The Xinjiang Uyghur Autonomous Region of China is the largest arid region in Central Asia, and is heavily dependent on glacier melt in high mountains for water supplies. In this paper, glacier and climate changes in Xinjiang during the past decades were comprehensively discussed based on glacier inventory data, individual monitored glacier observations, recent publications, as well as meteorological records. The results show that glaciers have been in continuous mass loss and dimensional shrinkage since the 1960s, although there are spatial differences between mountains and sub-regions, and the significant temperature increase is the dominant controlling factor of glacier change. The mass loss of monitored glaciers in the Tien Shan has accelerated since the late 1990s, but has a slight slowing after 2010. Remote sensing results also show a more negative mass balance in the 2000s and mass loss slowing in the latest decade (2010s) in most regions. This needs further investigation on whether the slowing is general and continuing. In addition, glacier surging occurs more frequently in the Karakoram and Kunlun Mountains.

This study deploys RTK-GNSS in 2012, TLS in 2015 and UAV in 2018 to monitor the changes of Urumqi Glacier No. 1 (UG1), eastern Tien Shan, and analyzes the feasibility of three technologies in monitoring the mountain glaciers. DEM differencing shows that UG1 has experienced a pronounced thinning and mass loss for the period of 2012–18. The glacier surface elevation change of −0.83 ± 0.57 m w.e. a−1 has been recorded for 2012–15, whereas the changes of glacier tongue surface elevation in 2015–18 and 2012–18 were −2.03 ± 0.95 and −1.34 ± 0.88 m w.e. a−1, respectively. The glacier area shrunk by 0.07 ± 0.07 × 10−3 km2 and the terminus retreat rate was 6.28 ± 0.83 m a−1 during 2012–18. The good agreement between the glaciological and geodetic specific mass-balances is promising, showing the combination of the three technologies is suitable to monitor glacier mass change. We recommend application of the three technologies to assess each other in different locations of the glacier, e.g. RTK-GNSS base stations, ground control points, glacier tongue and terminus, in order to avoid the inherent limitations of each technology and to provide reliable data for the future studies of mountain glacier changes in western China.

The seasonal surface changes of glaciers in Tien Shan have seen little prior investigation despite the increase in geodetic studies of multi-year changes. In this study, we analyzed the potential of an Unmanned Aerial Vehicle (UAV) to analyze seasonal surface change processes of the Urumqi Glacier No. 1 in eastern Tien Shan. We carried out UAV surveys at the beginning and the end of the ablation period in 2018. The high-precision evolution of surface elevation, geodetic mass changes, surface velocity, and terminus change in the surveyed ablation area were correspondingly derived in combination with ground measurements, including stake/snow-pit observation and GPS measurement. The derived mean elevation change in the surveyed ablation area was −1.64 m, corresponding to the geodetic mass balance of approximately −1.39 m w.e. during the ablation period in 2018. The mean surface velocity was 3.3 m/yr and characterized by the spatial change of the velocity, which was less in the East Branch than in the West Branch. The UAV survey results were a little less than those from the ground measurements, and the correlation coefficient was 0.88 for the surface elevation change and 0.87 for surface displacement. The relative error of the glacier terminus change was 4.5% for the East Branch and 6.2% for the West Branch. These results show that UAV photogrammetry is ideal for assessing seasonal glacier surface changes and has a potential application in the monitoring of detailed glacier changes.

In this study, energy and mass balance is quantified using an energy balance model to represent the glacier melt of Urumqi Glacier No. 1, Chinese Tian Shan. Based on data from an Automatic Weather Station (4025 m a.s.l) and the mass balance field survey data nearby on the East Branch of the glacier, the “COupled Snowpack and Ice surface energy and Mass balance model” (COSIMA) was used to derive energy and mass balance simulations during the ablation season of 2018. Results show that the modeled cumulative mass balance (−0.67 ± 0.03 m w.e.) agrees well with the in-situ measurements (−0.64 ± 0.16 m w.e.) (r² = 0.96) with the relative difference within 5% during the study period. The correlation coefficient between modeled and observed surface temperatures is 0.88 for daily means. The main source of melt energy at the glacier surface is net shortwave radiation (84%) and sensible heat flux (16%). The energy expenditures are from net longwave radiation (55%), heat flux for snow/ice melting (32%), latent heat flux of sublimation and evaporation (7%), and subsurface heat flux (6%). The sensitivity testing of mass balance shows that mass balance is more sensitive to temperature increase and precipitation decrease than temperature decrease and precipitation increase.

