Average Snow Depth Research Articles

Klima Jeseníků a jeho vývoj

The mountainous relief of the terrain has a significant effect especially on the temperature and precipitation characteristics. They mainly occur on windward slopes (mainly south-west to west), in the summer months, under conditions for convective weather manifestations, often accompanied by the formation of massive storm clouds, the appearance of intense precipitation and also storms. The relief of the Jeseníky Mountains, with high hills and narrow valleys, results in frequent occurrence of ground temperature inversions. Under suitable conditions, especially with clear night sky in spring and autumn, valley fogs arise resulting in lowering significantly air temperature before dawn. In most of the territory of Hrubý Jeseník Mountains and the massif of the Králický Sněžník climatological characteristics are dominantly influenced in relation to the altitude. For climatological processing and climate characteristics, we have defined the Jeseníky Mountain area, which is primarily based on geomorphological units. The area of the defined territory of the Jeseníky Mountains is approximately 3,251 square kilometres, which is approximately 4% of the territory of the Czech Republic. The average altitude is 613.1 meters above sea level. We calculated the area monthly and annual characteristics for the Jeseníky Mountains for the main processed meteorological elements, i.e. average monthly air temperature, monthly precipitation, monthly sunshine duration and in winter also for the monthly amount of new snow. Time series included air temperature and precipitation since 1875, new snow since 1896, and sunshine duration since 1932. The average annual air temperature for the normal period, or normal 1991–2020, is 7.2 °C. The highest average annual air temperature in the Jeseníky Mountain was 8.4 °C in 2019. According to the average annual air temperature (4.4 °C) in the Jeseníky Mountains, the year 1875 and 1940 were the coldest. Based on the evaluation of the linear trend, the average temperature has increased over the last 150 years annual air temperature in the Jeseníky Mountains by 1.5 °C. On average, the air temperature increases the most in November, by almost 2.2 °C in 150 years, and the least in September (by 0.7 °C). The average annual precipitation total for the defined area of the Jeseníky Mountains is 848.7mm. The highest annual precipitation total, 1,217.8mm, was recorded in 1890. The year 2015 was the lowest in terms of precipitation, with only 590mm of precipitation. In almost 150 years, the annual precipitation has decreased by around 77mm. The largest long-term decrease in precipitation is evident in October, almost 23mm in 150 years, and the lowest in February, August and December (between 2–3 mm). In the top parts of the Jeseníky Mountains, if the snow has not been blown, around 540cm snow cover occurs on an annual average. At the altitude of 1,300m above sea level, the annual total is around 460cm. Altitude zones around 1,100m above sea level have an average annual depth of new snow approximately 390cm. In areas around 1,000m above sea level, an average of 350cm of new snow cover appears in the Jeseníky Mountains. In the published literature, we searched for significant episodes related to the weather and climate of the Jeseníky Mountains, which we supplemented with data on the values of individual meteorological elements from local stations.

Application of HY-2B Satellite Data to Retrieve Snow Depth on Antarctic Sea Ice

Sea ice and its surface snow are crucial components of the energy cycle and mass balance between the atmosphere and ocean, serving as sensitive indicators of climate change. Observing and understanding changes in snow depth on Antarctic sea ice are essential for sea ice research and global climate change studies. This study explores the feasibility of retrieving snow depth on Antarctic sea ice using data from the Chinese marine satellite HY-2B. Using generic retrieval algorithms, snow depth on Antarctic sea ice was retrieved from HY-2B Scanning Microwave Radiometer (SMR) data, and compared with existing snow depth products derived from other microwave radiometer data. A comparison against ship-based snow depth measurements from the Chinese 35th Antarctic Scientific Expedition shows that snow depth derived from HY-2B SMR data using the Comiso03 retrieval algorithm exhibits the lowest RMSD, with a deviation of −1.9 cm compared to the Markus98 and Shen22 models. The snow depth derived using the Comiso03 model from HY-2B SMR shows agreement with the GCOM-W1 AMSR-2 snow depth product released by the National Snow and Ice Data Center (NSIDC). Differences between the two primarily occur during the sea ice ablation and in the Bellingshausen Sea, Amundsen Sea, and the southern Pacific Ocean. In 2019, the monthly average snow depth on Antarctic sea ice reached its maximum in January (36.2 cm) and decreased to its minimum in May (15.3 cm). Thicker snow cover was observed in the Weddell Sea, Ross Sea, and Bellingshausen and Amundsen seas, primarily due to the presence of multi-year ice, while thinner snow cover was found in the southern Indian Ocean and the southern Pacific Ocean. The derived snow depth product from HY-2B SMR data demonstrates high accuracy in retrieving snow depth on Antarctic sea ice, highlighting its potential as a reliable alternative for snow depth measurements. This product significantly contributes to observing and understanding changes in snow depth on Antarctic sea ice and its relationship with climate change.

