Hot Spots of Glacier Mass Balance Variability in Central Asia

Abstract

The Tien Shan and Pamir mountains host over 28,000 glaciers providing essential water resources for increasing water demand in Central Asia. A disequilibrium between glaciers and climate affects meltwater release to Central Asian rivers, challenging the region's water availability. Previous research has neglected temporal variability. We present glacier mass balance estimates based on transient snowline and geodetic surveys with unprecedented spatiotemporal resolution from 1999/00 to 2017/18. Our results reveal spatiotemporal heterogeneity characterized by two mass balance clusters: (a) positive, low variability, and (b) negative, high variability. This translates into variable glacial meltwater release (≈1-16%) of annual river runoff for two watersheds. Our study reveals more complex climate forcingrunoff responses and importance of glacial meltwater variability for the region than suggested previously.Plain Language Summary Glaciers in Central Asia act as water towers for millions of people by storing and releasing water in response to climate. Monitoring glaciers is difficult due to their often very remote locations. Satellite remote sensing has emerged as a powerful method but a drawback is their (semi-)decadal resolution for glacier mass change surveys. We present a methodology, combining multiyear elevation change maps with frequent snowline observations to estimate mass changes and variability at annual scale, which allows us identifying so far unrecognized regions of contrasting trends for the Tien Shan and Pamir mountains. These "hot spots" reveal a far more complex climate-glacier interplay than previously known. The additional meltwater released from the retreating glaciers varies considerably and contributes to the river flow for warm dry years by twice as much as for cold wet years. Our findings will help to better understand the impact of climate change on Central Asian glaciers and their meltwater release. BARANDUN ET AL.

Reconstruction of the Historical (1750–2020) Mass Balance of Bordu, Kara-Batkak and Sary-Tor Glaciers in the Inner Tien Shan, Kyrgyzstan

“…However, over the 18 years, the cumulative difference is only 0.69 m w.e. A comparison with the mass balance observations for the overlapping period shows that the reconstructed mass balance from Barandun et al (2021) deviates strongly in some years, while our model results are more in line with the observations. For example, for the 2015-2016 balance year, MB measurements revealed a mean specific mass balance of −0.39 m w.e.…”

“…In contrast, the meteorological gradients (especially the precipitation gradient) are more variable and constraining these is more complex. Such climatological variability was also observed in a recent study of Barandun et al (2021) which revealed a complex climate-glacier interplay in the study area. This likely hampers the model to be applied for glaciers for which there are no or insufficient quality observations to tune the gradients.…”

“…(see Figure 3), the model results showed a mean specific mass balance of −0.37 ± 0.30 m w.e. (see Figure 10B), while the results of Barandun et al (2021) revealed −1.69 m w.e. For all three studied glaciers, we find an average mass balance gradient between -0.50 and −0.60 m w.e.…”

“…A comparison between the modelled mass balance values for Kara-Batkak glacier with the reconstructed annual mass balance values (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018) of Barandun et al (2021), obtained from transient snowline and geodetic surveys, reveals a rather large mean absolute difference of 0.50 m w.e. yr −1 .…”

“…The mean specific mass balance depends on the climate, but also on the glacier geometry. Techniques for measuring and monitoring glacier mass balances have expanded over the last century from in situ point measurements, which require an extensive field campaign, to remote sensing and modeling approaches, mainly based on satellite measurements (e.g., Brun et al, 2017;Davaze et al, 2020;Sommer et al, 2020;Barandun et al, 2021;Miles et al, 2021;Thomson et al, 2021) and unoccupied aerial vehicle (UAV) measurements Vincent et al, 2021).…”