Performance evaluation of oil spill software systems in early fate and trajectory of oil spill: comparison analysis of OILMAP and PISCES 2 in Mersin bay spill

  • Ali Cemal TozEmail author
  • Muge Buber


The aim of this study is to evaluate the performance level of two advanced oil spill software systems in early transport and fate of oil spill through algorithms accepted in oil spill literature. To do this, the performance level of software systems mostly used in real cases have been compared. OILMAP (the oil spill prediction modeling system) and PISCES 2 (potential incident simulation, control and evaluation system) have been used for spill trajectory in the light of four spill scenarios. The findings reveal that the OILMAP has predicted a relatively larger area of spill. In addition, OILMAP has achieved closer results to the calculations of approaches adopted in the literature for evaporation calculations. Besides, OILMAP software has provided highly reliable results in the evaporation rates of oil compared to the calculations of PISCES 2. On the other hand, as for the determination of the risky area, both software systems have yielded results with high reliability values, which could be used in taking precautions against oil spill in such areas.


OILMAP PISCES 2 Oil spill Performance evaluation Mersin Bay 



The authors would like to thank Seagull Oil Spill Response Limited and MARSER Ltd. for their help in providing the data and Dokuz Eylul University Maritime Faculty for their support to this study.


  1. Balas, C. E., & Balas, L. (2002). Risk assessment of some revetments in Southwest Wales, United Kingdom. Journal of Waterway, Port, Coastal, and Ocean Engineering, 1285, 216–223. Scholar
  2. British Petroleum (BP). (2016). Great Australian Bight exploration drilling program Stromlo-1 and Whinham-1 Fate and effects oil spill modeling assumptions, parameters and results. Accessed 31 Dec 2017.
  3. Buffy, M., Puspa, L., Gregory, M., Edward, B., & Miles, M. S. (2017). Louisiana coastal marsh environments and mc252 oil biomarker chemistry, in oil spill environmental forensics case studies. In S. A. Stout & Z. Wang (Eds.), Oil spill environmental forensics case studies (pp. 737–756). Scholar
  4. Cekirge, H.M., Koch, M., Long, C., Giammona, C.P., Binkley, K., Engelhardt, R., et al. (2003). State-of-the-art technologies in oil spill modeling. International Oil Spill Conference Proceedings, 67-72. Scholar
  5. Chang, S. E., Stone, J., Demes, K., & Piscitelli, M. (2014). Consequences of oil spills: a review and framework for informing planning. Ecology and Society, 19(2), 26. Scholar
  6. Ciampalini, A., Raspini, F., Bianchini, S., Tarchi, D., Vespe, M., Moretti, S., & Casagli, N. (2016). The Costa Concordia last cruise: the first application of high frequency monitoring based on COSMO-SkyMed constellation for wreck removal. ISPRS Journal of Photogrammetry and Remote Sensing, 112, 37–49.CrossRefGoogle Scholar
  7. Coppini, G., De Dominicis, M., Zodiatis, G., Lardner, R., Pinardi, N., Santoleri, R., Colella, S., Bignami, F., Hayes, D. R., Soloviev, D., Georgiou, G., & Kallos, G. (2011). Hindcast of oil-spill pollution during the Lebanon crisis in the eastern Mediterranean. Marine Pollution Bulletin, 62(1), 140–153.CrossRefGoogle Scholar
  8. Daling, P.S., Aamo, O.M., Lewis, A., & Strøm-Kristiansen, T. (1997). Sintef/Iku oil-weathering model: predicting oil properties at sea. International Oil Spill Conference Proceedings, 1, 297–307. Scholar
  9. Davidson, W. F., Lee, K., & Cogswell, A. (2008). Oil spill response: a global perspective. Canada: Springer Science & Business Media ISBN-13: 978–1402085642.CrossRefGoogle Scholar
