Snow Layer Research Articles

Multi-physics ensemble modelling of Arctic tundra snowpack properties

Abstract. Sophisticated snowpack models such as Crocus and SNOWPACK struggle to properly simulate profiles of density and specific surface area (SSA) within Arctic snowpacks due to underestimation of wind-induced compaction, misrepresentation of basal vegetation influencing compaction and metamorphism, and omission of water vapour flux transport. To improve the simulation of profiles of density and SSA, parameterisations of snow physical processes that consider the effect of high wind speeds, the presence of basal vegetation, and alternate thermal conductivity formulations were implemented into an ensemble version of the Soil, Vegetation, and Snow version 2 (SVS2-Crocus) land surface model, creating Arctic SVS2-Crocus. The ensemble versions of the default and Arctic SVS2-Crocus were driven with in situ meteorological data and evaluated using measurements of snowpack properties (snow water equivalent, SWE; depth; density; and SSA) at Trail Valley Creek (TVC), Northwest Territories, Canada, over 32 years (1991–2023). Results show that both the default and Arctic SVS2-Crocus can simulate the correct magnitude of SWE (root-mean-square error, RMSE, for both ensembles – 55 kg m−2) and snow depth (default RMSE – 0.22 m; Arctic RMSE – 0.18 m) at TVC in comparison to measurements. Wind-induced compaction within Arctic SVS2-Crocus effectively compacts the surface layers of the snowpack, increasing the density, and reducing the RMSE by 41 % (176 kg m−3 to 103 kg m−3). Parameterisations of basal vegetation are less effective in reducing compaction of basal snow layers (default RMSE – 67 kg m−3; Arctic RMSE – 65 kg m−3), reaffirming the need to consider water vapour flux transport for simulation of low-density basal layers. The top 100 ensemble members of Arctic SVS2-Crocus produced lower continuous ranked probability scores (CRPS) than the default SVS2-Crocus when simulating snow density profiles. The top-performing members of the Arctic SVS2-Crocus ensemble featured modifications that raise wind speeds to increase compaction in snow surface layers and to prevent snowdrift and increase viscosity in basal layers. Selecting these process representations in Arctic SVS2-Crocus will improve simulation of snow density profiles, which is crucial for many applications.

Mechanism of ion migration from the substrate material into snow cover at the end of the cold period

Earlier, it was established that maximum mineralization of the contact layer of snow occurs in spring at the interface with substrates (soil or ice). This study analyzes the temperature and moisture conditions during this period at the interface of the contact layer of snow with substrates by examining frozen sand blocks saturated with a solution containing complex gold ions, or blocks filled with polystyrene containing ions of molybdenum, copper, etc. It is assumed that the migration of ions from the underlying substrate into the contact layer of snow cover in spring occurs along quasi-liquid films on the surface of snow crystals, the thickness of which exceeds the equilibrium one. Migration becomes noticeable when the temperature at the snow–substrate contact reaches −13 °С and above. The appearance of quasi-liquid films on the surface of snow particles under variable temperature and moisture conditions is possible due to the condensation of water vapor, which during the day, with general heating of the system, can enter the contact layer of snow both from above and below. With an increase in snow density in the spring, the mineralization of the near-contact layer of snow cover increases. At the same time, linear relationships were revealed between the content of substrate components migrating into the near-contact layer of snow and the gradient of water vapor density in it. The reliability of the approximation of these dependencies for the gold thiosulfate complex is 0.98; for copper ions – 0.52; for hydrogen ions – 0.88; for sodium ions – 0.69, for chloride anions – 0.89. The results of the study substantiate the increased efficiency of geochemical prospecting for mineral deposits using snow cover in the spring.

