Mean Snow Depth Research Articles

Changing snow conditions are challenging moose (Alces alces) surveys in Alaska

AbstractSnow conditions are changing rapidly across our planet, which has important implications for wildlife managers. In Alaska, USA, the later arrival of snow is challenging wildlife managers' ability to conduct aerial fall (autumn) moose (Alces alces) surveys. Complete snow cover is required to reliably detect and count moose using visual observation from an aircraft. With inadequate snow to help generate high‐quality moose survey data, it is difficult for managers to determine if they are effectively meeting population goals and optimizing hunting opportunities. We quantified past relationships and projected future trends between snow conditions and moose survey success across 7 different moose management areas in Alaska using 32 years (1987–2019) of moose survey data and modeled snow data. We found that modeled mean snow depth was 15 cm (SD = 11) when moose surveys were initiated, and snow depths were greater in years when surveys were completed compared to years when surveys were canceled. Further, we found that mean snow depth toward the beginning of the survey season (1 November) was the best predictor of whether a survey was completed in any given year. Based on modeled conditions, the trend in mean snow depth on 1 November declined from 1980 to 2020 in 5 out of 7 survey areas. These findings, coupled with future projections, indicated that by 2055, the delayed onset of adequate snow accumulation in the fall will prevent the completion of moose surveys over roughly 60% of Alaska's managed moose areas at this time of the year. Our findings can be used by wildlife managers to guide decisions related to the future reliability of aerial fall moose surveys and help to identify timelines for development of alternate measurement and monitoring methods.

Extremes of snow and temperature affect patterns of genetic diversity and differentiation in the alpine butterfly Parnassius smintheus.

Weather is an important short-term, local driver of population size and dispersal, which in turn contribute to patterns of genetic diversity and differentiation within species. Climate change is leading to greater weather variability and more frequent extreme weather events. While the effects of long-term and broad-scale mean climate conditions on genetic variation are well studied, our understanding of the effects of weather variability and extreme conditions on genetic variation is less developed. We assessed the influence of temperature and snow depth on genetic diversity and differentiation of populations of the alpine butterfly, Parnassius smintheus. We examined the relationships between a suite of variables, including those representing extreme conditions, and population-level genetic diversity and differentiation across 1453 single nucleotide polymorphisms, using both linear and gravity models. We additionally examined effects of land cover variables known to influence dispersal and gene flow in this species. We found that extreme low temperature events and the lowest recorded mean snow depth were significant predictors of genetic diversity. Extreme low temperature events, mean snow depth and land cover resistance were significant predictors of genetic differentiation. These results are congruent with known effects of early winter weather on population size and habitat connectivity on dispersal in P. smintheus. Our results demonstrate the potential for changes in the frequency or magnitude of extreme weather events to alter patterns of genetic diversity and differentiation.

Spatio‐temporal analysis of snow depth and snow water equivalent in a mountainous catchment: Insights from in‐situ observations and statistical modelling

AbstractThis research, conducted in the mountainous catchment near Abant Lake in the Western Black Sea region of Türkiye, aimed to investigate the spatiotemporal variations of snow depth (SD) and snow water equivalent (SWE) throughout the snow season from December 2019 to March 2020, encompassing both accumulation and melting periods. In total, 14 snow surveys were conducted, covering 58 permanent snow measurement points (PSMP) marked with snow poles. The classification and regression tree (CART) method was employed to statistically analyse their relationships with eight variables: snow period, forest canopy, aspect, slope, elevation, slope position, plan and profile curvature. The root mean square error (RMSE) for SD and SWE was determined to be 0.15 m and 46 mm, respectively. The study findings revealed that mean SD and SWE values were higher in forest gaps compared with under‐forest and open areas. Although the snow cover disappeared earliest in under‐forest areas, the melting rate was observed to be 43% and 17% slower compared with forest gaps and open areas, respectively. Wind redistribution resulted in minimum snow accumulation on western aspects, upper slope positions and ridges, while maximum accumulation was observed on southern aspects, valleys and lower slope positions. Higher elevations (>1580 meters) experienced faster snow melting rates, leading to earlier disappearance of snow cover. PSMPs located on slopes with lower degrees (<15°) exhibited lesser accumulation and earlier snow disappearance. The CART model identified the snow period as the most significant factor in predicting SD and SWE, based on variations in snowfall and air temperature. Other significant variables included forest canopy, aspect and elevation. The study suggests that the CART method is well‐suited for modelling complex snow dynamics, providing valuable insights into spatiotemporal variations in SD and SWE in mountainous regions.

