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.

South Asian agriculture increasingly dependent on meltwater and groundwater

Abstract

Irrigated agriculture in South Asia depends on meltwater, monsoon rains and groundwater. Climate change alters the hydrology and causes shifts in the timing, composition and magnitude of these sources of water supply. Simultaneously, socio-economic growth increases water demand. Here we use a high-resolution cryosphere–hydrology–crop model forced with an ensemble of climate and socio-economic projections to assess how the sources of irrigation water supply may shift during the twenty-first century. We find increases in the importance of meltwater and groundwater for irrigated agriculture. An earlier melt peak increases meltwater withdrawal at the onset of the cropping season in May and June in the Indus, whereas increasing peak irrigation water demand during July and August aggravates non-renewable groundwater pumping in the Indus and Ganges despite runoff increases. Increasing inter-annual variability in rainfall runoff increases the need for meltwater and groundwater to complement rainfall runoff during future dry years.

This is a preview of subscription content

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Fig. 1: Historical irrigation withdrawals by source and projected future changes.
Fig. 2: Future shifts in runoff and irrigation demand.
Fig. 3: Shifting composition of irrigation withdrawals in the Indus basin.
Fig. 4: Composition of irrigation withdrawals during years with highest irrigation demand.
Fig. 5: Contribution of meltwater and groundwater to irrigation for key crop types in the Indus river basin.
Fig. 6: Non-renewable groundwater abstraction.

Data availability

The data generated in this study (that is, outputs of model simulations) are available in an online archive at https://doi.org/10.24416/UU01-BY9O4S. Elevation data used in this study are available at https://www.hydrosheds.org. The reference climate data and downscaled climate change scenarios used in this study are available at https://rds.icimod.org/Home/Data?group=30. Glacier outlines used in this study are available at https://www.glims.org/RGI/. Snow-cover data used in this study are available at https://nsidc.org/data/modis/data_summaries#snow. Soil data used in this study are available at https://www.futurewater.eu/projects/hihydrosoil/. Land use data used in this study are available at http://due.esrin.esa.int/page_globcover.php and https://www.uni-frankfurt.de/45218031/Data_download_center_for_MIRCA2000. SSP data used in this study are available at https://tntcat.iiasa.ac.at/SspDb. Population data used in this study are available at https://dataportaal.pbl.nl/downloads/HYDE/HYDE3.2/. IMAGE v3.0 data used in this study are available at https://models.pbl.nl/image/index.php/Download.

Code availability

Code for the SPHY model is available at https://github.com/FutureWater/SPHY. Code for the LPJmL model is available at https://github.com/PIK-LPJmL/LPJmL.

References

  1. Wester, P., Mishra, A., Mukherji, A. & Bhakta Shrestha, A. (eds) The Hindu Kush Himalaya Assessment: Mountains, Climate Change, Sustainability and People (Springer, 2019).

  2. Jain, S. K., Agarwal, P. K. & Singh, V. P. in Hydrology and Water Resources of India (eds Jain, S.K. et al.) 473–511 (Springer, 2007).

  3. Biemans, H., Siderius, C., Mishra, A. & Ahmad, B. Crop-specific seasonal estimates of irrigation-water demand in South Asia. Hydrol. Earth Syst. Sci. 20, 1971–1982 (2016).

    Google Scholar 

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

    CAS  Google Scholar 

  5. 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. Change 4, 587–592 (2014).

    Google Scholar 

  6. Khanal, S. et al. Variable 21st century climate change response for rivers in High Mountain Asia at seasonal to decadal time scales. Water Resour. Res. https://doi.org/10.1029/2020WR029266 (2021).

  7. Immerzeel, W. W. et al. Importance and vulnerability of the world’ s water towers. Nature 577, 364–369 (2019).

    Google Scholar 

  8. Viviroli, D., Kummu, M., Meybeck, M., Kallio, M. & Wada, Y. Increasing dependence of lowland populations on mountain water resources. Nat. Sustain. 3, 917–928 (2020).

    Google Scholar 

  9. Nie, Y. et al. Glacial change and hydrological implications in the Himalaya and Karakoram. Nat. Rev. Earth Environ. https://doi.org/10.1038/s43017-020-00124-w (2021).

  10. 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 

  11. 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).

    CAS  Google Scholar 

  12. Döll, P., Schmied, H. M., Shuh, C., Portmann, F. T. & Eicker, A. Global-scale assessment of groundwater depletion and related groundwater abstractions: combining hydrological modeling with information from well observations and GRACE satellites. Water Resour. Res. 50, 5698–5720 (2014).

