Maximum Snow Research Articles

On the problem of estimating the snowiness of winters by different methods by the example of the Yuzhno-Sakhalinsk city

The problem of estimating a snowiness (a snow coverage) of a territory is considered by many authors, but still nowadays no common approach to its solution is reached. Different authors use different characteristics in their works for estimating the winter snowiness. In this work the snowiness of winters was estimated using different parameters to determine the most representative one. The area ща study is the Yuzhno-Sakhalinsk urban district. The data for the past 36 years (1986–2022) were used to determine the types of the winter snowiness in this district. The following nine methods have been applied: the maximum winter snow reserve; by the amount of solid precipitation; by the average winter thickness of snow cover from the weather station and by snow survey; by the average greatest ten-day thickness of snow cover; by the amount of precipitation in the form of snow; the method of N.N. Galakhov; the Schultz coefficient; and the maximum winter snow cover thickness. The results obtained are very contradictory. Thus, the types of snowiness completely coincided in only 17% of winters; while in 58% of winters the types of snowiness coincided by 2/3 of the above indicators. Estimation of snowiness using various parameters gives closer results when using data on snow reserve at the beginning of snowmelt and the average winter thickness of snow cover (coincidence in 78% of cases). The reason is that a major part of the above methods uses the values of only one parameter. But given that snowiness is a complex indicator, it would be reasonable to consider all possible parameters at the same time. It is impossible to develop a unified approach to estimating winter snowiness, since the parameters chosen for this problem depend on the goal of the determination. It would also be worthwhile to select a methodology for estimating winter snowiness depending on the tasks set, and on the availability and reliability of the initial meteorological data for the analysis. Such work is also complicated by the insufficient volume of meteorological data, as well as due to gaps in them.

Агроклиматическая оценка периода перезимовки озимой пшеницы в Центрально-Черноземном экономическом районе

This study was conducted in order to identify the most significant, in terms of influence, agroclimatic indicators of the overwintering period for winter wheat in the territories of the regions that are part of the Central Black Earth Economic Region. For this purpose, meteorological data on average, minimum and maximum air temperature, precipitation and snow cover height for the period from 1993 to 2022 were used, and a correlation and regression analysis of the dependence of the yield of this crop on the calculated indicators was carried out. During the analysis, statistically significant dependences of winter wheat yield on a number of agroclimatic indicators of the overwintering period were considered, revealed when using the Fisher criterion and estimating the P-values of the coefficients of the regression equation. According to the results of the analysis, the most significant indicators of the overwintering period are: the average air temperature for the coldest month and the coldest five-day period, the duration of the period between the dates of a steady transition of air temperature through 0℃ in autumn and spring, as well as the sum of negative air temperatures between these dates. The results obtained can potentially be more visible when using a larger amount of data, as well as when performing multiple regression.

Snowmelt duration controls red algal blooms in the snow of the European Alps

Algae populate multiple habitats, including snow and ice, where they can form red blooms. These decrease snow albedo, accelerating snowmelt and potentially feeding back on snow and glacier decline caused by climate change. Quantifying this feedback requires the understanding of bloom evolution with climate change. Little, however, is known about the drivers of red snow blooms. Here, we develop an algorithm to analyze 5 y of satellite data from the European Alps and separate bloom occurrences from similarly colored Saharan dust depositions. In a second step, we combine the occurrences of blooms with meteorological data and snow simulations to identify the drivers of blooms. Results show that the upward migration of algae from the ground and blooming requires the presence of liquid water throughout the whole snow column for at least 46 d. Our limited data suggest that moderate dust amounts provide nutrients favorable to bloom, whereas large dust amounts hasten snowmelt and reduce its duration below the threshold required for blooming. Over the period studied, blooms cover 1.3% of the area above 1,800 m elevation, advancing the snow melt-out date by 4 to 21 d in these areas. Under warmer climates, maximum snow mass will decrease whereas snowmelt duration, that controls algal blooms' occurrences, is less sensitive to global temperature increase. In this respect, the impact of bloom on snowmelt will either remain stable (RCP4.5) or decrease (RCP8.5). Algal blooms in the Alps therefore do not constitute a positive climate feedback.

