Search Results (36)

The Alborz Mountain range, serving as the strategic water tower of the Iranian Plateau, is experiencing the accelerating impacts of climate change. Given the critical role of snow reserves in this region for water security, understanding the mechanisms of snow degradation in response to warming is essential. Aiming to investigate the divergent responses of snow cover and snow depth to extreme temperature indices, this study analyzes a 23-year time series (2001–2023) of ERA5-Land data and MODIS imagery across 11 elevation bands. To this end, trends and correlations among the Warm Spell Duration Index (WSDI), the Percentage of Warm Days (TX90p), the Normalized Difference Snow Index (NDSI), and Average Snow Depth (ASD) were assessed using the Modified Mann–Kendall (MMK) test, Generalized Linear Modeling (GLM), and Spearman’s rank correlation. The findings reveal elevational heterogeneity in the snow regime of the Alborz. Notably, the decline in spatial snow cover (NDSI) is primarily concentrated in the mid-elevation transition zone (2000 to 3000 m), whereas the reduction in snow depth (ASD) is a widespread phenomenon, observed even at high altitudes above 4000 m. A key innovation of this research is demonstrating the dominant role of heat frequency over heat duration; GLM results indicate that the TX90p index (frequency of warm days) has a much stronger negative correlation with the degradation of snow resources than WSDI. These results confirm the transition of the Alborz hydrological system toward instability, the upward shift in the snowline in the transition zone, and the invisible thinning of the snowpack at higher elevations. Full article

Glaciers in the Canadian Arctic Archipelago (CAA) contribute significantly to sea-level rise, yet sparse in situ data limit regional climate assessments. This study presents the first decadal (2013–2024) satellite-derived time series of late-summer snowline altitude (SLA) for six CAA glaciers, utilising 9920 Landsat 8/9 and Sentinel-2 scenes. Glacier surface cover types (snow and bare ice) were mapped via machine learning, and SLA was extracted using elevation-binning and Snow-Elevation Histogram Analysis (SEHA). Elevation data were obtained from ArcticDEM v3; positive degree days (PDD) from Eureka, Pond Inlet, and Pangnirtung were used to characterize melt-season forcing. Satellite-derived SLA was validated against equilibrium-line altitude (ELA) observations from White Glacier. All glaciers exhibit a characteristic seasonal SCA cycle: maximum extent in June, minimum in August, and partial recovery in September, with extreme anomalies in 2020. Annual peak SLA correlates positively with summer warmth; sensitivities to PDD were 2.56, 0.67, and 0.83 m (°C d)⁻¹ for White, Highway, and Turner glaciers, respectively. Hypsometry strongly modulates climatic sensitivity: glaciers with limited high-elevation area (e.g., BylotD20s, Turner) frequently lose their accumulation zones in warm years. At White Glacier, SLA replicates interannual ELA variability with high correlation and lower error using the elevation-bin method (mean bias +53 m; RMSE 177 m) compared with SEHA (+165 m; 339 m). Meteorological records indicate significant summer and winter warming at Eureka, with increasing PDD; precipitation trends are spatially variable. A regionally calibrated, quality-assured elevation-bin method produces objective and transferable SLA time series, suitable for ELA estimation in data-sparse Arctic settings. The SLA–PDD relationship and hypsometry-dependent responses highlight increasing stress on accumulation zones under continued warming. Reporting SLA uncertainty and image quality, alongside expanded field observations, will enhance Arctic-wide glacier monitoring. 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

Understanding long-term snow cover dynamics is essential in mountain regions with limited meteorological or in situ observations. This study examines seasonal snow cover evolution across the Romanian Carpathians (2000–2025) using daily MODIS/Terra MOD10A1 Cloud-Gap-Filled data at 500 m resolution. Snow-covered pixels were identified using an NDSI ≥ 40 threshold, and snow cover duration (SCD), snow onset date (SOD), and snow end date (SED) were analyzed in relation to elevation and aspect from the FABDEM, complemented by snow-covered area (SCA) and snowline elevation (SLE) metrics. Across the entire range, the snow season shortens mainly due to later onset (+0.28 days/year) and earlier melt (−0.78 days/year), resulting in an SCD decrease of −1.14 days/year. High-elevation (>2000 m) areas show only small changes (SCD: −0.13 days/year; SOD: +0.46 days/year; SED: +0.32 days/year), while the strongest reductions occur at low and mid elevations, where snow persistence is most sensitive to warming; consistent declines in seasonal SCA and a pronounced monthly SLE cycle further document the spatial expression of this variability. Uncertainty was assessed by comparison with station-based snow cover duration (n = 230 station-years), indicating strong agreement (r = 0.95) with a modest negative bias (median: −8 days) and a mean absolute error (MAE) of 16.7 days. Climate correlations highlight air temperature as the dominant covariate of interannual snow-phenology variability, whereas precipitation associations are weaker. Overall, these shifts in snow phenology highlight increasing instability of the Carpathian snow regime and emphasize the value of long-term MODIS observations for tracking cryospheric change in a warming southeastern European mountain system. Full article