Snow Depth Estimation and Spatial and Temporal Variation Analysis in Tuha Region Based on Multi-Source Data

In the modelling of hydrological processes on a regional scale, remote-sensing snow depth products with a high spatial and temporal resolution are essential for climate change studies and for scientific decision-making by management. The existing snow depth products have low spatial resolution and are mostly applicable to large-scale studies; however, they are insufficiently accurate for the estimation of snow depth on a regional scale, especially in shallow snow areas and mountainous regions. In this study, we coupled SSM/I, SSMIS, and AMSR2 passive microwave brightness temperature data and MODIS, TM, and Landsat 8 OLI fractional snow cover area (fSCA) data, based on Python, with 30 m spatially resolved fractional snow cover area (fSCA) data obtained by the spatio-temporal dynamic warping algorithm to invert the low-resolution passive microwave snow depths, and we developed a spatially downscaled snow depth inversion method suitable for the Turpan–Hami region. However, due to the long data-processing time and the insufficient arithmetical power of the hardware, this study had to set the spatial resolution of the result output to 250 m. As a result, a day-by-day 250 m spatial resolution snow depth dataset for 20 hydrological years (1 August 2000–31 July 2020) was generated, and the accuracy was evaluated using the measured snow depth data from the meteorological stations, with the results of r = 0.836 (p ≤ 0.01), MAE = 1.496 cm, and RMSE = 2.597 cm, which are relatively reliable and more applicable to the Turpan–Hami area. Based on the spatially downscaled snow depth data produced, this study found that the snow in the Turpan–Hami area is mainly distributed in the northern part of Turpan (Bogda Mountain), the northwestern part of Hami (Barkun Autonomous Prefecture), and the central part of the area (North Tianshan Mountain, Barkun Mountain, and Harlik Mountain). The average annual snow depth in the Turpan–Hami area is only 0.89 cm, and the average annual snow depth increases with elevation, in line with the obvious law of vertical progression. The annual mean snow depth in the Turpan–Hami area showed a “fluctuating decreasing” trend with a rate of 0.01 cm·a−1 over the 20 hydrological years in the Turpan–Hami area. Overall, the spatially downscaled snow depth inversion algorithm developed in this study not only solves the problem of coarse spatial resolution of microwave brightness temperature data and the difficulty of obtaining accurate shallow snow depth but also solves the problem of estimating the shallow snow depth on a regional scale, which is of great significance for gaining a further understanding of the snow accumulation information in the Tuha region and for promoting the investigation and management of water resources in arid zones.

Observed changes in the climate and snow dynamics of the Third Pole

The Third Pole (TP) is the world’s largest highland and has one of the biggest reservoirs of glacier ice mass and snow cover on the Earth. Three major Asian rivers (the Indus, Ganga and Brahmaputra) are nourished by the melting of glaciers and snow in Central Himalaya, which are inevitable for the socioeconomic sustainability and water security of South Asia. Here, we investigate the long-term (1980–2020) changes in snow depth and precipitation in TP, where major precipitation occurs in the form of rainfall in summer, and snowfall in winter and spring. The seasonal mean snow depth is deep (≥1 m) in winter and shallow (≤0.2 m) in summer. The average snowmelt and snow water equivalent are higher in the central and western Himalaya and Karakoram ranges in spring, which are the regions with most glaciers in TP. There is a significant positive trend in total precipitation, about 0.01–0.03 mm d−1 yr−1 in the central and eastern TP during the South Asian Summer Monsoon for the 1980–2020 period. Snowmelt is also increasing (>0.5 × 10−3 mm yr−1) in the western Himalaya during spring, which is consistent with the temperature rise (0.04–0.06 °C yr−1) there. In addition, there is a notable increase in the annual mean glacier melt (here, the water equivalent thickness) in TP (−1 to −5 cm w.e. yr−1), with its highest values in the eastern and central Himalaya (−3 to −5 cm w.e. yr−1), as estimated for the period 2003–2020. On top of these, by the end of the 21st century, the Coupled Model Intercomparison Project Phase 6 (CMIP6) projections show that there would be a significant decrease in snow depth and an increase in temperature of TP in all shared socioeconomic pathways (SSPs). Henceforth, the increasing trend in temperature and melting of snow/glaciers in TP would be a serious threat to the regional climate, water security and livelihood of the people of South Asia.

