Mid-long term oil spill forecast based on logistic regression modelling of met-ocean forcings. (September 2019)

• Record Type: Journal Article
• Authors: Chiri, Helios; Abascal, Ana Julia; Castanedo, Sonia; Medina, Raul
• Is Part Of: Marine pollution bulletin. Volume 146(2019)
• Page Start: 962
• Page End: 976
• Publication Date: 2019-09
• Subjects: Mid-long term forecast -- Oil spill modelling -- Logistic regression -- Prestige accident -- Bay of Biscay; Marine pollution -- Periodicals; Marine Biology -- Periodicals; Water Pollution -- Periodicals; Mer -- Pollution -- Périodiques; Publications périodiques; Pollution des mers; Lutte antipollution; Electronic journals; 363.7394
• DOI: 10.1016/j.marpolbul.2019.07.053
• Languages: English
• ISSNs: 0025-326X

Abstract

Abstract: Past major oil spill disasters, such as the Prestige or the Deepwater Horizon accidents, have shown that spilled oil may drift across the ocean for months before being controlled or reaching the coast. However, existing oil spill modelling systems can only provide short-term trajectory simulations, being limited by the typical met-ocean forecast time coverage. In this paper, we propose a methodology for mid-long term (1–6 months) probabilistic predictions of oil spill trajectories, based on a combination of data mining techniques, statistical pattern modelling and probabilistic Lagrangian simulations. Its main features are logistic regression modelling of wind and current patterns and a probabilistic trajectory map simulation. The proposed technique is applied to simulate the trajectory of drifting buoys deployed during the Prestige accident in the Bay of Biscay. The benefits of the proposed methodology with respect to existing oil spill statistical simulation techniques are analysed. Highlights: Methodology presented for mid-long term probabilistic forecast of oil spill trajectories. Implementation of logistic regression for met-ocean pattern mid-long term prediction Performance of the methodology is assessed comparing results with observational data.