Search Results (514)

Over the past 70 years, Central Asia has emerged as a globally recognized water security hotspot due to its unique geographic location and uneven distribution of water resources. In arid and semi-arid regions, understanding runoff dynamics under climate change is essential for ensuring regional water security. This study addresses the data-sparse Central Asian region by applying the ISIMIP3b multi-scenario analysis framework, selecting three representative global hydrological models. Using model intercomparison, trend analysis, and geographically weighted regression, we assess the spatiotemporal evolution of runoff from 1950 to 2080 and investigate the spatial heterogeneity of runoff responses to precipitation and temperature. The results show that under the historical scenario, all models consistently identify similar spatial pattern of runoff, with higher values in southeastern mountainous regions and lower values in western and central regions. However, substantial differences exist in runoff magnitude, with regional annual means of 10, 26, and 68 mm across the three models, respectively. The spatial disparity of runoff distribution is projected to increase under higher SSP scenarios. During the historical period, most of Central Asia experienced a slight decreasing trend in runoff, but the overall trends were −0.022, 0.1, and 0.065 mm/year, respectively. In contrast, future projections indicate a transition to increasing trends, particularly in eastern regions, where trend magnitudes and statistical significance are notably greater than in the west. Meanwhile, the spatial extent of significant trends expands under high-emission scenarios. Precipitation exerts a positive influence on runoff in over 80% of the region, while temperature impacts exhibit strong spatial variability. In the WaterGAP2-2e and MIROC-INTEG-LAND models, temperature has a positive effect on runoff in glaciated plateau regions, likely due to enhanced snow and glacier melt under warming conditions. This study presents a multi-model framework for characterizing climate–runoff interactions in data-scarce and environmentally sensitive regions, offering insights for water resource management in Central Asia. Full article

Reliable monitoring of snow cover in mountainous regions remains a challenge due to frequent cloud cover and the revisit limitations of optical satellites. This study compares satellite snow-cover records with >99,000 ground-based time-lapse camera observations across northern Spain (2003–2025). Cloud cover caused major data loss, with up to 57% of satellite images affected. Effective revisit intervals (the average time between usable images) diverge substantially from nominal values: 2.3 days for MODIS, 6.9 days for Sentinel-2, and over 21 days for Landsat. A hierarchical multisensor approach with 5-day gap-filling reduced this to just 1.3 days. On dates when cameras confirmed snow, satellites underestimated snow presence by 61.6% (Sentinel-2), 71.5% (Landsat), and 79.7% (MODIS), though gap-filling approaches reduced underestimation to 49.4%—deficits largely attributable to cloud-obscured scenes. When both satellite and camera provided cloud-free observations for the same date and location, classification agreement exceeded 85%. Despite this, satellites consistently failed to detect short-lived snow events and introduced temporal biases. On average, Snow Onset Dates were detected 13–52 days later, and Snow Melt-Out Dates differed by up to 40 days compared to camera-derived records. These results have implications for snow-cover monitoring using satellite images and highlight the need for integrating ground-based observations to compensate for satellite limitations and improve snow cover seasonality assessments in complex terrains. Full article

Snow and ice accumulation on transportation infrastructure presents significant safety and maintenance challenges in cold regions, while conventional removal methods are both energy-intensive and environmentally detrimental. This study proposes a passive Heat Pipe–Coupled Geothermal Snow Melting System (HP-GSMS) that harnesses shallow geothermal energy to maintain snow-free surfaces without external energy input. Using Fluent-based CFD simulations, the system’s thermal performance was evaluated under various working fluids (ammonia, carbon dioxide, water) and pipe materials (stainless steel, aluminum). A one-dimensional thermal resistance model validated the CFD results under ammonia–stainless steel conditions, predicting a heat flux of 358.6 W/m² compared to 361.0 W/m² from the simulation, with a deviation of only 0.66%, confirming model accuracy. Ammonia demonstrated superior phase-change efficiency, with the aluminum–ammonia configuration yielding the highest heat flux (up to 677 W/m²), surpassing typical snow-melting thresholds. Aluminum pipes enhanced radial heat conduction without compromising phase stability, while water exhibited poor phase-change performance and CO₂ showed moderate but stable behavior. Additionally, a dynamic three-node RC thermal network was employed to assess transient performance under realistic diurnal temperature variations, revealing surface heat fluxes ranging from 230 to 460 W/m², with a daily average of approximately 340 W/m². These findings demonstrate the HP-GSMS’s practical viability in cold climates and underscore the importance of selecting low-boiling-point fluids and high-conductivity materials for scalable, energy-efficient, and low-carbon snow-melting applications in urban infrastructure. Full article

