Search

The article presents research findings on the content of tritium in the snow cover of test locations 'Balapan' and 'Sary-Uzen' in the territory of the Semipalatinsk Test Site. Based upon results, a number of tritium-contaminated areas were revealed. At the 'Balapan' site, elevated tritium concentrations in the snow were revealed in the vicinity of the 'Atomic lake' close to the 'Karazhyra' deposit and at the location of Lake 'Kishkensor'. At the 'Sary-Uzen' site, elevated tritium concentrations in the snow were discovered near borehole 101, at object 'Lazurite' and at an unnamed creek outflowing from the mountain range. Maximal of tritium activity concentration in the snow were 45 Bq/kg and 36 Bq/kg at the 'Balapan' and 'Sary-Uzen' sites, respectively. Background tritium concentrations in the snow cover of the STS territory were found to be 4.6-12 Bq/kg., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Turchenko reports administrative support and equipment, drugs, or supplies were provided by Institute of Radiation Safety and Ecology., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

The coverage of accumulated snow plays a significant role in inducing changes in both microbial activity and environmental factors within freeze-thaw soil systems. This study aimed to analyze the impact of snow cover on the dynamics of archeal communities in freeze-thaw soil. Furthermore, it seeks to investigate the role of fertilization in freeze-thaw soil. Four treatments were established based on snow cover and fertilization:No snow and no fertilizer (CK-N), snow cover without fertilizer (X-N), fertilizer without snow cover (T-N), and both fertilizer and snow cover (T-X). The research findings indicated that after snow cover treatment, the carbon, nitrogen, and phosphorus content in freeze-thaw soil exhibit periodic fluctuations. Snow covered effectively altered the community composition of bacteria and archaea in the soil, with a greater impact on archaeal communities than on bacterial communities. Snow covered improves the stability of archaeal communities in freeze-thaw soil. Additionally, the arrival of snow also enhanced the correlation between archaea and environmental factors, with the key archaeal phyla involved being Nanoarchaeota and Crenarchaeota. Further research showed that the application of organic fertilizers also had some impact on freeze-thaw soil, but this impact was smaller compared to snow cover. In summary, the arrival of snow could alter the archaeal community and protect nutrient elements in freeze-thaw soil, reducing their loss, and its effect is more pronounced compared to the application of organic fertilizers., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Introduction and Objective: Snow cover serves as a unique indicator of environmental pollution in both urban and rural areas. As a seasonal cover, it accumulates various pollutants emitted into the atmosphere, thus providing insight into air pollution types and the relative contributions of different pollution sources. The aim of the study is to analyze the distribution of trace elements in snow cover to assess the anthropogenic influence on pollution levels, and better understand ecological threats., Material and Methods: The study was conducted in rural areas around the village of Wólka in the Lublin Province of eastern Poland, and in urban districts of the city of Lublin, capital of the Province. Samples were analyzed using Inductively Coupled Plasma-Mass Spectrometry, the Enrichment Factor (EF), and ecological risk indices (RI), were calculated to evaluate the contamination and potential ecological risks posed by the metals., Results: The findings indicate higher concentrations of metals like sodium and iron in urban areas, likely due to road salt use and industrial activity, respectively. Enrichment factors showed significant anthropogenic contributions, particularly for metals like sodium, zinc, and cadmium, which had EF values substantially above natural levels. The potential ecological risk assessment highlighted a considerable ecological threat in urban areas compared to rural settings, primarily due to higher concentrations of metals., Conclusions: The variation in metal concentrations between urban and rural snow covers reflects the impact of human activities on local environments. Urban areas showed higher pollution levels, suggesting the need for targeted pollution control policies to mitigate the adverse ecological impacts. This study underscores the importance of continuous monitoring and comprehensive risk assessments to effectively manage environmental pollution.

Snow cover is an essential element of the Himalayan region (Third Pole), and it represents the impacts induced by climate change. Recently, studies have reported significant variations in the Himalayan snow cover area, which may impact the livelihood of a large portion of the global population. Therefore, in this study, efforts were made to estimate the association between key climate stressors (CSs), i.e., temperature and precipitation, topography, and temporal variability of snow cover area (SCA) in the Parvati River basin (PRB) of the Indian western Himalayas. In this regard, the PRB has been classified into different elevation zones, i.e., zone I to zone V, ranging from 1100 to 6200 amsl. The databases such as MODIS, MEERA-2, and ASTER DEM V2 have been used to estimate the changes in the SCA and the CSs with changes in elevation and seasons. The linear regression analysis of the dataset from 2001 to 2017 revealed a significant association and increasing trend in the SCA of zone III. However, a significant association could not be established between the elevation and the SCA for the rest of the zones. A zonal seasonal trend investigation of the SCA observed an increasing trend in zones IV and V during the summer season due to a momentous rise in snowfall and a decline in temperature. The SCA has shown a significant declining trend only during the monsoon season in zones IV and V, which is due to a strong negative relationship between the SCA and the temperature of the region. These results demonstrate the amount of SCA in zones of high elevation of the PRB has been declining at an alarming rate, which could negatively influence glaciers' retreat in the near future. Hence, it can be estimated that the outcomes of the study will act as a base for future studies, regional policy formulation, and climate modelling that can further prevent future drastic or extreme events., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

It is important to elucidate the changing distribution pattern of net primary productivity (NPP) to mechanistically understand the changes in aboveground and belowground ecosystem functions. In water-scarce desert environments, snow provides a crucial supply of water for plant development and the spread of herbaceous species. Yet uncertainty persists regarding how herbaceous plants' NPP allocation responds to variation in snow cover. The goal of this study was to investigate how variation in snow cover in a temperate desert influenced the NPP allocation dynamics of herbaceous species and their resistance to environmental change in terms aboveground and belowground productivity. In the Gurbantunggut Desert, wintertime snow cover depth was adjusted in plots by applying four treatments: snow removal (-S), ambient snow, double snow (+S), and triple snow (+2S). We examined their species richness, aboveground NPP (ANPP), belowground NPP (BNPP), and the resistance of ANPP and BNPP. We found that species diversity of the aboveground community increased significantly with increasing snow cover and decreased significantly Pielou evenness in plots. This resulted in greater ANPP with increasing snow cover; meanwhile, BNPP first increased and then decreased with increasing snow cover. However, this productivity in different soil layers responded differently to changed snow cover. In the 0-10 cm soil layer, productivity first rose and then declined, while it declined linearly in both the 10-20 cm and 20-30 cm soil layers, whereas in the 30-40 cm soil layer it showed an increasing trend. Belowground resistance would increase given that greater snow cover improved the BNPP in deeper soil and maintained the resource provisioning for plant growth, thus improving overall belowground stability. These results can serve as a promising research foundation for future work on how the functioning of desert ecosystems becomes altered due to changes in plant community expansion and, in particular, changes in snow cover driven by global climate change., Competing Interests: Declaration of competing interest We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled., (Copyright © 2023. Published by Elsevier B.V.)

This study analyzed total organic carbon (TOC), petroleum products (PP), suspended materials (SM), volatile aromatic hydrocarbons (toluene, o-xylene, etc.) and n-alkanes in the snow cover of Bol'shekhekhtsirsky, Zeysky state natural reserves and Khabarovsk, on 4, 5 and 9 stations in the south of the Russian Far East in March 2020. In Bol'shekhekhtsirsky reserve, the concentrations of TOC, PP, and SM in snow samples were in the range of 1.5-2.4, 0.06-0.11, and 11.4-1.9 mg/L, 1.4-1.9, 0.02-0.05, and 11-23 mg/L in Zeysky reserve, while in Khabarovsk were 1.7-23.7, 0.12-1.26, and 25-294 mg/L, respectively. In addition, the benzene, toluene, and o-xylene concentrations of snow samples ranges from not detected (ND) to 2.4, ND-3.1, and 1.1-2.7 µg/L in Khabarovsk, ND-1.3, ND-2.1, and ND-2.7 µg/L, respectively in Bol'shekhekhtsirsky reserve. Carbon preference index values of n-alkanes were consistent with anthropogenic sources for stations 7, 8 and 2 in Khabarovsk (Heat Power Plants 1, 2 and city roads). The snow of the Zeysky Reserve is not contaminated with organic pollutants, and can be used as a conditional background for the south of the Russian Far East., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Using a hemispherical directional reflectance factor instrument, spectral data of dirty snow containing black carbon (BC), mineral dust (MD), and ash was collected from multiple locations to investigate the impact of these light-absorbing impurities (LAIs) on snow reflectance characteristics. The findings revealed that the perturbation of snow reflectance caused by LAIs is characterized by nonlinear deceleration, indicating that the reduction in snow reflectance per unit ppm of LAIs declines as snow contamination increases. The reduction in snow reflectance caused by BC may reach saturation at elevated particle concentrations (thousands of ppm) on snow. Snowpacks loaded with MD or ash initially exhibit a significant reduction in spectral slope around 600 and 700 nm. The deposition of numerous MD or ash particles can increase snow reflectance beyond the wavelength of 1400 nm, with an increase of 0.1 for MD and 0.2 for ash. BC can darken the entire measurement range (350-2500 nm), while MD and ash can only affect up to 1200 nm (350-1200 nm). This study enhances our understanding of the multi-angle reflection characteristics of various dirty snow, which can guide future snow albedo simulations and improve the accuracy of LAIs' remote sensing retrieval algorithms.

