Glacier surging is an unusual instability of ice flow, and inventories of surging glaciers are important for regional glacier mass balance studies and glacier dynamic studies. Glacier surges in High Mountain Asia (HMA) have been widely reported. However, the completeness of available inventories of HMA surging glaciers is hampered by the insufficient spatial and temporal coverage of glacier change observations or by the limitations of the identification methods. In this study, we established a new inventory of HMA surging glaciers based on glacier surface elevation changes and morphological changes over 4 decades. Three elevation change datasets based on four elevation sources (the KH-9 DEM, NASA DEM, COP30 DEM, and HMA DEM) and long-term Landsat satellite image series were utilized to assess the presence of typical surge features over two time periods (1970s–2000 and 2000–2020). A total of 890 surging and 336 probably or possibly surging glaciers were identified in HMA. Compared to the most recent inventory of surging glaciers in HMA, our inventory incorporated 253 previously unidentified surging glaciers. The number and area of surging glaciers accounted for ∼2.49 % (excluding glaciers smaller than 0.4 km²) and ∼16.59 % of the total glacier number and glacier area in HMA, respectively. Glacier surges were found in 21 of the 22 subregions of HMA (except for the Dzhungarsky Alatau); however, the density of surging glaciers is highly uneven. Glacier surges occur frequently in the northwestern subregions (e.g., Pamir and Karakoram) but less often in the peripheral subregions. The inventory further shows that surge activity is more likely to occur for glaciers with a larger area, longer length, and wider elevation range. Among glaciers with similar areas, the surging ones usually have steeper slopes than non-surging ones. The inventory and elevation change products of identified surging glaciers are available at https://doi.org/10.5281/zenodo.7961207 (Guo et al., 2023).

The Hispar Glacier is a useful site for studying surge mechanisms. Prior to this study, only two‐dimensional (2‐D) flow velocities having low temporal resolution were available for this glacier, providing inadequate information about its surge evolution. In this study, 139 synthetic aperture radar (SAR) images from Sentinel‐1A were used to obtain 3‐D flow velocity time series for the Hispar Glacier during the recent surge (2014–2016). The 3‐D flow velocities were sampled at an interval of 11 days, which is much greater than in previous studies. Besides, the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) and two TanDEM‐X images were used to determine glacier thickness changes prior to and following the recent surge. Combining the results and geomorphologic features, we deduced that the recent surge was because of saturated basal water pressure in the Yutmaru tributary. The mass from the Yutmaru tributary squeezed into the trunk and rapidly flowed downslope along the northern margin, generating strong normal pressure to the trunk mass. Pushed by the Yutmaru tributary, the trunk began to surge in September 2014. The flow velocity reached a first peak in May 2015 and then decreased in October 2015, as part of the basal meltwater ran off. However, basal meltwater accumulated again during the following 4 months, and correspondingly, the trunk accelerated again after October 2015. Finally, as kinetic energy was released and resisting force increased, the trunk became almost stagnant in August 2016. The surge mass was blocked downstream in the trunk by the mass transferred from the Kunyang tributary, and consequently, the glacier did not advance.

Several glaciers in the Bukatage Massif are surge-type. However, previous studies in this region focused on glacier area and length changes, and more information is needed to support the deep analysis of glacier surge. We determined changes in glacier thickness, motion, and surface features in this region based on TanDEM-X, ALOS/PRISM, Sentinel-1A, and Landsat images. Our results indicated that the recent surge of the Monomah Glacier, the largest glacier in the Bukatage Massif, started in early 2009 and ceased in late 2016. From 2009 to 2016, its area and length respectively increased by 6.27 km² and 1.45 km, and its ice tongue experienced three periods of changes: side broadening (2009–2010), rapid advancing (2010–2013), and slow expansion (2013–2016). During 2000–2012, its accumulation zone was thinned by 50 m, while its ice tongue was thickened by 90 m. During 2015–2017, its flow velocity reduced from 1.2 to 0.25 m/d, and the summer velocities were much higher than winter velocities. We conclude that the recent Monomah Glacier surge is thermal-controlled. The subglacial temperature rose to the pressure-melting point because of substantial mass accumulation, and then the increased basal meltwater caused the surge.

