As the increasing demand for deep mineral resource extraction and the construction of deep vertical shafts by the artificial ground freezing method, the stability and safety of shaft that traverse thick alluvial depend significantly on their interaction with the surrounding deep frozen soil medium. Such interaction is directly conditioned by the mechanical properties of the deep frozen soil. To precisely capture these in-situ mechanical properties, the mechanical parameters tests using remodeled frozen specimens cannot ignore the disparities in consolidation history, stress environment and formation conditions between the deep and shallow soils. This study performs a series of long-term high-pressure K0 consolidation (where K0 represents the static earth pressure coefficient, describing the ratio of horizontal to vertical stress under zero lateral strain conditions), freezing under sustained load and unloading triaxial shear tests utilizing remodeled deep clay. This study presents the response of unloading strength and damage properties under varying consolidation stresses, durations, and freezing temperatures. The unloading strength increases sharply and then stabilizes with consolidation time. The unloading strength shows an approximate linear positive correlation with the consolidation stress, while a negative correlation with the freezing temperature. The strengthening rate of the unloading strength due to freezing temperature tends to decrease with increasing consolidation time. Additionally, an improved damage constitutive model was proposed and validated by incorporating the initial K0 stress state and a Weibull-based assumption for damage elements. Based on the back propagation (BP) neural network, a prediction method for the stress-strain curve was offered according to the consolidation stress level, initial stress state, and temperature. These results can provide references for improving the mechanical testing methods of deep frozen clay and revealing differences in mechanical properties between deep and shallow soils.

新疆内的高寒山区是高亚洲地区的重要组成部分，该区域内拥有大量规模较小的冰湖，部分冰湖在短时间内迅速扩大，并可能导致溃决洪水事件的发生。因此，提高对这些小型冰湖的制图精度对于深入理解冰川冰湖灾害机理至关重要。本研究利用Sentinel-2时序数据和DUNet语义分割模型，结合目视解译和质量控制，开展了2022年新疆高寒山区冰湖最大水域范围(≥新疆高寒山hm2)的提取；并根据冰湖与冰川的关系，将冰湖分为冰川补给湖（包括：冰面湖、冰川接触湖和冰川不接触湖3个亚类）和非冰川补给湖2类，最终得到2022年新疆高寒山区冰湖数据集。本数据集中冰湖总绝对面积误差为12.12 km，平均相对误差为6.14%。本数据集包括：（1）空间数据，即2022年研究区冰湖最大分布范围数据和新疆高寒山区分区；（2）表格数据，包括：2022年研究区不同区域、大小、类型、高程尺度下冰湖的数量与面积统计。数据集存储为shp和xlsx格式。可为新疆冰湖灾害预警、冰湖灾害评价提供数据支持和有效依据。

新疆内的高寒山区是高亚洲地区的重要组成部分，该区域内拥有大量规模较小的冰湖，部分冰湖在短时间内迅速扩大，并可能导致溃决洪水事件的发生。因此，提高对这些小型冰湖的制图精度对于深入理解冰川冰湖灾害机理至关重要。本研究利用Sentinel-2时序数据和DUNet语义分割模型，结合目视解译和质量控制，开展了2022年新疆高寒山区冰湖最大水域范围(≥新疆高寒山hm2)的提取；并根据冰湖与冰川的关系，将冰湖分为冰川补给湖（包括：冰面湖、冰川接触湖和冰川不接触湖3个亚类）和非冰川补给湖2类，最终得到2022年新疆高寒山区冰湖数据集。本数据集中冰湖总绝对面积误差为12.12 km，平均相对误差为6.14%。本数据集包括：（1）空间数据，即2022年研究区冰湖最大分布范围数据和新疆高寒山区分区；（2）表格数据，包括：2022年研究区不同区域、大小、类型、高程尺度下冰湖的数量与面积统计。数据集存储为shp和xlsx格式。可为新疆冰湖灾害预警、冰湖灾害评价提供数据支持和有效依据。

