Bulk meltwater flow and liquid water content of snowpacks mapped using the electrical self-potential (SP) method

Our ability to measure, quantify and assimilate hydrological properties and processes of snow in operational models is disproportionally poor compared to the significance of seasonal snowmelt as a global water resource and major risk factor in flood and avalanche forecasting. We show here that strong electrical self-potential fields are generated in melting in situ snowpacks at Rhone Glacier and Jungfraujoch Glacier, Switzerland. In agreement with theory, the diurnal evolution of self-potential magnitudes ( ∼ 60–250 mV) relates to those of bulk meltwater fluxes (0–1.2 × 10−6 m3 s−1) principally through the permeability and the content, electrical conductivity and pH of liquid water. Previous work revealed that when fresh snow melts, ions are eluted in sequence and electrical conductivity, pH and self-potential data change diagnostically. Our snowpacks had experienced earlier stages of melt, and complementary snow pit measurements revealed that electrical conductivity ( ∼ 1–5 × 10−6 S m−1) and pH ( ∼ 6.5–6.7) as well as permeabilities (respectively ∼ 9.7 × 10−5 and ∼ 4.3 × 10−5 m2 at Rhone Glacier and Jungfraujoch Glacier) were invariant. This implies, first, that preferential elution of ions was complete and, second, that our self-potential measurements reflect daily changes in liquid water contents. These were calculated to increase within the pendular regime from ∼ 1 to 5 and ∼ 3 to 5.5 % respectively at Rhone Glacier and Jungfraujoch Glacier, as confirmed by ground truth measurements. We conclude that the electrical self-potential method is a promising snow and firn hydrology sensor owing to its suitability for (1) sensing lateral and vertical liquid water flows directly and minimally invasively, (2) complementing established observational programs through multidimensional spatial mapping of meltwater fluxes or liquid water content and (3) monitoring autonomously at a low cost. Future work should focus on the development of self-potential sensor arrays compatible with existing weather and snow monitoring technology and observational programs, and the integration of self-potential data into analytical frameworks.