Projected future changes in the cryosphere and hydrology of a mountainous catchment in the upper Heihe River, China

Abstract. Climate warming exacerbates the degradation of the mountain cryosphere, including glacier retreat, permafrost degradation, and snow cover reduction. These changes dramatically alter the local and downstream hydrological regime, posing significant threats to basin-scale water resource management and sustainable development. However, this issue is still not adequately addressed, particularly in mountainous catchments. We developed an integrated cryospheric–hydrologic model, the FLEX-Cryo model, to comprehensively consider glaciers, snow cover, and frozen soil and their dynamic impacts on hydrological processes. Taking the mountainous Hulu catchment located in the upper Heihe River of China as a case study, we utilized the state-of-the-art climate change projection data under two scenarios (SSP2-4.5 and SSP5-8.5) from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) to simulate the future changes in the mountainous cryosphere and their impacts on hydrology. Our findings showed that under the medium-emission scenario (SSP2-4.5) and high-emission scenario (SSP5-8.5), by the end of the 21st century, the glacier will completely melt out around the years 2051 and 2045, respectively. The annual maximum snow water equivalent is projected to decrease by 41.4 % and 46.0 %, while the duration of snow cover will be reduced by approximately 45 and 70 d. The freeze onset of seasonally frozen soil is expected to be delayed by 10 and 22 d, while the thaw onset of permafrost is likely to advance by 19 and 32 d. Moreover, the maximum freeze depth of seasonally frozen soil is projected to decrease by 5.2 and 10.9 cm per decade, and the depth of the active layer will increase by 8.2 and 15.5 cm per decade. Regarding hydrology, catchment total runoff exhibits a decreasing trend, and the tipping point of glacier runoff occurs approximately between 2019 and 2021. Permafrost degradation will likely reduce the duration of low runoff in the early thawing season; the discontinuous baseflow recession gradually transitions into linear recessions, and the baseflow increases. Our results highlight the significant changes expected in the mountainous cryosphere and hydrology in the future. These findings enhance our understanding of cold-region hydrological processes and have the potential to assist local and downstream water resource management in addressing the challenges posed by climate change.

Development of a Daily Cloud-Free Snow-Cover Dataset Using MODIS-Based Snow-Cover Probability for High Mountain Asia during 2000–2020

Investigating the changes in snow cover caused by climate change is extremely important and has attracted increasing attention in cryosphere and climate research. Optimal remote sensing-based snow datasets can provide long-term daily and global spatial-temporal snow-cover distribution at regional and global scales. However, the application of these snow-cover products is inevitably limited because of the space–time discontinuities caused by cloud obscuration, which poses a significant challenge in snowpack-related studies, especially in High Mountain Asia (HMA), an area that has high-elevation mountains, complex terrain, and harsh environments and has fewer observation stations. To address this issue, we developed an improved five-step hybrid cloud removal strategy by integrating the daily merged snow-cover probability (SCP) algorithm, eight-day merged SCP algorithm, decision tree algorithm, temporal downscaling algorithm, and optimal threshold segmentation algorithm to produce a 21-year, daily cloud-free snow-cover dataset using two daily MODIS snow-cover products over the HMA. The accuracy assessment demonstrated that the newly developed cloud-free snow-cover product achieved a mean overall accuracy of 93.80%, based on daily classified snow depth observations from 86 meteorological stations over 10 years. The time series of the daily percentage of binary snow-cover over HMA was analyzed during this period, indicating that the maximum snow cover tended to change more dramatically than the minimum snow cover. The annual snow-cover duration (SCD) experienced an insignificantly increasing trend over most of the northeastern and southwestern HMA (e.g., Qilian, eastern Kun Lun, the east of Inner Tibet, the western Himalayas, the central Himalayas, and the Hindu Kush) and an insignificant declining trend over most of the northwestern and southeastern HMA (e.g., the eastern Himalayas, Hengduan, the west of Inner Tibet, Pamir, Hissar Alay, and Tien). This new high-quality snow-cover dataset will promote studies on climate systems, hydrological modeling, and water resource management in this remote and cold region.

