Snow Height Research Articles

Развитие модели динамики снежного покрова Гидрометцентра России

Conceptual models of runoff formation used in the operational practice of hydrological forecasting by the Hydrometeorological Center of Russia include simplified parameterization of snow cover dynamics based on the use of snow cover melting coefficient, water-retaining capacity of snow and secondary freezing of meltwater in case of return of negative air temperatures. This schematization of the process is well established for the reliable calculation of the snow water equivalent for subsequent use in the calculation of meltwater inflow to the catchment surface and the calculation of streamflow characteristics. At the same time, the lack of calculation of snow density and snow height in the used schematization restricts the use of schemes for calculating of soil freezing depth in hydrological models, which seems to be extremely important for modelling runoff on mid-latitudes rivers, i.e. for the most Russian rivers. To overcome this disadvantage, the snow density and snow height parameterization was added to the calculation scheme of the model, which was verified using Roshydromet’s snow measurement routes data and shown a good and satisfactory quality of modeling of model characteristics.

Six years of high-resolution climatic data collected along an elevation gradient in the Italian Alps

The complex meso- and microclimatic heterogeneity inherent to mountainous regions, driven by both topographic and biotic factors, and the lack of observations, poses significant challenges to using climate models to predict and understand impacts at various scales. We present here a six-year dataset (2017–2022) of continuous climatic measurements collected at five elevations from 983 m to 2705 m above sea level in the Val Mazia - Matschertal valley in the Italian Alps. The measurements include the air temperature, relative humidity, wind speed and direction, solar radiation, soil properties, precipitation, and snow height. Collected within the European Long-Term Ecological Research program (LTER), this dataset is freely available in an open access repository. The time series may be valuable for the validation of regional climate models, atmospheric exchange modelling, and providing support for hydrological models and remote sensing products in mountain environments. Additionally, our data may be useful for research on the influence of elevation on ecological processes such as vegetation growth, plant composition, and soil biology. Beyond its utility in advancing such fundamental research, meteorological monitoring data contribute to informed socio-political decisions on climate adaptation strategies, land management, and water resource planning, enhancing the safety and resilience of mountain communities and biodiversity.

Snow Depth Extraction From Time‐Lapse Imagery Using a Keypoint Deep Learning Model

AbstractSnow pole time‐lapse photography, in which a snow pole of a known height is installed in front of a camera and photographed repeatedly over the course of a snow season, allows a large network of sites to be established relatively quickly and affordably. However, current approaches for extracting snow depth from snow poles typically relies on time intensive manual photo processing. By integrating computer vision algorithms with snow pole photography, we present a method that uses a keypoint detection model to automatically observe snow height across a network of sites. At 20 snow pole locations from Grand Mesa, CO (n = 9,722 images), our model successfully predicts the top and bottom of the pole with a mean absolute error (MAE) of 1.30 cm. To assess model generalizability, we tested the model on 12 sites in Washington State (n = 1,770 images). When the Colorado trained model was fine‐tuned using a subset of Washington images, the model predicted snow depth with a MAE of 4.0 cm. Best performance was achieved when both data sets were included during training, with a MAE of 2.05 cm for Colorado images and a MAE of 1.14 cm for Washington images. We demonstrate that, especially when trained using a subset of site‐specific data, a keypoint detection model can accelerate snow pole automation. This algorithm brings the hydrology community one step closer to a generalized snow pole detection model, and we call for a future model that integrates across time‐lapse images from additional locations.

