Theory and numerical modeling of electrical self‐potential signatures of unsaturated flow in melting snow

Abstract

We have developed a new theory and numerical model of electrical self‐potential (SP) signals associated with unsaturated flow in melting snow. The model is applicable to continuous natural melt as well as transient flow phenomena such as meltwater pulses and is tested using laboratory column experiments. SP signals fundamentally depend on the temporal evolution of snow porosity and meltwater flux, electrical conductivity (EC), and pH. We infer a reversal of the sign of the zeta potential (a fundamental electrical property of grain surfaces in porous media) consistent with well‐known elution sequences of ions that cause progressive increases and decreases in meltwater pH and EC, respectively. Injection of fully melted snow samples, containing the entire natural range of ions, into melting snow columns caused additional temporary reversals of the sign of the zeta potential. Widely used empirical relationships between effective saturation, meltwater fraction, EC, and pH, as well as snow porosity, grain size, and permeability, are found to be robust for modeling purposes. Thus nonintrusive SP measurements can serve as proxies for snow meltwater fluxes and the temporal evolution of fundamental snow textural, hydraulic, or water quality parameters. Adaptation of automated multisensor SP acquisition technology from other environmental applications thus promises to bridge the widely acknowledged gap in spatial scales between satellite remote sensing and point measurements of snow properties. SP measurements and modeling may therefore contribute to solving a wide range of problems related to the assessment of water resource availability, avalanche or flood risk, or the amplification of climatic forcing of ice shelf, ice sheet, or glacier dynamics.