Search Results (149)

Climate change is reshaping hydrologic regimes in snow-dominated watersheds, with important implications for mine rock drainage quantity and contaminant mobilization. This study quantifies potential long-term changes in drainage quantity by coupling a previously published physics-informed machine learning model with a Monte Carlo framework driven by downscaled monthly climate projections from ClimateNA. The proposed methodology was applied to three drainage monitoring stations at a mine site in Western Canada to assess projected drainage responses over the 2011–2100 period. An ensemble of daily weather sequences was generated by sampling historical within-month variability and scaling the resulting series to match projected monthly climate statistics, which were then used as inputs for the drainage model. Trends were assessed using the Mann–Kendall test modified for serial correlation, and their magnitudes were summarized using the Theil–Sen slopes. The trend analysis results indicate scenario-dependent changes in annual drainage across stations, alongside consistent seasonal shifts toward higher spring (April–May) and lower early-summer (June–July) drainage. These patterns are consistent with earlier snowmelt and earlier snowpack depletion. Corresponding shifts in intra-annual flow timing suggest that a larger fraction of annual drainage occurs earlier in the year. Overall, these findings provide a physics-informed basis for changes in drainage quantity and for guiding monitoring, design, and mitigation strategies under a warming climate. Full article

Bolivia is home to approximately 20% of the tropical glaciers in South America, which are sensitive indicators of climate change and critical water resources. Glaciers in the Cordillera Real supply meltwater to Bolivia’s administrative capital, La Paz, making it important to accurately assess their evolution. This study reassesses the trajectory of glacier loss in the Cordillera Real between 1992 and 2024. We construct a time-evolving glacier inventory utilizing remote sensing data (Landsat) and techniques to limit the impact of ephemeral snow cover. Our inventory is at a temporal resolution (5- to 8-year spacing) that allows us to assess the trajectory of glacier loss using statistical models. Between 1992 and 2024, the Cordillera Real lost 103.67 ± 9.97 km² of glacierized area, representing a 42.0 ± 2.1% reduction. We find that glaciers in the Cordillera Real have been retreating at a constant absolute loss rate of 2.99 [2.32, 3.67] km² yr⁻¹ and a constant fractional loss rate of 1.6 [1.3, 1.9]% yr⁻¹, contrasting with past studies that suggest accelerating or decelerating loss rates. Our findings provide new insights into the current extent of glaciers in the Cordillera Real and their longevity. The time-evolving inventory is available for use in future studies on the evolution of glaciers in the Cordillera Real and the impacts of their continued loss. Full article

Aiming at the wind-blown snow disasters plaguing tunnel portals along the China-Tajikistan Highway Phase II Project, this study optimizes the protective parameters of wind deflectors through numerical simulation to improve the disaster prevention efficiency of tunnel portals in alpine mountainous areas. Three core control parameters of wind deflectors, namely horizontal distance from the tunnel portal (L), plate inclination angle (β), and top installation height (h), were selected as the research objects. Single-factor numerical simulation scenarios were designed for each parameter, and an L9(3³) orthogonal test was further adopted to formulate 9 groups of multi-parameter combination scenarios, with the snow phase volume fraction at 35 m on the leeward side of the tunnel portal set as the core evaluation index. A computational fluid dynamics (CFD) model was established to systematically investigate the influence laws of each parameter on the wind field structure and snow drift deposition characteristics at tunnel portals and clarify the flow field response rules under different parameter configurations. Single-factor simulation results show that the wind deflector exerts distinct regulatory effects on the wind-snow flow field with different parameter settings: when L = 6 m, the disturbance zone of the wind deflector precisely covers the main wind flow development area in front of the tunnel portal, which effectively lifts the main incoming flow path, compresses the recirculation zone (length reduced from 45.8 m to 22.3 m), and reduces the settlement of snow particles, achieving the optimal comprehensive prevention effect; when β = 60°, the leeward wind speed at the tunnel portal is significantly increased to 10–12 m/s (from below 10 m/s), which effectively promotes the transport of snow particles and mitigates the accumulation risk, being the optimal inclination angle; when h = 2 m, the wind speed on both the windward and leeward sides of the tunnel portal is significantly improved, and the snow accumulation risk at the portal reaches the minimum. Orthogonal test results further quantify the influence degree of each parameter on the snow prevention effect, revealing that the horizontal distance from the tunnel portal is the most significant influencing factor. The optimal parameter combination of the wind deflector is determined as L = 6 m, β = 60°, and h = 2 m. Under this optimal combination, the snow phase volume fraction at 35 m on the leeward side of the tunnel portal is 0.0505, a 12.3% reduction compared with the non-deflector condition; the high-concentration snow accumulation zone is shifted 25 m leeward, and the high-value snow phase volume fraction area (>0.06) disappears completely, which can effectively alleviate the adverse impact of wind-blown snow disasters on the normal operation of tunnel portals. The research results reveal the regulation mechanism of wind deflector parameters on the wind-snow flow field at alpine tunnel portals and determine the optimal protective parameter combination, which can provide important theoretical reference and technical support for the prevention and control of wind-blown snow disasters at tunnel portals in similar alpine mountainous areas. Full article

