Search Results (269)

When snow is present, the snow surface is the interface between the atmosphere and the Earth’s surface. The snowpack energy balance is dictated in part by snow surface roughness, which can be quite dynamic. At the Sierra Nevada ski resort in Spain, we measured several snow surface forms: natural, with the presence of dust, with the presence of sun cups, and groomed snow (tracked and between tracks). The snow surface was assessed in 2-dimensions from snow roughness boards and in 3-dimensions from iPad surface scanning to measure across resolutions. Both data collection methods yielded similar roughness estimates via random roughness (RR) and variogram analysis (scale break, SB, and fractal dimension, D) for each distinct surface, yet the roughness differences between the surfaces were substantial. The geometry-based aerodynamic roughness length ($z_0$) was computed for the iPad-scanned surfaces, yielding an order-of-magnitude variability in $z_0$. This produced an order-of-magnitude difference in modelled sublimation. This work can inform snow management at ski areas and reflects some of the snow-surface conditions encountered in a natural snowpack. Full article

Increasing temperature and decreasing precipitation threaten the extent, persistence, and dynamics of snow across spatial scales, particularly ephemeral snow in Mediterranean mountain regions. This study estimates ephemeral snow cover and snow water equivalent (SWE) in the Sierra de San Pedro Mártir, Baja California, Mexico, using open-access datasets and remote sensing. Camera trap images and limited in situ data were used to calibrate the normalized difference snow index (NDSI) for snow detection and to estimate SWE and topographic effects on SWE from 2002 to 2023, encompassing wet, dry, and normal years. The optimal NDSI threshold for snow detection was 6.4 for MODIS Terra and 5.3 for MODIS Aqua, substantially lower than thresholds commonly reported for seasonal snowpacks in forested regions. In wet years, snowfall contributed up to 20% of annual precipitation, compared with ~13% in dry years. In normal years, the average SWE is 70 mm (24% of annual precipitation). SWE increased by 30% (91 mm) during wet years and decreased by 21% (55 mm) during dry years. Eastness (aspect) was the only statistically significant topographic predictor of SWE for MTerra, with higher SWE values observed on west-facing slopes. This study provides the first quantitative assessment of ephemeral SWE dynamics in a Mexican Mediterranean mountain system and establishes a framework for monitoring marginal snowpacks under increasing climatic variability. Full article

Emerging contaminants (ECs) comprise diverse pollutant classes that are increasingly detected in remote environments due to their persistence and long-range transport potential. In cold regions, atmospheric cold-trapping processes favour their accumulation in high-altitude and high-latitude snow and ice, which act as sensitive archives and secondary sources of contamination. While previous studies have addressed individual environmental compartments (e.g., snowpack, glacier ice, meltwater), focusing on specific contaminant classes, a systematic review integrating the occurrence, behaviour and impacts of major EC groups in polar and alpine snow and ice is still lacking. To fill this gap, this work synthesised current knowledge on the environmental fate of three key EC categories in the cryosphere: metals and metalloids (MMs), industrial chemicals and by-products (ICBs), and pharmaceuticals and personal care products (PPCPs). PRISMA guidelines were accurately followed for research, which was based on a Google Scholar search combining keywords on cryospheric matrices (snow, firn, ice cores), geographical regions (Arctic, Antarctic, Alps, high mountains), and contaminant classes. Of 350 records initially identified, 300 met the eligibility criteria (post-industrial snow, firn, or ice cores studies) after excluding studies focused on aerosol or meltwater-only, method-focused papers, pre-industrial datasets, urban-only investigations, and duplicates. Risk of bias was qualitatively assessed through manual screening, evaluating matrix eligibility, temporal consistency, analytical methods, detection limits, and duplicate data, with particular attention to inconsistencies in ECs classification. Strict operational definitions were therefore applied to ensure methodological coherence. Concentration data were harmonised into a standardised database, and findings were synthesised through a structured narrative supported by tabulated datasets organised by matrix and site. Overall, the evidence indicates widespread occurrence of ECs in the global cryosphere, with spatial variability linked to emission sources, long-range transport pathways, and snow physicochemical properties. Climate-change-driven alterations of snow dynamics, glacier retreat and permafrost thaw are expected to modify partitioning equilibria and enhance the secondary release of legacy and contemporary contaminants. However, significant limitations persist, including geographical gaps, variability in analytical sensitivity, lack of long-term monitoring for certain EC classes, and inconsistencies in contaminant classification frameworks. Despite these constraints, the synthesis highlights consistent emerging patterns and underscores the need to strengthen existing environmental protocols to mitigate potential risks to ecosystems and human health. Full article