Climate warming is inducing widespread vegetation changes in Arctic tundra ecosystems, with the potential to alter carbon and nutrient dynamics between vegetation and soils. Yet, we lack a detailed understanding of how variation in vegetation and topography influences fine-scale temperatures ("microclimate") that mediate these dynamics, and at what resolution vegetation needs to be sampled to capture these effects. We monitored microclimate at 90 plots across a tundra landscape in western Greenland. Our stratified random study design covered gradients of topography and vegetation, while nested plots (0.8-100 m 2 ) enabled comparison across different sampling resolutions. We used Bayesian mixed-effect models to quantify the direct influence of plot-level topography, moisture and vegetation on soil, near-surface and canopy-level temperatures (-6, 2, and 15 cm). During the growing season, colder soils were predicted by shrub cover (-0.24°C per 10% increase), bryophyte cover (-0.35°C per 10% increase), and vegetation height (-0.17°C per 1 cm increase). The same three factors also predicted the magnitude of differences between soil and above-ground temperatures, indicating warmer soils at low cover/height, but colder soils under closed/taller canopies. These findings were consistent across plot sizes, suggesting that spatial predictions of microclimate may be possible at the operational scales of satellite products. During winter, snow cover (+0.75°C per 10 snow-covered days) was the key predictor of soil microclimate. Topography and moisture explained little variation in the measured temperatures. Our results not only underline the close connection of vegetation and snow with microclimate in the Arctic tundra but also point to the need for more studies disentangling their complex interplay across tundra environments and seasons. Future shifts in vegetation cover and height will likely mediate the impact of atmospheric warming on the tundra soil environment, with potential implications for below-ground organisms and ecosystem functioning., (© 2022 The Authors. Global Change Biology published by John Wiley & Sons Ltd.)

Premise: Future reductions in snow cover are expected in temperate climates, likely leading to more soil-freezing events and damage to plant tissues. However, whether and how plants can compensate for this damage may depend on the timing of damage and on plant allocations to seed size and number. We need more information about how seed production, germination, and seedling recruitment might respond to changes in snow cover., Methods: We manipulated snow cover over three seasons in a common garden experiment with four treatments: (1) "control," where snowpack was left unmanipulated throughout the winter season; (2) "late addition," where snowpack was experimentally increased at the end of the winter season in order to delay the onset of spring; (3) "late removal," where snowpack was experimentally reduced at the end of the winter season in order to advance the onset of spring; and (4) "freeze," a consistent removal treatment, where snowpack was experimentally reduced following every substantial snowfall in order to induce freeze-thaw events in the soil. In all treatments, we measured survival, growth, reproduction, and recruitment of a native perennial herb, Thalictrum dioicum., Results: Reduced snow cover minimally influenced adult survival. Instead, individuals that experienced reduced snow cover throughout the winter produced more massive seeds, whereas individuals that experienced a single snow removal at the end of the season produced less massive seeds. Seedling recruitment was lower in the removal treatments than in the control, as a result of failure to germinate in the freeze treatment and seedling mortality in the late removal treatment., Conclusions: Both reduced snow cover throughout the winter and a single late snow removal in the spring reduced seedling recruitment, but for different reasons, suggesting that a holistic approach to the life cycle is needed to understand responses to shifting climates., (© 2022 Botanical Society of America.)

Snow and rainfall are two main water resources required for vegetation growth in the Gurbantunggut Desert, China. Epehmerals, an important component of plant community in this temperate desert in early spring, tend to be more sensitive to water availability than other types of plants. While previous studies mainly focus on the separate effects of snowpack or rainfall on the growth parameters of ephemerals, it is unclear, whether there is any interaction between snowpack and rainfall. Here an in-situ field experiment was conducted with snowpack and rainfall manipulation in the southern part of this desert. During two consecutive years, we measured ephemeral density, composition, and biomass under three snowpack and three rainfall treatments. The results indicated that snow and rainfall independently affected the variation in the density, composition, and productivity of ephemerals in this temperate desert. Increased depth of snow increased the ephemeral density in dry year but did not affect the species richness and productivity in both dry and wet years. However, rainfall significantly affected these parameters in the dry year, but had no dramatic effects in the wet year. Snowpack and rainfall differentially affect seedling establishment and productivity, and their effects are independent no matter in a dry or wet year., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Land surface albedo plays a crucial role in the land surface energy budget and climate. This paper identified the spatiotemporal variations of surface albedo on the Tibetan Plateau (TP) from 1982 to 2015, and quantified the relationships between the spatial and temporal patterns of the albedo and associated influencing factors (snow cover, vegetation, and soil moisture) on the seasonal and interannual basis using satellite products and reanalysis data. It was determined that the albedo presented a distinct spatial variability, with high values in mountainous areas and low values on the southeastern TP. Spatially, average albedo exhibited a positive correlation with snow cover and negative correlations with vegetation and soil moisture. Average albedo over the whole TP had a clear seasonal cycle with a peak in winter and a minimum value in summer, which is dictated by seasonal changes in snow and vegetation covers. Annual average albedo exhibited a weakly downward trend, which was mainly contributed by a significant decrease in summer, pointing to the important role in vegetation dynamics for temporal change of the albedo. On the regional basis, interannual variation of albedo was more responsive to snow cover over the snow- and vegetation-coexisting area than the snow-covered area, and to changes in Normalized Difference Vegetation Index (NDVI) over the vegetation-covered area than the snow- and vegetation-coexisting area; albedo had a weakly negative correlation with soil moisture over bare soil. Furthermore, our results indicated that snow cover was the dominant factor for albedo change on mountainous areas, and vegetation change predominated the variation of albedo on the eastern, southern, and northwestern TP. Specifically, variations in snow cover contributed more than those of vegetation to the interannual albedo variation over the Three Rivers Headwater Region. These results would be beneficial for better understanding the climate and eco-environment changes over the TP., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

Snow insulates the soil from air temperature, decreasing winter cold stress and altering energy use for organisms that overwinter in the soil. As climate change alters snowpack and air temperatures, it is critical to account for the role of snow in modulating vulnerability to winter climate change. Along elevational gradients in snowy mountains, snow cover increases but air temperature decreases, and it is unknown how these opposing gradients impact performance and fitness of organisms overwintering in the soil. We developed experimentally validated ecophysiological models of cold and energy stress over the past decade for the montane leaf beetle Chrysomela aeneicollis, along five replicated elevational transects in the Sierra Nevada mountains in California. Cold stress peaks at mid-elevations, while high elevations are buffered by persistent snow cover, even in dry years. While protective against cold, snow increases energy stress for overwintering beetles, particularly at low elevations, potentially leading to mortality or energetic tradeoffs. Declining snowpack will predominantly impact mid-elevation populations by increasing cold exposure, while high elevation habitats may provide refugia as drier winters become more common., (© 2021 John Wiley & Sons Ltd.)

