title: Synergistic use of an oil drift model and remote sensing observations for oil spill monitoring.
abstract: In case of oil spills due to disasters, one of the environmental concerns is the oil trajectories and spatial distribution. To meet these new challenges, spill response plans need to be upgraded. An important component of such a plan would be models able to simulate the behaviour of oil in terms of trajectories and spatial distribution, if accidentally released, in deep water. All these models need to be calibrated with independent observations. The aim of the present paper is to demonstrate that significant support to oil slick monitoring can be obtained by the synergistic use of oil drift models and remote sensing observations. Based on transport properties and weathering processes, oil drift models can indeed predict the fate of spilled oil under the action of water current velocity and wind in terms of oil position, concentration and thickness distribution. The oil spill event that occurred on 31 May 2003 in the Baltic Sea offshore the Swedish and Danish coasts is considered a case study with the aim of producing three-dimensional models of sea circulation and oil contaminant transport. The High-Resolution Limited Area Model (HIRLAM) is used for atmospheric forcing. The results of the numerical modelling of current speed and water surface elevation data are validated by measurements carried out in Kalmarsund, Simrishamn and Kungsholmsfort stations over a period of 18 days and 17 h. The oil spill model uses the current field obtained from a circulation model. Near-infrared (NIR) satellite images were compared with numerical simulations. The simulation was able to predict both the oil spill trajectories of the observed slick and thickness distribution. Therefore, this work shows how oil drift modelling and remotely sensed data can provide the right synergy to reproduce the timing and transport of the oil and to get reliable estimates of thicknesses of spilled oil to prepare an emergency plan and to assess the magnitude of risk involved in case of oil spills due to disaster.
authors.affiliation: Department of Civil, Environmental, Building Engineering and Chemistry, Technical University of Bari, via Orabona 4, 70125, Bari, Italy. diana.depadova@poliba.it.
authors.forename: Diana
authors.initials: D
authors.lastname: De Padova
authors.affiliation: Department of Civil, Environmental, Building Engineering and Chemistry, Technical University of Bari, via Orabona 4, 70125, Bari, Italy.
authors.forename: Michele
authors.initials: M
authors.lastname: Mossa
authors.affiliation: National Research Council of Italy, Institute of Intelligent Systems for Automation (CNR-ISSIA), via Amendola 122/D, 70126, Bari, Italy.
authors.forename: Maria
authors.initials: M
authors.lastname: Adamo
authors.affiliation: National Research Council of Italy, Institute for the Electromagnetic Remote Sensing of the Environment (CNR-IREA), via Bassini 15, 20133, Milan, Italy.
authors.forename: Giacomo
authors.initials: G
authors.lastname: De Carolis
authors.affiliation: National Research Council of Italy, Institute of Intelligent Systems for Automation (CNR-ISSIA), via Amendola 122/D, 70126, Bari, Italy.
authors.forename: Guido
authors.initials: G
authors.lastname: Pasquariello
mesh_terms: D004190:Disasters; D004784:Environmental Monitoring / Q000379:methods*; D008962:Models, Theoretical; D010578:Petroleum; D059392:Petroleum Pollution; D058998:Remote Sensing Technology; D014874:Water Pollutants, Chemical; D014887:Weather; D014919:Wind
chemical_list: D010578:Petroleum; D014874:Water Pollutants, Chemical
keywords: Fun Shan Hai; Numerical model; Oil spill modelling; Oil thickness; Oil weathering; Remote sensing