СТАТИСТИЧЕСКИЙ АНАЛИЗ МНОГОЛЕТНИХ ДАННЫХ О СХОДЕ СНЕЖНЫХ ЛАВИН И СНЕЖНО-МЕТЕОРОЛОГИЧЕСКИХ УСЛОВИЙ В ГОРАХ ИЛЕ АЛАТАУ

Based on long-term observations at snow avalanche stations in the vicinity of Almaty, more than 5 thousand snow avalanches have been recorded since 1966. After statistical analysis of the data, the main characteristics of the avalanches were determined and snow and meteorological conditions were analyzed. The average volume of avalanches is 4 thousand m3, but this value is distorted due to the nonparametric distribution law. The median value of the volume of avalanches is 750-800 m3. 50% of cases within the upper and lower quartiles are 250-2500 m3. Catastrophic avalanches of more than 30 thousand m3 account for 3% of cases, and more than 100 thousand m3 only 0.3% of cases. Typical weather conditions during periods of avalanche danger are: snow height on the slopes 80-90 cm, on the meteorological site 50-60 cm, precipitation 15-19 mm, snow growth 25-30 cm. The dominant factors in avalanche formation were the height of the snow cover on the slopes, the amount of snowfall precipitation, snow cover increase, maximum air temperature. Spearman's correlation coefficients were found to be statistically significant for these parameters at the standard 5% significance level (a-level) with a probability of error (p-level) of less than 0.01. The results of factor analysis showed that avalanches are influenced by three groups of predictors: snow depth, precipitation and snow growth, and temperature conditions. Analysis of catastrophic avalanches in the indicator avalanche collection in the Kotyrbulak gorge showed typical conditions for mass avalanches in Ile Alatau in the spring - snow height at the weather site 50 cm, precipitation amount 53 mm, snow increase 28 cm. The agreement criteria we calculated turned out to be statistically significant by 5% level of significance. The standard errors of some data were within the recommended limits of 10%, which indicates the reliability of the results.

Antarstick: Extracting Snow Height From Time‐Lapse Photography

AbstractThe evolution and accumulation of snow cover are among the most important characteristics influencing Antarctica's climate and biotopes. The changes in Antarctica are also substantially impacting global climate change. Therefore, detailed monitoring of snow evolution is key to understanding such changes. One way to conduct this monitoring is by installing trail cameras in a particular region and then processing the captured information. This option is affordable, but has some drawbacks, such as the fully automatic solution for the extraction of snow height from these images is not feasible. Therefore, it still requires human intervention, manually correcting the inaccurately extracted information. In this paper, we present Antarstick, a tool for visual guidance of the user to potentially wrong values extracted from poor‐quality images and support for their interactive correction. This tool allows for much quicker and semi‐automated processing of snow height from time‐lapse photography.

A seasonal snowpack model forced with dynamically downscaled forcing data resolves hydrologically relevant accumulation patterns

The Mountain snowpack stores months of winter precipitation at high elevations, supplying snowmelt to lowland areas in drier seasons for agriculture and human consumption worldwide. Accurate seasonal predictions of the snowpack are thus of great importance, but such forecasts suffer from major challenges such as resolving interactions between forcing variables at high spatial resolutions. To test novel approaches to resolve these processes, seasonal snowpack simulations are run at different grid resolutions (50 m, 100 m, 250 m) and with variable forcing data for the water year 2016/2017. COSMO-1E data is either dynamically downscaled with the High-resolution Intermediate Complexity Atmospheric Research (HICAR) model or statistically downscaled to provide forcing data for snowpack simulations with the Flexible Snowpack Model (FSM2oshd). Simulations covering complex terrain in the Swiss Alps are carried out with the operational settings of the FSM2oshd model or with a model extension including wind- and gravitational-induced snow transport (FSM2trans). The simulated snow height is evaluated against observed snow height collected during LiDAR flights in spring 2017. Observed spatial snow accumulation patterns and snow height distribution are best matched with simulations using dynamically downscaled data and the FSM2trans model extension, indicating the importance of both accurate meteorological forcing data and snow transport schemes. This study demonstrates for the first time the effects of applying dynamical downscaling schemes to snowpack simulations at the seasonal and catchment scale.

