Search Results (19)

Sustainable small hydropower (SHP) is central to the clean-energy transition of mountainous Central Asia, yet its long-term reliability depends on a rapidly changing cryosphere. Winter river-ice dynamics—an underappreciated control on run-of-river generation—remain poorly characterized owing to the collapse of in situ hydrometeorological networks since 1991. We use a 112-month Sentinel-1 C-band SAR time series (February 2017–May 2026) over a 5320 km² headwater catchment of the Chu River basin, northern Tien Shan, Kyrgyzstan, to quantify river-ice phenology at 20 m resolution using a per-pixel summer-baseline anomaly approach. Mid-winter (December–February) ice cover declined significantly at −0.51%·yr⁻¹ (p = 0.013; Mann–Kendall p = 0.029), with the 2026 winter recording an unprecedented 2.6–2.8 σ departure from the 2017–2025 climatology. Contrasting the cold 2022 and warm 2026 winters revealed bidirectional climate sensitivity—early breakup versus persistent thin ice—posing distinct SHP hazards. ERA5-Land reanalysis (1992–2026) showed significant winter warming with no precipitation or snowfall trend, indicating thermally forced ice decline. Interpreted within a conceptual Peak Water scenario, this signals a closing window of opportunity for SHP generation, with direct relevance to sustainable water–energy management and the UN Sustainable Development Goals (SDG 7; SDG 13). Our results provide the first decadal, satellite-based evidence of river-ice loss for Central Asian mountain rivers and a transferable monitoring framework to support climate-resilient, sustainable hydropower planning in ungauged basins. Full article

Glacial lake outburst floods (GLOFs) are a major cryosphere-related hazard in High Mountain Asia (HMA), where glacier mass loss and changing hydroclimatic conditions are reshaping glacial-lake systems and increasing the prevalence of potentially unstable lake–dam configurations. However, current knowledge remains fragmented across HMA. Therefore, this review synthesizes historical evidence, future changes, and risk-reduction strategies of GLOFs across HMA from a remote-sensing perspective. Historical evidence derived from satellite archives, multi-temporal lake inventories, geomorphological analyses, and documented event records indicate that reported GLOFs in HMA are strongly clustered by sub-region and dam type, with moraine-dammed lakes representing the dominant source of documented events, while ice-dammed lakes remain important in several mountain belts. The compiled record also shows that GLOFs have caused severe human, economic, geomorphic, and ecological losses. Future projections based on glacier evolution, glacial-lake expansion, and climate-sensitive hazard assessments indicate continued glacial-lake growth under global warming. However, reliable prediction of future GLOF event timing, magnitude, and frequency remains constrained by uncertainties in glacier evolution, dam stability, and triggering processes. This review further shows that effective GLOF risk reduction in HMA requires integrated systems that combine hazard and risk mapping, early warning, structural interventions, and non-structural measures. It also highlights the need to better link remote sensing with monitoring, assessment, and implementation frameworks, and proposes an integrated management cycle to support practical risk reduction. It concludes that the most urgent research priorities are harmonized multi-temporal lake inventories, targeted field observations, explicit consideration of heatwaves and compound extremes, transparent uncertainty propagation, and stronger operationalization of monitoring and warning systems to support durable climate adaptation and disaster risk reduction across HMA. Full article

Glacial lake dynamics in high-mountain regions serve as a sensitive proxy for cryospheric responses to climate warming. This study utilizes multi-temporal Sentinel-2 imagery and digital elevation model (DEM) data to quantify glacial lake evolution in the Donglin Tsangpo Watershed, a strategically important section of the China–Nepal Economic Corridor, from 2016 to 2024. The results show a significant expansion in both the number (from 43 to 56) and total area (from 3.97 km² to 4.94 km², +24.43%) of glacial lakes, primarily driven by the rapid emergence of very small lakes (0.02–0.05 km²) and a clear upward shift in elevation distribution, with new lakes forming above 5300 m and extending to elevations exceeding 5500 m. Analysis of Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) reveals that this expansion coincided with pronounced positive thermal anomalies, particularly the 2020 extreme warm event (daytime +3.88 °C, nighttime +1.61 °C). Mechanistic analysis using the ERA5-Land reanalysis dataset further demonstrates that persistent positive downward longwave radiation (LW) anomalies (peaking at +10.71 W/m² in 2021) effectively compensated for reduced shortwave input, inhibiting nocturnal refreezing and extending the effective ablation period. Furthermore, a rising liquid-to-solid precipitation ratio and extreme melt-day anomalies (up to +39.36 days) provided intensified hydrothermal inputs, driving the pronounced expansion of glacier-contact lakes despite non-linear interannual responses. This study also estimates individual lake volumes, identifying a transition toward rapid lake development that elevates potential downstream hazard exposure. These findings provide a high-resolution dataset and a robust physical framework for transboundary environmental monitoring and risk assessment in this climate-sensitive region. Full article

