Abstract. Sub-seasonal glacier dynamics strongly influence the timing and magnitude of meltwater supply, a vital component of summer runoff in dry Central Asia region. Understanding of snowline evolution during the melt season is therefore essential for predicting seasonal water availability and glacier response to climate change. We present a novel method to infer 24-years of sub-seasonal snowline dynamic for four glaciers distributed throughout Pamir and Tien Shan mountain ranges using multi-sensor spaceborne observations. Our approach combines medium-resolution optical MODIS with high-resolution Sentinel-2 optical and Sentinel-1 radar imagery to produce close-to-daily estimates of the glacier Snow-Covered Area Fraction (SCAF – the ratio between snow covered area above the snowline and the total glacier area) throughout the melt season from 2000 to present. The method was validated against manually delineated Landsat snowlines, achieving RMSE values below 20 % for most sites. The resulting time series reveal substantial interannual and regional snowline variability with e.g. June SCAF ranging between 60–100 %. Recent warm years, show earlier exposure of bare ice and shifts in the melt season's end by as much as a month later in September. Accelerating snow depletion rates were found for all four glaciers, starting in 2000 and 2009 and reaching up to −1.25 %/day. Linking these dynamics to the annually measured and daily modelled mass balance data highlights that similar annual mass balance values can have large differences in sub-seasonal snow depletion and thus meltwater contribution, with implications for water availability during the critical dry-season months. Our findings demonstrate the potential of long-term, high-temporal-resolution snowline monitoring to improve understanding of glacier-climate interactions and to better constrain seasonal runoff forecasts in Central Asia's water-scarce river basins.
Kim et al. (Thu,) studied this question.