plume. With the addition of more dense seawater and loss of 

gas bubbles due to dissolution, the fluid in the plume increases in density. When the density becomes 

close to that of the surrounding water, there is no longer buoyancy to drive the plume, and the plume 

“breaks up” releasing the oil droplets and gas bubbles into the surrounding water where they will then rise 

independently and move with the currents. This is known as “trapping” the plume and the height at which 

the plume dynamics no longer dominate is known as the “trap height”. Depending on the parameters of 

the release and the water column, the plume can trap at mid-water column or rise to the surface and 

release the oil and gas at the surface. The water entrained in the plume near the bottom is typically more 
26

saline and colder, and therefore denser, than the water higher in the water column. Hence, as the plume 

loses buoyancy due to the dissolution of gas and entrainment of denser water, the plume can “slump” and 

move downward before it fully breaks up.

For both the oil droplets and gas bubbles, there is dissolution into the water as the plume develops. Oil 

has only a small fraction of soluble compounds, so this is typically a minor effect. Conversely, the bulk of 

natural gas is methane, which is highly soluble, so it rapidly dissolves into the water of the plume. 

Depending on the amount of gas and other factors (e.g., release depth, bubble diameter), the methane may 

fully dissolve before the plume reaches the surface or traps. However, if a large amount of methane 

reaches the surface it can create a hazardous atmosphere at the location of the surfacing plume. Dissolved 

methane in the water column has a low toxicity and is rapidly biodegraded. However, due to the high 

biodegradation rates, the dissolution of methane in seawater can result in local oxygen depletion that may 

lead to ecological effects. A decrease in oxygen was observed during the Deepwater Horizon incident 

associated with a deep plume of hydrocarbons located at approximately 1000 m depth (Kessler et al. 

2011). However, even with that very large release, the oxygen reduction never approached levels 

considered hypoxic. 

A.3.2 Droplet Size Distribution

Due to the high interfacial tension between oil and water, oil released in the water column tends to form 

individual droplets of varying sizes. The size of the droplets has a large influence on the fate and transport 

of the oil: larger droplets rise faster through the water column and dissolve and biodegrade more slowly. 

Because of this, the selection of the droplet sizes used in modeling is an important part of model setup.

In a release, droplets of a range of sizes are created, resulting in not a single size, but a distribution of 

sizes. The Droplet Size Distribution, or (DSD) resulting from a release is determined by the oil properties 

and the turbulent energy of the release. Critical oil properties are the density, viscosity, and interfacial 

tension with sea water. In practice, most petroleum products have a similar interfacial tension, unless 

chemical dispersants have been introduced. In an underwater release, the level of turbulence is determined 

by the flow rate, velocity and oil-gas ratio.

In the years since the Deepwater Horizon incident, many studies have attempted to characterize the DSD 

resulting from a subsurface blowout based on these parameters. There is still a lot of uncertainty in this 

work, as experiments must necessarily be conducted on much smaller scales than a real blowout, and it is 

unclear how well the results can be scaled up to field scale. Nevertheless, there have been a few models 

published in the peer reviewed literature that can guide analysis of transport of oil from a potential 

undersea release. For this study, the droplet size distribution model from Johansen et al. (2013) was used. 

A.3.3 Worst-case Discharge Scenario

In the worst-case discharge scenario, we simulate a spill originating from a total well blowout. In this case 

the orifice diameter for the release is set as 17.2 cm, which is approximately the well bore diameter. The 

release rate is based on a worst-case discharge number provided by BOEM of 33,986 bbl of oil per day 

with a gas-oil ratio of 1545 standard cubic feet (scf)/bbl (Susan Zaleski, personal communication). 

The water column properties were derived from the UCLA ROMS model at the location of the Heritage 

Platform (Figures A-1 and A-2). 
27

Figure A-1. Water column properties extracted from the UCLA Southern California ROMS model at 

the beginning of the simulation

Note that the salinity increases and the temperature decreases with depth, resulting in higher density water at the 

bottom. The small changes in temperature and salinity in the top 50 m indicate a mixed layer to that depth.