Snow is a crucial component of the hydrological cycle in the Western Himalaya. Water from snowmelt is used in various sectors in downstream regions, thus playing a critical role in securing the livelihoods of millions of people. In this study, we investigated the future evolution of snow cover and snowmelt in the Panjshir catchment of Afghanistan, a sub-basin of the Indus, in the Western Himalaya. We applied a three-step approach to select a few global climate model (GCM) simulations from CMIP5 climate datasets for RCP4.5 and RCP8.5, which showed reasonable performance with ERA5-Land dataset for the chosen historical period (1981-2010). The selected model simulations were then segregated into two groups: those projecting a cold-wet climate and those projecting a warm-dry climate by the end of the 21 st century (2071-2100). These GCMs were downscaled to a higher resolution using empirical statistical downscaling. To simulate the snow processes, we used the distributed cryospheric-hydrological J2000 model. The results indicate that the model captures well the snow cover dynamics for the historical period when compared with the daily MODIS-derived snow cover. The J2000 model was then forced by climate projections from the selected GCMs to quantify future changes in snow cover area, snow storage and snowmelt. While a 10-18% reduction in annual snow cover area is projected in the cold-wet models, a 22-36% reduction is projected in the warm-dry models. Similarly, the snow cover area is projected to decrease in all elevation bands under climate change. At the seasonal scale, across all models and scenarios, the snow cover in the autumn and spring seasons are projected to reduce by as much as 25%, with an increase in winter and spring snowmelt and a decrease in summer snowmelt. The projected changes in the seasonal availability of snowmelt-driven water resources are likely to have direct implications for water-dependent sectors in the region and call for a better understanding of water usage and future adaptation practices., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

Winter snow cover is a major driver of soil microbial processes in high-latitude and high-altitude ecosystems. Warming-induced reduction in snow cover as predicted under future climate scenarios may shift soil bacterial communities with consequences for soil carbon and nutrient cycling. The underlying mechanisms, however, remain elusive. In the present study, we conducted a snow manipulation experiment in a Tibetan spruce forest to explore the immediate and intra-annual legacy effects of snow exclusion on soil bacterial communities. We analyzed bacterial diversity and community composition in the winter (i.e., the deep snow season), in the transitional thawing period, and in the middle of the growing season. Proteobacteria, Acidobacteria, and Actinobacteria were dominant phyla across the seasons and snow regimes. Bacterial diversity was generally not particularly sensitive to the absence of snow cover. However, snow exclusion positively affected Simpson diversity in the winter but not in the thawing period and the growing season. Bacterial diversity further tended to be higher in winter than in the growing season. In the winter, the taxonomic composition shifted in response to snow exclusion, while composition did not differ between exclusion and control plots in the thawing period and the growing season. Soil bacterial communities strongly varied across seasons, and the variations differed in specific groups. Both soil climatic factors (i.e., temperature and moisture) and soil biochemical variables partly accounted for the seasonal dynamics of bacterial communities. Taken together, our study indicates that soil bacterial communities in Tibetan forests are rather resilient to change in snow cover, at least at an intra-annual scale., Competing Interests: Declaration of competing interest The authors declare that they have no competing financial interests or personal relationships that could have influenced the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

The paper presents the results of a study of the content of 12 polyaromatic hydrocarbons (PAHs) in the snow cover of the city of Ivanovo (Russian Federation). It is shown that their average content exceeds the background level by 6.6 times, which made it possible to identify for which compounds the admission channels are associated with transboundary transport (naphthalene, pyrene, benz [b]fluorantin, benzo [a]pyrene and dibenz [a,h]anthracene), and for which with local emission sources (anthracene, phenanthrene, fluoranthene, chrysene, benz [k]fluorantin, and benzo [g,h,i]perylene). According to the known indicator ratios of the concentrations of PAHs, the main sources of release (pyrogenic and mixed) PAHs into the environment were estimated. The combination of experimental data in combination with factor analysis allowed identifying priority PAHs (naphthalene, fluoren, fluoranthene, benzo [a]pyrene and benzo [g,h,i]perylene), which should be included in the environmental monitoring programs of the region. Environmental risk assessments are given, which showed that the level of pollutant does not always adequately reflect the environmental impact for the territories. Thus, the contribution to the total PAH concentration of benz [b]fluorantin is only 9%, and to the amount of environmental risk - 51%. This must be taken into account in order to prioritize the control of individual components of PAHs in environmental objects., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Aim is to study morphology of the traces of a large drop of blood formed on snow-covered surface when it falls from different heights. In the experiment, we studied the morphology of blood drop traces fallen from 20, 50 and 100 cm on a just settled dry snow cover in a moderately frosty environment (-8 °C) Dropping from a height of up to 20 cm, a blood drop formed a cylindrical channel in the snow cover and spread out to form a disc-shaped element. However if the bleeding source was located at a 50 and 100 cm distance, an element close to a ball, consisting of blood crystals and ice was formed in the final section of the cylindrical channel. Morphology of the trace in freshly settled dry snow is due to the realization of the kinetic energy of a falling drop on the one hand, and the cooling of blood and crystallization on the other. Data obtained may be used to identify and describe traces of blood found on the scene in the street in the winter.

Snow cover, which is undergoing significant change along with global climate change, has considerable impacts on the functioning of terrestrial ecosystems. However, how snow cover change affects the vegetation gross primary production (GPP) in temperate regions still requires in-depth exploration. In this study, we investigated how changes in the winter snow depth (WSD) and snowmelt date (SMD) affect spring GPP and summer GPP through their influences on the start date of the growing season (SGS) and the maximum daily GPP (GPP max ), respectively, across temperate China from 2001 to 2015, based on both in situ measurements and satellite products (i.e., GLASS GPP, WestDC snow depth and GLEAM soil moisture). Soil moisture is identified as an important factor in the snow-GPP relationship in temperate China. Since most of temperate China is water-limited, thicker snow cover along with later snowmelt generally resulted in earlier SGS via a significant increase in soil moisture (47% of the area), which lengthened the growth period and enhanced spring carbon uptake in these areas. However, in wetter regions (7% of the area), thicker snow cover with later snowmelt would be more likely to delay the SGS, thus reducing spring GPP. Moreover, although the direct impact mechanisms of snow cover dynamics on summer GPP have not been identified, the snow-induced SGS change was found to have delayed effects on summer photosynthesis capacity, as earlier SGS increased the GPP max , and thus summer GPP. However, the photosynthesis enhanced by earlier SGS meanwhile increased the plant water consumption, which would bring water stress and reduce summer GPP if the subsequent precipitation is unable to compensate for the water consumption. Our findings on the effects of snow cover change on carbon uptake would provide the basic mechanisms for assessing how future climate change will affect ecosystem productivity., (Copyright © 2019 Elsevier B.V. All rights reserved.)

In the Southern Alps, climate warming induced the use of artificial snow since two decades. In this area, two different ski piste management practices prevail: (1) large and medium ski resorts (M), which guarantee a ski season of four to five months using artificial snow, whereas (2) in the small, local ski resorts (S) it usually lasts two or three months. Our research addresses two main questions: 1) what is the impact of the ski pistes on the physico-chemical properties of the snow, on the soil and on the vegetation of mountain grassland ecosystems and 2) does the impact on the mountain grassland ecosystems change between medium and small ski resorts? Our experimental approach follows a pairwise design of plots on mountain grasslands of the ski pistes and control plots on mountain grasslands outside the pistes, where we examined the snow and soil properties and the vegetation composition. Under the long ski-season management (M) we found a significantly lower soil temperature below the snow cover of the ski pistes than the one below the natural snowpack, but this difference was limited to the period of natural snow cover. Only in M, pistes showed a lower biomass production and species richness in the mountain grassland plant communities compared to the controls, while there was no effect in S. The proportions of plant functional groups' cover changed in both ski resort types between piste and control. The most important factors affecting the observed differences in vegetation between pistes and controls were snow duration, snow and soil chemical properties, with more marked differences in the soil properties in M respect to S. The study concludes that reducing the ski season's length, therefore limiting the artificial snow's input, as in S, is more adequate to minimize the environmental impact in a changing climate., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Climate change scenarios anticipate decreased spring snow cover in boreal and subarctic regions. Forest lakes are abundant in these regions and substantial contributors of methane emissions. To investigate the effect of reduced snow cover, we experimentally removed snow from an anoxic frozen lake. We observed that the removal of snow increased light penetration through the ice, increasing water temperature and modifying microbial composition in the different depths. Chlorophyll a and b concentrations increased in the upper water column, suggesting activation of algal primary producers. At the same time, Chlorobiaceae , one of the key photosynthetic bacterial families in anoxic lakes, shifted to lower depths. Moreover, a decrease in the relative abundance of methanotrophs within the bacterial family Methylococcaceae was detected, concurrent with an increase in methane concentration in the water column. These results indicate that decreased snow cover impacts both primary production and methane production and/or consumption, which may ultimately lead to increased methane emissions after spring ice off. IMPORTANCE Small lakes are an important source of greenhouse gases in the boreal zone. These lakes are severely impacted by the winter season, when ice and snow cover obstruct gas exchange between the lake and the atmosphere and diminish light availability in the water column. Currently, climate change is resulting in reduced spring snow cover. A short-term removal of the snow from the ice stimulated algal primary producers and subsequently heterotrophic bacteria. Concurrently, the relative abundance of methanotrophic bacteria decreased and methane concentrations increased. Our results increase the general knowledge of microbial life under ice and, specifically, the understanding of the potential impact of climate change on boreal lakes., (Copyright © 2019 Garcia et al.)

