Snow Duration Research Articles

Sensitivity of snow magnitude and duration to hydrology model parameters

Process-based hydrology models are critical for understanding streamflow and water supply under global change. However, these models require parameterization which introduces additional uncertainty into the models. The role that these parameters play in driving uncertainty is under-studied, especially for intermediary processes outside of streamflow. An important example in snowmelt dominant regions are intermediary processes related to snowpack accumulation and ablation. We examine the sensitivity of snow magnitude and duration to eleven parameters relevant to snow processes in the coupled crop-hydrology model VIC-CropSyst using a hybrid global–local Distributed Evaluation of Local Sensitivity Analysis approach. With the Pacific Northwest US as a case study, our specific research questions are: (a) What is the sensitivity response of peak snow water equivalent (SWE) and snow duration and how does it vary in the parameter space? (b) What are the key drivers of the sensitivity response? and (c) Which of the most sensitive parameters can we immediately improve by leveraging existing data products? Both target variables were sensitive to less than four of the eleven parameters. We found that peak SWE was most sensitive to either the precipitation partitioning temperature threshold or the albedo of new snow, depending on the geography and associated interplay between hydro-meteorological factors. In contrast, snow duration was primarily sensitive to the albedo of new snow and the albedo decay coefficient during snowmelt. Machine learning explainability workflows applied on the sensitivity response explained the model behavior and determined key geographic and hydro-meteorological drivers of the sensitivity response. Regions where the significant precipitation co-occurred with near-freezing temperature exhibited higher sensitivity of peak SWE to precipitation partitioning. In contrast, much colder high-elevation regions that have a delayed snowmelt-driven runoff when downward shortwave radiation is higher, displayed more sensitivity to albedo parameters. We also noted differences in the list of key parameters and in the level of sensitivity between our work and the limited comparable existing work. This highlights the need for comprehensive sensitivity analysis of snow metrics to become a routine component of hydrology model application studies in addition to streamflow. This is critical for us to better understand model behavior, identify key model parameters that can benefit from more dynamic representation in the models, and strategically improve models to best support decision-makers.

Uneven changes in air and crown temperatures associated with snowpack changes affect the phenology of overwintering cereals

Time series analysis of overwintering cereals in snowy areas has revealed several phenological patterns associated with climate changes in winter. Herein, to investigate the recent effect of climatic variations on overwintering cereals, we investigated the phenology over multiple decades at three snowy region sites with an air temperature (Tair) increase trend of 0.48–1.09 °C/decade. Our findings were as follows: heading trends differed within the same cultivar at different sites; phenology was promoted with increasing temperatures in cooler regions and decreasing snow duration in regions with heavy snow; crown temperature (Tcrown) was a more direct determinant than Tair in phenology estimation model in regions with heavy snow. A thermal gap of more than a few degrees Celsius between Tair and Tcrown, owing to the insulation effect of snowpack, affected the phenology of overwintering cereals. A shorter snow cover period promoted phenology in locations with temperatures >0 °C. Subsequently, we found that when the thermal gap was >0 °C of the growing temperature range, Tcrown directly helped determine the phenology of overwintering cereals, and irrespective of the warming trend, the periodic inflow of cold air into the northern mid-latitudes of the Northern Hemisphere and associated snow cover changes dominated Tcrown, resulting in annual phenological anomalies with a range of fluctuations of approximately 1 month. The trend of increasing Tair during spring in northern Japan is consistent with the global trend, with a pronounced trend of advancing phenology reaching >4 days/decade in a typical cooler location experiencing snowmelt in March.