A novel approach for bridging the gap between climate change scenarios and avalanche hazard indication mapping

The influence of climate change on snow avalanches, particularly for the end of this century, remains uncertain, underscoring the need for further research. To assess the possible consequences of potential changes in snow accumulation and temperature and their impact on avalanche hazard, we introduce a comprehensive multi-step framework. It includes the analysis of climate change scenarios as well as the modeling of future snow covers and the simulation of avalanches in a case study region in central Switzerland.Using a downscaling and a quantile mapping approach, we considered the high emission RCP8.5 from the CH2018 Swiss climate change scenarios and simulated a potential snow cover of more than 100 future winters with the snow cover model SNOWPACK. Changing snow accumulation and snow cover temperature was taken into account for two future time frames. The changed parameters were used in the RAMMS::EXTENDED avalanche simulation software on large scale.The results indicate that changes in snow accumulation and temperature have a considerable impact on the run-out of avalanches. The results strongly depend on the climate model, without a clear overall trend in snow accumulation across the selected model chains. Snow accumulation and layer temperature can increase or decrease. However, for snow cover temperature, an increase in the mean snow temperature, especially towards the end of the century, can be expected. In future scenarios with reduced snow accumulation and rising temperatures, avalanche simulations show a decrease in the affected area.The workflow from climate scenario analysis to avalanche hazard modeling serves as an initial method for estimating future avalanche extents in the context of climate change on a large scale and can be useful for achieving future protection and adaptation goals.

On sea ice emission modeling for MOSAiC's L-band radiometric measurements

Abstract The retrieval of sea ice thickness using L-band passive remote sensing requires robust models for emission from sea ice. In this work, measurements obtained from surface-based radiometers during the MOSAiC expedition are assessed with the Burke, Wilheit and SMRT radiative transfer models. These models encompass distinct methodologies: radiative transfer with/without wave coherence effects, and with/without scattering. Before running these emission models, the sea ice growth is simulated using the Cumulative Freezing Degree Days (CFDD) model to further compute the evolution of the ice structure during each period. Ice coring profiles done near the instruments are used to obtain the initial state of the computation, along with Digital Thermistor Chain (DTC) data to derive the sea ice temperature during the analyzed periods. The results suggest that the coherent approach used in the Wilheit model results in a better agreement with the horizontal polarization of the in situ measured brightness temperature. The Burke and SMRT incoherent models offer a more robust fit for the vertical component. These models are almost equivalent since the scattering considered in SMRT can be safely neglected at this low frequency, but the Burke model misses an important contribution from the snow layer above sea ice. The results also suggest that a more realistic permittivity falls between the spheres and random needles formulations, with potential for refinement, particularly for L-band applications, through future field measurements.

Exploring the potential of forest snow modeling at the tree and snowpack layer scale

Abstract. Boreal and sub-alpine forests host seasonal snow for multiple months per year; however, snow regimes in these environments are rapidly changing due to rising temperatures and forest disturbances. Accurate prediction of forest snow dynamics, relevant for ecohydrology, biogeochemistry, cryosphere, and climate sciences, requires process-based models. While snow schemes that track the microstructure of individual snow layers have been proposed for avalanche research, so far, tree-scale processes resolving canopy representations only exist in a few snow-hydrological models. A framework that enables layer- and microstructure-resolving forest snow simulations at the meter scale is lacking to date. To fill this research gap, this study introduces the forest snow modeling framework FSMCRO, which combines two detailed, state-of-the art model components: the canopy representation from the Flexible Snow Model (FSM2) and the snowpack representation of the Crocus ensemble model system (ESCROC). We apply FSMCRO to discontinuous forests at boreal and sub-alpine sites to showcase how tree-scale forest snow processes affect layer-scale snowpack properties. Simulations at contrasting locations reveal marked differences in stratigraphy throughout the winter. These arise due to different prevailing processes at under-canopy versus gap locations and due to variability in snow metamorphism dictated by a spatially variable snowpack energy balance. Ensemble simulations allow us to assess the robustness and uncertainties of simulated stratigraphy. Spatially explicit simulations unravel the dependencies of snowpack properties on canopy structure at a previously unfeasible level of detail. Our findings thus demonstrate how hyper-resolution forest snow simulations can complement observational approaches to improve our understanding of forest snow dynamics, highlighting the potential of such models as research tools in interdisciplinary studies.