Accurate estimation of snowpack microwave backscatter is critical for retrieving key physical properties of snow, such as snow depth (SD) and snow water equivalent (SWE), typically modeled using radiative transfer models (RTM). Among the various sources of uncertainty in RTM simulations, snow–ground reflectivity—used as a boundary condition—plays a critical role in influencing the accuracy of simulated backscatter. This study leverages high-resolution X- and Ku-band synthetic aperture radar (SAR) backscatter aircraft measurements using SWESARR and SnowSAR from NASA’s SnowEx campaigns, co-located with in situ snow pit observations in Grand Mesa, Colorado, and uses a Bayesian MCMC parameter optimization model with RTM framework to estimate the key ground parameters such as surface roughness, moisture content, and specular-to-total reflectivity ratio (STRR) governing the estimation of the snow–ground reflectivity and quantify the uncertainties associated with them. At the X-band, increasing ground surface roughness reduced the simulated backscatter by ~1.5 dB across the tested range, increasing the STRR produced an additional ~1.0 dB decrease while the dielectric properties of the ground are highly sensitive to the moisture content of frozen soil, and increasing the moisture content even by 2% increased the backscatter by 2–3 dB. The retrieval sensitivity to the STRR is minimized in the 0.6–0.7 range and it can be fixed at 0.65 without having discernible impact. The Bayesian inversion reveals that the extreme parameter values act as diagnostic indicators of unmodeled complexity rather than retrieval failures, with representativeness error often dominating over instrument noise. The study provides a robust methodology for the estimation of the snow–ground backscatter boundary condition for forward modeling, ultimately aiding SWE and SD retrieval from active microwave observations. While this study relied on Grand Mesa, the framework developed here is general and, along with the model uncertainty, is directly transferable and broadly applicable to other snow-dominated mountain regions where active microwave observations can be used for snowpack monitoring. Full article

In this work, we present the Snow Depth and Snow Water Equivalent Dataset for specific areas located in the East Kazakhstan Region that can be exploited to monitor and understand water resource dynamics in mountain regions. The present dataset represents a georeferenced collection of snow depth, snow density, and derived snow water equivalent (SWE) measurements obtained through manual snow surveys. Snow survey observations were conducted during field campaigns in the East Kazakhstan Region during the period of maximum snow accumulation from 27 February to 6 March 2025. Snow survey sites were selected to maximize coverage of diverse landscape settings and snow accumulation conditions. In total, 111 snow survey sites were established across the East Kazakhstan Region, and 2331 snow depth measurements and 555 snow density measurements were collected. In post-field (laboratory) processing, snow water equivalent (SWE) was calculated for all snow survey sites based on measured snow depth and snow density values. Full article

The snowpack in the European Alps is declining due to global warming, which affects both the amount of seasonal snow and the timing of accumulation and melt. As the European Alps is the largest winter tourism destination in the world by revenue, this decline in natural snow poses an existential threat to the sector. Several smaller ski areas have closed permanently since 1980, and all Alpine regions face rising costs due to an increasing reliance on snowmaking. Professional winter sports are also affected, with several canceled events in recent years due to unsuitable snow conditions. In this study, we present the first remote sensing-based assessment of long-term snow reliability for winter tourism in the European Alps. Using snowline elevation (SLE) data derived from Landsat observations from 1985 to 2024, combined with OpenStreetMap ski infrastructure data and digital elevation models, we quantified the monthly snow coverage of ski area segments across 43 Alpine basins. Theil–Sen trends and Mann–Kendall significances were calculated for the full season and for three subseasons, with quality checks applied to guarantee sufficient data coverage. The results show predominantly negative trends across all seasons, with the strongest declines occurring in the late season. In this period, 97.8% of all downhill ski areas and 99.5% of the cross-country ski areas for which a trend was derived exhibited negative trends. For the full season, the corresponding shares were 94% for downhill ski areas and 99.2% for cross-country ski areas. In addition, areas located at the geographical edges of the European Alps showed more pronounced negative trends compared with the core regions. These findings align with previous studies on the subject and highlight the ongoing shortening of natural snow seasons and thus the increased challenges for the winter tourism sector in the Alps. Full article