    Google Scholar 

  13. Rodell, M., Velicogna, I. & Famiglietti, J. S. Satellite-based estimates of groundwater depletion in India. Nature 460, 999–1002 (2009).

    CAS  Google Scholar 

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

    Google Scholar 

  15. Turner, A. G. & Annamalai, H. Climate change and the South Asian summer monsoon. Nat. Clim. Change 2, 587–595 (2012).

    Google Scholar 

  16. Kirby, M., Mainuddin, M., Khaliq, T. & Cheema, M. Agricultural production, water use and food availability in Pakistan: historical trends, and projections to 2050. Agric. Water 179, 34–46 (2016).

    Google Scholar 

  17. 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 

  18. 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 https://doi.org/10.1038/nature23878 (2017).

  19. Rounce, D. R., Hock, R. & Shean, D. E. Glacier mass change in High Mountain Asia through 2100 using the open-source Python Glacier Evolution Model (PyGEM). Front. Earth Sci. https://doi.org/10.3389/feart.2019.00331 (2020).

  20. Hugonnet, R. et al. Accelerated global glacier mass loss in the early twenty-first century. Nature 592, 726–731 (2021).

    CAS  Google Scholar 

  21. Huss, M. & Hock, R. Global-scale hydrological response to future glacier mass loss. Nat. Clim. Change https://doi.org/10.1038/s41558-017-0049-x (2018).

  22. Kraaijenbrink, P. D. A., Stigter, E. E., Yao, T. & Immerzeel, W. W. Climate change decisive for Asia’s snow meltwater supply. Nat. Clim. Change https://doi.org/10.1038/s41558-021-01074-x (2021).

  23. Lutz, A. F. et al. South Asian river basins in a 1.5 °C warmer world. Reg. Environ. Change 19, 833–847 (2019).

    Google Scholar 

  24. Pritchard, H. D. Asia’s shrinking glaciers protect large populations from drought stress. Nature 569, 649–654 (2019).

    CAS  Google Scholar 

  25. Wijngaard, R. R. et al. Future changes in hydro-climatogical extremes in the upper Indus, Ganges, and Brahmaputra river basins. PLoS ONE 12, e0190224 (2017).

    Google Scholar 

  26. Van Tiel, M., Van Loon, A., Seibert, J. & Stahl, K. Hydrological response to warm and dry weather: do glaciers compensate? Hydrol. Earth Syst. Sci. Discuss. https://doi.org/10.5194/hess-2021-44 (2021).

  27. Pokhrel, Y. et al. Global terrestrial water storage and drought severity under climate change. Nat. Clim. Change 11, 226–233 (2021).

    Google Scholar 

  28. Qin, Y. et al. Agricultural risks from changing snowmelt. Nat. Clim. Change 10, 459–465 (2020).

    Google Scholar 

  29. KC, S. & Lutz, W. The human core of the shared socioeconomic pathways: population scenarios by age, sex and level of education for all countries to 2100. Glob. Environ. Change 42, 181–192 (2017).

    Google Scholar 

  30. Popp, A. et al. Land-use futures in the shared socio-economic pathways. Glob. Environ. Change 42, 331–345 (2017).

    Google Scholar 

  31. Munia, H. A. et al. Future transboundary water stress and its drivers under climate change: a global study. Earth’s Future 8, e2019EF001321 (2020).

    Google Scholar 

  32. Lutz, A. F., Maat, W., Biemans, H. & Shrestha, A. B. Selecting representative climate models for climate change impact studies: an advanced envelope-based selection approach. Int. J. Climatol. https://doi.org/10.1002/joc.4608 (2016).

  33. 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).

    Google Scholar 

  34. Wen, S. et al. Population exposed to drought under the 1.5 °C and 2.0 °C warming in the Indus River basin. Atmos. Res. 218, 296–305 (2019).

    Google Scholar 

  35. Cheema, M. J. M., Immerzeel, W. W. & Bastiaanssen, W. G. M. Spatial quantification of groundwater abstraction in the irrigated indus basin. Groundwater 52, 25–36 (2014).

    CAS  Google Scholar 

  36. Siderius, C. et al. Financial feasibility of water conservation in agriculture. Earth’s Future 9, e2020EF001726 (2021).

    Google Scholar 

  37. Grafton, R. Q. et al. The paradox of irrigation efficiency. Science 361, 748–750 (2018).

    CAS  Google Scholar 

  38. Shah, H., Siderius, C. & Hellegers, P. Limitations to adjusting growing periods in different agroecological zones of Pakistan. Agric. Syst. 192, 103184 (2021).

