Abstract

The dataset contains the numerical 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 oil concentrations and oil mass on a regular horizontal and vertical grid, as well as time evolution of the horizontally-cumulative oil mass, all of the data 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. The surface evaporation half-life was set to 250 hours; horizontal diffusion was set to 10 m^2/s in the present case. 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 parameterization used surface winds and wind stressed from the 0.5-degree Navy Operational Global Atmospheric Prediction System (NOGAPS). 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; the oil mass units are kg of crude oil. Horizontal 0.02-degree grid covers the entire Gulf of Mexico domain and beyond (18.0N-30.8N, 96.0W-77.0W), and vertical grid extends from the surface to the depth of 2500m at 20m increments, except for the top two layers which are 0-1m and 1-20m. Layer-averaged daily oil concentrations are also given for the following vertical layers: 0-1m, 1-20m, 0-20m, 20-400m, 400-1000m, 1000-1200m, and below 1200m. The data for horizontally-cumulative oil mass are in units of kg of crude oil, distributed in the water column on a vertical grid from the surface down to 2500m at 20m increments, and estimated bi-hourly corresponding to the oil-CMS model output interval. Post-processed NetCDF files were created using Matlab software package, v. R2016b. The data files with the 3-D data used compression capability of the NetCDF to keep file size small; maximum compression, or ‘DeflateLevel’ = 9 was used. Numerical simulations and post-processing were performed using a Pegasus supercomputer at the Center of Computational Science, University of Miami, in 2017.

Comments

Supplemental Information

Seconds passed after blowout (seconds), Latitude (degrees north), Longitude (degrees east), Depth (m), Oil mass (kg), Days after initial blowout (days), Oil concentration (ppb)|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.The computations of the oil mass are similar to the above, except item 5.|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 R2016b 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 3D oil concentrations: daily averages . Time scale for the 3D oil mass: daily averages. Horizontal grid: 0.02-degree spacing. Vertical grid: 0-2500m with 20-m increments, except the top two layers bounded as follows: 0-1m and 1-20m. Layer-average data: vertical layers as specified by layer boundaries Time scale for horizontally-cumulative oil mass: 2-h intervals||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. https://doi.org/10.1016/j.envsoft.2012.12.006 Reference for the previous version of the 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. https://doi.org/10.1021/es303197h 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 Proceedings, 2008(1), 621–626. . https://doi.org/10.7901/2169-3358-2008-1-621 Dataset update: This dataset was initially published on 2018-02-27 and was updated again on 2020-09-23 to better reflect the publication in which they were used, Perlin N., C.B. Paris, I.Berenshtein, A.C. Vaz, R. Faillettaz, Z.M. Aman, P.T. Schwing, I.C. Romero, M. Schlüter, A. Liese, N. Noirungsee, and S. Hackbusch. (2020): Far-Field Modeling of a Deep-Sea Blowout: Sensitivity Studies of Initial Conditions, Biodegradation, Sedimentation, and Subsurface Dispersant Injection on Surface Slicks and Oil Plume Concentrations. In: Murawski S. et al. (eds) Deep Oil Spills. Springer, Cham. https://doi.org/10.1007/978-3-030-11605-7_11. The newly added data are in the folder "DWH_CMS_sensitivity_added" and includes sensitivity study results investigating the effects of using the VDROPJ model droplet size distribution (DSD), different atmospheric forcing (same grid but fall season forcing), a differently-scaled DSD representing treatment by subsurface dispersant injection, temperature-dependent biodegradation, and prohibition of bottom sedimentation except in very shallow beach areas. Gridded distributions and trajectory summaries of individual particles and a detailed ReadMe document with the description of data are included.

Purpose

To provide state-of-the-art modeling data of the far-field hindcast simulations of the 2010 Deepwater Horizon oil spill, serving as a base ("control") case for untreated oil simulation. To be used for comparative analysis with the observations, other validation techniques, as well as with other modeling efforts in sensitivity studies. To be used as a spatiotemporal input data for ecosystem modeling applications.

Keywords

Oil modeling, Far-field oil modeling, Deepwater Horizon 2010 oil spill modeling, spatial and temporal oil distribution, deep sea oil distribution, Connectivity Modeling System, daily oil concentrations, subsurface hydrocarbon transport, Deepwater Horizon 2010 oil spill, Macondo Well

UDI

R4.x267.000:0084

Date

February 2018

Point of Contact

Name

Natalie Perlin

Organization

University of Miami / Center for Computational Science

Name

Claire B. Paris-Limouzy

Organization

University of Miami / Rosenstiel School of Marine and Atmospheric Science

Funding Source

RFP-4

DOI

10.7266/N7KD1WDB

Rights Information

Creative Commons License
This work is licensed under a Creative Commons Public Domain Dedication 1.0 License.

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