Ocean Dynamics

, Volume 66, Issue 8, pp 939–954 | Cite as

IT-OSRA: applying ensemble simulations to estimate the oil spill risk associated to operational and accidental oil spills

  • Antonio Augusto Sepp NevesEmail author
  • Nadia Pinardi
  • Flavio Martins
Part of the following topical collections:
  1. Topical Collection on the 47th International Liège Colloquium on Ocean Dynamics, Liège, Belgium, 4-8 May 2015


Oil Spill Risk Assessments (OSRAs) are widely employed to support decision making regarding oil spill risks. This article adapts the ISO-compliant OSRA framework developed by Sepp Neves et al. (J Environ Manag 159:158–168, 2015) to estimate risks in a complex scenario where uncertainties related to the meteo-oceanographic conditions, where and how a spill could happen exist and the risk computation methodology is not yet well established (ensemble oil spill modeling). The improved method was applied to the Algarve coast, Portugal. Over 50,000 simulations were performed in 2 ensemble experiments to estimate the risks due to operational and accidental spill scenarios associated with maritime traffic. The level of risk was found to be important for both types of scenarios, with significant seasonal variations due to the the currents and waves variability. Higher frequency variability in the meteo-oceanographic variables were also found to contribute to the level of risk. The ensemble results show that the distribution of oil concentrations found on the coast is not Gaussian, opening up new fields of research on how to deal with oil spill risks and related uncertainties.


ISO 31000 Oil spill risk assessment Ensemble oil spill modeling Algarve MEDSLIK-II 



This work was co-funded through the TESSA Project, a MACOMA Grant and the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 633211. MACOMA is a Joint Doctorate program selected under Erasmus Mundus coordinated by the University of Cadiz. “TESSA” (Sviluppo di TEcnologie per la Situational Sea Awareness) is an industrial research project supported by PON “Ricerca & Competitivita 2007–2013” program of the Ministry for Education, University and Research. The authors would also like to thank Catarina Frazao Santos for making the coastal vulnerability data available.

Supplementary material

10236_2016_960_MOESM1_ESM.pdf (857 kb)
(PDF 856 KB)


