Search Results (14)

Debris-free and debris-covered glaciers are both extensively present in the southeastern Tibetan Plateau. High-precision and rigorous comparative observational studies on different types of glaciers help us to accurately understand the overall state of water resource variability and the underlying mechanisms. In this study, we used multi-temporal simultaneous UAV surveys to systematically explore the surface elevation change, surface velocity, and surface mass balance of two representative glaciers. Our findings indicate that the thinning rate in the debris-free Parlung No. 4 glacier UAV survey area was consistently higher than that in the debris-covered 24K glacier in 2020–2021 (−1.16 ± 0.03 cm/d vs. −0.36 ± 0.02 cm/d) and 2021–2022 (−0.69 ± 0.03 cm/d vs. −0.26 ± 0.03 cm/d). Moreover, the surface velocity of the Parlung No. 4 glacier was also consistently higher than that of the 24K glacier across the survey period, suggesting a more dynamic glacial state. The surface mass balance of the Parlung No. 4 glacier (2020–2021: −1.82 ± 0.09 cm/d; 2021–2022: −1.30 ± 0.09 cm/d) likewise outpaced that of the 24K glacier (2020–2021: −0.81 ± 0.07 cm/d; 2021–2022: −0.70 ± 0.07 cm/d) throughout the observation period, which indicates that the debris cover slowed the glacier’s melting. Additionally, we extracted the melt contribution of the ice cliff area in the 24K glacier and found that the melt ratio of this ‘hotspot’ area ranged from 10.4% to 11.6% from 2020 to 2022. This comparative analysis of two representative glaciers provides evidence to support the critical role of debris cover in controlling surface elevation changes, glacier dynamics, and surface mass balance. Full article

Debris-covered glaciers have contrasting melting mechanisms and climate response patterns if compared with debris-free glaciers and thus show a unique influence on the hydrological process. Based on high-resolution satellite images and unpiloted aerial vehicle surveys, this study investigated the dynamic changes of Zhuxi Glacier, a thick debris-covered glacier in the southeastern Tibetan Plateau. Our result shows that the whole glacier can be divided into the active regime and stagnant regime along the elevation of 3400 m a.s.l. The mean surface velocity of the active regime was 13.1 m yr⁻¹, which was five times higher than that of the stagnant regime. The surface-lowing rate of this debris-covered glacier reaches more than 1 m yr⁻¹ and displays an accelerating trend. The majority of ice loss concentrates around ice cliffs and supraglacial ponds, the ablation hotspots. These hotspots can be roughly classified into three types, including persistent, expanding, and shrinking patterns, at different dynamic regimes on the Zhuxi Glacier. With the evolution of these hotpots and glacier dynamic changes, the supraglacial ponds showed significant change, with the total number fluctuating from 15 to 38 and the total area increasing from 1128 m² to 95790 m² during the past decade. The recent exponential expansion of the proglacial lake and the significant downwasting of stagnant ice inside the dammed terminus moraine possibly trigger the glacial lake outburst flood and thus threaten the security of livelihoods and infrastructure downstream. Full article

The rapid melting of glaciers has led to severe glacial-hydrological hazards in the Himalayas. An extreme example occurred on 7 February 2021, when a catastrophic mass flow descended from the Ronti glacier at Chamoli, Indian Himalaya, causing widespread devastation, with more than 200 people killed or missing, as well as severe damage to four hydropower projects. To disclose what happened to the Ronti glacier over the past several decades, here, we focused on glacier changes in the Dhauliganga catchment in Uttarakhand, India, over the past two decades. Another five glaciers in the catchment were also studied to map the regional detailed glacier changes. Our achievements are summarized as follows. (1) Based on Landsat images, we constructed two glacier inventories for the catchment in 2001 and 2020. We mapped nearly 413 debris-free glaciers in the catchment between 2001 and 2020 and analyzed the glacier area change at basin and altitude levels. (2) Debris-free glacier area decreased from 477.48 ± 35.23 km² in 2001 to 418.52 ± 36.18 km² in 2020, with a reduction of 58.95 km² or 12.35% over the past two decades. (3) The geodetic mass balance was −0.27± 0.10 m w.e.a⁻¹, with a glacier mass change of −0.12 Gt. a⁻¹ from 2000 to 2013. Based on the surface elevation difference between the Ice, Cloud, and land Elevation Satellite 2 (ICESat-2) footprints (acquired from 2018 to 2021) and the National Aeronautics and Space Administration (NASA) DEM from 2000 to 2021, the average glacier geodetic mass balance was −0.22 ± 0.005 m w.e.a⁻¹, and glacier mass change was −0.10 Gt a⁻¹. (4) Our results were cross verified by available published elevation difference datasets covering multiple temporal periods, where mass balance was by −0.22 ± 0.002 m w.e.a⁻¹ from 1975 to 2000 and −0.28 ± 0.0001 w.e.a⁻¹ from 2000 to 2020. (5) Glacier 1 and Glacier 2, the two largest glaciers in the catchment, experienced a decreasing melt rate from 2000 to 2020, while Glacier 3, Glacier 4, and Glacier 5 demonstrated an increasing melt rate. However, Glacier 6, also known as the collapsed Ronti glacier, had a negative mass balance of −0.04 m w.e.a⁻¹ from 2000 to 2005 and turned positive from 2005 onward with 0.06 m w.e.a⁻¹ from 2005 to 2010, 0.19 m w.e.a⁻¹ from 2010 to 2015, and 0.32 m w.e.a⁻¹ from 2015 to 2020. We postulate that the Ronti glacier collapsed solely because of the significant mass accumulation observed between 3700 to 5500 m a.s.l. Our study helps to understand the collapsed glacier’s mass changes over the past two decades and highlights the necessity to monitor mass-gaining glaciers from space to forecast the risks of disasters. Full article

