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Asia’s shrinking glaciers protect large populations from drought stress

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 These results update and reinforce a previous publication in Nature on this topic, which was retracted after an inadvertent error was discovered.

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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).

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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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.

Author information

Competing interests

The author declares no competing interests.

Correspondence to Hamish D. Pritchard.

Extended data figures and tables

  1. 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.

  2. 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.

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

    ag, 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, eg). See Extended Data Table 1c for precipitation uncertainty.

  4. 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.

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

    ag, 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.

  6. 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.

  7. 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.

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

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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.
Extended Data Fig. 1: Dams and water stress in high-mountain Asia.
Extended Data Fig. 2: Interannual precipitation variability.
Extended Data Fig. 3: Relative interannual precipitation variability by basin.
Extended Data Fig. 4: Datasets and processing flow used in this study.
Extended Data Fig. 5: Average- and drought-year monthly precipitation.
Extended Data Fig. 6: Definition and population of river-basin zones.
Extended Data Fig. 7: Precipitation data sources relative to dams, and precipitation uncertainty.

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