新疆内的高寒山区是高亚洲地区的重要组成部分，该区域内拥有大量规模较小的冰湖，部分冰湖在短时间内迅速扩大，并可能导致溃决洪水事件的发生。因此，提高对这些小型冰湖的制图精度对于深入理解冰川冰湖灾害机理至关重要。本研究利用Sentinel-2时序数据和DUNet语义分割模型，结合目视解译和质量控制，开展了2022年新疆高寒山区冰湖最大水域范围(≥新疆高寒山hm2)的提取；并根据冰湖与冰川的关系，将冰湖分为冰川补给湖（包括：冰面湖、冰川接触湖和冰川不接触湖3个亚类）和非冰川补给湖2类，最终得到2022年新疆高寒山区冰湖数据集。本数据集中冰湖总绝对面积误差为12.12 km，平均相对误差为6.14%。本数据集包括：（1）空间数据，即2022年研究区冰湖最大分布范围数据和新疆高寒山区分区；（2）表格数据，包括：2022年研究区不同区域、大小、类型、高程尺度下冰湖的数量与面积统计。数据集存储为shp和xlsx格式。可为新疆冰湖灾害预警、冰湖灾害评价提供数据支持和有效依据。

新疆内的高寒山区是高亚洲地区的重要组成部分，该区域内拥有大量规模较小的冰湖，部分冰湖在短时间内迅速扩大，并可能导致溃决洪水事件的发生。因此，提高对这些小型冰湖的制图精度对于深入理解冰川冰湖灾害机理至关重要。本研究利用Sentinel-2时序数据和DUNet语义分割模型，结合目视解译和质量控制，开展了2022年新疆高寒山区冰湖最大水域范围(≥新疆高寒山hm2)的提取；并根据冰湖与冰川的关系，将冰湖分为冰川补给湖（包括：冰面湖、冰川接触湖和冰川不接触湖3个亚类）和非冰川补给湖2类，最终得到2022年新疆高寒山区冰湖数据集。本数据集中冰湖总绝对面积误差为12.12 km，平均相对误差为6.14%。本数据集包括：（1）空间数据，即2022年研究区冰湖最大分布范围数据和新疆高寒山区分区；（2）表格数据，包括：2022年研究区不同区域、大小、类型、高程尺度下冰湖的数量与面积统计。数据集存储为shp和xlsx格式。可为新疆冰湖灾害预警、冰湖灾害评价提供数据支持和有效依据。

新疆内的高寒山区是高亚洲地区的重要组成部分，该区域内拥有大量规模较小的冰湖，部分冰湖在短时间内迅速扩大，并可能导致溃决洪水事件的发生。因此，提高对这些小型冰湖的制图精度对于深入理解冰川冰湖灾害机理至关重要。本研究利用Sentinel-2时序数据和DUNet语义分割模型，结合目视解译和质量控制，开展了2022年新疆高寒山区冰湖最大水域范围(≥新疆高寒山hm2)的提取；并根据冰湖与冰川的关系，将冰湖分为冰川补给湖（包括：冰面湖、冰川接触湖和冰川不接触湖3个亚类）和非冰川补给湖2类，最终得到2022年新疆高寒山区冰湖数据集。本数据集中冰湖总绝对面积误差为12.12 km，平均相对误差为6.14%。本数据集包括：（1）空间数据，即2022年研究区冰湖最大分布范围数据和新疆高寒山区分区；（2）表格数据，包括：2022年研究区不同区域、大小、类型、高程尺度下冰湖的数量与面积统计。数据集存储为shp和xlsx格式。可为新疆冰湖灾害预警、冰湖灾害评价提供数据支持和有效依据。

新疆内的高寒山区是高亚洲地区的重要组成部分，该区域内拥有大量规模较小的冰湖，部分冰湖在短时间内迅速扩大，并可能导致溃决洪水事件的发生。因此，提高对这些小型冰湖的制图精度对于深入理解冰川冰湖灾害机理至关重要。本研究利用Sentinel-2时序数据和DUNet语义分割模型，结合目视解译和质量控制，开展了2022年新疆高寒山区冰湖最大水域范围(≥新疆高寒山hm2)的提取；并根据冰湖与冰川的关系，将冰湖分为冰川补给湖（包括：冰面湖、冰川接触湖和冰川不接触湖3个亚类）和非冰川补给湖2类，最终得到2022年新疆高寒山区冰湖数据集。本数据集中冰湖总绝对面积误差为12.12 km，平均相对误差为6.14%。本数据集包括：（1）空间数据，即2022年研究区冰湖最大分布范围数据和新疆高寒山区分区；（2）表格数据，包括：2022年研究区不同区域、大小、类型、高程尺度下冰湖的数量与面积统计。数据集存储为shp和xlsx格式。可为新疆冰湖灾害预警、冰湖灾害评价提供数据支持和有效依据。

