Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1 -- 1560 2023-06-05 10:00:41 |
2 format correct -36 word(s) 1524 2023-06-05 10:12:40 |

Video Upload Options

Do you have a full video?

Confirm

Are you sure to Delete?
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Keramea, P.; Kokkos, N.; Zodiatis, G.; Sylaios, G. The Oil Spill Models. Encyclopedia. Available online: https://encyclopedia.pub/entry/45182 (accessed on 15 June 2024).
Keramea P, Kokkos N, Zodiatis G, Sylaios G. The Oil Spill Models. Encyclopedia. Available at: https://encyclopedia.pub/entry/45182. Accessed June 15, 2024.
Keramea, Panagiota, Nikolaos Kokkos, George Zodiatis, Georgios Sylaios. "The Oil Spill Models" Encyclopedia, https://encyclopedia.pub/entry/45182 (accessed June 15, 2024).
Keramea, P., Kokkos, N., Zodiatis, G., & Sylaios, G. (2023, June 05). The Oil Spill Models. In Encyclopedia. https://encyclopedia.pub/entry/45182
Keramea, Panagiota, et al. "The Oil Spill Models." Encyclopedia. Web. 05 June, 2023.
The Oil Spill Models
Edit

Oil spills may have devastating effects on marine ecosystems, public health, the economy, and coastal communities. To predict in near real time oil spill transport and fate with increased reliability, these models are usually coupled operationally to synoptic meteorological, hydrodynamic, and wave models. 

oil spill modeling meteorological and hydrodynamic forcing wave models met-ocean data forecasting biogeochemical models

1. Introduction

When crude oil is accidentally released in the marine environment, an oil slick is formed appearing as a thin oily layer, floating on the sea surface [1]. The slick is shaped by the slow, low-scale, and diffusive processes, responsible for changing the contaminants’ concentration, and is transported by the large-scale advective processes, advancing the center of the oil-slick mass to the direction of background currents, winds, and waves [2]. This implies that the ambient environment of the spill significantly determines its movement and fate. The amount of oil spilled in the ocean, the oil’s initial physicochemical properties, the prevailing weather and sea state conditions, and other spill-specific and environmental factors affect the timing, duration, and relative importance of each physical and biochemical oil-weathering process (known as OWP), affecting the slick [3][4][5]. Since hydrocarbons are nonconservative pollutants, OWPs cause longterm changes in their physicochemical properties, such as oil density and viscosity [6]. The most important OWPs are spreading, evaporation, dispersion/diffusion, emulsification, and dissolution. Photooxidation, biodegradation, and sedimentation act over longer time periods and determine oil’s ultimate behavior [7].
Planning for and responding to an oil spill requires rigorous comprehension of baseline environmental characteristics and processes [8]. Oil-spill models help the response agencies lessen the damaging impacts on the environment by predicting the path of at-sea oil spills. Predicting the spillage trajectory is the main outcome of oil-spill models, highlighting the potential for ecosystem harm as an incident develops, while, in parallel, assisting in the optimization of the cleanup efforts [9][10][11]. Any guidance that oil-spill modeling may offer could be extremely important for the authorities, given the tremendous effect and costs associated with oil spills. Risk evaluation, readiness planning and analysis of the environmental effects of the oil industry infrastructure, heavily rely on oil spill modeling [12]. When models are run, a wide range of input variables and actual met-ocean conditions might result in multiple alternative trajectories [13]. Following analysis, these trajectories are plotted on maps to create reaction strategies. Emergency responders must be knowledgeable about the type of oil, the location, and the marine and coastal habitats the spillage may affect. Thus, governments, oil exploration and production firms, insurance companies, and other stakeholders may evaluate whether the adequate resources, tools, and procedures are in place to respond to oil spill incidents. Simulating different scenarios may allow for assessing the potential environmental impacts and device plans on the movement of the response supplies to the necessary locations [14][15]. This procedure could lead to the assessment of the efficacy of various response strategies, as well as their benefits and drawbacks [10][12]. Additionally, it is expected to aid responders to organize and mobilize socioeconomic resources to limit environmental impacts along the oil’s potential course [8].