A global–land snow scheme (GLASS) v1.0 for the GFDL Earth System Model: formulation and evaluation at instrumented sites

Abstract. Snowpacks modulate water storage over extended land regions and at the same time play a central role in the surface albedo feedback, impacting the climate system energy balance. Despite the complexity of snow processes and their importance for both land hydrology and global climate, several state-of-the-art land surface models and Earth System Models still employ relatively simple descriptions of the snowpack dynamics. In this study we present a newly-developed snow scheme tailored to the Geophysical Fluid Dynamics Laboratory (GFDL) land model version 4.1. This new snowpack model, named GLASS (Global LAnd–Snow Scheme), includes a refined and dynamical vertical-layering snow structure that allows us to track the temporal evolution of snow grain properties in each snow layer, while at the same time limiting the model computational expense, as is necessary for a model suited to global-scale climate simulations. In GLASS, the evolution of snow grain size and shape is explicitly resolved, with implications for predicted bulk snow properties, as they directly impact snow depth, snow thermal conductivity, and optical properties. Here we describe the physical processes in GLASS and their implementation, as well as the interactions with other surface processes and the land–atmosphere coupling in the GFDL Earth System Model. The performance of GLASS is tested over 10 experimental sites, where in situ observations allow for a comprehensive model evaluation. We find that when compared to the current GFDL snow model, GLASS improves predictions of seasonal snow water equivalent, primarily as a consequence of improved snow albedo. The simulated soil temperature under the snowpack also improves by about 1.5 K on average across the sites, while a negative bias of about 1 K in snow surface temperature is observed.

Antarctic sea ice surface temperature bias in atmospheric reanalyses induced by the combined effects of sea ice and clouds

Sea-ice surface temperature from atmospheric reanalysis has been used as an indicator of ice melt and climate change. However, its performance in atmospheric reanalyses is not fully understood in Antarctica. Here, we quantified biases in six widely-used reanalyses using satellite observations, and found strong and persistent warm biases in most reanalyses examined. Further analysis of the biases revealed two main culprits: incorrect cloud properties, and inappropriate sea-ice representation in the reanalysis products. We found that overestimated cloud simulation can contribute more than 4 K warm bias, with ERA5 exhibiting the largest warm bias. Even in reanalysis with smaller biases, this accuracy is achieved through a compensatory relationship between relatively lower cloud fraction bias and overestimated sea ice insulation effect. A dynamic downscaling simulation shows that differences in sea-ice representation can contribute a 2.3 K warm bias. The representation of ice concentration is the primary driver of the spatial distribution of biases by modulating the coupling between sea ice and clouds, as well as surface heat conduction. The lack of a snow layer in all reanalyses examined also has an impact on biases.

Quantifying the influence of snow over sea ice morphology on L-band passive microwave satellite observations in the Southern Ocean

Abstract. Antarctic snow on sea ice can contain slush, snow ice, and stratified layers, complicating satellite retrieval processes for snow depth, ice thickness, and sea ice concentration. The presence of moist and brine-wetted snow alters microwave snow emissions and modifies the energy and mass balance of sea ice. This study assesses the impact of brine-wetted snow and slush layers on L-band surface brightness temperatures (TBs) by synergizing a snow stratigraphy model (SNOWPACK) driven by atmospheric reanalysis data and the RAdiative transfer model Developed for Ice and Snow in the L-band (RADIS-L) v1.0 The updated RADIS-L v1.1 further introduces parameterizations for brine-wetted snow and slush layers over Antarctic sea ice. Our findings highlight the importance of including both brine-wetted snow and slush layers in order to accurately simulate L-band brightness temperatures, laying the groundwork for improved satellite retrievals of snow depth and ice thickness using satellite sensors such as Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP). However, biases in modelled and observed L-band brightness temperatures persist, which we attribute to small-scale sea ice heterogeneity and snow stratigraphy. Given the scarcity of comprehensive in situ snow and ice data in the Southern Ocean, ramping up observational initiatives is imperative to not only provide satellite validation datasets but also improve process-level understanding that can scale up to improving the precision of satellite snow and ice thickness retrievals.