Dendrochronological analysis of conifers in avalanche catchments of the Northwestern Altai (Korgon River basin)

The purpose of the study was to date the powerful snow avalanches occurred in the avalanche catchment area in the river Korgon basin (Northwestern Altai) by the dendrochronological method. The paper presents the results of the analysis of a number of dendrochronological indicators of avalanches: the age of trees, the relation between widths of annual rings from opposite sides of the trunk, the presence of reactive (compressional) wood and traumatic resin canals, the dates of death and formation of wounds in trees, the presence of clearing effects (a sharp increase in growth) in three avalanche catchments, as well as a complex dendrochronological index of the avalanche activity. The dates of releases of the powerful avalanches were established down to 1570. It has been found that against the background of increasing amount of winter precipitation in these catchments, there are different trends in occurrence of the avalanches, which is determined by their morphological properties. In the avalanche areas with steeper slopes, due to repeated unloading of the avalanche centers in winter, the probability of releasing of powerful avalanches decreases. For the same reason, the powerful avalanches do not release during the years of maximum snow accumulation. The trends in the avalanche activity in the region under consideration had been obtained for the first time. The dendrochronological index of the avalanche activity, which is the ratio of the number of the tree growth failures to the number of the examined trees, is a good indicator of a release of a powerful avalanche even at a value of 0.85, provided that the number of examined trees in the lower part of the transit zone and in the zone of accumulation is 25–35 units. The results of this study may be used to predict the territorial differentiation of changes in the avalanche activity due to climate change. In connection with the recreational development of the Altai territory, they may also be of practical importance.

Mechanisms of spring freshet generation in southern Quebec, Canada

Seasonal forecasting of spring floods in snow-covered basins is challenging due to the ambiguity in the driving processes, uncertain estimations of antecedent catchment conditions, and the choice of predictor variables. In this study, we attempt to improve the prediction of spring flow peaks in southern Quebec, Canada, by studying the preconditioning mechanisms of runoff generation and their impact on inter-annual variations in the timing and magnitude of spring peak flow. Historical observations and simulated data from a hydrological and snowmelt model were used to study the antecedent conditions that control flood characteristics in 12 unregulated snow-dominated catchments. Maximum snow accumulation (peak SWE), snowmelt and rainfall volume and intensity, soil moisture, river baseflow, and the air freezing index, a proxy for soil freezing, were estimated during the pre-flood period. Stepwise multiple linear regression was used to identify the most relevant predictors and assess their relative contribution to interannual variability flood variability. Peak SWE was not by itself a strong predictor of spring flood magnitude and timing. The snowmelt rate during the pre-flood period was the most ubiquitous and skillful predictor of spring flood magnitude, followed by the rainfall intensity. The ‘soil memory’ effect, represented here by the simulated soil moisture content and freezing depth, was generally poorly related to flood characteristics. SWE and snowmelt dynamics dominated the interannual variability of flood magnitude in the southern and agricultural basins while rainfall intensity had a stronger influence in the northern, snowier forested basins. More complex machine learning models (random forest and support vector regression) did not perform better than the simple linear models to predict peakflow from antecedent hydroclimatic factors. These results highlight the impact of climate and land cover and use on spring flood generation mechanism and the moderate predictability potential of spring floods based on antecedent hydrological factors.

Measured black carbon deposition over the central Himalayan glaciers: Concentrations in surface snow and impact on snow albedo reduction

