Abstract
The dataset contains the post-processed results of the 2010 Deepwater Horizon oil spill incident at Macondo well in the Gulf of Mexico, as estimated from the simulations using the latest updated version of the oil application of the Connectivity Modeling System (CMS) or oil-CMS. In this version, the specified hydrocarbon pseudo-components are in the same droplet. The post-processing analysis yielded 4-D spatiotemporal data of the daily oil concentrations on a regular horizontal and vertical grid for the 167-day simulation period. CMS has a Lagrangian, particle-tracking framework, computing particle evolution and transport in the ocean interior. CMS simulation start date: April 20, 2010, 0000 UTC, and particles were tracked for 167 days. Oil particles release location: 28.736N, 88.365W, depth is 1222m or 300m above the oil well. 3000 particles were released every 2 hours, for 87 days, equivalent to total of 3132000 oil particles released during the simulation. Initial particle sizes were determined at random by the CMS in the range of 1-500 micron. Each particle contained three (3) pseudo-components accounting for the differential oil density as follows: 10% of light oil with the density of 800kg/m^3, 75% of the oil with 840 kg/m^3, and 15% of a heavy oil with 950 kg/m^3 density. The half-life decay rates of oil fractions were 30 days, 40 days, and 180 days, respectively. Ocean hydrodynamic forcing for the CMS model was used from the HYbrid Coordinate Ocean Model (HYCOM) for the Gulf of Mexico region on a 0.04-deg. horizontal grid and 40 vertical levels from the surface to 5500m. It provided daily average 3-D momentum, temperature and salinity forcing fields to the CMS model. The surface wind drift was also accounted for as follows. Wind stress components were used from the 0.5-degree Navy Operational Global Atmospheric Prediction System (NOGAPS), and 3% of their values were added to the top level ocean velocity components, considering the wind stress rotation. The transport and evolution of the oil particles were tracked by the oil-CMS model during the 167 days of the simulation, recording each particle’s horizontal position, depth, diameter, and density into the model output every 2 hours. Model data need to be post-processed to obtain oil concentrations estimates. The post-processing algorithm took into the account the total amount of oil spilled during the 87-day incident as estimated from the reports (730000 tons), and the assumptions about the oil particle size distribution at the time of the release as estimated in the prior studies. The current dataset assumes the oil was not treated with the chemical dispersants, and the modal peak in initial particle distribution is between 50-70 micron. The data for the oil concentrations are daily average values in ppb units. Horizontal 0.01-degree grid covers the Gulf of Mexico (25N-30.75N, 84W-93W), and vertical grid extends from the surface to the depth of 2400m at 20m increments. Daily oil concentrations are also estimated for the following vertical layers: 0-1m, 1-20m, 20-50m, 50-200m, and 200-200m; a separate file is for the layer of 0-1m and for the 0-20m layer; a separate file for the 0-1m surface layer concentrations is also provided for a greater region extending east of the Florida Peninsula (79W). Post-processed NetCDF files were created using Matlab software package, v. R2014b, and used compression to keep file size small. Maximum compression, or ‘DeflateLevel’ = 9 was used in most of the files. Numerical simulations and post-processing were performed using a Pegasus supercomputer at the Center of Computational Science, University of Miami, during the period of 2016-2017.
Purpose
Provide state-of-the-art modeling data of the far-field hind-cast simulations of the 2010 Deepwater Horizon oil spill, taking into the account surface wind drift. To be used for comparative analysis with the "control" case with untreated oil, with the observations, with other validation techniques, as well as with other modeling efforts. Comparative analysis with the "control" case could help to advance our understanding of the surface oil transport, and to improve modeling techniques in numerical models.
Keywords
oil modeling, Deepwater Horizon 2010 oil spill modeling, spatial oil distribution, deep sea oil distribution, Connectivity Modeling System, daily oil concentrations, subsurface hydrocarbon transport, far-field oil transport modeling, Macondo Well
UDI
R4.x267.000:0038
Date
July 2017
Point of Contact
Name
Claire B. Paris-Limouzy
Organization
University of Miami / Rosenstiel School of Marine and Atmospheric Science
Funding Source
RFP-4
DOI
10.7266/N7G44NQX
Rights Information
This work is licensed under a
Creative Commons Public Domain Dedication 1.0 License.
