Measured glacier ice thickness as constraint on the transition from ice storage to runoff

Abstract ID: 3.29
| Accepted as Talk
| TBA
| TBA
Siebenbrunner, A. (1)
Keuschnig, M. (1); and Delleske, R. (1)
(1) GEORESEARCH Forschungsgesellschaft mbH, Snow & Glaciers, Urstein Süd 15, 5412 Puch bei Hallein
How to cite: Siebenbrunner, A.; Keuschnig, M.; and Delleske, R.: Measured glacier ice thickness as constraint on the transition from ice storage to runoff, #WAH26-3.29
Categories: No categories defined
Keywords: ice thickness, GPR, runoff, meltwater, glacier
Categories: No categories defined
Keywords: ice thickness, GPR, runoff, meltwater, glacier
Abstract
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Alpine glaciers store water as ice, and as they shrink this ice is released as runoff. Both the magnitude and the timing of that runoff depend largely on the volume of ice present today. Ice volume is therefore a central control on glacial runoff, yet it is usually modelled rather than measured and remains poorly constrained. To address this gap, we surveyed ten glaciers in the Eastern Alps using ground-penetrating radar (GPR) over the last two years. The surveys provide distributed ice-thickness measurements, and a coincident digital elevation model is used to derive the underlying bed topography. Together, these datasets constrain the volume of water currently stored as ice, and thus the runoff this storage will release as the glaciers retreat. The bed topography adds a further dimension: it reveals overdeepenings that mark potential sites of future proglacial lakes, which would buffer and redistribute meltwater downstream. By replacing modelled volumes with direct measurements, such surveys reduce a central uncertainty in glacial runoff projections. Extending GPR-based thickness mapping across more catchments would provide an empirical foundation for the next generation of glacio-hydrological models and support water-resource planning in a region where glacial storage is rapidly declining.

AS acknowledges funding from the Austrian Research Promotion Agency (FFG), project MELT.AI, grant no. 61794780.

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