Project Grant 2146702

Award Date 8/15/22
Completion Date 7/31/25
Dollars Obligated $269K
Funding Federal Agency
National Science Foundation
Awarding Federal Agency
Division of Polar Programs
Federal Grant Program
47.078
Assistance Type
Project Grant
Place of Performance
Austin, TX 78758, USA
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This $127,350 project grant from the National Science Foundation Division of Polar Programs, under the Polar Programs federal grant program (CFDA 47.078), will support collaborative research to improve modeling of terminus ablation for Greenland's outlet glaciers. The researchers will use machine learning to analyze glaciological observations and better understand the variables influencing the ice-ocean boundary. They will develop an equation to represent ice-ocean interactions in an ice sheet...
The National Science Foundation (NSF) awarded a $1,281,805 Project Grant under its Polar Programs (CFDA 47.078) to the University of Texas at Austin. The grant, effective January 1, 2024 through September 30, 2026, aims to collect observations from the fronts of three Greenland outlet glaciers to determine if sediment deposition at the ice-ocean boundary is responsible for observed variability in glacier retreat. The project will measure sedimentation rates and processes, as well as monitor...
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The National Science Foundation awarded a $269,223 Project Grant to the University of Texas at Austin from the Polar Programs federal grant program (CFDA 47.078) to support research titled "Collaborative Research: Machine-Enabled Modeling of Terminus Ablation for Greenland's Outlet Glaciers." The research aims to improve projections of sea-level rise from Greenland through developing physics-based modeling of ice-ocean interactions at outlet glaciers using machine learning analysis of glaciological observations. Specifically, the team will use machine learning tools to analyze terminus position changes over time for all Greenland glaciers during a period of significant historical change to determine dominant controls on terminus position for individual and groups of glaciers. The machine learning model of terminus positions will then be used to refine projections of outlet glacier mass change from an ice flow model. A subaward of $129,000 is provided to the University of Nevada, Reno to support related research titled "Machine-Enabled Modeling of Terminus Ablation for Greenland's Outlet Glaciers." The research is intended to provide refined projections of dynamic ice loss from Greenland to inform critical infrastructure and resource decisions by policymakers.

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