Search Results (249)

Climate change is altering the frequency, duration, and seasonality of low flows, which are critical for water availability, ecosystem functioning, and river management. Low-flow characteristics, defining the minimum, often seasonal, flow levels in rivers or streams primarily fed by groundwater, snow or glacier melt, or lake drainage, are essential for assessing hydrological droughts and water resource vulnerability. In the Upper Vistula River Basin, variable precipitation and rising air temperatures increase the risk of droughts, impacting both natural systems and human water use. This study analyzed long-term trends in annual low flows and associated parameters, including drought frequency, duration, and deficit volume, across 41 small- and medium-sized catchments. Two datasets were considered: 25 stations with 58-year daily discharge records (1961–2019) and 41 stations with 38-year records (1981–2019). Low flows were identified using the threshold level method (TLM) at 70% and 90% exceedance (FDC70 and FDC90). Trends were assessed with the Mann–Kendall test, and spatial drought patterns were mapped to evaluate regional variability. Deep and shallow low flows occurred at all analyzed cross-sections. For the period 1961–2019, deep low flows (FDC90) occurred almost annually in 18 of the 25 cross-sections since 2012. Statistically significant increasing trends in deep low-flow parameters were detected in five cross-sections for 1961–2019 and in seven cross-sections for 1981–2019. Shallow low flows (FDC70) occurred in all sections; four rivers exhibited annual shallow droughts during 1961–2019, whereas 12 rivers showed annual events in 1981–2019. Summer droughts predominated over winter events, reflecting enhanced evapotranspiration and higher seasonal water demand. These findings highlight the relevance of analyzing low-flow parameters for understanding hydrological droughts. Such information can support water resource management, planning, and ecosystem protection under variable climatic conditions. Full article

Calving front position is a key indicator of glacier and ice-sheet dynamics and an important variable for assessing mass loss and sea-level rise. Rapid growth in satellite data availability and image analysis techniques has driven the development of numerous automated calving front detection algorithms; however, the methodological landscape remains fragmented. This scoping review aims to map the existing literature on automated calving front detection, characterize the types of algorithms and data sources used, and identify trends, gaps, and challenges in current approaches. A systematic search of major bibliographic databases and complementary sources was conducted to identify studies describing automated or semi-automated calving front detection from satellite imagery or derived datasets. Eligible studies included peer-reviewed articles and relevant grey literature using optical, synthetic aperture radar (SAR), or multi-sensor data. Data were charted using a predefined framework that captures the algorithmic approach, input data characteristics, spatial and temporal coverage, validation strategies, and reported performance metrics. The review identifies a wide range of methods, from early threshold- and edge-based techniques to recent machine learning and deep learning approaches, with a strong shift toward convolutional neural networks over the past few years. Despite methodological progress, validation practices and evaluation metrics remain heterogeneous, and standardized benchmark datasets are scarce. This scoping review provides a structured overview of the field and highlights priorities for future methodological development and benchmarking. 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

Cushion plants, renowned for their resilience to alpine and arctic extremes, remain challenging to delineate at the species level due to dense interwoven canopies and high spectral similarity among species. This study introduces CryoFlora, an instance segmentation framework tailored to cushion species, integrating a large-kernel attention backbone, UniRepLKNetBlock, with a bidirectional attention-based fusion neck, BIMAFPN, to enhance feature representation and boundary precision. CryoFlora was trained on 528 annotated UAV image tiles containing 17,488 instances of Thylacospermum caespitosum and Rhodiola rosea, representing the two principal morphological forms of cushion plants, flat and hemispherical. The model achieved a mean average precision of 0.975 at IoU 0.5 and 0.785 at IoU 0.5–0.95, with only 7.65 M parameters. Field validation across four 100 × 100 m plots in the Bayi Glacier forefield confirmed its ability to map total cover (575.5–999.3 m²), patch density (0.14–1.48 ind/m²), and individual canopy metrics, revealing clear elevational gradients in species dominance and morphology. By automating extraction of ecological indicators directly from UAV imagery, CryoFlora provides a scalable tool for dynamic monitoring of glacier-forefield ecosystems, supporting conservation and adaptive management under accelerating climate change. Full article