    Google Scholar 

  39. Gernaat, D. E. H. J., Bogaart, P. W., van Vuuren, D. P., Biemans, H. & Niessink, R. High-resolution assessment of global technical and economic hydropower potential. Nat. Energy https://doi.org/10.1038/s41560-017-0006-y (2017).

  40. Grill, G. et al. Mapping the world’s free-flowing rivers. Nature 569, 215–221 (2019).

    CAS  Google Scholar 

  41. Molden, D. J., Vaidya, R. A., Shrestha, A. B., Rasul, G. & Shrestha, M. S. Water infrastructure for the Hindu Kush Himalayas. Int. J. Water Resour. Dev. 30, 60–77 (2014).

    Google Scholar 

  42. Vinca, A. et al. Transboundary cooperation a potential route to sustainable development in the Indus basin. Nat. Sustain. 4, 331–339 (2021).

    Google Scholar 

  43. Rasul, G., Neupane, N., Hussain, A. & Pasakhala, B. Beyond hydropower: towards an integrated solution for water, energy and food security in South Asia. Int. J. Water Resour. Dev. 37, 466–490 (2021).

    Google Scholar 

  44. Wu, X., Jeuland, M., Sadoff, C. & Whittington, D. Interdependence in water resource development in the Ganges: an economic analysis. Water Policy 15, 89–108 (2013).

    Google Scholar 

  45. Gesch, D. B., Verdin, K. L. & Greenlee, S. K. New land surface digital elevation model covers the Earth. Eos Trans. AGU 80, 69–70 (2019).

    Google Scholar 

  46. Lehner, B. & Grill, G. Global river hydrography and network routing: baseline data and new approaches to study the world’s large river systems. Hydrol. Process. 27, 2171–2186 (2013).

    Google Scholar 

  47. Terink, W., Lutz, A. F., Simons, G. W. H., Immerzeel, W. W. & Droogers, P. SPHY v2.0: Spatial processes in HYdrology. Geosci. Model Dev. 8, 2009–2034 (2015).

    Google Scholar 

  48. Paul, F., Huggel, C. & Kääb, A. Combining satellite multispectral image data and a digital elevation model for mapping debris-covered glaciers. Remote Sens. Environ. 89, 510–518 (2004).

    Google Scholar 

  49. Frey, H. et al. Estimating the volume of glaciers in the Himalayan–Karakoram region using different methods. Cryosphere 8, 2313–2333 (2014).

    Google Scholar 

  50. Hock, R. Temperature index melt modelling in mountain areas. J. Hydrol. 282, 104–115 (2003).

    Google Scholar 

  51. Lutz, A. F., Immerzeel, W. W., Kraaijenbrink, P. D. A., Shrestha, A. B. & Bierkens, M. F. P. Climate change impacts on the upper Indus hydrology: sources, shifts and extremes. PLoS ONE 11, e0165630 (2016).

    CAS  Google Scholar 

  52. Droogers, P. & Allen, R. G. Estimating reference evapotranspiration under inaccurate data conditions. Irrig. Drain. Syst. 16, 33–45 (2002).

    Google Scholar 

  53. Gerten, D. et al. Global water availability and requirements for future food production. J. Hydrometeorol. 12, 885–899 (2011).

    Google Scholar 

  54. Schaphoff, S. et al. Contribution of permafrost soils to the global carbon budget. Environ. Res. Lett. 8, 014026 (2013).

    CAS  Google Scholar 

  55. Rost, S. et al. Agricultural green and blue water consumption and its influence on the global water system. Water Resour. Res. 44, W09405 (2008).

    Google Scholar 

  56. Lehner, B. et al. Global Reservoir and Dam Database, v.1 (GRanDv1): Reservoirs, Revision 01 (NASA Socioeconomic Data and Applications Center, 2011); https://sedac.ciesin.columbia.edu/data/set/grand-v1-dams-rev01

  57. Biemans, H. et al. Impact of reservoirs on river discharge and irrigation water supply during the 20th century. Water Resour. Res. 47, W03509 (2011).

    Google Scholar 

  58. Jägermeyr, J. et al. Water savings potentials of irrigation systems: global simulation of processes and linkages. Hydrol. Earth Syst. Sci. 19, 3073–3091 (2015).

    Google Scholar 

  59. Simons, G. W. H., Droogers, P., Contreras, S., Sieber, J. & Bastiaanssen, W. G. M. A novel method to quantify consumed fractions and non-consumptive use of irrigation water: application to the Indus Basin Irrigation System of Pakistan. Agric. Water Manag. 236, 106174 (2020).