Glide-snow avalanches pose a major challenge for operational forecasting and local avalanche authorities. Although their key prerequisite, a moist interface between the snowpack and smooth ground, is well known, predicting the timing of glide-snow avalanches remains difficult. We analyzed five seasons of avalanche monitoring data in the Planneralm area of Styria, Austria. Glide-snow avalanche activity in the study area follows typical temporal patterns, with the highest release probability in the early afternoon and peak activity from mid-March to mid-April. Using meteorological data and avalanche observations as input, we trained machine-learning models to predict hours with glide-snow avalanche release. The most significant predictors were the mean air temperature of the preceding 48h, the day of the winter season, the hour of the day, and the decrease in snow height. The combination of those variables suggests a longer-term predisposition toward glide-snow avalanche release, as well as short-term driving factors. Our decision tree model correctly identified the vast majority of avalanche hours (recall 90%) at the cost of a moderate false alarm rate (15%). Our model could support operational glide-snow avalanche forecasting by identifying hours with elevated glide-snow potential that warrant increased attention and may require warnings or temporary closures by local authorities. Full article

Accurate snowmelt runoff prediction is critical for water resource management in mountainous regions where seasonal snowpack constitutes the dominant water supply. This study demonstrates operational application of the degree-day-based Snowmelt Runoff Model (SRM) integrated with Geographic Information Systems (GIS) and multi-platform remote sensing for discharge forecasting in the Kirkgoze Basin (242.7 km², 1823–3140 m elevation), Eastern Anatolia, Turkey. Three automatic weather stations spanning 872 m elevation gradient provided meteorological forcing, while MODIS MOD10A2 8-day composite products supplied operational snow cover observations validated against Landsat-5/7 (30 m resolution, 87.3% agreement, Kappa = 0.73) and synthetic aperture radar imagery (RADARSAT-1 C-band, ALOS-PALSAR L-band). Uncalibrated model performance was modest (R² = 0.384, volumetric difference = 29.78%), demonstrating necessity of site-specific calibration. Systematic adjustment of snowmelt and rainfall runoff coefficients yielded excellent calibrated performance for 2009 melt season: R² = 0.8606, correlation coefficient R = 0.927, Nash–Sutcliffe efficiency = 0.854, and volumetric difference = 3.35%. Enhanced temperature lapse rate (0.75 °C/100 m vs. standard 0.65 °C/100 m) reflected severe continental climate. Multiple linear regression analysis identified temperature, snow-covered area, snow water equivalent, and calibrated runoff coefficients as significant discharge predictors (R² = 0.881). Results confirm SRM’s operational feasibility for seasonal forecasting and flood warning in data-scarce snow-dominated basins, with modest requirements (daily temperature, precipitation, and satellite snow cover) aligning with operational monitoring capabilities. The methodology provides a transferable framework for regional water resource management in climatically vulnerable mountain environments where snowmelt supports agriculture, hydropower, and municipal supply. Full article

Recent studies have shown that lidar observations from the Ice, Clouds, and Land Elevation Satellite-2 (ICESat-2) enable seasonal snow depth retrieval over land through two primary approaches. The snow-on–off method estimates snow depth by differencing surface elevations acquired during snow-covered and snow-free periods, while the pathlength method derives it from multiple-scattering photon distributions within the snowpack. In this study, we cross-validate ICESat-2-derived surface elevations and snow depths against in situ measurements from SnowEx field campaigns. ICESat-2 surface elevations agree closely with SnowEx data, which we consider closest to the truth, achieving centimeter-level accuracy (e.g., 1 cm) over flat, sparsely vegetated terrain, with larger biases in vegetated and steep areas. Snow depth estimates from both methods show comparable performance in the tundra area, with typical errors on the order of tens of centimeters; however, in vegetated or steep terrain, the pathlength method yields more reliable snow depth results, being less affected by slope and vegetation than the snow-on–off method. These findings show that ICESat-2 is a reliable tool for measuring snow depth from space. Full article