Global glacier models (GGMs) are effective tools for assessing changes in water resources in mountainous regions and studying glacier degradation. Moreover, with the rapid development and increasing complexity of Earth System Models (ESMs), the incorporation of mountain glaciation parametrizations into ESMs is only a matter of time. GGMs, being computationally efficient and physically well-founded, provide a solid basis for such parametrizations. In this study, we present a new global glacier model, IGRICE. Its dynamic core is based on the Oerlemans minimal model, and surface mass balance (SMB) is explicitly simulated, accounting for orographic precipitation, radiation redistribution on the glacier surface, turbulent heat fluxes, and snow cover evolution on ice. The model is tested on glaciers situated in climatically and topographically contrasting regions—the Caucasus and Svalbard—using observational data for validation. The model is forced with ERA5 reanalysis data and employs morphometric glacial and topographic parameters. The simulated components of the surface energy and mass balance, as well as glacier dynamics over the period of 1984–2021, are presented. The model results demonstrate good agreement with observations, with correlation coefficients for accumulation, ablation, and total SMB ranging from 0.6 to 0.9. The primary driver of glacier retreat in the Caucasus is identified as an increase in net shortwave radiation balance caused by reduced cloudiness and albedo. In contrast, rapid glacier degradation in Svalbard is linked to an increased fraction of liquid precipitation and an extended snow-free period, leading to a sharp decrease in albedo. Full article

Amid global climate change and extreme weather conditions, sudden dzud events in arid grassland regions inflict severe disasters on herders, livestock, transportation, and the economy. In particular, Mongolia experiences frequent dzud events in recent years, bringing devastating consequences. However, studies on the spatiotemporal distribution characteristics of snow cover during dzud events in Mongolia remain relatively scarce and fail to adequately explain the anomalous features and impacts of extreme snowfall. Therefore, this study examined the spatiotemporal distribution characteristics of snow in the five most severe dzud events in Mongolia from 2000 to 2024. We utilized the Normalized Difference Snow Index (NDSI) extraction method based on 500 m resolution MODIS10A1 data, with the results validated against 10 m resolution Sentinel-2 imagery. The study produces several interesting results: (1) Snow cover in Mongolia generally increases from south to north with rising terrain elevation. Although its interannual variation is highly unstable, a slight decreasing trend is observed over the past 25 years. (2) Significant regional differences form a fan-shaped snow distribution pattern centered around 45–52° N, with trend analysis indicating intensification in the west and weakening in the east, except for extreme weather events. (3) During dzud events, the snow cover fraction (SCF) generally exceeds the multi-year average, exhibiting a pronounced and abrupt rise, while snow cover and livestock mortality fluctuate in synchrony. By revealing the spatiotemporal distribution patterns of snow during dzud years in Mongolia, this research provides an evidence-based reference for the understanding of extreme winter climatic events and disaster risk reduction in arid grassland regions. Full article

