Meimei Zhang

Glacial lake outburst floods (GLOFs) in the Himalayas have caused severe consequences for downstream communities, including loss of life and substantial economic damage. The accelerated global atmospheric warming further heightens the GLOF risks. However, a gap in uncertainties underlying hazard and vulnerability constrains the accurate cognition of potential threats. Here we developed a glacial lake inventory and systematically assessed the hazard, exposure, vulnerability and comprehensive risks of all glacial lakes in ten hotspot sub-basins of the Himalayas using a DEM-based stochastic framework, numerical model and social census dataset. From 2015 to 2020, lake areas expanded by 6.52 ± 1.07 km² (0.8% annually). 38% of lakes pose high hazard, impacting 51% of the population and 76% of infrastructure. Northeastern Bhutan exhibits higher vulnerability than India, Nepal and Tibet, yet remains underexplored. The Kuri Chu and Dangme Chu sub-basins are particularly high-risk areas, with 10% of high-risk lakes previously underestimated. This work contributes to developing an effective risk assessment criteria, underscoring the need for systematic study of other glacierized regions.

Glacial lakes are among the most sensitive indicators of climate change. With the ongoing climate warming, the number and area of glacial lakes are dramatically increased, particularly in High Mountain Asia (HMA), thereby concomitantly arousing the overall number and frequency of glacial lake outburst flood (GLOF) events as well, which pose a threat to vulnerable communities. Regular glacial lake monitoring is needed to detect the abnormal changes that could signify instabilities. However, the existing methods rely on the scoring regressions from combinations of glacial lake properties to assess the outburst risks of glacial lakes in a large scale, neglecting the spatial structures of lake scenes and property dependencies which may result in diverging hazard estimates. In this work, we conducted an appraisal of GLOF risks in HMA by reasoning the association between the glacial lake spatial and property data. Concretely, the cascade of spatial features and property features were classified with semi-supervised mode to further identify the lake that shares a similar pattern with the outburst of glacial lakes. As a result, our model yielded 88.26% validation accuracy in historical GLOF events from 1990 to 2022 in HMA. We thus evaluated the glacial lakes with our model in HMA in 2020 and finally characterized 49 glacial lakes as high-risk lakes. From the lake investigation, 4 glacial lakes that had been outburst but were not mentioned previously in HMA from 1990 to 2020 were provided. Moreover, among the dependency analysis of lake properties, the pairs of (subregion, precipitation) and (distance, lake type) show a strong dependency on the state of glacial lakes. No significant dependency, by contrast, exists between the area change rate and the other properties.

Visual question answering (VQA) aims to build an interactive system that infers the answer according to the input image and text-based question. Recently, VQA for remote sensing has attracted considerable attention since it is essential and expedient for monitoring global resources and querying objective attributes. In reality, question-related semantic information is helpful for the reasoning and understanding capabilities, which is always contained in remote sensing images or complex questions. To capture the valuable information and extend the applications of remote-sensing VQA, we propose an end-to-end multiple-step question-driven VQA (MQVQA) system for remote sensing. In MQVQA, we employ a multiple-step attention mechanism to interactively reason and concomitantly mark the region that is most related to the question. To understand the semantic information in complex questions, we build a question-driven module that classifies the question types and keywords, which will further guide the combination of image feature maps in different scales. To benchmark this model, we construct a new complex remote sensing VQA dataset (CRSVQA), wherein the question is asked in complex forms and involves various remote sensing scenes. The evaluation results on CRSVQA, RSVQA and RSIVQA datasets indicate that the proposed MQVQA model surpasses other remote sensing VQA models. The visualization results demonstrate that MQVQA has a robust ability in reasoning and understanding the content from images and complex questions. Our code and dataset are publicly available at: https://github.com/MeimeiZhang-data/MQVQA.

The influence of ferric oxide content on the performance of melt-derived ferrimagnetic glass and glass fibers was investigated in this work. Ferrimagnetic glass fibers within ferrosilicate glass system were prepared by spinning method. The effect of ferric oxide content on the microstructure and mechanical properties of the glass fibers were examined. The X-ray diffraction analysis showed the presence of magnetite with spontaneous crystallization for all investigated samples. Transmission electron microscopy (TEM) analysis indicates that the size of magnetite is less than 30 nm in diameter. The surface microstructure, magnetic hysteresis circles, microwave absorption properties, Mössbauer spectra, thermal expansion performance, tensile properties, hardness, and elastic modulus of as-prepared samples were studied in detail.