Rock glaciers are key indicators of mountain permafrost and act as climatically resilient water reservoirs in arid mountains. This study presents the first inventory and kinematic classification of rock glaciers in Western Tien Shan (Kazakhstan and Kyrgyzstan), combining geomorphological mapping with InSAR time-series analysis. Using high-resolution optical imagery (Google Earth Pro (version 7.3.6.10441), Bing Maps, SAS Planet (version 200606.10075), digital elevation models, and Small Baseline Subset InSAR processing, 741 rock glaciers covering more than 70.5 km² were identified. Activity classification revealed 232 transitional and 509 active forms, with mean seasonal displacement rates of ~15 cm yr⁻¹ calculated based on August and September observations. Spatial analysis showed a strong rock glacier concentration on north-facing slopes (>66% of total area) with reduced potential incoming solar radiation. Rock glaciers mainly occur between 2800 and 3800 m a.s.l., with a mean elevation of 3340 m a.s.l. However, their kinematic activity varies across mid-altitudinal ranges, underscoring the influence of slope, aspect, shading, and local topography. Integration with the Global Permafrost Zonation Index (PZI) indicated a lower permafrost boundary at ~1922 m a.s.l., with the largest and most active glaciers occurring at intermediate PZI values (0.5–0.7). This first rock glacier inventory for the Western Tien Shan establishes a benchmark dataset that supports the validation and refinement of global models at a regional scale, guides priorities for permafrost monitoring, and provides a replicable framework for inventory development in other data-scarce mountain regions. Full article

Glacier inventories are critical for monitoring glacier response to climate change, providing constraints for glacier modeling studies and for assessing the impacts of glacier retreat on ecosystems and human societies. In the Bhutanese Himalaya, an up-to-date glacier inventory and a systematic analysis of decadal-scale glacier changes is lacking. Here, we present three glacier inventories (1976, 1998, and 2024) for this region. Manual mapping of glacier outlines from multi-source satellite imagery and the Copernicus digital elevation model (DEM) are used to derive a glacier inventory with associated topographic attributes. We found that 1871 glaciers existed in this region in 1976, covering an area of 2297.07 ± 117.15 km². By 1998 this number had reduced to 1803 glaciers, covering 2106.99 ± 90.60 km². In 2024, only 1697 glaciers remained, covering 1584.18 ± 36.37 km². A total of 89 (1976–1998) and 435 (1998–2024) glaciers became extinct in the Bhutanese Himalaya during these two time periods, and glacier area decrease accelerated from ~0.38% yr⁻¹ to ~0.95% yr⁻¹. Lake-terminating glaciers retreated almost three times faster (~32.2 m yr⁻¹) than land-terminating (~10.4 m yr⁻¹) glaciers during the observation period. Debris-covered glacier area increased from 112.79 ± 11.50 km² in 1976 to 128.89 ± 10.50 km² in 2024. Glaciers on the South Bhutanese Himalaya (draining into Bhutan) experienced faster glacier retreat than the glaciers of the North Bhutanese Himalaya (draining into the Tibetan Autonomous Region). ERA5-Land reanalysis data show that summer decadal average temperature in this region increased by 0.003 °C yr⁻¹ between 1976 and 1998 and 0.020 °C yr⁻¹ between 1998 and 2024, with the increase in warming rate coinciding with accelerated glacier retreat after 1998. Our updated glacier inventories will be useful for assessments of global sea level change, mountain hazards, and water resources. Full article