Glaciers are important sentinels of a changing climate, crucial components of the global cryosphere and integral to their local landscapes. However, many of the commonly used methods for mapping glacier change are labor-intensive and limit the temporal and spatial scope of existing research. This study addresses some of the limitations of prior approaches by developing a novel deep-learning-based method called GlacierCoverNet. GlacierCoverNet is a deep neural network that relies on an extensive, purpose-built training dataset. Using this model, we created a record of over three decades long at a fine temporal cadence (every two years) for the state of Alaska. We conducted a robust error analysis of this dataset and then used the dataset to characterize changes in debris-free glaciers and supraglacial debris over the last ~35 years. We found that our deep learning model could produce maps comparable to existing approaches in the capture of areal extent, but without manual editing required. The model captured the area covered with glaciers that was ~97% of the Randolph Glacier Inventory 6.0 with ~6% and ~9% omission and commission rates in the southern portion of Alaska, respectively. The overall model area capture was lower and omission and commission rates were significantly higher in the northern Brooks Range. Overall, the glacier-covered area retreated by 8425 km² (−13%) between 1985 and 2020, and supraglacial debris expanded by 2799 km² (64%) during the same period across the state of Alaska. Full article

Mapping the outlines of glaciers has primarily relied on the interpretation of satellite optical images. However, the accurate delineation of glaciers in complex terrain mountain regions remains challenging, mainly because the supraglacial debris-covered ablation zones and snow-covered accumulation zones often exhibit the same spectral properties as their adjacent grounds in optical images. This study presents a novel approach by exploring both the satellite synthetic aperture radar (SAR) amplitude and interferometric coherence to map mountain glaciers. This method explores the deviation of the glacier surface signal in the SAR time series to distinguish glacier ice from the surrounding stable ground. To this end, we explored the classifying capabilities of two indices from a set of SAR images, SAR interferometric coherence and amplitude deviation index (ADI), to determine glacier boundary. We found that the two indices complement each other for mapping glaciers. A ratio map based on ADI and SAR coherence (ACR) was then derived, from which the glacier outline was automatically tracked using a specified threshold, followed by manual modification. We validated this approach on two typical valley glaciers, the debris-covered Hailuogou Glacier and debris-free Mozigou Glacier, in Mount Gongga in the southeastern Tibetan Plateau. The results show that the proposed ACR criteria can significantly enhance the contrast between glaciers and their surroundings. By comparing our results with manually delineated glacier outlines from high-resolution cloud-free satellite optical imagery, we found that the misclassification rate and difference rate for our results were 2.6% and 4.2%, respectively. The approach presented in this study can be easily adapted to map the outlines of mountain glaciers worldwide efficiently and is useful for inferring glacier boundary changes in a climate warming context. Full article