Variable species establishment in response to microhabitat indicates different likelihoods of climate‐driven range shifts

Climate change is causing geographic range shifts globally, and understanding the factors that influence species' range expansions is crucial for predicting future biodiversity changes. A common, yet untested, assumption in forecasting approaches is that species will shift beyond current range edges into new habitats as they become macroclimatically suitable, even though microhabitat variability could have overriding effects on local population dynamics. We aim to better understand the role of microhabitat in range shifts in plants through its impacts on establishment by 1) examining microhabitat variability along large macroclimatic (i.e. elevational) gradients, 2) testing which of these microhabitat variables explain plant recruitment and seedling survival, and 3) predicting microhabitat suitability beyond species range limits. We transplanted seeds of 25 common tree, shrub, forb and graminoid species across and beyond their current elevational ranges in the Washington Cascade Range, USA, along a large elevational gradient spanning a broad range of macroclimates. Over five years, we recorded recruitment, survival, and microhabitat (i.e. high resolution soil, air and light) characteristics rarely measured in biogeographic studies. We asked whether microhabitat variables correlate with elevation, which variables drive species establishment, and whether microhabitat variables important for establishment are already suitable beyond leading range limits. We found that only 30% of microhabitat parameters covaried with elevation. We further observed extremely low recruitment and moderate seedling survival, and these were generally only weakly explained by microhabitat. Moreover, species and life stages responded in contrasting ways to soil biota, soil moisture, temperature, and snow duration. Microhabitat suitability predictions suggest that distribution shifts are likely to be species‐specific, as different species have different suitability and availability of microhabitat beyond their present ranges, thus calling into question low‐resolution macroclimatic projections that will miss such complexities. We encourage further research on species responses to microhabitat and including microhabitat in range shift forecasts.

Effects of reduced snowpack due to climate warming on abiotic and biotic soil properties in alpine and boreal forest systems

Reduction in snow cover, depth, onset, and duration of seasonal snow in mid-latitude regions due to climate warming has multiple global and local scale ecosystem impacts. These effects include modulations of the hydrological cycles and increases in land surface solar radiation absorption due to decreased albedo. Changes in snow cover characteristics also affect underlying soils. Snow has an insulating effect on soils by decoupling air and soil temperatures, thus seasonal snow cover reduction leads to overall lower soil temperatures and an increase in freeze-thaw cycles. This is especially prominent during the fall and spring thaw seasons when the snow cover is not as extensive. This in turn has downstream impacts on soil physical, chemical, and biological properties. Among these impacts are soil moisture reduction, temperature, frost regimes, soil pH shifts, and alteration in nutrient flux dynamics during winter, snowmelt period and the following summer growing season. These changes in soil physicochemical properties due to snowpack reduction can then impact the biological soil properties via increased plant root mortality, reduced abundance and diversity of soil arthropods, and shifts in composition, abundance and activity of soil microbial communities. All these soil biotic factors can in turn alter the dynamics of soil nutrient fluxes and future greenhouse gas emissions. Here, we integrate data on the effects of snow cover reduction on abiotic and biotic soil properties, with focus on temperate alpine and forest ecosystems and with an outlook on future impacts.

Global reduction of snow cover in ski areas under climate change.

Ongoing climate change substantially alters snowfall patterns with severe but diverging consequences for global ski areas. A global assessment as well as the investigation of potential implications for mountain ecosystems is currently lacking. We quantify future trends in natural snow cover days under different climate change scenarios until 2100 in seven major global skiing regions and discuss implications for mountainous biodiversity by analysing how natural snow cover days relate to regional human population density. Within all major skiing regions, snow cover days are projected to decrease substantially under every assessed climate change scenario. Thirteen percent of all current ski areas are projected to completely lose natural annual snow cover and one fifth will experience a reduction of more than 50% by 2071-2100 relative to historic baselines. Future skiable areas will concentrate in less populated areas, towards continental regions and inner parts of the mountain ranges. As skiable areas will be located at greater distances to highly populated areas in the future, we expect an expansion of infrastructure and increasing intervening actions (i.e., artificial snowmaking, slope grooming) to prolong snow duration. Our results are concerning for both the recreational and economic value of skiing as well as for mountain biodiversity since vulnerable high-altitude species might be threatened by space reductions with ski area expansion.

