Abstract. The widely used detailed SNOWPACK model has undergone constant development over the years. A notable recent extension is the introduction of a Richards equation (RE) solver as an alternative for the bucket-type approach for describing water transport in the snow and soil layers. In addition, continuous updates of snow settling and new snow density parameterizations have changed model behavior. This study presents a detailed evaluation of model performance against a comprehensive multiyear data set from Weissfluhjoch near Davos, Switzerland. The data set is collected by automatic meteorological and snowpack measurements and manual snow profiles. During the main winter season, snow height (RMSE: < 4.2 cm), snow water equivalent (SWE, RMSE: < 40 mm w.e.), snow temperature distributions (typical deviation with measurements: < 1.0 °C) and snow density (typical deviation with observations: < 50 kg m−3) as well as their temporal evolution are well simulated in the model and the influence of the two water transport schemes is small. The RE approach reproduces internal differences over capillary barriers but fails to predict enough grain growth since the growth routines have been calibrated using the bucket scheme in the original SNOWPACK model. However, the agreement in both density and grain size is sufficient to parameterize the hydraulic properties successfully. In the melt season, a pronounced underestimation of typically 200 mm w.e. in SWE is found. The discrepancies between the simulations and the field data are generally larger than the differences between the two water transport schemes. Nevertheless, the detailed comparison of the internal snowpack structure shows that the timing of internal temperature and water dynamics is adequately and better represented with the new RE approach when compared to the conventional bucket scheme. On the contrary, the progress of the meltwater front in the snowpack as detected by radar and the temporal evolution of the vertical distribution of melt forms in manually observed snow profiles do not support this conclusion. This discrepancy suggests that the implementation of RE partly mimics preferential flow effects.

The amount of liquid water in snow characterizes the wetness of a snowpack. Its temporal evolution plays an important role for wet-snow avalanche prediction, as well as the onset of meltwater release and water availability estimations within a river basin. However, it is still a challenge and a not yet satisfyingly solved issue to measure the liquid water content (LWC) in snow with conventional in situ and remote sensing techniques. We propose a new approach based on the attenuation of microwave radiation in the L-band emitted by the satellites of the Global Positioning System (GPS). For this purpose, we performed a continuous low-cost GPS measurement experiment at the Weissfluhjoch test site in Switzerland, during the snow melt period in 2013. As a measure of signal strength, we analyzed the carrier-to-noise power density ratio (C/N0) and developed a procedure to normalize these data. The bulk volumetric LWC was determined based on assumptions for attenuation, reflection and refraction of radiation in wet snow. The onset of melt, as well as daily melt-freeze cycles were clearly detected. The temporal evolution of the LWC was closely related to the meteorological and snow-hydrological data. Due to its non-destructive setup, its cost-efficiency and global availability, this approach has the potential to be implemented in distributed sensor networks for avalanche prediction or basin-wide melt onset measurements.

Snow stratigraphy and water percolation are key contributing factors to avalanche formation. So far, only destructive methods can provide this kind of information. Radar technology allows continuous, non-destructive scanning of the snowpack so that the temporal evolution of internal properties can be followed. We installed an upward-looking ground-penetrating radar system (upGPR) at the Weissfluhjoch study site (Davos, Switzerland). During two winter seasons (2010/11 and 2011/12) we recorded data with the aim of quantitatively determining snowpack properties and their temporal evolution. We automatically derived the snow height with an accuracy of about AE5 cm, tracked the settlement of internal layers (AE7 cm) and measured the amount of new snow (AE10 cm). Using external snow height measurements, we determined the bulk density with a mean error of 4.3% compared to manual measurements. Radar-derived snow water equivalent deviated from manual measurements by AE5%. Furthermore, we tracked the location of the dry-to-wet transition in the snowpack until water percolated to the ground. Based on the transition and an independent snow height measurement it was possible to estimate the volumetric liquid water content and its temporal evolution. Even though we need additional information to derive some of the snow properties, our results show that it is possible to quantitatively derive snow properties with upGPR.

Summary