Inventory and classification of the post Little Ice Age glacial lakes in Svalbard

Rapid changes of glacial lakes are among the most visible indicators of global warming in glacierized areas around the world. The general trend is that the area and number of glacial lakes increase significantly in high mountain areas and polar latitudes. However, there is a lack of knowledge about the current state of glacial lakes in the High Arctic. This study aims to address this issue by providing the first glacial lake inventory from Svalbard, with focus on the genesis and evolution of glacial lakes since the end of the Little Ice Age. We use aerial photographs and topographic data from 1936 to 2012 and satellite imagery from 2013 to 2020. The inventory includes the development of 566 glacial lakes (total area of 145.91 km2) that were in direct contact with glaciers in 2008–2012. From the 1990s to the end of the 2000s, the total glacial lake area increased by nearly a factor of six. A decrease in the number of lakes between 2012 and 2020 is related to two main processes: the drainage of 197 lakes and the merger of smaller reservoirs into larger ones. The changes of glacial lakes show how climate change in the High Arctic affect proglacial geomorphology by enhanced formation of glacial lakes, leading to higher risks associated with glacier lake outburst floods in Svalbard.

Spatio-temporal evolution of glacial lakes in the Tibetan Plateau over the past 30 years

As the Third Pole of the Earth and the Water Tower of Asia, Tibetan Plateau (TP) nurtures large numbers of glacial lakes, which are sensitive to global climate change. These lakes modulate the freshwater ecosystem in the region, but concurrently pose severe threats to the valley population by means of sudden glacial lake outbursts and consequent floods (GLOFs). Lack of high-resolution multi-temporal inventory of glacial lakes in TP hampers a better understanding and prediction of the future trend and risk of glacial lakes. Here, we created a multi-temporal inventory of glacial lakes in TP using 30 years record of satellite images (1990–2019), and discussed their characteristics and spatio-temporal evolution over the years. Results showed that their number and area had increased by 3285 and 258.82 km2, respectively in the last 3 decades. We noticed that different regions of TP exhibited varying change rates in glacial lake size; some regions even showed decreasing trend such as the western Pamir and the eastern Hindu Kush because of reduced rainfall rates. The mapping uncertainty is about 17.5 %, lower than other available datasets, thus making our inventory, a reliable one for the spatio-temporal evolution analysis of glacial lakes in TP. Our lake inventory data are freely available at https://doi.org/10.5281/zenodo.5574289 (Dou et al., 2021); it can help to study climate change-glacier-glacial lake-GLOF interactions in the third pole and serve input to various hydro-climatic studies.

ACFNet: A Feature Fusion Network for Glacial Lake Extraction Based on Optical and Synthetic Aperture Radar Images

Glacial lake extraction is essential for studying the response of glacial lakes to climate change and assessing the risks of glacial lake outburst floods. Most methods for glacial lake extraction are based on either optical images or synthetic aperture radar (SAR) images. Although deep learning methods can extract features of optical and SAR images well, efficiently fusing two modality features for glacial lake extraction with high accuracy is challenging. In this study, to make full use of the spectral characteristics of optical images and the geometric characteristics of SAR images, we propose an atrous convolution fusion network (ACFNet) to extract glacial lakes based on Landsat 8 optical images and Sentinel-1 SAR images. ACFNet adequately fuses high-level features of optical and SAR data in different receptive fields using atrous convolution. Compared with four fusion models in which data fusion occurs at the input, encoder, decoder, and output stages, two classical semantic segmentation models (SegNet and DeepLabV3+), and a recently proposed model based on U-Net, our model achieves the best results with an intersection-over-union of 0.8278. The experiments show that fully extracting the characteristics of optical and SAR data and appropriately fusing them are vital steps in a network’s performance of glacial lake extraction.