Experimental Whole‐Ecosystem Warming Enables Novel Estimation of Snow Cover and Depth Sensitivities to Temperature, and Quantification of the Snow‐Albedo Feedback Effect

AbstractClimate change is reducing the amount, duration, and extent of snow across high‐latitude ecosystems. But, in landscapes where persistent winter snow cover develops, experimental platforms to specifically investigate interactions between warming and changes in snowpack, and impacts on ecosystem processes, have been lacking. We leveraged a whole‐ecosystem warming experiment in a boreal peatland forest to quantify how snow duration, depth, and fractional cover vary with warming of up to +9°C. We found that every snow‐related quantity we examined declined precipitously as the amount of warming increased. The importance of deep, continuous snow cover for moderating shallow soil temperature is highlighted by an increase in soil temperature variance and the frequency of short‐duration freeze‐thaw cycles in the warmer plots. We used a paired‐plot approach to estimate the magnitude of the snow‐albedo feedback effect, and demonstrate that albedo‐driven warming linked to reduced snow cover varies between December (+0.4°C increase in maximum air temperature) and March (+1.2°C increase) because of differences in insolation. Overall, results show that even modest future warming will have profound impacts on northern winters and cold‐season ecosystem processes. Plot‐level data from this warming experiment, and emergent relationships between warming and quantities related to snow cover and duration, could be of enormous value for testing and improving the representation of snow processes in simulation models, especially under future climate scenarios that are outside of the range of historically observed variability.

Hydrological dynamics of snowmelt induced streamflow in a high mountain catchment of the Pyrenees under contrasting snow accumulation and duration years

AbstractSnowmelt drives a large portion of streamflow in many mountain areas of the world. However, the water paths from snowmelt to the arrival of the water in the streams are still largely unknown. This work analyzes for first time the influence of snowmelt on spring streamflow with different snow accumulation and duration, in an alpine catchment of the central Spanish Pyrenees. This study presents the water balance of the main melting months (May and June). Piezometric values, water temperature, electrical conductivity and isotope data (δ18O) allow a better understanding of the hydrological functioning of the basin during these months. Results of the water balance calculations showed that snow represented on average 73% of the water available for streamflow in May and June while precipitation during these months accounted for only 27%. However, rainfall during the melting period was important to determine the shape of the spring hydrographs. On average, 78% of the sum of both the snow water equivalent (SWE) accumulated at the beginning of May and the precipitation in May and June converted into runoff during the May–June melting period. The average evaporation‐sublimation during the 2 months corresponded to 8.4% of the accumulated SWE and rainfall, so that only a small part of the water input was ultimately available for soil and groundwater storage. When snow cover disappeared from the catchment, soil water storage and streamflow showed a sharp decline. Consequently, streamflow electrical conductivity, temperature and δ18O showed a marked tipping point towards higher values. The fast hydrological response of the catchment to snow and meteorological fluctuations, as well as the marked diel fluctuations of streamflow δ18O during the melting period, strongly suggests short meltwater transit times. As a consequence of this hydrological behaviour, independently of the amount of snow accumulated and of melting date, summer streamflow remained always low, with only small runoff peaks driven by rainfall events.

A method for robust estimation of snow seasonality metrics from Landsat and Sentinel-2 time series data over Atlas Mountains scale using Google Earth Engine

