Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Current and future global water scarcity intensifies when accounting for surface water quality

Abstract

The inadequate availability of clean water presents systemic risks to human health, food production, energy generation and ecosystem functioning. Here we evaluate population exposure to current and future water scarcity (both excluding and including water quality) using a coupled global hydrological and surface water quality model. We find that 55% of the global population are currently exposed to clean water scarcity at least one month per year, compared with 47% considering water quantity aspects only. Exposure to clean water scarcity at least one month per year increases to 56–66% by the end of the century. Increases in future exposure are typically largest in developing countries—particularly in sub-Saharan Africa—driven by a combination of water quantity and quality aspects. Strong reductions in both anthropogenic water use and pollution are therefore necessary to minimize the impact of future clean water scarcity on humans and the environment.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Spatial distribution, seasonal patterns and drivers of current hotspots of and population exposure to water scarcity.
Fig. 2: Global population exposure to water scarcity under uncertain climate and socio-economic change.
Fig. 3: Global population exposure to water scarcity under uncertain climate and socio-economic change.
Fig. 4: Regional exposure to water scarcity and key drivers at the end of the twenty-first century under uncertain climate and socio-economic change.

Similar content being viewed by others

Data availability

Output data from this study (that is, population exposure to water scarcity) per geographic region are available via Figshare at https://doi.org/10.6084/m9.figshare.24866310.v1 (ref. 53). Water quantity and quality data are available via Zenodo at https://doi.org/10.5281/zenodo.7811612 (ref. 54).

Code availability

The coupled global hydrological model and water resources model (PCR-GLOBWB 2) and global surface water quality model (DynQual) are freely available via Zenodo at https://doi.org/10.5281/zenodo.7932317 (ref. 55) and via GitHub at https://github.com/UU-Hydro/.

References

  1. van Vliet, M., Flörke, M. & Wada, Y. Quality matters for water scarcity. Nat. Geosci. 10, 800–802 (2017).

    Article  Google Scholar 

  2. van Vliet, M. T. H. et al. Global water scarcity including surface water quality and expansions of clean water technologies. Environ. Res. Lett. 16, 024020 (2021).

    Article  Google Scholar 

  3. Mekonnen, M. M. & Hoekstra, A. Y. Four billion people facing severe water scarcity. Sci. Adv. 2, e1500323 (2016).

    Article  Google Scholar 

  4. Transforming Our World: The 2030 Agenda for Sustainable Development (United Nations, 2015).

  5. Liu, J. et al. Water scarcity assessments in the past, present and future. Earth Future 5, 545–559 (2017).

    Article  Google Scholar 

  6. Kuzma, S. et al. Aqueduct 4.0: Updated Decision-Relevant Global Water Risk Indicators (World Resources Institute, 2023).

  7. Vanham, D. et al. Physical water scarcity metrics for monitoring progress towards SDG target 6.4: an evaluation of indicator 6.4.2 ‘Level of water stress’. Sci. Total Environ. 613614, 218–232 (2018).

    Article  Google Scholar 

  8. Liu, J., Liu, Q. & Yang, H. Assessing water scarcity by simultaneously considering environmental flow requirements, water quantity, and water quality. Ecol. Indic. 60, 434–441 (2016).

    Article  CAS  Google Scholar 

  9. Sullivan, C. A., Meigh, J. R. & Giacomello, A. M. The water poverty index: development and application at the community scale. Nat. Resour. Forum 27, 189–199 (2003).

    Article  Google Scholar 

  10. Vörösmarty, C. J. et al. Global threats to human water security and river biodiversity. Nature 467, 555–561 (2010).

    Article  Google Scholar 

  11. Zhao, X. et al. Burden shifting of water quantity and quality stress from megacity Shanghai. Water Resour. Res. 52, 6916–6927 (2016).

    Article  Google Scholar 

  12. Ercin, A. E. & Hoekstra, A. Y. Water footprint scenarios for 2050: a global analysis. Environ. Int. 64, 71–82 (2014).

    Article  Google Scholar 

  13. Vörösmarty, C. J., Green, P., Salisbury, J. & Lammers, R. B. Global water resources: vulnerability from climate change and population growth. Science 289, 284–288 (2000).

    Article  Google Scholar 

  14. Wada, Y., Wisser, D. & Bierkens, M. F. P. Global modeling of withdrawal, allocation and consumptive use of surface water and groundwater resources. Earth Syst. Dynam. 5, 15–40 (2014).