Dynamics of Lower-Barun Glacier and Glacial Lake and its GLOF Susceptibility Using Geospatial Analysis and Modelling

Shrinkage of some of the glaciers has direct impacts on the formation and expansion of glacial lakes. Sudden glacial lake outburst floods (GLOFs) are a major threat to lives and livelihoods downstream as they can cause catastrophic damage. In this study, we present the dynamics of the Lower-Barun glacier and glacial lakes and their GLOF susceptibility. We used multi temporal Landsat and Sentinel satellite imagery and extracted the lake outlines using the Normalized Difference Water Index (NDWI) with manual post-correction while the glacier outline was digitized manually. Multi-criteria decision-based method was used to assess the GLOF susceptibility. For the estimation of peak discharge and failure time, an empirical model developed by Froelich (1995) was used. The surface area of the Lower-Barun glacial lake was increased by 86% in the last 40 yrs (from 1979 to 2018), with a mean increase of 0.0432 km2/yr. The shrinkage in the glacier area is around 0.49 km2/yr and has shrunk by 8% in the last four decades. The retreat of the Lower-Barun glacier was 0.20% per year in the last four decades. The susceptibility index was 0.94, which suggests that the lake is very highly susceptible to the GLOF. The peak discharge of 5768 m3/s is produced when the breach depth is 20 m and the entire water volume is released. Likewise, in the case of 15 m breach depth, the peak discharge of 4038 m3/s is formed. Breach depth scenario of 10 m, peak discharge of 2442 m3/s is produced and in case of breach depth of 5 m produces the peak discharge of 1034 m3/s. If GLOF occurs, it can exert disastrous impacts on the livelihood and infrastructure in the downstream. So, it is necessary to examine such lakes regularly and mitigation measures to lower the GLOF susceptibility should be emphasized.

Glacial Lake Area Change and Potential Outburst Flood Hazard Assessment in the Bhutan Himalaya

Against the background of climate change-induced glacier melting, numerous glacial lakes are formed across high mountain areas worldwide. Existing glacial lake inventories, chiefly created using Landsat satellite imagery, mainly relate to 1990 onwards and relatively long (decadal) temporal scales. Moreover, there is a lack of robust information on the expansion and the GLOF hazard status of glacial lakes in the Bhutan Himalaya. We mapped Bhutanese glacial lakes from the 1960s to 2020, and used these data to determine their distribution patterns, expansion behavior, and GLOF hazard status. 2,187 glacial lakes (corresponding to 130.19 ± 2.09 km2) were mapped from high spatial resolution (1.82–7.62 m), Corona KH-4 images from the 1960s. Using the Sentinel-2 (10 m) and Sentinel-1 (20 m × 22 m), we mapped 2,553 (151.81 ± 7.76 km2), 2,566 (152.64 ± 7.83 km2), 2,572 (153.94 ± 7.83 km2), 2,569 (153.97 ± 7.79 km2) and 2,574 (156.63 ± 7.95 km2) glacial lakes in 2016, 2017, 2018, 2019 and 2020, respectively. The glacier-fed lakes were mainly present in the Phochu (22.63%) and the Kurichu (20.66%) basins. A total of 157 glacier-fed lakes have changed into non-glacier-fed lakes over the 60 years of lake evolution. Glacier-connected lakes (which constitutes 42.25% of the total glacier-fed lake) area growth accounted for 75.4% of the total expansion, reaffirming the dominant role of glacier-melt water in expanding glacial lakes. Between 2016 and 2020, 19 (4.82 km2) new glacial lakes were formed with an average annual expansion rate of 0.96 km2 per year. We identified 31 lakes with a very-high and 34 with high GLOF hazard levels. These very-high to high GLOF hazard lakes were primarily located in the Phochu, Kurichu, Drangmechu, and Mochu basins. We concluded that the increasing glacier melt is the main driver of glacial lake expansion. Our results imply that extending glacial lakes studies back to the 1960s provides new insights on glacial lake evolution from glacier-fed lakes to non-glacier-fed lakes. Additionally, we reaffirmed the capacity of Sentinel-1 and Sentinel-2 data to determine annual glacial lake changes. The results from this study can be a valuable basis for future glacial lake monitoring and prioritizing limited resources for GLOF mitigation programs.