Accurate equilibrium-line altitude (ELA) estimates are a valuable proxy for evaluating glacier mass balance conditions and interpreting climate-driven change in the Canadian high Arctic, where sustained in situ observations are limited. A scalable remote-sensing framework is evaluated to extract the snow cover ratio (SCR) and snowline altitude (SLA) on White Glacier (Axel Heiberg Island, Nunavut) and to assess the agreement with in situ ELA measurements. Ten-metre Sentinel-2 imagery (2019–2024) is processed with a hybrid pipeline comprising the principal component analysis (PCA) of four bands (B2, B3, B4, and B8), unsupervised K-means for pseudo-label generation, and a Random Forest (RF) classifier for snow/ice/ground mapping. SLA is defined based on the date of seasonal minimum SCR using (i) a snowline pixel elevation histogram (SPEH; mode) and (ii) elevation binning with SCR thresholds (0.5 and 0.8). Validation against field-derived ELAs (2019–2023) is performed; formal SLA precision from DEM and binning is quantified ($\pm 4.7$ m), and associations with positive degree days (PDDs) at Eureka are examined. The RF classifier reproduces the spectral clustering structure with >99.9% fidelity. Elevation binning at $\mathrm{SCR}\ge 0.8$ yields SLAs closely matching field ELAs (Pearson $r=0.994$, $p=0.0006$; RMSE $=30$ m), whereas SPEH and lower-threshold binning are less accurate. Interannual variability is pronounced as follows: minimum SCR spans 0.46–0.76 and co-varies with SLA; correlations with PDDs are positive but modest. Results indicate that high-threshold elevation-bin filtering with machine learning provides a reliable proxy for ELA in clean-ice settings, with potential transferability to other data-sparse Arctic sites, while underscoring the importance of image timing and mixed-pixel effects in residual SLA–ELA differences. Full article

Snow cover, as a critical component of the cryosphere, serves as a vital water resource for arid regions in Northwest China. The Qilian Mountains (QLM), situated on the northeastern margin of the Tibetan Plateau, function as an important ecological barrier and water conservation area in western China. This study presents the first high-resolution historical snow cover product developed specifically for the QLM, utilizing a multi-level snow classification algorithm tailored to the complex topography of the region. By employing Landsat satellite data from 1986–2024, we constructed a comprehensive 39-year snow cover dataset at a resolution of 30 m. A dual adaptive cloud masking strategy and spatial interpolation techniques were employed to effectively address cloud contamination and data gaps prevalent in mountainous regions. The spatiotemporal characteristics and driving mechanisms of snow cover changes in the QLM were systematically analyzed using Sen–Theil trend analysis and Mann–Kendall tests. The results reveal the following: (1) The mean annual snow cover extent in the QLM was 15.73% during 1986–2024, exhibiting a slight declining trend (−0.046% yr⁻¹), though statistically insignificant (p = 0.215); (2) The snowline showed significant upward migration, with mean elevation and minimum elevation rising at rates of 3.98 m yr⁻¹ and 2.81 m yr⁻¹, respectively; (3) Elevation-dependent variations were observed, with significant snow cover decline in high-altitude (>5000 m) and low-altitude (2000–3500 m) regions, while mid-altitude areas remained relatively stable; (4) Comparison with MODIS data demonstrated good correlation (r = 0.828) but revealed systematic differences (RMSE = 12.88%), with MODIS showing underestimation in mountainous environments (Bias: −8.06%). This study elucidates the complex response mechanisms of the QLM snow system under global warming, providing scientific evidence for regional water resource management and climate change adaptation strategies. Full article