  10. De Dominicis, M., Pinardi, N., Zodiatis, G., & Lardner, R. (2013). MEDSLIK-II, a Lagrangian marine surface oil spill model for short-term forecasting. Geoscientific Model Development, 6(6), 1851–1869. Scholar
  11. De Dominicis, M., Bruciaferri, D., Gerin, R., Pinardi, N., Poulain, P. M., Garreau, P., Zodiatis, G., Perivoilotis, L., Fazioli, L., Sorgente, R., & Manganiello, C. (2016). A multi-model assessment of the impact of currents, waves and wind in modelling surface drifters and oil spill. Deep Sea Research Part II: Topical Studies in Oceanography, 133, 21–38. Scholar
  12. Delgado, L., Kumzerova, E., & Martynov, M. (2006). Simulation of oil spill behavior and response operations in PISCES. WIT Transactions on Ecology and the Environment, 88, 279–292. Scholar
  13. Elhakeem, A. A., Elshorbagy, W., & Chebbi, R. (2007). Oil spill simulation and validation in the Arabian (Persian) gulf with special reference to the UAE coast. Water, Air, and Soil Pollution, 184(1-4), 243–254. Scholar
  14. Elliott, A. J., & Hurford, N. (1989). The influence of wind and wave shear on the spreading of a plume at sea. Oil and Chemical Pollution, 5(5), 347–363.CrossRefGoogle Scholar
  15. Fay, J.A. (1971). Physical processes in the spread of oil on a water surface. International Oil Spill Conference Proceedings, 1, 71. Scholar
  16. Fernandes, R., Neves, R., Viegas, C., Leitão P., & Hidromod, L. (2013). Integration of an oil and inert spill model in a framework for risk management of spills at sea: a case study for the Atlantic area. 36th AMOP Technical Seminar on Environmental Contamination and Response Proceedings, 4-6.Google Scholar
  17. Fingas, M. (1995). Literature review of the physics and predictive modeling of oil spill evaporation. Journal of Hazardous Materials, 42, 157–175. Scholar
  18. Fingas, M. (2010). Oil spill science and technology (1st ed.). USA: Gulf Professional Publishing (Elsevier).Google Scholar
  19. Fingas, M. (2015). Oil spill science and technology (2nd ed.). USA: Gulf professional publishing (Elsevier).Google Scholar
  20. French, D. P., Rines, H., & Masciangioli, P. (1997). Validation of an orimulsion spill fates model using observations from field test spills. 20th Arctic and Marine Oilspill Program (AMOP) Technical Seminar Proceedings. 1410.Google Scholar
  21. French-McCay, D. F. (2004). Oil spill impact modeling: development and validation. Environmental Toxicology and Chemistry, 23(10), 2441–2456. Scholar
  22. Galt, J. A. (1998). Uncertainty analysis related to oil spill modeling. Spill Science & Technology Bulletin, 4, 231–238. Scholar
  23. Hackett, B., Comerma, E., Daniel, P., & Ichikawa, H. (2009). Marine oil pollution prediction. Oceanography, 22(3), 168–175. Scholar
  24. Hodges, B. R., Orfila, A., Sayol, J. M., & Hou, X. (2015). Operational oil spill modeling: from science to engineering applications in the presence of uncertainty. In M. Ehrhardt (Ed.), Mathematical modeling and numerical simulation of oil pollution problems (pp. 99–126). Heidelberg: Springer International Publishing.Google Scholar
  25. Hou, X., Hodges, B. R., Feng, D., & Liu, Q. (2017). Uncertainty quantification and reliability assessment in operational oil spill forecast modeling system. Marine Pollution Bulletin, 116(1–2), 420–433. Scholar
  26. Howlett, E., Jayko, K., Isaji, T., Anid, P., Mocke, G., & Smit, F. (2008). Marine forecasting and oil spill modeling in Dubai and the Gulf region. The 31st Arctic and Marine Oil Spill Program (AMOP) Technical Seminar on Environmental Contamination and Response Proceedings, 1-12.Google Scholar
  27. IHS (2017). The module of maritime intelligence risk suite: Mediterranean region traffic density and risk visualization. U.K.Google Scholar
