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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 (Do), snow end day (De), snow cover duration days (Dd), and maximum snow depth (SDmax) 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. Dd 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 Dd in high-altitude regions and the Fergana Valley but increased Dd in the Ili Valley and upper reaches of the Chu and Aksu Rivers. Increased SDmax was exhibited in the central part of the TS, and decreased SDmax was observed in the western and eastern parts of the TS. Changes in Dd were dominated by earlier De, which was caused by increased melt-season temperatures (Tm). Earlier De with increased accumulation of seasonal precipitation (Pa) influenced the hydrological processes in the snowmelt recharge basin, increasing runoff and earlier peak runoff in the spring, which intensified the regional water crisis
Climate change; Snow cover duration; Snow depth; Passive microwave remote sensing; Runoff; Tianshan Mountains
Climate | Geotechnical Engineering | Hydraulic Engineering
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Changes in Snow Phenology from 1979 to 2016 over the Tianshan Mountains, Central Asia.
Remote Sensing, 11(5),