The Effects of Summer Snowfall on Arctic Sea Ice Radiative Forcing

AbstractSnow is the most reflective natural surface on Earth. Since fresh snow on bare sea ice increases the surface albedo, the impact of summer snow accumulation can have a negative radiative forcing effect, which would inhibit sea ice surface melt and potentially slow sea‐ice loss. However, it is not well known how often, where, and when summer snowfall events occur on Arctic sea ice. In this study, we used in situ and model snow depth data paired with surface albedo and atmospheric conditions from satellite retrievals to characterize summer snow accumulation on Arctic sea ice from 2003 to 2017. We found that, across the Arctic, ∼2 snow accumulation events occurred on initially snow‐free conditions each year. The average snow depth and albedo increases were ∼2 cm and 0.08, respectively. 16.5% of the snow accumulation events were optically thick (>3 cm deep) and lasted 2.9 days longer than the average snow accumulation event (3.4 days). Based on a simple, multiple scattering radiative transfer model, we estimated a −0.086 ± 0.020 W m−2 change in the annual average top‐of‐the‐atmosphere radiative forcing for summer snowfall events in 2003–2017. The following work provides new information on the frequency, distribution, and duration of observed snow accumulation events over Arctic sea ice in summer. Such results may be particularly useful in understanding the impacts of ephemeral summer weather on surface albedo and their propagating effects on the radiative forcing over Arctic sea ice, as well as assessing climate model simulations of summer atmosphere‐ice processes.

Influences of Snow Cover on the Thermal Regimes of Xing'an Permafrost in Northeast China in 1960s–2010s

ABSTRACTThe distributive characteristics of snow cover and their impacting mechanisms on ground thermal regimes in Northeast China remain evasive because of limited systematic studies. In this study, based on long‐term ground‐based observational data and auxiliary topographic data, geographically weighted regression kriging (GWRK) method and the temperature at the top of permafrost (TTOP) model were used to analyze the influences of snow cover duration (SCD) and average snow depth over the SCD (ASDSCD) on the thermal regimes of Xing'an permafrost in Northeast China in the 1960s–2010s. The results show a remarkable reduction of permafrost extent in Northeast China from the 1960s to 2010s, with an average reduction rate of 4.115 × 104 km2/decade. In permafrost regions, from the 1960s to 2010s, average SCD varied between 150 and 160 days, and the regional average of ASDSCD between 8 and 14 cm. Increases in ASDSCD led to a rise of TTOP. From the 1960s to 2010s, the regional average of ASDSCD increased by 3.53 cm, and that of TTOP by 2.02°C. The research results can provide scientific basis and data support for evaluating the responses of permafrost and cold region ecosystems to climate change in Northeast China.

Intraguild interactions and abiotic conditions mediate occupancy of mammalian carnivores: co‐occurrence of coyotes–fishers–martens

The widespread eradication of large carnivores and subsequent expansion of top mesopredators has the potential to impact species and community interactions with ecosystem‐wide implications. An example of these trophic dynamics is the widespread establishment of coyotes following extirpation of wolves and mountain lions in eastern North America. Here, we examined occupancy of three carnivores in northern New York considering both environmental/habitat factors and interspecific interactions. We estimated the co‐occurrence of coyotes, fishers, and martens from a landscape‐scale winter camera trap survey repeated annually for three years. Martens occurred independently of both coyotes and fishers, while fishers and coyotes displayed positive intraguild interactions that were constant across the landscape. Both marten and fisher first‐order occupancy were driven by a combination of biotic and abiotic factors, with both species displaying positive associations with forest cover but antithetical responses to average snow depth. The integral and antithetical role of snow depth in driving the occurrence of martens (positive) and fishers (negative) in the landscape indicates that future climatic warming could reduce the availability of current spatial refuges for martens created by severe winter conditions. Climate‐driven alterations to established competitive interactions and co‐existence patterns between marten and fisher have critical implications for the species survival and conservation. We provide correlational evidence consistent with the potential for positive top–down effects of dominant mesocarnivores on subordinate species, with fisher occupancy increasing conditional on the presence of coyotes across the landscape. These findings align with the hypothesis that under certain conditions, coyotes may facilitate certain subordinate carnivores. The evidence produced here is consistent with hypotheses on the dynamic nature of trophic niches. We demonstrate the need to consider the interplay between climate, habitat, and interspecific interactions to understand wildlife occupancy patterns and inform wildlife management in a rapidly changing world.

