Snow amounts and duration are susceptible to climate change and may significantly affect plant diversity and biomass in grassland ecosystems. Yet, the combined effects of grassland use (type and intensity) and snow depth on plant diversity and productivity remain poorly understood. We established two complementary field experiments to explore the mechanisms driving the effects of grassland use (type and intensity) and snow manipulation on plant diversity and productivity in the meadow steppe. An experiment on grassland use type and snow manipulation showed that lower snow cover in winter reduced soil moisture in the snowmelt period, significantly increased the abundance of ammonia-oxidizing archaea and ammonia-oxidizing bacteria, and initiated nitrification earlier, resulting in the loss of soil available nitrogen, and then reduced the aboveground biomass of early grasses. An experiment on grassland mowing intensity and snow manipulation showed that moderate mowing intensity can restrain the loss of grass biomass and soil nutrients and maintain grassland sustainability in winters with less snow. Stochasticity has played a more important role in plant community assembly in higher intensity of grassland use. Based on our results, we recommend that optimal defoliation height can restrain the loss of grass biomass and soil nutrients and maintain grassland sustainability in winters with less snow. This study has potential benefits for optimizing sustainable production and maintaining ecosystem function under winter snowfall changes in the future across large regions of arid and semiarid grasslands. (c) 2024 The Society for Range Management. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

The impact of global climate change and human-induced nitrogen (N) deposition on winter weather patterns will have consequences for soil N cycling and greenhouse gas emissions in temperate deserts. Biological soil crusts (referred to as biocrusts) are crucial communities in soil and significant sources of nitrous oxide (N2O) emission in desert ecosystems and are sensitive to environmental changes. The contribution of bacteria and fungi to N2O production in drylands has been acknowledged. However, the effect of changes in snow cover and N deposition on the N2O production of different microbial groups of microorganisms is not yet clear. In this study, we examine the responses of fungi and bacteria mediated pathways involved in soil N2O production from biocrusts to longterm snow cover manipulation and N addition experiments in the Gurbantunggut Desert. These soils were incubated and subjected to biocide treatments (such as cycloheximide and streptomycin, and fungal and bacterial inhibitors), after which rates of potential nitrification and N2O production were measured. Compared with controls, snow removal treatments from bare sand, lichen crust and moss crust reduced background rates of N2O production by 29.41 %, 26.21 % and 20.49 %, respectively; N2O production rates were 1.53-fold higher in bare sand, 1.38-fold higher in lichen crust, and 1.56-fold higher in moss crust after N addition. The addition of streptomycin significantly reduced the potential nitrification rates of bare sand and biocrusts, indicating that bacteria may be important sources of NO3- production in biocrusts rather than fungi. Conversely, fungi were main sources of N2O production in biocrusts. Additionally, fungi also played a major role in N2O production in biocrusts after snow cover manipulation and N addition. Both snow cover manipulation and N addition treatment indirectly affected the N2O production in biocrusts by considerably affecting the content of substrate N and the abundance of microbial groups. Our research suggests that fungi are main contributors for denitrification in biocrusts, and that snow cover changes (removal snow and double snow) and N addition alter the contribution of biotic pathways responsible for N cycling.

In Central Asia, the ground thermal regime is strongly affected by the interplay between topographic factors and ecosystem properties. In this study, we investigate the governing factors of the ground thermal regime in an area in Central Mongolia, which features discontinuous permafrost and is characterized by grassland and forest ecosystems. Miniature temperature dataloggers were used to measure near-surface temperatures at c. 100 locations throughout the 6 km2 large study area, with the goal to obtain a sample of sites that can represent the variability of different topographic and ecosystem properties. Mean annual near-surface ground temperatures showed a strong variability, with differences of up to 8 K. The coldest sites were all located in forests on north-facing slopes, while the warmest sites are located on steep south-facing slopes with sparse steppe vegetation. Sites in forests show generally colder near-surface temperatures in spring, summer and fall compared to grassland sites, but they are warmer during the winter season. The altitude of the measurement sites did not play a significant role in determining the near-surface temperatures, while especially solar radiation was highly correlated. In addition, we investigated the suitability of different hyperspectral indices calculated from Sentinel-2 as predictors for annual average near-surface ground temperatures. We found that especially indices sensitive to vegetation properties, such as the Normalized Difference Vegetation Index (NDVI), show a strong correlation. The presented observations provide baseline data on the spatiotemporal patterns of the ground thermal regime which can be used to train or validate modelling and remote sensing approaches targeting the impacts of climate change.

