Abstract. To improve the accuracy and timeliness of glacier surface-velocity retrieval in complex mountain terrain, we develop a high-resolution fusion method combining Landsat, Sentinel-1/2, and UAV (Unmanned Aerial Vehicle) data, and produce monthly velocity products for the Kangri Karpo region for 2015–2024. Compared with existing large-area public datasets, the products offer markedly higher spatial resolution and better detection of small mountain glaciers; relative to single-sensor inputs prior to fusion, the valid-pixel ratio increases by ~50 %, the average number of valid months per pixel over the decade rises by ~50, and spatial smoothness improves—demonstrating the method’s suitability for rugged terrain. Spatially, velocities follow the canonical “fast center, slow margins” pattern, with multi-year maxima >700 m·yr⁻¹ and values in lower reaches and most tributaries generally <100 m·yr⁻¹. Attribute analysis indicates significant correlations between velocity and area, slope, and aspect: larger glaciers flow faster overall; within individual glaciers, velocity responds more strongly to slope; after controlling for area and slope, south-facing glaciers are slightly faster. Temporally, the intra-annual series shows clear seasonality, with peaks at the beginning and end of the melt season and sustained high speeds throughout. At the interannual scale, most pixelwise decadal trends lie within −0.1 to +0.1 m·d⁻¹·dec⁻¹ (overall subdued change), and the median trend is slightly positive, indicating weak regional acceleration; ~38.3 % of glaciers accelerate significantly, 25.5 % decelerate significantly, and 36.2 % show no significant trend (p ≥ 0.05). By aspect, significant acceleration is concentrated on south- and west-facing glaciers, whereas significant deceleration occurs mainly on east- and north-facing glaciers. Month-resolved trends indicate acceleration primarily in April–May (~0.15–0.20 m·d⁻¹·dec⁻¹), likely linked to enhanced meltwater input from an advanced melt season, and deceleration concentrated in July–August (≤ −0.15 m·d⁻¹·dec⁻¹), plausibly associated with intensified mass deficit.

Study region Yanong Glacier is a large lake-terminating temperate glacier in the southeastern Tibetan Plateau, a hydrologically important region influenced by the Indian monsoon. Study focus This study investigates ablation-season glacier dynamics using six UAV surveys conducted over the glacier tongue from June to November 2023. High-resolution DSMs and DOMs were used to quantify elevation change and surface velocity, and to evaluate the links among ice-flow resupply, surface lowering, and velocity variability. New hydrological insights for the region The glacier tongue thinned by 3.43 ± 0.03 m and showed an average horizontal displacement of 0.412 ± 0.001 m d−1 during the ablation season. In the moraine-bounded section, ice-flow resupply partly compensated surface melt, with sensitivity bounds of approximately 14–26% under ±30% ice-thickness perturbations. Short-term velocity variability could not be explained by thickness- and slope-driven internal deformation alone; instead, a substantial residual basal-motion component remained. The covariation among air temperature, surface lowering, and residual basal motion suggests a plausible melt-velocity linkage, but does not provide direct proof of basal-sliding control. These monthly UAV observations help constrain the timing of meltwater production, lake-terminus dynamics, and seasonal glacier-related hazards in this data-scarce region.

To improve the accuracy and timeliness of glacier surface-velocity retrieval in complex mountain terrain, we develop a high-spatial-resolution fusion method combining Landsat, Sentinel-1/2, and UAV (Unmanned Aerial Vehicle) data, and produce monthly velocity products for the Kangri Karpo region for 2015-2024. Compared with existing large-area public datasets, the products offer markedly higher spatial resolution and better detection of small mountain glaciers; relative to single-sensor inputs prior to fusion, the valid-pixel ratio increases by similar to 50 %, the average number of valid months per pixel over the decade rises by similar to 50, and spatial smoothness improves - demonstrating the method's suitability for rugged terrain. Spatially, velocities follow the canonical "fast center, slow margins" pattern, with multi-year maxima >700 myr(-1) and values in lower reaches and most tributaries generally <100 myr(-1). Attribute analysis indicates significant correlations between velocity and area, slope, and aspect: larger glaciers flow faster overall; within individual glaciers, velocity responds more strongly to slope; and, with similar area and slope, south-facing glaciers are slightly faster than north-facing ones. Temporally, the intra-annual series shows clear seasonality, with peaks at the beginning and end of the melt season and sustained high speeds throughout. At the interannual scale, most pixelwise decadal trends lie within -36.5 to +36.5 myr(-1) per decade (overall subdued change), and the median trend is slightly positive, indicating weak regional acceleration; similar to 38.3 % of glaciers accelerate significantly, 25.5 % decelerate significantly, and 36.2 % show no significant trend (p >= 0.05). By aspect, significant acceleration is concentrated on south- and west-facing glaciers, whereas significant deceleration occurs mainly on east- and north-facing glaciers. Month-resolved trends indicate acceleration primarily in April-May (similar to 54.7-73.0 myr(-1) per decade), likely linked to enhanced meltwater input from an advanced melt season, and deceleration concentrated in July-August (<=-54.7 myr(-1) per decade), plausibly associated with intensified mass deficit.