As soil acidification occurs due to industrial and agricultural production processes, it can induce the release of rhizotoxic aluminium ions (Al3+) into the soil, ultimately causing aluminium (Al) stress. Excessive Al content in soil exhibits significant phytotoxicity, inhibiting the growth of roots and stems. In this study, we conducted an investigation into the Al stress tolerance of two apple rootstocks, namely 'YZ3' and 'YZ6', and discovered that 'YZ3' exhibited a superior ability to alleviate the inhibitory effects of Al stress on plant growth. By comparing the transcriptomes of two rootstocks, a differentially expressed gene, MdDUF506, containing an unknown functional (DUF) domain, was identified. Overexpression of MdDUF506 in apple and calli enhances the ability to scavenge reactive oxygen species (ROS), subsequently mitigating the oxidative damage induced by Al stress on plant growth and development. Furthermore, MdDUF506 regulates Al stress tolerance by modulating the expression of genes related to Al stress (MdSTOP1, MdRSL1, MdRSL4, MdGL2, and MdRAE1). MdDUF506 interacts with MdCNR8, positively regulating Al stress tolerance. Taken together, these discoveries offer crucial candidate genes for targeted breeding as well as fresh insights into resistance to Al stress.

In mid-July 2021, a quasi-stationary extratropical cyclone over parts of western Germany and eastern Belgium led to unprecedented sustained widespread precipitation, nearly doubling climatological monthly rainfall amounts in less than 72 h. This resulted in extreme flooding in many of the Eifel-Ardennes low mountain range river catchments with loss of lives, and substantial damage and destruction. Despite many reconstructions of the event, open issues on the underlying physical mechanisms remain. In a numerical laboratory approach based on a 52-member spatially and temporally consistent high-resolution hindcast reconstruction of the event with the integrated hydrological surface-subsurface model ParFlow, this study shows the prognostic capabilities of ParFlow and further explores the physical mechanisms of the event. Within the range of the ensemble, ParFlow simulations can reproduce the timing and the order of magnitude of the flood event without additional calibration or tuning. What stands out is the large and effective buffer capacity of the soil. In the simulations, the upper soil in the highly affected Ahr, Erft, and Kyll river catchments are able to buffer between about one third to half of the precipitation that does not contribute immediately to the streamflow response and leading eventually to widespread, very high soil moisture saturation levels. In case of the Vesdre river catchment, due to its initially higher soil water saturation levels, the buffering capacity is lower; hence more precipitation is transferred into discharge.

Study region: The study focuses on the Indus River Basin and southern Pakistan, severely affected by flooding in 2022. Study focus: This study assessed how land surface temperature, snow cover, soil moisture, and precipitation contributed to the deluge of 2022. This study mainly investigated MODIS-AIRS land surface temperature, MODIS snow cover (NDSI), SMAP soil moisture, and GPM IMERG precipitation accumulation. Furthermore, different flood visualization and mapping techniques were applied to delineate the flood extent map using Landsat 8-9, Sentinel-2 MSI, and Sentinel-1 SAR data. New hydrological insights for the region: The region experienced some of the most anomalous climatic events in 2022, such as prolonged heatwaves as observed with higher-than-average land surface temperatures and subsequent rapid decline in snow cover extent during the spring, increased soil moisture followed by an abnormal amount of extreme monsoon precipitation in the summer. The upper subbasins experienced more than 8 degrees C in positive temperature anomaly, indicating a warmer climate in spring. Subsequently, the snow cover declined by more than 25 % in the upper subbasins. Further, higher surface soil moisture values (> 0.3 m3/m3) were observed in the basin during the spring due to the rapid snow and ice melt. Furthermore, the basin received more than 200 mm of rainfall compared to the long-term average rainfall of about 98 mm, translating to about 300 % more rainfall than usual in July and August. The analysis helps understand the spatial and temporal variability within the basin and facilitates the understanding of factors and their intricate connections contributing to flooding.