Winters are limiting for many terrestrial animals due to energy deficits brought on by resource scarcity and the increased metabolic costs of thermoregulation and traveling through snow. A better understanding of how animals respond to snow conditions is needed to predict the impacts of climate change on wildlife. We compared the performance of remotely sensed and modeled snow products as predictors of winter movements at multiple spatial and temporal scales using a data set of 20,544 locations from 30 GPS-collared Dall sheep (Ovis dalli dalli) in Lake Clark National Park and Preserve, Alaska, USA from 2005 to 2008. We used daily 500-m MODIS normalized difference snow index (NDSI), and multi-resolution snow depth and density outputs from a snowpack evolution model (SnowModel), as covariates in step selection functions. We predicted that modeled snow depth would perform best across all scales of selection due to more informative spatiotemporal variation and relevance to animal movement. Our results indicated that adding any of the evaluated snow metrics substantially improved model performance and helped characterize winter Dall sheep movements. As expected, SnowModel-simulated snow depth outperformed NDSI at fine-to-moderate scales of selection (step scales < 112 h). At the finest scale, Dall sheep selected for snow depths below mean chest height (<54 cm) when in low-density snows (100 kg/m 3 ), which may have facilitated access to ground forage and reduced energy expenditure while traveling. However, sheep selected for higher snow densities (>300 kg/m 3 ) at snow depths above chest height, which likely further reduced energy expenditure by limiting hoof penetration in deeper snows. At moderate-to-coarse scales (112-896 h step scales), however, NDSI was the best-performing snow covariate. Thus, the use of publicly available, remotely sensed, snow cover products can substantially improve models of animal movement, particularly in cases where movement distances exceed the MODIS 500-m grid threshold. However, remote sensing products may require substantial data thinning due to cloud cover, potentially limiting its power in cases where complex models are necessary. Snowpack evolution models such as SnowModel offer users increased flexibility at the expense of added complexity, but can provide critical insights into fine-scale responses to rapidly changing snow properties., (© 2018 by the Ecological Society of America.)

The length of the snow-free season is a key factor regulating plant phenology and shaping plant community composition in cold regions. While global warming has significantly advanced the time of snowmelt and the growth period at all elevations in the Swiss Alps, it remains unclear if it has altered the likelihood of frost risk for alpine plants. Here, we analyzed the influence of the snowmelt timing on the risk of frost exposure for subalpine and alpine plants shortly after snowmelt, i.e., during their most vulnerable period to frost at the beginning of their growth period. Furthermore, we tested whether recent climate warming has changed the risk of exposure of plants to frost after snowmelt. We analyzed snow and air temperature data in the Swiss Alps using six weather stations covering the period 1970-2016 and 77 weather stations covering the period 1998-2016, spanning elevations from 1418 to 2950 m asl. When analyzed across all years within each station, our results showed strong negative relationships between the time of snowmelt and the frequency and intensity of frost during the most vulnerable period to frost for subalpine and alpine plants, indicating a higher frost risk damage for plants during years with earlier snowmelt. However, over the last 46 years, the time of snowmelt and the last spring frost date have advanced at similar rates, so that the frequency and intensity of frost during the vulnerable period for plants remained unchanged.

Birds use both the corticosterone stress response and immune system to meet physiological challenges during exposure to adverse climatic conditions. To assess the stress level and immune response of the Asian Great Bustard during conditions of severe winter weather, we measured fecal corticosterone (CORT) and Immunoglobulin A (IgA) before and after snowfall in a low snow cover year (2014) and a high snow cover year (2015). A total of 239 fecal samples were gathered from individuals in Tumuji Nature Reserve, located in eastern Inner Mongolia, China. We observed high CORT levels that rose further after snowfall both in high and low snow cover years. IgA levels increased significantly after snowfall in the low snow cover year, but decreased after snowfall in the high snow cover year. These results suggest that overwintering Asian Great Bustards are subjected to climatic stress during severe winter weather, and the hypothalamic-pituitary-adrenal axis and immune system react to this challenge. Extreme levels of stress, such as snowfall in already prolonged and high snow cover conditions may decrease immune function. Supplemental feeding should be considered under severe winter weather conditions for this endangered subspecies., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Variations in soil organic carbon (SOC) have implications for atmospheric CO 2 concentrations and the greenhouse effect. However, the effects of snow cover and straw mulching on the variations in SOC fractions across winter remain largely unknown. In this study, soil samples were collected during different stages of winter from an in situ experiment comprising three treatments: 1) snow removal with no straw mulching (Sn-SM-); 2) snow cover with no straw mulching (SC), and; 3) snow cover with straw mulching (SC + SM+). Results showed that labile organic carbon, semi-labile organic carbon, recalcitrant organic carbon (ROC), the light fraction of organic carbon (LFOC), and easily oxidized organic carbon (EOC) contents did not vary significantly (P > .05) during the unfrozen to hard frost stages. Compared to the unfrozen stage, microbial biomass carbon (MBC) contents decreased by 519.03 mg kg -1 , 325.21 mg kg -1 , and 244.09 mg kg -1 and dissolved organic carbon (DOC) contents increased by 473.36 mg kg -1 , 348.10 mg kg -1 , and 258.89 mg kg -1  at the hard frost stage in Sn-SM-, SC, and SC + SM + treatments, respectively. Throughout all thawing stages, > 61% and 59% of SOC and ROC accumulation, respectively in the three treatments were observed in thawing stage II, indicating that higher temperatures and microbial activities in thawing stage II accelerated the inputs of SOC and ROC. ROC accumulation accounted for >65% of the SOC accumulation and the proportions of ROC in SOC increased in the three treatments during the thawing stages. SC + SM + treatment maintained lower EOC contents during thawing stages than other treatments. The observation of lowest SOC and LFOC accumulation and contents in the SC + SM + treatment during thawing stages showed that SC + SM + experienced the least inputs of SOC in the soil., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Snow cover dynamics are considered to play a key role on spring phenological shifts in the high-latitude, so investigating responses of spring phenology to snow cover dynamics is becoming an increasingly important way to identify and predict global ecosystem dynamics. In this study, we quantified the temporal trends and spatial variations of spring phenology and snow cover across the Tibetan Plateau by calibrating and analyzing time series of the NOAA AVHRR-derived normalized difference vegetation index (NDVI) during 1983-2012. We also examined how snow cover dynamics affect the spatio-temporal pattern of spring alpine vegetation phenology over the plateau. Our results indicated that 52.21% of the plateau experienced a significant advancing trend in the beginning of vegetation growing season (BGS) and 34.30% exhibited a delaying trend. Accordingly, the snow cover duration days (SCD) and snow cover melt date (SCM) showed similar patterns with a decreasing trend in the west and an increasing trend in the southeast, but the start date of snow cover (SCS) showed an opposite pattern. Meanwhile, the spatial patterns of the BGS, SCD, SCS and SCM varied in accordance with the gradients of temperature, precipitation and topography across the plateau. The response relationship of spring phenology to snow cover dynamics varied within different climate, terrain and alpine plant community zones, and the spatio-temporal response patterns were primarily controlled by the long-term local heat-water conditions and topographic conditions. Moreover, temperature and precipitation played a profound impact on diverse responses of spring phenology to snow cover dynamics., (Copyright © 2017 Elsevier B.V. All rights reserved.)

In the present paper, the inter-seasonal Hg variability in snow cover was examined based on multivariate statistical analysis of chemical and meteorological data. Samples of freshly fallen snow cover were collected at the semi-urban site in Poznań (central Poland), during 3-month field measurements in winter 2013. It was showed that concentrations of atmospherically deposited Hg were highly variable in snow cover, from 0.43 to 12.5 ng L -1 , with a mean value of 4.62 ng L -1 . The highest Hg concentration in snow cover coincided with local intensification of fossil fuel burning, indicating large contribution from various anthropogenic sources such as commercial and domestic heating, power generation plants, and traffic-related pollution. Moreover, the variability of Hg in collected snow samples was associated with long-range transport of pollutants, nocturnal inversion layer, low boundary layer height, and relatively low air temperature. For three snow episodes, Hg concentration in snow cover was attributed to southerly advection, suggesting significant contribution from the highly polluted region of Poland (Upper Silesia) and major European industrial hotspots. However, the peak Hg concentration was measured in samples collected during predominant N to NE advection of polluted air masses and after a relatively longer period without precipitation. Such significant contribution to the higher Hg accumulation in snow cover was associated with intensive emission from anthropogenic sources (coal combustion) and atmospheric conditions in this area. These results suggest that further measurements are needed to determine how the Hg transformation paths in snow cover change in response to longer/shorter duration of snow cover occurrence and to determine the interactions between mercury and absorbing carbonaceous aerosols in the light of climate change.

