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Snowmelt water is an important freshwater resource in the Altay Mountains in north-west China; however, warming climate and rapid spring snowmelt can cause floods that endanger both public and personal property and safety. This study simulates snowmelt in the Kayiertesi River catchment using a temperature index model based on remote sensing coupled with high-resolution meteorological data obtained from National Centers for Environmental Prediction (NCEP) reanalysis fields that were downscaled using the Weather Research Forecasting model and then bias corrected using a statistical downscaled model. Validation of the forcing data revealed that the high-resolution meteorological fields derived from the downscaled NCEP reanalysis were reliable for driving the snowmelt model. Parameters of the temperature index model based on remote sensing were calibrated for spring 2014, and model performance was validated using Moderate Resolution Imaging Spectroradiometer snow cover and snow observations from spring 2012. The results show that the temperature index model based on remote sensing performed well, with a simulation mean relative error of 6.7% and a Nash-Sutcliffe efficiency of 0.98 in spring 2012 in the river of Altay Mountains. Based on the reliable distributed snow water equivalent simulation, daily snowmelt run-off was calculated for spring 2012 in the basin. In the study catchment, spring snowmelt run-off accounts for 72% of spring run-off and 21% of annual run-off. Snowmelt is the main source of run-off for the catchment and should be managed and utilized effectively. The results provide a basis for snowmelt run-off predictions, so as to prevent snowmelt-induced floods, and also provide a generalizable approach that can be applied to other remote locations where high-density, long-term observational data are lacking. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The glacier snowline altitude (SLA) at the end of the melt season is an indicator of the glacier equilibrium line altitude and can be used to estimate glacier mass balance and reconstruct past climate. This study analyzes the spatiotemporal variability in glacier SLA across High Mountain Asia, including the Altai Mountains, Karakoram Mountains, Western Himalayas, Gongga Mountains, Tian Shan, and Nyainqentanglha Mountains, over the past 30 years (1989–2019) to better elucidate the state of these mountain glaciers. Remote-sensing data are processed to delineate the glacier SLA across these mountainous regions, and nearby weather station data are incorporated to determine the potential relationships between SLA and temperature/precipitation. The mean SLAs across the Altai and Karakoram mountains ranged from 2860 ± 169 m to 3200 ± 152 m and from 5120 ± 159 m to 5320 ± 240 m, respectively, with both regions experiencing an average increase of up to 137 m over the past 30 years. Furthermore, the mean glacier SLAs across the Western Himalayas and Gongga Mountains increased by 190–282 m over the past 30 years, with both regions experiencing large fluctuations. In particular, the mean glacier SLA across the Western Himalayas varied from 4910 ± 190 m in 1989 to 5380 ± 164 m in 2000, and that across the Gongga Mountains varied from 4960 ± 70 m in 1989 to 5330 ± 100 m in 2012. Correlation analyses between glacier SLA and temperature/precipitation suggest that temperature is the primary factor influencing glacier SLA across these High Mountain Asia glaciers. There is a broad increase in glacier SLA from the Altai Mountains to the Karakoram Mountains, with a decrease in glacier SLA with decreasing latitude across the Himalayas; the maximum SLA occurs near the northern slopes of the Western Himalayas. The glacier SLA is lower on the eastern side of the Tibetan Plateau and exhibits a longitudinal distribution pattern. These results are expected to provide useful information for evaluating the state of High Mountain Asia glaciers, as well as their response and feedback to climate change. [ABSTRACT FROM AUTHOR]