Deposition of ambient black carbon (BC) aerosols over snow-covered areas reduces surface albedo and accelerates snowmelt. Based on in-situ atmospheric BC data and the WRF-Chem model, we estimated the dry and wet deposition of BC over the Yala glacier of the central Himalayan region in Nepal during 2016–2018. The maximum and minimum BC dry deposition was reported in pre- and post-monsoon respectively. Approximately 50% of annual dry deposition occurred in the pre-monsoon season (March to May) and 27% of the annual dry deposition occurred in April. The total dry BC deposition rate was estimated as ∼4.6 μg m−2 day−1 providing a total deposition of 531 μg m−2 during the pre-monsoon season. The contribution of biomass burning and fossil fuel sources to BC deposition on an annual basis was 28% and 72% respectively. The annual accumulated wet deposition of BC was 196 times higher than the annual dry deposition. The ten months of observed dry deposition of BC (October 1, 2016 to August 31, 2017 – except December 2016) was ∼39% lower than that of WRF-Chem's estimated annual dry deposition from September 1, 2016 to August 31, 2017 partially due to model bias. The deposited content of BC over the snow surface has an important role in albedo reduction, therefore snow samples were collected from the surface of the Yala Glacier and the surrounding region in April 2016, 2017, and 2018. Samples were analyzed for BC mass concentration through the thermal optical analysis and single particle soot photometer method. The BC calculated via the thermal optical method was in the range of 352–854 ng g−1, higher than the BC calculated through the particle soot photometer method and estimated BC in 2 cm surface snow (imperial equation). The maximum surface snow albedo reduction due to BC was 8.8%, estimated by a widely used snow radiative transfer model and a linear regression equation.

Snowpack variations and their hazardous effects under climate warming in the central Tianshan Mountains

Climate change alters snowpack evolution, which in turn influences the likelihood of snow avalanches and flood risks. The lack of systemic observational data on key snow characteristics in high mountains remains a scientific challenge in terms of systematically elucidating the dynamic chain of variations in climate–snowpack–snow disasters. This restricts our understanding and poses challenges in the prediction of snow-related disaster risks. As such, this study analysed the variations of temperature and snowfall and the physical characteristics of snowpacks based on ground-based observations from the Kunse River Valley situated in the Tianshan Mountains from 1967 to 2021. The results reveal that the temperature increased significantly by 0.32 °C per decade (p < 0.01) during the snow season, along with more extreme snowfall events. The snow-cover duration was observed to have been shortened by 4.77 d per decade (p < 0.01) from 1967 to 2021, which is characterised by later snow-cover onset and earlier snowmelt. Concurrently, average and maximum snow depths increased along with an increase in peak snow water equivalent, thus indicating a higher frequency of extremely scarce or abundant snow years. The low snowpack temperature gradient and earlier snowmelt dates in spring lead to earlier occurrences of snowmelt floods and wet avalanches. As the risks of these events increase, they pose greater threats to farmlands, road transportation, water–electricity infrastructure and several other human activities. Therefore, these insights are critical for providing vital information that can deepen our understanding of the impact of climate change on snowpack characteristics and improve management strategies for snow-related disaster prevention and mitigation.

The snow cover is more important than other climatic variables on the prediction of vegetation dynamics in the Pyrenees (1981–2014)

The dynamics of the mountain vegetation is governed by multiple climatic drivers including temperature, precipitation, radiation and snow cover variability. However, in the Mediterranean environment, little is known about the relative importance of each variable. In this study we assess how different snowpack indices (the maximum annual accumulation, the length of the snow season, and the melt-out date) and key climate variables (precipitation, temperature and shortwave solar radiation) control the interannual variability of the maximum Normalized Difference Vegetation Index (peak NDVI) in the Pyrenees. We use a 33 year long remote sensing dataset (1981–2014) to build a statistical model relating the annual peak NDVI with snow and climate variables. In elevated areas characterized by a well developed seasonal snowpack the melt-out date was the most important climatic variable for predicting the annual peak NDVI. However, at lower elevations where snow presence is ephemeral, shortwave solar radiation was the most important variable. This change in the relative importance of climatic variables occurs around 1300 m a.s.l. The results do not show a significant contribution of maximum snow accumulation, suggesting that indicators of snow presence (i.e. melt-out date or snow season duration), which are significantly easier to obtain than snow mass indicators from remote sensing, could be used to model the influence of the snowpack on peak NDVI at regional scale.