As explained above, met-ocean conditions, i.e., currents, wind, and waves, represent the fundamental components influencing the spreading of oil in the marine environment [7][16][17][18]. For this reason, it is crucial to be able to illustrate that oil-spill forecasts are accurate and reliable, as well as that the constraints of a model are well-understood when evaluating the model’s predicting capacity and performance [19][20]. An assessment of the ocean currents, water characteristics over the water column, and waves at a particular time and location is provided by the three-dimensional ocean-circulation models [21]. These models aid in determining how these factors will affect the transport of oil once it reaches the sea surface. Meteorological and atmospheric models provide information on air properties such as temperature, relative humidity, and barometric pressure, as well as on the surface winds that might transport and affect the evaporation rate of floating oil [22]. In parallel, wave models provide information about the significant wave height and Stokes drift fields, affecting wave turbulence, vertical mixing, and oil dispersion within the water column [23]. Furthermore, once the oil is discharged into the marine environment, the chemical and physical changes it will undergo could be predicted by the fate models [17][24].

2. Seven State-of-the-Art Oil-Spill Models

Seven state-of-the-art oil-spill models, namely: OpenOil [23][25], MEDSLIK [18][26][27], MEDSLIK-II [28][29], SIMAP [30][31], GNOME [32][33][34], BLOSOM [35][36][37], and STFM [38][39] are examined in terms of their meteorological, hydrodynamic, wave, and biogeochemical forcing in twenty-three oil accidental release cases studied worldwide. An analytical description and comparison of these oil-spill models in terms of their characteristics, capabilities, and simulated processes is presented in the comprehensive review of Keramea et al. [7].
OpenOil is a newly-developed, open-source oil-spill transport and fate model [40], part of the Python-based trajectory framework of OpenDrift [25]. To reach operational oil-spill forecasts with OpenOil, MET Norway employs in-house, high-resolution ocean-circulation, and meteorologic models [23]. However, the model allows the coupling with the coarser resolution forecasts from CMEMS (Copernicus Marine Environmental Service), FVCOM, SHYFEM, CYCOFOS, HYCOM, Norshelf for hydrodynamics and ocean state, and NOAA, ECMWF, and SKIRON wind fields with netCDF and many different files format. The OpenOil has been applied in several cases worldwide, such as the Norwegian Sea [23], the Gulf of Mexico and the Cuban coast [41][42][43][44], the Thracian Sea [45], and the Caribbean Sea [46].
MEDSLIK-II [28][29] is a version of the MEDSLIK oil-spill model [18][26]. MEDSLIK-II uses the experimental JONSWAP wave spectrum in terms of wind speed and fetch for the Stokes drift parameterization [47], while MEDSLIK directly uses the wave height and period to estimate the Stokes drift. MEDSLIK-II is also coupled in terms of input format with the forecasted atmospheric fields provided by the European Center for Medium-Range Weather Forecasts (ECMWF) and the oceanographic fields provided by CMEMS (currents, temperature, salinity, and density), while MEDSLIK is coupled in addition to the CMEMS with the downscaled CYCOFOS sea currents and the SKIRON winds. MEDSLIK-II has been implemented in many case studies in recent years, such as the Northern Atlantic [48], the Northwestern Mediterranean Sea [49], the Aegean Sea [50], the offshore of Southern Italy [51], and the Brazilian coast [52]. In addition, MEDSLIK has been implemented in real oil spill incidents in the Eastern Mediterranean Levantine basin [53] and in numerous test cases in the Levantine basin [54][55][56][57], in the Black Sea [58], and in the Red Sea [59].
SIMAP, the integrated oil-spill impact oil system, developed by ASA [30][31] simulates the three-dimensional trajectory, fate, and transport of spilled oil and fuels, as well as the biological effects and other impacts [30]. SIMAP has been validated against data from over 20 major spills, including the Exxon Valdez [30]. The analytical description of the SIMAP oil trajectory and fate model is presented in McCay [30][60]. Wind-driven wave drift (i.e., Stokes drift) and Ekman transport at the surface can be modeled, based on the results of Stokes drift and the Ekman transport formula produced by Youssef and Spaulding [61]. Moreover, the model has the capability to couple with three-dimensional hydrodynamic models (HYCOM (3–4 km), POM (10 km), and SABGOM (5 km)) and with wind data from NOAA and ECMWF [62]. Currently, SIMAP has been implemented in the Gulf of Mexico [60][62].