Testing the Suitability of Snow Depth in Winter Recreation Areas in Cities in the Republican Period with InSAR Technique: Abalı Ski Center Example

The Republican period is a period that coincides with the modernization process of Turkey, and the lifestyle in the cities has also changed significantly during this period. Winter recreation areas are the areas that people prefer to have fun, do sports and relax in the cold months. The Republic of Türkiye has a great importance in the tourism sector. In addition, the alternative tourism sector is developing rapidly and leading to different investments. Increasing winter recreation areas and facility investments in order to keep four seasons tourism alive with the advantage of climatic diversity is of great importance for alternative tourism. Different criteria such as slope, aspect, height, transportation, and sunshine duration are used to determine suitable areas for these investments. However, the most important suitability criterion is the snow factor. In order for the facilities to be established, the snow must be permanent and of a certain thickness in the winter season. In this study, the winter season (December-January-February) was examined temporally (2020-21-22), in the example of the Abalı Ski Center established in the republican period, and the average snow depth change in the area was determined by the InSAR (Interferometric Artificial Aperture Radar) technique. In addition, the accuracy of the results was tested with the real measurements of the 2021-22 winter season in the study area. The analysis conducted to assess the accuracy of Sentinel-1A C-band RADAR images by comparing snow depth data has yielded a high correlation value of 0.79, indicating a strong and consistent result. As a result, the importance of determining the snow factor both as a suitability criterion in determining the suitability of land use and the suitability of snow permanence in temporal change for establishment has been revealed.

Mapping snow depth distribution from 1980 to 2020 on the tibetan plateau using multi-source remote sensing data and downscaling techniques

Although passive microwave remote sensing has been widely used for snow depth (SD) retrieval, its coarse spatial resolution has led to large uncertainties in various applications, particularly in mountainous regions. Therefore, downscaling passive microwave data is an effective way to improve the accuracy of regional SD monitoring. In this study, we developed a SD downscaling algorithm based on multisource remote sensing data, generating a long-time series of daily 500 m resolution SD data from 1980 to 2020 over the Tibetan Plateau (TP). The downscaling SD product improved the spatial resolution and significantly reduced the overestimation of the SD for the TP. Based on this product, the spatial pattern and the spatiotemporal trend of the SD were analyzed by the Theil-Sen median method and the Mann–Kendall test. SD on the TP showed clear spatial heterogeneity by vertical zonal distribution, bimodal distribution features changing with longitude, and a thick SD distribution pattern at 30-32°N. The annual average SD, maximum SD, and accumulation of SD on the TP from 1980 to 2020 were 1.02 cm, 5.91 cm, and 39.45 cm, respectively, with trends of −0.06 cm/10a, −0.44 cm/10a, and 1.80 cm/10a (P > 0.05), respectively. SD on the TP increases with elevation above 5.5 km in mountainous areas and decreases in areas below 5.5 km.