Spatial and temporal stable water isotope data from the upper snowpack at the EastGRIP camp site, NE Greenland, sampled in summer 2018

Abstract. Stable water isotopes stored in snow, firn and ice are used to reconstruct climatic parameters. The imprint of these parameters at the snow surface and their preservation in the upper snowpack are determined by a number of processes influencing the recording of the environmental signal. Here, we present a dataset of approximately 3800 snow samples analysed for their stable water isotope composition, which were obtained during the summer season next to the deep drilling site of the East Greenland Ice Core Project in northeast Greenland (75.635411° N, 36.000250° W). Sampling was carried out every third day between 14 May and 3 August 2018 along a 39 m long transect. Three depth intervals in the top 10 cm were sampled at 30 positions with a higher resolution closer to the surface (0–1 and 1–4 cm depth vs. 4–10 cm). The sample analysis was carried out at two renowned stable water isotope laboratories that produced isotope data with the overall highest uncertainty of 0.09 ‰ for δ18O and 0.8 ‰ for δD. This unique dataset shows the strongest δ18O variability closest to the surface, damped and delayed variations in the lowest layer, and a trend towards increasing homogeneity towards the end of the season, especially in the deepest layer. Additional information on the snow height and its temporal changes suggests a non-uniform spatial imprint of the seasonal climatic information in this area, potentially following the stratigraphic noise of the surface. The data can be used to study the relation between snow height (changes) and the imprint and preservation of the isotopic composition at a site with 10–14 cm w.e. yr−1 accumulation. The high-temporal-resolution sampling allows additional analyses on (post-)depositional processes, such as vapour–snow exchange. The data can be accessed at https://doi.org/10.1594/PANGAEA.956626 (Zuhr et al., 2023a).

Extension of a Monolayer Energy-Budget Degree-Day Model to a Multilayer One

This paper presents the extension of the monolayer snow model of a semi-distributed hydrological model (HYDROTEL) to a multilayer model that considers snow to be a combination of ice and air, while accounting for freezing rain. For two stations in Yukon and one station in northern Quebec, Canada, the multilayer model achieves high performances during calibration periods yet similar to the those of the monolayer model, with KGEs of up to 0.9. However, it increases the KGE values by up to 0.2 during the validation periods. The multilayer model provides more accurate estimations of maximum SWE and total spring snowmelt dates. This is due to its increased sensitivity to thermal atmospheric conditions. Although the multilayer model improves the estimation of snow heights overall, it exhibits excessive snow densities during spring snowmelt. Future research should aim to refine the representation of snow densities to enhance the accuracy of the multilayer model. Nevertheless, this model has the potential to improve the simulation of spring snowmelt, addressing a common limitation of the monolayer model.

Temporospatial variability of snow's thermal conductivity on Arctic sea ice

Abstract. Snow significantly impacts the seasonal growth of Arctic sea ice due to its thermally insulating properties. Various measurements and parameterizations of thermal properties exist, but an assessment of the entire seasonal evolution of thermal conductivity and snow resistance is hitherto lacking. Using the comprehensive snow dataset from the Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, we have evaluated for the first time the seasonal evolution of the snow's and denser snow-ice interface layers' thermal conductivity above different ice ages (refrozen leads, first-year ice, and second-year ice) and topographic features (ridges). Our dataset has a density range of snow and ice between 50 and 900 kg m−3, and corresponding anisotropy measurements, meaning we can test the current parameterizations of thermal conductivity for this density range. Combining different measurement parameterizations and assessing the robustness against spatial heterogeneity, we found the average thermal conductivity of snow (<550 kg m−3) on sea ice remains approximately constant (0.26 ± 0.05 WK-1m-1) over time irrespective of underlying ice type, with substantial spatial and vertical variability. Due to this consistency, we can state that the thermal resistance is mainly influenced by snow height, resulting in a 2.7 times higher average thermal resistance on ridges (1.42 m2 K W−1) compared to first-year level ice (0.51 m2 K W−1). Our findings explain how the scatter of thermal conductivity values directly results from structural properties. Now, the only step is to find a quick method to measure snow anisotropy in the field. Suggestions to do this are listed in the discussion.

