Himalayan glaciers are important for freshwater supply, hydropower and ecosystem services and require continuous monitoring due to climate warming. Garhwal Himalayan glaciers are experiencing mass loss, but long-term studies on mass balance remain limited for Bhilangana Valley glaciers. This valley is an important sub-basin of the Bhagirathi River that contributes meltwater to the Tehri Reservoir and supports downstream agriculture and populations. Therefore, we assess elevation changes (Δh/Δt), mass balance, frontal retreat and area changes for six Bhilangana Valley glaciers (Khatling, Phating, Dudhganga, Ratangariyan, Jogin and an unnamed glacier) using satellite imagery and digital elevation models (DEMs) from 1973 to 2025. Analyses revealed glacier separation, accelerated retreat, thinning and mass loss, with rates expanding up-glacier toward accumulation zones. The unnamed and Khatling glaciers exhibited approximately four to fivefold increases in mass loss during 2014–2025 compared with 1973–2000, while Dudhganga and Jogin could not be evaluated for 1973–2000 due to DEM voids. Khatling retreated 5.33 ± 0.04 km, separating from Phating around 2000. The unnamed glacier receded 0.76 ± 0.04 km, forming a potentially hazardous proglacial lake ( 0.36 km2 by 2024). This study provides the first comprehensive long-term geodetic mass balance assessment that reveals heterogeneous glacier responses to warming, highlighting the need for hazard monitoring and sustainable water management.

The snow cover in the Teesta River basin (TRB), located in eastern Himalaya, plays crucial role in regional hydrology by influencing water availability, ecological processes, and socio-economic activities. This study assesses the spatio-temporal distribution of snow cover in the TRB for both the present (2000–2021) and future periods (2021–2040: early-century; 2041–2060: mid-century; 2081–2100: late-century). The analysis of spatio-temporal snow cover distribution was conducted using daily moderate resolution imaging spectroradiometer (MODIS) snow cover products (Terra and Aqua), which revealed a decreasing mean annual SCA trend at a rate of − 0.03

NISAR, a spaceborne L- and S-band radar mission, is set to launch in 2025. As part of mission testing, airborne L- and S-band radar sensors were employed to obtain dual-frequency quad-polarimetric (LS-ASAR) data in the United States. This study evaluates precursor NISAR data across the polarization channels of both L- and S-band radar frequencies for feature classification in glacierized regions. We use LS-ASAR data from July 2021, collected over Black Rapids Glacier and surrounding areas, to classify the region into snow, water, clean ice, debris-covered surface, rock, and vegetation. A support vector machine classifier is applied to both backscatter-intensity and polarimetric decomposition channels. Polarimetric decomposition improves classification accuracy by 28%, highlighting the influence of surface, volume, and double-bounce scattering mechanisms on feature discrimination. Resampled data at 4-m and 8-m resolutions yield comparable results for intensity-based classification, while the accuracy of polarimetric decomposition-based classification improves by 21.5% at 8 m. Combining all individual L- and S-band channels further enhances accuracy. In addition, our analysis of polarization ratios, particularly the S to L intensity ratio in VH cross-polarization, helps in differentiating landslide-covered ice from the usual debris-covered ice. This study highlights the effectiveness of individual channels and the potential benefits of integrating dual-frequency L- and S-band polarimetric decomposition data to achieve higher accuracy in mapping glacierized regions. The NISAR data is expected to be publicly available, offering the opportunity to study glacierized regions globally at regular intervals, contributing to higher level derived data products from NISAR.