Glacial lake outburst floods (GLOFs) are high-impact hazards in mountain regions, yet many events remain poorly documented because field access is limited and lake evolution can occur on sub-weekly time scales. Here, we used high spatiotemporal resolution PlanetScope imagery (3 m) to quantify the seasonal evolution and abrupt drainage of a moraine-dammed glacial lake in August 2025 in northern Pakistan. Historical lake dynamics were reconstructed using PlanetScope (2016–2024) imagery and multi-decadal Landsat observations (1992–2018). Climatic conditions were evaluated using ERA5-Land temperature data, and seasonal snow dynamics were characterized using MODIS and PlanetScope-based snow cover analyses. Multi-decadal satellite imagery indicates that lake formation in this catchment was historically intermittent, with no evidence of abrupt drainage before 2025, highlighting the anomalous nature of the event. PlanetScope observations show steady lake expansion throughout summer 2025, reaching a maximum area of 0.052 km² prior to the GLOF on August 22. Pre- and post-event imagery reveals no discernible landslide or impact trigger. Instead, the observations are most consistent with a failure mechanism driven by meltwater-driven lake growth and overtopping or erosion of the moraine dam. The 2025 summer season (June to September) was characterized by exceptionally warm conditions and unprecedented early snow depletion relative to the 2000–2024 baseline, suggesting a strong climatic and cryospheric contribution to the outburst. These results demonstrate the value of integrating dense time series of satellite observations and climatic data for capturing glacial-lake life cycles and diagnosing likely controls on outburst initiation. The study highlights the critical role of high-frequency satellite remote sensing for improving GLOF monitoring and early-warning capabilities in data-scarce mountain environments. Full article

The Upper Zarafshon River Basin (UZRB) in Tajikistan hosts numerous glaciers, of which the Zarafshon glacier is the largest and most important source of meltwater for both Tajikistan and Uzbekistan. In this study, we analyzed glacier retreat, surface displacement, and the evolution of supraglacial features from 2016 to 2024 using multi-temporal, high-resolution satellite imagery from Gaofen-2 and PlanetScope (80 cm and 3 m spatial resolution). We selected five representative glaciers-№ 168, 178, 185, 202, and 203 based on their size (greater than 1 km²) and hydrological significance. Our comprehensive investigation of the glaciers in 2024 includes data on glacier area, length, supraglacial lakes, and morphological classification. The results show a decrease in total glacier area from 254.1 km² in 2016 to 252.8 km² in 2024. Surface movement patterns, derived from visual and geomorphological assessments, reveal spatially heterogeneous displacement, especially in debris-covered areas. Supraglacial lakes and ponds showed dynamic changes, with the most significant expansion in 2022, driven by increased surface melt and subglacial hydrological reorganization. These findings highlight the need for ongoing glacier monitoring in the Zarafshon River Basin (ZRB) due to the significant implications that cryospheric changes hold for regional hydrology, water security, and the frequency of climate-induced natural hazards. Full article