Glaciers and snow in the Caucasus are major sources of runoff for populated places in many parts of this mountain region. These glaciers have shown a continuous area decrease; however, the magnitude of mass balance changes at the regional scale need to be further investigated. Here, we analyzed regional changes in surface elevation (or thickness) and geodetic mass balance for 1861 glaciers (1186.1 ± 53.3 km²) between 2000 and 2019 from recently published dataset and outlines of the Caucasus glacier inventory. We used a debris-covered glacier dataset to compare the changes between debris-free and debris-covered glaciers. We also used 30 m resolution ASTER GDEM (2011) to determine topographic details, such as aspect, slope, and elevation distribution of glaciers. Results indicate that the mean rate of glacier mass loss has accelerated from 0.42 ± 0.61 m of water equivalent per year (m w.e. a⁻¹) over 2000–2010, to 0.64 ± 0.66 m w.e. a⁻¹ over 2010–2019. This was 0.53 ± 0.38 m w.e. a⁻¹ in 2000–2019. Mass loss rates differ between the western, central, and eastern Greater Caucasus, indicating the highest mean annual mass loss in the western section (0.65 ± 0.43 m w.e. a⁻¹) in 2000–2019 and much lower in the central (0.48 ± 0.35 m w.e. a⁻¹) and eastern (0.38 ± 0.37 m w.e. a⁻¹) sections. No difference was found between the northern and southern slopes over the last twenty years corresponding 0.53 ± 0.38 m w.e. a⁻¹. The observed decrease in mean annual geodetic mass balance is higher on debris-covered glaciers (0.66 ± 0.17 m w.e. a⁻¹) than those on debris-free glaciers (0.49 ± 0.15 m w.e. a⁻¹) between 2000 and 2019. Thickness change values in 2010–2019 were 1.5 times more negative (0.75 ± 0.70 m a⁻¹) than those in 2000–2010 (0.50 ± 0.67 m a⁻¹) in the entire region, suggesting an acceleration of ice thinning starting in 2010. A significant positive trend of May-September air temperatures at two selected meteorological stations (Terskol and Mestia) along with a negative trend of October-April precipitation might be responsible for the negative mass balances and thinning for all Caucasus glaciers over the study period. These results provide insight into the change processes of regional glaciers, which is key information to improve glaciological and hydrological projections in the Caucasus region. Full article

An increasing number of satellite platforms provide daily images of the Earth’s surface that can be used in quantitative monitoring applications. However, their cost and the need for specific processing software make such products not often suitable for rapid mapping and deformation tracking. Google Earth images have been used in a number of mapping applications and, due to their free and rapid accessibility, they have contributed to partially overcome this issue. However, their potential in Earth’s surface displacement tracking has not yet been explored. In this paper, that aspect is analyzed providing a specific procedure and related MATLAB™ code to derive displacement field maps using digital image correlation of successive Google Earth images. The suitability of the procedure and the potential of such images are demonstrated here through their application to two relevant case histories, namely the Slumgullion landslide in Colorado and the Miage debris-covered glacier in Italy. Result validation suggests the effectiveness of the proposed procedure in deriving Earth’s surface displacement data from Google Earth images. Full article

Glacier surface temperature (GST) is influenced by both the energy flux from the atmosphere above and the thermal dynamics at the ice–water–debris interfaces. However, previous studies on GST are inadequate in time series research and mountain glacier surface temperature retrieval. We evaluate the GST variability at Hailuogou glacier, a temperate glacier located in Southeastern Tibetan Plateau, from 1990 to 2018. We utilized a modified mono-window algorithm to calculate the GST using the Landsat 8 thermal infrared sensor (TIRS) band 10 data and Landsat 5 thematic mapper (TM) band 6 data. Three essential parameters, including the emissivity of ice and snow, atmospheric transmittance, and effective mean atmospheric temperature, were employed in the GST algorithm. The remotely-sensed temperatures were compared with two other single-channel algorithms to validate GST algorithm’s accuracy. Results from different algorithms showed a good agreement, with a mean difference of about 0.6 ℃. Our results showed that the GST of the Hailuogou glacier, both in the upper debris-free part and the lower debris-covered tongue, has experienced a slightly increasing trend at a rate of 0.054 ℃ a⁻¹ during the past decades. Atmospheric warming, expanding debris cover in the lower part, and a darkening debris-free accumulation area are the main causes of the warming of the glacier surface. Full article

Meltwater from the glaciers in High Mountain Asia plays a critical role in water availability and food security in central and southern Asia. However, observations of glacier ablation and accumulation rates are limited in spatial and temporal scale due to the challenges that are associated with fieldwork at the remote, high-altitude settings of these glaciers. Here, using a remote-sensing-based mass-continuity approach, we compute regional-scale surface mass balance of glaciers in five key regions across High Mountain Asia. After accounting for the role of ice flow, we find distinctively different altitudinal surface-mass-balance gradients between heavily debris-covered and relatively debris-free areas. In the region surrounding Mount Everest, where debris coverage is the most extensive, our results show a reversed mean surface-mass-balance gradient of −0.21 ± 0.18 m w.e. a⁻¹ (100 m)⁻¹ on the low-elevation portions of glaciers, switching to a positive mean gradient of 1.21 ± 0.41 m w.e. a⁻¹ (100 m)⁻¹ above an average elevation of 5520 ± 50 m. Meanwhile, in West Nepal, where the debris coverage is minimal, we find a continuously positive mean gradient of 1.18 ± 0.40 m w.e. a⁻¹ (100 m)⁻¹. Equilibrium line altitude estimates, which are derived from our surface-mass-balance gradients, display a strong regional gradient, increasing from northwest (4490 ± 140 m) to southeast (5690 ± 130 m). Overall, our findings emphasise the importance of separating signals of surface mass balance and ice dynamics, in order to constrain better their contribution towards the ice thinning that is being observed across High Mountain Asia. Full article