Groundwater–surface water interactions were investigated in the Highwood River (3952 km²) and Sheep River watersheds (1568 km²), originating in the Rocky Mountains headwaters of the South Saskatchewan River (Alberta, Canada), to improve understanding of hydrological processes that potentially influence water use and vulnerability to climatic change in representative, alpine-fed mixed-use watersheds. Similar to adjacent regions of the Bow, Red Deer and Oldman watersheds, the upper reaches of these watersheds are sparsely populated with significant seasonal glacier and snowmelt influence, while the lower watersheds are currently under increasing water supply pressure from competing agricultural–municipal interests, with notable risk of flooding during high-flow events and drought during the growing season. Investigations included mapping of hydrologic and hydrogeologic controls (aquifers, buried channels, colluvial deposits, etc.,) and synoptic geochemical and isotopic surveys (δ²H, δ¹⁸O, δ¹³C-DIC, ²²²Rn) to characterize evolution in water type and seasonal progression in streamflow sources and underlying mechanisms. Our findings confirm seasonal progression in streamflow water sources, characterized by a pronounced snowmelt-dominated spring freshet, but with a sustained recession fed by colluvial, moraine, fluvial, and fractured bedrock sources. Seasonal isotopic variations establish that shallow groundwater sources are actively maintained throughout the spring freshet, often accounting for a dominant portion of streamflow, which indicates active displacement of groundwater storage by snowmelt recharge during spring melt. The contrast in the proportion of alpine contributions in each watershed suggests these systems may respond very differently to climate change, which needs to be carefully considered in developing sustainable water-use strategies for each watershed. Full article

A storage-aware water-scarcity risk assessment framework coupling satellite remote sensing, geospatial datasets with the IPCC exposure-hazard-vulnerability (EHV) paradigm was designed to evaluate the spatiotemporal dynamics of global water scarcity risk over the past two decades. To achieve this, a performance-weighted ensemble machine learning approach was employed to reconstruct long-term terrestrial water storage (TWS) from satellite observations, augmented with glacier-mass calibration to improve reliability in cryosphere-affected regions. Global water withdrawal dataset was generated by integrating remote sensing, geospatial dataset, and machine learning to mitigate the dependency of parameterized land surface hydrological models and enable consistent risk mapping. Satellite-derived results reveal obvious TWS declines in Asia, Northern Africa, and North America, particularly in irrigated drylands and glacier-dominated regions. EHV paradigm and big datasets further identified high-water scarcity risk in Asia and Africa, especially in agricultural regions. Water stress has intensified in Africa over the past two decades, while a decreasing trend is observed in parts of Asia. Vulnerability levels in Asia and Africa are approximately eight times higher than those in other global regions. Results reveal a strong connection between water stress and socioeconomic factors in Asia and Africa, reflecting global disparities in water resource availability. Full article

Rock glaciers have been recognized as key indicators of geomorphic and climatic processes in high mountain environments. In this study, Sentinel-2 MSI imagery and topographic data were integrated to construct enhanced feature sets for rock glacier identification. Three state-of-the-art deep learning models (U-Net, DeepLabV3+, and HRnet) were employed to perform semantic segmentation for extracting rock glacier boundaries in the Hunza River Basin, located in the eastern Karakoram Mountains. The combination of spectral and terrain features significantly improved the differentiation of rock glaciers from surrounding landforms, establishing a robust basis for model training. A series of comparative experiments were conducted to evaluate the performance of each model. The HRnet model achieved the highest overall accuracy, exhibiting superior capabilities in high-resolution feature representations and generalization. Using the HRnet framework, a total of 597 rock glaciers were identified, covering an area of 183.59 km². Spatial analysis revealed that these rock glaciers are concentrated between elevations of 4000 m and 6000 m, with maximum density near 5000 m, and a predominant south and southwest orientation. These spatial patterns reflect the combined influences of topography, thermal conditions, and snow accumulation on the formation and preservation of rock glaciers. The results confirm the effectiveness of deep learning-based semantic segmentation for large-scale rock glacier mapping. The proposed framework establishes a technical foundation for automated monitoring of alpine landforms and supports future assessments of rock glacier dynamics under climate variability. Full article