GNOME, the general NOAA operational modeling environment, is an oil spill model that forecasts the fate and transport of pollutants, as well as the movement of oil due to winds, currents, tides, and spreading [32][33]. Furthermore, this model is highly configurable and tunable to field conditions and it can be driven by a variety of data: measured point data, meteorologic, and hydrodynamic models with a variety of meshes (structured and triangular) (NOAA, ECMWF, CMEMS, POM, CROCO, RTOFS, GLB-HYCOM, FVCOM, and Salish Sea model). Since GNOME can integrate any ocean-circulation and meteorologic model that supports forecasts in various file formats, as well as observational data, NOAA has created the GNOME Operational Oceanographic Data Server (https://gnome.orr.noaa.gov/goods, accessed on 10 April 2023), a publicly accessible system that provides access to all available driver models and data sources. Moreover, GNOME has been applied in many regions over the latest years, such as Indonesia [63], the Gulf of Suez in Egypt [64], offshore Odisha in India [65], and the Red Sea in Egypt [66].
BLOSOM, the blowout and spill occurrence model has been generated by the National Energy Technology Laboratory (NETL) of the USA (https://edx.netl.doe.gov/offshore/blosom/, accessed on 10 April 2023) [35][37][67]. The model may be coupled to wind and current data from different models (Salish Sea model, FVCOM, NOAA, and NCOM AMSEAS) with multiple flexible file formats and output types. Finally, it incorporates a number of oil types from the ADIOS oil library [68]. Recent, the BLOSOM has been applied in the Gulf of Paria in Venezuela [69].
Finally, the STFM (Spill, Transport, and Fate Model), created by the Institute of Astronomy, Geophysics and Atmospheric Sciences, University of Sao Paulo (IAG/USP) of Brazil, is a transport and weathering model of spilled oil based on Lagrangian elements for operation in marine and environmental fields [38][39]. Moreover, STFM is a fully three-dimensional model that uses the Weather Research and Forecasting (WRF) atmospheric model and the hydrodynamic Hybrid Coordinate Ocean Model (HYCOM), feeding the oil-spill module with current speed and direction, water temperature, salinity and bathymetry data. In addition, it has the capability to couple with the ADIOS oil database. It has been recently applied on the Brazilian coast by Zacharias et al. [39].

References

  1. Crain, C.M.; Halpern, B.S.; Beck, M.W.; Kappel, C.V. Understanding and Managing Human Threats to the Coastal Marine Environment. Ann. N. Y. Acad. Sci. 2009, 1162, 39–62.
  2. Walker, A.H.; Pavia, R.; Bostrom, A.; Leschine, T.M.; Starbird, K. Communication Practices for Oil Spills: Stakeholder Engagement During Preparedness and Response. Hum. Ecol. Risk Assess. 2015, 21, 667–690.
  3. Fingas, M.; Brown, C. Review of oil spill remote sensing. Mar. Pollut. Bull. 2014, 83, 9–23.
  4. Azevedo, A.; Oliveira, A.; Fortunato, A.B.; Zhang, J.; Baptista, A.M. A cross-scale numerical modeling system for management support of oil spill accidents. Mar. Pollut. Bull. 2014, 80, 132–147.
  5. Zafirakou, A. Oil Spill Dispersion Forecasting Models. In Monitoring of Marine Pollution; IntechOpen: London, UK, 2019.
  6. Mishra, A.K.; Kumar, G.S. Weathering of Oil Spill: Modeling and Analysis. Aquat. Procedia 2015, 4, 435–442.
  7. Keramea, P.; Spanoudaki, K.; Zodiatis, G.; Gikas, G.; Sylaios, G. Oil Spill Modeling: A Critical Review on Current Trends, Perspectives, and Challenges. J. Mar. Sci. Eng. 2021, 9, 181.
  8. Webler, T.; Lord, F. Planning for the Human Dimensions of Oil Spills and Spill Response. Environ. Manag. 2010, 45, 723–738.
  9. Zhong, Z.; You, F. Oil spill response planning with consideration of physicochemical evolution of the oil slick: A multiobjective optimization approach. Comput. Chem. Eng. 2011, 35, 1614–1630.
  10. Davies, A.J.; Hope, M.J. Bayesian inference-based environmental decision support systems for oil spill response strategy selection. Mar. Pollut. Bull. 2015, 96, 87–102.