Glaciers worldwide, including those in the Himalaya, are retreating under climate change, often leading to the formation and expansion of glacial lakes and an increased risk of Glacial Lake Outburst Floods (GLOFs). This study examines the evolution of the proglacial Bhilangana Lake (similar to 0.37 km(2); similar to 4750 m asl) and the associated glacier changes, including thinning and retreat, between 1968 and 2025 using satellite imagery, field measurements and hydrodynamic modelling. Results show lake expansion from similar to 0.12 km(2) in 2001 to similar to 0.37 km(2) in 2025, with an estimated volume of similar to 10.7 x 10(6) m(3), indicating exponential growth over time. A potential GLOF could release peak discharge of 3645 m(3)/s, with average flow velocity similar to 12 m/s, inundating similar to 6.8 km(2) and threatening hydropower projects, settlements and infrastructure downstream. Rising mean, maximum and minimum air temperatures at rates of 0.028, 0.052 and 0.05 degrees C/year, respectively, are identified as the primary drivers of lake expansion and accelerated melt, particularly in July-August. The zero-degree isotherm is shifting to higher elevations, and bias-corrected ERA5 data show good agreement at such altitudes, making it reliable for climate change analysis. Several over-deepening sites were also mapped as potential future lakes, with CMIP6 projections (similar to 0.8 degrees C/decade) indicating substantial glacial and hydrological changes, further elevating GLOF risk by the century's end.

Glaciers of Jammu and Kashmir are retreating faster than those in the broader northwestern Himalayas, yet some glaciers in the Chenab River basin display signs of periodic advancement and mass gain (2005–2007). These features, such as coalescing lobate structures and blocked meltwater streams, raise intriguing questions about localized glacier dynamics. While global concerns over climate change and glacier retreat persist, the lack of detailed evidence regarding glacier advance in this region warrants further investigation. Consequently, a comprehensive investigation of the Bhut and Warwan sub-basin glaciers of the Chenab River basin, was conducted to understand their spatio-temporal evolution between 1993 and 2021. Our analysis revealed an area loss (6.7 %), surface thinning (−0.3 ± 0.4 ma−1), increased debris cover (11 %), and reduced glacial velocity (54 % in Bhut and 20 % in Warwan) between 1993 and 2021. In contrast, we also observed periodic insignificant glacier advancement on nine glaciers, a balanced state on twelve, and the complete disappearance of 113 glaciers in these three decades. Among Bhut and Warwan sub-basins, the former revealed higher average velocity, slowdown, and thinning compared to the latter. The higher average velocity in the Bhut sub-basin is controlled by relatively higher precipitation, and the increase in overall debris coverage possibly governs the enhanced slowdown and thinning. We conclude that while the climate controls the long-term and periodic glacier response, the spatial variability is governed largely by the debris thickness, which is variable among glaciers and might also be changing. Furthermore, the aforementioned geomorphological evidence of some glacier advances, while happening locally, does not well represent the state and recent dynamics of the glaciers in these regions overall.

On 3 October 2023, a multihazard cascade in the Sikkim Himalaya, India, was triggered by 14.7 million m3 of frozen lateral moraine collapsing into South Lhonak Lake, generating an ~20 m tsunami-like impact wave, breaching the moraine, and draining ~50 million m3 of water. The ensuing Glacial Lake Outburst Flood (GLOF) eroded ~270 million m3 of sediment, which overwhelmed infrastructure, including hydropower installations along the Teesta River. The physical scale and human and economic impact of this event prompts urgent reflection on the role of climate change and human activities in exacerbating such disasters. Insights into multihazard evolution are pivotal for informing policy development, enhancing Early Warning Systems (EWS), and spurring paradigm shifts in GLOF risk management strategies in the Himalaya and other mountain environments.

We present a new model called Bayesian Estimated Glacial Lake Volume (BE‐GLAV) to estimate the volume of proglacial lakes. Presuming the lake cross‐section as trapezoidal, BE‐GLAV uses a Bayesian calibration approach to adjust the cross‐sectional geometry to match modeled and observed lake surface widths. We validated our model using bathymetric measurements from lakes spread across High Mountain Asia (specifically, the Himalaya and Tien‐Shan), with aerial extents ranging from 0.01 to 5.5 km2. The modeled lake volumes agreed with the measured lake volume with a root‐mean‐square absolute uncertainty of ∼14%. With minimum and maximum errors of ∼0.3% and ∼61.2%, BE‐GLAV performed well compared to 10 other models in a model inter‐comparison experiment. Using the measured set of volumes, our model can constrain both the root mean square (RMS) error and the maximum percentage error in modeled lake volume, unlike other models, some of which can compute just the RMS uncertainty.