Snow serves as a crucial water source for vegetation growth on the Mongolian Plateau, and its temporal and spatial variations exert profound influences on terrestrial vegetation phenology. In recent years, global climate change has led to significant changes in snow and vegetation start of growing season (SOS). Therefore, it is necessary to study the mechanism of snow cover on vegetation growth and changes on the Mongolian Plateau. The study found that the spatial snow cover fraction (SCF) of the Mongolian Plateau ranged from 50% to 60%, and the snow melt date (SMD) ranged from day of the year (DOY) 88 to 220, mainly concentrated on the northwest Mongolian Plateau mountainous areas. Using different SOS methods to calculate the vegetation SOS distribution map. Vegetation SOS occurs earlier in the eastern part compared to the western part of the Mongolian Plateau. In this study, we assessed spatiotemporal distribution characteristics of snow on the Mongolian Plateau over the period from 2001 to 2023. The results showed that the SOS of the Mongolian Plateau was mainly concentrated on DOY 71-186. The Cox survival analysis model system established SCF and SMD on vegetation SOS. The SCF standard coefficient is 0.06, and the SMD standard coefficient is 0.02. The SOS_NDVI coefficient is −0.15, and the SOS_NDGI coefficient is −0.096. The results showed that the vegetation SOS process exhibited differential response characteristics to snow driving factors. These research results also highlight the important role of snow in vegetation phenology and emphasize the importance of incorporating the unique effects of vegetation SOS on the Mongolian Plateau. Full article

With the rapid development of wireless communication, Wireless Local Area Networks (WLANs) are widely deployed in high-density environments. Ensuring fast handovers and optimal AP selection during device roaming is critical for maintaining network throughput and user experience. However, frequent mobility, high access density, and dynamic channel fluctuations complicate throughput prediction. To address this, we propose a method combining the Snow-Melting Optimizer (SMO) with decision tree regression models to optimize feature selection and model transmission opportunities (TXOP) and AP throughput. Experimental results show that the Extreme Gradient Boosting (XGBoost) model performs best, achieving high prediction accuracy for TXOP (MSE = 1.3746, R² = 0.9842) and AP throughput (MAE = 2.5071, R² = 0.9896). This approach effectively captures the nonlinear relationships between throughput and network factors in dense WLAN scenarios, demonstrating its potential for real-world applications. Full article

The water regime in Pakistan’s northern region has experienced significant changes regarding hydrological extremes like floods because of climate change. Coupling hydrological models with remote sensing data can be valuable for flow simulation in data-scarce regions. This study focused on simulating the snow- and glacier-melt runoff using the snowmelt runoff model (SRM) in the Gilgit and Kachura River Basins of the upper Indus basin (UIB). The SRM was applied by coupling it with in situ and improved cloud-free MODIS snow and glacier composite satellite data (MOYDGL06) to simulate the flow under current and future climate scenarios. The SRM showed significant results: the Nash–Sutcliffe coefficient (NSE) for the calibration and validation period was between 0.93 and 0.97, and the difference in volume (between the simulated and observed flow) was in the range of −1.5 to 2.8% for both catchments. The flow tends to increase by 0.3–10.8% for both regions (with a higher increase in Gilgit) under mid- and late-21st-century climate scenarios. The Gilgit Basin’s higher hydrological sensitivity to climate change, compared to the Kachura Basin, stems from its lower mean elevation, seasonal snow dominance, and greater temperature-induced melt exposure. This study concludes that the simple temperature-based models, such as the SRM, coupled with improved satellite snow cover data, are reliable in simulating the current and future flows from the data-scarce mountainous catchments of Pakistan. The outcomes are valuable and can be used to anticipate and lessen any threat of flooding to the local community and the environment under the changing climate. This study may support flood assessment and mapping models in future flood risk reduction plans. Full article