The Alpine Periglacial Weathering Zone (APWZ) is a critical transitional belt between alpine vegetation and glaciers, and a highly sensitive region to climate change. Its dynamic variations profoundly reflect the surface environment’s response to climatic shifts. Taking Gongga Mountain as the study area, this study utilizes summer Landsat imagery from 1986 to 2024 and constructs a remote sensing method based on NDVI and NDSI indices using the Otsu thresholding algorithm on the Google Earth Engine platform to automatically extract the positions of the upper limit of vegetation and the snowline. Results show that over the past four decades, the APWZ in Gongga Mountain has exhibited a continuous upward shift, with the mean elevation rising from 4101 m to 4575 m. The upper limit of vegetation advanced at an average rate of 17.43 m/a, significantly faster than the snowline shift (3.9 m/a). The APWZ also experienced substantial areal shrinkage, with an average annual reduction of approximately 13.84 km², highlighting the differential responses of various surface cover types to warming. Spatially, the most pronounced changes occurred in high-elevation zones (4200–4700 m), moderate slopes (25–33°), and sun-facing aspects (east, southeast, and south slopes), reflecting a typical climate–topography coupled driving mechanism. In the upper APWZ, glacier retreat has intensified weathering and increased debris accumulation, while the newly formed vegetation zone in the lower APWZ remains structurally fragile and unstable. Under extreme climatic disturbances, this setting is prone to triggering chain-type hazards such as landslides and debris flows. These findings enhance our capacity to monitor alpine ecological boundary changes and identify associated disaster risks, providing scientific support for managing climate-sensitive mountainous regions. Full article

Understanding glacier and climate variations since pre-Industrial times is crucial for evaluating the present-day glacier response to climate change. Here, we focus on twelve small glaciers (≤2.0 km²) on both the northern and southern slopes of the Greater Caucasus to assess post-Little Ice Age glacier–climate fluctuations in this region. We reconstructed the Little Ice Age glacier extent using a manual detection method based on moraines. More recent glacier fluctuations were reconstructed using historical topographical maps and satellite imagery. Digital elevation models were used to estimate the topographic characteristics of glaciers. We also used the accumulation area ratio (AAR) method and a regional temperature lapse rate to reconstruct glacier snowlines and corresponding temperatures since the 1820s. The results show that all selected glaciers have experienced area loss, terminus retreat, and equilibrium line altitude (ELA) uplift over the last 200 years. The total area of the glaciers has decreased from 19.1 ± 0.9 km² in the 1820s to 9.7 ± 0.2 km² in 2020, representing a −49.2% loss, with an average annual reduction of −0.25%. The most dramatic reduction occurred between the 1960s and 2020, when the glacier area shrank by −35.5% or −0.59% yr⁻¹. The average terminus retreat for all selected glaciers was −1278 m (−6.4 m/yr⁻¹) during the last 200 years, while the average retreat over the past 60 years was −576 m (−9.6 m/yr⁻¹). AAR-based (0.6 ± 0.05) ELA reconstructions from all twelve glaciers suggest that the average ELA in the 1820s was about 180 m lower (3245 ± 50 m a.s.l.) than today (3425 ± 50 m a.s.l.), corresponding to surface air temperatures <1.1 ± 0.3 °C than today (2001–2020). The largest warming occurred between the 1960s and today, when snowlines rose by 105 m and air temperatures increased by <0.6 ± 0.3 °C. This study represents a first attempt at using glacier evidence to estimate climate changes in the Caucasus region since the Little Ice Age, and it can be used as a baseline for future studies. Full article

So far, most of the about 5700 exoplanets have been discovered mainly with radial velocity and transit methods. These techniques are sensitive to planets in close orbits, not being able to probearge star–planet separations. μ-lensing is the indirect method that allows us to probe the planetary systems at the snow-line and beyond, but it is not a repeatable observation. On the contrary, direct imaging (DI) allows for the detection and characterization ofow mass companions at wide separation (≤5–6 au). The main challenge of DI is that a typical planet–star contrast ranges from 10⁻⁶, for a young Jupiter in emittedight, to 10⁻⁹ for Earth in reflectedight. In theast two decades, aot of efforts have been dedicated to combiningarge (D ≥ 5 m) telescopes (to reduce the impact of diffraction) with coronagraphs and high-order adaptive optics (to correct phase errors induced by atmospheric turbulence), with sophisticated image post-processing, to reach such a contrast between the star and the planet in order to detect and characterize cooler and closer companions to nearby stars. Building on the first pioneering instrumentation, the second generation of high-contrast imagers, SPHERE, GPI, and SCExAO, allowed us to probe hundreds of stars (e.g., 500–600 stars using SHINE and GPIES), contributing to a better understanding of the demography and the occurrence of planetary systems. The DI offers a possible clear vision for studying the formation and physical properties of gas giant planets and brown dwarfs, and the future DI (space and ground-based) instruments with deeper detectionimits will enhance this vision. In this paper, we briefly review the methods, the instruments, the main sample of targeted stars, the remarkable results, and the perspective of this rising technique. Full article

