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Importance and vulnerability of the world’s water towers


Mountains are the water towers of the world, supplying a substantial part of both natural and anthropogenic water demands1,2. They are highly sensitive and prone to climate change3,4, yet their importance and vulnerability have not been quantified at the global scale. Here we present a global water tower index (WTI), which ranks all water towers in terms of their water-supplying role and the downstream dependence of ecosystems and society. For each water tower, we assess its vulnerability related to water stress, governance, hydropolitical tension and future climatic and socio-economic changes. We conclude that the most important (highest WTI) water towers are also among the most vulnerable, and that climatic and socio-economic changes will affect them profoundly. This could negatively impact 1.9 billion people living in (0.3 billion) or directly downstream of (1.6 billion) mountainous areas. Immediate action is required to safeguard the future of the world’s most important and vulnerable water towers.

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Fig. 1: The WTI, the population in WTUs and their downstream basins.
Fig. 2: The SI and DI.
Fig. 3: The vulnerability and projected change of the top five WTUs of each continent.
Fig. 4: WTI and vulnerabilities of the Indus basin.

Data availability

The data generated to support the findings of this study are available in an online data repository at at Third party data used in this study are available as follows. Hydrological basin boundaries5 used in this study are available online at Mountain definition data6 used in this study are available online at Precipitation and evaporation data used in this study32 are available online at Snow cover data used in this study7 are available online at Glacier volume data48 used in this study are available online at Glacier mass balance data17,49 are available online at Lake and reservoir storage data50 used in this study are available online at Water demand data used in this study are available upon request from Y.W. ( BWS data38 used in this study are available online at GE data39 used in this study are available online at Data on hydro-political tensions for transboundary river basins37 used in this study are available online at Data for future projections of population count9 used in this study are available online at Data for future projections of GDP41 used in this study are available online at Data for future projections of temperature and precipitation40 used in this study are available online at An online interactive visualization of the water tower index and vulnerability is available at

Code availability

The code developed for the WTI calculations performed for this study are publicly available in a Github repository at


  1. Viviroli, D., Dürr, H. H., Messerli, B., Meybeck, M. & Weingartner, R. Mountains of the world, water towers for humanity: typology, mapping, and global significance. Wat. Resour. Res. 43, 1–13 (2007).

    Google Scholar 

  2. Immerzeel, W. W., Van Beek, L. P. & Bierkens, M. F. P. Climate change will affect the Asian water towers. Science 328, 1382–1385 (2010).

    ADS  CAS  PubMed  Google Scholar 

  3. Viviroli, D. et al. Climate change and mountain water resources: overview and recommendations for research, management and policy. Hydrol. Earth Syst. Sci. 15, 471–504 (2011).

    ADS  Google Scholar 

  4. IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (IPCC, 2019).

  5. Lehner, B., Verdin, K. & Jarvis, A. New global hydrography derived from spaceborne elevation data. Eos 89, 93–104 (2008).

    ADS  Google Scholar 

  6. Körner, C. et al. A global inventory of mountains for bio-geographical applications. Alp. Bot. 127, 1–15 (2017).

    Google Scholar 

  7. Hall, D. K. & Riggs, G. A. MODIS/Terra Snow Cover Monthly L3 Global 0.05Deg CMG Dataset Version 6 (2015).

  8. Pfeffer, W. et al. The Randolph Glacier Inventory: a globally complete inventory of glaciers. J. Glaciol. 60, 537–552 (2014).

    ADS  Google Scholar 

  9. Klein Goldewijk, K., Beusen, A., Van Drecht, G. & De Vos, M. The HYDE 3.1 spatially explicit database of human-induced global land-use change over the past 12,000 years. Glob. Ecol. Biogeogr. 20, 73–86 (2011).

    Google Scholar 

  10. Xiao, C.-D., Wang, S. J. & Qin, D. H. A preliminary study of cryosphere service function and value evaluation. Adv. Clim. Chang. Res. 6, 181–187 (2015).

    Google Scholar 

  11. Wang, X., Liu, S. W. & Zhang, J. L. A new look at roles of the cryosphere in sustainable development. Adv. Clim. Chang. Res. 10, 124–131 (2019).

    Google Scholar 

  12. Chape, S., Spalding, M. D. & Jenkins, M. D. The World’s Protected Areas (UNEP-World Conservation Monitoring Centre, 2008).

