Glacier debris flow is one of the most critical categories of geological hazards in high-mountain regions. To reduce its potential negative effects, it needs to investigate the susceptibility of glacier debris flow. However, when evaluating the susceptibility of glacier debris flow, most research work considered the impact of existing glacier area, while ignoring the impact of changes in glacier ablation volume. In this paper, we considered the impact of the changes in the glacier ablation volume to investigate the susceptibility of glacier debris flow. We proposed to evaluate the susceptibility analysis in G217 gullies with frequent glacial debris flow on the Duku highway, Xinjiang Province. Specifically, by using the simple band ratio method with the manual correction to identify glacier outlines, we identified the ablation zone by comparing the glacier boundary in 2000 with that in 2015. We then calculated ablation volume by changes in glacier elevation and ablation area from 2000 to 2015. Finally, we used the volume of glacier melting in different watersheds as the main factor to evaluate the susceptibility based on the improved geomorphic information entropy (GIE) method. We found that, overall, the improved GIE method with a correction coefficient based on the glacier ablation volume is better than the previous method. Deglaciation can be adapted to analyze glacier debris flow susceptibility based on glaciology and geomorphology. Our presented work can be applied to other similar glacial debris flow events in high-mountain regions.

Debris‐covered glacier mapping for monitoring glacier fluctuations is necessary to prevent geohazards caused by glaciers. Recently, deep learning‐based methods have been widely utilized for the identification of debris‐covered glaciers. Compared with conventional geospatial methods, deep learning‐based approaches have the advantage of large‐scale coverage and outstanding accuracies in identifying glaciers. However, there are two main difficulties when using deep learning‐based approaches: (1) object misclassification, which causes the misclassifications of the surrounding bedrock, snow, and mountain‐shadowed areas as glaciers; and (2) the inaccurate segmentation of boundaries. In addition, the sample sets for training deep learning models are generally insufficient, which leads to unsatisfactory results. To address the above problems, a deep learning‐based approach is proposed for accurate and automatic mapping of the complex debris‐covered glacier from remote sensing imagery for glacial hazard prevention. First, high‐quality remote sensing imagery is acquired and pre‐processed. Second, we adopt a weight‐optimized glacier semantic segmentation model to our approach. Then, the post‐processing procedures can be used to obtain the glacier outline. Finally, an accurate and automatic mapping of complex debris‐covered glaciers can be achieved. To demonstrate the effectiveness, our approach is applied to observe spatiotemporal changes in the glacier outline in the Nyenchen Tanglha region. Results show that most evaluation metrics of our model are higher than 90%, which demonstrates that it is reasonable for the glacier boundary extraction. We also verify that the proposed approach can be used to observe the changes in glaciers to prevent and control glacial hazards in high‐mountain regions.

<p>Glacier is sensitive to climate warming, and changes in mountainous areas can lead to serious hazards to human society. Glacial debris flow is a type of geological hazards characterized by suddenness and high mobility in high-mountain regions due to deglaciation. The study of susceptibility analysis for glacial debris flow can effectively reduce its potential negative effects. However, when evaluating susceptibility of glacial mudflow, most research work takes the existing glacier area into consideration and ignores the effect of glacier ablation volume. The improved glacial geomorphological information entropy theory based on glacial correction coefficients can be used to evaluate the susceptibility. The correction coefficients can be calculated by investigating the changes in glacier ablation and distribution based on remote sensing applications. In addition, a deep learning-based approach for extracting glacier boundaries is proposed. We present a case study evaluating the susceptibility of along the Duku Highway in Tien Shan area. The results show that the improved method based on glacier ablation can effectively increase the accuracy of the susceptibility analysis. Based on the theory of glaciology and geomorphology, the changes of glacier can be used in the susceptibility of glacial debris flow. In the future, we will explore a new prediction method of geo-hazards based on glacier dynamics.</p>

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The discrete element method (DEM) can be effectively used in investigations of the deformations and failures of jointed rock slopes. However, when to appropriately terminate the DEM iterative process is not clear. Recently, a displacement-based discrete element modeling method for jointed rock slopes was proposed to determine when the DEM iterative process is terminated, and it considers displacements that come from rock blocks located near the potential sliding surface that needs to be determined before the DEM modeling. In this paper, an energy-based discrete element modeling method combined with time-series analysis is proposed to investigate the deformations and failures of jointed rock slopes. The proposed method defines an energy-based criterion to determine when to terminate the DEM iterative process in analyzing the deformations and failures of jointed rock slopes. The novelty of the proposed energy-based method is that, it is more applicable than the displacement-based method because it does not need to determine the position of the potential sliding surface before DEM modeling. The proposed energy-based method is a generalized form of the displacement-based discrete element modeling method, and the proposed method considers not only the displacement of each block but also the weight of each block. Moreover, the computational cost of the proposed method is approximately the same as that of the displacement-based discrete element modeling method. To validate that the proposed energy-based method is effective, the proposed method is used to analyze a simple jointed rock slope; the result is compared to that achieved by using the displacement-based method, and the comparative results are basically consistent. The proposed energy-based method can be commonly used to analyze the deformations and failures of general rock slopes where it is difficult to determine the obvious potential sliding surface.