Snow depth in high-resolution regional climate model simulations over southern Germany – suitable for extremes and impact-related research?

Abstract. Snow dynamics play a critical role in the climate system, as they affect the water cycle, ecosystems, and society. In climate modelling, the representation of the amount and extent of snow on the land surface is crucial for simulating the mass and energy balance of the climate system. Here, we evaluate simulations of daily snow depths against 83 station observations in southern Germany in an elevation range of 150 to 1000 m over the time period 1987–2018. Two simulations stem from high-resolution regional climate models – the Weather Research & Forecasting (WRF) model at 1.5 km resolution and the COnsortium for Small scale MOdelling model in CLimate Mode (COSMO-CLM; abbreviated to CCLM hereafter) at 3 km resolution. Additionally, the hydrometeorological snow model Alpine MUltiscale Numerical Distributed Simulation ENgine (AMUNDSEN) is run at point scale at the locations of the climate stations, based on the atmospheric output of CCLM. To complement the comparison, the ERA5-Land dataset (9 km), a state-of-the-art reanalysis land-surface product, is also compared. All four simulations are driven by the atmospheric boundary conditions of ERA5. Due to an overestimation of the snow albedo, the WRF simulation features a cold bias of 1.2 °C, leading to the slight overestimation of the snow depth in low-lying areas, whereas the snow depth is underestimated at snow-rich stations. The number of snow days (days with a snow depth above 1 cm) is reproduced well. The WRF simulation can recreate extreme snow depths, i.e. annual maxima of the snow depth, their timings, and inter-station differences, and thereby shows the best performance of all models. The CCLM reproduces the climatic conditions with very low bias and error metrics. However, all snow-related assessments show a strong systematic underestimation, which we relate to deficiencies in the snow module of the land-surface model. When driving AMUNDSEN with the atmospheric output of the CCLM, the results show a slight tendency to overestimate snow depth and number of snow days, especially in the northern parts of the study area. Snow depth extremes are reproduced well. For ERA5-Land (ERA5L), the coarser spatial resolution leads to larger differences between the model elevation and the station elevation, which contributes to a significant correlation of climatic biases with the elevation bias. In addition, the mean snow depth and number of snow days are strongly overestimated, with conditions that are too snowy in the late winter. Extreme snow depth conditions are reproduced well in the low-lying areas, whereas strong deviations occur with more complex topography. In sum, due to the high spatial resolution of convection-permitting climate models, they show the potential to reproduce the winter climate (temperature and precipitation) in southern Germany. However, different sources of uncertainties, i.e. the spatial resolution, the snow albedo parametrisation, and other parametrisations within the snow model, prevent their further use in a straightforward manner for impact research. Hence, careful evaluation is needed before any impact-related interpretation of the simulations, such as in the context of climate change research.