A Study on Avalanche-Triggering Factors and Activity Characteristics in Aerxiangou, West Tianshan Mountains, China

Through analyzing the triggering factors and activity characteristics of avalanches in Aerxiangou in the Western Tianshan Mountains, the formation and disaster-causing process of avalanches were studied to provide theoretical support and a scientific basis for avalanche disaster prevention. In this paper, based on remote sensing interpretation and field investigation, a spatial distribution map of avalanches was established, and the induced and triggering factors in disaster-prone environments were analyzed using the certainty factor model. The degree of influence (E) of the disaster-causing factors on avalanche triggering was quantified, and the main control conditions conducive to avalanche occurrence in different periods were obtained. The RAMMS-avalanche model was used to analyze the activity characteristics at points where multiple avalanches occurred. Research results: (1) The E values of the average temperature, average snowfall, and surface roughness in February were significantly higher than those of other hazard-causing factors, reaching 1.83 and 1.71, respectively, indicating strong control. The E values of the surface cutting degree, average temperature, and average snow depth in March were all higher than 1.8, indicating that these control factors were more prominent than the other factors. In contrast, there were four hazard-causing factors with E values higher than 1.5 in April: the mean temperature, slope, surface roughness, and mean wind speed, with clear control. (2) Under the influence of the different hazard-causing factors, the types of avalanches from February–April mainly included new full-layer avalanches, surface avalanches, and full-layer wet avalanches. (3) In the RAMMS-avalanche simulation test, considering the deposition effect, compared to the previous avalanche movement path, the secondary avalanche flow accumulation area impact range changes were slight, while the movement area within the avalanche path changes was large, as were the different categories of avalanches and their different movement characteristic values. Overall, wet snow avalanches are more hazardous, and the impact force is larger. The new snow avalanches start in a short period, the sliding rate is fast, and the avalanche sliding surface (full-snow surface and face-snow) of the difference is mainly manifested in the differences in the value of the flow height.

Variability and changes of the height and duration of snow cover on the Gąsienicowa Glade (Tatras)

AbstractThe oldest meteorological station in the Polish Tatra Mountains is that on the Gąsienicowa Glade (1520 m). In this study, two series of observations for the years 1927–1938 and 1947–2020 are presented for the number of days with snow cover higher than 1 cm (SCD—from 142 to 228 days), maximum snow depth (HSmax—from 43 to 271 cm) and average snow depth (HSmean—from 10 to 93 cm). For the remaining meteorological parameters discussed in this article, the observation series is slightly shorter. From 1927 to 2020, the number of days with snow cover show downward trends (SCD—from nearly 200 days in 1927/1928 to about 175 days in 2019/2020). The decrease in the values of SCD discussed is due to an increase in air temperature and an upward trend in the percentage of the number days with rain over the number of days with snowfall. The increase in mean air temperature started in the 1980s. The strongest temperature trends were recorded in spring (TG in June—from 8.2°C in 1927 to 10.0°C in 2020) and in summer (TG in August—from 9.7°C in 1927 to 12.0°C in 2020). The last two decades, in particular, are exceptional for the Tatra Mountains, both in terms of air temperature, days with snow cover, maximum and mean snow depth. Since the beginning of observations in the Gąsienicowa Glade, no such strong decade‐long increases in temperature and decreases in the number of days with snow cover and in maximum and mean snow depth have been recorded. The observed trends in days with snow cover and maximum and mean snow depth in the Tatras are similar to those observed in studies conducted in the Alps.

Brief communication: Comparison of in situ ephemeral snow depth measurements over a mixed-use temperate forest landscape

Abstract. The accuracy and precision of snow depth measurements depend on the measuring device and the conditions of the site and snowpack in which it is being used. This study compares collocated snow depth measurements from a magnaprobe snow depth probe and a Federal snow tube in an ephemeral snow environment. We conducted three snow depth sampling campaigns from December 2020 to February 2021 that included 39 open-field and coniferous-, mixed-, and deciduous-forest sampling sites in Durham, New Hampshire, United States. For all sampling campaigns and land cover types, with a total of 936 paired observations, the magnaprobe snow depth measurements were consistently deeper than those of the snow tube. There was a 12 % average difference between the magnaprobe (14.9 cm) and snow tube (13.2 cm) average snow depths with a greater difference in the forest (1.9 cm) than the field (1.3 cm). This study suggests that snow depth measurements using a Federal snow tube can avoid overprobing with an ephemeral snowpack in forested environments.