Glacial lakes provide insight into the melting rates of glaciers; thus, the ability to automatically detect and map them opens possibilities for improved monitoring of the changing size of glacial lakes. An accurate automated method for glacial lake segmentation would provide the means to perform constant monitoring without the need for tedious manual labeling. This work utilizes a deep learning approach using semantic segmentation in MATLAB with convolutional neural networks (CNNs) to automatically detect and map glacial lakes. This work can be used to produce quick estimates of lake area in order to monitor changes in their size. The CNN used is DeepLab v3+ with a Resnet 18 backbone. The algorithm correctly identified 93.322 % of lake pixels and has a mean BF score of 0.98652, meaning that the generated boundaries closely match the truth. These results show that this is a viable method for the detection and mapping of glacial lakes.

The Lower Barun Lake, the largest glacier-fed lake in the Nepal Himalaya, has been designated as critically or highly vulnerable to Glacier Lake Outburst Floods (GLOFs) due to the lake's massive volume and steep side walls that are susceptible to mass movements. The current study estimates the future evolution of the lake's extent and its exposure to potential avalanche under different climate scenarios by simulating the evolution of the Lower Barun glacier, which feeds the lake. We then assess this exposure (i) at the lake's current extent, (ii) when the lake length grows to 75 % of its maximum length, and (iii) when the lake length reaches its maximum possible length. We use a mass conservation based numerical flowline model for our analyses. The model was forced by the glacier surface mass balance (SMB) and meteorological data collected from weather stations in Kathmandu, Nepal and Darjeeling, India. Modelled lake lengths matched measured lengths within an RMSE of similar to 200 m. Analyses show that under SSP2-4.5 and SSP5-8.5 scenarios, the lake will reach its maximum length by 2075 +/- 2 and 2061 +/- 1, respectively. The largest uncertainty in future lake length fluctuations is approximately 200 m. Our study reveals that in current conditions, the zone where the angle of reach of potential avalanches is highest lies on the slopes along the right shore (south side) of the lake. The angle of reach shifts upstream and steepens-and the mass movement hazard increases-as the lake grows in the future.

Understanding the conditions that governed the distribution of coseismic landslide frequency and size from past earthquakes is imperative for quantifying the hazard potential of future events. However, it remains a challenge to evaluate the many factors controlling coseismic landsliding including ground shaking, topography, rock strength, and hydrology, among others, for any given earthquake, partly due to the lack of direct seismic observations in high mountain regions. To address the dearth of ground motion observations near triggered landslides, we develop simulated ground motions, including topographic amplification, to investigate these key factors that control the distribution of coseismic landslides from the Mw 7.6 2005 Kashmir earthquake. We show that the combination of strong peak ground motions, steep slopes, proximity to faults and rivers, and lithology control the overall spatial distribution of landslides. We also investigate the role of topographic amplification in triggering the largest landslide induced by this earthquake, the Hattian Bala landslide, finding that it is amplified at the landslide initiation point due to the trapping of energy within the ridge kink as it changes orientation from E to NE. This focusing effect combined with predisposing conditions for hillslope failure may have influenced the location and size of this devastating landslide.