  13. Körner, C. & Paulsen, J. A world-wide study of high altitude treeline temperatures. J. Biogeogr. 31, 713–732 (2004).

    Google Scholar 

  14. Körner, C., Paulsen, J. & Spehn, E. M. A definition of mountains and their bioclimatic belts for global comparisons of biodiversity data. Alp. Bot. 121, 73–78 (2011).

    Google Scholar 

  15. Nordhaus, W. D. Geography and macroeconomics: new data and new findings. Proc. Natl Acad. Sci. 103, 3510–3517 (2006).

    ADS  CAS  PubMed  Google Scholar 

  16. Pepin, N. et al. Elevation-dependent warming in mountain regions of the world. Nat. Clim. Chang. 5, 424–430 (2015).

    ADS  Google Scholar 

  17. Zemp, M. et al. Global glacier mass changes and their contributions to sea-level rise from 1961 to 2016. Nature 568, 382–386 (2019).

    ADS  CAS  PubMed  Google Scholar 

  18. Hammond, J. C., Saavedra, F. A. & Kampf, S. K. Global snow zone maps and trends in snow persistence 2001–2016. Int. J. Climatol. 38, 4369–4383 (2018).

    Google Scholar 

  19. Bormann, K. J., Brown, R. D., Derksen, C. & Painter, T. H. Estimating snow-cover trends from space. Nat. Clim. Chang. 8, 924–928 (2018).

    ADS  Google Scholar 

  20. Sarangi, C. et al. Impact of light-absorbing particles on snow albedo darkening and associated radiative forcing over High Mountain Asia: high resolution WRF-Chem modeling and new satellite observations. Atmos. Chem. Phys. Discuss. (2018).

  21. Painter, T. H. et al. Impact of disturbed desert soils on duration of mountain snow cover. Geophys. Res. Lett. 34, L12502 (2007).

    ADS  Google Scholar 

  22. Lutz, A. F., Immerzeel, W. W., Shrestha, A. B. & Bierkens, M. F. P. Consistent increase in High Asia’s runoff due to increasing glacier melt and precipitation. Nat. Clim. Chang. 4, 587–592 (2014).

    ADS  Google Scholar 

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

    ADS  Google Scholar 

  24. Kargel, J. S. S. et al. Geomorphic and geologic controls of geohazards induced by Nepal’s 2015 Gorkha earthquake. Science 351, aac8353 (2016).

    CAS  PubMed  Google Scholar 

  25. Kirschbaum, D. et al. The state of remote sensing capabilities of cascading hazards over high mountain Asia. Front. Earth Sci. (2019).

  26. Guha-Sapir, D., Below, R. & Hoyois, P. EM-DAT: International Disaster Database (2019).

  27. Mal, S. Climate Change, Extreme Events and Disaster Risk Reduction (Springer, 2018).

  28. Mann, M. E. et al. Influence of anthropogenic climate change on planetary wave resonance and extreme weather events. Sci. Rep. 7, 45242 (2017).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  29. Fischer, E. M. & Knutti, R. Anthropogenic contribution to global occurrence of heavy-precipitation and high-temperature extremes. Nat. Clim. Chang. 5, 560–564 (2015).

    ADS  Google Scholar 

  30. Haeberli, W., Schaub, Y. & Huggel, C. Increasing risks related to landslides from degrading permafrost into new lakes in de-glaciating mountain ranges. Geomorphology 293, 405–417 (2017).

    ADS  Google Scholar 

  31. Biemans, H. et al. Importance of snow and glacier meltwater for agriculture on the Indo-Gangetic Plain. Nat. Sustain. 2, 594–601 (2019).

    Google Scholar 

  32. Hersbach, H. et al. Operational global reanalysis: progress, future directions and synergies with NWP. ECMWF ERA Report Series No. 27, (ECMWF, 2018).

  33. Wada, Y., De Graaf, I. E. M. & van Beek, L. P. H. High-resolution modeling of human and climate impacts on global water resources. J. Adv. Model. Earth Syst. 8, 735–763 (2016).

    ADS  Google Scholar 

  34. Wada, Y. et al. Global depletion of groundwater resources. Geophys. Res. Lett. 37, (2010).

    Google Scholar 

  35. Wada, Y., Van Beek, L. P. H. & Bierkens, M. F. P. Nonsustainable groundwater sustaining irrigation: a global assessment. Wat. Resour. Res. 48, (2012).