Scholar Commons Citation
Natalie Perlin, Claire B. Paris-Limouzy. 2017. Distribution of oil concentrations in the Gulf of Mexico estimated from the Connectivity Modeling System simulation of the Deepwater Horizon 2010 oil spill; with wind drift. Distributed by: Gulf of Mexico Research Initiative Information and Data Cooperative (GRIIDC), Harte Research Institute, Texas A&M University–Corpus Christi. doi:10.7266/N7G44NQX
Comments
Supplemental Information
Time (days or seconds), Latitude (degrees north), Longitude (degrees east), Depth (m), Oil concentrations (ppb), Horizontally-cumulative oil mass (kg) |The output from the updated oil-CMS model contains the 3-D location of oil particles, their size and density. It is further processed in order to obtain the oil concentrations (or oil mass) in the time-space. The information used for that purpose is the following i) the total oil released during the case study, estimated as 730000 tons of crude oil; ii) the total number of particles released, 3132000 particles; iii) adopted droplet size distribution (DSD) at the time of release, for the untreated oil with no dispersants added. Knowing the i) and ii), yields the amount of oil of 233kg of oil that each released particle represents under the assumption of uniform distribution from 1-500 micron. A single particle therefore represents different amount of oil mass in different modal distributions. Here for untreated oil, i.e. not treated with chemical dispersants, we used the DSD with a modal peak between 50-70 microns and a range of 1-500 micron, following Li et al. (2008). The DSD is approximated with a binned distribution and particles falling within each individual bin are assumed to represent the same amount of oil for that bin; this value would vary for each bin. The post-processing method of computing the daily average oil concentrations includes the following: 1) estimate the mass of oil represented by each bin of the modal distribution; 2) compute the scaling factor for each particle at the initial time depending on particle mass; 3) multiply the evolving particle mass by the initial scaling factor unique for each particle; 4) sum the mass of all the particles found in a given 3-D grid box at a given time (2h intervals); 5) determine the oil concentration from the mass of oil in the volume of a ocean grid box, take into the account the bathymetry for coastal areas; 6) Compute daily average oil concentrations from 2-h concentration estimates.|1. Numerical model used: updated versions of the open-source Connectivity Modeling System (CMS v.2.0) and of the oil module (not open-source) where the multi-fraction algorithm represents all the hydrocarbon compounds within a single droplet. 2. Matlab software version R2014b was used for post-processing analysis and data preparation. 3. All numerical work has been done using a Pegasus supercomputer at the Center of Computational Science, University of Miami. 4. The dataset files are in NetCDF format, with *.nc extension; the headers of the *.nc files are also duplicated in text files with the corresponding names and *.txt extension. All the files have been archived in a *.zip file.|Time scale for the oil concentrations: daily averages Horizontal grid: 0.01-degree spacing Vertical grid: 0-2400m with 20-m increments Vertical grid alternative: vertical layers as specified by layer boundaries||References for the CMS model: Paris C.B., J. Helgers, E. van Sebille, and A. Srinivasan, 2013: Connectivity Modeling System: A probabilistic modeling tool for the multi-scale tracking of biotic and abiotic variability in the ocean. Environmental Modelling & Software, 42, 47-54. Reference for the previous version of oil-CMS model: Paris., C.B., M. Le Hénaff, Z.M. Aman, A. Subramaniam, J. Helgers, D.-P. Wang, V.H. Kourafalou, and A. Srinivasan, 2012: Evolution of the Macondo Well Blowout: Simulating the Effects of the Circulation and Synthetic Dispersants on the Subsea Oil Transport. Environ. Sci. Technol., 46, 13293−13302. http://dx.doi.org/10.1021/es303197h Reference for the previous study of the wind drift effect on oil transport: Le Hénaff, M., V.H. Kourafalou, C.B.Paris, J. Helgers, Z.M. Aman, P.J. Hogan, A. Srinivasan, 2012. Surface evolution of the deepwater horizon oil spill patch:combined effects of circulation and wind-induced drift. Environ. Sci. Technol. 7267−7273. http://dx.doi.org/10.1021/es301570w Reference for droplet size distribution (DSD) analysis study: Li, Z., K. Lee, T. King, M.C. Boufadel, A.D.Venosa, 2008: Oil droplet size distribution as a function of energy dissipation rate in an experimental wave tank. Conference: International Oil Spill Conference, DOI: 10.7901/2169-3358-2008-1-621.