A framework to quantify uncertainty in simulations of oil transport in the ocean

Rafael C. Gonçalves, Mohamed Iskandarani, Ashwanth Srinivasan, W. Carlisle Thacker, Eric Chassignet, Omar Knio

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12 Scopus citations

Abstract

An uncertainty quantification framework is developed for the DeepC Oil Model based on a nonintrusive polynomial chaos method. This allows the model's output to be presented in a probabilistic framework so that the model's predictions reflect the uncertainty in the model's input data. The new capability is illustrated by simulating the far-field dispersal of oil in a Deepwater Horizon blowout scenario. The uncertain input consisted of ocean current and oil droplet size data and the main model output analyzed is the ensuing oil concentration in the Gulf of Mexico. A 1331 member ensemble was used to construct a surrogate for the model which was then mined for statistical information. The mean and standard deviations in the oil concentration were calculated for up to 30 days, and the total contribution of each input parameter to the model's uncertainty was quantified at different depths. Also, probability density functions of oil concentration were constructed by sampling the surrogate and used to elaborate probabilistic hazard maps of oil impact. The performance of the surrogate was constantly monitored in order to demarcate the space-time zones where its estimates are reliable. © 2016. American Geophysical Union.
Original languageEnglish (US)
Pages (from-to)2058-2077
Number of pages20
JournalJournal of Geophysical Research: Oceans
Volume121
Issue number4
DOIs
StatePublished - Apr 1 2016

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This research was made possible in part by a grant from BP/The Gulf of Mexico Research Initiative to the Deep-C and CARTHE Consortia, by the Office of Naval Research, award N00014-101-0498, and by the US Department of the Interior, Bureau of Ocean Energy Management under the cooperative agreement MC12AC00019. R. Goncalves acknowledges support by the Brazilian Ministry of Science, Technology and Innovation (CNPq-Council for Scientific and Technological Development) through a PHD scholarship from the Science Without Borders program, grant 202263/2012-6. O. Knio acknowledges partial support from the US Department of Energy, Office of Advanced Scientific Computing Research, under award DE-SC0008789. This research was conducted in collaboration with and using the resources of the University of Miami Center for Computational Science. The outputs of the DeepC Oil Model used here are publicly available through the Gulf of Mexico Research Initiative Information & Data Cooperative (GRIIDC). [Available at https://data.gulfresearchinitiative.org/data/R1.x138.077:0026.]

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