Bayesian sunken oil tracking with SOSim v2: Inference from field and bathymetric data. (April 2021)

• Record Type: Journal Article
• Authors: Jacketti, Mary; Englehardt, James D.; Beegle-Krause, C.J.
• Is Part Of: Marine pollution bulletin. Volume 165(2021)
• Publication Date: 2021-04
• Subjects: Sunken oil -- Oil spill models -- SOSim -- Bayesian inference; 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.2021.112092
• Languages: English
• ISSNs: 0025-326X

Abstract

Abstract: Sunken oil is often difficult to detect, and few oil spill models are designed to locate and track such oil. Therefore, the multi-modal Bayesian inferential sunken oil model, SOSim (Subsurface Oil Simulator), was expanded in this work for use during emergency response and damage assessment. Rather than requiring hydrodynamic data as input, SOSim v2 accepts available field concentration data, along with default or custom bathymetric data, for inference of the location and trajectory of sunken oil. Novel aspects include inference based on bathymetry and the Coriolis Effect, by constructing a prior likelihood function from sampled bathymetric data, scaled proportionally with field concentration data. SOSim v2 is demonstrated versus field data on the ITB DBL-152 oil spill in the Gulf of Mexico, with sensitivity analysis. Results suggest that the inferential approach presented can be effective for modeling relatively slow-moving pollutant masses such as sunken oil, when field concentration data are available. Highlights: SOSim (Subsurface Oil Simulator) is a multi-modal Bayesian sunken oil model. SOSim was expanded to accept field concentration data and bathymetric data. Results suggest that SOSim can be an effective model when field data are available.