AN AUTOMATIC EXTRACTION METHOD FOR ANTARCTIC SUBGLACIAL LAKE BASED ON RADIO ECHO SOUNDING DATA

We combined a long-term InSAR derived surface deformation dataset with streamflow recession analysis to assess potential destruction effects of human activities on regional permafrost in the largest mining area of Qinghai-Tibet Plateau. We found that enhanced surface deformation coincides with significant recession slowdown after mining, indicating a strong signal of permafrost thaw. Our results highlight accelerated effects of human disturbance on permafrost degradation under regional warming.

We propose an innovative multi-satellite DEM adjustment model (MDAM) that removes biases in elevation between sub-DEMs across ice shelves. Our results reveal quantitative 3D structural and mélange features of a ~50 km long rift. For first time, we found that while the mélange elevation decreased from 2014–2021, the mélange inside the rift experienced a rapid expansion, attributing to newly calved shelf ice from rift walls, associated rift widening, and other rift-mélange interaction factors.

During the melting season, substantial quantities of surface meltwater converge in topographically depressed regions, forming supraglacial lakes (SGLs). We extract SGL area and profile depth using remote sensing data and then invert the depth of entire SGLs based on the machine learning method. By applying the above-mentioned methods, we capture the volumetric evolution of SGLs throughout the entire melt season of 2022 in southwestern Greenland.