    Article  Google Scholar 

  15. Konapala, G., Mishra, A. K., Wada, Y. & Mann, M. E. Climate change will affect global water availability through compounding changes in seasonal precipitation and evaporation. Nat. Commun. 11, 3044 (2020).

    Article  CAS  Google Scholar 

  16. Schewe, J. et al. Multimodel assessment of water scarcity under climate change. Proc. Natl Acad. Sci. USA 111, 3245–3250 (2014).

    Article  CAS  Google Scholar 

  17. Greve, P. et al. Global assessment of water challenges under uncertainty in water scarcity projections. Nat. Sustain. 1, 486–494 (2018).

    Article  Google Scholar 

  18. He, C. et al. Future global urban water scarcity and potential solutions. Nat. Commun. 12, 4667 (2021).

    Article  CAS  Google Scholar 

  19. Riahi, K. et al. The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: an overview. Glob. Environ. Change 42, 153–168 (2017).

    Article  Google Scholar 

  20. Desbureaux, S. et al. Mapping global hotspots and trends of water quality (1992–2010): a data driven approach. Environ. Res. Lett. 17, 114048 (2022).

    Article  Google Scholar 

  21. Jones, E. R. et al. Current wastewater treatment targets are insufficient to protect surface water quality. Commun. Earth Environ. 3, 221 (2022).

    Article  Google Scholar 

  22. Jones, E. R. et al. DynQual v1.0: a high-resolution global surface water quality model. Geosci. Model Dev. 16, 4481–4500 (2023).

    Article  CAS  Google Scholar 

  23. Hanasaki, N. et al. A global water scarcity assessment under Shared Socio-economic Pathways - part 1: water use. Hydrol. Earth Syst. Sci. 17, 2375–2391 (2013).

    Article  Google Scholar 

  24. 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).

    Article  Google Scholar 

  25. Wang, M. et al. A triple increase in global river basins with water scarcity due to future pollution. Nat. Commun. 15, 880 (2024).

    Article  CAS  Google Scholar 

  26. Kummu, M. et al. The world’s road to water scarcity: shortage and stress in the 20th century and pathways towards sustainability. Sci. Rep. 6, 38495 (2016).

    Article  CAS  Google Scholar 

  27. The United Nations World Water Development Report 2018 (UNESCO WWAP, 2018).

  28. Veldkamp, T. I. E. et al. Changing mechanism of global water scarcity events: impacts of socioeconomic changes and inter-annual hydro-climatic variability. Glob. Environ. Change 32, 18–29 (2015).

    Article  Google Scholar 

  29. Brunner, M. I., Zappa, M. & Stähli, M. Scale matters: effects of temporal and spatial data resolution on water scarcity assessments. Adv. Water Res. 123, 134–144 (2019).

    Article  Google Scholar 

  30. Boretti, A. & Rosa, L. Reassessing the projections of the World Water Development Report. npj Clean Water 2, 15 (2019).

    Article  Google Scholar 

  31. World Water Quality Assessment: First Global Display of a Water Quality Baseline (World Water Quality Alliance, 2021).

  32. Wada, Y. et al. Global monthly water stress: II. Water demand and severity of water. Water Resour. Res. 47, WO7518 (2011).

  33. Podgorski, J. & Berg, M. Global threat of arsenic in groundwater. Science 368, 845–850 (2020).

    Article  CAS  Google Scholar 

  34. de Graaf, I. E. M., Sutanudjaja, E. H., van Beek, L. P. H. & Bierkens, M. F. P. A high-resolution global-scale groundwater model. Hydrol. Earth Syst. Sci. 19, 823–837 (2015).

    Article  Google Scholar 

  35. Jones, E., Qadir, M., van Vliet, M. T. H., Smakhtin, V. & Kang, S.-M. The state of desalination and brine production: a global outlook. Sci. Total Environ. 657, 1343–1356 (2019).

    Article  CAS  Google Scholar 

  36. Jones, E. R., van Vliet, M. T. H., Qadir, M. & Bierkens, M. F. P. Country-level and gridded estimates of wastewater production, collection, treatment and reuse. Earth Syst. Sci. Data 13, 237–254 (2021).

    Article  Google Scholar 

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

    Article  Google Scholar 

  38. Jones, E. R. et al. Sub-Saharan Africa will increasingly become the dominant hotspot of surface water pollution. Nat. Water 1, 602–613 (2023).