Accurate estimation of young forest height is essential for assessing the carbon sequestration potential of vast Siberian boreal forests recovering from wildfires. Satellite radar interferometry, particularly PolInSAR, is a promising tool for this task. However, its application in winter conditions and over sparse young forests remains underexplored. This study proposes a novel method for estimating the height of sparse young pine (Pinus sylvestris) stands using fully polarimetric bistatic TerraSAR-X/TanDEM-X data acquired in winter. The method is based on an analysis of the multimodal distribution of the unwrapped interferometric phase of the surface scattering component, which was isolated via PolInSAR decomposition. We hypothesize that the phase centers correspond to the snow-covered ground (located between tree groups) and the rough surface formed by the upper layer of branches and needles (of the tree groups). The results demonstrate that the difference between the dominant modes of the surface scattering phase distribution correlates with the height of young trees. However, the measurable height difference is limited by the interferometric height of ambiguity. Furthermore, a temporal analysis of the phase and meteorological data revealed a strong correlation between sudden phase shifts and daytime temperature rises around 0 °C. This is interpreted as the formation of a layered snowpack structure with a dense ice crust. This study confirms the potential of X-band PolInSAR for monitoring the structure of young Siberian forests in winter but also highlights a significant limitation: the critical impact of snowpack metamorphism, particularly melt-freeze cycles, on the interferometric phase. The proposed method is only applicable to certain forest regeneration stages where tree height does not exceed the ambiguity limit and snow conditions are stable. Full article

Rain-on-snow (ROS) events profoundly influence mixed rain–snow flooding and the water resource cycle. However, current research regarding ROS events remains predominantly reliant on existing datasets, lacking detailed controlled experiments under variable conditions. This study employed control variables and an orthogonal experimental design to conduct laboratory-controlled experiments simulating ROS events with different temperatures, rainfall intensities, and rainfall durations. Observations and analyses were performed on the snowmelt volumes during and after events. The results indicate that ROS events significantly accelerate snowmelt rates and increase total snowmelt volume. Under low-intensity ROS, snowmelt volume exhibits greater sensitivity to temperature changes. A temperature threshold exists between 2 °C and 6 °C; beyond this threshold, the melting rate accelerates and ablation volume increases. Under high-intensity ROS, rainwater becomes the dominant factor driving snowpack ablation. When rainfall intensity exceeds 60 mm·h⁻¹, it triggers a sharp increase in snowmelt volume. Concurrently, following an ROS event, snowpacks subjected to low-intensity rainfall exhibit a stronger rainwater retention capacity, an effect that becomes more pronounced at lower temperatures. Additionally, snowmelt volume increases with prolonged rainfall duration, with the increment in snowmelt volume attributable to extended rainfall time being greater under weaker rainfall intensities. These findings provide a scientific reference for better understanding ROS-related disasters mechanisms. Full article

Wet-snow avalanches constitute a major geomorphic hazard in southeastern Tibet, where warm, humid climatic conditions and a steep, high-relief terrain generate failure mechanisms that are distinct from those in cold, dry snow environments. This study investigates the snowpack conditions underlying avalanche initiation in this region by integrating UAV-based multi-sensor surveys with field validation. Ground-penetrating radar (GPR), infrared thermography, and optical imaging were employed to characterize snow depth, stratigraphy, liquid water content (LWC), snow water equivalent (SWE), and surface temperature across an inaccessible avalanche channel. Calibration at representative wet-snow sites established an appropriate LWC inversion model and clarified the dielectric properties of avalanche-prone snow. Results revealed SWE up to 1092.98 mm and LWC exceeding 13.8%, well above the critical thresholds for wet-snow instability, alongside near-isothermal profiles and weak bonding at the snow–ground interface. Stratigraphic and UAV-based observations consistently showed poorly bonded, water-saturated snow layers with ice lenses. These findings provide new insights into the hydro-thermal controls of wet-snow avalanche release under monsoonal influence and demonstrate the value of UAV-based surveys for advancing the monitoring and early warning of snow-related hazards in high-relief mountain systems. Full article