Multiscale modeling of heat and mass transfer in dry snow: influence of the condensation coefficient and comparison with experiments

Abstract. Temperature gradient metamorphism in dry snow is driven by heat and water vapor transfer through snow, which includes conduction/diffusion processes in both air and ice phases, as well as sublimation and deposition at the ice–air interface. The latter processes are driven by the condensation coefficient α, a poorly constrained parameter in the literature. In the present paper, we use an upscaling method to derive heat and mass transfer models at the snow layer scale for values of α in the range 10−10 to 1. A transition α value arises, of the order of 10−4, for typical snow microstructures (characteristic length ∼ 0.5 mm), such that the vapor transport is limited by sublimation–deposition below that value and by diffusion above it. Accordingly, different macroscopic models with specific domains of validity with respect to α values are derived. A comprehensive evaluation of the models is presented by comparison with three experimental datasets, as well as with pore-scale simulations using a simplified microstructure. The models reproduce the two main features of the experiments: the non-linear temperature profiles, with enhanced values in the center of the snow layer, and the mass transfer, with an abrupt basal mass loss. However, both features are underestimated overall by the models when compared to the experimental data. We investigate possible causes of these discrepancies and suggest potential improvements for the modeling of heat and mass transport in dry snow.

Post-depositional modification on seasonal-to-interannual timescales alters the deuterium-excess signals in summer snow layers in Greenland

Abstract. We document the isotopic evolution of near-surface snow at the East Greenland Ice Core Project (EastGRIP) ice core site in northeast Greenland using a time-resolved array of 1 m deep isotope (δ18O, δD) profiles. The snow profiles were taken from May–August during the 2017–2019 summer seasons. An age–depth model was developed and applied to each profile, mitigating the impacts of stratigraphic noise on isotope signals. Significant changes in deuterium excess (d) are observed in surface snow and near-surface snow as the snow ages. Decreases in d of up to 5 ‰ occur during summer seasons after deposition during two of the three summer seasons observed. The d always experiences a 3 ‰–5 ‰ increase after aging 1 year in the snow due to a broadening of the autumn d maximum. Models of idealized scenarios coupled with prior work indicate that the summertime post-depositional changes in d (Δd) can be explained by a combination of surface sublimation, forced ventilation of the near-surface snow down to 20–30 cm, and isotope-gradient-driven diffusion throughout the column. The interannual Δd is also partly explained with isotope-gradient-driven diffusion, but other mechanisms are at work that leave a bias in the d record. Thus, d does not just carry information about source-region conditions and transport history as is commonly assumed, but also integrates local conditions into summer snow layers as the snow ages through metamorphic processes. Finally, we observe a dramatic increase in the seasonal isotope-to-temperature sensitivity, which can be explained solely by isotope-gradient-driven diffusion. Our results are dependent on the site characteristics (e.g., wind, temperature, accumulation rate, snow properties) but indicate that more process-based research is necessary to understand water isotopes as climate proxies. Recommendations for monitoring and physical modeling are given, with special attention to the d parameter.

