Using geophysical data to understand liquid water dynamics in seasonal snow

Using geophysical data to understand liquid water dynamics in seasonal snow Priestley, Alex Modelling and monitoring seasonal snow is critical for water resource management, flood forecasting and avalanche risk prediction. Snowmelt processes are of particular importance. The behaviour of liquid water in snow has a big influence on melting processes, but is difficult to measure and monitor non-invasively. Recent work has shown the promise of using electrical self potential and electrical resistivity measurements as snow hydrology sensors. Self potential magnitudes can be used to infer both liquid water content of snow and bulk meltwater runoff, and electrical resistivity is affected by liquid water content. In autumn 2018, a prototype geophysical monitoring array was installed at Col de Porte in the French Alps, alongside full hydrological and meteorological measurements made routinely at the site. Self potential measurements were taken throughout the following two winters, with manual snow pit data obtained in spring 2019. Electrical resistivity measurements were unsuccessful due to problems with power and control units. Observed self potential peaks preceded measured basal runoff peaks, indicating that self potential measurements are sensitive to water dynamics within the snowpack, most clearly during spring melting and rain-on-snow events. A physically-based snow hydrology model (Flexible Snow Model 2.0) was evaluated at Col de Porte against observations in order to select a best-performing configuration, by utilising the ability to easily change model parameters. Three different hydrology and two density configurations were tested, as well as investigating the effect of varying the irreducible water saturation and saturated hydraulic conductivity. It was found that an irreducible water saturation of 0.03 performed best, and that changing the saturated hydraulic conductivity had little effect on performance. This snow model was then coupled to an electrical model of liquid water in snow to create a synthetic set of self potential observations. These synthetic observations were compared to the observed self potential magnitudes to evaluate the effectiveness of the model and investigate the possibility of using the self potential array as part of a coupled geophysical monitoring and modelling system. It was found that modelled self potential magnitudes are extremely sensitive to small changes in prescribed snow properties, giving large uncertainties. Timings of modelled self potential peaks were able to be related to meteorological and hydrological observations, meaning self potential measurements could be used to improve liquid water flow representation in snow models. An empirical relationship between measured self potential and modelled internal water flow was trialled, which highlighted the potential for future empirical methods to exploit self potential observations. The combination of self potential, and meteorological and hydrological measurements has highlighted the value of combining observations with models both to guide future observation networks, and improve modelling capabilities.