Snowmelt from the Tianshan Mountains (TS) is a major contributor to the water resources of the Central Asian region. Thus, changes in snow phenology over the TS have significant implications for regional water supplies and ecosystem services. However, the characteristics of changes in snow phenology and their influences on the climate are poorly understood throughout the entire TS due to the lack of in situ observations, limitations of optical remote sensing due to clouds, and decentralized political landscapes. Using passive microwave remote sensing snow data from 1979 to 2016 across the TS, this study investigates the spatiotemporal variations of snow phenology and their attributes and implications. The results show that the mean snow onset day (D_o), snow end day (D_e), snow cover duration days (D_d), and maximum snow depth (SD_max) from 1979 to 2016 were the 78.2nd day of hydrological year (DOY), 222.4th DOY, 146.2 days, and 16.1 cm over the TS, respectively. D_d exhibited a spatial distribution of days with a temperature of <0 °C derived from meteorological station observations. Anomalies of snow phenology displayed the regional diversities over the TS, with shortened D_d in high-altitude regions and the Fergana Valley but increased D_d in the Ili Valley and upper reaches of the Chu and Aksu Rivers. Increased SD_max was exhibited in the central part of the TS, and decreased SD_max was observed in the western and eastern parts of the TS. Changes in D_d were dominated by earlier D_e, which was caused by increased melt-season temperatures (T_m). Earlier D_e with increased accumulation of seasonal precipitation (P_a) influenced the hydrological processes in the snowmelt recharge basin, increasing runoff and earlier peak runoff in the spring, which intensified the regional water crisis.

Mountain snowpacks play important roles in water resource and ecological system. However, existing snow depth products show great uncertainties across the Tianshan Mountains, Central Asia. This study evaluated and compared four snow depth datasets over the Tianshan Mountains, including snow depth datasets from ERA5, ERA5‐Land, passive microwave, as well as a dynamically downscaled simulation by Weather Research and Forecasting (WRF) model. The snow depth was evaluated against in situ observations while the snow cover extent was evaluated by interactive multisensor snow and ice mapping system (IMS) snow cover. Furthermore, the snow‐related metrics, such as annual mean snow depth and snow cover days, were compared among the four datasets. The results showed that the spatial patterns of snow‐related metrics were relatively consistent among datasets, although discrepancies existed in magnitude. Additionally, the temporal variations in snow‐related metrics depended on the dataset employed. The simulated snow depth from WRF got the lowest bias in daily snow depth value against the in situ observations (mean bias = 0.12 cm, root mean square error = 2.08 cm), additionally, it had the best performance in classifying correct snow grids compared with IMS (probability of detection = 0.766). It also outperformed in annual mean snow depth and snow cover days by 47.2 and 13.3% compared with ERA5‐Land snow depth. This study highlights the confidence in characterizing both the spatial pattern and temporal variations of snow depth products based on WRF model simulation.

The accumulation and ablation processes of seasonal snow significantly affect the land surface phenology in a mountainous ecosystem. However, the ability of snow to regulate the alpine land surface phenology in the arid regions is not well described in the context of climate change. The impact of snowpack changes on land surface phenology and its driving factors were investigated in the Tianshan Mountains using the land surface phenology metrics derived from satellited products and a snow dataset from downscaled regional climate model simulations covering the period from 1983 to 2015. The results demonstrated that the annual mean start of growing season (SOS) and length of growing season (LOS) experienced a significant (p < 0.05) decrease and increase with a rate of −2.45 days/decade and 2.98 days/decade, respectively. The significantly advanced SOS and increased LOS were mainly seen in the Western Tianshan Mountains and Ili Valley regions with elevations from 2500 to 3500 m a.s.l and below 3000 m a.s.l, respectively. During the early spring, the significant decline in snow cover fraction (SCF) could advance the SOS. In contrast, snowmelt amount and annual maximum snow water equivalent (SWE) have an almost equally substantial positive correlation with annual maximum vegetation greenness. In particular, the SOS of grassland was the most sensitive to variations of snow cover fraction during early spring than that of other vegetation types, and their strong relationship was mainly located at elevations from 1500 to 2500 m a.s.l. Its greenness was significantly controlled by the annual maximum snow water equivalent in all elevation bands. Both decreased SCF and increased temperature in the early spring caused a significant advance of the SOS, consequently prolonging the LOS. Meanwhile, more SWE and snowmelt amount could significantly promote vegetation greenness by regulating the soil moisture. The results can improve the understanding of the snow ecosystem services in the alpine regions under climate change.

Snow contributes one of the main water sources to runoff in the arid region of China. A clear understanding of the spatiotemporal variation of snowfall is not only required for climate change assessment, but also plays a critical role in water resources management. However, in-situ observations or gridded datasets hardly meet the requirement and cannot provide precise spatiotemporal details on snowfall across the region. This study attempted to apply the Weather Research and Forecasting (WRF) model to clarify the spatiotemporal variation of snowfall and the ratio of snowfall to total precipitation over Xinjiang in China during the 1979–2015 period. The results showed that the snowfall increased in the southern edge of the Tarim Basin, the Ili Valley, and the Altay Mountains, but decreased in the Tianshan Mountains and the Kunlun Mountains. The snowfall/precipitation (S/P) ratio revealed the opposite trends in low-elevation regions and mountains in the study area. The S/P ratio rose in the Tarim Basin and the Junggar Basin, but declined in the Altay Mountains, the Tianshan Mountains, and the west edge of the Junggar Basin. The study area comprises two major rivers in the middle of the Tianshan Mountains. Both the runoff magnitude increase and earlier occurrence of snowmelt recharge in runoff identified for the 1980s were compared with the 2000s level in decreasing S/P ratio regions.