  1. Álvarez Salgado X, Figueiras F, Pérez F, Groom S, Nogueira E, Borges a., Chou L, Castro C, Moncoiffé G, Ríos A.f, Miller AEJ, Frankignoulle M, Savidge G, Wollast R (2003) The Portugal coastal counter current off NW Spain: new insights on its biogeochemical variability. Prog Oceanogr 56(2):281–321. CrossRefGoogle Scholar
  2. Alves TM, Kokinou E, Zodiatis G (2014) A three-step model to assess shoreline and offshore susceptibility to oil spills: The south aegean (crete) as an analogue for confined marine basins. Mar Pollut Bull 86(12):443 –457CrossRefGoogle Scholar
  3. Aven T, Vinnem JE (2005) On the use of risk acceptance criteria in the offshore oil and gas industry. Reliab Eng Syst Saf 90:15–24CrossRefGoogle Scholar
  4. Burgherr P (2007) In-depth analysis of accidental oil spills from tankers in the context of global spill trends from all sources. J Hazard Mater 140(1–2):245–56. CrossRefGoogle Scholar
  5. Cailleau S, Chanut J, Lellouche J-M, Levier B, Maraldi C, Reffray G, Sotillo M G (2012) Towards a regional ocean forecasting system for the IBI (Iberia-Biscay-Ireland area): developments and improvements within the ECOOP project framework. Ocean Sci 8(2):143–159. CrossRefGoogle Scholar
  6. Canu DM, Solidoro C, Bandelj V, Quattrocchi G, Sorgente R, Olita A, Fazioli L, Cucco A (2015) Assessment of oil slick hazard and risk at vulnerable coastal sites. Mar Pollut Bull 94(12):84–95CrossRefGoogle Scholar
  7. Carpenter A (2007) The Bonn Agreement Aerial Surveillance programme: trends in North Sea oil pollution 1986-2004. Mar Pollut Bull 54(2):149–63. CrossRefGoogle Scholar
  8. Criado-Aldeanueva F, García-lafuente J, Vargas JM, Del Río J, Vázquez A, Reul A, Sánchez A (2006) Distribution and circulation of water masses in the Gulf of Cadiz from in situ observations. Deep-Sea Res II Top Stud Oceanogr 53(11–13):1144–1160. CrossRefGoogle Scholar
  9. De Dominicis M, Pinardi N, Zodiatis G, Lardner R (2013a) MEDSLIK-II, a Lagrangian marine oil spill model for short-term forecasting Part 1: Theory. Geosci Model Dev Discuss 6:1949–1997Google Scholar
  10. De Dominicis M, Pinardi N, Zodiatis G, Archetti R (2013b) Medslik-ii, a lagrangian marine surface oil spill model for short-term forecasting part 2: numerical simulations and validations. Geosci Model Dev 6(6):1871–1888.
  11. Delicado A, Gonçalves M (2007) Os portugueses e os novos riscos: resultados de um inquérito. Análise Social XLII 184:687–718.
  12. European Commission (2011) Roadmap to a single European Transport Area - Towards a Competitive and resource-efficient transport system. Tech. rep. European Commission. Brussels, BelgiumGoogle Scholar
  13. Fiúza A, DeMacedo M, Guerreiro M (1982) Climatological space and time-variation of the Portuguese coastal upwelling. Oceanol Acta 5(1):31–40. Google Scholar
  14. Frazão Santos C, Carvalho R, Andrade F (2012) Quantitative assessment of the differential coastal vulnerability associated to oil spills. J Coast ConservGoogle Scholar
  15. García-Lafuente J, Delgado J, Criado-Aldeanueva F, Bruno M, del Río J, Miguel Vargas J (2006) Water mass circulation on the continental shelf of the Gulf of Cádiz. Deep-Sea Res II Top Stud Oceanogr 53(11–13):1182–1197. CrossRefGoogle Scholar
  16. Goldman R, Biton E, Brokovich E, Kark S, Levin N (2015) Oil spill contamination probability in the southeastern levantine basin. Mar Pollut Bull 91(1):347 –356CrossRefGoogle Scholar
  17. Hampton S, Kelly PR, Carter HR (2003) Tank vessel operations, seabirds, and chronic oil pollution in California. Mar Ornithol 31:29–34Google Scholar
  18. Hassler B (2011) Accidental versus operational oil spills from shipping in the Baltic Sea: risk governance and management strategies. Ambio 40(2):170–178. CrossRefGoogle Scholar
  19. Huijer K (2005) Trends in oil spills from tanker ships 1995-2004. Tech. rep., International Tanker Owners Pollution Federation, London, UK.
  20. I N E – Instituto Nacional de Estatistica (2012) Anuário Estatísstico da Regiãso Algarve. Tech. rep. Instituto Nacional de Estatistica. Lisbon, PortugalGoogle Scholar
  21. International Atomic Energy Agency (2001) Severity, probability and risk of accidents during maritime transport of radioactive material. Tech. Rep. July International Atomic Energy Agency, Vienna AustriaGoogle Scholar
  22. ITOPF - International Tanker Owners Pollution Federation (2013) Oil tanker spill statistics. ITOPF, London, UK. Google Scholar
  23. Kallos G, Nickovic S, Papadopoulos A, Jovic D, Kakaliagou O, Misirlis N, Boukas L, Mimikou N, Sakellaridis G, Papageordiou J, Anadranistakis E, Manousakis M (1997) The regional weather forecasting system SKIRON: an overview. In: International symposium on regional weather prediction on parallel computer environments. Athens, Greece, pp 109–122Google Scholar
  24. Lagring R, Degraer S, de Montpellier G, Jacques T, Van Roy W, Schallier R (2012) Twenty years of Belgian North Sea aerial surveillance: a quantitative analysis of results confirms effectiveness of international oil pollution legislation. Mar Pollut Bull 64(3):644–52. CrossRefGoogle Scholar
  25. Lellouche J-M, Le Galloudec O, Drévillon M, Régnier C, Greiner E, Garric G, Ferry N, Desportes C, Testut C-E, Bricaud C, Bourdallé-Badie R, Tranchant B, Benkiran M, Drillet Y, Daudin A, De Nicola C (2013) Evaluation of global monitoring and forecasting systems at Mercator Océan. Ocean Sci 9(1):57–81. CrossRefGoogle Scholar
  26. Musk S (2012) Trends in oil spills from tankers and ITOPF non-tanker attended incidents. ITOPF, London, UK. Google Scholar
  27. Nunes M, Ferreira O, Schaefer M, Clifton J, Baily B, Moura D, Loureiro C (2009) Hazard assessment in rock cliffs at Central Algarve (Portugal): a tool for coastal management. Ocean Coast Manag 52 (10):506–515. CrossRefGoogle Scholar
  28. Olita A, Cucco A, Simeone S, Ribotti A, Fazioli L, Sorgente B, Sorgente R (2012) Oil spill hazard and risk assessment for the shorelines of a Mediterranean coastal archipelago. Ocean Coast Manag 57:44–52CrossRefGoogle Scholar
  29. Price JM, Johnson WR, Marshall CF, Ji Z. -G., Rainey GB (2003) Overview of the oil spill risk analysis (OSRA) model for environmental impact assessment. Spill sci Technol B 8:529–533CrossRefGoogle Scholar
  30. Relvas P (2002) Mesoscale patterns in the Cape São Vicente (Iberian Peninsula) upwelling region. J Geophys Res 107(C10):3164. CrossRefGoogle Scholar
  31. Relvas P, Barton E, Dubert J, Oliveira PB, Peliz A, da Silva J, Santos a. MP, Peliz a., Dasilva J (2007) Physical oceanography of the western Iberia ecosystem: latest views and challenges. Progress In Oceanography 74(2–3):149–173. CrossRefGoogle Scholar
  32. Samaras AG, de Dominicis M, Archetti R, Lamberti R, Pinardi N (2014) Towards improving the representation of beaching in oil spill models: a case study. Mar Pollut Bull 88:91–101CrossRefGoogle Scholar
  33. Sepp Neves AA, Pinardi N, Martins F, Janeiro J, Samaras A, Zodiatis G, De Dominicis M (2015) Towards a common oil spill risk assessment framework adapting ISO 31000 and addressing uncertainties. J Environ Manag 159:158–168. CrossRefGoogle Scholar
  34. US National Research Council (2009) Oil in the sea: inputs, fates, and effects. National Academy of Sciences, Washington DC. USAGoogle Scholar
  35. Volckaert F, Kayens G (2000) Aerial surveillance of operational oil pollution in Belgium’s maritime zone of interest. Mar Pollut Bull 40(11):1051–1056. CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Antonio Augusto Sepp Neves
    • 1
    Email author
  • Nadia Pinardi
    • 1
  • Flavio Martins
    • 2
  1. 1.Department of Physics and AstronomyUniversity of BolognaBolognaItaly
  2. 2.CIMAUniversity of AlgarveFaroPortugal

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