  28. International Maritime Organization (IMO). (1995). Manual on oil pollution. Section 2-contingency planning. London, UK.Google Scholar
  29. International Tanker Owners Pollution Federation Limited (ITOPF). (2017). Weathering. Technical Information Paper. Accessed 25 June 2017.
  30. Juszkiewicz, W., & Lazuga, K. (2011). Simulations to prevent pollution from maritime transport. Swedish National Road and Transport Research Institute (VTI) Project Baltic Master 2. Accessed 26 June 2017.
  31. King, B., Brushett, B., & Lemckert, C. (2010). A case study of consensus modeling for tracking oil spills. Earth and Environmental Science, 11. Scholar
  32. Kolluru, V., Spaulding, M.L., & Anderson, E., (1994). A three dimensional subsurface oil dispersion model using a particle based technique. 17th Arctic and Marine Oil Spill Program Technical Seminar, 767-784.Google Scholar
  33. Lardner, R., Zodiatis, G., Hayes, D., & Pinardi, N. (2006). Application of the MEDSLIK oil spill model to the Lebanese spill of July 2006. European Group of Experts on Satellite Monitoring of Sea Based Oil Pollution, European Communities, 1018–5593.Google Scholar
  34. Lazuga, K. (2012). Testing of an oil spill mathematical model contained in PISCES II simulator. Scientific Journals of the Maritime University of Szczecin, 32(104), 105–109.Google Scholar
  35. Lazuga, K., Gucma, L., & Perkovic, M. (2013). M/t “Baltic carrier” accident. The reconstruction of oil spill with PISCES II simulator application. Scientific Journals of Maritime University of Szczecin, 36(108), 110–115.Google Scholar
  36. Lebreton, L. C. M. (2015). Stochastic analysis of deep sea oil spill trajectories in the great Australian bight. Wellington: Cuba.Google Scholar
  37. Lee, C. (2012). Water-based oil spill modeling software: benefits, requirements & recommendations. Undergraduate Research.
  38. Lehr, W. J. (2001). Review of modeling procedures for oil spill weathering behavior. Advances in Ecological Sciences, 9, 51–90.Google Scholar
  39. Lehr, W. J., Fraga, R. J., Belen, M. S., & Cekirge, H. M. (1984). A new technique to estimate initial spill size using a modified Fay-type spreading formula. Marine Pollution Bulletin, 15(9), 326–329. Scholar
  40. Lehr, W., Jones, R., Evans, M., Simecek-Beatty, D., & Overstreet, R. (2002). Revisions of the ADIOS oil spill model. Environmental Modeling & Software, 17(2), 189–197. Scholar
  41. Liu, Z., Liu, J., Zhu, Q., & Wu, W. (2012). The weathering of oil after the deepwater horizon oil spill: insights from the chemical composition of the oil from the sea surface, salt marshes and sediments. Environmental Research Letters, 7(3), 5302. Scholar
  42. Mackay, D., Buistt, I.A., Marcarenhas, R., & Paterson, S. (1980a). Oil spill processes and models. Environment Canada. Manuscript report no. EE-8. Ottawa, Ontario.Google Scholar
  43. Mackay, D., Paterson S., & Nadeau, S. (1980b). Calculation of the evaporation rate of volatile liquids. National Conference on Control of Hazardous Material Spill, 415–435.Google Scholar
  44. Marine Services (MARSER Seagull Oil Spill response Limited). (2015). Mersin bay oil spill response plan. Turkey.Google Scholar
  45. McCay, D. F. (2003). Development and application of damage assessment modeling: example assessment for the north cape oil spill. Marine Pollution Bulletin, 47(9–12), 341–359.CrossRefGoogle Scholar
  46. McCay, D. F. (2004). Oil spill impact modeling: development and validation. Environmental Toxicology and Chemical, 23(10), 2441–2456. Scholar