Rock glaciers are ice-debris landforms commonly found in high mountain environments. Shaped by long-term creep of ice-rich permafrost, they provide critical information for permafrost studies, mountain hydrology, and hazard assessment. Although the characteristics and controlling factors of rock glacier velocities across various temporal scales have been studied at individual sites, their environmental drivers in the spatial domain over large regions remain poorly understood. In this study, we employ four machine learning methods, i.e. support vector machine, extreme gradient boost, random forest, and backpropagation neural network, to model the relationship between rock glacier velocities and environmental variables for 5,163 rock glaciers in the Pamir-Karakoram-Kunlun region. Subsequently, we use SHapley Additive exPlanations to quantify variable importance. Results show that the upslope connection to a glacier is a critical factor controlling rock glacier velocities. In our study area, glacier-connected rock glaciers exhibit on average faster movement (median velocity = 38 cm/year) than talus-connected ones (median velocity = 28 cm/year). We also find that geomorphological properties exert stronger controls on the spatial variability of rock glacier velocities than regional climate variability. Rock glacier area and slope are identified as the second and third most important variables, with larger areas and steeper slopes associated with higher velocities. The snow cover duration ranks fourth, followed by precipitation, while air temperature shows minimal influence on velocity. Overall, these findings bridge a critical knowledge gap regarding the environmental controls on rock glacier dynamics at the regional scale, extending our understanding of rock glacier kinematics beyond site-specific investigations.

Glacier mass balances across High Mountain Asia exhibit striking regional contrasts. While glaciers in the Himalaya have undergone rapid retreat, those in the Karakoram and West Kunlun ranges have remained anomalously stable—a long-standing climatic puzzle known as the Karakoram Anomaly. The physical drivers underlying this contrast remain poorly understood. Here, we show that this divergence is governed by a widespread summer cooling of diurnal maximum air temperatures localized over westernmost glaciated regions. Combining climatic reanalysis, ground observations, and remote sensing data, we demonstrate that this cooling is driven by multi-scale atmospheric coupling. Synoptically, upper-troposphere temperature trends are modulated by a summer Eurasian teleconnection. Locally, warming air temperatures enhance sensible heat exchange over melting ice, intensifying daytime katabatic winds and reinforcing near-surface cooling. Integrating these local temperature trends with regional precipitation regimes—which dictate glaciers' intrinsic climate sensitivity—our multi-regression model explains 78% of the regional mass balance variance, providing a mechanistic explanation for the Karakoram Anomaly.

Proglacial lakes at glacier termini have received widespread attention in the literature for their role in accelerating melt, velocity and contributing to cryospheric hazards. Although global and regional inventories for both glaciers and lakes exist, lake-terminating glaciers have not been consistently identified at the global scale. Based on the most recent global glacier inventory (RGI 7.0), which so far identifies marine-terminating glaciers in most regions, but not lake-terminating glaciers, we present a global inventory of lake-terminating glaciers, differentiating between three categories based on the degree of contact between the glacier terminus and any proglacial lakes. Contributors manually assigned categories to glaciers using satellite imagery from as close to the target date of 2000 as possible, aided by regional lake inventories where available. The resulting dataset corresponds to the year 2000 (+/- 1.5), matching to the timestamp of RGI 7.0 outlines (2001 +/- 6.2). We find that of 274 531 glaciers worldwide, 1.4 % terminate in lakes, with regional percentages varying between 0.5 % and 6.7 % across the 19 RGI regions. These glaciers account for 11.4 % of global glacier area (0.2 % to 41.8 % across regions). With multiple submissions available for 1260 individual glaciers, we find mapping conflicts between contributors to be low (6.7 %). The lake termini data set is available at 10.5281/zenodo.15524733 as well as at https://github.com/GLIMS-RGI/lake_terminating (last access: 2 February 2026). This dataset is integrated into the forthcoming update to the RGI, v7.1.

