scholarly journals Application of the SOSim v2 Model to Spills of Sunken Oil in Rivers

2020 ◽  
Vol 8 (9) ◽  
pp. 729
Author(s):  
Mary Jacketti ◽  
James D. Englehardt ◽  
C.J. Beegle-Krause
Keyword(s):  
Emergency Response ◽  
Oil Spill ◽  
Likelihood Function ◽  
Transport Processes ◽  
Longitudinal Distribution ◽  
Concentration Data ◽  
Deterministic Models ◽  
Oil Transport ◽  
Bathymetric Data ◽  
Sunken Oil

Sunken oil transport processes in rivers differ from those in oceans, and currently available models may not be generally applicable to sunken oil in river settings. The open-source Subsurface Oil Simulator (SOSim) model has been expanded to handle spills of sunken oil in navigable rivers, utilizing Bayesian inference to integrate field concentration data with bathymetric data to predict the location and movement of sunken oil. A novel prior likelihood function incorporates bathymetric input, with sampling grid and default parameters adapted appropriately for rivers. SOSim v2 was demonstrated versus field observations taken following the M/T (Motor Tanker) Athos I oil spill. The model was also modified to operate in 1-D, to assess the longitudinal distribution of sunken oil in a non-navigable river using available poling data collected following the Enbridge Kalamazoo River oil spill in 2010. Results of both case studies were consistent with observed data and local bathymetry in 2-D and 1-D, and the model is suggested as a complement to deterministic models for oil spill emergency response in rivers.

scholarly journals Innovative Approaches for Geometric Uncertainty Quantification in an Operational Oil Spill Modeling System

2019 ◽  
Vol 7 (8) ◽  
pp. 259 ◽  
Author(s):  
Dongyu Feng ◽  
Paola Passalacqua ◽  
Ben R. Hodges
Keyword(s):  
Emergency Response ◽  
Oil Spill ◽  
Learning Algorithm ◽  
Coarse Grid ◽  
Data Driven ◽  
Model Integration ◽  
Fine Grid ◽  
Oil Transport ◽  
Uncertainty Model ◽  
Geometric Uncertainty

Reliable and rapid real-time prediction of likely oil transport paths is critical for decision-making from emergency response managers and timely clean-up after a spill. As high-resolution hydrodynamic models are slow, operational oil spill systems generally rely on relatively coarse-grid models to provide quick estimates of the near-future surface-water velocities and oil transport paths. However, the coarse grid resolution introduces model structural errors, which have been called “geometric uncertainty”. Presently, emergency response managers do not have readily-available methods for estimating how geometric uncertainty might affect predictions. This research develops new methods to quantify geometric uncertainty using fine- and coarse-grid models within a lagoonal estuary along the coast of the northern Gulf of Mexico. Using measures of geometric uncertainty, we propose and test a new data-driven uncertainty model along with a multi-model integration approach to quantify this uncertainty in an operational context. The data-driven uncertainty model is developed from a machine learning algorithm that provides a priori assessment of the prediction’s confidence degree. The multi-model integration generates ensemble predictions through comparison with limited fine-grid predictions. The two approaches provide explicit information on the expected scale of modeling errors induced by geometric uncertainty in a manner suitable for operational modeling.

scholarly journals Review of the Science behind Oil Spill Fate Models: Are Updates Needed?

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Lin Zhao ◽  
Timothy Nedwed ◽  
Douglas Mitchell
Keyword(s):  
Oil Spill ◽  
Particle Tracking ◽  
Chemical Properties ◽  
Transport Processes ◽  
Future Research ◽  
Near Surface ◽  
Oil Transport ◽  
Oil Spill Response ◽  
Planning Processes ◽  
Physical And Chemical

Abstract Oil spill models play an important role in the oil spill response decision making and contingency planning processes. The current generation of spill models mostly use Lagrangian based particle tracking random walk methods for oil transport processes combined with individual algorithms for oil fate processes (Spaulding, 2017). The fate of near surface oil movement is modeled using algorithms describing oil spreading, evaporation, emulsification, entrainment, dissolution, and biodegradation. These fate processes are applied to oil in the Lagrangian particle tracking elements to alter the physical and chemical properties of the oil, and subsequently the oil behavior. In this paper, we review the major algorithms used in oil spill models and identify the science and physics underpinning them. For each, we evaluated how far the science has advanced since the algorithms were developed to identify those that could be upgraded based on current understanding. We also identified algorithms where future research is needed because the physical and chemical behaviors are not fully understood. These areas include the spreading behavior of surface slicks, surface-slick emulsification, and the physical transport of small oil droplets near the air-water interface.