As a key component of the hydrological cycle, knowledge and comprehension of precipitation formation and evolution are of leading significance. This study investigates the statistical characteristics of raindrop size distribution for heavy precipitation events with observations collected by a Present Weather Sensor (PWS100) disdrometer located in the alpine area of eastern Tianshan, China. The characteristics are quantified based on heavy rain, heavy snow, and hail precipitation events classified using the rainfall intensity and the precipitation-related weather codes (US National Weather Service). On average, the heavy precipitation events in the headwaters of the Urumqi River are dominated by medium-sized (2–4 mm) raindrops. As well, we investigate mass-weighted mean diameter–normalized intercept parameter scatterplots, which demonstrate that the heavy precipitation events in alpine regions of the Tianshan Mountains can be identified as maritime-like clusters. The concentration of raindrops in heavy precipitation is the highest overall, while the concentration of raindrops in heavy snow is the lowest when the diameter is lower than 1.3 mm. The power–law relationships of radar reflectivity (Z) and rain rate (R) [Z = AR^b] for the heavy rain, heavy snow, and hail precipitation events are also calculated. The Z–R relationship of heavy rain and heavy snow in this work has a lower coefficient value of A (10 and 228.7, respectively) and a higher index value of b (2.6 and 2.1, respectively), and the hail events are the opposite (A = 551.5, b = 1.3), compared to the empirical relation (Z = 300R^1.4). Furthermore, the possible thermodynamics and general atmospheric circulation that cause the distinctions in the raindrop size distribution characteristics between alpine areas and other parts of the Tianshan Mountains are also debated in this work. The headwaters of the Urumqi River in alpine areas have relatively colder and wetter surroundings in the near-surface layer than the foothills of the Tianshan Mountains during the precipitation process. Meanwhile, a lower temperature, a higher relative humidity, a more efficient collision coalescence mechanism, and glacier local microclimate effects (temperature jump, inverse glacier temperature, glacier wind) at the headwaters of the Urumqi River during the precipitation process are probably partly responsible for more medium- and large-size drops in the mountains.

As one of the major water supply systems for inland rivers, especially in arid and semi-arid regions, snow cover strongly affects hydrological cycles. In this study, remote sensing datasets combined with in-situ observation data from a route survey of snow cover were used to investigate the changes in snow cover parameters on the Chinese Altai Mountains from 2000 to 2022, and the responses of snow cover to climate and hydrology were also discussed. The annual snow cover frequency (SCF), snow cover area, snow depth (SD), and snow density were 45.03%, 2.27 × 10⁴ km², 23.4 cm, and ~0.21 g·cm⁻³, respectively. The snow water equivalent ranged from 0.58 km³ to 1.49 km³, with an average of 1.12 km³. Higher and lower SCF were mainly distributed at high elevations and on both sides of the Irtysh river. The maximum and minimum snow cover parameters occurred in the Burqin River Basin and the Lhaster River Basin. In years with high SCF, abnormal westerly airflow was favorable for water vapor transport to the Chinese Altai Mountains, resulting in strong snowfall, and vice versa in years with low SCF. There were significant seasonal differences in the impact of temperature and precipitation on regional SCF changes. The snowmelt runoff ratios were 11.2%, 25.30%, 8.04%, 30.22%, and 11.56% in the Irtysh, Kayit, Haba, Kelan, and Burqin River Basins. Snow meltwater has made a significant contribution to the hydrology of the Chinese Altai Mountains.