Retrieval of Snow Depth on Arctic Sea Ice From Surface‐Based, Polarimetric, Dual‐Frequency Radar Altimetry

AbstractSnow depth on sea ice is an Essential Climate Variable and a major source of uncertainty in satellite altimetry‐derived sea ice thickness. During winter of the MOSAiC Expedition, the “KuKa” dual‐frequency, fully polarized Ku‐ and Ka‐band radar was deployed in “stare” nadir‐looking mode to investigate the possibility of combining these two frequencies to retrieve snow depth. Three approaches were investigated: dual‐frequency, dual‐polarization and waveform shape, and compared to independent snow depth measurements. Novel dual‐polarization approaches yielded r2 values up to 0.77. Mean snow depths agreed within 1 cm, even for data sub‐banded to CryoSat‐2 SIRAL and SARAL AltiKa bandwidths. Snow depths from co‐polarized dual‐frequency approaches were at least a factor of four too small and had a r2 0.15 or lower. r2 for waveform shape techniques reached 0.72 but depths were underestimated. Snow depth retrievals using polarimetric information or waveform shape may therefore be possible from airborne/satellite radar altimeters.

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.

Snow depth and the associated offset in ground temperatures in a landscape manipulated with snow-fences

Seasonal snow cover has an important impact on the difference between soil- and air temperature because of the insulation effect, and is therefore a key parameter in ecosystem models. However, it is still uncertain how specific variations in soil moisture, vegetation composition, and surface air warming, combined with snow dynamics such as compaction affect the difference between soil- and air temperature. Here, we present an analysis of 8 years (2012–2020) of snow dynamics in an Arctic ecosystem manipulation experiment (using snow fences) on Disko Island, West Greenland. We explore the snow insulation effect under different treatments (mesic tundra heath as a dry site and fen area as a wet site, snow addition from snow fences, warming using open top chambers, and shrub removal) on a plot-level scale. The snow fences significantly changed the inter-annual variation in snow depths and -phenology. The maximum annual mean snow depths were 90 cm on the control side and 122 cm on the snow addition side during all study years. Annual mean snow cover duration across 8 years was 234 days on the control side and 239 days on the snow addition side. The difference between soil- and air temperature was significantly higher on the snow addition side than on the control side of the snow fences. Based on a linear mixed-effects model, we conclude that the snow depth was the decisive factor affecting the difference between soil- and air temperature in the snow cover season (p < 0.0001). The change rate of the difference between soil- and air temperature, as a function of snow depth, was slower during the period before maximum snow depth than during the period between the day with maximum snow depth until snow ending day. During the snow-free season, the effects of the open top chambers were stronger than the effects of the shrub removal, and the combination of both contributed to the highest soil temperature in the dry site, but the warming effect of open top chambers was limited and shrub removal warmed soil temperature in the wet site. The warming effects of open top chambers and shrub removal were weakened on the snow addition side, which indicates a lagged effect of snow on soil temperature. This study quantifies important dynamics in soil-air temperature offsets linked to both snow and ecosystem changes mimicking climate change and provides a reference for future surface process simulations.

Assessment of L-band InSAR snow estimation techniques over a shallow, heterogeneous prairie snowpack

Snow water equivalent (SWE) is a critical input for weather, climate, and water resource management models at local to global scales. Despite its importance, global SWE measurements that are accurate, consistent, and at sufficiently high spatiotemporal resolutions are not currently available. L-band interferometric synthetic aperture radar (InSAR) techniques have been used to measure SWE at local to regional scales, and two upcoming L-band SAR satellite missions have renewed interest in these techniques to provide regular SWE measurements at the global scale. However, previous research demonstrating the capabilities of L-band InSAR-SWE measurement has been limited to mountain or tundra snowpack regimes. Here we examine the feasibility of applying the same techniques over a prairie snowpack, which are typically characterized by shallow snow depths (mean snow depth of 0.22 m in this study), exposed agricultural vegetation, and high spatial variability over short distances. Our study area in central Montana, USA (47.060, -109.951) was a validation site for NASA SnowEx 2021, as part of the UAVSAR snow timeseries. Airborne L-band SAR imagery was acquired by the UAVSAR platform while concurrent snow measurements were collected using uncrewed aerial vehicle (UAV)-based LiDAR, UAV-based photogrammetry, and ground-based manual techniques. This validation dataset enables an investigation of the effects of sub-pixel snow cover heterogeneity and exposed agricultural vegetation stubble on SAR data and the resulting SWE estimations. Results based on repeated application of the Kolmogorov–Smirnov test show that UAVSAR VV phase change is sensitive to differences in snow cover but relatively unaffected by differences in agricultural stubble height. However, we did not find similarly definitive results when we used the same phase change data to estimate SWE. Although broad spatial patterns were similar in both LiDAR-derived and InSAR-derived SWE estimates, considerable differences in the two estimates were apparent in areas with large sub-pixel snow depth variability. Our results indicate that additional work is necessary to derive accurate SWE estimates in prairie environments. Regular measurements from L-band SAR satellites will provide an excellent opportunity to refine InSAR-based snow estimation techniques over shallow, heterogeneous snowpacks.

