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Towards the development of an automated electrical self-potential sensor of melt and rainwater flow in snow
Journal of Glaciology, Volume: 68, Issue: 270, Pages: 720 - 732
Swansea University Author: Bernd Kulessa
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To understand snow structure and snowmelt timing, information about flows of liquid water within the snowpack is essential. Models can make predictions using explicit representations of physical processes, or through parameterization, but it is difficult to verify simulations. In situ observations g...
|Published in:||Journal of Glaciology|
Cambridge University Press (CUP)
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To understand snow structure and snowmelt timing, information about flows of liquid water within the snowpack is essential. Models can make predictions using explicit representations of physical processes, or through parameterization, but it is difficult to verify simulations. In situ observations generally measure bulk quantities. Where internal snowpack measurements are made, they tend to be destructive and unsuitable for continuous monitoring. Here, we present a novel method for in situ monitoring of water flow in seasonal snow using the electrical self-potential geophysical method. A prototype geophysical array was installed at Col de Porte (France) in October 2018. Snow hydrological and meteorological observations were also collected. Results for two periods of hydrological interest during winter 2018-19 (a marked period of diurnal melting and refreezing, and a rain-on-snow event) show that the electrical self-potential method is sensitive to internal water flow. Water flow was detected by self-potential signals before it was measured in conventional snowmelt lysimeters at the base of the snowpack. This initial feasibility study shows the utility of the self-potential method as a non-destructive snow sensor. Future development should include combining self-potential measurements with a high-resolution snow physics model to improve prediction of melt timing.
Glacier geophysics; glaciological instruments and methods; snow
Faculty of Science and Engineering
NERC E3 Doctoral Training Partnership studentship under grant NE/L002558/1 in partnership with British Geological Survey