Back analysis of a building collapse under snow and rain loads in a Mediterranean area

Abstract. At the end of February 2018 the Mediterranean area of Montpellier in France was struck by a significant snowfall that turned into an intense rain event caused by an exceptional atmospheric situation. This rain-on-snow event produced pronounced damage to many buildings of different types. In this study, we report a detailed back analysis of the roof collapse of a large building, namely Irstea Cévennes. Attention is paid to the dynamics of the climatic event, on the one hand, and the mechanical response of the metal roof structure to different snow and rain loads, on the other hand. The former aspect relies on multiple sources of information that provide reliable estimates of snow heights in the area before the rain came into play and substantially modified the load on the roof. The latter aspect relies on detailed finite-element simulations of the mechanical behavior of the roof structure in order to assess the pressure due to snow and rain loading, which could theoretically lead to failure. By combining the two approaches, it is possible to reconstruct the most probable scenario for the roof failure before its full collapse. As an example of building behavior and vulnerability to an atypical rain-on-snow event in the Mediterranean area of France, this detailed case study provides useful key points to be considered in the future for a better mitigation of such events in non-mountainous areas.

Prediction of natural dry-snow avalanche activity using physics-based snowpack simulations

Abstract. Predicting the timing and size of natural snow avalanches is crucial for local and regional decision makers but remains one of the major challenges in avalanche forecasting. So far, forecasts are generally made by human experts interpreting a variety of data and drawing on their knowledge and experience. Using avalanche data from the Swiss Alps and one-dimensional physics-based snowpack simulations for virtual slopes, we developed a model predicting the probability of dry-snow avalanches occurring in the region surrounding automated weather stations based on the output of a recently developed instability model. This new avalanche day predictor was compared with benchmark models related to the amount of new snow. Evaluation on an independent data set demonstrated the importance of snow stratigraphy for natural avalanche release, as the avalanche day predictor outperformed the benchmark model based on the 3 d sum of new snow height (F1 scores: 0.71 and 0.65, respectively). The averaged predictions of both models resulted in the best performance (F1 score: 0.75). In a second step, we derived functions describing the probability for certain avalanche size classes. Using the 24 h new snow height as proxy of avalanche failure depth yielded the best estimator of typical (median) observed avalanche size, while the depth of the deepest weak layer, detected using the instability model, provided the better indicator regarding the largest observed avalanche size. Validation of the avalanche size estimator on an independent data set of avalanche observations confirmed these findings. Furthermore, comparing the predictions of the avalanche day predictors and avalanche size estimators with a 21-year data set of re-analysed regional avalanche danger levels showed increasing probabilities for natural avalanches and increasing avalanche size with increasing danger level. We conclude that these models may be valuable tools to support forecasting the occurrence of natural dry-snow avalanches.

An Experimental Investigation on the Size Distribution of Snow Particles during Artificial Snow Making

For artificial snowfall, snow particle size can have a direct impact on snow quality. The operating conditions of the snow-makers and environmental factors will influence the atomization and crystallization processes of artificial snow making, which consequently affect snow particle size. This paper investigates the size distribution of snow particles during artificial snow making under different operating conditions and environmental parameters. For this purpose, an environmental chamber is designed and structured. The laser scattering method was used to measure the size distribution of snow under different parameters in the room. The results show that the distribution of snow crystal particle size aligns closely with the Rosin–Rammler (R-R) distribution. The higher the height of the snowfall, the longer the snow crystals grow and the larger the snow crystal particle size. It has been found that a higher air pressure favors atomization, while the opposite is true for water pressure, which results in a higher air–water pressure ratio, producing smaller snow particle sizes. Additionally, an ambient temperature in the range of −5 °C to −15 °C contributes to the snow crystal form transforming from plates to columns and then back to plates; the snow particle size first decreases and then increases. Snow crystal particles at −10 °C have the smallest size. Outdoor snow-makers should be operated at the highest possible air–water pressure ratio and snow height, and at a suitable ambient temperature.