  36. Immerzeel, W. W. & Bierkens, M. F. P. Asia’s water balance. Nat. Geosci. 5, 841–842 (2012).

    ADS  CAS  Google Scholar 

  37. De Stefano, L., Petersen-Perlman, J. D., Sproles, E. A., Eynard, J. & Wolf, A. T. Assessment of transboundary river basins for potential hydro-political tensions. Glob. Environ. Change 45, 35–46 (2017).

    Google Scholar 

  38. Hofste, R. W. et al. Aqueduct 3.0: Updated Decision-Relevant Global Water Risk Indicators. Technical Note (World Resources Institute, 2019).

  39. Kaufmann, D., Kraay, A. & Mastruzzi, M. The Worldwide Governance Indicators. Methodology and Analytical Issues. World Bank Policy Research Working Paper No. 5430 (The World Bank Development Research Group, 2010).

  40. Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).

    ADS  Google Scholar 

  41. Murakami, D. & Yamagata, Y. Estimation of Gridded Population and GDP Scenarios with Spatially Explicit Statistical Downscaling. Sustainability 11, 2106 (2019).

    Google Scholar 

  42. Wijngaard, R. R. et al. Climate change vs. socio-economic development: understanding the South-Asian water gap. Hydrol. Earth Syst. Sci. 22, 6297–6321 (2018).

    ADS  Google Scholar 

  43. Rockström, J. et al. Planetary boundaries: exploring the safe operating space for humanity. Ecol. Soc. 14, 32 (2009).

    Google Scholar 

  44. Jaramillo, F. & Destouni, G. Comment on “Planetary boundaries: Guiding human development on a changing planet”. Science 348, 1217 (2015).

    CAS  PubMed  Google Scholar 

  45. Springmann, M. et al. Options for keeping the food system within environmental limits. Nature 562, 519–525 (2018).

    ADS  CAS  PubMed  Google Scholar 

  46. Roy, J. et al. Exploring futures of the Hindu Kush Himalaya: scenarios and pathways. In The Hindu Kush Himalaya Assessment: Mountains, Climate Change, Sustainability and People (eds Wester, P., Mishra, A., Mukherji, A. & Shrestha, A. B.) 99–125 (Springer International Publishing, 2019).

  47. United Nations. Transforming Our World: The 2030 Agenda For Sustainable Development. A/RES/70/1 Resolution adopted by the United Nations General Assembly. (UN, 2015).

  48. Farinotti, D. et al. A consensus estimate for the ice thickness distribution of all glaciers on Earth. Nat. Geosci. 12, 168–173 (2019).

    ADS  CAS  Google Scholar 

  49. World Glacier Monitoring Service Fluctuations of Glaciers (FoG) Database (2018).

  50. Messager, M. L., Lehner, B., Grill, G., Nedeva, I. & Schmitt, O. Estimating the volume and age of water stored in global lakes using a geo-statistical approach. Nat. Commun. 7, 13603 (2016).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  51. Gleeson, T., Wada, Y., Bierkens, M. F. P. & Van Beek, L. P. H. Water balance of global aquifers revealed by groundwater footprint. Nature 488, 197–200 (2012).

    ADS  CAS  PubMed  Google Scholar 

  52. Smakhtin, V., Revenga, C. & Döll, P. A pilot global assessment of environmental water requirements and scarcity. Water Int. 29, 307–317 (2004).

    CAS  Google Scholar 

  53. Harris, I., Jones, P. D., Osborn, T. J. & Lister, D. H. Updated high-resolution grids of monthly climatic observations—the CRU TS3.10 dataset. Int. J. Climatol. (2013).

    ADS  Google Scholar 

  54. Dee, D. P. et al. The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc. 137, 553–597 (2011).

    ADS  Google Scholar 

  55. Weedon, G. P. et al. The WFDEI meteorological forcing data set: WATCH Forcing Data methodology applied to ERA-Interim reanalysis data. Wat. Resour. Res. 50, 7505–7514 (2014).

    ADS  Google Scholar 

  56. Weedon, G. P. et al. Creation of the WATCH Forcing Data and its use to assess global and regional reference crop evaporation over land during the twentieth century. J. Hydrometeorol. 12, 823–848 (2011).

    ADS  Google Scholar 

  57. Schneider, U. et al. GPCC’s new land surface precipitation climatology based on quality-controlled in situ data and its role in quantifying the global water cycle. Theor. Appl. Climatol. 115, 15–40 (2014).