Historical remote sensing imagery, including archival aerial photographs and declassified satellite imagery, has been increasingly used to extend earth observation records into periods not covered by modern satellite missions. However, the broader application of these data remains constrained by georeferencing challenges related to incomplete metadata, uncertain acquisition geometry, and heterogeneous image characteristics. This systematic review examines georeferencing practices for historical remote sensing imagery. Out of the 2547 studies identified in the literature, 205 peer-reviewed journal articles were deemed eligible for analysis. This systematic review provides the first comprehensive, PRISMA-compliant synthesis of georeferencing practices for historical remote sensing imagery, analyzing 205 peer-reviewed studies to establish methodological patterns and identify critical gaps. The review considers imagery types, spatial and temporal distributions of case studies, georeferencing workflows, geometric constraints, and accuracy reporting practices. The results indicate a strong reliance on ground control points and a clear preference for manual or semi-automatic georeferencing approaches, while fully automatic methods remain rare. Although the use of historical imagery has increased over time, its potential has not been fully exploited due to persistent georeferencing difficulties, and study areas are often spatially limited or selectively processed to achieve acceptable accuracy. Nevertheless, properly georeferenced historical imagery has been widely applied to land-cover analysis, geomorphology, cryosphere research, hazard assessment, and archeology by extending observation records into earlier decades. Full article

Catastrophic mass flows originating from the high mountain cryosphere often cause cascading hazards. With increasing human activities in the alpine region and the sensitivity of the cryosphere to climate warming, cryospheric hazards are becoming more frequent in the mountain regions. Monitoring the evolution and impact of the glaciers and ice-rock avalanches and hazard consequences in the mountain regions is crucial to understand nature and drivers of mass flow process in order to prevent and mitigate potential hazard risks. In this study, the glacier and ice-rock avalanches that occurred in the Tibetan Plateau (TP) were investigated based on the Sentinel-2 satellite data and in situ observations, and the main driving forces and impacts on the regional environment, landscape, and geomorphological conditions were also analyzed. The results showed that the avalanche deposit of Arutso glacier No. 53 completely melted away in 2 years, while the deposit of Arutso glacier No. 50 melted in 7 years. Four large-scale ice-rock avalanches in the Sedongpu basin not only had significant impacts on the river flow, landscape, and geomorphologic shape in the basin, but also caused serious disasters in the region and beyond. These glacier and ice-rock avalanches were caused by temperature anomaly, heavy precipitation, climate warming, and seismic activity, etc., which act on the specific glacier properties in the high mountain regions. The study highlights scientific advances should support and benefit the remote and vulnerable mountain communities to make mountain regions safer. 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

Glacial lakes in the Upper Indus Basin (UIB) are rapidly evolving due to accelerated glacier retreat driven by climate change. Here we present a comprehensive inventory of glacial lakes using Sentinel-1 SAR data with adaptive backscatter thresholding, enabling consistent detection under challenging conditions and improving delineation accuracy. In August 2023, we identified 6019 glacial lakes at scales from 0.001 to 5.80 km², covering a cumulative area of 266 km² (~0.06% of the basin). Although more than 90% of the lakes are smaller than 0.1 km², large lakes (>0.1 km²) account for over 57% of the total lake area. Most lakes are concentrated between 4000 and 4600 m, coinciding with the main glacierized zone. Regional patterns reveal that the Hindu Kush and Himalayas are dominated by glacier erosion lakes (GELs) and moraine-dammed lakes (MDLs), reflecting widespread glacier retreat, whereas the Karakoram is characterized by numerous supraglacial lakes (SGLs) associated with extensive debris-covered glaciers. Compared to previous optical-based inventories, our SAR-based approach captures more lakes and better represents small and transient features such as SGLs. These findings provide a more accurate baseline for assessing cryospheric change and glacial lake hazards in one of the world’s most heavily glacierized basins. Full article

Glacial lake outburst floods (GLOFs) represent increasingly common and high-magnitude geohazards across the cryosphere of the Tibetan Plateau, particularly under ongoing climate warming and glacier retreat. This study combines multi-temporal remote sensing imagery and detailed Flo-2D hydrodynamic modeling to investigate the erosive dynamics of the 2020 Jinwuco GLOF in Southeastern Tibetan Plateau. Key conclusions include: (1) The 2.35 km-long flood routing channel exhibits pronounced non-uniformity in horizontal curvature, channel width, and cross-sectional shape, significantly influencing flood propagation; five representative cross-sections divide the channel into six distinct segments. (2) Prominent lateral erosion occurred proximally to the dam, attributable to extreme erosive forces and high sediment transport capacity during peak discharge, with horizontal channel curvature further amplifying local impact and erosion. (3) Erosion rates were highest near the dam and in downstream narrow segments, while mid-reach sections with greater width experienced lower erosion. (4) Maximum flow depths reached 28.12 m in topographically confined reaches, whereas peak velocities occurred in upstream and downstream curved sections. (5) The apparent critical erosive shear stress of bank material is controlled not only by soil strength but also by flood dynamics and pre-existing channel morphology, indicating strong feedback between flow dynamics, channel morphology, and critical erosive shear stress of bank material. This study provides a generalized and transferable framework for analyzing GLOF-related erosion in data-scarce high-altitude regions, offering critical insights for hazard assessment, regional planning, and risk mitigation strategies. Full article