  47. Reed, M., Johansen, Ø., Brandvik, P. J., Dailing, P., Lewis, A., Fiocco, R., Mackay, D., & Prentki, R. (1999). Oil spill modeling towards the close of the 20th century: overview of the state of the art. Spill Science and Technology Bulletin, 5(1), 3–16. Scholar
  48. Samaras, A. G., De Dominicis, M., Archetti, R., Lamberti, A., & Pinardi, N. (2014). Towards improving the representation of beaching in oil spill models: a case study. Marine Pollution Bulletin, 88(1–2), 91–101.CrossRefGoogle Scholar
  49. Sebastiao, P., & Soares, C. G. (2006). Uncertainty in predictions of oil spill trajectories in a coastal zone. Journal of Marine Systems, 63(3–4), 257–269. Scholar
  50. Simecek-Beatty, D. (2011). Oil spill trajectory forecasting uncertainty and emergency response. Oil Spill Science and Technology, 275–299. Scholar
  51. Snow, B. J., Moulitsas, I., Kolios, A. J., & De Dominicis, M. (2014). CranSLIK v1.0: stochastic prediction of oil spill transport and fate using approximation methods. Geoscientific Model Development, 7, 1507–1516. Scholar
  52. Spaulding, M.L., Howlett, E., Anderson, E. & Jayko, K., (1992). OILMAP—a global approach to spill modeling. 15th annual Arctic and marine Oilspill program technical seminar, Edmonton, Canada.Google Scholar
  53. Spaulding, M. L., Kolluru, V. S., Anderson, E., & Howlett, E. (1994). Application of three dimensional oil spill model (WOSM/ OILMAP) to hindcast the Braer spill. Spill Science and Technology Bulletin, 1(1), 23–35. Scholar
  54. Stevens, C. C. (2014). Sinking of hydrocarbon mixtures due to evaporative and/or dissolution weathering on the surface and submerged in water. Master of science in chemical engineering, Louisiana State University and Agricultural and Mechanical College.Google Scholar
  55. Stiver, W., & Mackay, D. (1984). Evaporation rate of spills of hydrocarbons and petroleum mixtures. Environmental Science & Technology, 18(11), 834–840.CrossRefGoogle Scholar
  56. Tippett, M. K., Delsole, T., & Barnston, A. G. (2014). Reliability of regression corrected climate forecasts. Journal of Climate, 27, 3393–3404. Scholar
  57. Toz, A. C., & Koseoglu, B. (2017). Trajectory prediction of oil spill with Pisces 2 around bay of Izmir, Turkey. Marine Pollution Bulletin, 126, 215–227. Scholar
  58. Transas (2008). PISCES II (version 2.93) Instruction Manual. Transas Ltd. Russia.Google Scholar
  59. Turkish State Meteorological Service (TSMS) (2014). Accessed 10 Nov 2017.
  60. Wirtz, K. W., Baumberger, N., Adam, S., & Liu, X. (2007). Oil spill impact minimization under uncertainty: evaluating contingency simulations of the prestige accident. Ecological Economics, 61(2–3), 417–428. Scholar
  61. Yapa, P. D. (1994). Oil spill processes and model development. Journal and Advanced Marine Technology, 11, 1–22.Google Scholar
  62. Zodiatis, G., De Dominicis, M., Perivoliotis, L., Radhakrishnan, H., Georgoudis, E., Sotillo, M., Lardner, R. W., Krokos, G., Bruciaferri, D., Clementi, E., Guarnieri, A., Ribotti, A., Drago, A., Bourma, E., Padorno, E., Daniel, P., Gonzalez, G., Chazot, C., Gouriou, V., Kremer, X., Sofianos, S., Tintore, J., Garreau, P., Pinardi, N., Coppini, G., Lecci, R., Pisano, A., Sorgente, R., Fazioli, L., Soloviev, D., Stylianou, S., Nikolaidis, A., Panayidou, X., Karaolia, A., Gauci, A., Marcati, A., Caiazzo, L., & Mancini, M. (2016). The Mediterranean decision support system for marine safety dedicated to oil slicks predictions. Deep Sea Research Part II: Topical Studies in Oceanography, 133, 4–20. Scholar

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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Marine Transportation Engineering, Maritime FacultyDokuz Eylul UniversityIzmirTurkey

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