Historical and real-time estimation of snow depth in Eurasia based on multiple passive microwave data

Current snow depth datasets demonstrate large discrepancies in the spatial pattern in Eurasia, and the lagging updates of datasets do not meet the operational requirements of the meteorological service department. This study developed a dynamic retrieval method for daily snow depth over Eurasia based on cross-sensor calibrated microwave brightness temperatures to enhance retrieval accuracy and meet the requirements of operational work. These brightness temperatures were detected by microwave radiometer imager carried on the FengYun 3 (FY-3) satellite and the special sensor microwave imager/sounder carried on the USA Defense Meteorological Satellite Program series satellites, which use the fewest sensors to provide the longest data and consequently introduce minimal errors during inter-sensor calibration. Firstly, inter-sensor calibration was conducted amongst brightness temperatures collected by the three sensors. A spatiotemporal dynamic relationship between snow depth and microwave brightness temperature gradient was then established, overcoming the large uncertainties induced by varying snow characteristics. This relationship can be utilised in FY-3 satellite data for operational service to obtain real-time snow depth. The generated daily snow depth dataset from 1988 to 2021 presents similar spatial patterns of snow depth to those observed in situ. Against in situ snow depth, the overall bias and root mean square error are −2.04 and 6.49 cm, respectively, facilitating considerable improvements in accuracy compared with the Advanced Microwave Scanning Radiometer 2 snow depth product, which adopts the static algorithm. Further analysis shows an overall decreasing trend from 1988 to 2021 for annual and monthly mean snow depths, demonstrating a noticeable reduction since around 2000. The reduction in monthly mean snow depth started earlier in shallow snow months than in deep snow months.

Evaluation of snow depth from multiple observation-based, reanalysis, and regional climate model datasets over a low-altitude Central European region

This study evaluates snow depth (SD) from several data sources: a combined satellite-based and in situ snow water equivalent product from the Copernicus Global Land Service (CGLS), a dataset constructed from temperature, precipitation, and relative humidity using a snow model (CARPATCLIM), two state-of-the-art reanalyses by ECMWF (ERA5 and ERA5-Land), and Weather Research and Forecasting (WRF) regional climate simulations at grid spacings of 50 km and 10 km. SD observations from weather stations are used as a reference for the pointwise comparison. The study area covers the Pannonian Basin region (part of Central and Eastern Europe). Results are presented for the 2006–2010 and 1985–2010 periods. All datasets adequately reproduce the average day-to-day variation of SD but with different error magnitudes. The ERA5 reanalysis and the CGLS product represent SD remarkably well, with correlation coefficients above 0.9 and mean errors close to zero. On the other hand, ERA5-Land and CARPATCLIM overestimate daily mean SD by 2–3 cm for some stations and display lower correlations (0.7–0.9) during the 26-year time span. The WRF simulations significantly overestimate SD in the melting period (February–March). Reduction of the grid spacing from 50 to 10 km does not improve the results. The excessive snow cover might negatively impact land–atmosphere interactions in the model and lead to biases like temperature underestimation found in previous regional climate model evaluation studies. The results indicate that even in regions where snow is not a major climatic factor, SD errors can be substantial and should be considered in model evaluation and adaptation. Over the Carpathian Mountain ranges, SD from the different data sources diverges to the extent that the sign of the monthly mean model bias changes depending on the choice of the reference dataset.