A shallow, seasonal snowpack is rarely homogeneous in depth, layer characteristics, or surface structure throughout an entire winter. Aerodynamic roughness length ($z_0$) is typically considered a static parameter within hydrologic and atmospheric models. Here, we present observations showing $z_0$ as a dynamic variable that is a function of snow depth ($d_s$). This has a significant impact on sublimation modeling, especially for shallow snowpacks. Terrestrial LiDAR data were collected at nine different study sites in northwest Colorado from the 2019 to 2020 winter season to measure the spatial and temporal variability of the snowpack surface. These data were used to estimate the geometric $z_0$ from 91 site visits. Values of $z_0$ decrease during initial snow accumulation, as the snow conforms to the underlying terrain. Once the snowpack is sufficiently deep, which depends on the height of the ground surface roughness features, the surface becomes more uniform. As melt begins, $z_0$ increases, when the snow surface becomes more irregular. The correlation value of $z_0$ was altered by human disturbance at several of the sites. The $z_0$ versus $d_s$ correlation was almost constant, regardless of the initial roughness conditions that only affected the initial $z_0$. Full article

We present a stochastic method for classifying high-elevation coniferous forest coverage that includes an uncertainty estimate using Landsat images. We evaluate trends in coniferous coverage from 1986 to 2024 in a sub-basin of the Great Salt Lake basin in the western United States This work was completed before the recent release of the extended National Land Cover Database (NLCD) data, so we use the 9 years of NLCD data previously available over the period from 2001 to 2021 for training and validation. We perform 100 draws of 5130 data points each using stratified sampling from the paired NLCD and Landsat data to generate 100 Random Forest Models. Even though extended NLCD data are available, our model is unique as it is trained on high elevation dense coniferous stands and does not classify wester pinyon (Pinus edulis) or Utah juniper (Juniperus osteosperma) shrub trees as “coniferous”. We apply these models, implemented in Google Earth Engine, to the nearly 40-year Landsat dataset to stochastically classify coniferous forest extent to support trend analysis with uncertainty. Model accuracy for most years is better than 94%, comparable to published NLCD accuracy, though several years had significantly worse results. Coniferous area standard deviations for any given year ranged from 0.379% to 1.17% for 100 realizations. A linear fit from 1985 to 2024 shows an increase of 65% in coniferous coverage over 38 years, though there is variation around the trend. The method can be adapted for other specialized land cover categories and sensors, facilitating long-term environmental monitoring and management while providing uncertainty estimates. The findings support ongoing research forest management impacts on snowpack and water infiltration, as increased coniferous coverage of dense fir and spruce increases interception and sublimation, decreasing infiltration and runoff. NLCD data cannot easily be used for this work in the west, as the pinyon (Pinus edulis) and juniper (Juniperus osteosperma) forests are classified as coniferous, but have much lower impact on interception and sublimation. Full article

After heavy snowfall in the Uinta Basin, Utah, elevated surface ozone occurs if a cold-air pool persists and traps emissions from oil and gas industry operations. Sunlight and actinic flux from a high-albedo snowpack drive ozone buildup via photolysis. Snow coverage is paramount in initiating the cold pool and driving ozone generation. Its depth is critical for predicting ozone concentrations. The Basin’s location leeward of the Wasatch Mountains provides conditions for a precipitation shadow, where sinking air suppresses snowfall. We analyzed multiple years of ground-based snow depth measurements, surface ozone data, and meteorological observations; we found that ozone levels track with snow coverage, but diagnosing a shadow effect (and any impact on ozone levels) was difficult due to sparse, noisy data. The uncertainty in linking snowfall variation to ozone levels hinders forecast quality in, e.g., machine-learning training. We highlight the importance of a better understanding of regional variation when issuing outlooks to protect the local economy and health. A wider sampling of snow depth across the Basin would benefit operational forecasters and, likely, predictive skill. Full article