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Importance and vulnerability of the world’s water towers

Nature volume 577, pages 364–369 (2020)Cite this article

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Abstract

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.

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Data availability

The data generated to support the findings of this study are available in an online data repository at zenodo.org at https://doi.org/10.5281/zenodo.3521933. Third party data used in this study are available as follows. Hydrological basin boundaries5 used in this study are available online at http://www.fao.org/nr/water/aquamaps/. Mountain definition data6 used in this study are available online at https://ilias.unibe.ch/goto_ilias3_unibe_file_1047348.html. Precipitation and evaporation data used in this study32 are available online at https://cds.climate.copernicus.eu. Snow cover data used in this study7 are available online at https://nsidc.org/data/MOD10CM. Glacier volume data48 used in this study are available online at https://doi.org/10.3929/ethz-b-000315707. Glacier mass balance data17,49 are available online at https://wgms.ch/. Lake and reservoir storage data50 used in this study are available online at https://www.hydrosheds.org/pages/hydrolakes. Water demand data used in this study are available upon request from Y.W. (wada@iiasa.ac.at). BWS data38 used in this study are available online at https://www.wri.org/aqueduct. GE data39 used in this study are available online at https://info.worldbank.org/governance/wgi/#home. Data on hydro-political tensions for transboundary river basins37 used in this study are available online at https://transboundarywaters.science.oregonstate.edu/content/transboundary-freshwater-spatial-database. Data for future projections of population count9 used in this study are available online at ftp://ftp.pbl.nl/hyde/SSPs/SSP2/zip/. Data for future projections of GDP41 used in this study are available online at http://www.cger.nies.go.jp/gcp/population-and-gdp.html. Data for future projections of temperature and precipitation40 used in this study are available online at https://climexp.knmi.nl. An online interactive visualization of the water tower index and vulnerability is available at https://www.nationalgeographic.com/environment/perpetual-planet/.

Code availability

The code developed for the WTI calculations performed for this study are publicly available in a Github repository at https://github.com/mountainhydrology/pub_ngs-watertowers.

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Acknowledgements

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

Author notes

  1. These authors contributed equally: W. W. Immerzeel, A. F. Lutz

Authors and Affiliations

  1. Faculty of Geosciences, Department of Physical Geography, Utrecht University, Utrecht, The Netherlands

    W. W. Immerzeel, A. F. Lutz & P. D. A. Kraaijenbrink

  2. FutureWater, Wageningen, The Netherlands

    W. W. Immerzeel & A. F. Lutz

  3. Universidad Mayor de San Andrés, Institute for Physics Research, La Paz, Bolivia

    M. Andrade

  4. University of Maryland, Department of Atmospheric and Oceanic Science, College Park, MD, USA

    M. Andrade

  5. National Geographic Society, Washington, DC, USA

    A. Bahl, S. Hyde, S. Brumby, A. C. Elmore & J. E. M. Baillie

  6. Wageningen University and Research, Water and Food Group, Wageningen, The Netherlands

    H. Biemans

  7. School of Geography and Sustainable Development, University of St Andrews, St Andrews, UK

    T. Bolch

  8. Centre for Quaternary Research, Department of Geography, Royal Holloway University of London, Egham, UK

    B. J. Davies

  9. Czech Academy of Sciences, Global Change Research Institute, Brno, Czech Republic

    A. Emmer

  10. Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China

    M. Feng & T. Yao

  11. Department of Geography, Universidad de Concepción, Concepción, Chile

    A. Fernández

  12. Department of Geology, University of Dayton, Dayton, OH, USA

    U. Haritashya

  13. Planetary Science Institute, Tucson, AZ, USA

    J. S. Kargel

  14. Department of Geography, University of British Columbia, Vancouver, British Columbia, Canada

    M. Koppes

  15. Indian Institute of Science, Divecha Center for Climate Change, Bangalore, India

    A. V. Kulkarni

  16. University of Maine, Climate Change Institute, Orono, ME, USA

    P. A. Mayewski

  17. International Centre for Integrated Mountain Development, Kathmandu, Nepal

    S. Nepal, A. Sinisalo & A. B. Shrestha

  18. Agua Sustentable, Irpavi, La Paz, Bolivia

    P. Pacheco

  19. Joint Institute for Regional Earth System Science and Engineering, University of California, Los Angeles, CA, USA

    T. H. Painter

  20. Swiss Federal Research Institute WSL, Birmensdorf, Switzerland

    F. Pellicciotti

  21. Johns Hopkins University, Department of Environmental Health and Engineering, Baltimore, MD, USA

    H. Rajaram

  22. University of Utah, Department of Geography, Salt Lake City, UT, USA

    S. Rupper

  23. University of Zurich, Department of Geography, Zurich, Switzerland

    D. Viviroli

  24. International Institute for Applied Systems Analysis, Laxenburg, Austria

    Y. Wada

  25. State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, China

    C. Xiao

Authors
  1. W. W. Immerzeel
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  2. A. F. Lutz
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  32. J. E. M. Baillie
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Contributions

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.

Additional information

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

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

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. b–e, 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
Full size table
Extended Data Table 2 List of WTUs and the GMBA mountain ranges that are (partly) covered by each WTU, for Europe, Asia and Oceania
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Extended Data Table 3 Overview of WTU supply indicators used
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Extended Data Table 4 Overview of WTU demand indicators used
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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). https://doi.org/10.1038/s41586-019-1822-y

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