Towards the development of an automated electrical self-potential sensor of melt and rainwater flow in snow

• Published in: Journal of Glaciology
• Main Authors: Priestley, Alex, Kulessa, Bernd, Essery, Richard, Lejeune, Yves, Le Gac, Erwan, Blackford, Jane
• Other Authors: Natural Environment Research Council
• Format: Article in Journal/Newspaper
• Language: English
• Published: Cambridge University Press (CUP) 2021
• Subjects: Earth-Surface Processes; Journal of Glaciology
• Online Access: http://dx.doi.org/10.1017/jog.2021.128; https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0022143021001283

Abstract 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 (SP) 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 SP method is sensitive to internal water flow. Water flow was detected by SP signals before it was measured in conventional snowmelt lysimeters at the base of the snowpack. This initial feasibility study shows the utility of the SP method as a non-destructive snow sensor. Future development should include combining SP measurements with a high-resolution snow physics model to improve prediction of melt timing.