Glacial Lake Outburst Floods (GLOFs) can generate catastrophic flash floods when the damming structure is breached or overtopped. Some of these glacial lakes are located in transboundary regions where floods originating from the lake in one country could inundate a neighboring country, devastating the population and infrastructure of both nations and influencing socio-political relationships. Therefore, assessing the lakes' hazard is crucial. This study investigates transboundary glacial lakes, considering their GLOF hazard, including potential mass movement intrusion, moraine's stability, upstream and downstream process cascades, downstream flood extents, and the exposure and vulnerability of the downstream infrastructure and affected population. GLOF exposure assessments were carried out to identify exposed buildings, bridges, and hydropower systems in transboundary regions. China currently has the highest number of transboundary lakes, with most of them potentially impacting India and Nepal. Most of the transboundary lakes in China, and many in India and Nepal, are susceptible to mass movements. Among the 230 transboundary glacial lakes in the Hindu Kush Karakoram Himalaya, 55 lakes can potentially impact other glacial lakes along their flow path, creating a cascade of events. Five transboundary lakes could potentially impact over 1000 buildings, and 16 lakes could impact over 500 buildings. A total of 35 lakes can impact at least one hydropower station along their flow path, and 4 lakes can impact two hydropower stations. This research emphasizes the critical importance of conducting comprehensive risk analyses of GLOFs in transboundary regions to inform policy-makers. It calls for investing in broad-scale assessments and data-driven decision-making for mitigating and adapting to GLOF risks effectively. Finally, by raising awareness among policy-makers, the study aims to drive actions that safeguard communities and infrastructure vulnerable to GLOF.

Himalayan glaciers represent both an important source of water and a major suite of geohazards for inhabitants of their downstream regions. Recent climate change has intersected with local topographic, geomorphic, and glaciological factors to drive complex patterns of glacier thinning, retreat, velocity change, and lake development. In this study, we analyze the long-term variations in surface elevation change and velocity of the glaciers in the Central and Eastern Himalaya using existing and newly generated datasets spanning 1975 to 2018. We have used modelled (e.g., debris and ice thickness) and remote sensing datasets (e.g., Corona, Hexagon, and Landsat images) to investigate the impact of debris cover and the evolution of proglacial lakes on the glacier response in the region. We found that lake-terminating glaciers (lake TGs) have significantly higher thinning, velocity, and deceleration over time than land-terminating glaciers (land TGs). Lakes have shown an overall growth of 98 % in area and 40 % in number during 1975-2017. New proglacial lakes will likely continue to develop, and existing ones will keep expanding, influencing the frontal changes and dynamics of the lake-terminating glaciers. Debriscovered glaciers have undergone similar thinning compared to clean-ice glaciers, both for lake and land TGs; however, variations exist across the ablation zones between clean and debris-covered glaciers which this study further explores using a data-driven approach. Overall, the proglacial lakes development, changes in debris coverage, and topography significantly affect the glacier responses in the regions.

Limited ground-based surveys and extensive remote sensing analyses have confirmed glacier thinning in the Garhwal Himalaya. More detailed studies on specific glaciers and the drivers of reported changes are essential to comprehend small-scale differences in the effects of climatic warming on Himalayan glaciers. We computed elevation changes and surface flow distribution for 205 (≥0.1 km2) glaciers in the Alaknanda, Bhagirathi, and Mandakini basins, all located in the Garhwal Himalaya, India. This study also investigates a detailed integrated analysis of elevation changes and surface flow velocities for 23 glaciers with varying characteristics to understand the impact of ice thickness loss on overall glacier dynamics. We observed significant heterogeneity in glacier thinning and surface flow velocity patterns using temporal DEMs and optical satellite images with ground-based verification. The average thinning rate was found to be 0.07 ± 0.09 m a-1 from 2000 to 2015, and it increased to 0.31 ± 0.19 m a-1 from 2015 to 2020, with pronounced differences between individual glaciers. Between 2000 and 2015, Gangotri Glacier thinned nearly twice as much as the neighbouring Chorabari and Companion glaciers, which have thicker supraglacial debris that protects the beneath ice from melting. The transitional zone between debris-covered and clean ice glaciers showed substantial flow during the observation period. However, the lower reaches of their debris-covered terminus areas are almost stagnant. These glaciers experienced a significant slowdown (~25 %) between 1993-1994 and 2020-2021, and only the Gangotri Glacier was active even in its terminus region during most observational periods. The decreasing surface gradient reduces the driving stress and causes slow-down surface flow velocities and an increase in stagnant ice. Surface lowering of these glaciers may have substantial long-term impacts on downstream communities and lowland populations, including more frequent cryospheric hazards, which may threaten future water and livelihood security.

