International E-publication: Publish Projects, Dissertation, Theses, Books, Souvenir, Conference Proceeding with ISBN.  International E-Bulletin: Information/News regarding: Academics and Research

Hindcasting and Validation of Mumbai Oil Spills using Gnome

    , , ,

Author Affiliations

  • 1Indian National Centre for Ocean Information Services, Hyderabad, INDIA
  • 2 Jawaharlal Nehru Technological University, Hyderabad,INDIA

Int. Res. J. Environment Sci., Volume 3, Issue (12), Pages 18-27, December,22 (2014)

Abstract

Oil spill trajectory forecasting became mandatory for providing advisory services to the regulatory authorities during the event of oil spill, for planning their remediation and clean up measures. The present study describes a method to simulate the trajectory of the spilled oil using GNOME and validating it using available Radar data. The trajectory forecasting of two oil spill events, happened in mumbai high region, during 2010- 2011 has been executed in hindcast mode using General NOAA Operational Modeling Environment. The forcing parameters such as, forecasted European Center of Medium Range Weather Forecast winds and Regional Ocean Modeling system currents were used for the execution. The likely areas which are to be affected are found from the prediction. The trajectory obtained from GNOME is compared with oil spill signatures obtained from the radar data of a particular time step. The observed oil slicks were found within the average distance of 3.73 km and 4.16 km from the prediction for MSC chitra spill and Mumbai uran trunk pipeline spill respectively. This trajectory model can be used for making the contingency plans, conducting the mock drills and during oil spill response & preparedness operations.

References

  1. Venkatraman, Coastal and Marine biodiversity in India, Indian Journal of marine science, 34(1), 57-75, (2005)
  2. Ganguly Subha, Environmental and Ecological Importance of Coral Reefs: A Review, Int. Res. J. Environment Sci.,2(7), 85-86, (2013)
  3. Kumar Goutam and Ramanathan Al, Microbial Diversity in the Surface Sediments and its Interaction with Nutrients of Mangroves of Gulf of Kachchh, Gujarat, India, Int. Res. J. Environment Sci.,2(1), 25-30, (2013)
  4. Ladwani K.D. and Ramteke D.S., Bulletin of Environmental Contamination and Toxicology,90(5), 1-6, (2013)
  5. Parab S.R., Pandit R.A., Kadam A.N. and Indap M.M., Effect of Bombay high crude oil and its water-soluble fraction on growth and metabolism of diatom Thalassiosira sp, Indian Journal of Marine Sciences,37, 251-255, (2008)
  6. Beegle-Krause C.J, General NOAA Oil Modeling Environment (GNOME) : A New Spill Trajectory Model.IOSC 2001 Proceedings, Tampa, FL, March 26-29, St. Louis, MO: Mira Digital Publishing, Inc, , 865-871 (2001)
  7. Brown C. and Fingas M., A review of current global oil spill surveillance, monitoring and remote sensing capabilities, In Proc. 28th Arctic and Marine Oil Spill Program (AMOP) Tech. Seminar Calgary, Canada, 7–9, 789-798, (2005)
  8. Brekke C. and Solberg A., Oil spill detection by satellite remote sensing. Remote Sensing of Environment, 95, 1–13 (2005)
  9. Gade M. and Alpers. W., Using ERS-2 SAR images for routine observation of marine pollution in European coastal waters, Sci. Total Environ., 237/238, 441-448 (1999)
  10. Alpers W. and H. Espedal, Oils and Surfactants, Chapter 11 in Synthetic Aperture Radar Marine User's Manual, National Oceanic and Atmospheric Administration, Center for Satellite Application and Research, NOAA/NESDIS, Ch.R. Jackson, J.R. Apel, editors, Washington, D.C., USA, ISBN 0-16-073214-X, 263-275 (2004)
  11. Yu. Ivanov and V. V. Zatyagalova b a P. P., A GIS approach to mapping oil spills in a marine environment Shirshov Institute of Oceanology, Russian Academy of Sciences, Nakhimovsky prospect 36, Moscow 117997, Russia b Centre ‘Almaz’, NPO Mashinostroenia, Gagarina St. 33, Reutov, Moscow Region 143966, Russia Version of record first published, 23 Oct (2008)
  12. Xu L., Li J., Brenning A., A comparative study of different classification techniques for marine oil spill identification using RADARSAT-1 imagery, Remote Sensing of Environment, 141, 14-23 (2014)
  13. Lee Jong-Sen, Speckle suppression and analysis for synthetic aperture radar images, Optical Engineering, 25 (5), 636-643 (1986)
  14. Chang L.a, Cheng C.M.a and Tang Z.S.b, An automatic detection of oil spills in SAR images by using image segmentation approach, International Geoscience and Remote Sensing Symposium (IGARSS), Article number 1525287, 2, 1021-1024 (2005)
  15. Solberg A.H. and Volden E., Incorporation of Prior Knowledge in Automatic Classification of Oil Spill in ERS SAR Images, IEEE Transactions on Geoscience and Remote Sensing, 157- 159 (1997)
  16. Sebastiao P. and Guedes Soares C., Uncertainty in predictions of oil spill trajectories in a coastal zone, Journal of Marine Systems,63, 257-269 (2006)