CranSLIK v2.0: improving the stochastic prediction of oil spill transport and fate using approximation methods

Rutherford, R.; Moulitsas, Irene; Snow, Ben; Kolios, Athanasios; Dominicis, Michela De

doi:10.5194/gmd-8-3365-2015

Cited by 8 publications

(7 citation statements)

References 15 publications

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“…Moreover, the model has been validated with in situ data, surface drifters data, and with satellite data [90,176]. Interesting applications of the model are included in [176,177,179,[253][254][255][256][257]. MEDSLIK-II is used operationally in the Mediterranean region, allowing also support to REMPEC [258] for oil spill emergencies in the entire Mediterranean basin.…”

Section: The New Generation Of Oil Spill Modelsmentioning

confidence: 99%

Oil Spill Modeling: A Critical Review on Current Trends, Perspectives, and Challenges

Keramea

Spanoudaki

Zodiatis

et al. 2021

JMSE

147

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Several oil spill simulation models exist in the literature, which are used worldwide to simulate the evolution of an oil slick created from marine traffic, petroleum production, or other sources. These models may range from simple parametric calculations to advanced, new-generation, operational, three-dimensional numerical models, coupled to meteorological, hydrodynamic, and wave models, forecasting in high-resolution and with high precision the transport and fate of oil. This study presents a review of the transport and oil weathering processes and their parameterization and critically examines eighteen state-of-the-art oil spill models in terms of their capacity (a) to simulate these processes, (b) to consider oil released from surface or submerged sources, (c) to assimilate real-time field data for model initiation and forcing, and (d) to assess uncertainty in the produced predictions. Based on our review, the most common oil weathering processes involved are spreading, advection, diffusion, evaporation, emulsification, and dispersion. The majority of existing oil spill models do not consider significant physical processes, such as oil dissolution, photo-oxidation, biodegradation, and vertical mixing. Moreover, timely response to oil spills is lacking in the new generation of oil spill models. Further improvements in oil spill modeling should emphasize more comprehensive parametrization of oil dissolution, biodegradation, entrainment, and prediction of oil particles size distribution following wave action and well blow outs.

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Section: The New Generation Of Oil Spill Modelsmentioning

confidence: 99%

Oil Spill Modeling: A Critical Review on Current Trends, Perspectives, and Challenges

Keramea

Spanoudaki

Zodiatis

et al. 2021

JMSE

147

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“…They concluded that the causes of the assemblages after spill were the limited use of aggressive cleanup methods and fuel deposition on the shores that weren't intense but rather extensive. Rutherford et al (2015) examined how the stochastic prediction of oil spill transport can be improved using the approximation methods. Their sole aim was on how they can identify parameters that could further improve the forecasting algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

Application of the Stochastic Markov Model in Predicting the Volume of Oil Spill in Nigeria: A Case of the Niger-Delta Region

Christy

Ikechukwu

2019

IJEEP

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Oil spillage in the Niger Delta region of Nigeria and its associated hazard is on the increase and there is urgent need to combat its increasing volume by predicting the volume in the future thus, the objective of this study is on the prediction of the volume of oil spill in Nigeria via the Stochastic Markov Model. Two States Markov analysis were employed and it was discovered that the volume of oil spill incident were mostly maintained in a high state than in a low state and the predicted values were approximately steady at a probability value of 0.519 which is in favour of the high state. The study concluded that for the Nigerian Federal government to combat the volume of oil spill, she should in addition to enforcing the laws governing the volume of oil spill incident, employ remediation process that would help clean up the mess caused the spillage

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“…"Beaching" is a term commonly used in the literature to describe the interaction between the oil and the shoreline and is an essential part of oil spill modeling and impact assessment due to the environmental, economic, and social impor-tance of coastal areas (Samaras et al, 2014). In this study, we assess the uncertainty of beached oil in the context of an oil spill ensemble, investigating equally possible states of coastal pollution.…”

Section: Uncertainty Assessment Of Beached Oilmentioning

confidence: 99%

“…Mariano et al (2011) performed an ensemble to assess uncertainties in the oil spill state and spreading. Perturbed forcing fields have been used to assess their impact on an oil spill forecasting system by Jorda et al (2007) and stochastic methods have been applied on the transport and oil spill transformations by Snow et al (2014) and Rutherford et al (2015). Khade et al (2017) investigated the potential of atmospheric ensemble forecasting on the Deep Water Horizon oil spill accident in the Gulf of Mexico.…”

Section: Introductionmentioning

confidence: 99%

Oil spill model uncertainty quantification using an atmospheric ensemble

et al. 2021

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Abstract. We investigate the impact of atmospheric forcing uncertainties on the prediction of the dispersion of pollutants in the marine environment. Ensemble simulations consisting of 50 members were carried out using the ECMWF ensemble prediction system and the oil spill model MEDSLIK-II in the Aegean Sea. A deterministic control run using the unperturbed wind of the ECMWF high-resolution system served as reference for the oil spill prediction. We considered the oil spill rates and duration to be similar to major accidents of the past (e.g., the Prestige case) and we performed simulations for different seasons and oil spill types. Oil spill performance metrics and indices were introduced in the context of probabilistic hazard assessment. Results suggest that oil spill model uncertainties were sensitive to the atmospheric forcing uncertainties, especially to phase differences in the intensity and direction of the wind among members. An oil spill ensemble prediction system based on model uncertainty of the atmospheric forcing, shows great potential for predicting pathways of oil spill transport alongside a deterministic simulation, increasing the reliability of the model prediction and providing important information for the control and mitigation strategies in the event of an oil spill accident.

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CranSLIK v2.0: improving the stochastic prediction of oil spill transport and fate using approximation methods

Cited by 8 publications

References 15 publications

Oil Spill Modeling: A Critical Review on Current Trends, Perspectives, and Challenges

Oil Spill Modeling: A Critical Review on Current Trends, Perspectives, and Challenges

Application of the Stochastic Markov Model in Predicting the Volume of Oil Spill in Nigeria: A Case of the Niger-Delta Region

Oil spill model uncertainty quantification using an atmospheric ensemble

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