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Asia’s shrinking glaciers protect large populations from drought stress

Nature volume 569, pages 649–654 (2019)Cite this article

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Abstract

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 These results update and reinforce a previous publication in Nature on this topic, which was retracted after an inadvertent error was discovered.

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Fig. 1: Annual and mean river-basin precipitation.
Fig. 2: Glacier temperature, water inputs and seasonally delayed balance melt.
Fig. 3: Precipitation and glacial melt inputs in an average year.
Fig. 4: Precipitation and glacial melt inputs in a drought year.
Fig. 5: The demographic and political context of the contribution of glacial melt.

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Data availability

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.

Code availability

I used the following publicly available software in this study: ArcGIS 10.1 with Spatial Ecology GME plugin (http://www.spatialecology.com/gme/); ArcSWAT Version 2012.10_1.18 (http://swat.tamu.edu/software/arcswat/); CDO Climate Data Operators (https://code.zmaw.de/projects/cdo).

References

  1. Zarfl, C., Lumsdon, A., Berlekamp, J., Tydecks, L. & Tockner, K. A global boom in hydropower dam construction. Aquat. Sci. 77, 161–170 (2015).

    Article  Google Scholar 

  2. 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).

    Article  ADS  Google Scholar 

  3. 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).

    Article  ADS  CAS  Google Scholar 

  4. 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).

  5. Himalayan Glaciers: Climate Change, Water Resources, and Water Security https://doi.org/10.17226/13449 (National Research Council, 2012).

  6. Natural Capital at Risk: The Top 100 Externalities of Business https://www.trucost.com/publication/natural-capital-risk-top-100-externalities-business/ (TRUCOST, 2013).

  7. The Global Risks Report 2016 11th Edition http://www3.weforum.org/docs/GRR/WEF_GRR16.pdf (World Economic Forum, 2016).

  8. AQUASTAT http://www.fao.org/nr/water/aquastat/main/index.stm (Food and Agriculture Organization of the United Nations, accessed 27 May 2015).

  9. 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).

    Article  ADS  CAS  Google Scholar 

  10. Andermann, C. et al. Impact of transient groundwater storage on the discharge of Himalayan rivers. Nat. Geosci. 5, 127 (2012).

    Article  ADS  CAS  Google Scholar 

  11. 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).

    Article  ADS  Google Scholar 

  12. 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).

  13. 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).

    Article  ADS  CAS  Google Scholar 

  14. Schaner, N., Voisin, N., Nijssen, B. & Lettenmaier, D. P. The contribution of glacier melt to streamflow. Environ. Res. Lett. 7, 034029 (2012).

    Article  ADS  Google Scholar 

  15. 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).

    Article  ADS  CAS  Google Scholar 

  16. 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).

    Article  ADS  CAS  Google Scholar 

  17. Xu, J. et al. The melting Himalayas: cascading effects of climate change on water, biodiversity, and livelihoods. Conserv. Biol. 23, 520–530 (2009).

    Article  CAS  Google Scholar 

  18. Azam, M. F. et al. Review of the status and mass changes of Himalayan-Karakoram glaciers. J. Glaciol. 64, 61–74 (2018).

    Article  ADS  Google Scholar 

  19. 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).

  20. Sakai, A. et al. Climate regime of Asian glaciers revealed by GAMDAM glacier inventory. Cryosphere 9, 865–880 (2015).

    Article  ADS  Google Scholar 

  21. Pritchard, H. D. Asia’s glaciers are a regionally important buffer against drought. Nature 545, 169–174 (2017); retraction 555, 274 (2018).

    Article  ADS  CAS  Google Scholar 

  22. Pritchard, H. D. Retraction: Asia’s glaciers are a regionally important buffer against drought. Nature 555, 274 (2018).

    Article  ADS  CAS  Google Scholar 

  23. 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).

    Article  ADS  Google Scholar 

  24. Huss, M. & Hock, R. Global-scale hydrological response to future glacier mass loss. Nat. Clim. Change 8, 135–140 (2018).

    Article  ADS  Google Scholar 

  25. IPCC Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects (eds Barros, V. R. et al.) (Cambridge Univ. Press, 2014).

  26. 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).

  27. 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).

  28. 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).

    Article  ADS  Google Scholar 

  29. 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).

    Article  ADS  Google Scholar 

  30. 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).

    Article  Google Scholar 

  31. 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).

    Article  CAS  Google Scholar 

  32. 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).

    Article  ADS  Google Scholar 

  33. 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).

