Using geophysical data to understand liquid water dynamics in seasonal snow
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...
| Main Author: | |
|---|---|
| Other Authors: | , , , |
| Format: | Doctoral or Postdoctoral Thesis |
| Language: | English |
| Published: |
The University of Edinburgh
2022
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/1842/39521 https://doi.org/10.7488/era/2771 |
| Summary: | 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 ... |
|---|