Trend Analysis of Maximum Snow Height in Eastern Anatolia Region According to Provincial Meteorology Station Data

Trend Analysis of Maximum Snow Height in Eastern Anatolia Region According to Provincial Meteorology Station Data

Investigating the Role of Shrub Height and Topography in Snow Accumulation on Low-Arctic Tundra using UAV-Borne Lidar

Abstract Expanding shrubs in the Arctic trap blowing snow, increasing snow height and accelerating permafrost warming. Topography also affects snow height as snow accumulates in hollows. The respective roles of topography and erect vegetation in snow accumulation were investigated using a UAV-borne lidar at two nearby contrasted sites in northern Quebec, Canada. The North site featured tall vegetation up to 2.5 m high, moderate snow height, and smooth topography. The South site featured lower vegetation, greater snow height, and rougher topography. There was little correlation between topography and vegetation height at both sites. Vegetation lower than snow height had very little effect on snow height. When vegetation protruded above the snow, snow height was well correlated with vegetation height. The topographic position index (TPI) was well correlated with snow height when it was not masked by the effect of protruding vegetation. The North site with taller vegetation therefore showed a good correlation between vegetation height and snow height, R2 = 0.37, versus R2 = 0.04 at the South site. Regarding topography, the reverse was observed between TPI and snow height, with R2 = 0.29 at the North site and R2 = 0.67 at the South site. The combination of vegetation height and TPI improved the prediction of snow height at the North site (R2 = 0.59) but not at the South site because vegetation height has little influence there. Vegetation was therefore the main factor determining snow height when it protruded above the snow. When it did not protrude, snow height was mostly determined by topography. Significance Statement Wind-induced snow drifting is a major snow redistribution process in the Arctic. Shrubs trap drifting snow, and drifting snow accumulates in hollows. Determining the respective roles of both these processes in snow accumulation is required to predict permafrost temperature and its emission of greenhouse gases, because thicker snow limits permafrost winter cooling. Using a UAV-borne lidar, we have determined snow height distribution over two contrasted sites in the Canadian low Arctic, with varied vegetation height and topography. When snow height exceeds vegetation height, topography is a good predictor of snow height, with negligible effect of buried vegetation. When vegetation protrudes above the snow, combining both topography and vegetation height is required for a good prediction of snow height.

Latest Advances in the Global Navigation Satellite System—Reflectometry (GNSS-R) Field

The global navigation satellite system-reflectometry (GNSS-R) field has experienced an exponential growth as it is becoming relevant to many applications and has captivated the attention of an elevated number of research scholars, research centers and companies around the world. Primarily based on the contents of two Special Issues dedicated to the applications of GNSS-R to Earth observation, this review article provides an overview of the latest advances in the GNSS-R field. Studies are reviewed from four perspectives: (1) technology advancements, (2) ocean applications, (3) the emergent land applications, and (4) new science investigations. The technology involved in the GNSS-R design has evolved from its initial GPS L1 LHCP topology to include the use of other GNSS bands (L2, L5, Galileo, etc.), as well as consider RHCP/LHCP-receiving polarizations in order to perform polarimetric studies. Ocean applications have included developments towards ocean wind speed retrievals, swell and altimetry. Land applications have evolved considerably in the past few years; studies have used GNSS-R for soil moisture, vegetation opacity, and wetland detection and monitoring. They have also determined flood inundation, snow height, and sea ice concentration and extent. Additionally, other applications have emerged in recent years as we have gained more understanding of the capabilities of GNSS-R.