Understanding how organisms will respond to altered winter conditions is hampered by a paucity of information on the winter ecology for many species. Amphibians are sensitive to environmental temperature and moisture conditions and may be vulnerable to changes in winter climate. We used a combination of radio telemetry and field enclosures to monitor survival of the freeze-tolerant wood frog (Lithobates sylvaticus) during the unusually cold winter of 2013-2014. We experimentally manipulated snow cover to determine the effect of snow removal on winter survival. In addition, we placed a group of untracked frogs at locations used by tracked frogs prior to long-distance late fall movement to investigate whether late fall movement entailed survival consequences. Winter survival was highest (75.3 %) among frogs at post-movement locations that received natural snow cover. The odds of surviving the winter for frogs in the snow removal treatment was only 21.6 % that of frogs in the natural snow treatment. Likewise, paired frogs placed at pre-fall movement locations had only 35.1 % the odds of surviving as tracked frogs at post-fall movement locations. A comparison of a priori models that included microhabitat conditions measured at wood frog overwintering locations revealed that the minimum temperature experienced and the depth of the frog in the substrate explained additional variation in winter survival. Our results suggest that acute exposure to lethal temperature conditions is the most likely cause of mortality during this study, rather than energy exhaustion or desiccation. They also demonstrate the importance of snow cover to the winter survival of wood frogs.

The paper presents the results of investigation of tritium content in the layers of snow located in the streambeds of the "Degelen" massif contaminated with tritium. The objects of investigation were selected watercourses Karabulak, Uzynbulak, Aktybai located beyond the "Degelen" site. We studied the spatial distribution of tritium relative to the streambed of watercourses and defined the borders of the snow cover contamination. In the centre of the creek watercourses the snow contamination in the surface layer is as high as 40 000 Bq/L. The values of the background levels of tritium in areas not related to the streambed, which range from 40 to 50 Bq/L. The results of snow cover measurements in different seasonal periods were compared. The main mechanisms causing tritium transfer in snow were examined and identified. The most important mechanism of tritium transfer in the streams is tritium emanation from ice or soil surface., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

Alternative mating tactics (AMTs) are intrasexual variants in mating behaviour of several species ranging from arthropods to mammals. Male AMTs coexist between and within populations. In particular, male ungulates rarely adopt just one tactic throughout their lifetime. Tactics commonly change according to internal factors (age, body size, condition) and external conditions (weather, resources, predation, animal density). However, the influence of weather has not yet been investigated in upper vertebrates. Such influence may be relevant in species whose rutting period occurs late in fall or in winter, when environmental conditions and the snow cover in particular may vary considerably. We detected two AMTs in Alpine ibex (Capra ibex) males: older and full-grown males mainly adopted the tending tactic, while younger males usually pursued an alternative one (coursing tactic). Weather was found to influence the use of AMTs by males: in snowy mating seasons, the coursing tactic was no longer used due to difficulties in moving through deep snow. In snowy rutting periods, males appeared to delay or even avoid mating activities and a decrease of births was reported in the second part of the following birth season. Snow cover may have a negative effect on population dynamics by reducing the recruitment and on population genetic variability, as a consequence of poorer mating opportunities. Studies on factors affecting mating behaviour and leading to a reduced availability of mates and a decrease in female productivity are especially relevant in species, like Alpine ibex, whose genetic variability is low., (Copyright © 2013 Elsevier B.V. All rights reserved.)

This research studies the characteristics of snow-covered area (SCA) from two vastly different sensors: optical (Moderate-Resolution Imaging Spectroradiometer, or MODIS, equipped on board the Terra satellite) and radar (Synthetic Aperture Radar (SAR) on-board Sentinel-1 satellites). The focus are the five mountain ranges of the Iberian Peninsula (Cantabrian System, Central System, Iberian Range, Pyrenees, and Sierra Nevada). The MODIS product was selected to identify SCA dynamics in these ranges using the Probability of Snow Cover Presence Index (PSCPI). In addition, we evaluate the potential advantage of the use of SAR remote sensing to complete optical SCA under cloudy conditions. For this purpose, we utilize the Copernicus High-Resolution Snow and Ice SAR Wet Snow (HRS&I SWS) product. The Pyrenees and the Sierra Nevada showed longer-lasting SCA duration and a higher PSCPI throughout the average year. Moreover, we demonstrate that the latitude gradient has a significant influence on the snowline elevation in the Iberian mountains (R2 ≥ 0.84). In the Iberian mountains, a general negative SCA trend is observed due to the recent climate change impacts, with a particularly pronounced decline in the winter months (December and January). Finally, in the Pyrenees, we found that wet snow detection has high potential for the spatial gap-filling of MODIS SCA in spring, contributing above 27% to the total SCA. Notably, the additional SCA provided in winter is also significant. Based on the results obtained in the Pyrenees, we can conclude that implementing techniques that combine SAR and optical satellite sensors for SCA detection may provide valuable additional SCA data for the other Iberian mountains, in which the radar product is not available. [ABSTRACT FROM AUTHOR]

As photovoltaic (PV) deployment increases worldwide, PV systems are being installed more frequently in locations that experience snow cover. The higher albedo of snow, relative to the ground, increases the performance of PV systems in northern and high-altitude locations by reflecting more light onto the PV modules. Accurate modeling of the snow's albedo can improve estimates of PV system production. Typical modeling of snow albedo uses a simple two-value model that sets the albedo high when snow is present, and low when snow is not present. However, snow albedo changes over time as snow settles and melts and a binary model does not account for transitional changes, which can be significant. Here, we present and validate a model for estimating snow albedo as it changes over time. The model is simple enough to only require daily snow depth and hourly average temperature data, but can be improved through the addition of site-specific factors, when available. We validate this model to quantify its ability to more accurately predict snow albedo and compare the model's performance against satellite imagery-based methods for obtaining historical albedo data. In addition, we perform modeling using the System Advisor Model (SAM) to show the impact of changes in albedo on energy modeling for PV systems. Overall, our albedo model has a significantly improved ability to predict the solar insolation on PV modules in real time, especially on bifacial PV modules where reflected irradiance plays a larger role in energy production. [ABSTRACT FROM AUTHOR]

Snow cover is a good indicator of air pollution and accumulates trace elements and organic pollutants. At the same time, the analysis of snow cover is simple and allows you to assess the presence of these pollutants in the atmosphere. We have studied the snow cover of an urban agglomeration located in the northern part of Europe, with a large number of inhabitants, and therefore with a large anthropogenic load on the atmosphere. We have identified trace elements in the snow cover at many city crossroads. Also we determined the presence of PAHs as the main organic pollutants that are typical for urban agglomerations. Inorganic pollutants were detected in all samples. We found that trace elements contamination advised 1 to 5 according to Nemerow index (PiNemerow). In 50% of the samples, the contamination index corresponded to type V (heavy pollution); 30% to type III and 20% to type IV, respectively. We also determined PAHs in the same places. We can conclude that the values of the investigated substances are low throughout the city. About half of PAHs come from the combustion of wood, the other from the combustion of petroleum products. Since different sources prevail at different sampling points, therefore, it can be assumed about anthropogenic sources, which is also confirmed by the HMW/LMW ratio. No correlations we were found between heavy PAH and Trace elements, no associations were found. [ABSTRACT FROM AUTHOR]