The pilot study is devoted to the assessment of both the accumulation and spatial distribution of microplastics in the snow cover of the Zhetysu region. The height of snow cover in the study area varied from 4.0 to 80.5 cm, with a volume of melt water ranging from 1.5 to 143 L. The analysis of 53 snow samples taken at different altitudes (from 350 to 1500 m above sea level) showed the presence of microplastics in 92.6% of samples in concentrations from 1 to 12 particles per square meter. In total, 170 microplastic particles were identified. The main polymers identified by Raman spectroscopy were polyethylene (PE), polypropylene (PP), and polystyrene (PS). These are typical components of plastic waste. The spatial distribution of microplastics showed elevated concentrations near settlements and roads. Notable contaminations were also recorded in remote mountainous areas, confirming the significant role of long-range atmospheric transport. Particles smaller than 0.5 mm dominated, having high aerodynamic mobility and capable of long-range atmospheric transport. Quantitative and qualitative characteristics of microplastics in snow cover have been realized for the first time both in Kazakhstan and in the Central Asian region, which contributes to the formation of primary ideas and future approaches about microplastic pollution in continental inland regions. The obtained results demonstrate the importance of atmospheric transport in the distribution of microplastics. They indicate the need for further monitoring and microplastic pollution analyses in Central Asia, taking into account its detection even in hard-to-reach and remote areas. Full article

The observations of atmospheric, oceanic, and sea ice data from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition were used to analyze the influence of snow redistribution and melt-pond processes on the evolution of sea ice thickness (SIT) in 2019 and 2020. To mitigate the effect of missing atmospheric observations from the time of the expedition, we used ERA5 atmospheric reanalysis along the MOSAiC drift trajectory to force the single-column sea ice model Icepack. SIT simulations from six combinations of two melt-pond schemes and three snow-redistribution configurations of Icepack were compared with observations and analyzed to investigate the sources of model–observation discrepancies. The three snow-redistribution configurations are the bulk scheme, the snwITDrdg scheme, and one simulation conducted without snow redistribution. The bulk scheme describes snow loss from level ice to leads and open water, and snwITDrdg describes wind-driven snow redistribution and compaction. The two melt-pond schemes are the TOPO scheme and the LVL scheme, which differ in the distribution of melt water. The results show that Icepack without snow redistribution simulates excessive snow–ice formation, resulting in an SIT thicker than that observed in spring. Applying snow-redistribution schemes in Icepack reduces snow–ice formation while enhancing the congelation rate. The bulk snow-redistribution scheme improves the SIT simulation for winter and spring, while the bias is large in simulations using the snwITDrdg scheme. During the summer, Icepack underestimates the sea ice surface albedo, resulting in an underestimation of SIT at the end of simulation. The simulations using the TOPO scheme are characterized by a more realistic melt-pond evolution compared to those using the LVL scheme, resulting in a smaller bias in SIT simulation. Full article

Snow-dominated watersheds experience a snowmelt-driven peak in streamflow that occurs in the spring or early summer. Some of the headwater basins in Colorado, USA have two or more peaks in streamflow, including the Uncompahgre River, a Colorado River tributary. The timing of peak streamflow is important for water management and recreational planning. As such, we examined the connection between the timing of each streamflow peak and readily available snow measurement information in the forest and alpine zones. These station data are the date of the initiation of snowmelt, 50% melt-out, and complete melt-out or the snow disappearance date (SDD). When it occurs before mid-June (14 of 20 years), the timing of the first peak is well correlated with the forested snow measurement station SDD. The second streamflow peak timing is well correlated with SDD from the alpine station except for very early (3 years) and very late (2 years) SDD. We also examine the spatial variability of snow disappearance and peak snow water equivalent (SWE) across the four seasonally snow-covered headwater sub-basins using a dataset from a coupled meteorological–snowpack model. Full article