Himalaya is experiencing frequent catastrophic mass movement events such as avalanches and landslides, causing loss of human lives and infrastructure. Millions of people reside in critical zones potentially exposed to such catastrophes. Despite this, a comprehensive assessment of mass movement exposure is lacking at a regional scale. Here, we developed a novel method of determining mass movement trajectories and applied it to the Himalayan Mountain ranges for the first time to quantify the exposure of infrastructure, waterways, roadways, and population in six mountain ranges, including Hindu Kush, Karakoram, western Himalaya, eastern Himalaya, central Himalaya, and Hengduan Shan. Our results reveal that the exposure of buildings and roadways to mass movements is highest in Karakoram, whereas central Himalaya has the highest exposed waterways. The hotspots of exposed roadways are concentrated in Nepal, the North Indian states of Uttarakhand, Himachal Pradesh, the Union Territory of Ladakh, and China's Sichuan Province. Our analysis shows that the population in the central Himalaya is currently at the highest exposure to mass movement impacts. Projected future populations based on Shared Socio-economic and Representative Concentration Pathways suggest that changing settlement patterns and emission scenarios will significantly influence the potential impact of these events on the human population. Assessment of anticipated secondary hazards (glacial lake outburst floods) shows an increase in probable headward impacts of mass movements on glacial lakes in the future. Our findings will support researchers, policymakers, stakeholders, and local governments in identifying critical areas that require detailed investigation for risk reduction and mitigation.

Glacial lake outburst floods (GLOFs) are a severe threat to communities in the Himalayas; however, GLOF mitigation strategies have been implemented for only a few lakes, and future changes in hazard are rarely considered. Here, we present a comprehensive assessment of current and future GLOF hazard for Gepang Gath Lake, Western Himalaya, considering rock and/or ice avalanches cascading into the lake. We consider ground surface temperature and topography to define avalanche source zones located in areas of potentially degrading permafrost. GLOF process chains in current and future scenarios, also considering engineered lake lowering of 10 and 30 m, were evaluated. Here, varied avalanche impact waves, erosion patterns, debris flow hydraulics, and GLOF impacts at Sissu village, under 18 different scenarios were assessed. Authors demonstrated that a larger future lake does not necessarily produce larger GLOF events in Sissu, depending, among other factors, on the location from where the triggering avalanche initiates and strikes the lake. For the largest scenarios, 10 m of lowering reduces the high-intensity zone by 54% and 63% for the current and future scenarios, respectively, but has little effect on the medium-intensity flood zone. Even with 30 m of lake lowering, the Sissu helipad falls in the high-intensity zone under all moderate-to-large scenarios, with severe implications for evacuations and other emergency response actions. The approach can be extended to other glacial lakes to demonstrate the efficiency of lake lowering as an option for GLOF mitigation and enable a robust GLOF hazard and risk assessment.