Wildfire-induced disturbances to soil and vegetation can significantly impact streamflows for years, depending upon the degree of burn severity. Accurately predicting the effects of wildfire on streamflow at the watershed scale is essential for effective water budget management. This study presents a novel approach to generating a burn severity map on a small scale by integrating unmanned aerial vehicle (UAV)-based thermal imagery with Landsat-derived Differenced Normalized Burn Ratio (dNBR) and upscaling burned severity to the entire burned area. The approach was applied to the Thompson Ridge Fire perimeter, and the upscaled UAV-Landsat-based burn severity map achieved an overall accuracy of ~73% and a kappa coefficient of ~0.62 when compared with the Burned Area Emergency Response’s (BAER) fire product as a reference map, indicating moderate accuracy. We then tested the transferability of burn severity information to a Beaver River watershed by applying Random Forest models. Predictors included topography, spectral bands, vegetation indices, fuel, land cover, fire information, and soil properties. We calibrated and validated the Distributed Hydrology Soil Vegetation Model (DHSVM) against observed streamflow and Snow Water Equivalent (SWE) data within the Beaver River watershed and measured model performance using Nash–Sutcliffe Efficiency (NSE), Kling–Gupta Efficiency (KGE), and Percent Bias (PBIAS) metrics. We adjusted soil (maximum infiltration rate) and vegetation (fractional vegetation cover, snow interception efficiency, and leaf area index) parameters for the post-fire model setup and simulated streamflow for the post-fire years without vegetation regrowth. Streamflow simulations using the upscaled and transferred UAV-Landsat burn severity map and the Burned Area Emergency Response’s (BAER) fire product produced similar post-fire hydrologic responses, with annual average flows increasing under both approaches and the UAV-Landsat-based simulation yielding slightly lower values, by less than 6% compared to the BAER-based simulation. Our results demonstrate that the UAV-satellite integration method offers a cost- and time-effective method for generating a burn severity map, and when combined with the transferability method and hydrologic modeling, it provides a practical framework for predicting post-fire streamflow in both burned and unburned watersheds. 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

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

Remote sensing observations of snow-covered areas (SCA) are important for monitoring and modeling energy balances, hydrologic processes, and climate change. For an agricultural field, we produced 12 snow cover maps from UAS imagery during an approximately 3-week-long spring snowmelt period. SCA maps were used to characterize snow cover patterns, validate satellite snow cover products, translate satellite Normalized Difference Snow Index (NDSI) to fractional SCA (fSCA), and downscale satellite SCA observations. Compared to manually delineated SCA, the UAS SCA accuracy was 85%, with misclassifications due to shadows, ice, and patchy snow conditions. During snowmelt, UAS-derived maps of bare earth patches exhibited self-similarity, behaving as fractal objects over scales from 0.01 to 100 m². As a validation tool, the UAS-derived SCA showed that satellite snow cover observations accurately captured the fSCA evolution during snowmelt (R² = 0.93−0.98). A random forest satellite downscaling model, trained using 20 m Sentinel-2 NDSI observations and 20 cm vegetation and terrain features, produced realistic (>90% accuracy), high-resolution SCA maps. While similar to traditional Sentinel-2 SCA in most conditions, downscaling snow cover significantly improved performance during periods of patchy snow cover and produced more realistic bare patches. UAS optical sensing demonstrates the potential uses for high-resolution snow cover mapping and recommends future research avenues for using UAS SCA maps. Full article

Lake ice has a major impact on the functioning of lake ecosystems, the thermal and gas regimes of lakes, habitat conditions, socio-economic aspects of human life, local climate, etc. The multifaceted influence of lake ice makes it important to study its changes associated with global warming, including lake ice phenology, ice thickness, and the snow–ice fraction. This article presents a study of lake ice changes in different regions of Eurasia: the Arctic (Lake Imandra in the Murmansk region and Lake Kilpisjärvi in Finland), the temperate zone (six small and medium lakes in Karelia, Mozhaysk Reservoir in the Moscow region, and Lake Pääjärvi in Finland), the arid zone (Lake Ulansuhai in China), and the highlands (lakes Arpi and Sevan in Armenia). In the study regions, a statistically significant increase in winter air temperature has been recorded over the past few decades. The number of days with thaw (air temperature above 0 °C) has increased, while the number of days with severe frost (air temperature below −10 °C and −20 °C) has decreased. The share of liquid or mixed precipitation in winter increases most rapidly in the temperate zone. For two Finnish lakes, lakes Vendyurskoe and Vedlozero in Karelia, and Mozhaysk Reservoir, a decrease in the duration of the ice period was revealed, with later ice-on and earlier ice-off. The most dramatic change occurred in the large high-mountain Lake Sevan, where the water area has no longer been completely covered with ice every winter. In contrast, the small high-mountain Lake Arpi showed no significant changes in ice phenology over a 50-year period. Changes in the ice composition with an increase in the proportion of white ice and a decrease in the proportion of black ice have occurred in some lakes. In the temperate lakes Pääjärvi and Vendyurskoe, inverse dependences of the thickness of black ice on the number of days with thaw and frost in December–March for the first lake and on the amount of precipitation in the first month of ice for the second were observed. In the arid study region of China, due to the very little winter precipitation (usually less than 10 mm) only black ice occurs, and significant interannual variability in its thickness has been identified. Full article

