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

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.

Snow model comparison to simulate snow depth evolution and sublimation at point scale in the semi-arid Andes of Chile

Physically based snow models provide valuable information on snow cover evolution and are therefore key to provide water availability projections. Yet, uncertainties related to snow modelling remain large as a result of differences in the representation of snow physics and meteorological forcing. While many studies focus on evaluating these uncertainties, no snow model comparison has been done in environments where sublimation is the main ablation process. This study evaluates a case study in the semi-arid Andes of Chile and aims to compare two snow models with different complexities, SNOWPACK and SnowModel, at a local point over one snow season and to evaluate their sensitivity relative to parameterisation and forcing. For that purpose, the two models are forced with (i) the most ideal set of input parameters, (ii) an ensemble of different physical parameterisations, and (iii) an ensemble of biased forcing. Results indicate large uncertainties depending on forcing, the snow roughness length z0, albedo parameterisation, and fresh snow density parameterisation. The uncertainty caused by the forcing is directly related to the bias chosen. Even though the models show significant differences in their physical complexity, the snow model choice is of least importance, as the sensitivity of both models to the forcing data was on the same order of magnitude and highly influenced by the precipitation uncertainties. The sublimation ratio ranges are in agreement for the two models: 36.4 % to 80.7 % for SnowModel and 36.3 % to 86.0 % for SNOWPACK, and are related to the albedo parameterisation and snow roughness length choice for the two models.

Snow model comparison to simulate snow depth evolution and sublimation at point scale in the semi-arid Andes of Chile

Physically-based snow models provide valuable information on snow cover evolution and are therefore key to provide water availability projections. Yet, uncertainties related to snow modelling remain large as a result of differences in the representation of snow physics and meteorological forcing. While many studies focus on evaluating these uncertainties, issues still arise, especially in environments where sublimation is the main ablation process. This study evaluates a case study in the semi-arid Andes of Chile and aims to compare two snow models with different complexities, SNOWPACK and SnowModel, at a local point, over one snow season. Their sensitivity relative i) to physical calibration for albedo and fresh snow density and ii) to forcing perturbation is evaluated based on ensemble approaches. Results indicate larger uncertainty depending on the model calibration than between the two models (even though the significant differences in their physical complexity). We also confirm the importance of albedo parameterization, even though ablation is driven by sublimation. SnowModel is particularly sensitive to this choice as it strongly affects both the sublimation and the melt rates. However, the day of snow-free snow surface is not sensitive to the parameterization as it only varies every eight days. The albedo parameterization of SNOWPACK has stronger consequences on melt at the end of the season leading to a date difference of the end of the season of 41 days. However, despite these differences, the sublimation ratio ranges are in agreement for the two models: 42.7–63.5 % for SnowModel and 51.3 and 64.6 % for SNOWPACK, and are related to the albedo calibration choice for the two models. Finally, the sensitivity of both models to the forcing data was in the same order of magnitude and highly influenced by the precipitation uncertainties.

Permafrost Area Is Sensitive to Key Soil and Snow Physics

Accounting for key soil and snow variables shows a much higher impact on simulated permafrost area than uncertainties in land cover and climate data.