Figure A-2. Flow velocity throughout the water column at the beginning of the simulation

The “ua” is the flow in the east-west direction, and the “va” is the flow in the north-south direction. Note that the flow 

decreases from a maximum at the surface to near zero at the bottom. There is a change in direction between 100 

and 150 m depth indicated by the reversal in the N-S (va) direction. Note that the scales are different; the flow is 

mostly in the westerly direction.

Under these conditions, the high release rate coupled with the buoyancy of the oil and gas results in a 

buoyant plume that quickly reaches the ocean surface (Figures A-3 through A-6). The plume entrains 

water on its way up, so it does not release oil into the water column; i.e., all the oil reaches the surface. 

Due to the rapid rise time (oil surfaces in less than ten minutes), the extent of surfacing oil is constrained 

to within an approximately 80-m diameter around the well head location. Under these circumstances, the 
28

surface transport can thus be adequately modeled as a surface point release using a similar approach to the 

TAP modeling results presented in this report.

Figure A-3. Results of the TAMOC simulation for the worst-case discharge

The two views in the top plots are of the centerline of the plume from the south and the east. The plume has risen 

quickly, while being bent over by the cross current, with the current velocity strongest near the surface. The lower left 

plot is the view from the top. Note that the plume reaches the surface just over 100 m from the source. The lower

right plot is the total mass in the plume along the plume centerline.
29

Figure A-4. Results of the TAMOC simulation for the worst-case discharge

The two views are from the south and the east. The centerline of the plume has been bent over by the currents, 

strongest near the surface. Near the surface, the plume no longer has clearly defined edges. The plume reached the 

mixed layer before trapping.

Figure A-5. Results of the TAMOC simulation for the worst-case discharge

The plume width increases as it entrains water, reaching a maximum width of 180 m before reaching the surface. 
30

Figure A-6. Methane flux in the plume for the worst-case discharge

The blue line is the amount of methane in the bubbles (of all size classes), the orange line is the amount of methane 

in the water of the plume. As the bubbles dissolve, the methane in the bubbles transfers to the water; in this case, all 

the methane has dissolved into the water before the plume reaches the surface.

A.3.4 Plume-trapping Scenario

If a release were of a smaller rate, the resulting plume would be weaker, and might reach its trap height 

below the ocean surface. This would result in the oil droplets being released from the plume to be 

advected by ambient currents. Simulating these conditions in the relatively shallow water locations of 

wells in the Southern California Planning region necessitated reducing the flow rate and hence the 

buoyancy force of the oil-gas mixture. A reduction of the flow rate by an order of magnitude resulted in 

the plume “trapping” in the mid-water column.

The plume trapping simulation used the same water column conditions as the previous one, with a slower 

release rate of 2000 bbl/day and a hole diameter of 5 cm (assuming a slower release rate would likely 

result from a smaller orifice than the full bore hole). The resulting plume loses buoyancy and releases oil 

droplets at a depth of approximately 120 m above the well depth (180 m below the surface) as depicted in 

Figures A-7 through A-10. 
31

Figure A-7. Results of the TAMOC simulation for a moderate discharge

The varying colors are different size classes of oil and gas droplets; larger droplets rise more quickly. The two views 

in the top plots are from the south and the east. Note that the droplets are moving independently once they reach the 

trap height of approximately 120 m above the release (180 m below the surface). At this point, the oil droplets are 

released from the plume, and will move with the ambient currents (and their own rise velocity) The lower left plot is 

the view from the top, and the lower right plot is the mass in the plume along the plume centerline.
32

Figure A-8. Results of the TAMOC simulation for a moderate discharge

The two views are from the south and the east. The centerline of the plume has only moderately been bent over by 

the modest currents at depth.

Figure A-9. Results of the TAMOC simulation for a moderate discharge

The plume width increases as it entrains water, reaching a maximum width of 32 m before trapping.
33

Figure A-10. Methane flux in the plume for a moderate discharge

The blue line is the amount of methane in the bubbles (of all size classes), the orange line is the amount of methane 

in the water of the plume. As the bubbles dissolve, the methane in the bubbles transfers to the water; in this case, 

about half of the methane has dissolved into the water before the plume traps, releasing the bubbles into the water 

column. Those bubbles will rise through the water column and continue to dissolve on their way up.