  39. Büchner, S. L. A. ISIMIP3b bias-adjusted atmospheric climate input data. ISIMIP https://doi.org/10.48364/ISIMIP.581124.1 (2021).

  40. Gilbert, M. et al. Global distribution data for cattle, buffaloes, horses, sheep, goats, pigs, chickens and ducks in 2010. Sci. Data 5, 180227 (2018).

    Article  Google Scholar 

  41. Graham, N. T. et al. Water sector assumptions for the Shared Socioeconomic Pathways in an integrated modeling framework. Water Resour. Res. 54, 6423–6440 (2018).

    Article  Google Scholar 

  42. Hurtt, G. C. et al. Harmonization of global land use change and management for the period 850–2100 (LUH2) for CMIP6. Geosci. Model Dev. 13, 5425–5464 (2020).

    Article  CAS  Google Scholar 

  43. Jones, B. & O’Neill, B. C. Spatially explicit global population scenarios consistent with the Shared Socioeconomic Pathways. Environ. Res. Lett. 11, 084003 (2016).

    Article  Google Scholar 

  44. Lohrmann, A., Farfan, J., Caldera, U., Lohrmann, C. & Breyer, C. Global scenarios for significant water use reduction in thermal power plants based on cooling water demand estimation using satellite imagery. Nat. Energy 4, 1040–1048 (2019).

    Article  Google Scholar 

  45. A Snapshot of the World’s Water Quality: Towards a Global Assessment (UNEP, 2016).

  46. van Puijenbroek, P. J. T. M. et al. Quantifying future sanitation scenarios and progress towards SDG targets in the shared socioeconomic pathways. J. Environ. Manag. 346, 118921 (2023).

    Article  Google Scholar 

  47. Falkenmark, M., Lundqvist, J. & Widstrand, C. Macro-scale water scarcity requires micro-scale approaches. Nat. Resour. Forum 13, 258–267 (1989).

    Article  CAS  Google Scholar 

  48. Falkenmark, M., Rockström, J. & Karlberg, L. Present and future water requirements for feeding humanity. Food Secur. 1, 59–69 (2009).

    Article  Google Scholar 

  49. Oki, T. & Kanae, S. Global hydrological cycles and world water resources. Science 313, 1068–1072 (2006).

    Article  CAS  Google Scholar 

  50. Pastor, A. V., Ludwig, F., Biemans, H., Hoff, H. & Kabat, P. Accounting for environmental flow requirements in global water assessments. Hydrol. Earth Syst. Sci. 18, 5041–5059 (2014).

    Article  Google Scholar 

  51. Wan, L., Cai, W., Jiang, Y. & Wang, C. Impacts on quality-induced water scarcity: drivers of nitrogen-related water pollution transfer under globalization from 1995 to 2009. Environ. Res. Lett. 11, 074017 (2016).

    Article  Google Scholar 

  52. Hoekstra, A. & Mekonnen, M. The water footprint of humanity. Proc. Natl Acad. Sci. USA 109, 3232–3237 (2012).

    Article  CAS  Google Scholar 

  53. Jones, E. R., Bierkens, M. F. P. & van Vliet, M. T. H. Population exposed to clean water scarcity under (uncertain) climate change and socioeconomic development. Figshare https://doi.org/10.6084/m9.figshare.24866310.v1 (2024).

  54. Jones, E. R. et al. Global hydrology and water quality datasets under uncertain climate and socio-economic change, derived from the dynamical surface water quality model (DynQual) at 10 km spatial resolution. Zenodo https://doi.org/10.5281/zenodo.7811612 (2023).

    Article  Google Scholar 

  55. Jones, E. R. et al. UU-Hydro/DYNQUAL: DynQual (v1.0). Zenodo https://doi.org/10.5281/zenodo.7932317 (2023).

    Article  Google Scholar 

Download references

Acknowledgements

E.R.J. and M.T.H.v.V. were financially supported by the Netherlands Scientific Organisation (NWO) by a VIDI grant (VI.Vidi.193.019). M.T.H.v.V. was also financially supported by the European Research Council (ERC) under the European Union’s Horizon Europe research and innovation programme (grant agreement no. 101039426 B-WEX). E.R.J. acknowledges and thanks the Netherlands Organisation for Scientific Research (NWO) for the grant that enabled us to use the national supercomputer Snellius (project no. EINF-3999).

