Knowledge of englacial and subglacial conditions are critical for ice sheet models and predictions of sea-level change. Some of the critical variables that are poorly known but essential for improving flow models and predictions of sea-level change are: basal roughness, subglacial sedimentary and hydrologic conditions, and the temporal and spatial variability of the ice sheet flow field. Seismic reflection and refraction imaging and dense arrays of continuously operating GPS receivers can determine these parameters. The PIs propose to develop a network of wirelessly interconnected geophysical sensors (geoPebble) that will allow glaciologists to carry out these experiments simultaneously. This sensor web will provide a new way of imaging the ice sheet that is not possible with current instruments. With this sensor web, the PIs will extend the range of existing instruments from 2D to 3D, from low resolution to high resolution, but more importantly, all the geophysical measurements will be conducted synchronously. By the end of the proposal period the PIs will produce a network of 150-200 geoPebbles that will be available for NSF-sponsored glaciology research projects.
Improved knowledge of the flow law of ice, the sliding of glaciers and ice streams, and paleoclimate history will contribute to assessments of the potential for abrupt ice-sheet mass change, with consequent sea-level effects and significant societal impacts. This improved modeling ability will be a direct consequence of better knowledge of the physical properties of ice sheets, which this project will facilitate. The development effort will be integrated with the undergraduate education program via the capstone design classes in EE and the senior thesis requirement in Geoscience. The PIs will also form a cohort of first-year and sophomore students who will work in their labs from the beginning of the project to develop specifications through the commissioning of the network.
|Effective start/end date
|9/15/10 → 8/31/15
- National Science Foundation: $1,035,512.00