    Google Scholar 

  60. Keenan, T. F. et al. A constraint on historic growth in global photosynthesis due to increasing CO2. Nature 600, 253–258 (2021).

    CAS  Google Scholar 

  61. Elliott, J. et al. Constraints and potentials of future irrigation water availability on agricultural production under climate change. Proc. Natl Acad. Sci. USA 111, 3239–3244 (2014).

    CAS  Google Scholar 

  62. Bijl, D. L. et al. A global analysis of future water deficit based on different allocation mechanisms. Water Resour. Res. 54, 5803–5824 (2018).

    Google Scholar 

  63. de Vos, L., Biemans, H., Doelman, J. C., Stehfest, E. & Van Vuuren, D. P. Trade-offs between water needs for food, utilities, and the environment—a nexus quantification at different scales. Environ. Res. Lett. 16, 115003 (2021).

    Google Scholar 

  64. Alcamo, J. et al. Development and testing of the WaterGAP 2 global model of water use and availability. Hydrol. Sci. J. 48, 317–337 (2003).

    Google Scholar 

  65. Wada, Y. et al. Global depletion of groundwater resources. Geophys. Res. Lett. 37, L20402 (2010).

    Google Scholar 

  66. Weedon, G. P. et al. The WFDEI meteorological forcing data set: WATCH Forcing Data methodology applied to ERA-Interim reanalysis data. Water Resour. Res. https://doi.org/10.1002/2014WR015638 (2014).

  67. Immerzeel, W. W., Wanders, N., Lutz, A. F., Shea, J. M. & Bierkens, M. F. P. Reconciling high altitude precipitation with glacier mass balances and runoff. Hydrol. Earth Syst. Sci. 12, 4755–4784 (2015).

    Google Scholar 

  68. Harmonized World Soil Database (v.1.2) (FAO/IIASA/ISRIC/ISSCAS/JRC, 2012); https://www.fao.org/soils-portal/soil-survey/soil-maps-and-databases/harmonized-world-soil-database-v12/ru/

  69. Boer, F. D. HiHydroSoil: A High Resolution Soil Map of Hydraulic Properties v.1.2 (FutureWater, 2016).

  70. Portmann, F. T., Siebert, S. & Döll, P. MIRCA2000—Global monthly irrigated and rainfed crop areas around the year 2000: a new high-resolution data set for agricultural and hydrological modeling. Glob. Biogeochem. Cycles 24, GB1011 (2010).

    Google Scholar 

  71. Defourny, P. et al. GLOBCOVER: A 300 m global land cover product for 2005 using ENVISAT MERIS time series. In Proc. ISPRS Commission VII Mid-Term Symposium: Remote Sensing: From Pixels to Processes (eds Kerle, N. & Skidmore, A.) (International Society of Photogrammetry and Remote Sensing, 2007).

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

    Google Scholar 

  73. 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 

  74. AQUASTAT database (FAO, 2016); https://www.fao.org/aquastat/en/

  75. Arendt, A. et al. Randolph Glacier Inventory [5.0]: A Dataset of Global Glacier Outlines, v.5.0 (Global Land Ice Measurements from Space (GLIMS), 2015); https://www.glims.org/RGI/

  76. van Vuuren, D. P. et al. A new scenario framework for climate change research: scenario matrix architecture. Climatic Change 122, 373–386 (2014).

    Google Scholar 

  77. 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).

    Google Scholar 

  78. 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 

  79. Stehfest, E., et al. Integrated Assessment of Global Environmental Change with IMAGE 3.0.—Model Description and Policy Applications (Netherlands Environmental Assessment Agency, 2014).

  80. Bijl, D. L., Bogaart, P. W., Kram, T., de Vries, B. J. M. & van Vuuren, D. P. Long-term water demand for electricity, industry and households. Environ. Sci. Policy 55, 75–86 (2016).

    Google Scholar 

  81. Doelman, J. C. et al. Exploring SSP land-use dynamics using the IMAGE model: regional and gridded scenarios of land-use change and land-based climate change mitigation. Glob. Environ. Change 48, 119–135 (2018).

    Google Scholar 

  82. Hall, D. K. & Riggs, G. A. MODIS/Terra Snow Cover Monthly L3 Global 0.05Deg CMG, v.6. National Snow and Ice Data Center https://doi.org/10.5067/MODIS/MOD10CM.006 (2015).