    ADS  Google Scholar 

  58. Uppala, S. M. et al. The ERA-40 re-analysis. Q. J. R. Meteorol. Soc. 131, 2961–3012 (2005).

    ADS  Google Scholar 

  59. Kalnay, E. et al. The NCEP/NCAR 40-Year Reanalysis Project. Bull. Am. Meteorol. Soc. 77, 437–471 (1996).

    ADS  Google Scholar 

  60. Martens, B. et al. GLEAM v3: Satellite-based land evaporation and root-zone soil moisture. Geosci. Model Dev. 10, 1903–1925 (2017).

    ADS  Google Scholar 

  61. Rienecker, M. M. et al. MERRA: NASA’s modern-era retrospective analysis for research and applications. J. Clim. 24, 3624–3648 (2011).

    ADS  Google Scholar 

  62. Sutanudjaja, E. H. et al. PCR-GLOBWB 2: a 5 arcmin global hydrological and water resources model. Geosci. Model Dev. 11, 2429–2453 (2018).

    ADS  Google Scholar 

  63. Riggs, G. A., Hall, D. K. & Román, M. O. Overview of NASA’s MODIS and VIIRS Snow-Cover Earth System Data Records. Earth Syst. Sci. Data 9, 765–777 (2017).

    ADS  Google Scholar 

  64. Wada, Y. et al. Modeling global water use for the 21st century: the Water Futures and Solutions (WFaS) initiative and its approaches. Geosci. Model Dev. 9, 175–222 (2016).

    ADS  Google Scholar 

  65. Wada, Y. et al. Multimodel projections and uncertainties of irrigation water demand under climate change. Geophys. Res. Lett. 40, 4626–4632 (2013).

    ADS  Google Scholar 

  66. van Vuuren, D. P. et al. The representative concentration pathways: an overview. Clim. Change 109, 5–31 (2011).

    ADS  Google Scholar 

  67. O’Neill, B. C. et al. The roads ahead: narratives for shared socioeconomic pathways describing world futures in the 21st century. Glob. Environ. Change 42, 169–180 (2017).

    Google Scholar 

  68. Kummu, M., Taka, M. & Guillaume, J. H. A. Gridded global datasets for gross domestic product and human development index over 1990-2015. Sci. Data 5, 180004 (2018).

    PubMed  PubMed Central  Google Scholar 

  69. McDowell, G. et al. Adaptation action and research in glaciated mountain systems: Are they enough to meet the challenge of climate change? Glob. Environ. Change 54, 19–30 (2019).

    Google Scholar 

  70. Conway, D. et al. The need for bottom-up assessments of climate risks and adaptation in climate-sensitive regions. Nat. Clim. Chang. 9, 503–511 (2019).

    ADS  Google Scholar 

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This project was funded as part of the National Geographic Society and Rolex Partnership to Support a Perpetual Planet. We are grateful to the Strategic Priority Research Program of the Chinese Academy of Sciences for their support, to D. Farinotti for providing the data on glacier volume, and to N. Wanders for providing precipitation datasets used in the uncertainty analysis.

Author information

Authors and Affiliations



W.W.I. and A.F.L. contributed equally to the study; they designed the study, performed the analysis, prepared figures and tables and drafted the manuscript. P.D.A.K. contributed to the data analysis and prepared Fig. 3. Y.W. provided the dataset used to calculate demand indicators. S.B., S.H., A.B. and A.C.E contributed to the design of the index and analysis methods. All authors contributed to developing the theory and conception of the study by providing regional (M.A., A.F. and P.P. for the Andes; T.B., U.H., P.D.A.K., A.V.K., P.A.M., S.N., F.P., A.B.S., A.S., C.X. and T.Y. for High Mountain Asia; T.B., A.E., F.P. and D.V. for the Alps; and S.R., T.H.P., J.S.K. and M.K. for North America) or subject-specific expertise (B.J.D., J.S.K., A.B.S., P.P., A.S. and S.R. for glacial volume; U.H., M.K. and F.P. for meltwater discharge; H.B., A.F. and Y.W. on irrigation demand; T.B., A.E., J.S.K. and A.V.K. for glacial lakes; M.F. and T.H.P. for global snow cover, P.D.A.K. for volume ice loss; A.F., P.A.M., A.S. and T.Y. for climatology; S.N. and S.R. for hydrology; M.K., A.B.S. and D.V. for water demand, conflicts and vulnerability; H.R. for preferential flow; S.R. for glacier accumulation mass loss and its effects on downstream populations; D.V. for water management capacity; C.X. for global cryospheric functions and processes; and Y.W. for environmental flow requirements). All authors discussed and provided feedback on the manuscript. The study was initiated by J.E.M.B. and facilitated by A.C.E.