Seasonal snow cover provides the majority of freshwater supplies for human society and natural ecosystems especially in semi-arid regions. For water resource managers, precise data regarding the spatiotemporal variability of snow cover and snow phenology is of paramount importance. Owing to the great spatial and temporal heterogeneity of the snowpack and the inaccessibility of high mountainous areas, gapless satellite remote sensing presents an unprecedented opportunity to monitor snow cover effectively and affordably on a fine scale, from different aspects, and with regular revisit time. This study derives the snow seasonality metrics (first day of snowfall, last day of snow melt; and snow cover duration) over a large semi-arid region in Morocco’s Atlas Mountains. We calculate these metrics by combining over 10,000 images from Landsat 8 and Sentinel 2 satellites for four hydrological years (2016–2021) to create a harmonized product with a time interval of about 3 days using the Google Earth Engine (GEE) platform. This dense and large time series facilitates a gap-filling method to minimize and overcome the effect of cloud cover, and its assessment shows a positive correlation between the masked pixels and the interpolated ones. These methods allowed us to realize a map of the snow cover area and extract a homogeneous Normalized Difference Snow Index (NDSI) profile over the four years whereby we were able to determine the optimal threshold to separate the presence of snow from its absence. The results showed that derived snow cover metrics provide considerable variation in both time and space, where an increase in snowpack measurement values at higher elevations can be noted. Overall, the snow duration ranges between November and April depending on the characteristics of each hydrological year. The retrieved Landsat 8 and Sentinel 2 snow dates had a high level of agreement with in-situ data observations with almost a day-and-a-half delay with an overall accuracy equal to 0.96. The analysis of snow cover dynamics via GEE has offered the ability to calculate the first day of snowfall, last day of snowmelt, and snow cover duration annually at a pixel level, providing the user with the ability to track the seasonal and interannual variability in the timing of snowmelt toward a better understanding of how the hydrological cycles of higher latitude and mountainous regions are responding to climate change.

Snow Persistence and Snow Line Elevation Trends in a Snowmelt-Driven Basin in the Central Andes and Their Correlations with Hydroclimatic Variables

The mountain cryosphere is crucial for socio-economic processes, especially during the dry seasons. However, anthropogenic climate change has had a detrimental impact on the cryosphere due to its sensitivity. Over the past two decades, there has been a decline in precipitation and a temperature rise, leading to a substantial reduction in the timing and extent of snow cover. This increase in temperature also elevates the snow line elevation (SLE), further diminishing the volume of available freshwater in the snow-driven basins of the Andes. In this study, we use 22 years (2000–2021) of 8-day snow product (MOD10A2) from the Moderate Resolution Imaging Spectroradiometer (MODIS) to analyze the annual and seasonal variability of snow cover area, SLE, and snow persistence (SP, an indicator of the duration of snow) in the Yeso River basin in Central Chile and the correlation of SP and SLE with hydrometeorological variables and climatic indices. We introduce a new approach called the Maximum Dissimilarity Method to obtain the SLE even on cloudy days. The results are as follows: (1) Snow cover area reductions of 34.0 km2 at low elevations in spring and 86.5 km2 at mid elevations in summer were found when comparing the period 2016–2021 to 2000–2004; (2) SP trends at the annual scale revealed a significant decrease in 89% of its area and an average of 3.6 fewer days of snow cover per year; (3) an upward and significant trend of 21 m‧year−1 in the annual SLE was found; and (4) annual SP and SLE were highly correlated with annual hydrometeorological variables, and spring and summer snow variables were significantly correlated with dry streamflow. This methodology can potentially serve as a valuable tool for detecting trends in snow-covered surfaces, and thereby associate these changes with climate change or other anthropogenic effects in future research.

Reconstruction of Snow Cover in Kaidu River Basin via Snow Grain Size Gap-Filling Based on Machine Learning

Fine spatiotemporal resolution snow monitoring at the watershed scale is crucial for the management of snow water resources. This research proposes a cloud removal algorithm via snow grain size (SGS) gap-filling based on a space–time extra tree, which aims to address the issue of cloud occlusion that limits the coverage and time resolution of long-time series snow products. To fully characterize the geomorphic characteristics and snow duration time of the Kaidu River Basin (KRB), we designed dimensional data that incorporate spatiotemporal information. Combining other geographic and snow phenological information as input for estimating SGS. A spatiotemporal extreme tree model was constructed and trained to simulate the nonlinear mapping relationship between multidimensional inputs and SGS. The estimation results of SGS can characterize the snow cover under clouds. This study found that when the cloud cover is less than 70%, the model’s estimation of SGS meets expectations, and snow cover reconstruction achieves good results. In specific cloud removal cases, compared to traditional spatiotemporal filtering and multi-sensor fusion, the proposed method has better detail characterization ability and exhibits better performance in snow cover reconstruction and cloud removal in complex mountainous environments. Overall, from 2000 to 2020, 66.75% of snow products successfully removed cloud coverage. This resulted in a decrease in the annual average cloud coverage rate from 52.46% to 34.41% when compared with the MOD10A1 snow product. Additionally, there was an increase in snow coverage rate from 21.52% to 33.84%. This improvement in cloud removal greatly enhanced the time resolution of snow cover data without compromising the accuracy of snow identification.

