Abstract
The explosion of the Deepwater Horizon (DWH) oil drilling rig on April 20 of 2010 in the Gulf of Mexico resulted in an uncontrolled oil spill from 5000 feet below the sea surface, requiring an extraordinarily rapid response. A decade ago, the technology to model oil spills from the deep ocean did not exist. A few oil models are now capable of forecasting the 3D transport and fate of the oil from such blowout. An important limitation in estimating the fate of the oil is, still today, the lack of spatially explicit photooxidation model parameterization. Weathering of oil near the sea surface depends on the physicochemical and biological characteristics of the petroleum hydrocarbons and the environment. While most fate processes, such as evaporation, dissolution, and biodegradation are well understood and typically accounted for, the effects of sunlight on photooxidation of oil are not fully modeled. During the DWH blowout, photooxidation of surface oil led to the formation of persistent oxidized compounds, still found in shoreline sediments. Additionally, studies demonstrated that photooxidation modified both biodegradation rates of the surface oil as well as the effectiveness of aerial dispersant applications. Despite the significant consequences of this weathering pathway, it was not considered in DWH oil budget calculations nor in predictive models. Here we develop a Lagrangian photooxidation module that estimates the dose of solar radiation individual oil droplets receive while moving in the ocean, and quantifies changes in photooxidation. The dose of incoming solar radiation is computed with the intensity of the surface irradiance from the earth system model NOGAPS (Navy Operational Global Atmospheric Prediction System), the coefficient of absorption (Kd) of specific wavelengths from the literature, and the depth of the oil droplets from the Connectivity Modeling System (CMS). Coupling the net shortwave radiation from NOGAPS to CMS, we estimate the photooxidation rates in oil droplets for the DWH case. Our new photooxidation module can be used to test hypotheses of oil fate during the DWH, and inform rapid response in future oil spills. This dataset supports the publication: Vaz, Ana C., Robin Faillettaz, and Claire B. Paris. (2021). A Coupled Lagrangian-Earth System Model for Predicting Oil Photooxidation. Frontiers in Marine Science, 8. E576747. doi:10.3389/fmars.2021.576747
Purpose
Our new photo-oxidation module integrates spatially-temporally explicit solar irradiation from Earth Systems models to estimate changes in individual droplets composition.
Keywords
photooxidation, weathering, solar irradiance, Droplet size distribution (DSD), Oil spill model, Connectivity Modeling System (CMS), Oil Photooxidation, oil-CMS, Earth System Model
UDI
R6.x805.000:0110
Date
July 2023
Point of Contact
Name
Claire B. Paris-Limouzy
Organization
University of Miami / Rosenstiel School of Marine and Atmospheric Science
Name
Ana Vaz
Organization
University of Miami / Rosenstiel School of Marine and Atmospheric Science
Funding Source
RFP-6
DOI
10.7266/ADGH8VGH
Rights Information
This work is licensed under a
Creative Commons Public Domain Dedication 1.0 License.
Scholar Commons Citation
Paris-Limouzy, Claire B., Ana C. Vaz, and Robin Faillettaz. 2023. Dataset for: A Coupled Lagrangian-Earth System Model for Predicting Oil Photooxidation. Distributed by: Gulf of Mexico Research Initiative Information and Data Cooperative (GRIIDC), Harte Research Institute, Texas A&M University–Corpus Christi. doi:10.7266/ADGH8VGH
Comments
Supplemental Information
Oil particles are stored in text files with 11 columns representing the following parameters and units: #1 Release line ID: Number corresponding to the line containing the release information for this droplet release. #2 Droplet ID: Droplets within each release are sequentially numbered from 1 to 3000 (number of droplets for each release). Taken together, #1 and #2 create unique droplet IDs for each droplet. #3 Runtime: Time droplets dispersed from their release (seconds) #4 Longitude: Longitude of droplets at each writing time step (every 2 hours) #5 Latitude: Latitude of droplets at each writing time step (every 2 hours) #6 Depth: Depth of droplets (m) at each writing time step (every 2 hours) #7 Status: Status of droplet: 0 moving, -2 beached, -3 evaporated, -5 out of domain, -6 dissolved #8 Release Date: Date of droplet release in Julian days #9 Density: Density (kg/m3) of the droplet at current time step #10 Diameter: Diameter (m) of droplet at current time step #11 Irradiance: Incident solar irradiance (W/m2) for the last two hours (since last writing time step)|In our study, the far-field modeling is done using an integrated oil spill application developed during and post DWH in the Connectivity Modeling System (CMS), the oil-CMS (Paris et al., 2012). To account for the photooxidation in oil-CMS, we calculated the incident solar irradiance received by each droplet and time-step, following the method described in Faillettaz et al., 2021. The environmental variables (horizontal and vertical velocity, temperature, and salinity profiles) used to disperse and calculate droplet fate were obtained from the Gulf of Mexico HYCOM (GoM-HYCOM) hindcast (0.04-degree horizontal resolution, and 20 vertical layers). 3000 droplets are released every 2 hours at trap height (1222m) during the 87 days of the Deepwater Horizon spill. The initial droplet size distribution (DSD) follows a log-normal distribution with a mean of 117 µm and a standard deviation of 0.72 µm. For the estimation of photo-oxidation, we used a conservative attenuation coefficient k=5, which attenuates UV radiation to 0.7% of surface irradiance at 1m depth. For more detailed methods and modeling scenarios, please refer to the associated publication Vaz et al., 2021.|Model: Oil application of Connectivity Modeling System (Paris et al., 2013, Perlin et al., 2020, Faillettaz et al., 2021).|||Faillettaz, R., Paris, C. B., Vaz, A. C., Perlin, N., Aman, Z. M., Schlüter, M., & Murawski, S. A. (2021). The choice of droplet size probability distribution function for oil spill modeling is not trivial. Marine Pollution Bulletin, 163, 111920. doi:10.1016/j.marpolbul.2020.111920 Paris, C. B., Helgers, J., van Sebille, E., and Srinivasan, A. (2013). Connectivity Modeling System: a probabilistic modeling tool for the multi-scale tracking of biotic and abiotic variability in the ocean. Environ. Model. Softw. 42, 47–54. doi: 10.1016/j.envsoft.2012.12.006 Paris., C.B., Hénaff, M.L., Aman, Z.M., Subramaniam, A., Helgers, J., Wang, D.P., Kourafalou, V.H. and Srinivasan, A. (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(24), 13293−13302. doi: 10.1021/es303197h 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. Facts, Fate, and Effects. Springer, Cham. doi:10.1007/978-3-030-11605-7_11