Rock glaciers manifest the creep of mountain permafrost occurring in the past or at present. Their presence and dynamics are indicators of permafrost distribution and changes in response to climate forcing. There is a complete lack of knowledge about rock glaciers in the Western Kunlun Mountains, one of the driest mountain ranges in Asia, where extensive permafrost is rapidly warming. In this study, we first mapped and quantified the kinematics of active rock glaciers based on satellite Interferometric Synthetic Aperture Radar (InSAR) and Google Earth images. Then we trained DeepLabv3+, a deep learning network for semantic image segmentation, to automate the mapping task. The well-trained model was applied for a region-wide, extensive delineation of rock glaciers from Sentinel-2 images to map the landforms that were previously missed due to the limitations of the InSAR-based identification. Finally, we mapped 413 rock glaciers across the Western Kunlun Mountains: 290 of them were active rock glaciers mapped manually based on InSAR and 123 of them were newly identified and outlined by deep learning. The rock glaciers are categorized by their spatial connection to the upslope geomorphic units. All the rock glaciers are located at altitudes between 3,390 m and 5,540 m with an average size of 0.26 km2 and a mean slope angle of 17°. The median and maximum surface downslope velocities of the active ones are 17±1 cm yr-1 and 127±6 cm yr-1, respectively. Characteristics of the inventoried rock glaciers provided insights into permafrost distribution in the Western Kunlun Mountains.

Rock glaciers manifest the creep of mountain permafrost occurring in the past or at present. Their presence and dynamics are indicators of permafrost distribution and changes in response to climate forcing. Knowledge of rock glaciers is completely lacking in the West Kunlun, one of the driest mountain ranges in Asia, where widespread permafrost is rapidly warming. In this study, we first mapped and quantified the kinematics of active rock glaciers based on satellite Interferometric Synthetic Aperture Radar (InSAR) and Google Earth images. Then we trained DeepLabv3+, a deep learning network for semantic image segmentation, to automate the mapping task. The well-trained model was applied for a region-wide, extensive delineation of rock glaciers from Sentinel-2 images to map the landforms that were previously missed due to the limitations of the InSAR-based identification. Finally, we mapped 413 rock glaciers across the West Kunlun: 290 of them were active rock glaciers mapped manually based on InSAR and 123 of them were newly identified and outlined by deep learning. The rock glaciers are categorized by their spatial connection to the upslope geomorphic units. All the rock glaciers are located at altitudes between 3,389 m and 5,541 m with an average size of 0.26 km2 and a mean slope angle of 17°. The mean and maximum surface downslope velocities of the active ones are 24 cm yr-1 and 127 cm yr-1, respectively. Characteristics of the rock glaciers of different categories hold implications on the interactions between glacial and periglacial processes in the West Kunlun.

Geonor T-200B weighing precipitation gauge (Geonor) and Chinese standard precipitation gauge (CSPG) are widely used for measuring precipitaion in the Qinghai-Tibet Plateau. However, their measurements must be adjusted due to wetting, evaporation loss and wind-induced undercatch. Some transfer functions had been proposed in previous studies, but their applicability in the Qinghai-Tibetan Plateau has not been evaluated. In our study, a precipitation measurement intercomparison experiment was carried out from August 2018 to September 2020 at a station in the central Qinghai-Tibet Plateau, and these transfer functions are also evaluated based on the results of the experiment. The results show that: (1) the catch efficiency of Geonor for rain, mixed, snow, hail are 92.06%, 85.32%, 68.08% and 91.82% respectively, and the catch efficiency of CSPG are 92.59%, 81.32%, 46.43% and 95.56% respectively. (2) K2017b has the most accurate correction results for Geonor solid and mixed precipitation at 30 minutes time scale, and the M2007e scheme has the most accurate correction results for Geonor solid precipitation at event scale. (3) The current transfer functions for CSPG underestimate the solid precipitation, while overestimate the liquid precipitation. Based on the results of the comparative observation in our study, new CSPG transfer functions are proposed for the central Qinghai-Tibet Plateau. (4) Hail is also an important precipitation type in the central Qinghai-Tibet Plateau. Because the capture rate of hail precipitation is close to that of rain, and the temperature when hail precipitation occurs is high, it is not necessary to determine the hail precipitation type, and the transfer functions recommended in this study can also get a good correction results.