Significant decreasing trends in snow cover and duration in Northeast China during the past 40 years from 1980 to 2020

Snow cover in Northeast China (NC) is not only a vital variable in water availability but also a significant indicator of climate change. Limited by the short time span, incomplete spatial coverage, large uncertainty, and low spatial resolution of the current snow cover products, the long-term snow cover changes in NC remain unclear. To resolve this issue, a high-quality, long-term, and daily 500 m fractional snow cover (NCFSC) data were generated by integrating ERA5-land FSC data and several auxiliary data, with the help of a random forest (RF) regression model, during the period 1980–2020. The validation against in situ snow-depth observations from 2000 to 2018 indicated that the NCFSC effectively distinguished snow cover conditions (the overall accuracy (OA) was 0.88, producer’s accuracy (PA) was 0.78, user’s accuracy (UA) was 0.81, and Cohen’s kappa (CK) value was 0.71). The spatiotemporal comparison between the NCFSC and MODIS FSC data (MDFSC) during 2015–2018 showed that NCFSC was reliable and highly comparable, with bias, RMSE, and R values of −0.02, 0.14, and 0.73, respectively. Additionally, based on NCFSC, the spatiotemporal variations in snow cover in NC over the past 40 years were analyzed for the first time, and the results showed that the FSC had a significant decreasing trend (0.0017/year, p < 0.05). The FSC in farmland had the fastest decline rate, 0.0020/year (p < 0.05), and the snow cover areas decreased by approximately 20 % from 1.55×105 km2 in 1980 to 1.19×105 km2 in 2020. The snow onset date had a delayed trend (0.141 day/year, p > 0.05), the snow end date had a significant advanced trend (0.243 day/year, p < 0.05), and the snow duration days had a significant decreasing trend (0.384 day/year, p < 0.05) in NC. In summary, the NCFSC seamlessly depicts the spatiotemporal changes in snow cover in NC over the past 40 years, providing a scientific basis and decision-making support for hydrological studies and climate change.

Understanding the Snow Cover Climatology over Turkey from ERA5-Land Reanalysis Data and MODIS Snow Cover Frequency Product

Understanding the distribution, patterns, and characteristics of snowfall and snow cover within a given region over extended periods is important. Snow climatology provides valuable insights into the seasonal and long-term variations in snowfall, helping researchers and meteorologists understand the impacts of climate change on snow accumulation, melt rates, and snowmelt runoff. In this study, in order to understand the spatial and temporal variation in snow cover in Turkey, the temporal and spatial dynamics of snow cover in the country were analyzed during the latest and longest period from 1970 to 2022 using ERA5-Land reanalysis product. It is aimed (1) to show snow-covered area (SCA), snow duration, and snow depth trends over the country; (2) to examine the altitudinal difference of snow phenology response to climate change; and (3) to evaluate the Snow Cover Frequency Maps from MODIS Snow Cover Products with the reanalysis snow depth data. It is found that the “false snow” mapping problem still exists in the MOD10C1_CGF Snow Cover Frequency maps over Turkey, especially in the melting period. We found that an increasing trend of 0.4 °C/decade and snow duration have a decreasing trend due to the early melting between 1970 and 2022. This trend is even more noticeable at elevations below 2000 m. Another important finding is the decreasing trend in snow duration at altitudes below 500 m, indicating a shift from snow to rain for precipitation types.