Characteristics of snow depth and period variations in snowy areas in Japan for 60 years from 1962 to 2021 winter season using annual average snow depth and annual maximum snow depth

Characteristics of snow depth and period variations in snowy areas in Japan for 60 years from 1962 to 2021 winter season using annual average snow depth and annual maximum snow depth

Quality of Snow Cover Characteristics Derived from ERA 5-Land Reanalysis for the Territory of Perm Krai

Received March 31, 2023; revised June 6, 2023; accepted June 27, 2023Agreement between values of the mean monthly snow depth provided by the ERA 5-Land reanalysis and similar characteristics of snow cover calculated on the basis of the extended hydrometeorological monitoring performed in the Perm Region for 1990–2020 is analyzed. It was found that ERA 5-Land in 73% of cases reproduces the presence/absence of snow during the onset period, and in 53% – during the period of snow loss. The conclusions made in the authors' previous studies based on more limited material were generally confirmed. It is shown that the reanalysis values of the snow depth are overestimated in relation to instrumental measurements for most of the hydrometeorological stations of the Perm Region. In space, the magnitude of the reanalysis error increases from the southwest to the northeast of the region, with the exception of its central part, where the influence of the Kama water reservoir is perceptible. But the interannual variability of the average snow depth in the Perm Region was reproduced by the ERA 5-Land reanalysis adequately. For 30 years, the magnitude of the reanalysis error decreased as it was compared with 61% observation points. The analysis of seasonal variability showed that in ERA 5-Land time of the maximum snow depth was shifted to earlier onset. The complete coincidence of the seasonal course was recorded only in 5% of hydrometeorological monitoring sites. The value of the average monthly discrepancies between the data of the reanalysis and the information of the posts as a whole exceeds the similar characteristic for the stations, which is especially evident during the period of active snowmelt.

Weather conditions affecting white‐tailed deer harvest in Iowa

AbstractWeather conditions, such as temperature and precipitation, likely influence white‐tailed deer (Odocoileus virginianus) activity and hunter participation, both of which are major factors contributing to deer harvest. Because harvest is often used to manage white‐tailed deer population trends and guide management decisions, understanding how certain abiotic factors may bias such data is important. We examined the effects of weather on deer harvest during the Iowa, USA, firearms seasons from 2006–2021 using a generalized mixed‐effects linear model. Weather conditions during the hunting season were related to overall deer harvest. Specifically, deer harvest was positively correlated with snowfall on opening day and negatively correlated with average minimum temperature, average snow depth, rainfall on opening day, and number of rainfall and snowfall days. Abiotic factors have the ability to substantially influence deer harvest. Because many white‐tailed deer population models use harvest data, incorporating the effects of weather variability into such models may substantially improve population estimates. Our study provides a compelling case for considering hunting season weather conditions when interpreting harvest data as a population index for white‐tailed deer. The results of this study also demonstrate potential for retrospective modeling of past harvest rates and incorporating the effects of abiotic factors on harvest into future age‐based population models.

Analysis of Snow Cover in the Sibillini Mountains in Central Italy

Research on solid precipitation and snow cover, especially in mountainous areas, suffers from problems related to the lack of on-site observations and the low reliability of measurements, which is often due to instruments that are not suitable for the environmental conditions. In this context, the study area is the Monti Sibillini National Park, and it is no exception, as it is a mountainous area located in central Italy, where the measurements are scarce and fragmented. The purpose of this research is to provide a characterization of the snow cover with regard to maximum annual snow depth, average snow depth during the snowy period, and days with snow cover on the ground in the Monti Sibillini National Park area, by means of ground weather stations, and also analyzing any trends over the last 30 years. For this research, in order to obtain reliable snow cover data, only data from weather stations equipped with a sonar system and manual weather stations, where the surveyor goes to the site each morning and checks the thickness of the snowpack and records, it were collected. The data were collected from 1 November to 30 April each year for 30 years, from 1991 to 2020; six weather stations were taken into account, while four more were added as of 1 January 2010. The longer period was used to assess possible ongoing trends, which proved to be very heterogeneous in the results, predominantly negative in the case of days with snow cover on the ground, while trends were predominantly positive for maximum annual snow depth and distributed between positive and negative for the average annual snow depth. The shorter period, 2010–2022, on the other hand, ensured the presence of a larger number of weather stations and was used to assess the correlation and presence of clusters between the various weather stations and, consequently, in the study area. Furthermore, in this way, an up-to-date nivometric classification of the study area was obtained (in terms of days with snow on the ground, maximum height of snowpack, and average height of snowpack), filling a gap where there had been no nivometric study in the aforementioned area. The interpolations were processed using geostatistical techniques such as co-kriging with altitude as an independent variable, allowing fairly precise spatialization, analyzing the results of cross-validation. This analysis could be a useful tool for hydrological modeling of the area, as well as having a clear use related to tourism and vegetation, which is extremely influenced by the nivometric variables in its phenology. In addition, this analysis could also be considered a starting point for the calibration of more recent satellite products dedicated to snow cover detection, in order to further improve the compiled climate characterization.