Low-altitude palaeoglaciation in Atlantic mountain regions provides important insights into past climatic conditions and moisture dynamics during the Last Glacial Cycle. This study presents the first quantitative reconstruction of palaeoglaciers in Serra da Cabreira (northwest Portugal), a mid-altitude granite massif located along the Atlantic fringe of the Iberian Peninsula. Detailed geomorphological mapping (1:14,000) and field surveys identified 48 glacial and periglacial landforms, enabling reconstruction of two small valley glaciers in the Gaviões and Azevedas valleys using GlaRe numerical modelling. The spatial distribution of palaeoglacial landforms shows a pronounced west–east asymmetry: periglacial features prevail on wind-exposed west-facing slopes, whereas glacial erosion and depositional landforms characterise the more protected east-facing valleys. The reconstructed glaciers covered 0.24–0.98 km², with maximum ice thicknesses of 72–89 m. Equilibrium-line altitudes were estimated using AABR, AAR, and MELM methods, yielding consistent palaeo-ELA values of ~1020–1080 m. These results indicate temperature depressions of ~6–10 °C and enhanced winter precipitation associated with humid, Atlantic-dominated conditions. Comparison with regional ELA datasets situates Cabreira within a clear Atlantic–continentality gradient across northwest Iberia, aligning with other low-altitude maritime palaeoglaciers in the northwest Iberian mountains. The findings highlight the strong influence of the orographic barrier position, moisture availability, valley hypsometry, and structural controls in sustaining small, climatically sensitive glaciers at low elevations. Serra da Cabreira thus provides a key reference for understanding Last Glacial Cycle palaeoclimatic variability along the Western Iberian margin. 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

With an area of 13,000 km² and more than 60 outlet glaciers (tidewater or lake-terminating), the Southern Patagonian Icefield (SPI) stores a substantial volume of freshwater, and its accelerating melt contributes to global sea level rise. In addition to monitoring frontal retreat and ice thinning, tracking near-terminus glacier surface velocity can provide key insight into glacier dynamics. Here, we aimed to understand the current state of the SPI and to explore the dynamic restructuring of the glaciers in comparison with previous results. Considering that ice velocity acceleration near termini can be indicative of a drastic ice thinning and calving, during 2019–2020, we investigated the surface velocity of glaciers in the SPI using feature and speckle tracking. We calculated velocity maps (450 in total) from Sentinel-1 SAR images. Velocity ranged from 0 to 6571 myr⁻¹ for the whole study period, taking into account the 846 one square kilometer subsamples. Mean values of the topographic parameters (elevation, slope, aspect) have variable correlation with the mean velocity values, while mean ice thickness does not have a strong correlation with velocity. Nevertheless, mean velocities show association between near-frontal motion acceleration and calving, as observed in tidewater glaciers and four lake-terminating glaciers. Considering along-length changes in the glaciers, it is found that there are glaciers with upward increasing velocities, downward increasing velocities, and with a single velocity peak and multiple velocity peaks. Comparing our measurements with previous studies, we found major dynamic changes in several glaciers. A massive calving event at Pío XI Glacier significantly affected its velocity for months. The slowdown observed at 13–14 km from the terminus of the Jorge Montt Glacier contrasts with all previous studies that showed an acceleration of the glacier in this area. Our observations indicate rapid changes in some of the SPI glaciers, which suggests their unstable state. Full article