Snow Height and Snow Water Equivalent Estimation from Snow Cover Fraction Using Sentinel-2 Satellite Images in North Kazakhstan

Climate change's influence on snowpack can significantly affect natural and anthropogenic processes. Water resources and agri-business, which depend on winter precipitation, are highly affected by variations in a snowbank and melting regimes. This paper demonstrates the comparison results of the snowpack thickness estimation in the LLP "North Kazakhstan AES" adopting distinctive techniques (quadratic, exponential, and linear functions) for assessing Fractional Snow Cover (SCF) and demonstrating Snow Water Equivalent (SWE) on the one hand, and in-situ perspective on the other. Between the 26–29 of February 2020, a field measurement was managed on the territory of 25,000 hectares. Accordingly, the thickness of the snowpack was surveyed at 560 points, and its density was measured at 70 points. Applying existing methodologies of SCF computation, it became apparent that the quadratic equation provides more reliable results at RMSE of 0.01 m, followed by linear -0.12 m and exponential -0.13 m methods. This work showed a strong correlation between snow height and SCF, namely the quadratic function in Northern Kazakhstan. Thus, we strongly suggest using Sentinel-2 MSI and the quadratic SCF estimation function for snow cover estimation, further spring flood forecasting, and other hydrological studies. Keywords: snow height, snow cover fraction, normalized-difference snow index, snow water equivalent, Sentinel-2 MSI, North Kazakhstan

Snow cover prediction in the Italian central Apennines using weather forecast and land surface numerical models

Abstract. Italy is a territory characterized by complex topography with the Apennines mountain range crossing the entire peninsula and its highest peaks in central Italy. Using the latter as our area of interest and the snow seasons 2018/19, 2019/20 and 2020/21, the goal of this study is to investigate the ability of a simple single-layer and a more sophisticated multi-layer snow cover numerical model to reproduce the observed snow height, snow water equivalent and snow extent in the central Apennines, using for both models the same forecast weather data as meteorological forcing. We here consider two well-known ground surface and soil models: (i) Noah LSM, an Eulerian model which simulates the snowpack as a bulk single layer, and (ii) Alpine3D, a multi-layer Lagrangian model which simulates the snowpack stratification. We adopt the Weather Research and Forecasting (WRF) model to produce the meteorological data to drive both Noah LSM and Alpine3D at a regional scale with a spatial resolution of 3 km. While Noah LSM is already online-coupled with the WRF model, we develop here a dedicated offline coupling between WRF and Alpine3D. We validate the WRF simulations of surface meteorological variables in central Italy using a dense network of automatic weather stations, obtaining correlation coefficients higher than 0.68, except for wind speed, which suffered from the model underestimation of the real elevation. The performances of both WRF–Noah and WRF–Alpine3D are evaluated by comparing simulated and measured snow height, snow height variation and snow water equivalent, provided by a quality-controlled network of automatic and manual snow stations located in the central Apennines. We find that WRF–Alpine3D can predict better than WRF–Noah the snow height and the snow water equivalent, showing a correlation coefficient with the observations of 0.9 for the former and 0.7 for the latter. Both models show similar performances in reproducing the observed daily snow height variation; nevertheless WRF–Noah is slightly better at predicting large positive variations, while WRF–Alpine3D can slightly better simulate large negative variations. Finally we investigate the abilities of the models in simulating the snow cover area fraction, and we show that WRF–Noah and WRF–Alpine3D have almost equal skills, with both models overestimating it. The equal skills are also confirmed by Jaccard and the average symmetric surface distance indices.

A Snapshot on the Buildup of the Stable Water Isotopic Signal in the Upper Snowpack at EastGRIP on the Greenland Ice Sheet

AbstractThe stable water isotopic composition in firn and ice cores provides valuable information on past climatic conditions. Because of uneven accumulation and post‐depositional modifications on local spatial scales up to hundreds of meters, time series derived from adjacent cores differ significantly and do not directly reflect the temporal evolution of the precipitated snow isotopic signal. Hence, a characterization of how the isotopic profile in the snow develops is needed to reliably interpret the isotopic variability in firn and ice cores. By combining digital elevation models of the snow surface and repeated high‐resolution snow sampling for stable water isotope measurements of a transect at the East Greenland Ice‐core Project campsite on the Greenland Ice Sheet, we are able to visualize the buildup and post‐depositional changes of the upper snowpack across one summer season. To this end, 30 cm deep snow profiles were sampled on six dates at 20 adjacent locations along a 40 m transect. Near‐daily photogrammetry provided snow height information for the same transect. Our data shows that erosion and redeposition of the original snowfall lead to a complex stratification in the δ18O signature. Post‐depositional processes through vapor‐snow exchange affect the near surface snow with d‐excess showing a decrease in surface and near‐surface layers. Our data suggests that the interplay of stratigraphic noise, accumulation intermittency, and local post‐depositional processes form the proxy signal in the upper snowpack.

