In-ice oil spill trajectory modeling based on a satellite-derived ice drift dataset for the Beaufort Sea

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AuteurRechercher : ; Rechercher : ; Rechercher : ; Rechercher :
TypeArticle
ConférenceThe 39th AMOP Technical Seminar on Environmental Contamination and Response, June 7-9, 2016, Halifax, NS Canada
RésuméKnowing where an oil-spill would go is crucial to assess explorations and developments risks and to plan for an optimized and effective clean-up. Having this knowledge is even more crucial for spills in the harsh climate of the hydrocarbon-rich Beaufort Sea where the dominance of harsh ice and darkness during colder seasons make detection and clean-up very challenging. We have modelled and analyzed several in-ice oil spill scenarios for this location and seasons during which the concentration of ice is very high. A satellite-derived ice drift dataset is employed as the driver of the in-ice spills with the assumption that oil only moves with ice. Shallow and deep water spills at different locations, starting at different times and with different spill durations are modelled. Trajectories were modelled assuming that the ice drift dataset is and is not error-free. Uncertainties were modelled through a Monte-Carlo approach. Some of the conclusions follow: (1) the extent of the spill is generally larger when the spill starts on 1 Nov. than when it starts on 15 Dec. (2) deep water spills extend farther than shallow water spills, and (3) shallow water, earlier in the colder winter season, and longer-lasting spills are generally associated with elongated contaminated territorial water boundaries.
Date de publication
Langueanglais
AffiliationConseil national de recherches Canada; Génie océanique, côtier et fluvial
Publications évaluées par des pairsNon
Numéro du CNRCOCRE-PR-2016-015
Numéro NPARC23001848
Exporter la noticeExport en format RIS
Signaler une correctionSignaler une correction
Identificateur de l’enregistrementfa350f21-f053-4009-acb1-2af0a2aac3a6
Enregistrement créé2017-04-21
Enregistrement modifié2017-04-21
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