Himalayan glaciers have shrunk rapidly in recent decades, but the spatial pattern of ice loss is highly variable and appears to be modulated by factors relating to individual glacier characteristics. This hinders our ability to predict their future evolution, which is vital for water resource management. The aim of this study is to assess recent glacier changes in the little-studied Annapurna Conservation Area (ACA; area: 7629 km²) in Nepal, and to explore local controls influencing their behaviour. We map changes in glacier area, surface elevation, and ice flow velocity on a large sample of glaciers (n = 162) in the ACA between 2000 and 2016. We found that total glacier area decreased by 8.5% between 2000 and 2014/15. Ice surface velocity changes between 2002 and 2016 were variable, with no clear trend of acceleration or deceleration. The mean surface elevation change for a smaller sample of glaciers (n = 72) was −0.33 ± 0.22 m a⁻¹ between 2000 and 2013/16, which equates to a mean mass balance of −0.28 ± 0.24 m w.e. a⁻¹. There was a trend of increasingly less negative mass balance towards the north. Glaciers that lost the most mass in the north of the ACA tended to have lower maximum elevations, bottom-heavy hypsometries, and were more likely to be avalanche-fed. However, these patterns were not apparent in glaciers in central ACA. There was no significant difference in the mean surface elevation change rate on the ablation zones of debris-covered compared with debris-free glaciers. Our work shows that glaciers in the ACA are losing area and mass at variable rates, but that the influence of local controls is complex, which introduces large uncertainties when predicting their future evolution. Full article

Glaciers are recognized as key indicators of climate change on account of their sensitive reaction to minute climate variations. Extracting more accurate glacier boundaries from satellite data has become increasingly popular over the past decade, particularly when glacier outlines are regarded as a basis for change assessment. Automated multispectral glacier mapping methods based on Landsat imagery are more accurate, efficient and repeatable compared with previous glacier classification methods. However, some challenges still exist in regard to shadowed areas, clouds, water, and debris cover. In this study, a new index called the automated glacier extraction index (AGEI) is proposed to reduce water and shadow classification errors and improve the mapping accuracy of debris-free glaciers using Landsat imagery. Four test areas in China were selected and the performances of four commonly used methods: Maximum-likelihood supervised classification (ML), normalized difference snow and ice index (NDSI), single-band ratios Red/SWIR, and NIR/SWIR, were compared with the AGEI. Multiple thresholds identified by inspecting the shadowed glacier areas were tested to determine an optimal threshold. The confusion matrix, sub-pixel analysis, and plot-scale validation were calculated to evaluate the accuracies of glacier maps. The overall accuracies (OAs) created by AGEI were the highest compared to the four existing automatic methods. The sub-pixel analysis revealed that AGEI was the most accurate method for classifying glacier edge mixed pixels. Plot-scale validation indicated AGEI was good at separating challenging features from glaciers and matched the actual distribution of debris-free glaciers most closely. Therefore, the AGEI with an optimal threshold can be used for mapping debris-free glaciers with high accuracy, particularly in areas with shadows and water features. Full article