Permafrost warming has been observed all around the Arctic, however, variations in temperature trends and their drivers remain poorly understood. We present a comprehensive analysis of climatic changes spanning 25 years (1998–2023) at Bayelva (78.92094°N, 11.83333°E) on Spitzbergen, Svalbard. The quality controlled hourly data set includes air temperature, radiation fluxes, snow depth, rainfall, active layer temperature and moisture, and, since 2009, permafrost temperature. Our Bayesian trend analysis reveals an annual air temperature increase of 0.9 ± 0.5°C/decade and strongest warming in September and October. We observed a significant shortening of the snow cover by −14 ± 8 days/decade, coupled with reduced winter snow depth. The active layer simultaneously warmed by 0.6 ± 0.7°C/decade at the top and 0.8 ± 0.5°C/decade at the bottom. While the soil surface got drier, in particular during summer, soil moisture below increased in accordance with the longer unfrozen period and higher winter temperatures. The thawed period prolonged by 10–15 days/decade at different depths. In contrast to earlier top‐soil warming, we observed stable temperatures since 2010 and only little permafrost warming (0.14 ± 0.13°C/decade). This is likely due to recently stable winter air temperature and continuously decreasing winter snow depth. This recent development highlights a complex interplay among climate and soil variables. Our distinctive long‐term data set underscores (a) the changes in seasonal warming patterns, (b) the influential role of snow cover decline, and (c) that air temperature alone is not a sufficient indicator of change in permafrost environments, thereby highlighting the importance of investigating a wider range of parameters, such as soil moisture and snow characteristics. Plain Language Summary: Permafrost is warming across the Arctic, but it is not yet well understood why temperature trends vary and what affects them the most. Our detailed study investigates 25 years (1998–2023) of data at the Bayelva permafrost observatory on Svalbard. We analyzed a quality‐controlled data set, including hourly measurements of air temperature, radiation, snow depth, rainfall, permafrost temperature, and active layer conditions. We looked beyond annual averages, examining changes in each month. The air warmed strongly by 0.9°C/decade and even stronger in September and October. Continuous snow cover shortened by −14 days/decade and winter snow depth decreased. Simultaneously, the active layer warmed by 0.6°C/decade at the top and 0.8°C/decade at the bottom. While the surface dried in summer, deeper soil layers became moister due to a longer unfrozen period and higher temperatures in winter. The thawed period extended by 10–15 days/decade, with slightly stronger changes toward later freezing in autumn. We found that soil warming stopped in recent years and attributed this effect to the lower winter snow depth since 2010. Therefore, if we want to know how permafrost warms or cools in the future, we need to consider additional measurements such as soil moisture and snow properties. Key Points: Snow cover thinning and winter air temperature variability are the most important drivers of trends in permafrost temperatureWhile mean annual air temperature continues to increase, the top soil at Bayelva, Svalbard, stopped warming since 2010The fully snow‐covered season shortened by −14 days per decade since 1998, which led to longer unfrozen conditions in the active layer [ABSTRACT FROM AUTHOR]

The Ice, Cloud, and Land Elevation Satellite‐2 (ICESat‐2) mission has collected surface elevation measurements for over 5 years. ICESat‐2 carries an instrument that emits laser light at 532 nm, and ice and snow absorb weakly at this wavelength. Previous modeling studies found that melting snow could induce significant bias to altimetry signals, but there is no formal assessment on ICESat‐2 acquisitions during the melting season. We performed two case studies over the Greenland Ice Sheet to quantify bias in ICESat‐2 signals over snow: one to validate Airborne Topographic Mapper (ATM) data against Next Generation Airborne Visible/Infrared Imaging Spectrometer (AVIRIS‐NG) grain sizes, and a second to estimate ICESat‐2 bias relative to ATM. We used snow optical grain sizes derived from ATM and AVIRIS‐NG to attribute altimetry bias to snowpack properties. For the first case study, the mean and standard deviation of optical grain sizes were 340 ± 65 µm (AVIRIS‐NG) and 670 ± 420 µm (ATM). A mean altimetry bias of 4.81 ± 1.76 cm was found for ATM, with larger biases linked to increases in grain size. In the second case study, we found a mean grain size of 910 ± 381 µm and biases of 6.42 ± 1.77 cm (ICESat‐2) and 9.82 ± 0.97 cm (ATM). The grain sizes and densities needed to recreate biases with a model are uncommon in nature, so we propose that additional surface attributes must be considered to characterize ICESat‐2 bias over snow. The altimetry biases are within the accuracy requirements of the ICESat‐2 mission, but we cannot rule out more significant errors over coarse‐grained snow. Plain Language Summary: The Ice, Cloud, and Land Elevation Satellite‐2 (ICESat‐2) mission has been used to measure changes in land ice, vegetation cover, and sea ice, and there is growing interest to use ICESat‐2 for snow science. ICESat‐2 uses a green laser to estimate the elevation of the Earth's surface, and recent work has shown that snow can introduce errors in green laser measurements such as ICESat‐2. In this study, we used ICESat‐2 and airborne data to (a) identify errors in the ICESat‐2 data and (b) link the errors to snow properties over the Greenland Ice Sheet. We found that ICESat‐2 errors are linked to two snow properties: the size of individual snow grains and the density of the snow. Two retrieval methods were used to estimate snow grain size, though the methods show significant differences when large snow grains are observed. Snow grain size and density are insufficient to fully explain ICESat‐2 biases, so we propose that other factors also play a role. The ICESat‐2 errors are within the accuracy requirements of ICESat‐2, but more significant errors may be possible during the Northern Hemisphere melting season. Key Points: Very large snow grains and densities are needed to explain centimeter‐level differences between ICESat‐2 and airborne lidarBiases from snow grain size and other factors may be influenced by different height measurement methods between ICESat‐2 and airborne lidarSnow grain size retrievals strongly differ between airborne lidar and AVIRIS‐NG over regions with coarse‐grained snow [ABSTRACT FROM AUTHOR]

Wildfires in the snow zone can brighten winter and spring landscapes by removing forest canopy, revealing underlying snow cover. Land surface albedo (LSA) alterations associated with transitioning from a canopied, snow‐hiding vegetation regime to a snow‐revealing landscape have impacts on the surface energy balance, with implications for climate and water supply. Forest fires are increasing in frequency, size, and elevation, but the change in LSA due to fire in the seasonal snow zone (SSZ) is poorly understood. This study addresses this knowledge gap for the Sierra Nevada, where recent climatic changes have contributed to droughts, earlier and more rapidly declining snowpacks, and worsening wildfire impacts. Remotely sensed snow fraction and LSA data from Moderate Resolution Imaging Spectrometer were used to assess the impact of wildfire on landscapes in the Sierra Nevada SSZ by comparing LSA in burn scars to unburned control areas and the historical average LSA, then quantifying the surface radiative forcing (RF) associated with change in LSA. Among high and moderate burn severity fires, winter LSA varied depending on snow cover, land characteristics, and burn severity, ranging from 0.12 in low‐snow fire scars to 0.47 in snow‐covered fire scars. This study adds to understanding of how landscapes respond to wildfires and the subsequent impacts on the surface energy balance. Plain Language Summary: Wildfires alter how the landscape absorbs and reflects sunlight by removing forest canopy and exposing the underlying ground surface, leading to changes in surface temperature. This impact is particularly pronounced in areas that are snow‐covered due to the brightness of snow contrasted with dark canopy. As wildfires become more frequent, intense, and larger, it is crucial to understand how they affect seasonally snow‐covered landscapes. This study focuses on the Sierra Nevada, CA, where recent climate changes have led to more droughts, earlier melting of snow, and intensifying wildfire. We used satellite data to study how wildfires impact landscape reflectance by comparing burned areas to both nearby unburned areas and historical data. We found that great variability was present in landscape response but overall, in the first 4 years after a high or moderate severity wildfire, the snow‐covered areas were brighter in winter compared to unburned areas. Quantifying how absorption of sunlight changes following wildfire helps us understand how landscapes respond to wildfires and how they affect the local climate. Key Points: Land surface albedo response to fire in the Sierra Nevada seasonal snow zone varies with snow cover, land characteristics, and burn severityContrary to current understanding, post‐fire snow‐free dates were variable and not consistently earlier than in surrounding unburned areasFire‐induced albedo changes led to greater cooling effect (net negative radiative forcing) in moderate compared to high‐burn severity areas [ABSTRACT FROM AUTHOR]

The spatial and temporal variation of the seasonal snowpack in mountain regions is recognized as a clear knowledge gap for climate, ecology and water resources applications. Here, we identify three salient topics where recent developments in snow remote sensing and data assimilation can lead to significant progress: snowwater equivalent, high resolution snow-covered area and long term snow cover observations including snow albedo. These topics can be addressed in the near future with institutional support. [ABSTRACT FROM AUTHOR]