Greenland's firn responds more to warming than to cooling

Abstract. The porous layer of snow and firn on the Greenland Ice Sheet stores meltwater and limits the rate at which the ice sheet contributes to sea level rise. This buffer is threatened in a warming climate. To better understand the nature and timescales of firn's response to air temperature change on the Greenland Ice Sheet, we use a physics-based model to assess the effects of atmospheric warming and cooling on Greenland's firn air content in idealized climate experiments. We identify an asymmetric response of Greenland's firn to air temperature: firn loses more air content due to warming compared to the amount gained from commensurate cooling. 100 years after a 1 °C temperature perturbation, warming decreases the spatially integrated air content by 9.7 %, and cooling increases it by 8.3 %. In dry firn, this asymmetry is driven by the highly nonlinear relationship between temperature and firn compaction, as well as the dependence of thermal conductivity on the composition of the firn. The influence of liquid water accentuates this asymmetry. In wet firn areas, melt increases nonlinearly with atmospheric warming, thus enhancing firn refreezing and further warming the snowpack through increased latent heat release. Our results highlight the vulnerability of Greenland firn to temperature change and demonstrate that firn air content is more efficiently depleted than generated. This asymmetry in the temperature–firn relationship may contribute to the overall temporally asymmetric mass change of the Greenland Ice Sheet in a changing climate across many timescales.

Comparison of bulk snow density measurements using different methods

Snow density is one of the basic properties used to describe snow cover characteristics, and it is critical for remote sensing retrieval, water resources assessment and modeling inputs. There are many instruments available to measure snow density in situ. However, there are measurement errors of snow density for bulk and layers or gravimetric and electronic instruments, which may affect the accuracy of remote sensing retrieval and model simulation. Especially in China, due to the noticeable heterogeneity of snowpacks, it is necessary to evaluate in detail the performance and applicability of snow density instruments in different snowpack conditions. This study evaluated the performance of different snow density instruments: the Federal Sampler, the model VS–43 snow density cylinder (VS–43), the wedge snow density cutter (WC1000 and WC250), and the Snow Fork. The average bulk snow density of all instrument measurements was set as the reference value for evaluation. The results showed that as compared with the reference, the VS–43 cylinder presented the best performance for bulk snow density measurement in the measured range with the lowest RMSE (11 kg m−3), BIAS (3 kg m−3), and MRE (1.6%). For layer observation, bulk snow density was overestimated by 8.1% with WC1000 and underestimated by 11.4% with Snow Fork which was the worst performance compared with the reference value, and there were greater measurement errors of snow density in the depth hoar than other snow layers. Compared with grassland, the uncertainty of snow density measurements was slightly lower in forests. Overall, the Federal Sampler and VS–43 cylinder are more suitable for bulk snow density measurement in deep snowpack regions across China, and it is recommended to use WC1000, WC250 and Snow Fork to measure the snow density of snow layers in the snow stratigraphy.

Transition from a mixotrophic/heterotrophic protist community during the dark winter to a photoautotrophic spring community in surface waters of Disko Bay, Greenland.

Unicellular eukaryotic plankton communities (protists) are the major basis of the marine food web. The spring bloom is especially important, because of its high biomass. However, it is poorly described how the protist community composition in Arctic surface waters develops from winter to spring. We show that mixotrophic and parasitic organisms are prominent in the dark winter period. The transition period toward the spring bloom event was characterized by a high relative abundance of mixotrophic dinoflagellates, while centric diatoms and the haptophyte Phaeocystis pouchetii dominated the successive phototrophic spring bloom event during the study. The data shows a continuous community shift from winter to spring, and not just a dormant spring community waiting for the right environmental conditions. The spring bloom initiation commenced while sea ice was still scattering and absorbing the sunlight, inhibiting its penetration into the water column. The initial increase in fluorescence was detected relatively deep in the water column at ~55 m depth at the halocline, at which the photosynthetic cells accumulated, while a thick layer of snow and sea ice was still obstructing sunlight penetration of the surface water. This suggests that water column stratification and a complex interplay of abiotic factors eventually promote the spring bloom initiation.