Snow distribution has been altered over the past decades under global warming, with a significant reduction in duration and extent of snow cover and an increase in unprecedented snowstorms across large areas in cold regions. The altered snow conditions are likely to have immediate (in winter) and carry-over or legacy (which an extended effect might continue in the following spring, summer and autumn) impacts on soil processes and functioning, but a quantification of the legacy effect of snow coverage alternation is still lacking. Furthermore, studies investigating the effect of snow cover changes on soil respiration, soil carbon pools and microbial activity are increasing, but contrasting results of different studies makes it difficult to assess the overall effect of snow cover changes and the underlying mechanisms, thus a systematic and comprehensive meta-analysis is required. In this study, we synthesized the results from 60 papers based on field snow manipulation experiments and conducted a meta-analysis to evaluate immediate and prolonged effects on eight variables related to soil carbon dynamics and microbial activity to snow coverage alternation. Results showed that snow removal had no significant effect on soil respiration, but increased dissolved organic carbon (DOC) (11.5%) and fungal abundance (32.0%). By contrast, snow addition significantly increased soil respiration (16.3%) and microbial biomass carbon (MBC) (6.6%). Snow addition had immediate and prolonged impacts on soil carbon dynamics and microbial activity lasting from winter to the following autumn, whereas an effect of snow removal on total organic carbon (TOC) and DOC was detectable only in the following spring. Snow depth, ecosystem and soil types determined the extent of the impact of snow treatments on soil respiration, DOC, MBC and microbial biomass nitrogen (MBN). Our findings provide critical insights into understanding how changes in snow coverage affect soil respiration and microbial activity. We suggest future field-based experiments to enhance our understanding the effect of climate change on soil processes and functioning in the winter and the following seasons.

Snow is an important factor controlling vegetation functions in high latitudes/altitudes. However, due to the lack of reliable in -situ measurements, the effects of snow on vegetation phenology remains poorly understood. Here, we examine the effects of snow cover duration (SCD) on the start of growing season (SOS) for different vegetation types. SOS and SCD were extracted from in -situ carbon flux and albedo data, respectively, at 51 eddy covariance flux sites in the northern mid -high latitudes. The effects of SCD on SOS vary substantially among different vegetation types. For grassland, preseason SCD outperforms other factors controlling grassland SOS. However, for forests and cropland, the preseason air temperature is the dominant factor in controlling SOS. Preseason SCD mainly influences the SOS by regulating preseason air and soil temperature rather than soil moisture. The CMIP6 Earth system models (ESMs) fail to capture the effect of SCD on SOS. Thus, Random Forest (RF) models were established to predict future SOS changing trends considering the effect of SCD. For grassland and evergreen needleleaf forest, the projected SOS advance rate is slower when SCD is considered. These findings can help us better understand impacts of snow on vegetation phenology and carbon -climate feedbacks in the warming world.

