A predictive Bayesian data-derived multi-modal Gaussian model of sunken oil mass. (July 2015)

• Record Type: Journal Article
• Authors: Echavarria-Gregory, M. Angelica; Englehardt, James D.
• Is Part Of: Environmental modelling & software. Volume 69(2015:Jul.)
• Page Start: 1
• Page End: 13
• Publication Date: 2015-07
• Subjects: Sunken oil -- Bayesian -- Gaussian -- Stochastic -- Emergency response -- Statistical model; API American Petroleum Institute -- SOSim Sunken Oil Simulator -- NOAA National Oceanographic and Atmospheric Administration -- CRRC Coastal Response Research Center; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2015.02.014
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Hydrodynamic modeling of sunken oil is hindered by insufficient knowledge of bottom currents. In this paper, the development of a predictive Bayesian model, SOSim, for inferring the location of sunken oil in time, based on sparse, qualitative or quantitative near-real time field data collected immediately following a spill, is described. Mapped output represents unconditional multi-modal Gaussian relative probabilities of finding oil at points across a relatively flat bay bottom, in time. The method of images is extended to address curvilinear reflecting shorelines. The model is demonstrated to locate the entire DBL-152 spill, given field data covering part of the area affected, and to project oil movement near curvilinear shoreline boundaries given simulated field data at two points in time. Limitations include accountability for discontinuous boundary conditions. Further development is recommended, including development of capability for accepting bathymetric data, for modeling continuous oil releases, and for 3-D modeling of suspended oil. Highlights: Tracking of sunken oil is hindered by insufficient knowledge of bottom currents. We model sunken oil given sparse emergency field data on oil location in time. Oil location is projected in time using predictive Bayesian inference. Map output denotes unconditional multimodal Gaussian probabilities of finding oil. We demonstrate the model versus synthetic and real oil spill data.