  11. Grubesic, T.H.; Wei, R.; Nelson, J. Optimizing oil spill cleanup efforts: A tactical approach and evaluation framework. Mar. Pollut. Bull. 2017, 125, 318–329.
  12. Chang, S.E.; Stone, J.; Demes, K.; Piscitelli, M. Consequences of oil spills a review and framework for informing planning. Ecol. Soc. 2014, 19, 25.
  13. Li, C.; Miller, J.; Wang, J.; Koley, S.S.; Katz, J. Size Distribution and Dispersion of Droplets Generated by Impingement of Breaking Waves on Oil Slicks. J. Geophys. Res. Ocean. 2017, 122, 7938–7957.
  14. Wenning, R.J.; Robinson, H.; Bock, M.; Rempel-Hester, M.A.; Gardiner, W. Current practices and knowledge supporting oil spill risk assessment in the Arctic. Mar. Environ. Res. 2018, 141, 289–304.
  15. Barker, C.H.; Kourafalou, V.H.; Beegle-Krause, C.J.; Boufadel, M.; Bourassa, M.A.; Buschang, S.G.; Androulidakis, Y.; Chassignet, E.P.; Dagestad, K.-F.; Danmeier, D.G.; et al. Progress in Operational Modeling in Support of Oil Spill Response. J. Mar. Sci. Eng. 2020, 8, 668.
  16. Zodiatis, G.; Lardner, R.; Alves, T.M.; Krestenitis, Y.; Perivoliotis, L.; Sofianos, S.; Spanoudaki, K. Oil spill forecasting (prediction). J. Mar. Res. 2017, 75, 923–953.
  17. Spaulding, M.L. State of the art review and future directions in oil spill modeling. Mar. Pollut. Bull. 2017, 115, 7–19.
  18. Zodiatis, G.; Lardner, R.; Spanoudaki, K.; Sofianos, S.; Radhakrishnan, H.; Coppini, G.; Liubartseva, S.; Kampanis, N.; Krokos, G.; Hoteit, I.; et al. Chapter 5—Operational Oil Spill Modelling Assessments. In Marine Hydrocarbon Spill Assessments; Makarynskyy, O., Ed.; Elsevier: Amsterdam, The Netherlands, 2021; pp. 145–197.
  19. De Dominicis, M.; Bruciaferri, D.; Gerin, R.; Pinardi, N.; Poulain, P.M.; Garreau, P.; Zodiatis, G.; Perivoliotis, L.; Fazioli, L.; Sorgente, R.; et al. A multi-model assessment of the impact of currents, waves and wind in modelling surface drifters and oil spill. Deep-Sea Res. Part II Top. Stud. Oceanogr. 2016, 133, 21–38.
  20. Dearden, C.; Culmer, T.; Brooke, R. Performance Measures for Validation of Oil Spill Dispersion Models Based on Satellite and Coastal Data. IEEE J. Ocean. Eng. 2022, 47, 126–140.
  21. Pisano, A.; De Dominicis, M.; Biamino, W.; Bignami, F.; Gherardi, S.; Colao, F.; Coppini, G.; Marullo, S.; Sprovieri, M.; Trivero, P.; et al. An oceanographic survey for oil spill monitoring and model forecasting validation using remote sensing and in situ data in the Mediterranean Sea. Deep-Sea Res. Part II Top. Stud. Oceanogr. 2016, 133, 132–145.
  22. Le Hénaff, M.; Kourafalou, V.H. Mississippi waters reaching South Florida reefs under no flood conditions: Synthesis of observing and modeling system findings. Ocean Dyn. 2016, 66, 435–459.
  23. Röhrs, J.; Dagestad, K.F.; Asbjørnsen, H.; Nordam, T.; Skancke, J.; Jones, C.E.; Brekke, C. The effect of vertical mixing on the horizontal drift of oil spills. Ocean Sci. 2018, 14, 1581–1601.
  24. Spaulding, M.L. A state-of-the-art review of oil spill trajectory and fate modeling. Oil Chem. Pollut. 1988, 4, 39–55.
  25. Dagestad, K.F.; Röhrs, J.; Breivik, O.; Ådlandsvik, B. OpenDrift v1.0: A generic framework for trajectory modelling. Geosci. Model Dev. 2018, 11, 1405–1420.