Potential impacts of climate change on the spatial distribution of Chinese ski resorts

Chinese skiing tourism is rapidly developing, but it will be potentially affected by snow resources and climate conditions. This study systematically analysed the spatial characteristics of snow resources, climatic conditions and ski resorts in China; revealed the potential impacts of climatic environment on current ski resorts by using Regional Climate Model (RegCM4.4); and proposed suggestions for climate resource assessment and spatial planning of ski resorts. The mean snow depth and snow cover days are more than 4.50 cm and 150 d between the years 2000 and 2014, respectively, in high-latitude areas, where fewer ski resorts are located. In the middle-low latitude areas, the mean snow depth and snow cover days are less than 3.00 cm and 100 d, respectively, with ski resorts being intensively distributed. In most of these areas, the pronounced climate warming exceeded 0.50 °C during the ski period of 2009–2015 relative to 1961–2015, and mean annual precipitation was less than 800 mm, with some even less than 400 mm. In the 2030s and 2050s, the regions with drastic temperature increases are mainly in the Qinghai–Tibetan Plateau, Northeast China, and the Yangtze River Basin. The upper and middle reaches of the Yangtze River are expected to experience reduced snowfalland temperature increases with an almost 1–1.6 °C, respectively. In this context, some ski resorts will face major threats because of shortened skiing durations and rising management costs. Skiing tourism should not pursue a one-sided growth rate in the number of ski resorts in areas with short snowpack periods. Instead, it should rely highly on snow resources, climatic conditions, geographical environment, population and economic basis, and rationalise its spatial layout to promote the industry towards sustainable development.

A novel fine-resolution snow depth retrieval model to reveal detailed spatiotemporal patterns of snow cover in Northeast China

ABSTRACT Seasonal snow cover is a key component of the global climate and hydrological system, it has drawn considerable attention under global warming conditions. Although several passive microwave (PMW) snow depth (SD) products have been developed since the 1970s, they inherit noticeable errors and uncertainties when representing spatial distributions and temporal changes of SD, especially in complex mountainous regions. In this paper, we developed a fine-resolution SD retrieval model (FSDM) using machine learning to improve SD estimation quality for Northeast China and produced a long-term, fine-resolution, daily SD dataset. The accuracies of the FSDM dataset were evaluated against in-situ SD data along with existing SD products. The results showed the FSDM dataset provided satisfactory inversion accuracy in spatiotemporal evaluation, with the root-mean-square error (RMSE), bias, and correlation coefficient (R) of 7.10 cm, −0.13 cm, and 0.60. Additionally, we analyzed the spatiotemporal variations of SD in Northeast China and found that snow cover was mainly distributed in the Greater Khingan Range, Lesser Khingan Mountains, and Changbai Mountain regions. The SD exhibited high-low distribution patterns with the increased latitude. The annual mean SD slightly increased at the rate of 0.029 cm/year during 1987-2018.

Investigating Extreme Snowfall Changes in China Based on an Ensemble of High-Resolution Regional Climate Models

Anthropogenically induced global warming intensifies the water cycle around the world. As a critical sector of the water cycle, snow depth and its related extremes greatly impact agriculture, animal husbandry, and food security, yet lack investigation. In this study, five high-resolution climate models are selected to simulate and project snow depth and its extremes over China. The simulation capabilities of models in reproducing the basic climate variables in winter are gauged in terms of spatial and temporal patterns over nine subregions. It is found that the driving global climate model (GCM) can contribute to similar patterns, while the different regional climate model (RCM) schemes lead to large variations in the snowfall accumulating on the land surface. The warming magnitude is larger under a higher representative concentration pathway (RCP) scenario (2.5 °C greater under RCP8.5 than RCP4.5). The distribution of ensemble mean winter precipitation changes is more fragmented because of the relatively low skill in reproducing water-related content in the climate system. The projected precipitation change is larger under RCP8.5 than under RCP4.5 due to the amplification of the hydrological cycle by temperature warming. The projected changes in the ensemble mean snow depth mainly occur over the Tibetan Plateau with a decreasing trend. Only several grids over the Himalayas Mountains and the upper stream of the Yarlung Zangbo River are projected with a slight increase in snow depth. Both the intensity and frequency of extreme snow events are projected to increase in Northeast China and Inner Mongolia, which are important agricultural and animal husbandry production areas in China. The reason behind this projection can be explained by the fact that the hydrological cycle intensified by temperature warming leads to excessive snowfall stacking up during winter. The changes in extreme snowfall events in the future will have a significant impact on China’s agricultural and animal husbandry production and threaten food security.

