Annual Snow Research Articles

Rapid decline in extratropical Andean snow cover driven by the poleward migration of the Southern Hemisphere westerlies.

Seasonal snow in the extratropical Andes is a primary water source for major rivers supplying water for drinking, agriculture, and hydroelectric power in Central Chile. Here, we used estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) to analyze changes in snow cover extent over the period 2001-2022 in a total of 18 watersheds spanning approximately 1,100km across the Chilean Andes (27-36°S). We found that the annual snow cover extent is receding in the watersheds analyzed at an average pace of approximately 19% per decade. These alarming trends have impacted meltwater runoff, resulting in historically low river streamflows during the dry season. We examined streamflow records dating back to the early 1980s for 10 major rivers within our study area. Further comparisons with large-scale climate modes suggest that the detected decreasing trends in snow cover extent are likely driven by the poleward migration of the westerly winds associated with a positive trend in the Southern Annular Mode (SAM).

Investigating spatial-temporal trend of snow cover over the three provinces of Northeast China based on a cloud-free MODIS snow cover product

Snow serves as a pivotal water resource in the three provinces of Northeast China (TPNC), the investigation of spatial and temporal distribution changes of snow cover is essential for the region’s efficient water resource management. The Moderate Resolution Spectroradiometer (MODIS) snow cover product has proven effective in providing detailed spatial–temporal distribution and identification of snow cover. However, its accuracy and reliability are significantly hampered by cloud obscuration. To address this issue, we have developed a novel four-step cloud removal approach, which includes Terra and Aqua combination, 5-day temporal combination, snowline method (IMS) as well as 8-pixel and 24-pixel adjacent pixel method. Results indicate that the proposed approach successfully eliminates all cloud cover, yielding a highly accurate cloud-free snow cover product with validation accuracies of 92% and 97% when using in situ snow depth measurements and cloud masking method, respectively. We investigated the spatial and temporal variations of snow cover for the TPNC during 2003–2018 with snow coverage and snow cover days (SCD) and analyzed the correlation between snow cover and climate factors. The results showed that the annual basin-averaged snow coverage increased at a rate of 1.27% yr−1 before 2009 and decreased at a rate of 1.80% yr−1 after 2009. The Mann-Kendall tests indicated that about 72% of the total area showed an increasing trend of annual SCD during 2003–2018. Conversely, 94% of the total area exhibited a decreasing trend of annual SCD for the period of 2009–2018 due to the increasing trend of temperature and decreasing trend of precipitation. Our approach shows a promising application of generating accurate cloud-free products and its potential to examine spatial–temporal variations of snow cover under climate change for other snow-covered regions globally.

Phasing and climate forcing potential of the Millennium Eruption of Mt. Baekdu

The Millennium Eruption of Mt. Baekdu, one of the largest volcanic eruptions in the Common Era, initiated in late 946. It remains uncertain whether its two main compositional phases, rhyolite and trachyte, were expelled in a single eruption or in two. Investigations based on proximal and medial ash have not resolved this question, prompting us to turn to high-resolution ice-core evidence. Here, we report a suite of glaciochemical and tephra analyses of a Greenlandic ice core, identifying the transition from rhyolitic to trachytic tephra with corresponding spikes in insoluble particle fallout. By modeling annual snow accumulation, we estimate an interval of one to two months between these spikes, which approximates the hiatus between two eruptive phases. Additionally, negligible sulfur mass-independent fractionation, near-synchroneity between particle and sulfate deposition, and peak sulfur fallout in winter all indicate an ephemeral aerosol veil. These factors limited the climate forcing potential of the Millennium Eruption.

Dark brown carbon from wildfires: a potent snow radiative forcing agent?

Deposition of wildfire smoke on snow contributes to its darkening and accelerated snowmelt. Recent field studies have identified dark brown carbon (d-BrC) to contribute 50–75% of shortwave absorption in wildfire smoke. d-BrC is a distinct class of water-insoluble, light-absorbing organic carbon that co-exists in abundance with black carbon (BC) in snow across the world. However, the importance of d-BrC as a snow warming agent relative to BC remains unexplored. We address this gap using aerosol-snow radiative transfer calculations on datasets from laboratory and field measurement. We show d-BrC increases the annual mean snow radiative forcing between 0.6 and 17.9 W m−2, corresponding to different wildfire smoke deposition scenarios. This is a 1.6 to 2.1-fold enhancement when compared with BC-only deposition on snow. This study suggests d-BrC is an important contributor to snowmelt in midlatitude glaciers, where ~40% of the world’s glacier surface area resides.

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.

