About 800 million people depend in part on meltwater from the thousands of glaciers in the high mountains of Asia. Water stress makes this region vulnerable to drought, but glaciers are a uniquely drought-resilient source of water. Here I show that seasonal glacier meltwater is equivalent to the basic needs of 221 ± 59 million people, or most of the annual municipal and industrial needs of Pakistan, Afghanistan, Tajikistan, Turkmenistan, Uzbekistan and Kyrgyzstan. During drought summers, meltwater dominates water inputs to the upper Indus, Aral and Chu/Issyk-Kul river basins. This reduces the risk of social instability, conflict and sudden migrations triggered by water scarcity, which is already associated with the large, rapidly growing populations and hydro-economies of these basins. Regional meltwater production is, however, unsustainably high—at 1.6 times the balance rate—and is expected to increase in future decades before ultimately declining. These results update and reinforce a previous publication in Nature on this topic, which was retracted after an inadvertent error was discovered.
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I used the following published datasets: Randolph RGI50 (version 5, http://www.glims.org/RGI/); SRTM version 4.1 (https://cgiarcsi.community/data/srtm-90m-digital-elevation-database-v4-1/; APHRODITE APHRO_MA_025_V1101 and APHRO_MA_TAVE_025deg_V1204R1 (http://www.chikyu.ac.jp/precip/english/products.html); River basins from The Global Runoff Data Centre, 56068 Koblenz, Germany (2007) (http://www.bafg.de/GRDC/EN/02_srvcs/22_gslrs/221_MRB/riverbasins.html?nn=201570); NCEP-CFSR Global Weather data for SWAT (http://globalweather.tamu.edu/); WaterBase Landuse Maps (https://forobs.jrc.ec.europa.eu/products/glc2000/products.php); Harmonised World Soil Database version 1.2 (http://www.fao.org/soils-portal/soil-survey/soil-maps-and-databases/harmonized-world-soil-database-v12/en/); United Nations Environment Programme (UNEP) 2015 population density data for Asia, as compiled from World Population Prospects, the 2012 Revision (WPP2012), United Nations Population Division, United Nations Environment Programme, (http://ede.grid.unep.ch), NASA Global Land Data Assimilation System (GLDAS) Noah Version 2.0 L4 gridded evaporation (https://gcmd.nasa.gov). With permission of the authors, I used datasets from ref. 1 for dams and from ref. 20 for mountain (Sakai) precipitation. See also Extended Data Fig. 4. Source data for Figs. 1, 2, 3, 4 and Extended Data Figs. 3, 5, 6b are available in the online version of the paper.
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Zarfl, C., Lumsdon, A., Berlekamp, J., Tydecks, L. & Tockner, K. A global boom in hydropower dam construction. Aquat. Sci. 77, 161–170 (2015).
Laghari, A. N., Vanham, D. & Rauch, W. The Indus basin in the framework of current and future water resources management. Hydrol. Earth Syst. Sci. 16, 1063–1083 (2012).
Immerzeel, W. W., van Beek, L. P. H. & Bierkens, M. F. P. Climate change will affect the Asian water towers. Science 328, 1382–1385 (2010).
Issues in Managing Water Challenges and Policy Instruments: Regional Perspectives and Case Studies https://www.imf.org/external/pubs/ft/sdn/2015/sdn1511tn.pdf (International Monetary Fund, 2015).
Himalayan Glaciers: Climate Change, Water Resources, and Water Security https://doi.org/10.17226/13449 (National Research Council, 2012).
Natural Capital at Risk: The Top 100 Externalities of Business https://www.trucost.com/publication/natural-capital-risk-top-100-externalities-business/ (TRUCOST, 2013).
The Global Risks Report 2016 11th Edition http://www3.weforum.org/docs/GRR/WEF_GRR16.pdf (World Economic Forum, 2016).
AQUASTAT http://www.fao.org/nr/water/aquastat/main/index.stm (Food and Agriculture Organization of the United Nations, accessed 27 May 2015).
Schleussner, C.-F., Donges, J. F., Donner, R. V. & Schellnhuber, H. J. Armed-conflict risks enhanced by climate-related disasters in ethnically fractionalized countries. Proc. Natl Acad. Sci. USA 113, 9216–9221 (2016).
Andermann, C. et al. Impact of transient groundwater storage on the discharge of Himalayan rivers. Nat. Geosci. 5, 127 (2012).