High spatial resolution snow depth (SD) is crucial for hydrological, ecological, and disaster research. However, passive microwave SD product (10/25 km) is increasingly insufficient to meet contemporary requirements due to its coarse spatial resolution, particularly in heterogeneous alpine areas. In this study, we develop a superior SD downscaling algorithm based on the FT-Transformer (Feature Tokenizer + Transformer) model, termed FTSD. This algorithm fuses the latest calibrated enhanced resolution brightness temperature (CETB) (3.125/6.25 km) with daily cloud-free optical snow data (500 m), including snow cover fraction (SCF) and snow cover days (SCD). Developed and evaluated using 42,692 ground measurements across China from 2000 to 2020, FTSD demonstrated notable improvements in accuracy and spatial resolution of SD retrieval. Specifically, the RMSE of temporal and spatiotemporal independent validation for FTSD is 7.64 cm and 9.74 cm, respectively, indicating reliable generalizability and stability. Compared with the long-term series of SD in China (25 km, RMSE = 10.77 cm), FTSD (500 m, RMSE = 7.67 cm) provides superior accuracy, especially improved by 48% for deep snow (> 40 cm). Moreover, with the higher spatial resolution, FTSD effectively captures the SD’s spatial heterogeneity in the mountainous regions of China. When compared with downscaling algorithms utilizing the raw TB data and the traditional random forest model, the CETB data and FT-Transformer model optimize the RMSE by 10.08% and 4.84%, respectively, which demonstrates the superiority of FTSD regarding data sources and regression methods. Collectively, these results demonstrate that the innovative FTSD algorithm exhibits reliable performance for SD downscaling and has the potential to provide a robust data foundation for meteorological and environmental research. Full article

In this work, we present two datasets for specific areas located on the Alpine arc that can be exploited to monitor and understand water resource dynamics in mountain regions. The idea is to provide the reader with information about the different sources of water supply over five defined test areas over the South Tyrol (Italy) and Tyrol (Austria) areas in alpine environments. The snow cover fraction (SCF) and Soil Moisture Content (SMC) datasets are derived from machine learning algorithms based on remote sensing data. Both SCF and SMC products are characterized by a spatial resolution of 20 m and are provided for the period from October 2020 to May 2023 (SCF) and from October 2019 to September 2022 (SMC), respectively, covering winter seasons for SCF and spring–summer seasons for SMC. For SCF maps, the validation with very high-resolution images shows high correlation coefficients of around 0.9. The SMC products were originally produced with an algorithm validated at a global scale, but here, to obtain more insights into the specific alpine mountain environment, the values estimated from the maps are compared with ground measurements of automatic stations located at different altitudes and characterized by different aspects in the Val Mazia catchment in South Tyrol (Italy). In this case, an MAE between 0.05 and 0.08 and an unbiased RMSE between 0.05 and 0.09 m³·m⁻³ were achieved. The datasets presented can be used as input for hydrological models and to hydrologically characterize the study alpine area starting from different sources of information. Full article