  26. Lardner, R.; Zodiatis, G.; Loizides, L.; Demetropoulos, A. An operational Oil Spill Model for the Levantine Basin (Eastern Mediterranean Sea). In Proceedings of the International Symposium on Marine Pollution, Monaco, Monte-Carlo, 5–6 October 1988; International Atomic Energy Agency (IAEA): Vienna, Austria, 1998; pp. 542–543.
  27. Lardner, R.; Zodiatis, G. MEDSLIK oil spill model recent developments. In Proceedings of the EGU General Assembly, Vienna, Austria, 17–22 April 2016; p. EPSC2016-16240.
  28. De Dominicis, M.; Pinardi, N.; Zodiatis, G.; Lardner, R. MEDSLIK-II, a Lagrangian marine surface oil spill model for short-term forecasting—Part 1: Theory. Geosci. Model Dev. 2013, 6, 1851–1869.
  29. De Dominicis, M.; Pinardi, N.; Zodiatis, G.; Archetti, R. MEDSLIK-II, a Lagrangian marine surface oil spill model for short-term forecasting—Part 2: Numerical simulations and validations. Geosci. Model Dev. 2013, 6, 1871–1888.
  30. McCay, D.F. Development and application of damage assessment modeling: Example assessment for the North Cape oil spill. Mar. Pollut. Bull. 2003, 47, 341–359.
  31. McCay, D.F.; Li, Z.; Horn, M.; Crowley, D.; Spaulding, M.; Mendelsohn, D.; Turner, C. Modeling oil fate and subsurface exposure concentrations from the Deepwater Horizon oil spill. In Proceedings of the 39th AMOP Technical Seminar on Environmental Contamination and Response, Ottawa, ON, Canada, 7–9 June 2016; pp. 115–150.
  32. Beegle-Krause, C.J. General NOAA oil modeling environment (GNOME): A new spill trajectory model. Int. Oil Spill Conf. IOSC 2001, 2001, 3277–3283.
  33. Zelenke, B.; O’Connor, C.; Barker, C.H.; Beegle-Krause, C.J.; Eclipse, L. General NOAA Operational Modeling Environment (GNOME) Technical Documentation. 2012. Available online: https://repository.library.noaa.gov/view/noaa/2620 (accessed on 10 February 2023).
  34. Duran, R.; Romeo, L.; Whiting, J.; Vielma, J.; Rose, K.; Bunn, A.; Bauer, J. Simulation of the 2003 Foss Barge—Point Wells Oil Spill: A Comparison between BLOSOM and GNOME Oil Spill Models. J. Mar. Sci. Eng. 2018, 6, 104.
  35. Nelson, J.R.; Grubesic, T.H.; Sim, L.; Rose, K.; Graham, J. Approach for assessing coastal vulnerability to oil spills for prevention and readiness using GIS and the Blowout and Spill Occurrence Model. Ocean Coast. Manag. 2015, 112, 1–11.
  36. Sim, L.H. Blowout and Spill Occrrence Model. 2013, pp. 1–90. Available online: https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/mp48sh78g (accessed on 10 February 2023).
  37. Murray, K.J.; Boehm, P.D.; Prince, R.C. The Importance of Understanding Transport and Degradation of Oil and Gasses from Deep-Sea Blowouts. In Deep Oil Spills; Murawski, S.A., Ainsworth, C.H., Gilbert, S., Hollander, D.J., Paris, C.B., Schlüter, M., Wetzel, D.L., Eds.; Springer International Publishing: Cham, Switzerland, 2020; pp. 86–106.
  38. Zacharias, D.C.; Rezende, K.F.O.; Fornaro, A. Offshore petroleum pollution compared numerically via algorithm tests and computation solutions. Ocean Eng. 2018, 151, 191–198.
  39. Zacharias, D.C.; Gama, C.M.; Harari, J.; da Rocha, R.P.; Fornaro, A. Mysterious oil spill on the Brazilian coast—Part 2: A probabilistic approach to fill gaps of uncertainties. Mar. Pollut. Bull. 2021, 173, 113085.
  40. GitHub. OpenDrift. Available online: https://github.com/OpenDrift/opendrift/ (accessed on 25 February 2022).
  41. Androulidakis, Y.; Kourafalou, V.; Robert Hole, L.; Le Hénaff, M.; Kang, H. Pathways of Oil Spills from Potential Cuban Offshore Exploration: Influence of Ocean Circulation. J. Mar. Sci. Eng. 2020, 8, 535.