Quantifying the influencing factors of the thermal state of permafrost in Northeast China

In Northeast China, permafrost is controlled by a combination of biotic, climatic, physiographic, and anthropogenic factors. Due to the complexity of these governing or influencing factors, it is challenging to exactly describe the features of the Xing’an permafrost in Northeast China. By integrating remote sensing (RS) and geographic information system (GIS) technologies, we have quantified these influencing factors of permafrost changes as an important approach to understanding the nature of latitudinal and mountain permafrost in Northeast China at the mid-latitudes in the Northern Hemisphere. In this study, we combine Geographical Detector (Geodetector) model, trend analysis, and multi-source RS data to quantify the controlling or influencing factors of permafrost thermal state and of permafrost changes, and explain the interactions among permafrost, environment, and climate. The results indicate that, at the regional scale, changes in the thermal state of permafrost are primarily governed or influenced by mean annual land surface temperature (MALST), precipitation, and snow cover duration (SCD). Topographic factors also affect the spatial patterns of permafrost development. Additionally, in the context of climate warming, the insulation effect of snow cover on the permafrost is weakened, or has been weakening. Moreover, the interactive effects among various factors significantly enhance their explanatory power for changes in the thermal state of permafrost. The study emphasizes the complexity of the interactions among permafrost, climate, and the environment, and highlights the significance of understanding these interactions for regional socio-economic development, ecological management, carbon pool stabilization, and research on future climate change in Northeast China.

Changes in Snow Cover and Its Surface Temperature across the Tibetan Plateau Region from 2000 to 2020

Currently, the global climate system is complex and ever-changing, with multiple factors influencing climate change. The Qinghai–Tibet Plateau, known as the “Third Pole” of the Earth, is particularly sensitive to global climate change. Without timely and scientific research on the ecological environment of the Qinghai–Tibet Plateau and without summarizing relevant adaptive strategies, global climate change will impact the sustainable development of the plateau. This study utilized Landsat remote sensing images from 2000 to 2020 to extract the snow cover area and snow temperature of the Qinghai–Tibet Plateau using the snow frequency threshold method. The study analyzed the spatiotemporal characteristics of snow cover and temperature over the 20-year period and investigated some of the climate and topographical driving factors influencing their changes. The results showed that from 2000 to 2020, the permanent snow cover area in the Qinghai–Tibet Plateau region showed a fluctuating decreasing trend, reducing from approximately 12.34 thousand km2 to around 9.01 thousand km2; the permanent snow temperature showed an initial increase followed by a decrease during the same period. The highest annual average snow temperature was approximately −3.478 °C, while the lowest annual average temperature was around −8.150 °C. Over the 20-year period, the snow cover area in the plateau was negatively correlated with temperature and precipitation, while snow temperature was positively correlated with temperature and precipitation. The snow cover in the weak wind areas of the plateau showed a significant reduction. Areas with higher average wind speeds, such as shaded slopes and semi-shaded slopes, had larger snow cover areas. These research findings provide important insights into the protection and management of the ecological environment of the Qinghai–Tibet Plateau.

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

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

Spatiotemporal Variations in Snow Cover on the Tibetan Plateau from 2003 to 2020

The variations in snow cover on the Tibetan Plateau play a pivotal role in comprehending climate change patterns and governing hydrological processes within the region. This study leverages daily snow cover data and the NASA Digital Elevation Model (DEM) from 2003 to 2020 to analyze spatiotemporal snow cover days and assess their responsiveness to climatic shifts by integrating meteorological data. The results reveal significant spatial heterogeneity in snow cover across the Plateau, with a slight decreasing trend in annual average snow cover duration. Snow cover is predominantly observed during the spring and winter seasons, constituting approximately 32% of the total snow cover days annually. The onset and cessation of snow cover occur within a range of 120–220 days. Additionally, an increasing trend in snow cover duration below 5000 m altitude was observed, in addition to a decreasing trend above 5000 m altitude. Sub-basin analysis delineates the Tarim River Basin as exhibiting the lengthiest average annual snow cover duration of 83 days, while the Yellow River Basin records the shortest duration of 31 days. The decreasing trend in snow cover duration closely aligns with climate warming trends, characterized by a warming rate of 0.17 ± 0.54 °C per decade, coupled with a concurrent increase in precipitation at a rate of 3.09 ± 3.81 mm per year. Temperature exerts a more pronounced influence on annual snow cover duration variation compared to precipitation, as evidenced by a strong negative correlation (CC = −0.67). This study significantly augments the comprehension of hydrological cycle dynamics on the Tibetan Plateau, furnishing essential insights for informed decision-making in water resource management and ecological conservation efforts.