Elevation dependent climate assessment and its influence on snow cover variability in Hindu Kush Himalayan region using Google Earth Engine for the period of 2003–2021

The Hindu Kush Himalayan (HKH) regions are taken for this study, which is predominantly characterized by amplified climatic uncertainty and rapid snow melting. Because at the present there is a lack of studies estimating the elevation dependent and climate influenced snow cover variability in the HKH region. This present study examines elevation-dependent (2003–2021) distribution and trends in precipitation, temperature, and snow cover area (SCA) by employing numerous non-parametric statistical applications using the Google Earth Engine (GEE). The Modified Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua snow cover data is employed to estimate SCA along with the Climate Hazards Group Infrared Precipitation with Station Data version 2.0 (CHIRPS-V2.0) for precipitation and MODIS land surface temperature (LST). The study reveals statistically significant decreasing annual, seasonal, and monthly trends of SCA in most of the elevation zones typically during winter season (−0.256%/year), whereas temperature and precipitation exhibit strong spatial variability. Inclusive investigation portrays maximum mean annual and seasonal increased precipitation at the low-elevated regions, whereas seasonal disparities in temperature ostensible marginal decreased at high-elevation, albeit increased at lowland. Moreover, topographic importance on SCA distribution showcases maximum snow (43.4%) at north-facing less surface inclination with flat topography (14.6%). The comprehensive elevation wise analysis improves our understanding of the overall hydrological challenge and assist in the development of more reliable flood forecasting and water resource management.

An overwinter protocol for detecting wolverines and other carnivores at camera traps paired with automated scent dispensers.

Camera traps deployed with olfactory attractants are used to survey rare and elusive carnivores. Study areas with deep snowpack and rugged terrain present challenges and risks to field personnel, who traditionally must revisit camera stations regularly to refresh attractants. In such locations, alternative overwinter survey protocols that include a persistent attractant would improve both the safety and efficiency of camera-trap surveys. We present a protocol for installing camera traps and automated scent dispensers on trees at above-average maximum snow depth to eliminate the need for interim service visits and to enable standardized surveys to be conducted throughout the year. Our protocol proved to be effective at attracting and detecting numerous and repeated visits by wolverines, fishers, and other carnivores in two montane regions of the western contiguous United States. The volume, timing, and composition of liquid scent lure released by automated scent dispensers can be varied to target multiple species of interest, and the dispenser can be used insituations where bait rewards may influence the behavior of target species and/or pose human safety concerns.

Climate change-induced shifts in survival and size of the worlds' northernmost oviparous snake: A 68-year study.

Because of their dependence on ambient temperature ectothermic animals can serve as sentinels of conservation problems related to global warming. Reptiles in temperate areas are especially well suited to study such effects, as their annual and daily activity patterns directly depend on ambient temperature. This study is based on annual data spanning 68 years from a fringe population of Grass Snakes (Natrix natrix), which is the world's northernmost oviparous (egg-laying) reptile, and known to be constrained by temperature for reproduction, morphology, and behavior. Mark-recapture analyses showed that survival probability was generally higher in males than in females, and that it increased with body length. Body condition (scaled mass index) and body length increased over time, indicative of a longer annual activity period. Monthly survival was generally higher during winter (i.e., hibernation) than over the summer season. Summer survival increased over time, whilst winter survival decreased, especially during recent decades. Winter survival was lower when annual maximum snow depth was less than 15 cm, implying a negative effect of milder winters with less insulating snow cover. Our study demonstrates long-term shifts in body length, body condition and seasonal survival associated with a warming climate. Although the seasonal changes in survival ran in opposite directions and though changes were small in absolute terms, the trends did not cancel out, but total annual survival decreased. We conclude that effects of a warming climate can be diverse and pose a threat for thermophilic species in temperate regions, and that future studies should consider survival change by season, preferably in a long-term approach.

Extending the utility of space-borne snow water equivalent observations over vegetated areas with data assimilation