Severity of winters in the Czech Republic during the 1961–2021 period and related environmental impacts and responses

AbstractThis paper analyses temperature and snow patterns of winters (December–February) averaged for the territory of the Czech Republic during the 1961–2021 period and their broad environmental impacts and responses. Series of mean, maximum, minimum, absolute maximum and absolute minimum temperatures show significant increasing linear trends, while decreasing trends were detected in numbers of frost, ice and extremely cold days, duration of cold waves, snowfall days, sums of heights of new snow, days with snow depths ≥1 cm, mean and maximum snow depths. The winter severity, derived from five temperature and five snow variables and expressed by temperature/snow scores, indicates decreasing severity of winters for 1961–2021, in which temperature severity is more pronounced than that of snow. Decreasing winter severity is in line with decreasing frequency of cyclonic and directional circulation types according to objective classification, while the trend in anticyclonic types was opposite. Types with maritime airflow from the Atlantic Ocean and the Mediterranean contribute to milder and types with continental airflow from the east to colder winters. The coldest winters, 1962/1963 and 1984/1985, and the mildest winters, 2006/2007 and 2019/2020, were analysed in greater detail. Concerning of different analysed environmental impacts, they are influenced not only by severe winter weather, but also by political, socioeconomic and general environmental changes in the country. In line with decreasing winter severity were only statistically significant decreasing trends in proportions of traffic accidents connected with snow and glaze ice on the roads and volumes of damaged wood due to high weights of snow and ice deposit, expressed as salvage felling. Series of other environmental impacts (e.g., fatalities attributed to weather, impacts on the economy and society) reflect rather severity of individual winters and express high interannual variability without any representative trends.

Spatial differentiation and influencing factors of active layer thickness in the Da Hinggan Ling Prefecture

Active layer thickness (ALT) of permafrost changes significantly under the combined influence of human activities and climate warming, which has a significant impact on the ecological environment, hydrology, and engineering construction in cold regions. The spatial differentiation of Active layer thickness and its influencing factors have become one of the hot topics in the field of cryopedology in recent years, but there are few studies in the Da Hinggan Ling Prefecture (DHLP). In this study, the Stefan equation was used to simulate the Active layer thickness in the Da Hinggan Ling Prefecture, and the factor detection and interaction detection functions of geodetector were used to analyze the factors affecting the spatial differentiation of Active layer thickness from both natural and humanity aspects. The results showed that Active layer thickness in the Da Hinggan Ling Prefecture ranges from 58.82 cm to 212.55 cm, the determinant coefficientR2, MAE, RMSE between simulation results and the sampling points data were 0.86, 11.25 (cm) and 13.25 (cm), respectively. Lower Active layer thickness values are mainly distributed higher elevations in the west, which are dominated by forest (average ALT: 136.94 cm) and wetlands (average ALT: 71.88 cm), while the higher values are distributed on cultivated land (average ALT: 170.35 cm) and construction land (average ALT: 176.49 cm) in the southeast. Among the influencing factors, elevation is significantly negatively correlated with ALT. followed by summer mean LST, SLHF and snow depth. NDVI and SM has the strong explanation power for the spatial differentiation of ALT in factor detection. Regarding interactions, the explanatory power of slope ∩ snow depth is the highest of 0.83, followed by the elevation ∩ distance to settlements. The results can provide reference for the formulation of ecological environmental protection and engineering construction policies in cold regions.