NDVI Analysis for Monitoring Land-Cover Evolution on Selected Deglaciated Areas in the Gran Paradiso Group (Italian Western Alps)

The ongoing climate warming is affecting high-elevation areas, reducing the extent and the duration of glacier and snow covers, driving a widespread greening effect on the Alpine region. The impact assessment requires therefore the integration of the geomorphological context with altitudinal and ecological features of the study areas. The proposed approach introduces chronologically-constrained zones as geomorphological evidence for selecting deglaciated areas in the alpine and non-alpine belts. In the present study, the protected and low-anthropic-impacted areas of the Gran Paradiso Group (Italian Western Alps) were analysed using Landsat NDVI time series (1984–2022 CE). The obtained results highlighted a progressive greening even at a higher altitude, albeit not ubiquitous. The detected NDVI trends showed, moreover, how the local factors trigger the greening in low-elevation areas. Spectral reflectance showed a general decrease over time, evidencing the progressive colonisation of recently deglaciated surfaces. The results improved the discrimination between different greening rates in the deglaciated areas of the Alpine regions. The geomorphological-driven approach showed significant potential to support the comprehension of these processes, especially for fast-changing areas such as the high mountain regions.

Forest gap effects on snow storage in the transitional climate of the Eastern Cascade Range, Washington, United States

Forest thinning and gap creation are being implemented across the western United States of America (USA) to reduce wildfire and forest mortality risk as the climate warms. The Eastern Cascades in Washington, USA, is in a transitional zone between maritime and continental climate conditions and represents a data gap in observations describing the relationship between forest density and snowpack. We collected 3 years of snow observations across a range of forest densities to characterize how forest management efforts in this region may influence the magnitude and duration of snow storage. Observations indicate that peak snow storage magnitude in small gaps ranges from the same to over twice that observed in unburned forest plots in the Eastern Cascades. However, differences in snow duration are generally small. Across all Eastern Cascade sites and years, we observed a median difference of snow storage lasting 7 days longer in gaps as compared to nearby forest plots. A notable exception to this pattern occurred at one north-facing site, where snow lasted 30 days longer in the gap. These observations of similar snow storage duration in the Eastern Cascades are attributed to minimal differences in canopy snow interception processes between forests and gaps at some sites, and to higher ablation rates that counterbalance the higher snow accumulation in the gaps at other sites. At the north-facing site, more snow accumulated in the gap, and ablation rates in the open gap were similar to the shaded forest due to the aspect of the site. Thus, snow storage duration was much longer in the gap. Together, these data suggest that prescriptions to reduce forest density through thinning and creating gaps may increase the overall amount of snow storage by reducing loss due to sublimation and melting of canopy-intercepted snow. However, reducing forest density in the Eastern Cascades is unlikely to buffer climate-induced shortening of snow storage duration, with the possible exception of gap creation in north-facing forests. Lastly, these observations fill a spatial and climatic data gap and can be used to support hydrological modeling at spatial and temporal scales that are relevant to forest management decisions.

Snow cover persistence as a useful predictor of alpine plant distributions

AbstractAimSnow cover persistence (SCP) has significant effects on plants in high‐elevation ecosystems. It determines the length of the growing season, provides insulation against low temperatures and influences water availability, thereby shaping the vegetation mosaic. Despite its importance, SCP is rarely used in plant species distribution modelling. In this study, we examine whether incorporating SCP in plant species distribution models (SDMs) improves their predictive power. We investigate the link between species' ecology and SDM improvements by the addition of various SCP predictors.LocationWestern Swiss Alps.Taxon206 alpine flowering plants (angiosperms).MethodsWe produced three maps of landsat satellite‐based SCP indices over an entire mountain region, one of them using an online open access platform allowing quick and easy replication and used them as a predictor in plant SDMs alongside commonly used predictors. We tested whether this improved the predictive performance of plant SDMs.ResultsAll three SCP indices improved the overall SDM predictive accuracy, but the overall improvement was potentially limited by their correlation with other climatic predictors. Alpine plant species known for their dependence on snow benefited more from the additional snow information.Main ConclusionsSCP should be used for predicting at least the distribution of alpine, snow‐related plant species. Given that adding snow cover improves SDMs and that snow duration decreases as climate warms, future predictions of alpine plant distributions should account for both snow predictor and associated snow change scenarios.