Abstract. Snow is a vital component of the earth system, yet no snow-focused satellite remote sensing platform currently exists. In this study, we investigate how synthetic observations of snow water equivalent (SWE) representative of a synthetic aperture radar remote sensing platform could improve spatiotemporal estimates of snowpack. We use a fraternal twin observing system simulation experiment, specifically investigating how much snow simulated using widely used models and forcing data could be improved by assimilating synthetic observations of SWE. We focus this study across a 24∘×37∘ domain in the western USA and Canada, simulating snow at 250 m resolution and hourly time steps in water year 2019. We perform two data assimilation experiments, including (1) a simulation excluding synthetic observations in forests where canopies obstruct remote sensing retrievals and (2) a simulation inferring snow distribution in forested grid cells using synthetic observations from nearby canopy-free grid cells. Results found that, relative to a nature run, or assumed true simulation of snow evolution, assimilating synthetic SWE observations improved average SWE biases at maximum snowpack timing in shrub, grass, crop, bare-ground, and wetland land cover types from 14 %, to within 1 %. However, forested grid cells contained a disproportionate amount of SWE volume. In forests, SWE mean absolute errors at the time of maximum snow volume were 111 mm and average SWE biases were on the order of 150 %. Here the data assimilation approach that estimated forest SWE using observations from the nearest canopy-free grid cells substantially improved these SWE biases (18 %) and the SWE mean absolute error (27 mm). Simulations employing data assimilation also improved estimates of the temporal evolution of both SWE and runoff, even in spring snowmelt periods when melting snow and high snow liquid water content prevented synthetic SWE retrievals. In fact, in the Upper Colorado River region, melt-season SWE biases were improved from 63 % to within 1 %, and the Nash–Sutcliffe efficiency of runoff improved from −2.59 to 0.22. These results demonstrate the value of data assimilation and a snow-focused globally relevant remote sensing platform for improving the characterization of SWE and associated water availability.

Numerical Simulation of Falling‐Snow Deposition Pattern Over 3D‐Hill

AbstractSnow distribution over complex terrain is an important input quantity for hydrological modeling. Earlier numerical studies on falling‐snow deposition pattern mostly focused on two‐dimensional (2D) hills. This is simulated over a three‐dimensional (3D) hill using a large‐eddy simulation model. It is shown that for both 2D and 3D cases, the hill blocking effect leads to the formation of a snow accumulation zone on the windward slope. For the 3D case, the maximum snow deposition occurs on the leeward toe, but not for the 2D case under the same background conditions. The main reason for these differences is that the spanwise terrain change in the 3D case weakens this hill blocking effect and concurrently generates terrain‐scale vortices which cause spatially variable snow depositions. It is found that an additional snow accumulation zone forms on the leeward slope of the 3D‐hill due to the presence of a horseshoe vortex there. The low wind velocity regime bracketed by the vorticity streamers forms another large snow accumulation zone on the leeward toe. While the results here are only applicable to small hills in a mountain chain, they provide the necessary information for the parameterization of large‐scale snow patterns for hydrological modeling.

Long‐Term Variability and Trends in Snow Depth and Cover Days Throughout Iranian Mountain Ranges

AbstractIn 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 &lt; 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.

Аналіз тенденцій довготривалих змін кліматичних навантажень

The purpose of the work is to identify changes in the characteristic values of loads from snow and wind over the past decades, as well as to justify the need to take these changes into account when developing load standards for buildings and structures. Characteristic values of wind pressure are determined by the probabilistic model of a random process. The necessary parameters of the Weibull distribution for 13 weather stations of Ukraine are determined by the histograms of the distribution of the results of wind speed measurements obtained in three different periods of meteorological observations during the years 1961...2020. The characteristic values of the snow load are determined by the probabilistic model of the sequence of annual maxima with the Gumbel distribution. For this purpose, the average values and standards of the annual maximum snow cover weight at 54 weather stations, obtained from the results of snow measurement surveys in three different observation periods during 1950...2020, were used. According to the data of each weather station, the rates of characteristic values of loads change over time are determined. Characteristic values of wind pressure at 11 weather stations out of 13 that were considered decrease by an average of 4.5 Pa/year. For two weather stations, the intensity of the decrease is much greater and equals to 13.4 Pa/year and 25.6 Pa/year. At 41 weather stations out of 54 studied, a decrease in the characteristic values of snow cover weight is observed, and at 13 weather stations, the snow load increases over time. Annual increases in the characteristic values of the weight of the snow cover vary within the range of -22...+25 Pa/year with an average decrease of 4.5 Pa/year. As a result of the study, it was found that the significant spread of the rates of change of the characteristic values of loads over time at different weather stations does not allow to generalize this indicator on the territory of Ukraine. In the presence of several groups of data, it is recommended to determine the characteristic values of wind and snow load through the statistical characteristics of wind speed and weight of snow cover, averaged over the data of all available periods of meteorological observations, without taking into account the detected changes in time.