  42. Hole, L.R.; Dagestad, K.-F.; Röhrs, J.; Wettre, C.; Kourafalou, V.H.; Androulidakis, Y.; Kang, H.; Le Hénaff, M.; Garcia-Pineda, O. The DeepWater Horizon Oil Slick: Simulations of River Front Effects and Oil Droplet Size Distribution. J. Mar. Sci. Eng. 2019, 7, 329.
  43. Hole, L.R.; de Aguiar, V.; Dagestad, K.F.; Kourafalou, V.H.; Androulidakis, Y.; Kang, H.; Le Hénaff, M.; Calzada, A. Long term simulations of potential oil spills around Cuba. Mar. Pollut. Bull. 2021, 167, 112285.
  44. Kourafalou, V.; Justic, D.; Androulidakis, Y.; Bracco, A. From the deep ocean to the coasts and estuaries through the shelf: Linking coastal response to a deep blow-out. Int. Oil Spill Conf. Proc. 2021, 2021, 685087.
  45. Keramea, P.; Kokkos, N.; Gikas, G.D.; Sylaios, G. Operational Modeling of North Aegean Oil Spills Forced by Real-Time Met-Ocean Forecasts. J. Mar. Sci. Eng. 2022, 10, 411.
  46. Devis Morales, A.; Rodríguez Rubio, E.; Rincón Martínez, D. Numerical modeling of oil spills in the Gulf of Morrosquillo, Colombian Caribbean. CTF Cienc. Tecnol. Futuro 2022, 12, 69–83.
  47. Hasselmann, K. On the spectral dissipation of ocean waves due to white capping. Bound. Layer Meteorol. 1974, 6, 107–127.
  48. Sepp Neves, A.A.; Pinardi, N.; Navarra, A.; Trotta, F. A General Methodology for Beached Oil Spill Hazard Mapping. Front. Mar. Sci. 2020, 7, 65.
  49. Liubartseva, S.; Smaoui, M.; Coppini, G.; Gonzalez, G.; Lecci, R.; Cretì, S.; Federico, I. Model-based reconstruction of the Ulysse-Virginia oil spill, October–November 2018. Mar. Pollut. Bull. 2020, 154, 111002.
  50. Kampouris, K.; Vervatis, V.; Karagiorgos, J.; Sofianos, S. Oil spill model uncertainty quantification using an atmospheric ensemble. Ocean Sci. 2021, 17, 919–934.
  51. Liubartseva, S.; Federico, I.; Coppini, G.; Lecci, R. Stochastic oil spill modeling for environmental protection at the Port of Taranto (southern Italy). Mar. Pollut. Bull. 2021, 171, 112744.
  52. Siqueira, P.G.S.C.; Silva, J.A.M.; Gois, M.L.B.; Duarte, H.O.; Moura, M.C.; Silva, M.A.; Araújo, M.C. Numerical simulations of potential oil spills near Fernando de Noronha archipelago. In Trends in Maritime Technology and Engineering Volume 2; CRC Press: London, UK, 2022; pp. 273–282.
  53. Zodiatis, G.; Coppini, G.; Peña, J.; Benjumeda, P.; Sepp-Neves, A.A.; Lardner, R.; Liubartseva, S.; Soloviev, D.; Scuro, M.; Viola, F. Operational Response to the Syrian Oil Pollution Crisis in 2021. In Operational Response to the Syrian Oil Pollution Crisis in 2021. In Proceedings of the EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022; p. EGU22-1098.
  54. Alves, T.M.; Kokinou, E.; Zodiatis, G.; Lardner, R.; Panagiotakis, C.; Radhakrishnan, H. Modelling of oil spills in confined maritime basins: The case for early response in the Eastern Mediterranean Sea. Environ. Pollut. 2015, 206, 390–399.
  55. Zodiatis, G.; Liubartseva, S.; Loizides, L.; Pellegatta, M.; Coppini, G.; Lardner, R.; Kallos, G.; Kalogeri, C.; Bonarelli, R.; Sepp Neves, A.A.; et al. Cmems and cycofos assessing the pollution risk from the leviathan offshore platform in the eastern mediterranean sea cmems et cycofos: Évaluation du risque de pollution de la plate-forme offshore leviathan en mer méditerranée orientale. In Proceedings of the 9th EuroGOOS International conference, Brest, France, 5 March 2021; pp. 169–177.