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 km(2) were identified. Activity classification revealed 232 transitional and 509 active forms, with mean seasonal displacement rates of similar to 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 similar to 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.

Rock glaciers situated within interconnected complexes of glaciers, moraines and glacier forefields are common in the Tien Shan. This study investigates four rock glaciers within ice-debris complexes in the Ulken Almaty valley, northern Tien Shan, Kazakhstan, through the integration of geophysical data on internal structure and remotely sensed surface velocities. Combined ground-penetrating radar (GPR) and electrical resistivity tomography (ERT) surveys were conducted on the larger Morennyi and Gorodetsky complexes, with GPR profiles on further two rock glaciers. Geophysical analyses identified a heterogeneous internal structure, with ice content and structural features varying across morphological units. We characterised the geomorphic units within the two larger complexes, delineating an upper glacially dominated zone and a lower periglacially dominated rock glacier zone. Buried glacial ice bodies were present within the upper rock glacier zone on both the Morennyi and Gorodetsky complexes, likely deposited during advances of the upslope glaciers. These ice bodies were identified through high ERT resistivities of similar to 7 M Omega m and supported by structural reflections within the GPR radargrams. Surface velocities increased in both magnitude and coherence across the transition between the glacial and periglacial zones, reflecting changes in internal structure and surface morphology. We also emphasise the importance of debris availability and connectivity in the development and flow activity of individual units. Understanding the interplay between the glacial and periglacial components of these systems is essential when considering their hydrological and geomorphic role.

Abstract. Ice-debris complexes are glacial-periglacial transitional landforms including rock glaciers that provide important insights into geomorphological evolution and represent substantial yet overlooked water reservoirs. However, their formation and evolution remain poorly constrained. Here, we investigate ice-debris complexes at Muztagh Ata, Eastern Pamir, using historical Corona KH-4A stereo images acquired in 1967 and very-high-resolution Pléiades tri-stereo data acquired in 2013 and 2019 to reconstruct elevation changes over five decades and derive surface velocities for the recent period. Our results reveal a three-zone geomorphological organization comprising a glacier, a glacier-affected, and a periglacial zone. The glacier-affected zone exhibits extensive thermokarst features and substantial surface lowering, locally exceeding 100 m between 1967 and 2019, whereas the periglacial zone is dominated by rock glaciers with maximum surface velocities of up to 6 m/yr. These zones occupy approximately 39.4 km², 6.5 km², and 9.4 km², respectively. Despite their considerably smaller area, the glacier-affected and periglacial zones experienced ice volume losses comparable to those of the glacier zone during 1967–2019. Our observations suggest that multiple evolutionary pathways can operate simultaneously within a single ice-debris complex. Glacier-affected landforms may progressively evolve into rock glaciers, whereas debris-covered glaciers may advance onto or become embedded within pre-existing rock glaciers. This study provides new insights into the evolution of ice-debris complexes and highlights the need to incorporate these landforms into glacier inventories and hydrological models for water resource assessments in High Mountain Asia and other mountainous regions on Earth.

Dry-snow zones, the highest and coldest parts of a glacier where summer melt is absent, remain poorly constrained in High Mountain Asia (HMA) due to limited high-altitude observations. Here we present the first decadal, region-wide assessment of dry-snow distribution across HMA from 2015 to 2024 based on Sentinel-1 SAR observations. A physically guided approach combining SAR backscatter thresholds with an elevation constraint is used to delineate dry-snow zones. Dry snow is mainly confined to high-altitude western and southern regions, while largely absent elsewhere across HMA. Its extent shows strong interannual sensitivity to summer air temperature, weak correlation to precipitation, and positive associations with glacier mass balance across subregions. The consistency between SAR-derived dry-snow patterns and independent climatic and mass-balance signals indicates that this simple, physically based approach provides reliable large-scale estimates of accumulation-state conditions in data-sparse, high-elevation environments. Dry-snow extent therefore emerges as a sensitive and previously underexplored indicator of glacier–climate interactions, providing new observational constraints for future glacier-change projections.

