With the global climate change, glaciers on the Qinghai-Tibet Plateau (QTP) and its adjacent mountainous regions are retreating rapidly, leading to an increase in active rock glaciers (ARGs) in front of glaciers. As crucial components of water resources in alpine regions and indicators of permafrost boundaries, ARGs reflect climatic and environmental changes on the QTP and its adjacent mountainous regions. However, the extensive scale of rock glacier development poses a challenge to field investigations and sampling, and manual visual interpretation requires substantial effort. Consequently, research on rock glacier cataloging and distribution characteristics across the entire area is scarce. This study statistically analyzed the geometric characteristics of ARGs using high- resolution GF-2 satellite images. It examined their spatial distribution and relationship with local factors. The findings reveal that 34,717 ARGs, covering an area of approximately 6873.54 km2, with an average area of 0.19 +/- 0.24 km2, a maximum of 0.0012 km2, and a minimum of 4.6086 km2, were identified primarily in north-facing areas at elevations of 4300-5300 m and slopes of 9 degrees-25 degrees, predominantly in the Karakoram Mountains and the Himalayas. Notably, the largest concentration of ARGs was found on north-facing shady slopes, constituting about 42 % of the total amount, due to less solar radiation and lower near-surface temperatures favorable for interstitial ice preservation. This research enriches the foundational data on ARG distribution across the QTP and its adjacent mountainous regions, offering significant insights into the response mechanisms of rock glacier evolution to environmental changes and their environmental and engineering impacts.

Hydrological processes in mid-latitude mountainous regions are greatly affected by changes in vegetation cover that induced by the climate change. However, studies on hydrological processes in mountainous regions are limited, be-cause of difficulties in building and maintaining basin-wide representative hydrological stations. In this study, a new method, remote sensing technology for monitoring river discharge by combining satellite remote sensing, un-manned aerial vehicles and hydrological surveying, was used for evaluating the runoff processes in the Changbai Mountains, one of the mid-latitude mountainous regions in the eastern part of Northeast China. Based on this method, the impact of vegetation cover change on hydrological processes was revealed by combining the data of hydrological processes, meteorology, and vegetation cover. The results showed a decreasing trend in the monitored river discharge from 2000 to 2021, with an average rate of -5.13 x 105 m3 yr-1. At the monitoring mainly influenced by precipitation, the precipitation-induced proportion of changes in river discharge to annual average river discharge and its change significance was only 6.5 % and 0.23, respectively, showing the precipitation change was not the cause for the decrease in river discharge. A negative impact of evapotranspiration on river discharge was found, and the decrease in river discharge was proven to be caused by the increasing evapotranspiration, which was induced by the drastically increased vegetation cover under a warming climate. Our findings suggested that increases in vege-tation cover due to climate change could reshape hydrological processes in mid-latitude mountainous regions, leading to an increase in evapotranspiration and a subsequent decrease in river discharge.

Mountains are the water towers of the world, so it is critical to obtain accurate precipitation data for mountainous areas. Due to the complex topography of high mountainous areas, precipitation ground stations are sparse and unevenly distributed in such areas, so precipitation products such as remote sensing and reanalysis products are used to obtain gridded precipitation data for these areas. However, no single precipitation product performs best in all areas of mountainous regions. Therefore, this study first evaluated the performance of 12 precipitation products in estimating precipitation in the Qilian Mountains at the station scale and sub-basin scale, and then compared the performance of precipitation estimates for the Qilian Mountains generated by 8 multimodel averaging methods. The evaluation results for 29 meteorological stations in the Qilian Mountains showed that the China Meteorological Forcing Dataset product was the best-performing precipitation product, while the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record product was the worst-performing precipitation product. The evaluation results for 18 sub-basins showed that at these sub-basins, the WorldClim was the best-performing precipitation product, while the High Asia Refined analysis was the worst-performing precipitation product. Thus, station-scale evaluations may not necessarily be applicable to the basin scale. Multi-model averaging methods effectively improved the accuracy of precipitation estimates both at station scale and at sub-basin scale. The Granger-Ramanathan variant C was the best multi-model averaging method for estimating precipitation at station scale. As the Granger-Ramanathan methods allow negative weights, they are not recommended to interpolate the Granger-Ramanathan weight values of stations to grids. The Bayesian model averaging (BMA) was found to be the most suitable multi-model averaging method for estimating precipitation in the Qilian Mountains by interpolation of weight values of stations to grids. The precipitation estimates generated by BMA show that the mean annual precipitation in the Qilian Mountains from 2001 to 2018 was approximately 336.1 mm, and the annual precipitation during this period increased linearly by 2.4 mm per year.