Study of snow cover/depth evolution characteristics in Tianshan region of China based on geographical partition

Based on the digital elevation data, snow depth and snow cover remote sensing data, this paper divides six snow evolution areas and geographical partitions, extracts the geographical partitions of each evolution area and obtains the geographical characteristics of the evolution area for analysis. The results show that: (1) From 2003 to 2017, the average snow area decreased at a rate of − 0.004, and the average snow depth increased at a rate of 0.03. (2) The snow in the middle altitude hill with shady gentle slope area is the most obvious in the seasonal evolution, and the percentage of this region in the seasonal snow evolution area is 5.46%, the snow depth in the middle altitude hill with sunny and gentle slopes area increased and decreased significantly in the past 15 years, and the percentage of this region in the SD significant changes evolution area was 6.32%. The snow in the low relief middle altitude mountain with shady and moderate slope area not only shows obvious seasonal evolution, but also increases and decreases significantly in snow depth. And the percentage of this region in the seasonal snow significant evolution area is 5.82%. (3) The geographical partitions with the largest area in all evolution areas is the middle altitude hill with sunny and gentle slopes area (4.75%). (4) The geographical partition with the largest variation of snow depth in Tianshan region is the low relief middle altitude mountain with shady and moderate slope area (12.02 cm). (5) The snow accumulation and melting are obvious in the range of 1000–3500 m above altitude, different geomorphology types lead to obvious differences in snow characteristics. The snow melting is most obvious in the gentle slope area of the low topographic relief geomorphology types, and the snow accumulation is most obvious in the steep slope area of the middle relief geomorphology types.

The benefits of homogenising snow depth series – Impacts on decadal trends and extremes for Switzerland

Abstract. Our current knowledge of spatial and temporal snow depth trends is based almost exclusively on time series of non-homogenised observational data. However, like other long-term series from observations, they are prone to inhomogeneities that can influence and even change trends if not taken into account. In order to assess the relevance of homogenisation for time-series analysis of daily snow depths, we investigated the effects of adjusting inhomogeneities in the extensive network of Swiss snow depth observations for trends and changes in extreme values of commonly used snow indices, such as snow days, seasonal averages or maximum snow depths in the period 1961–2021. Three homogenisation methods were compared for this task: Climatol and HOMER, which apply median-based adjustments, and the quantile-based interpQM. All three were run using the same input data with identical break points. We found that they agree well on trends of seasonal average snow depth, while differences are detectable for seasonal maxima and the corresponding extreme values. Differences between homogenised and non-homogenised series result mainly from the approach for generating reference series. The comparison of homogenised and original values for the 50-year return level of seasonal maximum snow depth showed that the quantile-based method had the smallest number of stations outside the 95 % confidence interval. Using a multiple-criteria approach, e.g. thresholds for series correlation (>0.7) as well as for vertical (<300 m) and horizontal (<100 km) distances, proved to be better suited than using correlation or distances alone. Overall, the homogenisation of snow depth series changed all positive trends for derived series of snow days to either no trend or negative trends and amplifying the negative mean trend, especially for stations >1500 m. The number of stations with a significant negative trend increased between 7 % and 21 % depending on the method, with the strongest changes occurring at high snow depths. The reduction in the 95 % confidence intervals of the absolute maximum snow depth of each station indicates a decrease in variation and an increase in confidence in the results.