During a research expedition to the Kanchenjunga Conservation Area (KCA), eastern Nepal, in April–June 2024, local concern was expressed about the rapid development of meltwater ponds upon the terminus of the Kanchenjunga glacier since 2020, especially in terms of the possible formation of a large and potentially dangerous glacial lake. Our resultant study of the issue included informal interviews with local informants, comparison of time series satellite composite images acquired by Sentinel-2 Multispectral Instrument, and modeling of different lake development, outburst flood scenarios, and prospective downstream impacts. Assuming that the future glacial lake will be formed by the merging of present-day supraglacial ponds, filling the low-gradient area beneath the present-day glacier terminal complex, we estimated the potential volume of a Kanchenjunga proglacial lake to be 33 × 10⁶ m³. Potential mass movement-triggered outburst floods would travel downstream distances of almost 120 km even under the small magnitude scenario, and under the worst-case scenario would reach the Indo-Gangetic Plain and cross the border into India, exposing up to 90 buildings and 44 bridges. In response, we suggest that the lower Kanchenjunga glacier region be regularly monitored by both local communities and Kathmandu-based research entities over the next decade. The development of user-friendly early warning systems, hazard mapping and zoning programs, cryospheric hazards awareness building programs, and construction of locally appropriate flood mitigation measures are recommended. Finally, the continued development and refinement of the models presented here could provide governments and remote communities with a set of inexpensive and reliable tools capable of providing the basic information needed for communities to make informed decisions regarding hazard mitigation, adaptive, and/or preventive measures related to changing glaciers. Full article

Climate change is resulting in significant transformations in mountain areas all over the world, causing the melting of glacier ice, reduction in snow accumulation, and permafrost loss. Changes in the mountain cryosphere are not only modifying flora and fauna distributions but also affecting the stability of slopes in those regions. For all these reasons, and because of the risks these phenomena pose to the population, the dentification of dangerous areas is a crucial step in the development of risk reduction strategies. While several methods and examples exist that cover the assessment and computation of single sub-components, there is still a lack of application of risk assessment due to glacier melting over large areas in which the final result can be directly employed in the design of risk mitigation policies at regional and municipal levels. This research is focused on landslides and gravitational movements on slopes resulting from rapid glacier melting phenomena in the Valle d’Aosta region in Italy, with the aim of providing a tool that can support spatial planning in response to climate change in Alpine environments. Through the conceptualization and development of a GIS-based and multi-criteria approach, risk is then estimated by defining hazard indices that consider different aspects, combining the experience acquired from studies carried out in various disciplinary fields, to obtain a framework at the regional level. This first assessment is then deepened for the Lys River Valley, where the mapping of hazardous areas was implemented, obtaining a classification of buildings according to their hazard score to estimate the potential damage and total risk relating to possible slope instability events due to ice melt at the local scale. Full article

Continuously monitoring and mapping glacial lake variation is of great importance for determining changes in water resources and potential hazards in alpine cryospheric regions. The semi-automated glacial lake mapping methods used currently are hampered by inherent subjectivity and inefficiency. This study used improved YOLOv5 strategies to extract glacial lake boundaries from Sentinel-2 imagery. These strategies include using the space-to-depth technique to identify small glacial lakes, and adopting the coordinate attention and the convolution block attention modules to improve mapping performance and adaptability. In terms of glacial lake extraction, the improved YOLOv5-seg network achieved values of 0.95, 0.93, 0.96, and 0.94 for precision (P), recall (R), mAP_0.5, and the F1 score, respectively, indicating an overall improvement in performance of 12% compared to that of the newest YOLOv8 networks. In High Mountain Asia (HMA), 23,108 glacial lakes with a total area of 1847.5 km² were identified in imagery from 2022 using the proposed method. Compared with the use of manual interpretation for lake boundary extraction in test sites of HMA, the proposed method achieved values of 0.89, 0.87, and 0.86 for P, R, and the F1 score, respectively. Our proposed deep learning method has improved accuracy in glacial lake extraction because it can address the challenge represented by frozen or high-turbidity glacial lakes in HMA. Full article