Mountain glaciers are sensitive indicators of climate variability, and their retreat since the end of the Little Ice Age (LIA) has strongly reshaped alpine environments worldwide. In the Greater Caucasus, glacier shrinkage has accelerated over the past century, yet detailed multi-temporal reconstructions remain limited for many glaciers. Here, we reconstruct the post-LIA evolution of Tsaneri–Nageba Glacier, one of largest ice bodies in the Georgian Caucasus, and document the development of its newly formed proglacial lake. Using a combination of geomorphological mapping, historical maps, multi-temporal satellite imagery, Uncrewed Aerial Vehicle (UAV) photogrammetry, and sonar bathymetry, we quantify glacier change from ~1820 to 2025 and provide the first direct measurements of a proglacial lake in the Tsaneri–Nageba system—and indeed in the Georgian Caucasus as a whole. Our results reveal that Tsaneri–Nageba Glacier has shrunk from ~48 km² at its LIA maximum to ~30.6 km² in 2025, a loss of −43.5% (or −0.21% yr⁻¹). The pace of shrinkage intensified after 2000, with the steepest losses recorded between 2014 and 2025. Terminus positions shifted up-valley by nearly 3.9 km (Tsaneri) and 4.3 km (Nageba), accompanied by fragmentation of the former compound valley glacier into smaller ice bodies. Long-term meteorological records confirm strong climatic forcing, with pronounced summer warming since the 1990s and declining winter precipitation. A proglacial lake started to form in mid-summer 2015, which by 03/09/15 had a surface area of ~14,366 m², expanding to ~106,945 m² by 10/07/2025. The lake is in contact with glacier ice and is thus prone to calving. It is dammed by unconsolidated moraines and bounded by steep, active slopes, making it susceptible to generating a glacial lake outburst flood (GLOF). By providing the first quantitative measurements of a proglacial lake in the region, this study establishes a baseline for future monitoring and risk assessment. The findings highlight the urgency of integrating glaciological, geomorphological, and hazard studies to support community safety and water resource planning in the Caucasus. Full article

Accurate delineation of glacier extent is crucial for monitoring glacier degradation in the context of global warming. Satellite remote sensing with high spatial and temporal resolution offers an effective approach for large-scale glacier mapping. However, persistent cloud cover limits its application on the Tibetan Plateau, leading to substantial omissions in glacier identification. Therefore, this study proposed a novel sub-cloudy glacier extraction model (SCGEM) designed to extract glacier boundaries from cloud-affected satellite images. First, the cloud-insensitive characteristics of topo-graphic (Topo.), synthetic aperture radar (SAR), and temporal (Tempo.) features were investigated for extracting glaciers under cloud conditions. Then, a transformer-based generative adversarial network (GAN) was proposed, which incorporates an image reconstruction and an adversarial branch to improve glacier extraction accuracy under cloud cover. Experimental results demonstrated that the proposed SCGEM achieved significant improvements with an IoU of 0.7700 and an F1 score of 0.8700. The Topo., SAR, and Tempo. features all contributed to glacier extraction in cloudy areas, with the Tempo. features contributing the most. Ablation studies further confirmed that both the adversarial training mechanism and the multi-task architecture contributed notably to improving the extraction accuracy. The proposed architecture serves both to data clean and enhance the extraction of glacier texture features. Full article

The Puruogangri Ice Field (PIF) in the central Tibetan Plateau, known as the world’s Third Pole, is the largest modern ice field in the Tibetan Plateau and a crucial indicator of climate change. Although it was thought to be quiet, recent studies identified possible surging behaviors. But comprehensive velocity fields remain largely unknown. Here we present the first comprehensive and high spatiotemporal resolution 3D displacement field of the PIF from 2017 to 2024 using synthetic aperture radar (SAR) imaging geodesy. Using time-series InSAR and time-series pixel offset tracking and integrating ascending and descending Sentinel-1 SAR images, we invert the time-series 3D displacement over eight years. Our results reveal significant seasonal variations and three surging glaciers, with peak displacements exceeding 110 m in 12 days. Combined with ERA5 reanalysis and SAR backscatter coefficients analysis, we demonstrate that these surges are hydrologically controlled, likely initiated by damaged subglacial drainage systems. This study enhances our understanding of glacier dynamics in the central Tibetan Plateau and highlights the potential of using SAR imaging geodesy to monitor glacial hazards in High Mountain Asia. Full article