When a plume traps below the water surface, the movement of the oil droplets are no longer governed by 

plume dynamics, and can be more accurately modeled with a 3D simulation that takes into account the 

currents at depth, as well as the rise velocity of the oil droplets. The rise velocity of the droplets is a 

function of the droplet size. For reference, Table A-2 shows rise times for droplets from 300 m and 180 m 

depth based on their diameter, from GNOME based on Galt and Overstreet 2014. In a calm water column, 

a 100-μm droplet could take almost a week to rise from 300 m, while a 5000-μm (5 millimeter [mm]) 

diameter droplet could surface in approximately 1 hour from the same depth. In the ocean, droplets 

smaller than about 50 μm are unlikely to reach the surface and form a slick as the turbulence in the water 

column will keep them suspended. 
34

Table A-2. Rise velocities for droplets of varying sizes from GNOME

With the bulk of the mass in droplets larger than 1000 μm, most of the oil droplets will reach the surface within about 

12 hours.

Droplet size

(μm) 

Rise velocity

(m/sec)

Rise time from a

300 m release

(hours)

Rise time from a

180 m release

(hours)

100 0.000468 178.0627 106.8376

200 0.00187 44.5633 26.7380

300 0.00237 35.1617 21.0970

400 0.00292 28.5388 17.1233

500 0.00353 23.6072 14.1643

1000 0.00737 11.3071 6.7843

1500 0.0124 6.7204 4.0323

2000 0.0186 4.4803 2.6882

3000 0.0336 2.4802 1.4881

4000 0.0513 1.6244 0.9747

5000 0.0704 1.1837 0.7102

As discussed in Section A.3.2, the DSD is a function of the oil properties and the level of turbulence of 

the release. For the plume trapping scenario with the reduced spill rate and orifice, the median droplet size 

was estimated to be 1.8 mm, with the resulting distribution shown in Figure A-11. Referring back to 

Table A-2, a median droplet leaving the plume at the trap height would take approximately 3 hours to 

reach the surface.

Figure A-11. Estimated droplet size distribution for a moderate discharge

Median (D50) droplet size is 1.8 mm (1,800 μm). Note that most of the mass is in droplets above about 500 μm.

A.3.5 Subsurface Transport Modeling

For a moderate subsurface release that results in a plume that is trapped in the mid-water column, the 

trajectory of the oil exiting the plume can be modeled with a 3D oil transport model. For this study, the 
35

GNOME model was used; it simulates the oil transport by simulating a range of droplet size classes, and 

computes the rise of the droplets as a function of oil properties and the droplet size (Galt and Overstreet 

2014). The simulated droplets rise through the water column, while being transported by the 3D currents 

provided by the ROMS model, as well as being mixed and diluted by diffusion in both the vertical and 

horizontal directions. When a given class of droplets reach the surface, they are then also advected by the 

near surface winds and currents, just as a surface release would be.

For this simulation, the oil was released at a rate of 2000 bbl/day into the water column at the location of 

the Heritage Platform, and a water depth of 180 m below sea level, where the plume is trapped and no 

longer controlling the transport of the oil. The oil is released as droplets from a DSD with a D50 of 1.8 mm 

and the distribution described above. 

Figure A-12 shows the oil horizontal locations after a 4 day trajectory run with a continuous release. The 

predicted oil location reflects the strong westward flow often seen in the Santa Barbara Channel in winter 

months. Note that this plot only displays the horizontal location of the modeled oil - the oil depth is not 

shown. Figure A-13 presents a 3D view of the trajectory. Since a continuous release was simulated, oil 

droplets are still seen to be rising through the water column, generally within approximately 10 km of the 

source. Further away from the source, the number of droplets in the water column is dwarfed by those at 

the surface. In total, approximately 95% of the oil is at the surface after 4 days of model simulation. 