Arctic and red fox population responses to climate and cryosphere changes at the Arctic's edge.

Responses of one species to climate change may influence the population dynamics of others, particularly in the Arctic where food webs are strongly linked. Specifically, changes to the cryosphere may limit prey availability for predators. We examined Arctic (Vulpes lagopus) and red fox (V. vulpes) population dynamics near the southern edge of the Arctic fox distribution using fur harvest records from Churchill, Manitoba, Canada between 1955 and 2012. Arctic foxes showed a declining population trend over time (inferred from harvest records corrected for trapping effort), whereas the red fox population trend was relatively stable. The positive relationship between the annual Arctic and red fox harvests suggested interspecific competition did not promote the Arctic fox decline. To investigate alternative mechanisms, we evaluated the relative influence of sea-ice phenology, snow depth, snow duration, winter thaws, and summer temperature on the harvest dynamics of both species in the most recent 32years (1980-2012; n = 29) of our data. Arctic fox harvests were negatively related to the length of time Hudson Bay was free of sea ice. Shorter sea ice duration may reduce access to seal carrion as an alternative winter food source when lemming densities decline. Contrary to our prediction, red fox harvest was not related to summer temperature but was positively related to snow depth, suggesting winter prey availability may limit red fox population growth. Predators have an important ecological role, so understanding the influence of changes in the cryosphere on predator-prey interactions may better illuminate the broader influence of climate change on food-web dynamics.

Changes in Climate and Snow Cover in View of Functioning of Ski Resorts in Russia in the Early 21th Century

Received December 14, 2023; revised April 21, 2023; accepted June 27, 2023The development of winter ski tourism and characteristics of ski resorts in various regions of Russia are closely related to climatic conditions, the most important of which are the presence and duration of snow cover. For the period 2000–2021, a study of snow cover, availability of “optimal ski days” and climatic indicators necessary for artificial snowmaking at ski resorts located in different regions of Russia was performed, using data of the reanalysis ERA5-Land. The characteristics of snow cover and temperature from the reanalysis data were compared with data of the meteorological network. The ERA5-Land data for temperature, precipitation, and snow cover thickness are well synchronized with the observational data, and estimates of the error of trends in air temperature and snow cover depth according to the reanalysis data relative to the station data give satisfactory results. In the conditions of the current climate, the average and maximum thickness of snow cover in all resorts is sufficient for their functioning, but in 2000–2021, a decrease in both the maximum and average values of snow cover is noted in most resorts. The study shows that in terms of snow and weather conditions, the highest mountain resorts of the North Caucasus and Kirovsk (Murmansk region) are the most prosperous, where thickness of the snow cover and duration of its occurrence as well as a significant number of “optimal ski days” sustains stability of the resorts and creates favorable conditions for their further development.

Differences in regeneration niche mediate how disturbance severity and microclimate affect forest species composition

Climate change is altering forest composition through species-specific responses to fire and drought. Future forest composition will depend on how the different regeneration niches of co-occurring species align with current environmental conditions, especially after fire, which can promote germination by exposing mineral soil. Few studies, however, have examined the effects of disturbance severity and microclimate on post-fire regeneration to define and compare the regeneration niches of co-occurring tree species. We used seven years of annual demography and microenvironment data from a 25.6-ha fully censused, stem-mapped forest dynamics plot in California, USA, to examine how disturbance severity, snow duration, and temperature extremes affect the survival of Abies concolor and Pinus lambertiana seedlings that germinated naturally after a low- to moderate-severity fire. We defined disturbance severity at the microsite level, based on characteristics of the substrate, and at the neighborhood level, based on tree mortality. Both disturbance severity and snow duration had species-specific effects on seedling survival, but these differed by life stage. During the germination year, later snow disappearance was associated with a 0.5 increase in survival probability for A. concolor but hardly affected P. lambertiana; in contrast, higher neighborhood disturbance severity increased survival of both species. After the germination year, higher substrate burn severity was associated with a 0.8 increase in survival probability for A. concolor but hardly affected P. lambertiana; higher neighborhood disturbance severity and later snow disappearance increased annual survival of both species, but maximum summer temperature had minimal effect. Overall, available seed, higher substrate burn severity, higher neighborhood disturbance severity, and later snow disappearance promoted natural regeneration. However, lower substrate burn severity and earlier snow disappearance in the germination year disadvantaged A. concolor seedlings, increasing the relative abundance of P. lambertiana seedlings compared to the local tree population. Our results indicate that natural post-fire compositional shifts toward drought-tolerant Pinus species–and away from less drought-tolerant Abies species–are possible in the Sierra Nevada, with potential benefits for forest persistence under climate change. Broadly, we show that species differences in regeneration niches shape how disturbance severity and microclimate affect forest species composition.