Aiming at the problems of serious pavement temperature diseases, low efficiency and high loss of ice-breaking methods, high occupancy rate of waste tires and the low utilization rate and insufficient durability of rubber particles, this paper aims to improve the service level of roads and ensure the safety of winter pavements. A pavement material with high efficiency, low carbon and environmental friendliness for active snow melting and ice breaking is developed. Firstly, NaOH, NaClO and KH550 were used to optimize the treatment of rubber particles. The hydrophilic properties, surface morphology and phase composition of rubber particles before and after optimization were studied, and the optimal treatment method of rubber particles was determined. Then, the optimized rubber particles were used to replace the natural aggregate in the polyurethane gel mixture by the volume substitution method, and the optimum polyurethane gel dosages and molding and curing processes were determined. Finally, the influence law of the road performance of RPGM was compared and analyzed by means of an indoor test, and the ice-breaking effect of RPGM was explored. The results showed that the contact angles of rubber particles treated with three solutions were reduced by 22.5%, 30.2% and 36.7%, respectively. The surface energy was improved, the element types on the surface of rubber particles were reduced and the surface impurities were effectively removed. Among them, the improvement effect of the KH550 solution was the most significant. With the increase in rubber particle content from 0% to 15%, the dynamic stability of the mixture gradually increases, with a maximum increase of 23.5%. The maximum bending strain increases with the increase in its content. The residual stability increases first and then decreases with the increase in rubber particle content, and the increase ranges are 1.4%, 3.3% and 0.5%, respectively. The anti-scattering performance increases with the increase in rubber content, and an excessive amount will lead to an increase in the scattering loss rate, but it can still be maintained below 5%. The fatigue life of polyurethane gel mixtures with 0%, 5%, 10% and 15% rubber particles is 2.9 times, 3.8 times, 4.3 times and 4.0 times higher than that of the AC-13 asphalt mixture, respectively, showing excellent anti-fatigue performance. The friction coefficient of the mixture increases with an increase in the rubber particle content, which can be increased by 22.3% compared with the ordinary asphalt mixture. RPGM shows better de-icing performance than traditional asphalt mixtures, and with an increase in rubber particle content, the ice-breaking ability is effectively improved. When the thickness of the ice layer exceeds 9 mm, the ice-breaking ability of the mixture is significantly weakened. Mainly through the synergistic effect of stress coupling, thermal effect and interface failure, the bonding performance of the ice–pavement interface is weakened under the action of driving load cycle, and the ice layer is loosened, broken and peeled off, achieving efficient de-icing. Full article

Snow cover phenology (SCP) serves as a critical regulator of hydrological cycles and ecosystem stability across the Mongolian Plateau (MP). Despite its importance, the spatiotemporal patterns of SCP and their climatic drivers remain poorly quantified, constrained by persistent gaps in satellite snow cover observations. Leveraging a high-resolution (500 m) daily gap-filled Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover dataset combined with reanalysis climate datasets, we systematically quantified SCP dynamics and identified the dominant controls during the 2000–2022 hydrological years using trend analysis and ridge regression. Our results reveal a significant divergence in SCP parameters: snow end dates (D_e) advanced markedly across the entire plateau (0.29 days yr⁻¹, p < 0.01), accounting for 90.39% of SCP anomalies. In contrast, snow onset date (D_o) exhibited unnoticeable changes, explaining 9.58% of SCP changes. Attribution analysis demonstrates that 47.72% of D_e variability stems from increased net shortwave radiation (+0.38 Wm⁻² yr⁻¹) and rising temperatures (+0.06 °C yr⁻¹) during the melting season, with net shortwave radiation exerting stronger control (R² = 0.73) than temperature (R² = 0.63). This study establishes the first continuous, high-resolution SCP climatology for the MP, providing mechanistic insights into cryosphere–atmosphere interactions that inform adaptive water resource strategies for climate-vulnerable arid ecosystems in this region. Full article