Comparing spatiotemporal species distribution models: A case study of a Scotian Shelf sea cucumber (Cucumaria frondosa)

AbstractNumerous spatiotemporal species distribution modeling frameworks are now available to the ecological practitioner. This study compared three such frameworks accessible in the R programming language: generalized additive models with spatiotemporal smooths as implemented by mgcv, spatiotemporal generalized linear mixed models based on nearest neighbor Gaussian processes as implemented by starve, and spatiotemporal generalized linear mixed models based on the stochastic partial differential equations approach as implemented by sdmTMB. The primary focus was to compare the inferences obtained from applying these frameworks to the case study of the orange‐footed sea cucumber, Cucumaria frondosa, on the Scotian Shelf off Nova Scotia, Canada. Each model was fit to catch data (2000–2019) from Fisheries and Oceans Canada's annual Research Vessel and Snow Crab surveys. Environmental covariates were sourced from high‐resolution data layers, including physical oceanographic, bathymetric, and seafloor morphometric datasets. The three models captured variability in sea cucumber distribution that would have been overlooked without a spatiotemporal approach. Although their predictions were similar, including within C. frondosa spatial reserves, the models provided different inferences regarding covariate effects. This suggests that while practitioners primarily interested in mapping species distributions need only apply the most familiar framework, those most concerned with identifying predictive environmental covariates may benefit from comparing the output from multiple approaches. Employing multiple approaches can also serve as a validation technique.

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.

An Intercomparison of Snow Mass Budget over Arctic Sea Ice Simulated by CMIP6 Models

Abstract The Arctic has experienced rapid changes in recent decades. For the first time, we intercompare five snow mass budget processes over Arctic sea ice simulated by 22 models from the Coupled Model Intercomparison Project phase 6 (CMIP6) using new diagnostics that have not been available for previous CMIPs. Our analysis suggests that snowfall accumulation (melt) is the dominant process contributing to nearly 100% (70.4% ± 10.1%) of the annual snow growth (loss). Snow mass change through sea ice dynamics, snow–ice conversion, and sublimation contribute 10.9% ± 4.9%, 9.7% ± 5.9%, and 9.0% ± 7.7% to the total snow mass loss. The seasonal cycle of various snow processes simulated by most of the CMIP6 models generally follows similar variations. There is reduced snowfall accumulation, melt, and sea ice dynamics during 1993–2014. However, substantial temporal and spatial discrepancies are noteworthy between the CMIP6 models. There is a large spread of snowfall accumulation and snowmelt in summer and fall, snow–ice conversion from autumn to spring, sublimation in late spring and summer, and snow mass change due to sea ice dynamics from winter to midspring. About half the models show decreasing trends of snowfall accumulation during 1993–2014, with no trends in others. Divergent trends in snow–ice conversion and sublimation occur in the Greenland and Barents Seas. The discrepancies are attributed equally to internal variability and model structural differences. Future projections that remove the identified outlier models suggest a significant reduction in snowfall accumulation, snowmelt, and snow mass change due to sea ice dynamics in the Arctic Ocean from 2015 to 2099. Snow–ice conversion and sublimation are also projected to be reduced but with less confidence.

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.

Seasonal Modulation of Crustal Seismicity in Northeastern Japan Driven by Snow Load

AbstractNumerous studies have reported that surface hydrological loading can seasonally modulate seismicity rates at crustal depths. For example, substantial winter snow accumulation occurs across the Japanese Islands, and these snowy regions appear to have seasonally modulated the occurrence of previous large inland earthquakes. Therefore, it is important to investigate the impact of seasonal stress changes on crustal seismicity to deepen our understanding of earthquake generation. Here we constrain seasonal changes in the surface load across northeastern Japan using Global Navigation Satellite System surface displacements and evaluate the potential relationship between temporal trends in inland seismicity and estimated seasonal stress changes. The spatial distribution of the seasonal surface load is consistent with snow depth along the Sea of Japan. The inland seismicity beneath northeastern Japan is modestly modulated by the seasonal stress changes that are induced by the annual snow load. However, this seasonal response is weaker than that in other regions. This weak modulation may be due to the small surface‐load‐induced stress perturbation relative to the long‐term‐averaged stressing rate and/or the limited presence of crustal fluids to trigger seismicity in Japan.

Spatial stratified heterogeneity analysis of field scale permafrost in Northeast China based on optimal parameters-based geographical detector.