Application of a simple model for ice growth to the Lake St. Moritz, Switzerland

AbstractWe present a Simple Lake Ice Model to calculate the growth rate of lake ice in a cold and relatively dry climate. The focus is on Lake St. Moritz, Switzerland, which has an area of 0.78 km2 and is about 45 m deep. In winter the lake is extensively used for recreational purposes, including horse racing with thousands of spectators. Safety on the ice cover is essential, and there is a great need to have a simple tool with which the growth rate of the ice layer can be calculated for given meteorological conditions. The approach is based on a simple formulation of the upper temperature of the ice layer, which depends on air temperature and snow cover. Input data are the date on which the lake freezes over, daily mean air temperatures and snow depth. For the winter 2021/22 calculated ice growth compares well with ice thickness measurements. We demonstrate that grooming of the snow has a significant positive effect on the ice thickening rate. We also evaluate the sensitivity of the simulated ice thickness to increasing mean temperature.

Applicability of alpine snow depth estimation based on multitemporal UAV-LiDAR data: A case study in the Maxian Mountains, Northwest China

Unmanned aerial vehicle (UAV) LiDAR has emerged as a viable approach to generate high-resolution snow depth (SD) maps. However, implementing this technology in high-elevation mountains is difficult, and few studies have examined the SD on mountains in Asia until now. Here, we used UAV-LiDAR to estimate the SD on the Maxian Mountains along the eastern edge of High Mountain Asia to assess the reliability of this technology for obtaining SD distributions on a typical alpine periglacial geomorphology. Using LiDAR data from different periods (i.e., 2020/10/19, 2020/11/25 and 2021/04/08) and evenly-distributed in situ measurements, we obtained and validated SDs for both shallow (mean SD less than 15 cm) and deep (mean SD ≥ 15 cm) snow conditions. Our results indicated that the quality of the LiDAR point cloud data met the requirements of the relevant specifications, and the SD distribution map captured the spatial heterogeneity caused by the hummocks. The multitemporal SDs derived from LiDAR were all overestimated, but the accuracy for shallow snow was better than that for deep snow. The root mean square error (RMSE) and mean absolute error (MAE) are 2.23 cm and 1.57 cm, respectively, for deep snow, which were almost three times those of shallow snow. The seasonal deformation of the hummock and manual sampling errors caused by the ice layer over the soil were the main factors affecting accuracy. In contrast, snow surface conditions played a minor role in the error estimators. This study helps refine the use of UAV-LiDAR for SD estimation in similar environments.

Estimation of Soil Freeze Depth in Typical Snowy Regions Using Reanalysis Dataset: A Case Study in Heilongjiang Province, China

Soil freeze depth variations greatly affect energy exchange, carbon exchange, ecosystem diversity, and the water cycle. Given the importance of these processes, obtaining freeze depth data over large scales is an important focus of research. This paper presents a simple empirical algorithm to estimate the maximum seasonally frozen depth (MSFD) of seasonally frozen ground (SFG) in snowy regions. First, the potential influences of driving factors on the MSFD variations were quantified in the baseline period (1981–2010) based on the 26 meteorological stations within and around the SFG region of Heilongjiang province. The three variables that contributed more than 10% to MSFD variations (i.e., air freezing index, annual mean snow depth, and snow cover days) were considered in the analysis. A simple multiple linear regression to estimate soil freeze depth was fitted (1981–2010) and verified (1975–1980 and 2011–2014) using ground station observations. Compared with the commonly used simplified Stefan solution, this multiple linear regression produced superior freeze depth estimations, with the mean absolute error and root mean square error of the station average reduced by over 20%. By utilizing this empirical algorithm and the ERA5-Land reanalysis dataset, the multi-year average MSFD (1981–2010) was 132 cm, ranging from 52 cm to 186 cm, and MSFD anomaly exhibited a significant decreasing trend, at a rate of −0.38 cm/decade or a net change of −28.14 cm from 1950–2021. This study provided a practical approach to model the soil freeze depth of SFG over a large scale in snowy regions and emphasized the importance of considering snow cover variables in analyzing and estimating soil freeze depth.