The Shaksgam Valley, located on the north side of the Karakoram Mountains of western China, is situated in the transition zone between the Indian monsoon system and dry arid climate zones. Previous studies have reported abnormal behaviors of the glaciers in this region compared to the global trend of glacier retreat, so the region is of special interest for glacier-climatological studies. For this purpose, long-term monitoring of glaciers in this region is necessary to obtain a better understanding of the relationships between glacier changes and local climate variations. However, accurate historical and up-to-date glacier inventory data for the region are currently unavailable. For this reason, this study conducted glacier inventories for the years 1970, 1980, 1990, 2000 and 2014 (i.e., a ~10-year interval) using multi-temporal remote sensing imagery. The remote sensing data used included Corona KH-4A/B (1965–1971), Hexagon KH-9 (1980), Landsat Thematic Mapper (TM) (1990/1993), Landsat Enhanced Thematic Mapper Plus (ETM+) (2000/2001), and Landsat Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) (2014/2015) multispectral satellite images, as well as digital elevation models (DEMs) from the Shuttle Radar Topography Mission (SRTM), DEMs generated from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images (2005–2014), and Advanced Land Observing Satellite (ALOS) World 3D 30 m mesh (AW3D30). In the year 2014, a total of 173 glaciers (including 121 debris-free glaciers) (>0.5 km²), covering an area of 1478 ± 34 km² (area of debris-free glaciers: 295 ± 7 km²) were mapped. The multi-temporal glacier inventory results indicated that total glacier area change between 1970–2014 was not significant. However, individual glacier changes showed significant variability. Comparisons of the changes in glacier terminus position indicated that 55 (32 debris-covered) glaciers experienced significant advances (~40–1400 m) between 1970–2014, and 74 (32 debris-covered) glaciers experienced significant advances (~40–1400 m) during the most recent period (2000–2014). Notably, small glaciers showed higher sensitivity to climate changes, and the glaciers located in the western part of the study site were exhibiting glacier area expansion compared to other parts of the Shaksgam Valley. Finally, regression analyses indicated that topographic parameters were not the main driver of glacier changes. On the contrary, local climate variability could explain the complex behavior of glaciers in this region. Full article

Himalayan glacier changes in the context of global climate change have attracted worldwide attention due to their profound cryo-hydrological ramifications. However, an integrated understanding of the debris-free and debris-covered glacier evolution and its interaction with glacial lake is still lacking. Using one case study in the Gyirong River Basin located in the central Himalayas, this paper applied archival Landsat imagery and an automated mapping method to understand how glaciers and glacial lakes interactively evolved between 1988 and 2015. Our analyses identified 467 glaciers in 1988, containing 435 debris-free and 32 debris-covered glaciers, with a total area of 614.09 ± 36.69 km². These glaciers decreased by 16.45% in area from 1988 to 2015, with an accelerated retreat rate after 1994. Debris-free glaciers retreated faster than debris-covered glaciers. As a result of glacial downwasting, supraglacial debris coverage expanded upward by 17.79 km² (24.44%). Concurrent with glacial retreat, glacial lakes increased in both number (+41) and area (+54.11%). Glacier-connected lakes likely accelerated the glacial retreat via thermal energy transmission and contributed to over 15% of the area loss in their connected glaciers. On the other hand, significant glacial retreats led to disconnections from their proglacial lakes, which appeared to stabilize the lake areas. Continuous expansions in the lakes connected with debris-covered glaciers, therefore, need additional attention due to their potential outbursts. In comparison with precipitation variation, temperature increase was the primary driver of such glacier and glacial lake changes. In addition, debris coverage, size, altitude, and connectivity with glacial lakes also affected the degree of glacial changes and resulted in the spatial heterogeneity of glacial wastage across the Gyirong River Basin. Full article

We developed an automated approach for mapping persistent ice and snow cover (glaciers and perennial snowfields) from Landsat TM and ETM+ data across a variety of topography, glacier types, and climatic conditions at high latitudes (above ~65°N). Our approach exploits all available Landsat scenes acquired during the late summer (1 August–15 September) over a multi-year period and employs an automated cloud masking algorithm optimized for snow and ice covered mountainous environments. Pixels from individual Landsat scenes were classified as snow/ice covered or snow/ice free based on the Normalized Difference Snow Index (NDSI), and pixels consistently identified as snow/ice covered over a five-year period were classified as persistent ice and snow cover. The same NDSI and ratio of snow/ice-covered days to total days thresholds applied consistently across eight study regions resulted in persistent ice and snow cover maps that agreed closely in most areas with glacier area mapped for the Randolph Glacier Inventory (RGI), with a mean accuracy (agreement with the RGI) of 0.96, a mean precision (user’s accuracy of the snow/ice cover class) of 0.92, a mean recall (producer’s accuracy of the snow/ice cover class) of 0.86, and a mean F-score (a measure that considers both precision and recall) of 0.88. We also compared results from our approach to glacier area mapped from high spatial resolution imagery at four study regions and found similar results. Accuracy was lowest in regions with substantial areas of debris-covered glacier ice, suggesting that manual editing would still be required in these regions to achieve reasonable results. The similarity of our results to those from the RGI as well as glacier area mapped from high spatial resolution imagery suggests it should be possible to apply this approach across large regions to produce updated 30-m resolution maps of persistent ice and snow cover. In the short term, automated PISC maps can be used to rapidly identify areas where substantial changes in glacier area have occurred since the most recent conventional glacier inventories, highlighting areas where updated inventories are most urgently needed. From a longer term perspective, the automated production of PISC maps represents an important step toward fully automated glacier extent monitoring using Landsat or similar sensors. Full article