Author information

Authors and Affiliations

Authors

Contributions

The study was designed by E.R.J., M.F.P.B. and M.T.H.v.V. Data processing, analysis and interpretation were led by E.R.J. in consultation with M.F.P.B. and M.T.H.v.V. E.R.J. led the paper writing, and all authors approved the paper.

Corresponding author

Correspondence to Edward R. Jones.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Climate Change thanks Ting Ma, Mesfin Mekonnen and Bridget Scanlon for their contribution to the peer review of this work.

Additional information

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

Extended data

Extended Data Fig. 1 Population exposure to water scarcity in the East Asia & Pacific region under uncertain climate and socio-economic change.

a) Number of people exposed to water scarcity from 2005–2100, based on indicators considering water quantity only (WS) and including water quality (WSq). Thick lines and thin lines display the annual average and monthly average exposure to water scarcity, respectively, while shaded areas represent uncertainty arising from variations in GCM simulations as ±1 s.d. b) Percentage of the population exposed to seasonal (that is at least one month per year) and year-round (>9 months per year) water scarcity from 2005–2100, as indicated by WS and WSq. Lines display the mean average exposure per year averaged over the five GCMs considered, while shaded areas represent uncertainty arising from variations in GCM simulations as ±1 s.d. c) Percentage of the population exposed to water scarcity in each month, as indicated by WS and WSq. Boxplots are made based on monthly exposure to water scarcity across all GCMs for a historical reference period (2005–2020) and under three global change scenarios at the end of the century (2081–2100).

Extended Data Fig. 2 Population exposure to water scarcity in the Eastern Europe & Central Asia region under uncertain climate and socio-economic change.

a) Number of people exposed to water scarcity from 2005–2100, based on indicators considering water quantity only (WS) and including water quality (WSq). Thick lines and thin lines display the annual average and monthly average exposure to water scarcity, respectively, while shaded areas represent uncertainty arising from variations in GCM simulations as ±1 s.d. b) Percentage of the population exposed to seasonal (that is at least one month per year) and year-round (>9 months per year) water scarcity from 2005–2100, as indicated by WS and WSq. Lines display the mean average exposure per year averaged over the five GCMs considered, while shaded areas represent uncertainty arising from variations in GCM simulations as ±1 s.d. c) Percentage of the population exposed to water scarcity in each month, as indicated by WS and WSq. Boxplots are made based on monthly exposure to water scarcity across all GCMs for a historical reference period (2005–2020) and under three global change scenarios at the end of the century (2081–2100).

Extended Data Fig. 3 Population exposure to water scarcity in the Latin America & Caribbean region under uncertain climate and socio-economic change.

a) Number of people exposed to water scarcity from 2005–2100, based on indicators considering water quantity only (WS) and including water quality (WSq). Thick lines and thin lines display the annual average and monthly average exposure to water scarcity, respectively, while shaded areas represent uncertainty arising from variations in GCM simulations as ±1 s.d. b) Percentage of the population exposed to seasonal (that is at least one month per year) and year-round (>9 months per year) water scarcity from 2005–2100, as indicated by WS and WSq. Lines display the mean average exposure per year averaged over the five GCMs considered, while shaded areas represent uncertainty arising from variations in GCM simulations as ±1 s.d. c) Percentage of the population exposed to water scarcity in each month, as indicated by WS and WSq. Boxplots are made based on monthly exposure to water scarcity across all GCMs for a historical reference period (2005–2020) and under three global change scenarios at the end of the century (2081–2100).

Extended Data Fig. 4 Population exposure to water scarcity in the Middle East & North Africa region under uncertain climate and socio-economic change.

a) Number of people exposed to water scarcity from 2005–2100, based on indicators considering water quantity only (WS) and including water quality (WSq). Thick lines and thin lines display the annual average and monthly average exposure to water scarcity, respectively, while shaded areas represent uncertainty arising from variations in GCM simulations as ±1 s.d. b) Percentage of the population exposed to seasonal (that is at least one month per year) and year-round (>9 months per year) water scarcity from 2005–2100, as indicated by WS and WSq. Lines display the mean average exposure per year averaged over the five GCMs considered, while shaded areas represent uncertainty arising from variations in GCM simulations as ±1 s.d. c) Percentage of the population exposed to water scarcity in each month, as indicated by WS and WSq. Boxplots are made based on monthly exposure to water scarcity across all GCMs for a historical reference period (2005–2020) and under three global change scenarios at the end of the century (2081–2100).