A Study on Temporal Variation of Snow and Glacier in Langtang Basin

Snow cover and glacier in the Himalaya play a major role for generating stream flow in South Asia. It is already observed that the snowline is retreating in the Himalaya region. While there are relatively more studies on glaciers fluctuation in this region, studies on snow cover are relatively rare. Present study is aimed to analyze the temporal variation of snow and glacier cover (both debris and clean ice covered) of Langtang area within the catchment 432.32 sq.km at outlet 28°12'10.78"N and 85°27'44.58"E. Currently, we used intermittent medium resolution satellite imageries (Landsat) to study the fluctuation in snow cover and glacier area in the Langtang catchment. Our analysis revealed that the glaciers in the study area are tentatively shrinking in average. More specifically, the Landsat Satellite images of the concerned area processed through ERDAS Imagine and their detailed analysis conducted in ArcGIS show that the minimum snow coverage area is declining and maximum snow coverage is in the average range and in the case of debris coverage glacier and clean ice glacier, the trend is declining. The novelty of this study was to develop a robust process having potentiality to predict snow coverage that would be ultimately applicable to agricultural sectors.

The Variability of Snow Cover and Its Contribution to Water Resources in the Chinese Altai Mountains from 2000 to 2022

As one of the major water supply systems for inland rivers, especially in arid and semi-arid regions, snow cover strongly affects hydrological cycles. In this study, remote sensing datasets combined with in-situ observation data from a route survey of snow cover were used to investigate the changes in snow cover parameters on the Chinese Altai Mountains from 2000 to 2022, and the responses of snow cover to climate and hydrology were also discussed. The annual snow cover frequency (SCF), snow cover area, snow depth (SD), and snow density were 45.03%, 2.27 × 104 km2, 23.4 cm, and ~0.21 g·cm−3, respectively. The snow water equivalent ranged from 0.58 km3 to 1.49 km3, with an average of 1.12 km3. Higher and lower SCF were mainly distributed at high elevations and on both sides of the Irtysh river. The maximum and minimum snow cover parameters occurred in the Burqin River Basin and the Lhaster River Basin. In years with high SCF, abnormal westerly airflow was favorable for water vapor transport to the Chinese Altai Mountains, resulting in strong snowfall, and vice versa in years with low SCF. There were significant seasonal differences in the impact of temperature and precipitation on regional SCF changes. The snowmelt runoff ratios were 11.2%, 25.30%, 8.04%, 30.22%, and 11.56% in the Irtysh, Kayit, Haba, Kelan, and Burqin River Basins. Snow meltwater has made a significant contribution to the hydrology of the Chinese Altai Mountains.

Snow Load Shape Coefficients and Snow Prevention Method for Stepped Flat Roofs

Excessive snow load and nonuniform snow deposition are the main factors leading to building collapses. The snow load shape coefficient represents the dimensionless snow load, and its value is related to the unbalanced distribution of snow. The snow load shape coefficients for stepped flat roofs vary greatly in the codes of different regions, which always leads to underestimation of snow loads. We need a widely used standard for snow load shape coefficients. Therefore, through a combination of field measurements and numerical simulations, this study probes the snow accumulation processes and snow load shape coefficients on stepped flat roofs and proposes an equation to calculate snow load shape coefficients and the optimal slope of snow protection for lower roofs. It is found that the maximum snow load shape coefficient emerges at the roof junction with a value of 3.44. The nonuniform length of the snow accumulation is equal to two times the level difference. Based on these, the equation of the snow load shape coefficients is summarized, which is combined with the discrepancies between different codes and the regularity of snow distributions. In this study, the dynamic grid technology under the Eulerian framework is used to successfully predict snow accumulation on stepped flat roofs, and it is noted that snow erosion and deposition are closely related to the location and size of vortexes. Finally, we consider that the ideal slope for the lower roof to prevent snow should be 11°.