Affected by global warming, the permafrost in Northeast China (NEC) has been continuously degrading in recent years. Many researchers have focused on the spatial and temporal distribution characteristics of permafrost in NEC, however, few studies have delved into the field scale. In this study, based on the Optimal Parameters-based Geographical Detector (OPGD) model and Receiver Operating Characteristic (ROC) test, the spatial stratified heterogeneity of permafrost distribution and the indicating performance of environmental variables on permafrost in NEC at the field scale were analyzed. Permafrost spatial distribution data were obtained from the Engineering Geological Investigation Reports (EGIR) of six highways located in NEC and a total of 19 environmental variables related to heat transfer, vegetation, soil, topography, moisture, and ecology were selected. The H-factors (variables with the highest contribution in factor detector results and interaction detector results): slope position (γ), surface frost number (SFN), elevation (DEM), topographic diversity (TD), and annual snow cover days (ASCD) were found to be the major contributors to the distribution of permafrost at the field scale. Among them, γ has the highest contribution and is a special explanatory variable for permafrost. In most cases, interaction can improve the impact of variables, especially the interaction between H-factors. The risk of permafrost decreases with the increase of TD, RN, and SBD, and increases with the increase of SFN. The performance of SFN to indicate permafrost distribution was found to be the best among all variables (AUC = 0.7063). There is spatial heterogeneity in the distribution of permafrost on highways in different spatial locations. This study summarized the numerical and spatial location between permafrost and different environmental variables at the field scale, and many results were found to be informative for environmental studies and engineering construction in NEC.

Predictive Analytics of Air Temperature in Alaskan Permafrost Terrain Leveraging Two-Level Signal Decomposition and Deep Learning

Local weather forecasts in the Arctic outside of settlements are challenging due to the dearth of ground-level observation stations and high computational costs. During winter, these forecasts are critical to help prepare for potentially hazardous weather conditions, while in spring, these forecasts may be used to determine flood risk during annual snow melt. To this end, a hybrid VMD-WT-InceptionTime model is proposed for multi-horizon multivariate forecasting of remote-region temperatures in Alaska over short-term horizons (the next seven days). First, the Spearman correlation coefficient is employed to analyze the relationship between each input variable and the forecast target temperature. The most output-correlated input sequences are decomposed using variational mode decomposition (VMD) and, ultimately, wavelet transform (WT) to extract time-frequency patterns intrinsic in the raw inputs. The resulting sequences are fed into a deep InceptionTime model for short-term forecasting. This hybrid technique has been developed and evaluated using 35+ years of data from three locations in Alaska. Different experiments and performance benchmarks are conducted using deep learning models (e.g., Time Series Transformers, LSTM, MiniRocket), and statistical and conventional machine learning baselines (e.g., GBDT, SVR, ARIMA). All forecasting performances are assessed using four metrics: the root mean squared error, the mean absolute percentage error, the coefficient of determination, and the mean directional accuracy. Superior forecasting performance is achieved consistently using the proposed hybrid technique.

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 < 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.

Assessing snow cover patterns in the Indus-Ganga-Brahmaputra River Basins of the Hindu Kush Himalayas using snow persistence and snow line as metrics

This study investigates the utilization of snow persistence (SP) as a metric to assess snow cover patterns in the Upper Indus-Ganga-Brahmaputra (UIGB) river basins, an area notably susceptible to climate change. The objectives of the study are to establish seasonal snow persistence, calculate seasonal snow lines, and quantify the temporal trend of snow lines. It also underscores the importance of accounting for climate zones, elevation, and seasonal variations when investigating snow persistence. Furthermore, the study estimates the snow line elevation using the Regional Snow Line Elevation (RSLE) approach with remote sensing data. Over a span of 19 years (2003–2021), the analysis of SP% in UIGB basins indicates a notable reduction in the mean annual permanent snow zone area, decreasing by -2.61 %, -2.37 %, and -4.18 % from winter (DJF) to summer (JJA). The results uncover considerable fluctuations in snow cover across various climate zones and seasons. The impact of warming is clearly visible on snow cover, instigating a transition from permanent and seasonal snow zones towards intermittent and minimal or no snow zones. The seasonal analysis of the snow line from 2003 to 2021 illustrates fluctuation in the snow line for UIGB basins, from 2349 ± 117 m to 4802 ± 51.7 m, 3003 ± 286 m to 4871 ± 64.4 m, and 3238 ± 127 m to 5047 ± 73 m between winter and summer seasons, with the most significant fluctuation occurring in the Brahmaputra basin. A substantial increase in Snow Line Elevation (SLE) is observed for Ganga (6.67 m yr−1), and Brahmaputra (7.26 m yr−1) at the conclusion of the melting season. These findings offer invaluable insights for formulating targeted mitigation strategies in the snow accumulation regions of each river basin. They equip us with the ability to respond effectively to the spatiotemporal variability of snow cover caused by climate change.

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.