Yatagai, A. et al. APHRODITE: constructing a long-term daily gridded precipitation dataset for Asia based on a dense network of rain gauges. Bull. Am. Meteorol. Soc. 93, 1401–1415 (2012).
Arendt, A. et al. Randolph Glacier Inventory – A Dataset of Global Glacier Outlines: Version 5.0. Global Land Ice Measurements from Space https://www.glims.org/RGI/rgi50_dl.html (Digital Media, 2015).
Kaser, G., Großhauser, M. & Marzeion, B. Contribution potential of glaciers to water availability in different climate regimes. Proc. Natl Acad. Sci. USA 107, 20223–20227 (2010).
Schaner, N., Voisin, N., Nijssen, B. & Lettenmaier, D. P. The contribution of glacier melt to streamflow. Environ. Res. Lett. 7, 034029 (2012).
Brun, F., Berthier, E., Wagnon, P., Kaab, A. & Treichler, D. A spatially resolved estimate of High Mountain Asia glacier mass balances from 2000 to 2016. Nat. Geosci. 10, 668–673 (2017).
Kraaijenbrink, P. D. A., Bierkens, M. F. P., Lutz, A. F. & Immerzeel, W. W. Impact of a global temperature rise of 1.5 degrees Celsius on Asia’s glaciers. Nature 549, 257 (2017).
Xu, J. et al. The melting Himalayas: cascading effects of climate change on water, biodiversity, and livelihoods. Conserv. Biol. 23, 520–530 (2009).
Azam, M. F. et al. Review of the status and mass changes of Himalayan-Karakoram glaciers. J. Glaciol. 64, 61–74 (2018).
Bolch, T. et al. in The Hindu Kush Himalaya Assessment: Mountains, Climate Change, Sustainability and People (eds Wester, P., Mishra, A., Mukherji, A. & Bhakta Shrestha, A.) 209–255 (Springer, 2019).
Sakai, A. et al. Climate regime of Asian glaciers revealed by GAMDAM glacier inventory. Cryosphere 9, 865–880 (2015).
Pritchard, H. D. Asia’s glaciers are a regionally important buffer against drought. Nature 545, 169–174 (2017); retraction 555, 274 (2018).
Pritchard, H. D. Retraction: Asia’s glaciers are a regionally important buffer against drought. Nature 555, 274 (2018).
Gardelle, J., Berthier, E., Arnaud, Y. & Kaab, A. Region-wide glacier mass balances over the Pamir-Karakoram-Himalaya during 1999–2011 (vol 7, pg 1263, 2013). Cryosphere 7, 1263–1286, (2013); corrigendum 7, 1885–1886 (2013).
Huss, M. & Hock, R. Global-scale hydrological response to future glacier mass loss. Nat. Clim. Change 8, 135–140 (2018).
IPCC Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects (eds Barros, V. R. et al.) (Cambridge Univ. Press, 2014).
The UNEP Environmental Data Explorer, as compiled from World Population Prospects, the 2012 Revision (WPP2012), United Nations Population Division http://ede.grid.unep.ch (United Nations Environment Programme, 2016).
Savoskul, O. S. & Smakhtin, V. Glacier systems and seasonal snow cover in six major Asian river basins: water storage properties under changing climate. IWMI Research Report No. 149 (International Water Management Institute (IWMI), 2013).
Radić, V. & Hock, R. Glaciers in the Earth’s hydrological cycle: assessments of glacier mass and runoff changes on global and regional scales. Surv. Geophys. 35, 813–837 (2014).
McCarthy, M., Pritchard, H., Willis, I. A. N. & King, E. Ground-penetrating radar measurements of debris thickness on Lirung Glacier, Nepal. J. Glaciol. 63, 543–555 (2017).
New, M., Lister, D., Hulme, M. & Makin, I. A high-resolution data set of surface climate over global land areas. Clim. Res. 21, 1–25 (2002).
Malsy, M., aus der Beek, T. & Flörke, M. Evaluation of large-scale precipitation data sets for water resources modelling in Central Asia. Environ. Earth Sci. 73, 787–799 (2015).
Qi, W., Zhang, C., Fu, G., Sweetapple, C. & Zhou, H. Evaluation of global fine-resolution precipitation products and their uncertainty quantification in ensemble discharge simulations. Hydrol. Earth Syst. Sci. 20, 903–920 (2016).