    Article  ADS  Google Scholar 

  34. Hewitt, K. Glacier change, concentration, and elevation effects in the Karakoram Himalaya, Upper Indus Basin. Mt. Res. Dev. 31, 188–200 (2011).

    Article  Google Scholar 

  35. Leclercq, P. W. & Oerlemans, J. Global and hemispheric temperature reconstruction from glacier length fluctuations. Clim. Dyn. 38, 1065–1079 (2012).

    Article  Google Scholar 

  36. 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).

    Article  ADS  Google Scholar 

  37. 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).

    Article  ADS  Google Scholar 

  38. Andermann, C., Bonnet, S. & Gloaguen, R. Evaluation of precipitation data sets along the Himalayan front. Geochem. Geophys. Geosyst. 12, (2011).

  39. 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).

    Article  ADS  Google Scholar 

  40. 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).

    Article  Google Scholar 

  41. 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).

    Article  ADS  Google Scholar 

  42. Huss, M. et al. Toward mountains without permanent snow and ice. Earths Future 5, 418–435 (2017).

    Article  ADS  Google Scholar 

  43. 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).

    Article  ADS  Google Scholar 

  44. 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).

    Article  ADS  Google Scholar 

  45. 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).

    Article  ADS  Google Scholar 

  46. 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).

    Article  ADS  Google Scholar 

  47. Gardner, A. S. et al. A reconciled estimate of glacier contributions to sea level rise: 2003 to 2009. Science 340, 852–857 (2013).

    Article  ADS  CAS  Google Scholar 

  48. Farinotti, D. et al. Substantial glacier mass loss in the Tien Shan over the past 50 years. Nat. Geosci. 8, 716–722 (2015).

    Article  ADS  CAS  Google Scholar 

  49. 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).

    Article  ADS  Google Scholar 

  50. Barandun, M. et al. Re-analysis of seasonal mass balance at Abramov glacier 1968–2014. J. Glaciol. 61, 1103–1117 (2015).

    Article  ADS  Google Scholar 

  51. 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).

    Article  Google Scholar 

  52. 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).

    Article  ADS  Google Scholar 

  53. Beaudoing, H. & Rodell, M. (ed NASA/GSFC/HSL) (Goddard Earth Sciences Data and Information Services Center (GES DISC), 2015).

  54. 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).

    Article  ADS  Google Scholar 

  55. 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).

  56. George, C. & Leon, L. F. WaterBase: SWAT in an open source GIS. Open Hydrol. J. 2, 1–6 (2008).

    Article  Google Scholar 

  57. 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).

  58. 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).

    Article  Google Scholar 

  59. 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).

    Article  CAS  Google Scholar 

  60. 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).

    Article  Google Scholar 

  61. 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).

    Article  ADS  Google Scholar 

  62. 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).

    Article  ADS  Google Scholar 

  63. Gassert, F., Luck, M., Landis, M., Reig, P. & Shiao, T. Aqueduct Global Maps 2.0. (World Resources Institute, 2013).

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Acknowledgements

I thank A. Sakai (ref. 19) and C. Zarfl (ref. 1) for providing data on glacier accumulation and dam locations.

Reviewer information

Nature thanks Tobias Bolch and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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Authors and Affiliations

  1. British Antarctic Survey, Cambridge, UK

    Hamish D. Pritchard

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Correspondence to Hamish D. Pritchard.

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Extended data figures and tables

Extended Data Fig. 1 Dams and water stress in high-mountain Asia.

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.

Extended Data Fig. 2 Interannual precipitation variability.

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.

Extended Data Fig. 3 Relative interannual precipitation variability by basin.

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.

Extended Data Fig. 4 Datasets and processing flow used in this study.

Processing scheme to derive NMFs for each catchment. See Methods section ‘Data availability’ for references.

Extended Data Fig. 5 Average- and drought-year monthly precipitation.

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.

Extended Data Fig. 6 Definition and population of river-basin zones.

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.

Extended Data Fig. 7 Precipitation data sources relative to dams, and precipitation uncertainty.

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.

Extended Data Table 1 Correlation coefficients of precipitation, their confidence intervals, and drought extremes
Full size table
Extended Data Table 2 Inputs, losses and melt fractions for basins and glaciers
Full size table
Extended Data Table 3 Comparison to other studies
Full size table

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Pritchard, H.D. Asia’s shrinking glaciers protect large populations from drought stress. Nature 569, 649–654 (2019). https://doi.org/10.1038/s41586-019-1240-1

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