An oil risk management system based on high-resolution hazard and vulnerability calculations

Azevedo, Alberto; Fortunato, A. B.; Epifânio, Bruno; Boer, Sérgio den; Oliveira, Elise Botteselle de; Alves, Fátima L.; Jesus, Gonçalo de; Gomes, João L.; Oliveira, Anabela

doi:10.1016/j.ocecoaman.2016.11.014

Cited by 44 publications

(23 citation statements)

References 55 publications

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“…Return periods were calculated from Equation 2, derived from a Weibull distribution (Equation (3)), where, Tx is the period expressed in number of years, Px is the probability of exceedance, n is the number of The study area includes the 11 municipalities of the Baixo Vouga Subregion (NUTS III), corresponding to the lower section of the Vouga River Watershed. This river is the main fluvial course draining into the Aveiro Lagoon, a shallow coastal lagoon, with a length of 45 km (N-S direction) and a maximum width of 10 km (W-E direction) [85]. The lagoon and adjacent areas are recognized as a complex system, integrating urban areas and a wide range of natural and semi-natural habitats, classified as a Special Protection Area (Natura 2000 Network) and Site of Community Importance [86].…”

Section: Selection Of Eventsmentioning

confidence: 99%

Multi-Index Image Differencing Method (MINDED) for Flood Extent Estimations

et al. 2019

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Satellite remote sensing data are often used to extract water surfaces related to extreme events like floods. This study presents the Multi INDEx Differencing (MINDED) method, an innovative procedure to estimate flood extents, aiming at improving the robustness of single water-related indices and threshold-based approaches. MINDED consists of a change detection approach integrating specific sensitivities of several indices. Moreover, the method also allows to quantify the uncertainty of the Overall flood map, based on both the agreement level of the stack of classifications and the weight of every index obtained from the literature. Assuming the lack of ground truths to be the most common condition in flood mapping, MINDED also integrates a procedure to reduce the subjectivity of thresholds extraction focused on the analysis of water-related indices frequency distribution. The results of the MINDED application to a case study using Landsat images are compared with an alternative change detection method using Sentinel-1A data, and demonstrate consistency with local fluvial flood records.

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Section: Selection Of Eventsmentioning

confidence: 99%

Multi-Index Image Differencing Method (MINDED) for Flood Extent Estimations

et al. 2019

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“…Lastly, the use of normative impact modeling represents a hybrid approach, combining the best of both vulnerability and risk analysis [17]. For example, Azevedo and colleagues [57] determined risk via oil transport and shoreline exposure which is combined with a vulnerability metric based on biologic and physical indicators related to their sensitivity to oil. The combination of these two metrics into a spatially explicit normalized impact index is used to characterize different segments of coastline.…”

Section: Oil Spill Impact Modelingmentioning

confidence: 99%

“…As detailed previously, the Deepwater Horizon event triggered a substantial resurgence in the development and testing of methods to quantify actual and potential impact [57,69]. This process is necessarily data driven, relying on the incorporation of data sets to represent the vulnerability of coastal communities which can be distinguished by sector (biologic, economic, social) and then grouped together for a composite vulnerability measure (Table A1).…”

Section: Impact Calculationmentioning

confidence: 99%

The Implications of Oil Exploration off the Gulf Coast of Florida

Nelson

Grubesic

2018

JMSE

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In the United States (U.S.), oil exploration and production remain critical economic engines for local, state, and federal economies. Recently, the U.S. Department of the Interior expressed interest in expanding offshore oil production by making available lease areas in the U.S. Gulf of Mexico, the U.S. West Coast and East Coast, as well as offshore Alaska. With the promise of aiding in energy independence, these new lease areas could help solidify the U.S. as one of the world's largest oil-producing countries, while at the same time bolstering the local and regional energy job sectors. Of all the newly proposed lease areas, the Gulf Coast of Florida is particularly contentious. Opponents of drilling in the area cite the sensitive ecosystems and the local and state tourism economy that depends heavily on the numerous beaches lining Florida's coast. In this analysis, we use a data-driven spatial analytic approach combined with advanced oil spill modeling to determine the potential impact of oil exploration off of Florida's Gulf Coast given a loss-of-control event. It is determined that plume behavior varies drastically depending on the location of the spill but that overall impacts are comparable across all spill scenario sites, highlighting the necessity of contingency-type analyses. Implications for spill response are also discussed.

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“…The underestimation of such processes increases the local geometric uncertainty. Additionally, such uncertainty is likely to increase in time/space regions with strong flow dynamics as oil particles will transport and diffuse in a more rapid manner [17].…”

Section: Introductionmentioning

confidence: 99%

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

Feng

Passalacqua

Hodges

2019

JMSE

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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.

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An oil risk management system based on high-resolution hazard and vulnerability calculations

Cited by 44 publications

References 55 publications

Multi-Index Image Differencing Method (MINDED) for Flood Extent Estimations

Multi-Index Image Differencing Method (MINDED) for Flood Extent Estimations

The Implications of Oil Exploration off the Gulf Coast of Florida

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

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