Winter pavement maintenance faces challenges in balancing large-scale upkeep and driving safety, particularly regarding the application of active slow-release materials. This study proposes a gel-modified salt-storing ceramsite asphalt mixture to enhance ice-melting capabilities through controlled salt release. By replacing a conventional coarse aggregate with salt-storing ceramsite in SMA-10 graded mixtures (0–80% content), we systematically evaluate its mechanical performance and de-icing functionality. The experimental results demonstrate that 40% salt-storing ceramsite content optimizes high-temperature stability while maintaining acceptable low-temperature performance and water resistance. Microstructural analysis reveals that silicone–acrylic emulsion forms a hydrophobic film on ceramsite surfaces, enabling uniform salt distribution and sustained release. The optimal 10% gel modification achieves effective salt retention and controlled release through pore-structure regulation. These findings establish a 40–60% salt-storing ceramsite content range as the practical range for winter pavement applications, offering insights into the design of durable snow-melting asphalt surfaces. Full article

Snow cover constitutes a medium that can be used as a way of assessing air pollution. The chemical composition of snow layers from the same snowfall event reflects the composition of atmospheric aerosols and dry precipitates, depending on the properties of the adsorbing surface and prevailing weather conditions. Analyzing snow samples provides reliable insights into anthropogenic pollution accumulated in soil and groundwater of different land use type areas, as well as allows the evaluation of the degree and sources of environmental pollution. The aim of the research was to determine the distribution of polycyclic aromatic hydrocarbons in various sites of Zawoja village and identify their possible sources and factors influencing their differentiation. A total of 15 surface snow samples of the same thickness and snowfall origin were collected from different locations in the village in the winter of 2024. The samples were pre-concentrated by solid phase extraction and analyzed by gas chromatography—tandem mass spectrometry. The sampling set was invented, and the extraction procedure and analysis parameters were optimized. A spatial distribution map of PAHs was created. The contamination of ∑16PAHs varied from 710 to 2310 ng/L in melted snow with the highest concentrations detected in Zawoja Markowa by the border of the Babia Góra National Park, which is interpreted mainly as a result of the topographical setting. Medium molecular weight PAHs were the dominant fraction, which, combined with specific PAH ratios, indicate the combustion of biomass and coal as the main source of contamination. Full article

The assessment of the maximum snow-water equivalent in mountains is important for understanding the mechanism of their formation, as well as for hydrological calculations. The low density of the observation network and the high complexity of ground-based snow-measuring operations have led to the widespread use of remote methods to obtain such data. In this study, the maximum water reserve of the Uba River basin was calculated for the period of 2020–2023, based on data from the Sentinel-2 satellite regarding the position of the seasonal snow line, obtained using the temperature-based melt-index method. This study determined the snowmelt coefficients for the meteorological stations at Zmeinogorsk, Shemonaikha, and Ridder. Maps were constructed to show the distribution of the maximum snow-water equivalent in the Uba River basin. The spatial differentiation features of the snow cover were revealed, depending on the elevation, slope exposure, and distance from the watersheds. It was established that the altitudinal distribution of snow cover on the northern and southern macro-slopes of the ridges is asymmetric: in the western part of the basin, within the elevation range of 500–1200 m, the maximum water reserves of snow cover are greater on the southern slopes, but they become higher on the northern slopes above 1200 m. In the eastern part of the basin, they are always larger on the northern slopes. The greatest differences in the distribution of snow cover between the slopes occur near the watersheds. Full article

This study reviews the melting rate of anthropogenic snow patches formed as a result of cleaning the territory of Yuzhno-Sakhalinsk city from snow and collecting it in designated areas known as snow dumps. Snow patches persisted at absolute altitudes of less than 50 m in the summers during the period of 2010–2022, except in 2017. The positive factor was the ratio of the relatively small area occupied by the anthropogenic snow patch and its significant height at the beginning of the melting period. The detailed observations of anthropogenic snow patch growth and melting were conducted by the authors starting in the winter season of 2017–2018. The snow volume collected in snow dumps during the winter season in Yuzhno-Sakhalinsk can reach 3000 m³. That is why it is necessary to determine how the anthropogenic snow patch will loosen the water through the warm season. Special models of anthropogenic snow patch melting do not exist. So, the authors review the ability of four glacier and snow cover melting model applications for such objects. The contribution of various parameters affecting the snow path melting rate was also determined. The collected factual data allowed for the development of empirical snow patch melting models. The largest errors resulting in the usage of reviewed models are related to the beginning (April) and ending (September–October) of the melting periods. Full article