Figure A-12. Trajectory results for the moderate subsurface release

The image shows predicted oil horizontal positions 4 days after release from a continuous blowout spill of 

2000 bbl/day. The color represents depth - light blue is on the surface, medium blue is shallower than 50 m (in the 

mixed layer) and dark blue is between 50 and 180 m deep.
36

.

Figure A-13. Trajectory results for the moderate subsurface release

The image shows predicted oil positions three-dimensionally, 4 days after release from a continuous blowout spill of 

2000 bbl/day at 180 m depth. Colors correspond to oil depth.

Maximum subsurface concentrations of oil are shown in Figure A-14, as a function of longitude and 

depth. Again, the higher subsurface values are seen to be close to the source, as the oil rises to the surface. 

Subsurface values above 0.2 parts per million (ppm) are only seen within 10 km of the source. The color 

scale for this plot is selected to focus on the subsurface, meaning the values in the surface are beyond the 

limits of the color. The higher values in the surface layer close to the source are up to 4.0 ppm. 
37

Figure A-14. Maximum concentration of oil as a function of longitude and depth, looking from the 

south

The color limits are set to focus on the subsurface concentrations, meaning the surface colors are all above 1 ppm.

This 3D test case was designed to examine whether the surface-only runs used in the TAP development 

for the Southern California Bight region would reflect the fate of oil released from a sub-surface incident. 

The release time was chosen to be during a time when horizontal velocities were at a maximum, meaning 

that the horizontal transport while droplets rose through the water column would be maximized. In most 

cases this horizontal transport will be less, further limiting the surface expression (the area where oil 

comes to the surface) of the spill.

A.4 Conclusions 

These results confirm that from a planning perspective, focusing on surface transport and statistics 

resulting from those trajectories may be sufficient for scaling the extent of regional impacts. In the 

scenarios modeled, the bulk of the oil reached the surface fairly rapidly (within hours). Below the mixed 

layer, oil concentrations fell to <0.1 ppm within 10 km from the source location.

While the surface focused TAP modeling results may be sufficient for scaling regional impacts in terms 

of illustrating the likely extent of surface oil footprints and threatened shorelines, there are response 

questions that could only be examined through full 3D modeling simulations. For example, subsurface 

dispersant application could significantly alter the droplet size distribution and allow more oil to remain 

in the water column. Although undertaking a full 3D statistical analysis may be unnecessary for 

answering these planning focused questions, in the event of an actual subsurface spill, a 3D modeling 

approach like that described here would be essential for examining many response related questions. 
38

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capability. US Department of the Interior, Bureau of Ocean Energy Management. 83 p. OCS Study 

BOEM 2017-055. https://espis.boem.gov/final%20reports/5636.pdf

Galt JA, Overstreet R. 2014. Development of spreading algorithms for the ROC. Edmonds, WA: Genwest 

Systems, Inc. Technical Report. 

Johansen Ø, Brandvik PJ, Farooq U. 2013. Droplet breakup in subsea oil releases – Part 2: Predictions of 

droplet size distributions with and without injection of chemical dispersants. Mar. Pollut. Bull., 73(1): 

327-335. http://dx.doi.org/10.1016/j.marpolbul.2013.04.012

Kessler JD, Valentine DL, Redmond MC, Du M, Chan EW, Mendes SD, Quiroz EW, Villanueva CJ, 

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Calculator (TAMOC): Modeling suite for subsea spills. Proceedings of the 38th AMOP Technical 

Seminar on Environmental Contamination and Response. 153-168. 

Socolofsky SA, Adams EE, Paris CB, Yang D. 2016. How do oil, gas, and water interact near a subsea 

blowout? Oceanography 29(3):64-75. http://dx.doi.org/10.5670/ oceanog.2016.63

Zelenke B, O'Connor C, Barker C, Beegle-Krause CJ, Eclipse L (Eds.). 2012. General NOAA 

Operational Modeling Environment (GNOME) Technical Documentation. US Department. of 

Commerce, NOAA Technical Memorandum NOS OR&R 40. Seattle, WA: Emergency Response 

Division, NOAA. 105 p. http://response.restoration.noaa.gov/gnome_manual
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