Over the past decades, the cryosphere has changed significantly in High Mountain Asia (HMA), leading to multiple natural hazards such as rock–ice avalanches, glacier collapse, debris flows, landslides, and glacial lake outburst floods (GLOFs). Monitoring cryosphere change and evaluating its hydrological effects are essential for studying climate change, the hydrological cycle, water resource management, and natural disaster mitigation and prevention. However, knowledge gaps, data uncertainties, and other substantial challenges limit comprehensive research in climate–cryosphere–hydrology–hazard systems. To address this, we provide an up-to-date, comprehensive, multidisciplinary review of remote sensing techniques in cryosphere studies, demonstrating primary methodologies for delineating glaciers and measuring geodetic glacier mass balance change, glacier thickness, glacier motion or ice velocity, snow extent and water equivalent, frozen ground or frozen soil, lake ice, and glacier-related hazards. The principal results and data achievements are summarized, including URL links for available products and related data platforms. We then describe the main challenges for cryosphere monitoring using satellite-based datasets. Among these challenges, the most significant limitations in accurate data inversion from remotely sensed data are attributed to the high uncertainties and inconsistent estimations due to rough terrain, the various techniques employed, data variability across the same regions (e.g., glacier mass balance change, snow depth retrieval, and the active layer thickness of frozen ground), and poor-quality optical images due to cloudy weather. The paucity of ground observations and validations with few long-term, continuous datasets also limits the utilization of satellite-based cryosphere studies and large-scale hydrological models. Lastly, we address potential breakthroughs in future studies, i.e., (1) outlining debris-covered glacier margins explicitly involving glacier areas in rough mountain shadows, (2) developing highly accurate snow depth retrieval methods by establishing a microwave emission model of snowpack in mountainous regions, (3) advancing techniques for subsurface complex freeze–thaw process observations from space, (4) filling knowledge gaps on scattering mechanisms varying with surface features (e.g., lake ice thickness and varying snow features on lake ice), and (5) improving and cross-verifying the data retrieval accuracy by combining different remote sensing techniques and physical models using machine learning methods and assimilation of multiple high-temporal-resolution datasets from multiple platforms. This comprehensive, multidisciplinary review highlights cryospheric studies incorporating spaceborne observations and hydrological models from diversified techniques/methodologies (e.g., multi-spectral optical data with thermal bands, SAR, InSAR, passive microwave, and altimetry), providing a valuable reference for what scientists have achieved in cryosphere change research and its hydrological effects on the Third Pole. Full article

Glacier, snow, and ice are the essential components of the Himalayan cryosphere and provide a sustainable water source for different applications. Continuous and accurate monitoring of glaciers allows the forecasting analysis of natural hazards and water resource management. In past literature, different methodologies such as spectral unmixing, object-based detection, and a combination of various spectral indices are commonly utilized for mapping snow, ice, and glaciers. Most of these methods require human intervention in feature extraction, training of the models, and validation procedures, which may create bias in the implementation approaches. In this study, the deep learning classifier based on ENVINet5 (U-Net) architecture is demonstrated in the delineation of glacier boundaries along with snow/ice over the Bara Shigri glacier (Western Himalayas), Himachal Pradesh, India. Glacier monitoring with Landsat data takes the advantage of a long coverage period and finer spectral/spatial resolution with wide coverage on a larger scale. Moreover, deep learning utilizes the semantic segmentation network to extract glacier boundaries. Experimental outcomes confirm the effectiveness of deep learning (overall accuracy, 91.89% and Cohen’s kappa coefficient, 0.8778) compared to the existing artificial neural network (ANN) model (overall accuracy, 88.38% and kappa coefficient, 0.8241) in generating accurate classified maps. This study is vital in the study of the cryosphere, hydrology, agriculture, climatology, and land-use/land-cover analysis. Full article