High-magnitude mass flows can have a pervasive geomorphological legacy, yet the shortterm response of valley floors to such intense disturbances is poorly known and poses significant observational challenges in unstable landscapes. We combined satellite remote sensing, numerical modeling, and field observations to reconstruct the short-term geomorphological response of river channels directly affected by the 7 February 2021 ice-rock avalanche-debris flow in Chamoli district, Uttarakhand, India. The flow deposited 10.4 & PLUSMN; 1.6 Mm3 of sediment within the first 30 km and in places reset the channel floor to a zero-state condition, requiring complete fluvial re-establishment. In the 12 months post-event, 7.0 & PLUSMN; 1.5 Mm3 (67.2%) of the deposit volume was removed along a 30-km-long domain and the median erosion rate was 2.3 & PLUSMN; 1.1 m a-1. Most sediment was removed by pre-monsoon and monsoon river flows, which conveyed bedload waves traveling at 0.1-0.3 km day-1 and sustained order-of-magnitude increases in suspended sediment concentrations as far as 85 km from the event source. Our findings characterize a high-mountain fluvial cascade with a short relaxation time and high resilience to a high-magnitude geomorphological perturbation. This system response has wider implications, notably for water quality and downstream hydropower projects, which may be disrupted by elevated bedload and suspended sediment transport.

California's Central Valley, one of the most agriculturally productive regions, is also one of the most stressed aquifers in the world due to anthropogenic groundwater over-extraction primarily for irrigation. Groundwater depletion is further exacerbated by climate-driven droughts. Gravity Recovery and Climate Experiment (GRACE) satellite gravimetry has demonstrated the feasibility of quantifying global groundwater storage changes at uniform monthly sampling, though at a coarse resolution and is thus impractical for effective water resources management. Here, we employ the Random Forest machine learning algorithm to establish empirical relationships between GRACE-derived groundwater storage and in situ groundwater level variations over the Central Valley during 2002-2016 and achieved spatial downscaling of GRACE-observed groundwater storage changes from a few hundred km to 5 km. Validations of our modeled groundwater level with in situ groundwater level indicate excellent Nash-Sutcliffe Efficiency coefficients ranging from 0.94 to 0.97. In addition, the secular components of modeled groundwater show good agreements with those of vertical displacements observed by GPS, and CryoSat-2 radar altimetry measurements and is perfectly consistent with findings from previous studies. Our estimated groundwater loss is about 30 km3 from 2002 to 2016, which also agrees well with previous studies in Central Valley. We find the maximum groundwater storage loss rates of -5.7 ± 1.2 km3 yr-1 and -9.8 ± 1.7 km3 yr-1 occurred during the extended drought periods of January 2007-December 2009, and October 2011-September 2015, respectively while Central Valley also experienced groundwater recharges during prolonged flood episodes. The 5-km resolution Central Valley-wide groundwater storage trends reveal that groundwater depletion occurs mostly in southern San Joaquin Valley collocated with severe land subsidence due to aquifer compaction from excessive groundwater over withdrawal.

In recent decades, climate change has significantly affected glacier dynamics, resulting in mass loss and an increased risk of glacier-related hazards including supraglacial and proglacial lake development, as well as catastrophic outburst flooding. Rapidly changing conditions dictate the need for continuous and detailed observations and analysis of climate-glacier dynamics. Thematic and quantitative information regarding glacier geometry is fundamental for understanding climate forcing and the sensitivity of glaciers to climate change, however, accurately mapping debris-cover glaciers (DCGs) is notoriously difficult based upon the use of spectral information and conventional machine-learning techniques. The objective of this research is to improve upon an earlier proposed deep-learning-based approach, GlacierNet, which was developed to exploit a convolutional neural-network segmentation model to accurately outline regional DCG ablation zones. Specifically, we developed an enhanced GlacierNet2 architecture that incorporates multiple models, automatic post-processing, and basin-level hydrological flow techniques to improve the mapping of DCGs such that it includes both the ablation and accumulation zones. Experimental evaluations demonstrate that GlacierNet2 improves the estimation of the ablation zone and allows a high level of intersection over union (IOU: 0.8839) score, which is higher than the GlacierNet (IOU: 0.8599). The proposed architecture provides complete glacier (both accumulation and ablation zone) outlines at regional scales, with an overall IOU score of 0.8619. This is a crucial first step in automating complete glacier mapping that can be used for accurate glacier modeling or mass-balance analysis.