Global warming is profoundly altering soil freeze -thaw cycle (FTC) patterns, and the formation of different thicknesses and durations of snow cover by snowfall results in heterogeneity of environmental and biological factors, which can have complex effects on soil water and carbon cycle processes. In order to better develop rational regulation strategies to increase the potential of soil carbon sequestration and emission reductions under climate change conditions, a three-year in situ control trial of field snow was set up to simulate climate scenarios using two treatments: snow removal and natural snow. The effects of FTCs and biochar on soil CO 2 emission flux (CO 2 Flux) were analyzed by constructing a driven coupling model between soil hydrothermal environmental factors, unstable organic carbon components and stable organic carbon components. The results showed that CO 2 Flux decreased by 9.36% to 11.34% for 1% biochar treatment, while CO 2 Flux increased by 15.41% to 18.32% for 2% biochar treatment. Moreover, the snow removal treatment increased CO 2 Flux by 9.86% to 13.99% compared to the natural snow treatment. The snow during freezing and thawing has a dual effect on soil hydrothermal dynamics, with snow removal making the freeze -thaw action more intense in perturbing the soil carbon matrix, while the interfacial behavior of biochar with soil minerals protects the stability of the soil structure. Biochar reduces soil carbon emissions thanks to its highly stabilized components and unique surface structure, which enhances the carbon sequestration and emission reduction effect by increasing the proportion of inert organic carbon, promoting the formation of organic -inorganic complexes, and encapsulating and adsorbing soil organic matter. The results of the study can provide important theoretical support and practical models for the assessment of the environmental effects of biochar and the reduction of carbon sequestration in agriculture under climate change conditions.

Observations from 1,047 meteorological stations from September 1, 2006 to August 31, 2015 revealed regional differences in the freezing and thawing processes of seasonally frozen ground (SFG) across China. SFG generally undergoes a one-way freezing process (i.e., top-down), and the stations with a large freeze depth generally experienced long freeze durations. During the thawing process, soil is generally characterized by two-way thawing (i.e., top-down and bottom-up) in the region north of 35 ' N, ' N, especially north of 30 ' N ' N (except in northeastern China). The onset of thawing from the bottom occurs earlier than that from the top at most stations in the two-way thawing region. The stations exhibiting one-way thawing (i.e., bottom-up) were mainly located on the southern edge of eastern China (east of 110 degrees E) degrees E) and in southern part of Xinjiang and southeast part of the Qinghai-Tibet Plateau. The freezing process lasts several days to more than four months longer than the soil thawing process, and this difference tends to be larger in high-latitude and high-altitude regions. All of the sites experienced a discontinuous freeze-thaw process, the station-average duration of which was less than a quarter of that of the continuous freeze-thaw process. Strong associations of soil freeze depth with air temperature (as characterized by the air freezing index and air thawing index) implied a dominant influence of air temperature on the soil freeze-thaw process. During the freezing process, this relationship was partially modulated by snow cover in snowy regions, such as northeast China, northwest China, and the eastern Tibetan Plateau. This paper provides the first overview of regional differences in the freezing and thawing processes of SFG over China, and the findings improve our understanding of the soil freeze-thaw process and provide important information to support research into regional landscapes, ecosystems, and hydrological processes.

A critical comprehension of the impact of snow cover on urban bidirectional reflectance is pivotal for precise assessments of energy budgets, radiative forcing, and urban climate change. This study develops a numerical model that employs the Monte Carlo ray-tracing technique and a snow anisotropic reflectance model (ART) to simulate spectral albedo and bidirectional reflectance, accounting for urban structure and snow anisotropy. Validation using three flat surfaces and MODIS data (snow-free, fresh snow, and melting snow scenarios) revealed minimal errors: the maximum domain-averaged BRDF bias was 0.01% for flat surfaces, and the overall model-MODIS deviation was less than 0.05. The model's performance confirmed its accuracy in reproducing the reflectance spectrum. A thorough investigation of key factors affecting bidirectional reflectance in snow-covered urban canyons ensued, with snow coverage found to be the dominant influence. Urban coverage, building height, and soot pollutant concentration significantly impact visible and infrared reflectance, while snow grain size has the greatest effect on shortwave infrared. The bidirectional reflectance at backward scattering angles (0.5-0.6) at 645 nm is lower than forward scattering (around 0.8) in the principal plane as snow grain size increases. These findings contribute to a deeper understanding of snow-covered urban canyons' reflectance characteristics and facilitate the quantification of radiation interactions, cloud-snow discrimination, and satellite-based retrieval of aerosol and snow parameters.

