Date of Award

12-17-2023

Document Type

Dissertation

Abstract

The retreat and thinning of Arctic sea ice, driven by climate change, have increased the potential for maritime navigation in the region, thereby heightening concerns about the environmental impacts of potential oil spills in the Arctic. This dissertation, with a focus on the Beaufort and Chukchi Seas, seeks to develop a remote predictive method for assessing the subsurface features of Arctic sea ice, thereby facilitating rapid responses to Arctic oil spills without depending on time-consuming in situ measurements. The first paper of this dissertation addresses the need for oil spill modelers to understand oil movement along the subsurface of sea ice. Employing sonar data from the Chukchi Sea, the study investigates whether the subsurface topography of sea ice exhibits fractal scaling behavior. It was found that young sea ice exhibits multifractal scaling geometry, with parameters α, c1, and H determined as 1.2, 0.03, and 0.12, respectively. Fractal scaling behavior was not observed in other types of sea ice, highlighting the need for further research in this area. These findings are instrumental in enhancing predictive models for oil slick migration under sea ice, a crucial aspect of Arctic oil spill preparedness and mitigation. The dissertation's second paper analyzed five years of field data to determine the statistical distributions of subsurface features beneath various ice stages, using indirect assessment techniques. The analysis revealed that, with few exceptions, the subsurface features of sea ice predominantly follow lognormal distribution patterns, each characterized by distinct mean (mu) and standard deviation (sigma) values. This research represents a significant step forward in remote sea-ice characterization and is vital for formulating effective oil spill responses in the Arctic. The final paper utilized Arctic sea ice stage data, interpreted from satellite imagery, and sea ice draft data from moored sensors in the Beaufort and Chukchi Seas. This data was pivotal in accurately modeling the under-ice morphology, essential for establishing boundary conditions for realistic gravity-driven flow simulations. The study found that potential oil sequestration volumes could range from 30,000 to 1 million cubic meters per square kilometer, varying with the ice stage conditions. Additionally, the models suggest that under-ice morphology significantly influences oil slick movement, with only 20-40% of the ice surface encountering oil. This complexity highlights the intricate nature of Arctic oil spill cleanup and the potential for oil encapsulated in sea ice to cross international boundaries, emphasizing the need for comprehensive preparedness and international cooperation in Arctic oil spill response strategies.

Handle

http://hdl.handle.net/11122/14954

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