Rock glaciers are distinctive geomorphological features acting as indicators for climatic changes and permafrost occurrence. They store significant amounts of ice and are widespread in mountain ranges, making them potentially important for regional hydrology. However, existing inventories for the Southeastern Tibetan Plateau (STP) exhibit significant discrepancies and varying levels of completeness, highlighting the need for a systematic reassessment to overcome the limitations of previous manual or coarse-resolution mapping efforts. Here, we present a detailed rock glacier inventory covering 545,400 km2 of STP, derived from 4.7-m resolution Planet Basemap imagery. The dataset was generated using a SegFormer deep learning model trained on a globally constructed dataset of 19,096 rock glacier samples, designed to maximize generalization across diverse terrains. The inventory was refined and quality-controlled through an inventory-wide human-in-the-loop workflow, including manual inspection of raw polygons, expert review of uncertain cases, and cross-verification with high-resolution imagery and existing datasets. In addition, Uncrewed Aerial Vehicle (UAV)-supported field surveys at three sites provided independent high-resolution reference examples for assessing boundary reliability. The final inventory contains 47,916 rock glaciers, significantly expanding previous inventories by over 20,000 entries. Each record includes comprehensive geometric attributes and topographic information. This dataset provides an important baseline dataset for monitoring permafrost dynamics, hydrological modeling, and geohazard assessment in the context of climate change.

Glaciers in the Chandra-Bhaga basin, western Indian Himalaya, are critical to the cryosphere-hydrosphere system, yet their long-term climate responses remain poorly understood due to sparse in-situ data. Our geodetic mass balance assessment reveal substantial ice loss from 1971 to 2022, with glaciers shrinking by 0.72 ± 0.08 km2 a-1 and losing mass at 0.26 ± 0.10 m w.e. a-1. Debris-covered glaciers experienced greater ice loss (0.28 ± 0.10 m w.e. a-1) than clean-ice glaciers (0.20 ± 0.12 m w.e. a-1). CMIP6-based regression indicates modest pre-2000 loss, then average loss rates of -0.5 m w.e. a-1 until ∼2035, after which trajectories diverge depending on SSP scenarios. Temperature sensitivity is strongest in summer (-0.49 m w.e. a-1 °C-1) and weakest in winter (-0.38 m w.e. a-1 °C-1). Precipitation sensitivity is highest for winter and lowest for summer. ERA5 Land reanalysis-based sensitivities show annual temperature has stronger influence than seasonal, with lower magnitudes than CMIP6. Winter precipitation from ERA5 Land reanalysis data show stronger correlation to glacier mass gain compared to CMIP6. These differences emphasize uncertainty over which dataset better represents regional climate, particularly for temperature-mass balance relationships and winter precipitation that largely governs glacier accumulation. Despite this, sensitivities align with broader Himalayan trends. Projections suggest stable winter precipitation, combined with increased summer and annual warming, will accelerate mass loss through the 21st century. This study proves that long-term geodetic data can provide an alternative solution to understand glacier-climate interactions in data-scarce regions such as the Himalaya, enabling reconstructions, forecasts, and targeted adaptation for glacier-dependent communities.

Glaciers play a critical role as freshwater reserves and indicators of climate change, yet their automatic delineation, especially for debris-covered glaciers, remains challenging due to spectral similarity with surrounding terrain. This study introduces CryoNet, a deep learning framework that leverages a rich multi-modal dataset combining Sentinel-2 optical imagery, DEM-derived topographic variables, spectral indices, Principal Component Analysis (PCA), InSAR coherence and phase, tasseled-cap features, and GLCM texture to discriminate clean-ice glaciers, debris-covered glaciers, and glacial lakes. CryoNet is an encoder-decoder CNN with nested skip connections and spatial-channel Squeeze-and-Excitation (scSE) attention, built upon a ResNet101 encoder to capture hierarchical contextual and spatial features. The study is conducted in the Poiqu Basin in the central Himalaya, and transferability is evaluated by applying the trained model to the Mont Blanc Massif in the Alps. We additionally analyse the importance of each data layer in improving glacier mapping performance. The proposed model achieves an overall IoU of 90.52

Glacier response to climate change results in rapid glacier water resource loss. Positive glacial regulatory processes (GRPs) are processes that buffer glacier water resource losses. Nevertheless, these processes are not systematically quantified, which may lead to uncertainty in glacier water resource sustainability assessments. Here, we employ a glacier water balance model to track glacier water resource transport trajectories and quantify contributions of positive GRPs across High Mountain Asia (HMA). The combined impacts of positive GRPs could mitigate 236-255 Gt of HMA glacier water resource loss ( 9