The continuous melting of valley glaciers can impact the water levels of glacial lakes and create glacial lake outburst floods (GLOFs). The Xixabangma massif is one of the most populated areas in the Himalayas and has suffered from multiple GLOFs. To estimate the glacier melting rate in the past four decades and analyze the outburst risk of glacial lakes in the Xixabangma massif, we determined changes in glacier mass balance, glacier area and glacial lake area based on KH-9 images, TanDEM-X images, Landsat images, SRTM DEM and ICESat-2 elevations. Our results show that, from 1974 to 2018, the total glacier area shrank from 954.01 km² to 752.46 km², whereas the total glacial lake area grew from 20.90 km² to 38.71 km². From 1974 to 2000, 2000 to 2013 and 2013 to 2018, the region-wide glacier mass balance values were −0.16 m w.e./a, −0.31 m w.e./a and −0.29 m w.e./a, respectively. Three glacial lakes, named Gangxico, Galongco and Jialongco, respectively, expanded by 127.14%, 373.45% and 436.36% from 1974 to 2018, and the mass loss rates of their parent glaciers from 2000 to 2013 increased by 81.72%, 122.22% and 160.00% relative to those during 1974 to 2000. The dams of these three lakes are unstable, and their drainage valleys directly connect to a major town and its infrastructure. Due to current high-water levels, possible external events such as ice collapse, landslide, heavy rainfall and earthquakes can easily trigger GLOFs. Hence, we deemed that the Gangxico, Galongco and Jialongco glacial lakes are dangerous and require special attention.

Arctic sea ice prediction is of great practical significance in facilitating Arctic route planning, optimizing fisheries management, and advancing the field of sea ice dynamics research. While various deep learning models have been developed for sea ice prediction, they predominantly operate at the seasonal or sub-seasonal scale, often focusing on localized areas, and few cater to full-region daily-scale prediction. This study introduces the use of spatiotemporal sequence data prediction models, namely, the convolutional LSTM (ConvLSTM) and predictive recurrent neural network (PredRNN), for the prediction of sea ice concentration (SIC). Our analysis reveals that, when solely utilizing SIC historical data as the input, the ConvLSTM model outperforms the PredRNN model in SIC prediction. To enhance the models’ capacity to capture spatiotemporal relationships between multiple variables, we expanded the range of input data types to form the ConvLSTM-multi and PredRNN-multi models. Experimental findings demonstrate that the prediction accuracy of the four models significantly surpasses the CMIP6 model in three prospective climate scenarios (SSP126, SSP245, and SSP585). Of the four models, the ConvLSTM-multi model excels in assimilating the influence of reanalysis data on sea ice within the sea ice edge region, thus exhibiting superior performance than the PredRNN-multi model in predicting daily Arctic SIC over the subsequent 10 days. Furthermore, sensitivity tests on various model parameters highlight the substantial impact of sea surface temperature and prediction date on the accuracy of daily sea ice prediction, and meteorological and oceanographic parameters primarily affect the prediction accuracy of the thin-ice region at the edge of the sea ice.

Simulating potential glacier collapses can provide crucial support for local disaster prevention and mitigation efforts. The Xiaomagou Glacier in the Amnye Machen Mountains, Qinghai–Tibetan Plateau, has experienced five collapses in the past two decades. Field investigation and remote sensing observations indicate that the topography and bedrock characteristics of the Qushi’an No. 22 Glacier, which is 3.5 km south of the Xiaomagou Glacier, are similar to those of the Xiaomagou Glacier. More importantly, the mass movement of the Qushi’an No. 22 Glacier since 2018 closely resembles that of the Xiaomagou Glacier exhibited before its previous collapses. Therefore, in the context of rising temperatures, it is possible that the Qushi’an No. 22 Glacier will collapse in the near future. Based on remote sensing imagery and the glacier’s surface elevation changes, we reconstructed the 2004 collapse process of the Xiaomagou Glacier via numerical simulation. The key parameters of the mass flow model were optimized based on the actual deposition area of the 2004 collapse. The model with optimized parameters was then used to simulate the potential Qushi’an No. 22 Glacier collapse. Two collapse scenarios were set for the Qushi’an No. 22 Glacier. In Scenario 1, the lower half of the tongue detaches; in Scenario 2, the whole tongue detaches. Simulation results show that, in Scenario 1, the maximum mass flow depth is 72 m, the maximum mass flow speed is 51.6 m/s, and the deposition area is 5.40 × 10⁶ km²; in Scenario 2, the maximum mass flow depth is 75 m, the maximum mass flow speed is 59.7 m/s, and the deposition area is 6.32 × 10⁶ km². In both scenarios, the deposition area is much larger than that of the Xiaomagou Glacier 2004 collapse, which had a deposition area of 2.21 × 10⁶ km². The simulation results suggest that the Qushi’an No. 22 Glacier collapse could devastate the pastures and township roads lying in front of the glacier, seriously affecting local transportation and livestock farming; furthermore, it may deposit in the Qinglong River, forming a large, dammed lake. At present, the Qushi’an No. 22 Glacier remains in an unstable state. It is crucial to strengthen monitoring of its surface morphology, flow speed, and elevation.