Extended Data Fig. 5 Population exposure to water scarcity in the North America region under uncertain climate and socio-economic change.

a) Number of people exposed to water scarcity from 2005–2100, based on indicators considering water quantity only (WS) and including water quality (WSq). Thick lines and thin lines display the annual average and monthly average exposure to water scarcity, respectively, while shaded areas represent uncertainty arising from variations in GCM simulations as ±1 s.d. b) Percentage of the population exposed to seasonal (that is at least one month per year) and year-round (>9 months per year) water scarcity from 2005–2100, as indicated by WS and WSq. Lines display the mean average exposure per year averaged over the five GCMs considered, while shaded areas represent uncertainty arising from variations in GCM simulations as ±1 s.d. c) Percentage of the population exposed to water scarcity in each month, as indicated by WS and WSq. Boxplots are made based on monthly exposure to water scarcity across all GCMs for a historical reference period (2005–2020) and under three global change scenarios at the end of the century (2081–2100).

Extended Data Fig. 6 Population exposure to water scarcity in the Southern Asia region under uncertain climate and socio-economic change.

a) Number of people exposed to water scarcity from 2005–2100, based on indicators considering water quantity only (WS) and including water quality (WSq). Thick lines and thin lines display the annual average and monthly average exposure to water scarcity, respectively, while shaded areas represent uncertainty arising from variations in GCM simulations as ±1 s.d. b) Percentage of the population exposed to seasonal (that is at least one month per year) and year-round (>9 months per year) water scarcity from 2005–2100, as indicated by WS and WSq. Lines display the mean average exposure per year averaged over the five GCMs considered, while shaded areas represent uncertainty arising from variations in GCM simulations as ±1 s.d. c) Percentage of the population exposed to water scarcity in each month, as indicated by WS and WSq. Boxplots are made based on monthly exposure to water scarcity across all GCMs for a historical reference period (2005–2020) and under three global change scenarios at the end of the century (2081–2100).

Extended Data Fig. 7 Population exposure to water scarcity in the Sub-Saharan Africa region under uncertain climate and socio-economic change.

a) Number of people exposed to water scarcity from 2005–2100, based on indicators considering water quantity only (WS) and including water quality (WSq). Thick lines and thin lines display the annual average and monthly average exposure to water scarcity, respectively, while shaded areas represent uncertainty arising from variations in GCM simulations as ±1 s.d. b) Percentage of the population exposed to seasonal (that is at least one month per year) and year-round (>9 months per year) water scarcity from 2005–2100, as indicated by WS and WSq. Lines display the mean average exposure per year averaged over the five GCMs considered, while shaded areas represent uncertainty arising from variations in GCM simulations as ±1 s.d. c) Percentage of the population exposed to water scarcity in each month, as indicated by WS and WSq. Boxplots are made based on monthly exposure to water scarcity across all GCMs for a historical reference period (2005–2020) and under three global change scenarios at the end of the century (2081–2100).

Extended Data Fig. 8 Population exposure to water scarcity in the Western Europe region under uncertain climate and socio-economic change.

a) Number of people exposed to water scarcity from 2005–2100, based on indicators considering water quantity only (WS) and including water quality (WSq). Thick lines and thin lines display the annual average and monthly average exposure to water scarcity, respectively, while shaded areas represent uncertainty arising from variations in GCM simulations as ±1 s.d. b) Percentage of the population exposed to seasonal (that is at least one month per year) and year-round (>9 months per year) water scarcity from 2005–2100, as indicated by WS and WSq. Lines display the mean average exposure per year averaged over the five GCMs considered, while shaded areas represent uncertainty arising from variations in GCM simulations as ±1 s.d. c) Percentage of the population exposed to water scarcity in each month, as indicated by WS and WSq. Boxplots are made based on monthly exposure to water scarcity across all GCMs for a historical reference period (2005–2020) and under three global change scenarios at the end of the century (2081–2100).

Extended Data Table 1 Future population exposure to water scarcity

Supplementary information

Supplementary Information

Supplementary Figs. 1–4, Tables 1–4 and Discussion.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jones, E.R., Bierkens, M.F.P. & van Vliet, M.T.H. Current and future global water scarcity intensifies when accounting for surface water quality. Nat. Clim. Chang. 14, 629–635 (2024). https://doi.org/10.1038/s41558-024-02007-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41558-024-02007-0

Search

Quick links

Nature Briefing Anthropocene

Sign up for the Nature Briefing: Anthropocene newsletter — what matters in anthropocene research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: Anthropocene