High Mountain Asia (HMA), encompassing the Tibetan Plateau and surrounding mountain ranges, is the primary water tower for much of Asia, serving more than a billion downstream users. Many catchments receive the majority of their yearly water budget in the form of snow, which is poorly monitored by sparse in-situ weather networks. Both the timing and volume of snowmelt play critical roles in downstream water provision, as many applications &ndash; such as agriculture, drinking-water generation, and hydropower &ndash; rely on consistent and predictable snowmelt runoff. Here, we leverage passive microwave data across five sensors (SSMI, SSMIS, AMSR-E, AMSR2, and GPM) from 1987&ndash;2016 to track the onset and end of snowmelt across HMA. Compared against a control dataset (n&thinsp;=&thinsp;2100, 3 variables at 25 locations over 28 years), our algorithm is generally within 3&ndash;5 days of the onset and end dates of melt. Using the algorithm-generated snowmelt dates, we examine the spatiotemporal patterns of the snowmelt season across HMA. The climatically short (29 year) time series, along with complex inter-annual snowfall variations, makes determining trends in melt onset and end dates at a single point difficult. We instead identify trends in snowmelt timing by using hierarchical clustering of the passive microwave data to determine trends in self-similar regions. We make the following four key observations: (1) The end of the snowmelt season is trending almost universally earlier in HMA (negative trends). Changes in the end of the snowmelt season are generally between 2 and 8 days/decade over the 29-year study period (5&ndash;25 days total). The length of the snowmelt season is thus shrinking in many, though not all, regions of HMA. Some areas exhibit later snowmelt onset dates (positive trends), but with a generally smaller magnitudes than trends in snowmelt end. (2) Areas with long snowmelt periods, such as the Tibetan Plateau, show the strongest compression of the snowmelt season (negative trends). These trends are apparent regardless of the time period over which the regression is performed. (3) While trends averaged over three decades indicate earlier snowmelt onset and end, data from the last 14 years (2002&ndash;2016) exhibit positive trends in both snowmelt onset and end dates in many regions, such as parts of the Pamir and Kunlun Shan. Due to the short nature of the time series, it is not clear whether this change is a reversal in a long-term trend or simply inter-annual variability. (4) Some regions with stable or growing glaciers &ndash; such as the Karakoram and Kunlun Shan &ndash; see slightly later snowmelt onset and longer snowmelt periods. It is likely that changes in the snowmelt regime of HMA account for some of the observed heterogeneity in glacier response to climate change. While the decadal increases in regional temperature have in general caused earlier snowmelt onset and shortened melt seasons, changes in HMA's crysophere have been spatially and temporally heterogeneous. The complex response of HMA's cryosphere to climate change highlights the importance of both regional and small-scale studies for effective water planning.
Smith, T., Bookhagen, B., & Rheinwalt, A. (2017). Spatiotemporal patterns of High Mountain Asia’s snowmelt season identified with an automated snowmelt detection algorithm, 1987-2016. Cryosphere, 11(5), 2329–2343. https://doi.org/10.5194/tc-11-2329-2017