Factors Influencing the Spatiotemporal Changes of Permafrost in Northeast China from 1982 to 2020

Permafrost in northeast China, which is at the southern edge of the high-latitude permafrost belt in Eurasia, is extremely sensitive to climate warming. However, the distribution of permafrost in the region in recent years has been poorly studied, and there is a lack of understanding of the relative importance of environmental factors affecting the region. Based on observed ground surface temperature (GST) data, this study quantifies changes in the permafrost area in northeast China from 1982 to 2020 using a surface frost number model, and the influencing factors are identified based on dominance analysis and spatial correlation analysis. The results suggest that the permafrost in northeast China during the observation period underwent degradation with a degradation rate of 0.33 × 104 km2/a. In addition, the permafrost degradation also exhibited altitudinal and latitudinal zonality. Permafrost degradation under typical grassland, deciduous forest, and savannah cover was more significant than that under evergreen forest, mixed forest, and shrubbery cover. As revealed by the dominance analysis results, the annual average snow cover, annual average snow depth and annual average normalized difference vegetation index (NDVI) had the largest contributions to the variance of the permafrost area in northeast China, accounting for 88.3% of the total variance contribution of the six influencing factors. The spatial correlation results reveal that areas with a significantly increased NDVI and significantly reduced snow depth and snow cover were coincident with areas with significantly degraded permafrost. Hence, the snow cover, snow depth, and NDVI were found to have the greatest influence on the permafrost distribution in northeast China. The results of this study evidently increase the understanding of the changing permafrost in northeast China, providing important knowledge about permafrost for researchers and the related community.

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.

Snow Depth Inversion in Forest Areas from Sentinel-1 Data Based on Phase Deviation Correction

At present, snow depth inversion based on active microwave remote sensing is concerned essentially with areas having a relatively simple underlying surface. The existence of forests reduces the sensitivity of microwaves to snow, which often makes the snow depth inversion results uncertain. This paper presents a snow depth estimation algorithm for forest areas by introducing a forest phase to characterize the effect of forests on backscattering electromagnetic wave. Firstly, the interferogram is generated with the differential interference of two-pass master-slave Synthetic Aperture Radar (SAR) images, and the real phase under snow cover condition is obtained by phase unwrapping. Secondly, the phase models for forest and non-forest areas are constructed. The effects of forest cover are modeled as forest phase in the forest phase model, which is estimated under the assumption of snow depth consistency on both sides of the boundaries between forest and non-forest areas. Finally, snow depth is estimated by the snow phase-depth model. The correctness of the proposed forest snow depth inversion algorithm was verified by taking the Jiagedaqi area of Greater Xing’an Mountains as the study area and sentinel-1 dual polarization images as the data source. Finally, the snow depth distribution of the study area was obtained with a spatial resolution of 30 m on 7 December 2020. The experimental results show that the snow depth values estimated in Jiagedaqi area are mainly between 40–120 cm, and the average snow depth value is 80.27 cm. Taking the snow depth value of 84.69 cm reckoned from hourly accumulated snowfall in Jiagedaqi as the reference snow depth, the results of the estimated snow depth are relatively consistent and well-founded. With the introduction of the forest phase, the average snow depth values estimated in the forest area increase by 5.98 cm, which reduces the underestimation of the snow depth in forest areas.

Influence of snow cover on soil freeze depth across China

Using 1980–2014 observation data of 378 meteorological stations, this study quantified the correlations between snow cover and the annual maximum seasonally freeze depth (MSFD) across China and the contributions of snow cover to MSFD. Snow cover exhibited a weak effect on MSFD across China, which may be related to the thinness and short duration of the snow cover. Regional differences in the effects of snow cover on MSFD were relatively large, and the effects of snow cover were more significant than those of air temperature (characterized by the air-freezing index) at stations in Northeast China, northeastern Inner Mongolia, and the north of the Tianshan Mountains. In regions with a thin snow cover or short snow cover duration (SCD), the cooling effect of snow cover can dominate, but it only slightly influences MSFD owing to the short SCD. With increasing average snow depth (ASD) and SCD, the relative contributions of snow cover to MSFD gradually increased, peaking at SCDs of 120–140 days or ASDs of 8–10 cm, which is attributable to the increasing insulating effect. However, with further increase in ASD and SCD, the insulating effect decreases because of high albedo and the latent heat effect of snow melting. This decrease resulted in declining MSFD–snow cover correlations and snow cover contributions to MSFD. Compared with SCD, ASD has a greater influence on MSFD. By adding an ASD variable to the Stefan formula, this modified formula outperformed the conventional formula in MSFD estimation at SCDs of 60–140 days. This study demonstrated the importance of snow cover variables for soil freeze depth analysis and simulation in areas with large snow cover and further elucidated the effects of snow cover on soil freeze depth.