Snow Penetration Depth Inversion in Tibetan Plateau Based on Coherence Scattering Model with X-band Data

Abstract. The snow penetration depth is a necessary input parameter to accurately evaluate the thickness and vertical structure of snow layer, which is conducive to understanding the freeze-thaw process of ice and snow and predicting the change of ice and snow mass balance. Interferometric Synthetic Aperture Radar (InSAR) exhibits great potential in estimating snow penetration depth due to the emission of microwave band and the ability of operating under all-day and all-weather conditions. In previous studies, the penetration depth of snow was obtained based on InSAR coherence, but the external factors such as temperature, humidity, topography, dielectric constant and so on also affect the penetration dept. In order to extract the penetration depth in detail, this paper this paper adopts a uniform volume model as the basis, introduces two parameters describing the physical properties of snow: the volume ambiguity height and the volumetric decorrelation. Then, the magnitude of volumetric decorrelation is used to establish the internal relationship between the magnitude and the volume ambiguity height to estimate the snow penetration depth. In order to verify the effectiveness and feasibility of this method, a TSX/TDX InSAR pair covering southeast Tibetan Plateau in 2012 is applied to estimate the snow penetration depth. And results indicate that X-band penetration depths range mainly between −2.753 meters and −4.589 meters with an average value of −3.671 meters m and standard deviation of 0.918 meters in snowy regions on the plateau.

Numerical investigation of crack propagation regimes in snow fracture experiments

A snow slab avalanche releases after failure initiation and crack propagation in a highly porous weak snow layer buried below a cohesive slab. While our knowledge of crack propagation during avalanche formation has greatly improved over the last decades, it still remains unclear how snow mechanical properties affect the dynamics of crack propagation. This is partly due to a lack of non-invasive measurement methods to investigate the micro-mechanical aspects of the process. Using a DEM model, we therefore analyzed the influence of snow cover properties on the dynamics of crack propagation in weak snowpack layers. By focusing on the steady-state crack speed, our results showed two distinct fracture process regimes that depend on slope angle, leading to very different crack propagation speeds. For long experiments on level terrain, weak layer fracture is mainly driven by compressive stresses. Steady-state crack speed mainly depends on slab and weak layer elastic moduli as well as weak layer strength. We suggest a semi-empirical model to predict crack speed, which can be up to 0.6 times the slab shear wave speed. For long experiments on steep slopes, a supershear regime appeared, where the crack propagation speed reached approximately 1.6 times the slab shear wave speed. A detailed micro-mechanical analysis of stresses revealed a fracture principally driven by shear. Overall, our findings provide new insight into the micro-mechanics of dynamic crack propagation in snow, and how these are linked to snow cover properties.Graphical

Snow water equivalent retrieved from X- and dual Ku-band scatterometer measurements at Sodankylä using the Markov Chain Monte Carlo method

Abstract. Radar at high frequency is a promising technique for fine-resolution snow water equivalent (SWE) mapping. In this paper, we extend the Bayesian-based Algorithm for SWE Estimation (BASE) from passive to active microwave (AM) application and test it using ground-based backscattering measurements at three frequencies (X and dual Ku bands; 10.2, 13.3, and 16.7 GHz), with VV polarization obtained at a 50° incidence angle from the Nordic Snow Radar Experiment (NoSREx) in Sodankylä, Finland. We assumed only an uninformative prior for snow microstructure, in contrast with an accurate prior required in previous studies. Starting from a biased monthly SWE prior from land surface model simulation, two-layer snow state variables and single-layer soil variables were iterated until their posterior distribution could stably reproduce the observed microwave signals. The observation model is the Microwave Emission Model of Layered Snowpacks 3 and Active (MEMLS3&a) based on the improved Born approximation. Results show that BASE-AM achieved an RMSE of ∼ 10 cm for snow depth and less than 30 mm for SWE, compared with the RMSE of ∼ 20 cm snow depth and ∼ 50 mm SWE from priors. Retrieval errors are significantly larger when BASE-AM is run using a single snow layer. The results support the potential of X- and Ku-band radar for SWE retrieval and show that the role of a precise snow microstructure prior in SWE retrieval may be substituted by an SWE prior from exterior sources.