UNNECESSARILY NEGLECTED IN PLANNING: ILLUSTRATION OF A PRACTICAL APPROACH TO IDENTIFY HUMAN DIMENSION IMPACTS OF MARINE OIL SPILLS

2012 ◽  
Vol 14 (02) ◽  
pp. 1250012 ◽  
Author(s):  
FABIENNE LORD ◽  
SETH TULER ◽  
THOMAS WEBLER ◽  
KIRSTIN DOW
Keyword(s):  
Emergency Response ◽  
Oil Spill ◽  
Damage Assessment ◽  
Oil Spills ◽  
Social Systems ◽  
Human Dimensions ◽  
Contingency Planning ◽  
Human Dimension ◽  
Contingency Plans ◽  
Negative Impacts

Technological hazards research, including that on oil spills and their aftermath, is giving greater attention to human dimension impacts resulting from events and response. While oil spill contingency planners recognize the importance of human dimension impacts, little systematic attention is given to them in contingency plans. We introduce an approach to identifying human dimensions impacts using concepts from hazard and vulnerability assessment and apply it to the Bouchard-120 oil spill in Buzzards Bay, MA. Our assessment covers the spill, emergency response, clean-up, damage assessment, and mid-term recovery. This approach, while still exploratory, did demonstrate that the spill produced a range of positive and negative impacts on people and institutions and that these were mediated by vulnerabilities. We suggest ways in which the framework may help spill managers to learn from events and improve contingency planning by anticipating risks to social systems and identifying strategies to reduce impacts.

scholarly journals Formation, Detection, and Modeling of Submerged Oil: A Review

2020 ◽  
Vol 8 (9) ◽  
pp. 642
Author(s):  
Chao Ji ◽  
Cynthia Juyne Beegle-Krause ◽  
James D. Englehardt
Keyword(s):  
Real Time ◽  
Oil Spill ◽  
Probabilistic Models ◽  
Deepwater Horizon ◽  
Detection Methods ◽  
Formation Mechanisms ◽  
Constant Density ◽  
Deepwater Horizon Oil Spill ◽  
Deterministic Models ◽  
In Situ Detection

Submerged oil, oil in the water column (neither at the surface nor on the bottom), was found in the form of oil droplet layers in the mid depths between 900–1300 m in the Gulf of Mexico during and following the Deepwater Horizon oil spill. The subsurface peeling layers of submerged oil droplets were released from the well blowout plume and moved along constant density layers (also known as isopycnals) in the ocean. The submerged oil layers were a challenge to locate during the oil spill response. To better understand and find submerged oil layers, we review the mechanisms of submerged oil formation, along with detection methods and modeling techniques. The principle formation mechanisms under stratified and cross-current conditions and the concepts for determining the depths of the submerged oil layers are reviewed. Real-time in situ detection methods and various sensors were used to reveal submerged oil characteristics, e.g., colored dissolved organic matter and dissolved oxygen levels. Models are used to locate and to predict the trajectories and concentrations of submerged oil. These include deterministic models based on hydrodynamical theory, and probabilistic models exploiting statistical theory. The theoretical foundations, model inputs and the applicability of these models during the Deepwater Horizon oil spill are reviewed, including the pros and cons of these two types of models. Deterministic models provide a comprehensive prediction on the concentrations of the submerged oil and may be calibrated using the field data. Probabilistic models utilize the field observations but only provide the relative concentrations of the submerged oil and potential future locations. We find that the combination of a probabilistic integration of real-time detection with trajectory model output appears to be a promising approach to support emergency response efforts in locating and tracking submerged oil in the field.

scholarly journals Predicting the impact of future oil-spill closures on fishery-dependent communities—a spatially explicit approach

2019 ◽  
Author(s):  
Igal Berenshtein ◽  
Shay O’Farrell ◽  
Natalie Perlin ◽  
James N Sanchirico ◽  
Steven A Murawski ◽  
...  
Keyword(s):  
Oil Spill ◽  
Oil Spills ◽  
Transport Model ◽  
Gulf Coast ◽  
Three Dimensional ◽  
Management Tool ◽  
Spatially Explicit ◽  
Oil Transport ◽  
Fishing Fleets ◽  
The Impact

Abstract Major oil spills immensely impact the environment and society. Coastal fishery-dependent communities are especially at risk as their fishing grounds are susceptible to closure because of seafood contamination threat. During the Deepwater Horizon (DWH) disaster for example, vast areas of the Gulf of Mexico (GoM) were closed for fishing, resulting in coastal states losing up to a half of their fishery revenues. To predict the effect of future oil spills on fishery-dependent communities in the GoM, we develop a novel framework that combines a state-of-the-art three-dimensional oil-transport model with high-resolution spatial and temporal data for two fishing fleets—bottom longline and bandit-reel—along with data on the social vulnerability of coastal communities. We demonstrate our approach by simulating spills in the eastern and western GoM, calibrated to characteristics of the DWH spill. We find that the impacts of the eastern and western spills are strongest in the Florida and Texas Gulf coast counties respectively both for the bandit-reel and the bottom longline fleets. We conclude that this multimodal spatially explicit quantitative framework is a valuable management tool for predicting the consequences of oil spills at locations throughout the Gulf, facilitating preparedness and efficient resource allocation for future oil-spill events.

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