Stratospheric ozone, which has been depleted in recent decades by the release of anthropogenic gases, is critical for shielding the biosphere against ultraviolet‐B (UV‐B) radiation. Although the ozone layer is expected to recover before the end of the 21st century, a hole over Antarctica continues to appear each year. Ozone depletion usually peaks between September and October, when fortunately, most Antarctic terrestrial vegetation and soil biota is frozen, dormant and protected under snow cover. Similarly, much marine life is protected by sea ice cover. The ozone hole used to close before the onset of Antarctic summer, meaning that most biota were not exposed to severe springtime UV‐B fluxes. However, in recent years, ozone depletion has persisted into December, which marks the beginning of austral summer. Early summertime ozone depletion is concerning: high incident UV‐B radiation coincident with snowmelt and emergence of vegetation will mean biota is more exposed. The start of summer is also peak breeding season for many animals, thus extreme UV‐B exposure (UV index up to 14) may come at a vulnerable time in their life cycle. Climate change, including changing wind patterns and strength, and particularly declining sea ice, are likely to compound UV‐B exposure of Antarctic organisms, through earlier ice and snowmelt, heatwaves and droughts. Antarctic field research conducted decades ago tended to study UV impacts in isolation and more research that considers multiple climate impacts, and the true magnitude and timing of current UV increases is needed. [ABSTRACT FROM AUTHOR]

This study proposes a framework to develop a high-resolution snow cover area (SCA) product from freely available spaceborne remote sensing data and utilizes the Sentinel-1 multi-temporal products and MODIS surface reflectance data. The proposed methodology focuses on using the sensitivity of the parameters retrievable from the Sentinel-1 datasets to snow. Different parameters such as the dual polarimetric entropy, mean scattering angle, backscatter coefficients, and the interferometric coherence are integrated with a spatially resampled normalized difference snow index (NDSI) from MODIS data to estimate an equivalent NDSI, which is used for the determination of the SCA at 15 m spatial resolution. The equivalent NDSI is derived using a machine learning-based regression based on support vector machines (SVMs) and the multilayer perceptron (MLP). The experiments are performed for the high elevated regions of the Kunduz and Khanabad watershed of the northern Hindu Kush mountains for the peak winter and early melt season of 2019, corresponding to February and March. The reference SCA for evaluating the results is generated by thresholding the NDSI derived from pan-sharpened Landsat-8 imagery. As compared to MLP, the SCA generated based on the SVM regression showed better performance. Further, compared to spatially resampled MODIS NDSI, both the SVM and MLP results showed better accuracy for snow classification, as determined by the mean conditional kappa coefficients of 0.75, 0.83, respectively, over 0.62. [ABSTRACT FROM AUTHOR]

Variations in the productivity of subnivean vegetation can substantially affect the ecology of regions more susceptible to increasing warming levels and lead to significant feedback effects on the global climate. Due to its importance, this topic is at the center of a broad scope of interdisciplinary studies supported by field and remote sensing observations. However, the current knowledge about environmental factors affecting the penetration of photosynthetically active radiation (PAR) through snow is still constrained by the paucity of transmittance data. In this work, we aim to further the understanding about these interconnected processes. We conduct a systematic investigation about the effects of independent and combined changes in key nivological characteristics, namely thickness, saturation, density and grain size, on snow transmittance in the photosynthetic spectral domain. Our investigation is carried out through controlled in silico (computational) experiments supported by measured radiometric data. Its outcomes unveil fundamental quantitative and qualitative trends related to the role played by these nivological characteristics on the spectral quality of transmitted PAR, which is quantified in terms of red to blue ( R / B ), red to far-red ( R / F R ) and blue to far-red ( B / F R ) ratios. These trends include increases in the R / B ratio as well as decreases in the R / F R and B / F R ratios following thickness reductions or grain size increases, with opposite variations in these ratios being observed for saturation or density increases. Accordingly, the pairing of our findings with in situ and remotely collected information contributes to cement the scientific foundation required for the effective assessment of cause-effect loops linking accentuated vegetation greening to accelerated rates of snow cover recession. [ABSTRACT FROM AUTHOR]

Premise of research. In boreal forests with snow-covered conditions, local adaptation in spring root exploration can be expected due to soil freezing or competition for the fundamental niche. However, few studies have investigated adaptive strategies for acquiring soil space and resources after snowmelt. Methodology. To conduct a common-garden trial, Abies sachalinensis was used as a representative boreal conifer species with local adaptation. Five-year-old seedlings derived from an eastern provenance with low snow cover and a northern provenance with high snow cover were grown for 2 yr in a common garden in a high–snow cover region. Seedlings were dug up in early spring just after snowmelt and in late spring after 1 mo had passed to evaluate patterns of spring root growth. Pioneer roots exploring soil space and fibrous roots acquiring soil resources were collected, and specific root length and relative central-cylinder thickness were measured. Pivotal results. In pioneer roots, a significant increase in specific root length and a decrease in relative central cylinder thickness from early to late spring were observed only in the northern provenance. These results demonstrated that the pioneer roots in the northern provenance were more responsive to spring soil conditions than those in the eastern provenance. In fibrous roots, the decline rate in specific root length from early to late spring averaged approximately 14% in the northern provenance versus 37% in the eastern provenance. This intraspecific difference may be induced by the plastic responses in the low–snow cover provenance to the condition of a high–snow cover region. Conclusions. Our results indicated that the plasticity of the number and specific root length of pioneer roots was related to local adaptation. The rate of soil space exploration by pioneer roots might be relevant to adaptation to the spring soil environments with various snow-covered conditions. [ABSTRACT FROM AUTHOR]

The spatial and temporal variation of the seasonal snowpack in mountain regions is recognized as a clear knowledge gap for climate, ecology and water resources applications. Here, we identify three salient topics where recent developments in snow remote sensing and data assimilation can lead to significant progress: snow water equivalent, high resolution snow-covered area and long term snow cover observations including snow albedo. These topics can be addressed in the near future with institutional support.

Solar radiative heating andmelting of lake and sea ice is a geophysical problem that has attracted the attention of researchers for many years. This problem is important in connection with the current global change of the climate. Physical and computational models of the process are suggested in the paper. Analytical solutions for the transfer of solar radiation in lightscattering snow cover and ice are combined with numerical calculations of heat transfer in amultilayer system. The thermal boundary conditions take into account convective heat losses to the ambient air and radiative cooling in the mid-infrared window of transparency of the cloudless atmosphere. The study begins with an anomalous spring melting of ice on the large high-mountain lakes of Tibet. It was found that a thick ice layer not covered with snow starts to melt at the ice-water interface due to volumetric solar heating of ice. The results of the calculations are in good agreement with the field observations. The computational analysis showed a dramatic change in the process when the ice is covered with snow. A qualitative change in the physical picture of the process occurs when the snow cover thickness increases to 20-30 cm. In this case, the snow melting precedes ice melting and water ponds are formed on the ice surface. This is typical for the Arctic Sea in polar summer. Known experimental data are used to estimate the melting of sea ice under the melt pond. Positive or negative feedback related to the specific optical and thermal properties of snow, ice, and water are discussed. [ABSTRACT FROM AUTHOR]

Accurate prediction of snow water equivalent (SWE) can be valuable for water resource managers. Recently, deep learning methods such as long short-term memory (LSTM) have exhibited high accuracy in simulating hydrologic variables and can integrate lagged observations to improve prediction, but their benefits were not clear for SWE simulations. Here we tested an LSTM network with data integration (DI) for SWE in the western United States to integrate 30-day-lagged or 7-day-lagged observations of either SWE or satellite-observed snow cover fraction (SCF) to improve future predictions. SCF proved beneficial only for shallow-snow sites during snowmelt, while lagged SWE integration significantly improved prediction accuracy for both shallow- and deep-snow sites. The median Nash–Sutcliffe model efficiency coefficient (NSE) in temporal testing improved from 0.92 to 0.97 with 30-day-lagged SWE integration, and root-mean-square error (RMSE) and the difference between estimated and observed peak SWE values dmax were reduced by 41% and 57%, respectively. DI effectively mitigated accumulated model and forcing errors that would otherwise be persistent. Moreover, by applying DI to different observations (30-day-lagged, 7-day-lagged), we revealed the spatial distribution of errors with different persistent lengths. For example, integrating 30-day-lagged SWE was ineffective for ephemeral snow sites in the southwestern United States, but significantly reduced monthly-scale biases for regions with stable seasonal snowpack such as high-elevation sites in California. These biases are likely attributable to large interannual variability in snowfall or site-specific snow redistribution patterns that can accumulate to impactful levels over time for nonephemeral sites. These results set up benchmark levels and provide guidance for future model improvement strategies. [ABSTRACT FROM AUTHOR]