Climate change has caused substantial shifts in species’ ranges and vegetation distributions in local areas of the Himalayas. However, the spatial patterns and dynamic changes of the vegetation lines in the Himalayas remain poorly understood due to the lack of comprehensive vegetation line dataset. This study developed a method to identify vegetation lines by combining the Canny edge detection algorithm with elevation parameters and produced comprehensive vegetation line datasets with 30 m resolution in the Himalayas. First, the Modified Soil-Adjusted Vegetation Index (MSAVI) was applied to indicate vegetation presence. The image was then smoothed by filling (or removing) small non-vegetated (or vegetated) patches scattered within vegetated (or unvegetated) areas. Subsequently, the Canny edge detection algorithm was applied to identify vegetation edge pixels, and elevation differences were utilized to determine the upper edges of the vegetation. Finally, Gaussian function-based thresholds were used across 24 sub-basins to determine the vegetation lines. Field surveys and visual interpretations demonstrated that this method can effectively and accurately identify vegetation lines in the Himalayas. The R² was 0.99, 0.93, and 0.98, respectively, compared with the vegetation line verification points obtained through three different ways. The mean absolute errors were 11.07 m, 29.35 m, and 13.99 m, respectively. Across the Himalayas, vegetation line elevations ranged from 4125 m to 5423 m (5th to 95th percentile), showing a trend of increasing and then decreasing from southeast to northwest. This pattern closely parallels the physics-driven snowline. The method proposed in this study enhances the toolkit for identifying vegetation lines across mountainous regions. Additionally, it provides a foundation for evaluating the responses of mountain vegetation to climate change in the Himalayas. Full article

As a focal point in contemporary land system science research, land use transitions significantly impact the ecological environment. Zhaosu County, a typical ecological county in the arid region of Northwest China, offers significant insights into the processes of land use transition and their effects on the ecological environment. Studying these dynamics is crucial for the county’s rational spatial allocation and sustainable development. Based on the remote sensing monitoring data of land use in Zhaosu County in 2000, 2010, and 2018, this article classified land use according to three primary functions: “production, living, and ecological”. By comprehensively applying the research methods of land use transfer matrix model, land use center of gravity shift model, eco-environmental quality index, and regional ecosystem contributions, this paper quantitatively analyzed the process of land use functional transition and spatial transition in Zhaosu County from 2000 to 2018 and scientifically investigated the spatiotemporal distribution characteristics of land use transitions as well as their varying impacts on the ecological environment. This research indicates that: (1) From 2000 to 2018, the ecological land in Zhaosu County initially experienced an increase followed by a decrease; in contrast, production land underwent a decline before rebounding, while living land has shown a continuous upward trajectory. (2) The spatial distribution of the three primary functional land uses in Zhaosu is unbalanced, and the center of gravity of all land uses has shifted during the study period. Among them, the center of gravity of water area and other ecological land underwent the most pronounced displacements, and the spatial migration intensified initially before gradually diminishing, while the degree of deviation of urban living land was the least pronounced. (3) The comprehensive eco-environmental quality index of Zhaosu County continued to decline from 0.584 in 2000 to 0.549 in 2018, indicating a persistent degradation trend of the ecological environment quality. (4) The negative effects of the ecological environment in Zhaosu County outweighed the positive effects, and the main factors contributing to the decline in ecological environment quality were grassland degradation and the decline of the snowline. Full article

The Cenozoic glaciation of Antarctica proceeded through two distinct steps around 35 and 15 million years ago. The first icing was attributed to thermal isolation due to the opening of the Drake/Tasman passages and the development of the Antarctic circumpolar current. I also subscribe to this “thermal isolation” but posit that, although the snowline was lowered below the Antarctic plateau for it to be iced over, the glacial line remains above sea level to confine the ice sheet to the plateau, a “partial” glaciation that would be sustained over time. The origin of the second icing remains unknown, but based on the sedimentary evidence, I posit that it was triggered when the isostatic rebound of West Antarctica caused by heightened erosion rose above the glacial line to be iced over by the expanding plateau ice, and the ensuing cooling lowered the glacial line to sea level to cause the “full” glaciation of Antarctica. To test these hypotheses, I formulate a minimal box model, which is nonetheless subjected to thermodynamic closure that allows a prognosis of the Miocene climate. Applying representative parameter values, the model reproduces the observed two-step icing followed by the stabilized temperature level, in support of the model physics. Full article

