Improving oil spill trajectory modelling in the Arctic

Nordam, Tor; Beegle-Krause, CJ; Skancke, Jørgen; Nepstad, Raymond; Reed, Mark

doi:10.1016/j.marpolbul.2019.01.019

Cited by 48 publications

(32 citation statements)

References 24 publications

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“…In addition, OSCAR has been widely applied in oil spill risk evaluation and for response planning and operations [121] and for numerous hindcasts and predictions of oil spill accidents in the Northern and Baltic Sea, Gulf of Mexico, Mediterranean Sea [272,273], and in the Caspian Sea [274]. Further effort to upgrade the OSCAR model has been provided by Nordam et al [275]. The comparison of OSCAR with the Prestige's first and second oil slick evolution with drifter buoys trajectories have demonstrated the capacity of the model to identify the areas with the strong possibility of being influenced by a specific oil spill and the arrival time of the oil spilled at a specific coastal region.…”

Section: Models Performance Against Field Datamentioning

confidence: 99%

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

Keramea

Spanoudaki

Zodiatis

et al. 2021

JMSE

161

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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: Models Performance Against Field Datamentioning

confidence: 99%

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

Keramea

Spanoudaki

Zodiatis

et al. 2021

JMSE

161

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“…Given the uncertainties associated with modelling velocities in the MIZ, it makes sense to use both ice and ocean velocities to derive a general transport velocity. This is precisely the approach used for oil spill modelling in ice-covered waters (Nordam et al, 2019), where a mean transport velocity is calculated and the ice and surface ocean components are weighted by a function of ice concentration. The model used by the oil spill community uses some empirical estimates for the weighting function as well as for the leeway.…”

Section: Transport Equations For the Mizmentioning

confidence: 99%

“…where u o is the oil velocity, u w is the water velocity, u i is the ice velocity, U 10 is the wind velocity at 10 m, α w is a leeway coefficient for oil in water which is typically about 3% (Spaulding, 2017;Nordam et al, 2019) and k i is the ice transfer coefficient and a function of ice concentration A. Often k i is presented as a piece-wise linear function of ice concentration (Nordam et al, 2019), commonly referred to as the "80/30" rule, and defined as…”

Section: Oil Transport Equation In the Mizmentioning

confidence: 99%

Determining an optimal transport velocity in the marginal ice zone using operational ice-ocean prediction systems

Sutherland

Aguiar

Hole

et al. 2021

Preprint

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Abstract. Knowledge of transport in the marginal ice zone (MIZ) is critical for operations in the Arctic and associated emergency response applications, for example, the transport of pollutants, such as oil, as well as predicting drift associated with search and rescue operations. This paper proposes a general transport equation for the MIZ that can be used for operational purposes in the MIZ. This equation is designed to use a mean velocity of the ice and water velocity, which is weighted by the ice concentration. A key component is the introduction of a leeway coefficient for both the ocean and ice components. These leeway coefficients are determined by minimizing the velocity error between the transport model and observed drifter velocity in the MIZ. These leeway values are found to be 3 % of the wind for the water leeway and 2 % and 30° to the right of the wind for the ice leeway, which are consistent with "rule of thumb" values for surface drifters and sea ice respectively. This general transport model is compared with other transport models and the error is reduced by a factor of 2 compared with traditional transport models for 48 hour lead times. The inclusion of a leeway coefficient in the ice is the key component to reduce trajectory errors in the MIZ.

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“…Amir-Heidari and Raie [194] used wind (advection) and ocean current as metrological parameters for the creation of an active and passive decision support system (DSS) based on consequence modeling of GNOME for response planning for accidental oil spills in the Persian Gulf. To address the peculiarities of regions where snow and ice cover large sections of the water body, Nordam et al [51] adapted the OSCAR model by using sea-ice velocity to predict the trajectory of oil spill. An evaluation of the three case studies revealed that the use of sea-ice velocity, ocean wind, wave, and current is not always feasible for predicting trajectory, especially when there is constraint by land.…”

Section: Oil Spill Trajectory Modeling For Vulnerability Assessmentmentioning

confidence: 99%

“…Further, predicting the trajectory of oil spill is valuable in classifying the vulnerability of surrounding areas and prompt and accurate delineation of vulnerable areas is essential for decision making in disaster risk reduction. Several mathematical models based on Lagrangian particle works have been developed for this purpose but the ability to provide clear risk information with respect to the potential flow of the spilled oil is limited [50][51][52][53]. Recent advances in spatial data science, remote sensing technology and digitalization, particularly novel machine learning and deep learning algorithms, offer new opportunities to improve existing processes and address the challenges of oil spill disaster.…”

Section: Introductionmentioning

confidence: 99%

Advances in Remote Sensing Technology, Machine Learning and Deep Learning for Marine Oil Spill Detection, Prediction and Vulnerability Assessment

Yekeen

Balogun

2020

Remote Sensing

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Although advancements in remote sensing technology have facilitated quick capture and identification of the source and location of oil spills in water bodies, the presence of other biogenic elements (lookalikes) with similar visual attributes hinder rapid detection and prompt decision making for emergency response. To date, different methods have been applied to distinguish oil spills from lookalikes with limited success. In addition, accurately modeling the trajectory of oil spills remains a challenge. Thus, we aim to provide further insights on the multi-faceted problem by undertaking a holistic review of past and current approaches to marine oil spill disaster reduction as well as explore the potentials of emerging digital trends in minimizing oil spill hazards. The scope of previous reviews is extended by covering the inter-related dimensions of detection, discrimination, and trajectory prediction of oil spills for vulnerability assessment. Findings show that both optical and microwave airborne and satellite remote sensors are used for oil spill monitoring with microwave sensors being more widely used due to their ability to operate under any weather condition. However, the accuracy of both sensors is affected by the presence of biogenic elements, leading to false positive depiction of oil spills. Statistical image segmentation has been widely used to discriminate lookalikes from oil spills with varying levels of accuracy but the emergence of digitalization technologies in the fourth industrial revolution (IR 4.0) is enabling the use of Machine learning (ML) and deep learning (DL) models, which are more promising than the statistical methods. The Support Vector Machine (SVM) and Artificial Neural Network (ANN) are the most used machine learning algorithms for oil spill detection, although the restriction of ML models to feed forward image classification without support for the end-to-end trainable framework limits its accuracy. On the other hand, deep learning models’ strong feature extraction and autonomous learning capability enhance their detection accuracy. Also, mathematical models based on lagrangian method have improved oil spill trajectory prediction with higher real time accuracy than the conventional worst case, average and survey-based approaches. However, these newer models are unable to quantify oil droplets and uncertainty in vulnerability prediction. Considering that there is yet no single best remote sensing technique for unambiguous detection and discrimination of oil spills and lookalikes, it is imperative to advance research in the field in order to improve existing technology and develop specialized sensors for accurate oil spill detection and enhanced classification, leveraging emerging geospatial computer vision initiatives.

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Improving oil spill trajectory modelling in the Arctic

Cited by 48 publications

References 24 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

Determining an optimal transport velocity in the marginal ice zone using operational ice-ocean prediction systems

Advances in Remote Sensing Technology, Machine Learning and Deep Learning for Marine Oil Spill Detection, Prediction and Vulnerability Assessment

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