GAN-GL: Generative Adversarial Networks for Glacial Lake Mapping

Remote sensing is a powerful tool that provides flexibility and scalability for monitoring and investigating glacial lakes in High Mountain Asia (HMA). However, existing methods for mapping glacial lakes are designed based on a combination of several spectral features and ancillary data (such as the digital elevation model, DEM) to highlight the lake extent and suppress background information. These methods, however, suffer from either the inevitable requirement of post-processing work or the high costs of additional data acquisition. Signifying a key advancement in the deep learning models, a generative adversarial network (GAN) can capture multi-level features and learn the mapping rules in source and target domains using a minimax game between a generator and discriminator. This provides a new and feasible way to conduct large-scale glacial lake mapping. In this work, a complete glacial lake dataset was first created, containing approximately 4600 patches of Landsat-8 OLI images edited in three ways—random cropping, density cropping, and uniform cropping. Then, a GAN model for glacial lake mapping (GAN-GL) was constructed. The GAN-GL consists of two parts—a generator that incorporates a water attention module and an image segmentation module to produce the glacial lake masks, and a discriminator which employs the ResNet-152 backbone to ascertain whether a given pixel belonged to a glacial lake. The model was evaluated using the created glacial lake dataset, delivering a good performance, with an F1 score of 92.17% and IoU of 86.34%. Moreover, compared to the mapping results derived from the global–local iterative segmentation algorithm and random forest for the entire Eastern Himalayas, our proposed model was superior regarding the segmentation of glacial lakes under complex and diverse environmental conditions, in terms of accuracy (precision = 93.19%) and segmentation efficiency. Our model was also very good at detecting small glacial lakes without assistance from ancillary data or human intervention.

Changes in glacial lakes in the Poiqu River basin in the central Himalayas

The Poiqu River basin is an area of concentration for glaciers and glacial lakes in the central Himalayas, where 147 glacial lakes were identified, based on perennial remote sensing images, with lake area ranging from 0.0002 to 5.5 km2 – a total of 19.89 km2. Since 2004, the retreat rate of glacier has reached as high as 5.0 km2 a−1, while the growth rate of glacial lake has reached 0.24 km2 a−1. We take five typical lakes as our case study and find that the retreat of glacier area reaches 31.2 %, while the glacial lake area has expanded by 166 %. Moreover, we reconstruct the topography of the lake basin to calculate the water capacity and propose a water balance equation (WBE) to explore the lake evolution. By applying the WBE to the five lakes, we calculate the water supplies of the last few years and compare this with the results of field surveys, which are in agreement, within an error of only 1.86 % on average. The WBE also reveals that the water supplies to the lake depend strongly on the altitude. Lakes at low altitudes are supplied by glacier melting, and lakes at high altitudes are supplied by snowmelts. The WBE is not only applicable for predicting future changes in glacial lakes under climate warming conditions but is also useful for assessing water resources from rivers in the central Himalayas.

Spatiotemporal dynamics of glacial lakes (1990–2018) in the Kashmir Himalayas, India using Remote Sensing and GIS

This study is perhaps the first attempt to use satellite data (1990–2018) to analyze spatiotemporal changes in glacial lakes over the Kashmir Himalayas supplemented by field studies. Landsat images were used to delineate the spatial extent of glacial lakes at four-time points, i.e., 1990, 2000, 2010 and 2018. The total count of lakes as well as their spatial extent showed a discernible increase. The number increased from 253 in 1990 to 324 in 2018, with a growth rate of 21.4%. The area has increased from 18.84 ± 0.1 km2 in 1990 to 22.13 ± 0.12 km2 in 2018 with a growth rate of 14.7%. The newly formed glacial lakes, including supraglacial lakes, were greater in number than the lakes that disappeared over the study period. All glacial lakes are situated at elevations of 2700 m asl and 4500 m asl. More than 78% of lake expansion in the study region is largely due to the growth of existing glacial lakes. Through area change analysis, our findings reveal that certain lakes show rapid expansion needing immediate monitoring and observation. The analysis of the meteorological variables reveals that minimum and maximum temperatures in the Jhelum basin have shown an increasing trend. Tmax showed an increase of 1.25 °C, whereas Tmin increased to 0.7 °C from 1980 to 2020. On the other hand, precipitation has shown a decreasing trend, which can be attributed to one of the major causes of glacier recession and the expansion of glacial lakes in the Upper Jhelum basin. Consequently, this study could play a significant role in devising a comprehensive risk assessment plan for potential Glacial Lake Outburst Floods (GLOFs) and developing a mechanism for continuous monitoring and management of lakes in the study region.