Glaciers in High Mountain Asia (HMA) are important water sources for millions of people in arid regions. However, their mass balance exhibits strong regional heterogeneity. The reasons are still not well understood mainly due to limited high-resolution temperature and precipitation datasets in high-altitude zones. Here, we propose a glacier ablation and accumulation index based on firn and snow observation from Sentinel-1 synthetic aperture radar data, which represent the seasonal ablation and accumulation patterns across HMA. Evaluations of the index with the summer snow line, precipitation, and temperature records from supraglacial weather stations confirm their reliability. Comparisons to available mass balance data show that glaciers with the lowest ablation and lowest summer-accumulation index which are located in the center of the HMA region, have the most balanced state (+0.05 m w.e. a-1). In contrast, glaciers with a high ablation and median summeraccumulation index, show highly negative mass balances (-0.47 m w.e. a-1). Under similar ablation index, the type of glaciers with higher summer-accumulation index is more negative. This work provides novel methods and knowledge to understand and project the glacier mass balance heterogeneity in HMA.

Abstract. We present the new Austrian glacier inventory, AGI 5. Glacier outlines were manually digitized from high-resolution orthoimagery and digital elevation models (DEM), using older inventory data as a baseline. The delineation of debris-covered ice was supported by visual analysis of multi-temporal imagery and DEM differencing, depending on data availability. We assessed discrepancies with older inventories and differences in interpretation between analysts using a round robin experiment (mapping of selected glaciers by several analysts). The updated inventory reflects glacier extent in 2023 (55 % of total glacier area in the study region), 2022 (43 %), and 2021 (2 %). The total glacier area in AGI5 is 285±12 km2. Most glaciers in Austria (87 %) are smaller than 0.5 km2. These "very small" glaciers comprise 22 % of the total glacier area. Nine glaciers remained larger than 5 km2 and account for more than a quarter of Austria's glacierized area. Area losses since the previous inventory (2004–2012) amount to 129±23 km2, corresponding to 31 % of the total glacier area. Median area loss rates differ between regions, ranging from 2–3 % per year in more heavily glacierized regions to almost 7 % per year in regions with smaller glaciers. Of 894 glaciers listed in the previous inventory, 95 have disappeared completely or were no longer mappable. Compared to other glacierized regions, Austria's glacier recession since the Little Ice Age (LIA) maximum is well constrained with a LIA inventory, four high-resolution, consistent AGIs from 1969 to 2022/23, and additional coverage in complementary inventories using different data sources. As glacier loss accelerates, more frequent updates to the AGIs are needed to keep pace with rapid changes.

Glaciers lost 408 ± 132 Gt of mass during the hydrological year 2025, equivalent to 1.1 ± 0.4 mm sea-level rise. Since 1975, glacier mass loss has totalled 9,583 ± 1,211 Gt, equivalent to 26.4 ± 3.3 mm of sea-level rise, with six of the highest mass-loss years on record occurring in the past seven years.

Abstract. Snowmelt is a vital contributor to river discharge across the Northern Hemisphere, supplying freshwater to over 1.5 billion people and supporting key economic sectors such as agriculture and hydropower. However, climate change has led to a decline in snow water equivalent (SWE) on almost the entire Northern Hemisphere, reducing snow-based water availability. Despite the importance of snowmelt and the pressure imposed by climate change, no large-scale studies have examined the connection of snowmelt dynamics and river discharge beyond statistical measures, which fail to capture the complexity of hydrological regimes. To address this gap, we perform causal discovery using PCMCI, a method that adapts the PC proposed by Peter Spirtes and Clark Glymour to the time series setting, to obtain qualitative causal structure across 119 basins from 1980 to 2022 and then quantify using causal effect estimation with a 20-year moving window and a random forest estimator. Our results show that the role of snowmelt in streamflow generation is changing. In various basins where the method allows for trend detection, the ratio of the causal effect of snowmelt on river discharge to the mean of the river discharge is increasing despite declining SWE. This suggests that as precipitation patterns shift and intra-annual variability increases, snowmelt may become more important for streamflow generation in certain basins despite a generally declining SWE. While regional differences emerge, causal effects do not consistently correlate with geographical factors such as latitude or basin characteristics. Analyses in six basins that serve as illustrative examples, indicate that changes in seasonal hydrology, particularly the timing and distribution of precipitation, influence the relative role snowmelt plays for river discharge. These findings highlight the power of causal inference over conventional statistical measures in enhancing the analysis of large-scale snow hydrological regimes by adding depth to existing approaches.