Evaluation of the bioclimate of the Piatra-Neamț tourist resort

Abstract: Background: The study responds to real needs of knowing the bioclimate of some tourist resorts in order to develop different types of tourism, in which the bioclimate plays a significant role. In Romania, a similar study was carried out for the resort towns of Vatra Dornei and Tg. Ocna. Methods: The research is based on time series of weather elements from 1961-2021. These series were used for the calculation of the bioclimatic index of the physiological equivalent temperature (PET) which allows the analysis of the temporal peculiarities of the bioclimate. In order to highlight in a new perspective the relationship between biocli-mate and tourism in the Piatra Neamț resort, we created climate-tourism schemes (CTIS). Results: PET values ranged between a maximum of 35.7°C (severe thermal stress due to heating) and a minimum of - 29.3°C (extreme thermal stress due to cooling). The mean PET value was 8.8°C (indicating moderate cold stress). The period of the year with biocli-matic comfort, for hiking, visiting, outdoor sports, climatotherapy, balneotherapy cor-re-sponds to the interval April (3rd decade) - October (1st decade). Conclusions: Tourists who want to plan their vacation in Piatra Neamț will benefit from non-restrictive bioclimatic conditions from the end of April to the beginning of October. During the cold season, with a layer of snow, winter sports can be practiced.

Modification and Validation of the Soil–Snow Module in the INM RAS Climate Model

This paper describes the modification of a simple land snow cover module of the INM RAS climate model. The possible liquid water and refreezing of meltwater in the snow layer are taken into account by the proposed parameterization. This is particularly important for modelling the transition season, as this phenomenon is mainly observed during the formation and melting of the snow cover when the surface temperature fluctuates around 0 °C. The snow density evolution simulation is also added. This parameterization is implemented in the INM-CM snow module and verified on observation data using the ESM-SnowMIP-like protocol. As a result, the INM-CM mean climate snow melt periods are refined, particularly in middle and high latitudes. The snow-covered area according to the model is also improved. In the future, a modified version of the land snow module can be used, coupled with a snow albedo model that takes into account snow metamorphism. This module can also be applied to sea ice snow.

Constraints on habitat possibilities: overwintering of a micro snail species facing climate change consequences in a harsh environment

The aim of this study was to establish the overwintering strategy and cold tolerance in V. moulinsiana. We observed a seasonal change in the SCP measurements, indicating that the species' preparation for winter results in a decrease in the SCP. Along with the results obtained from the frost survival experiment, this suggests the presence of a freezing avoidance strategy in this species. It overwinters in a buffered zone, separated from harsh conditions by a layer of leaf litter and snow, which protects it from temperature fluctuations and severe frost. The results of our research may indicate a possible poor resistance of these snails to snow-free conditions. Therefore, climate change-induced alterations could profoundly impact this species, especially considering the predicted disappearance of snow before the frost.

Bevameter pressure-sinkage testing on snow

Pressure-sinkage tests for determining vehicle sinkage on soft soils can be done using a bevameter. In this study, pressure-sinkage tests were performed on snow, which, like soil, is a granular material. However, unlike soil, snow layers are inhomogeneous with varying properties. For tracked vehicles, the shape of the track print is rectangular, which is why rectangular plates are often used for pressure-sinkage tests. The aim of this study was to see if smaller circular plate or smaller rectangular plates can be used instead of larger rectangular plates, and to understand the possible limitations of using small plates. Radius for the circular plate was chosen to be equal to the width of the rectangular plate. Three measuring sessions were performed at different locations during different snow conditions using circular pressure plates and rectangular pressure plates of different aspect ratios. The results show that smaller rectangular plates can be used if the width of the plates remains the same, or circular plates can be used if the radius of the circular plate is equal to the width of the rectangular plate. Limitation comes with increasing pressure, which occurs more quickly with larger-area plates, as larger plates sense solid ground more rapidly than smaller plates. To avoid this, snowpack thickness should be a minimum of five times thicker than maximum sinkage.