Applied thermal process for a hydronic snow-melting system in the coldest provincial capital of China

• T ars , R s , t d , and q a of the applied thermal process resented and discussed. • Weather conditions of ambient temperature, snow density and height were analyzed. • Snow-melting rates were similar within 1.5 o C for ambient temperature differences. • Thermal performances were similar with the snow density of 100 ∼ 150 kg/m 3 . • Snow-melting system was effective when the snow height was below 43 mm. A hydronic experimental snow-melting system was implemented in the coldest provincial capital of China (Harbin) to investigate the thermal process. The average road-surface temperature, snow-free area ratio, period duration, and average energy consumption of the applied thermal process as affected by weather conditions of ambient temperature (-14.3 ∼ -5.7 o C), snow density (50 ∼ 150 kg/m 3 ), and snow height (14 ∼ 90 mm) were presented and discussed. The results revealed that the average road-surface temperatures of starting, rapid, and slow periods were in the ranges of T initial ∼ 0, 0 ∼ 1, and 1 ∼ 10 o C and the snow-free area ratios were 0, 0 ∼ 0.7, and 0.7 ∼ 1.0, respectively. When the ambient temperature declined from -5.7 to -14.3 o C, the whole process duration and average energy consumption increased by 50 min and 0.2 kW/m 2 , respectively. As the snow density improved from 50 to 150 kg/m 3 , the whole process duration and average energy consumption increased by 135 min and 0.4 kW/m 2 , respectively. When the snow height increased from 14 to 90 mm, the whole process duration increased by 320 min whereas the average energy consumption increased and decreased in the range of 1.6 ∼ 2.1 kW/m 2 . The thermal processes were similar when the ambient temperature differences were less than 1.5 o C or the snow densities were in the range of 100 ∼ 150 kg/m 3 , and they were effective when the snow height was less than 43 mm.

Многолетняя динамика снежного покрова в Приэльбрусье

Introduction. The snow accumulation regime is an important parameter in climate models, in assessing soil moisture reserves, in calculating the level of runoff, flood forecasting, and also for studying atmospheric pollution. In connection with global warming observed in recent decades, there are noticeable changes in climatic characteristics that affect the regime of snow accumulation in the mountains. Materials and research methods. To analyze and evaluate long-term changes in snow cover height, we used data from observations of precipitation and snow cover (from 1951 to 2022) at the Terskol meteorological station closest to the Elbrus resort, located at the altitude of 2100 m above the sea level. The materials of the study were long-term observations of the amount of precipitation in the cold season and the height of the snow cover at the Terskol meteorological station. Using the methods of statistical analysis, according to long-term data on snow depth for the cold period of the year, the mean and the standard deviation were calculated. Linear trends were obtained, the rate of change of the meteorological parameter (snow depth), anomalies were revealed relative to the average value for 1961 ÷ 1990 for each of the studied series. Research results and their discussion. The results of the analysis are the regularities of the temporal course of snow cover height at the Terskol meteorological station from 1951 to 2022. It was found that the value of the average snow depth in the full time series was 46 cm, the maximum value was 82 cm, the minimum – 16 cm, the average deviation was 15,65 cm. The time series of snow cover height is characterized by a negative trend expressed as a linear relationship. The values of anomalies of snow height values in different years are calculated. Conclusions. It is shown that the period from 1951 to 2022, the snow accumulation regime was characterized by an annual decrease in the average annual snow cover height by 0,1 cm per year. Snow height anomalies were obtained in different years.