Validation of Oil Spill Transport and Fate Modeling in Arctic Ice

French-McCay, Deborah; Tajalli-Bakhsh, Tayebeh; Jayko, Kathy; Spaulding, Malcolm L.; Li, Zhengkai

doi:10.1139/as-2017-0027

Cited by 14 publications

(16 citation statements)

References 19 publications

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“…Moreover, SIMAP has been validated against data of more than 20 large spills, such as the Exxon Valdez [37,38,208] and [117]. In parallel, the simulation of SIMAP model in the pack ice provides results same as the experimental observations [271].…”

Section: Models Performance Against Field Datamentioning

confidence: 97%

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

Keramea

Spanoudaki

Zodiatis

et al. 2021

JMSE

156

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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: 97%

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

Keramea

Spanoudaki

Zodiatis

et al. 2021

JMSE

156

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“…An effective way to display this information could be by means of a model ranking based on the averaged model performance over the area of interest. This can be addressed by computing the spatiotemporal average of SS considering all available drifter observations during a specific period of observations (as in Röhrs et al, 2012;De Dominicis et al, 2014;Roarty et al, 2016;Sotillo et al, 2016;French-McCay et al, 2017;Phillipson and Toumi, 2017;Roarty et al, 2018). However, we show in this article that averages of the SS should be applied and interpreted with caution.…”

Section: Introductionmentioning

confidence: 93%

“…After being applied in the context of the Deepwater Horizon oil spill (Liu and Weisberg, 2011;Mooers et al, 2012;Halliwell et al, 2014), this metric has been one of the most widely used statistics for trajectory evaluation. The SS has been used to evaluate different parameterizations in operational oil spill trajectory models (Ivichev et al, 2012;Röhrs et al, 2012;De Dominicis et al, 2014;Berta et al, 2015;Wang et al, 2016;French-McCay et al, 2017;Janeiro et al, 2017;Chen et al, 2018;Zhang et al, 2018;Tamtare et al, 2019), to assess the impact of data assimilation in the model's Lagrangian predictability (Sperrevik et al, 2015;Phillipson and Toumi, 2017), to estimate the accuracy of the gap-filled method for HFR data (Fredj et al, 2017), to test the ability of ocean models in simulating surface transport (Sotillo et al, 2016), and to evaluate the relative performance of ocean models and HFR surface currents in predicting trajectories for SAR operations (Roarty et al, 2016(Roarty et al, , 2018.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity of Skill Score Metric to Validate Lagrangian Simulations in Coastal Areas: Recommendations for Search and Rescue Applications

et al. 2021

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Search and rescue (SAR) modeling applications, mostly based on Lagrangian tracking particle algorithms, rely on the accuracy of met-ocean forecast models. Skill assessment methods are therefore required to evaluate the performance of ocean models in predicting particle trajectories. The Skill Score (SS), based on the Normalized Cumulative Lagrangian Separation (NCLS) distance between simulated and satellite-tracked drifter trajectories, is a commonly used metric. However, its applicability in coastal areas, where most of the SAR incidents occur, is difficult and sometimes unfeasible, because of the high variability that characterizes the coastal dynamics and the lack of drifter observations. In this study, we assess the performance of four models available in the Ibiza Channel (Western Mediterranean Sea) and evaluate the applicability of the SS in such coastal risk-prone regions seeking for a functional implementation in the context of SAR operations. We analyze the SS sensitivity to different forecast horizons and examine the best way to quantify the average model performance, to avoid biased conclusions. Our results show that the SS increases with forecast time in most cases. At short forecast times (i.e., 6 h), the SS exhibits a much higher variability due to the short trajectory lengths observed compared to the separation distance obtained at timescales not properly resolved by the models. However, longer forecast times lead to the overestimation of the SS due to the high variability of the surface currents. Findings also show that the averaged SS, as originally defined, can be misleading because of the imposition of a lower limit value of zero. To properly evaluate the averaged skill of the models, a revision of its definition, the so-called SS∗, is recommended. Furthermore, whereas drifters only provide assessment along their drifting paths, we show that trajectories derived from high-frequency radar (HFR) effectively provide information about the spatial distribution of the model performance inside the HFR coverage. HFR-derived trajectories could therefore be used for complementing drifter observations. The SS is, on average, more favorable to coarser-resolution models because of the double-penalty error, whereas higher-resolution models show both very low and very high performance during the experiments.

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“…To model the transport, fate, and distribution of oil, we used the Spill Impact Model Application Package (SIMAP; French-McCay, 2004;French-McCay et al, 2017a). The SIMAP model has been validated with numerous actual oil spills (French-McCay et al, 2017b, 2004French and Rines, 1997;McCay, 2003) and has been used in many risk assessment studies (French-McCay et al, 2005), including in the Arctic (French-McCay et al, 2014;French-McCay et al, 2017b). In the model, the distribution and fate of oil was primarily affected by wind, ocean currents, and sea ice.…”

Section: Oil Spill Modelingmentioning

confidence: 99%

Potential impacts of offshore oil spills on polar bears in the Chukchi Sea

Wilson

Perham

French-McCay

et al. 2018

Environmental Pollution

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a b s t r a c tSea ice decline is anticipated to increase human access to the Arctic Ocean allowing for offshore oil and gas development in once inaccessible areas. Given the potential negative consequences of an oil spill on marine wildlife populations in the Arctic, it is important to understand the magnitude of impact a large spill could have on wildlife to inform response planning efforts. In this study we simulated oil spills that released 25,000 barrels of oil for 30 days in autumn originating from two sites in the Chukchi Sea (one in Russia and one in the U.S.) and tracked the distribution of oil for 76 days. We then determined the potential impact such a spill might have on polar bears (Ursus maritimus) and their habitat by overlapping spills with maps of polar bear habitat and movement trajectories. Only a small proportion (1 e10%) of high-value polar bear sea ice habitat was directly affected by oil sufficient to impact bears. However, 27e38% of polar bears in the region were potentially exposed to oil. Oil consistently had the highest probability of reaching Wrangel and Herald islands, important areas of denning and summer terrestrial habitat. Oil did not reach polar bears until approximately 3 weeks after the spills. Our study found the potential for significant impacts to polar bears under a worst case discharge scenario, but suggests that there is a window of time where effective containment efforts could minimize exposure to bears. Our study provides a framework for wildlife managers and planners to assess the level of response that would be required to treat exposed wildlife and where spill response equipment might be best stationed. While the size of spill we simulated has a low probability of occurring, it provides an upper limit for planners to consider when crafting response plans.Published by Elsevier Ltd.

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Validation of Oil Spill Transport and Fate Modeling in Arctic Ice

Cited by 14 publications

References 19 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

Sensitivity of Skill Score Metric to Validate Lagrangian Simulations in Coastal Areas: Recommendations for Search and Rescue Applications

Potential impacts of offshore oil spills on polar bears in the Chukchi Sea

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