  56. Liubartseva, S.; Zodiatis, G.; Coppini, G.; Sepp Neves, A.A.; Peña, J.; Benjumeda, P.; Lecci, R.; Soloviev, D. Operational simulations of a Mediterranean oil spill in February 2021. In Proceedings of the EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022; p. EGU22-2276.
  57. Lardner, R.; Zodiatis, G.; Hayes, D.; Pinardi, N. Application of the MEDSLIK oil spill model to the Lebanese spill of July 2006. In Proceedings of the European Group of Experts on satellite monitoring of sea based oil pollution, European Communities ISSN, Brussels, Belgium, 4–5 April 2006; pp. 1018–5593.
  58. Zodiatis, G.; Lardner, R.; Solovyov, D.; Panayidou, X.; De Dominicis, M. Predictions for oil slicks detected from satellite images using MyOcean forecasting data. Ocean Sci. 2012, 8, 1105–1115.
  59. Hoteit, I.; Abualnaja, Y.; Afzal, S.; Ait-El-Fquih, B.; Akylas, T.; Antony, C.; Dawson, C.; Asfahani, K.; Brewin, R.J.; Cavaleri, L.; et al. Towards an End-to-End Analysis and Prediction System for Weather, Climate, and Marine Applications in the Red Sea. Bull. Am. Meteorol. Soc. 2021, 102, E99–E122.
  60. French-McCay, D.P.; Jayko, K.; Li, Z.; Spaulding, M.L.; Crowley, D.; Mendelsohn, D.; Horn, M.; Isaji, T.; Kim, Y.H.; Fontenault, J.; et al. Oil fate and mass balance for the Deepwater Horizon oil spill. Mar. Pollut. Bull. 2021, 171, 112681.
  61. Youssef, M.; Spaulding, M. Drift Current under the Action of Wind and Waves; International Nuclear Information System (INIS): Ottawa, ON, Canada, 1993; pp. 587–615.
  62. French-McCay, D.P.; Spaulding, M.L.; Crowley, D.; Mendelsohn, D.; Fontenault, J.; Horn, M. Validation of Oil Trajectory and Fate Modeling of the Deepwater Horizon Oil Spill. Front. Mar. Sci. 2021, 8, 618463.
  63. Nugroho, D.; Pranowo, W.S.; Gusmawati, N.F.; Nazal, Z.B.; Rozali, R.H.B.; Fuad, M.A.Z. The application of coupled 3d hydrodynamic and oil transport model to oil spill incident in karawang offshore, indonesia. IOP Conf. Ser. Earth Environ. Sci. 2021, 925, 012048.
  64. Abdallah, I.M.; Chantsev, V.Y. Simulating oil spill movement and behavior: A case study from the Gulf of Suez, Egypt. Model. Earth Syst. Environ. 2022, 8, 4553–4562.
  65. Pradhan, B.; Das, M.; Pradhan, C. Trajectory modelling for hypothetical oil spill in Odisha offshore, India. J. Earth Syst. Sci. 2022, 131, 205.
  66. Abdallah, I.M.; Chantsev, V.Y. Modeling marine oil spill trajectory and fate off Hurghada, Red Sea coast, Egypt. Egypt. J. Aquat. Biol. Fish. 2022, 26, 41–61.
  67. Sim, L.; Graham, J.; Rose, K.; Duran, R.; Nelson, J.; Umhoefer, J.; Vielma, J. Developing a Comprehensive Deepwater Blowout and Spill Model; National Energy Technology Laboratory (NETL): Pittsburgh, PA, USA, 2015; p. 48.
  68. Lehr, W.; Jones, R.; Evans, M.; Simecek-Beatty, D.; Overstreet, R. Revisions of the ADIOS oil spill model. Environ. Model. Softw. 2002, 17, 189–197.
  69. Grubesic, T.H.; Nelson, J.R. Estimating potential oil spill trajectories and coastal impacts from near-shore storage facilities: A case study of FSO Nabarima and the Gulf of Paria. Int. J. Disaster Risk Reduct. 2022, 78, 103117.
More
Information
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register : , , ,
View Times: 267
Revisions: 2 times (View History)
Update Date: 05 Jun 2023
1000/1000
Video Production Service