As a vital freshwater resource for one-sixth of the world's population, snowmelt provides great convenience for residents in terms of livelihood and production, agricultural irrigation, and hydroelectric power generation. However, snowmelt can also have an important impact on the formation of surface runoff and the process of soil erosion. In contrast to glacier melt, snowmelt erosion has received relatively little attention in the past. This paper reviewed the generation of snowmelt runoff, the characteristics of erosion and sediment yield during snowmelt, the snowmelt erosion mechanism, and the applications of snowmelt modeling. The published results of sediment yield driven from snowmelt runoff ranged from 1 to 300 t km-2 a-1, with the largest value of 1114 t km-2 a-1. Snowmelt erosion is extremely sensitive to warming climate. With global warming, there is a trend towards earlier snowmelt periods and a significant increase in runoff volume, as well as a significant increase in sediment yield from snowmelt in most of the study cases. Moreover, snowmelt erosion compared to rainfall erosion has more complex mechanistic processes which can be influenced by various factors such as snowfall, freeze-thaw, topography, etc. In particular, the occurrence of rain-on-snow events will lead to more severe soil erosion. In addition, current studies of sediment yield from snowmelt erosion account for a small percentage of snowmelt, and snowmelt erosion modeling is rarely applied in practical studies. In future research, the field monitoring of snowmelt erosion in the context of climate change needs to be further strengthened and the effects of multiple factors on snowmelt erosion need to be investigated. The inclusion of rain-on-snow and specific erosion types in the model will improve the applicability of models under climate change scenarios and in multiscale environments. This paper is intended to show the achievements as well as the limitations of snowmelt erosion research, while suggesting future research directions that need to be further explored and developed for better understanding and forecasting of snowmelt erosion.

Climate change has had a significant impact on the seasonal transition dates of Arctic tundra ecosystems, causing diverse variations between distinct land surface classes. However, the combined effect of multiple controls as well as their individual effects on these dates remains unclear at various scales and across diverse land surface classes. Here we quantified spatiotemporal variations of three seasonal transition dates (start of spring, maximum normalized difference vegetation index (NDVImax) day, end of fall) for five dominating land surface classes in the ice-free Greenland. Using a distributed snow model, structural equation modeling, and a random forest model, based on ground observations and remote sensing data, we assessed the indirect and direct effects of climate, snow, and terrain on seasonal transition dates. We then presented new projections of likely changes in seasonal transition dates under six future climate scenarios. The coupled climate, snow cover, and terrain conditions explained up to 61% of seasonal transition dates across different land surface classes. Snow ending day played a crucial role in the start of spring and timing of NDVImax. A warmer June and a decline in wind could advance the NDVImax day. Increased precipitation and temperature during July-August are the most important for delaying the end of fall. We projected that a 1-4.5 degrees C increase in temperature and a 5%-20% increase in precipitation would lengthen the spring-to-fall period for all five land surface classes by 2050, thus the current order of spring-to-fall lengths for the five land surface classes could undergo notable changes. Tall shrubs and fens would have a longer spring-to-fall period under the warmest and wettest scenario, suggesting a competitive advantage for these vegetation communities. This study's results illustrate controls on seasonal transition dates and portend potential changes in vegetation composition in the Arctic under climate change. Employing the distributed snow modeling, structural equation modeling, and random forest model, we assessed the effect of climatic, snow, and terrain conditions on the seasonal transition dates of Arctic tundra ecosystems and projected their changes by 2050 in ice-free Greenland, between diverse land surface classes. We found that the start of spring and the end of fall respond differently to various environmental conditions. Future temperature and precipitation changes could significantly affect the spring-to-fall period for different land surface classes, with potential implications for Arctic vegetation composition, providing valuable insights into the controls on seasonal transition dates under climate change.image