Local surface-depressed areas in an urban microrelief are geochemical traps for sediments deposited at the surface. These sediments accumulate pollutants over space and time. The aim of this study was to estimate the total amount of surface sediment in residential areas of small towns with different industrial specialisations. Snow-dirt sludge, snow, and surface sediment samples were collected in towns of the Sverdlovsk region, Russia: Alapaevsk, Kachkanar, Serov, and Verkhnyaya Pyshma. Snow and snow-dirt sludge were collected in the cold season, and surface sediment was collected in the warm season. This study was carried out in 2024. The solid matter of the samples was divided by sieving into particle size fractions: dust (<0.1 mm), fine sand (0.1–1 mm), and coarse sand (1–3 mm). The method used to estimate the total amount of sediment took into account data on the concentration of solid matter in snow-dirt sludge, the volume of melt water, and the contribution of the dust fraction in surface sediment and residential areas. The concentration of solid matter in snow-dirt sludge was about the same in the three cities (up to 6.6 g/L), but differed significantly in Kachkanar (60 g/L). The total amount of surface sediment per unit area was about the same in the three towns (1.1–1.4 kg/m²), but differed significantly in Kachkanar (10.8 kg/m²). The contribution of the dust fraction to the total amount of sediment was estimated to be 10–20% in the cities. The total amount of surface deposited sediments in the residential areas of the small towns was 1.6 × 10⁷ t in Alapaevsk, 5.9 × 10⁷ t in Kachkanar, 1.7 × 10⁷ t in Serov, and 1.3 × 10⁷ t in Verkhnyaya Pyshma. The values obtained for the total amount of surface sediments characterise the contemporary sedimentation processes in residential areas and the environmental quality of small towns. Full article

Waste slag and rubber particles are commonly used to modify concrete, offering benefits such as reduced cement consumption and lower greenhouse gas emissions during cement production. In this study, these two environmentally friendly, sustainable waste materials were proposed for the preparation of mortar intended for snow-melting pavements. A series of experiments were conducted to evaluate the performance of the material and to determine whether its compressive and flexural strengths meet the requirements of pavement specifications. The mortar’s suitability for snow-melting pavements was assessed based on its thermal conductivity, impermeability, and freeze–thaw resistance. The results indicate that slag, when used in different volume fractions, can enhance the compressive and flexural strength of the mortar. Slag also provides excellent thermal conductivity, impermeability, and resistance to freeze–thaw cycles, contributing to the overall performance of snow-melting pavements. When the slag content was 20%, the performance was optimal, with the compressive strength and flexural strength reaching 58.5 MPa and 8.1 MPa, respectively. The strength loss rate under freeze–thaw cycles was 8.03%, the thermal conductivity reached 2.2895 W/(m * K), and the impermeability pressure value reached 0.5 MPa. Conversely, the addition of rubber particles was found to decrease the material’s mechanical and thermal properties. However, when used in small amounts, rubber particles improved the mortar’s impermeability and resistance to freeze–thaw cycles. When the rubber content was 5% by volume, the impermeability pressure value reached 0.5 MPa, which was 166.7% lower than that of ordinary cement mortar. Under freeze–thaw cycles, the strength loss rate of the test block with a rubber content of 25% volume fraction was 9.83% lower than that of ordinary cement mortar. Full article

Snow cover is a relevant component of the Earth’s climate system, influencing water supply, ecosystem health, and natural hazard management. This study aims to monitor daily snow cover in the Cantabrian Mountains using Sentinel-2, Landsat (5–8), and MODIS data processed in Google Earth Engine (GEE). The main purpose is to extract metrics on snow cover extent, duration, frequency, and trends. Key findings reveal significant spatial and temporal variability in Snow-Cover Days (SCDs) across the region. Over the past 23 years, there has been a notable overall decrease in snow-cover days (−0.26 days per year, and −0.92 days per year in areas with a significant trend). Altitudes between 1000–2000 m a.s.l. showed marked decreases. The analysis of Snow-Cover Fraction (SCF) indicates high interannual variability and records the highest values at the end of January and the beginning of February. The effectiveness of satellite data and GEE is highlighted in providing detailed, long-term snow cover analysis, despite some limitations in steep slopes, forests, and prolonged cloud-cover areas. These results underscore the capacity for continuous monitoring with satellite imagery, especially in areas with sparse snow observation networks, where studies could be enhanced with more localized studies or additional ground-based observations. Full article