Effect of Snow Cover on Detecting Spring Phenology from Satellite-Derived Vegetation Indices in Alpine Grasslands

The accurate estimation of phenological metrics from satellite data, especially the start of season (SOS), is of great significance to enhance our understanding of trends in vegetation phenology under climate change at regional or global scales. However, for regions with winter snow cover, such as the alpine grasslands on the Tibetan Plateau, the presence of snow inevitably contaminates satellite signals and introduces bias into the detection of the SOS. Despite recent progress in eliminating the effect of snow cover on SOS detection, the mechanism of how snow cover affects the satellite-derived vegetation index (VI) and the detected SOS remains unclear. This study investigated the effect of snow cover on both VI and SOS detection by combining simulation experiments and real satellite data. Five different VIs were used and compared in this study, including four structure-based (i.e., NDVI, EVI2, NDPI, NDGI) VIs and one physiological-based (i.e., NIRv) VI. Both simulation experiments and satellite data analysis revealed that the presence of snow can significantly reduce the VI values and increase the local gradient of the growth curve, allowing the SOS to be detected. The bias in the detected SOS caused by snow cover depends on the end of the snow season (ESS), snow duration parameters, and the snow-free SOS. An earlier ESS results in an earlier estimate of the SOS, a later ESS results in a later estimate of the SOS, and an ESS close to the snow-free SOS results in small bias in the detected SOS. The sensitivity of the five VIs to snow cover in SOS detection is NDPI/NDGI &lt; NIRv &lt; EVI2 &lt; NDVI, which has been verified in both simulation experiments and satellite data analysis. These findings will significantly advance our research on the feedback mechanisms between vegetation, snow, and climate change for alpine ecosystems.

Performance assessment of gridded snow products in the Upper Euphrates

Snow observations are important in many mountain areas to quantify the water stored in snowpacks and to predicting runoff during the melting period. In this study we compare the performance of five different regional-scale gridded snow products to reproduce snow water equivalent (SWE) in the Upper Euphrates region (Karasu Basin, 10,275 km2), with observations from automatic weather stations in the catchment through Taylor diagrams. The products compared are the ERA5, ERA5-Land, MERRA-2, snow data from a dynamical downscaling of ERA-5 (period 2000-2018) and SWE generated from microwave satellite data (SWE-E(H13) period 2013-2015 product of the EUMETSAT H SAF project). The H13 product presented deficiencies in terms of not being able to reproduce the spatial and temporal variability of the snowpack. ERA-5 and, in particular, ERA-Land products, at 30 and 9 km grid size, respectively, showed good performance in reproducing snow evolution compared to four available observation sites. MERRA2 at 50 km resolution showed lower skills compared to the above-mentioned products. Resulting snow data from WRF at 10 km resolution did not show any improvement with respect to the global datasets. The impossibility of testing different configurations due to the lack of observations to compare and the computational constraints to test different parametrizations may be the reasons to explain the low performance although they remain speculative. All the gridded datasets showed good performance in reproducing snow duration over the basin, compared to remotely sensed data. Results highlight ERA-Land dataset as a very promising tool for regional snow studies in mountainous regions with limited observations, in a cost-effective way.