South and Southeast Asia (SSA) emitted black carbon (BC) exerts potential effects on glacier and snow melting and regional climate change in the Tibetan Plateau. In this study, online BC measurements were conducted for 1 year at a remote village located at the terminus of the Mingyong Glacier below the Meili Snow Mountains. The Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) was used to investigate the contribution and potential effect of SSA-emitted BC. In addition, variations in the light absorption characteristics of BC and brown carbon (BrC) were examined. The results indicated that the annual mean concentration of BC was 415 ± 372 ng m−3, with the highest concentration observed in April (monthly mean: 930 ± 484 ng m−3). BC exhibited a similar diurnal variation throughout the year, with two peaks observed in the morning (from 8:00 to 9:00 AM) and in the afternoon (from 4:00 to 5:00 PM), with even lower values at nighttime. At a short wavelength of 370 nm, the absorption coefficient (babs) reached its maximum value, and the majority of babs values were < 20 Mm−1, indicating that the atmosphere was not overloaded with BC. At the same wavelength, BrC substantially contributed to babs, with an annual mean of 25.2 % ± 12.8 %. SSA was the largest contributor of BC (annual mean: 51.1 %) in the study area, particularly in spring (65.6 %). However, its contributions reached 20.2 % in summer, indicating non-negligible emissions from activities in other regions. In the atmosphere, the SSA BC-induced radiative forcing (RF) over the study region was positive. While at the near surface, the RF exhibited a significant seasonal variation, with the larger RF values occurring in winter and spring. Overall, our findings highlight the importance of controlling BC emissions from SSA to protect the Tibetan Plateau against pollution-related glacier and snow cover melting.

The fatigue and damage characteristics of frozen soil under cyclic loading are highly dependent on the three-dimensional (3D) stress state, due to the anisotropic properties of the ground. Measuring and researching the deformation behavior and fatigue failure characteristics of frozen soil under complex 3D cyclic stress states are significant for the stability assessment of frozen soil when it is subjected to earthquakes and vehicular traffic. In this paper, a hollow cylindrical apparatus was used to simulate a cyclic stress state with constant values of principal stress direction angle (α), coefficient of intermediate principal stress(b), and amplitude of the first principal stress under −6℃ conditions. The influences of 3D stress parameters (α and b) on the deformation behavior, damage evolution, and fatigue failure characteristics of frozen silty clay were systematically investigated. The results indicated that the deformation of the samples was dominated by axial strain, when α < 15° and b = 0. Furthermore, as the value b increased, both the accumulated axial strain and accumulated torsional shear strain exhibited a decreasing-then-increasing trend. When 30°≤α ≤ 60°, the deformation feature is primarily dominated by torsional shear direction. With the increase of the value b, the accumulated torsional shear strain increased rapidly, while the axial strain gradually decreases, and then in turn to compressive elongation deformation. The increase of 3D stress parameters leads to a decrease in accumulated torsional shear strain, absolute value of accumulated axial strain, number of cycles, and accumulated torsional shear dissipated energy density at the failure of frozen soil. This indicated that under cyclic stress conditions, the increase of 3D stress characteristic parameters accelerates the damage evolution and fatigue failure process of frozen soil samples. Essentially, the increase of 3D stress parameters accelerates the damage of soil particle and ice lens structures in horizontally layered and the growth of micro-crack of frozen soil, thereby reducing the transverse shear resistance of frozen soil samples.

Extreme heat events in the summer of 2022 were observed in Eurasia, North America and China. Glaciers are a unique indicator of climate change, and the European Alps experienced substantial glacier mass loss as a result of the conditions in 2022, which prompted a wide range of community concerns. However, relevant findings for glaciers in China have not been currently reported. Here, we document the response of Urumqi Glacier No. 1 in the eastern Tien Shan to the extreme heat observed in 2022 based on in situ measurements that span more than 60 years. In 2022, Urumqi Glacier No. 1 exhibited the second largest annual mass loss on record, and the summer mass balance was the most negative on record. The hottest summer on record and relatively lower solid precipitation ratio contributed to the exceptional mass losses at Urumqi Glacier No. 1 in 2022, demonstrating the significant influence of heatwaves on extreme glacier melt in China.

Recently, ensemble multiple deep learning (DL) classifiers has been reported to be an effective method for improving remote sensing classification accuracy. Although these approaches still follow the conventional pattern of inputting instance features and outputting corresponding classes, they often overlook the intrinsic relationships between pixels beyond their spatial features. As a result, the diversity in the ensemble classification results primarily relies on different DL models. However, training the DL models consumes a significant amount of time, and training multiple networks not only incurs additional time costs but also affects the overall efficiency. To address this, a new approach has been proposed in this paper, which takes advantage of the relationships between pixels and their combinations to generate diverse classification results. It’s a novel ensemble classification framework, termed as the Doublet-Based Ensemble Classification Framework (DBECF), which eliminates the need for multiple classifiers. The DBECF starts by utilizing the training set to combine different samples to generate doublets. Then, features are assigned to these doublets through an exponentiation operation, resulting in a doublet training set. Using both the original training set and the derived doublet datasets, the DBECF is trained. For each input pixel, the DBECF produces multiple classification results, which are then integrated to obtain a more accurate output. To validate the proposed approach, experiments were conducted on three datasets, including multispectral images, hyperspectral images, and time series images. The maximum accuracies achieved by DBECF on the three datasets are 87.80 %, 97.71 %, and 83.51 %, respectively. In comparison to the contrastive methods, the incremental improvements in accuracy are 3.73 %, 7.66 %, and 9.16 %, respectively. The experimental results indicate that no matter using DL or non-deep learning for training, our proposed framework achieves progress on accuracy improvement outperforming classifications using comparative approach that based on single instance. This research provides a new perspective on the combination of DL and ensemble learning, highlighting its important implications and practical value in enhancing classification accuracy and efficiency.

规范名称及编号承担角色发布实施时间·青藏铁路多年冻土区工程勘察暂行规定 铁建设【2001】32号（企标）第三单位2001年·青藏铁路高原多年冻土区工程设计暂行规定 铁建设【2001】79号（企标）第三单位2001年·渠系抗冻胀设计规范 SL23-2006（国标）第五单位2006年·热棒 GB/T27880-2011（国标）第三单位2011年·水工建筑物抗冰冻设计规范 GB/T50662-2011（国标）第四单位2012年·冻土地区建筑地基基础设计规范 JGJ118-2011（行标）第二单位2012年·多年冻土地区公路设计与施工技术细则 JTG/T D31-04-2012（行标）第二单位2012年·冻土工程地质勘察规范 GB 50324-2014（国标）第二单位2014年·冻土地区架空输电线路基础设计技术规程 DL/T 5501-2015 （行标）第二单位2015年·土工试验法方法标准 GB/T 50123-2019（国标）第三单位2019年