Glacial lake mapping is essential for understanding the response of glacial lakes to climate change and the risk assessment of glacial lake outburst floods (GLOFs). Given that glacial lakes have little area compared with background objects, extracting glacial lakes high precisely is still challenging. Recently, U-Net, a deep learning (DL) method, has shown great potential in glacial lake extraction, due to its elaborate encoder-decoder structure and powerful skip connections. However, the skip connections transmit a lot of information irrelevant to glacial lakes from the low-level appearance features to the high-level semantic features, leading to inefficient utilization of the low-level features. In this letter, we propose a normalized difference water index (NDWI) attention U-Net (NAU-Net) for pixel-wise glacial lake segmentation, which utilizes NDWI as a spatial attention to highlight the signals of water regions in low-level feature maps, thereby making the network pay more attention to glacial lakes. Compared with the classical networks and the state-of-the-art non-DL methods, our NAU-Net shows better performance on glacial lake extraction. The source code can be found at https://github.com/JinxiaoWang/NAU-Net .

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.

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.

The current glacial lake datasets in the High Mountain Asia (HMA) region still need to be improved because their boundary divisions in the land–water transition zone are not precisely delineate, and also some very small glacial lakes have been lost due to their mixed reflectance with backgrounds. In addition, most studies have only focused on the changes in the area of a glacial lake as a whole, but do not involve the actual changes of per pixel on its boundary and the potential controlling factors. In this research, we produced more accurate and complete maps of glacial lake extent in the HMA in 2008, 2012, and 2016 with consistent time intervals using Landsat satellite images and the Google Earth Engine (GEE) cloud computing platform, and further studied the formation, distribution, and dynamics of the glacial lakes. In total, 17,016 and 21,249 glacial lakes were detected in 2008 and 2016, respectively, covering an area of 1420.15 ± 232.76 km² and 1577.38 ± 288.82 km²; the lakes were mainly located at altitudes between 4400 m and 5600 m. The annual areal expansion rate was approximately 1.38% from 2008 to 2016. To explore the cause of the rapid expansion of individual glacial lakes, we investigated their long-term expansion rates by measuring changes in shoreline positions. The results show that glacial lakes are expanding rapidly in areas close to glaciers and had a high expansion rate of larger than 20 m/yr from 2008 to 2016. Glacial lakes in the Himalayas showed the highest expansion rate of more than 2 m/yr, followed by the Karakoram Mountains (1.61 m/yr) and the Tianshan Mountains (1.52 m/yr). The accelerating rate of glacier ice and snow melting caused by global warming is the primary contributor to glacial lake growth. These results may provide information that will help in the understanding of detailed lake dynamics and the mechanism, and also facilitate the scientific recognition of the potential hazards associated with glacial lakes in this region.

Atmospheric warming is intensifying glacier melting and glacial-lake development in High Mountain Asia (HMA), and this could increase glacial-lake outburst flood (GLOF) hazards and impact water resources and hydroelectric-power management. There is therefore a pressing need to obtain comprehensive knowledge of the distribution and area of glacial lakes and also to quantify the variability in their sizes and types at high resolution in HMA. In this work, we developed an HMA glacial-lake inventory (Hi-MAG) database to characterize the annual coverage of glacial lakes from 2008 to 2017 at 30 m resolution using Landsat satellite imagery. Our data show that glacial lakes exhibited a total area increase of 90.14 km² in the period 2008–2017, a +6.90 % change relative to 2008 (1305.59±213.99 km²). The annual increases in the number and area of lakes were 306 and 12 km², respectively, and the greatest increase in the number of lakes occurred at 5400 m elevation, which increased by 249. Proglacial-lake-dominated areas, such as the Nyainqêntanglha and central Himalaya, where more than half of the glacial-lake area (summed over a 1^∘ × 1^∘ grid) consisted of proglacial lakes, showed obvious lake-area expansion. Conversely, some regions of eastern Tibetan mountains and Hengduan Shan, where unconnected glacial lakes occupied over half of the total lake area in each grid, exhibited stability or a slight reduction in lake area. Our results demonstrate that proglacial lakes are a main contributor to recent lake evolution in HMA, accounting for 62.87 % (56.67 km²) of the total area increase. Proglacial lakes in the Himalaya ranges alone accounted for 36.27 % (32.70 km²) of the total area increase. Regional geographic variability in debris cover, together with trends in warming and precipitation over the past few decades, largely explains the current distribution of supraglacial- and proglacial-lake area across HMA. The Hi-MAG database is available at https://doi.org/10.5281/zenodo.4275164 (Chen et al., 2020), and it can be used for studies of the complex interactions between glaciers, climate and glacial lakes, studies of GLOFs, and water resources.