PROBABILISTIC LONG RANGE FORECAST SYSTEM FOR OIL SPILL TRAJECTORIES IN THE GULF OF BISCAY (SPAIN)

Abstract

The increasing number of accidental oil spills has motivated the development and implementation of operational oceanography systems (OOS) to help in the decision process during oil spill emergency situations. Currently, most of the national and regional OOS have been setup for short-term (up to 5 days) oil spill forecast. However, recent accidental oil spills such as Prestige in Spain (2002) or Deep Horizon in Gulf of Mexico (2010), have revealed the importance of having larger prediction horizons (up to 30 days) in regional-scale areas. In this work, we have developed a stochastic methodology based on the combination of clustering algorithms and Markov chains of first order to provide medium term (15–30 days) probabilistic oil spill forecasts. The method encompasses the following steps: (1) classification of representative atmospheric patterns using clustering techniques (PCA and k-means [1]); (2) determination the transition probability matrix associated with the Markov chain. The element of the transition matrix (pij) represents the probability of moving from a cluster “i” to a cluster “j” in one time step. In case an accident occurs, the Markov chain provides through the transition probability matrix, the evolution of ocean-atmospheric conditions during the forecasting period; (3) this result is used to force TESEO Lagrangian transport model [2] which allows the characterization of trajectories in probabilistic terms during the forecasting period. The methodology has been applied in the Gulf of Biscay (Spain) to simulate the evolution of oil slick observations and drifter buoys gathered during the Prestige accident. The cumulative probability maps have been compared with these data (oil slicks observations and drifter data), showing that actual trajectories are consistent with the probability of contamination obtained. Results seem promising and we expect to reduce uncertainty by incorporating autoregressive logistic models to help improving the possible evolution of the ocean-atmospheric conditions. A detailed description of the methodology, application and validation will be shown in the presentation and in the final paper.