Influenced by global warming, glaciers in High Mountains Asia (HMA) generally show a trend of retreat and thinning, but in Karakoram, Pamir, and West Kunlun there is a trend of glacier stabilization or even a weak advance. In this study, using a bibliometric analysis, we systematically sorted the area, mass balance, and elevation changes of the glaciers in Karakoram and summarized the glacier surges in HMA. The study shows that, since the 1970s, the glaciers in the Karakoram region have experienced a weak positive mass balance, with weakly reducing area and the increasing surface elevation. The north slope of Chogori Peak and the Keltsing River Basin presented a glacier retreat rate with a fast to slow trend. The anomaly is mainly due to low summer temperatures and heavy precipitation in winter and spring in the Karakoram region. There are a large number of surging glaciers in the Karakoram Mountains, the Pamir Plateau, and the West Kunlun region in the western part of HMA, especially in the Karakoram Mountains and the Pamir Plateau, which account for more than 70% of the number of surging glaciers in the entire HMA. The glaciers in the Karakoram and Kunlun Mountains are mainly affected by the synergistic influence of various factors, such as hydrothermal conditions, atmospheric circulation, and topography. However, the glaciers in the Pamir region are mainly influenced by the thermal mechanism of the glacier surge. The glaciers in and around Karakoram are critical to the hydrological response to climate change, and glacial meltwater is an important freshwater resource in arid and semi-arid regions of South and Central Asia, as well as in western China. Therefore, changes in the Karakoram anomaly will remain a hot research topic in the future.

Runoff from the high-cold mountains area (HCMA) is the most important water resource in the arid zone, and its accurate forecasting is key to the scientific management of water resources downstream of the basin. Constrained by the scarcity of meteorological and hydrological stations in the HCMA and the inconsistency of the observed time series, the simulation and reconstruction of mountain runoff have always been a focus of cold region hydrological research. Based on the runoff observations of the Yurungkash and Kalakash Rivers, the upstream tributaries of the Hotan River on the northern slope of the Kunlun Mountains at different time periods, and the meteorological and atmospheric circulation indices, we used feature analysis and machine learning methods to select the input elements, train, simulate, and select the preferences of the machine learning models of the runoffs of the two watersheds, and reconstruct the missing time series runoff of the Kalakash River. The results show the following. (1) Air temperature is the most important driver of runoff variability in mountainous areas upstream of the Hotan River, and had the strongest performance in terms of the Pearson correlation coefficient (ρ_XY) and random forest feature importance (FI) (ρ_XY = 0.63, FI = 0.723), followed by soil temperature (ρ_XY = 0.63, FI = 0.043), precipitation, hours of sunshine, wind speed, relative humidity, and atmospheric circulation were weakly correlated. A total of 12 elements were selected as the machine learning input data. (2) Comparing the results of the Yurungkash River runoff simulated by eight machine learning methods, we found that the gradient boosting and random forest methods performed best, followed by the AdaBoost and Bagging methods, with Nash–Sutcliffe efficiency coefficients (NSE) of 0.84, 0.82, 0.78, and 0.78, while the support vector regression (NSE = 0.68), ridge (NSE = 0.53), K-nearest neighbor (NSE = 0.56), and linear regression (NSE = 0.51) were simulated poorly. (3) The application of four machine learning methods, gradient boosting, random forest, AdaBoost, and bagging, to simulate the runoff of the Kalakash River for 1978–1998 was generally outstanding, with the NSE exceeding 0.75, and the results of reconstructing the runoff data for the missing period (1999–2019) could well reflect the characteristics of the intra-annual and inter-annual changes in runoff.