Dahri, Z. H. et al. An appraisal of precipitation distribution in the high-altitude catchments of the Indus basin. Sci. Total Environ. 548–549, 289–306 (2016).
Hewitt, K. Glacier change, concentration, and elevation effects in the Karakoram Himalaya, Upper Indus Basin. Mt. Res. Dev. 31, 188–200 (2011).
Leclercq, P. W. & Oerlemans, J. Global and hemispheric temperature reconstruction from glacier length fluctuations. Clim. Dyn. 38, 1065–1079 (2012).
Bliss, A., Hock, R. & Radić, V. Global response of glacier runoff to twenty-first century climate change. J. Geophys. Res. Earth Surf. 119, 717–730 (2014).
Remesan, R. & Holman, I. P. Effect of baseline meteorological data selection on hydrological modelling of climate change scenarios. J. Hydrol. (Amst.) 528, 631–642 (2015).
Andermann, C., Bonnet, S. & Gloaguen, R. Evaluation of precipitation data sets along the Himalayan front. Geochem. Geophys. Geosyst. 12, (2011).
Wortmann, M., Bolch, T., Menz, C., Tong, J. & Krysanova, V. Comparison and correction of high-mountain precipitation data based on glacio-hydrological modeling in the Tarim River headwaters (High Asia). J. Hydrometeorol. 19, 777–801 (2018).
Reggiani, P. & Rientjes, T. H. M. A reflection on the long-term water balance of the Upper Indus Basin. Hydrol. Res. 46, 446–462 (2015).
Duethmann, D. et al. Evaluation of areal precipitation estimates based on downscaled reanalysis and station data by hydrological modelling. Hydrol. Earth Syst. Sci. 17, 2415–2434 (2013).
Huss, M. et al. Toward mountains without permanent snow and ice. Earths Future 5, 418–435 (2017).
Vincent, C. et al. Balanced conditions or slight mass gain of glaciers in the Lahaul and Spiti region (northern India, Himalaya) during the nineties preceded recent mass loss. Cryosphere 7, 569–582 (2013).
Vijay, S. & Braun, M. Elevation change rates of glaciers in the Lahaul-Spiti (Western Himalaya, India) during 2000–2012 and 2012–2013. Remote Sens. 8, 1038 (2016).
Shangguan, D. H. et al. Mass changes of Southern and Northern Inylchek glacier, central Tian Shan, Kyrgyzstan, during ~1975 and 2007 derived from remote sensing data. Cryosphere 9, 703–717 (2015).
Pieczonka, T., Bolch, T., Junfeng, W. & Shiyin, L. Heterogeneous mass loss of glaciers in the Aksu-Tarim Catchment (Central Tien Shan) revealed by 1976 KH-9 Hexagon and 2009 SPOT-5 stereo imagery. Remote Sens. Environ. 130, 233–244 (2013).
Gardner, A. S. et al. A reconciled estimate of glacier contributions to sea level rise: 2003 to 2009. Science 340, 852–857 (2013).
Farinotti, D. et al. Substantial glacier mass loss in the Tien Shan over the past 50 years. Nat. Geosci. 8, 716–722 (2015).
Kääb, A., Treichler, D., Nuth, C. & Berthier, E. Brief Communication: Contending estimates of 2003–2008 glacier mass balance over the Pamir–Karakoram–Himalaya. Cryosphere 9, 557–564 (2015).
Barandun, M. et al. Re-analysis of seasonal mass balance at Abramov glacier 1968–2014. J. Glaciol. 61, 1103–1117 (2015).
Lehner, B. et al. High-resolution mapping of the world’s reservoirs and dams for sustainable river-flow management. Front. Ecol. Environ. 9, 494–502 (2011).
Abbaspour, K. C. et al. Modelling hydrology and water quality in the pre-alpine/alpine Thur watershed using SWAT. J. Hydrol. (Amst.) 333, 413–430 (2007).
Beaudoing, H. & Rodell, M. (ed NASA/GSFC/HSL) (Goddard Earth Sciences Data and Information Services Center (GES DISC), 2015).
Singh, P. & Bengtsson, L. Impact of warmer climate on melt and evaporation for the rainfed, snowfed and glacierfed basins in the Himalayan region. J. Hydrol. 300, 140–154 (2005).
Arnold, J. G., Kiniry, J. R., Srinivasan, R., Williams, J. R. & Neitsch, S. L. Soil and Water Assessment Tool Theoretical Documentation, Version 2012 http://swat.tamu.edu/documentation/2012-io/ (Texas A&M Univ., 2012).
