@article{3069364,
    title = "Oil spill forecasting (prediction)",
    author = "Zodiatis, G. and Lardner, R. and Alves, T.M. and Krestenitis, Y. and Perivoliotis, L. and Sofianos, S. and Spanoudaki, K.",
    journal = "Journal of Marine Research",
    year = "2017",
    volume = "75",
    number = "6",
    pages = "923-953",
    publisher = "Yale University",
    issn = "0022-2402",
    doi = "10.1357/002224017823523982",
    keywords = "algorithm;  forecasting method;  marine pollution;  modeling;  oil spill;  prediction;  shoreline;  synthetic aperture radar, Libellulidae",
    abstract = "Oil spills in the ocean are a matter of concern due to the damaging effect they can have on coastal and offshore resources. This work presents a review of present-day modeling techniques used in the mitigation of oil spills by booms, skimmers, chemical dispersants, and other equipment and the importance of the controlling parameters of these techniques. Three basic questions need to be addressed by oil spill models: (1) where the spill will move, (2) when will the spill get to the modeled endpoints, and (3) what will be its state when it arrives. The first two questions are relatively urgent, as far as response measures are concerned, and depend closely on the use of accurate data on winds, sea currents, and wave action as oil spill accidents evolve. Obtaining a reasonable answer to the third question lies in the use of reliable fate algorithms. Oil spill models can be divided in two types: Euleurian and Langragian. Adding to information regarding the oil type and its initial location, all oil spill models require data for the wind fields, sea state, sea-surface temperature, and currents, as well as other environmental parameters, if available. Such reliable data suit the needs of oil spill modeling predictions and are available daily at global, regional, and coastal scales within the broader scope of operational oceanography. Advanced oil spill models available at present use satellite synthetic aperture radar (SAR) images/data to detect possible oil slicks and assimilate slick and drifter observations to correct slick predictions. The emphasis of research and governmental institutions has been on improving 4D predictions obtained through simulation of oil spills backward in time to track the slicks back to their source. Such backward simulations, when integrated with ships’ Automatic Identification Systems (AIS), will be used to locate the sources of oil slicks around the world’s oceans and seas. © 2017 George Zodiatis, Robin Lardner, Tiago M. Alves, Yiannis Krestenitis, Leonidas Perivoliotis, Sarantis Sofianos, and Katerina Spanoudaki."
}