Corresponding authors

Correspondence to W. W. Immerzeel or A. F. Lutz.

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Competing interests

The authors declare no competing interests.

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Peer review information Nature thanks Günther Grill and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 Concept and global spread of WTUs.

a, The WTUs are defined as the intersection of Earth’s major hydrological basins5 and mountain ranges6 meeting predefined thresholds for ice volume or snow persistence (see Methods section). One WTU can consist of (parts of) multiple mountain ranges and one mountain range can be part of multiple WTUs. The example shows two hydrological basins in North America: the Great Basin (red outline) and California (blue outline). The striped areas indicate two mountain ranges: the Sierra Nevada and the Cascade Range. The intersection of the hydrological basins and the mountain ranges defines the WTUs (dark colours). For example, the Great Basin WTU is defined as the portion of the Sierra Nevada that is part of the Great Basin hydrological basin (dark red), and the California WTU is defined as the portion of the Sierra Nevada that is part of the California hydrological basin as well as a portion of the Cascade Range that is part of the California hydrological basin (dark blue). The WTU’s dependent area (light colours) is defined as the sub-basins within the hydrological basin that are overlapping the WTU or downstream of sub-basins overlapping the WTU. be, The WTUs (dark colours) and associated WTU basins (light colours) for all 78 WTUs and WTU basins, grouped by continents: North America (b), Europe (c), Asia and Oceania (d), South America (e). Number labels indicate the WTU IDs (see Extended Data Tables 1, 2 for corresponding names).

Extended Data Fig. 2 SI and DI.

a, The WTU SI (blue colourscale) and downstream DI (brown colourscale) for all 78 WTUs and WTU basins. b, Supply index (SI) and demand index (DI) for each WTU grouped per continent. Background colour gradient indicates water tower importance (that is, darker shades represent higher SI and DI values). Points are labelled with WTU IDs (see Extended Data Tables 1, 2, Extended Data Fig. 1).

Extended Data Fig. 3 Annual precipitation and snow cover.

a, Average annual precipitation between 2001 and 2017, resampled bilinearly to 0.05° resolution based on ERA532. b, Average snow persistence between 2001 and 2017, resampled to 0.05° resolution based on MODIS MOD10CM17.

Extended Data Fig. 4 Glacier ice volume and lake and reservoir volume.

a, Total aggregated glacier ice volume per WTU48. b, Total aggregated lake and reservoir water volume per WTU50.

Extended Data Fig. 5 Water use for irrigation and industry.

a, Average annual irrigation water use per 0.05 × 0.05° grid cell 2001–201433. b, Average annual industrial water use per 0.05 × 0.05° grid cell 2001–201433.

Extended Data Fig. 6 Domestic water use and natural water demand.

a, Average annual domestic water use per 0.05 × 0.05° grid cell 2001–201433. b, Total aggregated average annual natural water demand 2001–2014 per WTU basin based on the Environmental Flow Requirement33,51,52.

Extended Data Fig. 7 Sensitivity of WTU ranking to uncertainty in input data and indicator weights.

Position change in ranking of WTUs by WTI resulting from uncertainty in input data (blue), expressed as a percentage of 1,000 realizations of the WTI index calculation. Position change in ranking of WTUs by WTI resulting from uncertainty in the weights of individual indicators (red), expressed as a percentage of 10,000 realizations of the WTI index calculation.

Extended Data Table 1 List of WTUs and the GMBA mountain ranges that are (partly) covered by each WTU, for North America and South America
Extended Data Table 2 List of WTUs and the GMBA mountain ranges that are (partly) covered by each WTU, for Europe, Asia and Oceania
Extended Data Table 3 Overview of WTU supply indicators used
Extended Data Table 4 Overview of WTU demand indicators used

Supplementary information

Supplementary Table 1 | Indicator and Water Tower Index values

Excel table presenting an overview of all values for the supply indicators and subindicators, supply index, demand indicators, demand index, and Water Tower Index, for each Water Tower Unit. Explanations of all variables are provided in Methods and Extended Data Tables 3, 4.

Supplementary Table 2 | Vulnerabilities

Excel table presenting an overview of all values of vulnerability and future change indicators for each Water Tower Unit. Explanations of all variables are provided in Methods.

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Immerzeel, W.W., Lutz, A.F., Andrade, M. et al. Importance and vulnerability of the world’s water towers. Nature 577, 364–369 (2020).

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