George, C. & Leon, L. F. WaterBase: SWAT in an open source GIS. Open Hydrol. J. 2, 1–6 (2008).
Saha, S. et al. Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory https://climatedataguide.ucar.edu/climate-data/climate-forecast-system-reanalysis-cfsr (2010).
Singh, P. & Jain, S. K. Snow and glacier melt in the Satluj River at Bhakra Dam in the western Himalayan region. Hydrol. Sci. J. 47, 93–106 (2002).
Srinivasan, R., Zhang, X. & Arnold, J. SWAT ungauged: hydrological budget and crop yield predictions in the Upper Mississippi River basin. Trans. ASABE 53, 1533–1546 (2010).
Zhang, D., Zhang, Q., Werner, A. D. & Gu, R. Assessment of the reliability of popular satellite products in characterizing the water balance of the Yangtze River Basin, China. Hydrol. Res. 47, 8–23 (2016).
Wang, W., Cui, W., Wang, X. & Chen, X. Evaluation of GLDAS-1 and GLDAS-2 forcing data and Noah model simulations over China at the monthly scale. J. Hydrometeorol. 17, 2815–2833 (2016).
Bookhagen, B. & Burbank, D. W. Toward a complete Himalayan hydrological budget: Spatiotemporal distribution of snowmelt and rainfall and their impact on river discharge. J. Geophys. Res. 115, F03019 (2010).
Gassert, F., Luck, M., Landis, M., Reig, P. & Shiao, T. Aqueduct Global Maps 2.0. (World Resources Institute, 2013).
Extended data figures and tables
a, Circles indicate existing irrigation and water supply barrages (dark blue), existing hydroelectric dams (orange)51, and planned hydroelectric dams or those under construction (light blue; which constitute a 121-GW increase in capacity)1. The background shading shows the 4-km-gridded 2015 population26 (more than 3,000 people per cell) and the distribution of glaciers12. b, Baseline water stress (total annual water withdrawals (municipal, industrial and agricultural) as a percentage of the total annual available blue water) for the major HMA river basins in 201563. The dashed line represents the Line of Control in Jammu and Kashmir, which is agreed upon by India and Pakistan. The final status of Jammu and Kashmir has not yet been agreed upon by the parties. IAK refers to Indian-administered Kashmir, PAK to Pakistan-administered Kashmir.
Annual relative precipitation variability (coefficient of variation normalized by mean, 1961–199030; colour scale) for the Aral (1), Indus (2), Brahmaputra (3), Ganges (4), Tarim (5), Chu/Issyk-Kul (6) and Balkhash (7) basins in the global context.
a–g, Precipitation is shown relative to the mean (blue); ±1 coefficient of variation is indicated by the dashed grey lines. Relative interannual variability is lowest in the Brahmaputra (d) and Ganges (c) basins, intermediate in the Indus (b) and highest in the four northern basins (a, e–g). See Extended Data Table 1c for precipitation uncertainty.
Processing scheme to derive NMFs for each catchment. See Methods section ‘Data availability’ for references.
a–g, Monthly basin precipitation for an average year (green) and the driest year on record (blue) for the Aral (a), Indus (b), Ganges (c), Brahmaputra (d), Tarim (e), Chu/Issyk-Kul (f) and Balkhash (g) basins. See Extended Data Table 1c for precipitation uncertainty in average and drought years.
a, River-basin hypsometric zones 2–4 are defined13 as covering the area above the area-weighted mean glacier terminus height for each basin, plus the upper 75%, 50% or 25%, respectively, of the remaining basin area. b, Cumulative population distribution by zone within each river basin.
a, The precipitation-gauge colour scale refers to the sum of the APHRODITE RSTN parameter11, a measure of relative gauge density and duration within each APHRODITE grid cell (higher numbers indicate better temporal and spatial sampling). Sakai W- and L-average zones show the areas where precipitation is constrained by glacier hypsometry20. Labelled and coloured polygons show catchments Kaqun (KQ), Wuluwati (WW), Tongguziluoke (TG), Xiehela (XH), Shaliguilanke (SH), Besham Qila (BQ), Cholma (CH), Marala (MR), Mangla (MG), Beas (BE) and Tarbela (TB), with independent evidence of precipitation referred to in the Methods. b, Precipitation uncertainty classes.