Increasing temperatures and winter precipitation can influence the carbon (C) exchange rates in arctic ecosystems. Feedbacks can be both positive and negative, but the net effects are unclear and expected to vary strongly across the Arctic. There is a lack of understanding of the combined effects of increased summer warming and winter precipitation on the C balance in these ecosystems. Here we assess the short‐term (1–3 years) and long‐term (5–8 years) effects of increased snow depth (snow fences) (on average + 70 cm) and warming (open top chambers; 1–3°C increase) and the combination in a factorial design on all key components of the daytime carbon dioxide (CO2) fluxes in a wide‐spread heath tundra ecosystem in West Greenland. The warming treatment increased ecosystem respiration (ER) on a short‐ and long‐term basis, while gross ecosystem photosynthesis (GEP) was only increased in the long term. Despite the difference in the timing of responses of ER and GEP to the warming treatment, the net ecosystem exchange (NEE) of CO2 was unaffected in the short term and in the long term. Although the structural equation model (SEM) indicates a direct relationship between seasonal accumulated snow depth and ER and GEP, there were no significant effects of the snow addition treatment on ER or GEP measured over the summer period. The combination of warming and snow addition turned the plots into net daytime CO2 sources during the growing season. Interestingly, despite no significant changes in air temperature during the snow‐free time during the experiment, control plots as well as warming plots revealed significantly higher ER and GEP in the long term compared to the short term. This was in line with the satellite‐derived time‐integrated normalized difference vegetation index of the study area, suggesting that more factors than air temperature are drivers for changes in arctic tundra ecosystems. [ABSTRACT FROM AUTHOR]

In Iran, the mountain snow cover generally feeds major rivers and thereby largely provides water resources required for improving human lives and protecting nature. Hence, understanding historical variability and trends in mountainous snowpack water resources in Iran in response to global warming and climate change can play a critical role in the sustainable development of this country. Accordingly, this study investigated long‐term (1982–2018) snowpack climatology at 13 hydrometeorological measurement stations scattered throughout the Iranian mountain ranges, with a focus on Elburz, Azerbaijan, Zagros, and Khorasan mountainous regions. The non‐parametric Mann‐Kendall test was used to detect statistically significant (p < 0.05) trends, the Pettitt test to identify possible abrupt shift years, the Pearson's correlation coefficient to measure relationships among different time series, and the partial correlation to determine the most important climate factor influencing snowpack dynamics The annual snow depth (maximum snow depth) significantly declined throughout Iranian mountain ranges during 1982–2018, with an average rate of 1.0 (3.4) cm decade−1. The annual snow cover days (SCDs) also showed significant decreasing trends, ranging from 3 to 15 days decade−1 during 1982–2018, in 69% of the stations studied. Such considerable reductions in snow depth and cover days were mainly related to the compound effects of substantial increases in temperature, sunshine, and wind speed as well as decreases in precipitation and cloudiness during the SCDs across the Iranian mountain ranges. However, precipitation was the most influential climate factor controlling snow resources throughout both the Elburz and Zagros mountains in Iran. [ABSTRACT FROM AUTHOR]

Suspended particulate matter (SPM), polychlorinated biphenyls (PCBs), and organochlorine pesticides (OCP) were studied in the snow cover at urban and suburban localities in the Irkutsk region, Eastern Siberia for their temporal variations in 2009–2023, daily deposition fluxes (DDFs), and effects of some meteorological factors, as well as the effects of different technogenic activities in the industrial area of the former organochlorine enterprises of Usol'ekhimprom. SPM loads at both stations were found to be at a low level of pollution. The levels of HCB, α + γ-HCH, and ∑p,p′-DDX were lower than Russian maximum permissible levels (MPLs) in drinking water, groundwater, and surface water for household drinking and cultural purposes. The sums of all organochlorine compounds studied in snow were higher than the MPL in freshwater water bodies for fishery purposes. The levels of the DDFs of HCHs, DDTs, and heptachlorinated PCB decreased, di- and trichlorinated PCB levels increased, and HCB levels changed at a polynomial line during 2009–2023. The change in the relative composition of PCBs was found as a result of recultivation activities at the industrial area of the former organochlorine enterprise of Usol'ekhimprom. The air humidity and temperature are the key meteorological factors affecting the DDFs of PCBs and OCPs. [ABSTRACT FROM AUTHOR]

The absence of vegetation in most ice‐free areas of Antarctica makes the soil surface very sensitive to atmosphere dynamics, especially in the western sector of the Antarctic Peninsula, an area within the limits of the permafrost zone. To evaluate the possible effects of regional warming on frozen soils, we conducted an analysis of ground surface temperatures (GSTs) from 2007 to 2021 from different monitoring sites in Livingston and Deception islands (South Shetlands archipelago, Antarctica). The analysis of the interannual evolution of the GST and their daily regimes and the freezing and thawing indexes reveals that climate change is showing impacts on seasonal and perennially frozen soils. Freezing Degree Days (FDD) have decreased while Thawing Degree Day (TDD) have increased during the study period, resulting in a balance that is already positive at the sites at lower elevations. Daily freeze–thaw cycles have been rare and absent since 2014. Meanwhile, the most common thermal regimes are purely frozen – F1 (daily temperatures < = −0.5°C), isothermal – IS (ranging between −0.5°C to +0.5°C), and purely thawed – T1 (> = +0.5°C). A decrease in F1 days has been observed, while the IS and T1 days increased by about 60 days between 2007 and 2021. The annual number of days with snow cover increased between 2009 and 2014 and decreased since then. The GST and the daily thermal regimes evolution point to general heating, which may be indicative of the degradation of the frozen soils in the study area. [ABSTRACT FROM AUTHOR]

For two years (2021 and 2022), snow sampling was carried out in the coastal zone of the Onega and Dvina Bays of the White Sea in the period of maximum snow accumulation (March). The snow was analyzed for the content of the major ions (Cl–, , Na+, Ca2+, K+); nutrients (phosphorus, silicon, nitrogen), pH, and TDS. The algological composition of the snow cover was also studied. The results showed that the snow was slightly acidic (average pH 5.4). Cl– and Na+ were the main ions in the coastal zone and and Ca2+, in the estuary zone. A high content of marine ions and mineralization were determined near the Paranikha Bay (Dvina Bay), where releases of sea water onto the ice occur annually. Compared to previous studies, in which snow samples were taken in the coastal zone, the concentration of marine ions in the territory under consideration is an order of magnitude higher. The effect of marine aerosols results in that the values of snow cover mineralization can reach 140–680 mg/L. The concentration of dissolved silicon in melt water is higher in the zone influenced by atmospheric emissions from urbanized territories (Arkhangelsk, Novodvinsk, Severodvinsk), as well as near the granite quarry on the coast of Onega Bay. During the study period, 14 taxa of microalgae (species and supraspecific taxa), belonging to the division Bacillariophyta, were found in snow samples. The maximum value of the total number of microalgae (1.293 thousand cells/L) in the snow cover was determined in 2022. [ABSTRACT FROM AUTHOR]

The shape of the surface is important for geomorphologists because the morphology influences other environmental components. One of the important environmental variables that strongly influence is the Snow-Cover Duration on elevations. SCDura in mountain reservoirs has a direct effect on temperature, hydrological, erosion, vegetation, and biological feedbacks, and as a result of studying the interaction of the topographic surface of the earth with the durability of snow cover, it becomes necessary and important. In this research, the relationship between the geomorphological components of the elevation, slope, and aspect with the max Snow-Cover Duration was calculated and investigated based on the data of Aqua and Terra satellites in the statistical period of 2003 to 2021. The Modis daily snow products named MOD10A1 and MYD10A1 were used for this purpose. The data was downloaded from NASA's official site and was prepared in the main database. In the first step, the data was binarized based on a threshold of 50%, then, the effect of the cloudiness was reduced using a spatial and temporal filter. Then, the data from Terra and Aqua satellites were combined and, the Max Snow-Cover Duration per cell in the raster network was calculated on an annual basis. The relationship between Max Snow-Cover Duration and elevational bands was investigated. The trend of changes in the Max Snow-Cover Duration shows that lower valleys and foothills of eastern Talash have a sharper decline. This situation can be attributed to human interventions, anthropogenic activities, as well as these areas locating in the transitional zone and to be more effective from environmental changes. [ABSTRACT FROM AUTHOR]