This research studies the characteristics of snow-covered area (SCA) from two vastly different sensors: optical (Moderate-Resolution Imaging Spectroradiometer, or MODIS, equipped on board the Terra satellite) and radar (Synthetic Aperture Radar (SAR) on-board Sentinel-1 satellites). The focus are the five mountain ranges of the Iberian Peninsula (Cantabrian System, Central System, Iberian Range, Pyrenees, and Sierra Nevada). The MODIS product was selected to identify SCA dynamics in these ranges using the Probability of Snow Cover Presence Index (PSCPI). In addition, we evaluate the potential advantage of the use of SAR remote sensing to complete optical SCA under cloudy conditions. For this purpose, we utilize the Copernicus High-Resolution Snow and Ice SAR Wet Snow (HRS&I SWS) product. The Pyrenees and the Sierra Nevada showed longer-lasting SCA duration and a higher PSCPI throughout the average year. Moreover, we demonstrate that the latitude gradient has a significant influence on the snowline elevation in the Iberian mountains (R² ≥ 0.84). In the Iberian mountains, a general negative SCA trend is observed due to the recent climate change impacts, with a particularly pronounced decline in the winter months (December and January). Finally, in the Pyrenees, we found that wet snow detection has high potential for the spatial gap-filling of MODIS SCA in spring, contributing above 27% to the total SCA. Notably, the additional SCA provided in winter is also significant. Based on the results obtained in the Pyrenees, we can conclude that implementing techniques that combine SAR and optical satellite sensors for SCA detection may provide valuable additional SCA data for the other Iberian mountains, in which the radar product is not available. Full article

Albedo is a primary driver of the glacier surface energy balance and consequent melting. As glacier albedo decreases, it further accelerates glacier melting. Over the past 20 years, glaciers on the Tibetan Plateau have experienced significant melting. However, our understanding of the variations in glacier albedo and its driving factors in this region remains limited. This study used MOD10A1 data to examine the average characteristics and variations in glacier albedo on the Tibetan Plateau from 2001 to 2022; the MOD10A1 snow cover product, developed at the National Snow and Ice Data Center, was employed to analyze spatiotemporal variations in surface albedo. The results indicate that the albedo values of glaciers on the Tibetan Plateau predominantly range between 0.50 and 0.60, with distinctly higher albedo in spring and winter, and lower albedo in summer and autumn. Glacier albedo on the Tibetan Plateau decreased at an average linear regression rate of 0.06 × 10⁻² yr⁻¹ over the past two decades, with the fastest declines occurring in autumn at an average rate of 0.18 × 10⁻² yr⁻¹, contributing to the prolongation of the melting period. Furthermore, significant variations in albedo change rates with altitude were found near the snowline, which is attributed to the transformation of the snow and ice surface. The primary factors affecting glacier albedo on the Tibetan Plateau are temperature and snowfall, whereas in the Himalayas, black carbon and dust primarily influence glacier albedo. Our findings reveal a clear decrease in glacier albedo on the Tibetan Plateau and demonstrate that seasonal and spatial variations in albedo and temperature are the most important driving factors. These insights provide valuable information for further investigation into surface albedo and glacier melt. Full article

Surface albedo plays a pivotal role in the Earth’s energy balance and climate. This study conducted an analysis of the spatial distribution patterns and temporal evolution of albedo, normalized difference vegetation index (NDVI), normalized difference snow index snow cover (NSC), and land surface temperature (LST) within the Qilian Mountains (QLMs) from 2001 to 2022. This study evaluated the spatiotemporal correlations of albedo with NSC, NDVI, and LST at various temporal scales. Additionally, the study quantified the driving forces and relative contributions of topographic and natural factors to the albedo variation of the QLMs using geographic detectors. The findings revealed the following insights: (1) Approximately 22.8% of the QLMs exhibited significant changes in albedo. The annual average albedo and NSC exhibited a minor decline with rates of −0.00037 and −0.05083 (Sen’s slope), respectively. Conversely, LST displayed a marginal increase at a rate of 0.00564, while NDVI experienced a notable increase at a rate of 0.00178. (2) The seasonal fluctuations of NSC, LST, and vegetation collectively influenced the overall albedo changes in the Qilian Mountains. Notably, the highly similar trends and significant correlations between albedo and NSC, whether in intra-annual monthly variations, multi-year monthly anomalies, or regional multi-year mean trends, indicate that the changes in snow albedo reflected by NSC played a major role. Additionally, the area proportion and corresponding average elevation of PSI (permanent snow and ice regions) slightly increased, potentially suggesting a slow upward shift of the high mountain snowline in the QLMs. (3) NDVI, land cover type (LCT), and the Digital Elevation Model (DEM, which means elevation) played key roles in shaping the spatial pattern of albedo. Additionally, the spatial distribution of albedo was most significantly influenced by the interaction between slope and NDVI. Full article