  83. Kääb, A., Berthier, E., Nuth, C., Gardelle, J. & Arnaud, Y. Contrasting patterns of early twenty-first-century glacier mass change in the Himalayas. Nature 488, 495–498 (2012).

    Google Scholar 

  84. Pellicciotti, F., Buergi, C., Immerzeel, W. W., Konz, M. & Shrestha, A. B. Challenges and uncertainties in hydrological modeling of remote Hindu Kush–Karakoram–Himalayan (HKH) basins: suggestions for calibration strategies. Mt. Res. Dev. 32, 39–50 (2012).

    Google Scholar 

  85. Nash, J. E. & Sutcliffe, J. V. River flow forecasting through conceptual models part I—a discussion of principles. J. Hydrol. 10, 282–290 (1970).

    Google Scholar 

  86. Moriasi, D. N. et al. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans. ASABE 50, 885–900 (2007).

    Google Scholar 

  87. Food Balance Sheets. A Handbook (FAO, 2001).

Download references

Acknowledgements

Part of this work was carried out by the Himalayan Adaptation, Water and Resilience (HI-AWARE) consortium under the Collaborative Adaptation Research Initiative in Africa and Asia (CARIAA) with financial support from the UK government’s Department for International Development and the International Development Research Centre, Ottawa, Canada. Part of this work was performed for the project ‘Targeting a climate change hotspot: science to support the SDGs and sustainable water management in the transboundary Indus river basin (SustainIndus)’ and received funding from the Netherlands Organization for Scientific Research under the WOTRO Joint Sustainable Development Goals (SDG) research programme (grant W 07.30318.002). The views expressed in this work are those of the creators and do not necessarily represent those of the UK government’s Department for International Development, the International Development Research Centre, Canada, or its board of governors and are not necessarily attributable to their organizations.

Author information

Authors and Affiliations

Authors

Contributions

A.F.L., H.B., C.S. and W.W.I. designed the study. R.R.W., A.F.L. and S.N. developed and ran the upstream model. H.B. developed the downstream model with help from C.S. A.F.L. downscaled future climate forcing, and H.B. and R.R.W. implemented socio-economic scenarios. A.F.L. and H.B. analysed the data and prepared the figures. A.F.L. wrote the article with major contributions from all co-authors.

Corresponding author

Correspondence to A. F. Lutz.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Climate Change thanks Yong Nie, Xiaoming Wang and the other, anonymous, reviewer(s) 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 Historical irrigation withdrawal and projected future changes.

Average annual irrigation withdrawals during 1981–2010 (IWREF) (panel a). Change in average annual irrigation withdrawal (ΔIW) between 2071–2100 and 1981–2010 for the ensemble mean of RCP4.5-SSP1 (panel b) and RCP8.5-SSP3 (panel c). Background digital elevation model (GTOPO30) from ref. 45.

Extended Data Fig. 2 Historical irrigation withdrawals by source and projected future changes (RCP8.5-SSP3).

a) Average contribution of glacier and snowmelt (GS) contribution to irrigation withdrawal during 1981–2010 (IWREF). Grid cells with IWREF < 10 mm yr−1 are excluded. b) RCP8.5-SSP3 ensemble mean of projected changes in GS contribution to irrigation withdrawal for 2071–2100 vs 1981–2010. c) Thirty-year average monthly irrigation withdrawals for the Indus, Ganges and Brahmaputra basins during 1981–2010 differentiated by source. GS = glacier and snowmelt, RB = rainfall-runoff and baseflow, GW = groundwater. Black line indicates RCP8.5-SSP3 ensemble mean of projected change in average monthly total irrigation withdrawal 2071–2100 vs 1981–2010 (ΔIW). Error bars indicate the ensemble spread in projections. d) Average monthly projected changes in irrigation withdrawal per source for 2071–2100 vs 1981–2010 (ΔIW). Lines and shading indicate the ensemble mean and ensemble range for RCP8.5-SSP3. Upstream and downstream river basin boundaries (light grey tones) and main rivers (dark grey tones) are indicated in panels a and b. Background digital elevation model (GTOPO30) from ref. 45. River data from ref. 46.

Extended Data Fig. 3 Historical and projected future contributions to irrigation withdrawals for groundwater and rainfall and baseflow.

Contributions of groundwater (GW, panel a) and rainfall and baseflow (RB, panel b) to irrigation withdrawals (IW) during the reference period. Ensemble mean projections for change in groundwater contribution (∆IWGW) and rainfall and baseflow contribution (∆IWRB) between 2071–2100 and 1981–2010 for RCP4.5-SSP1 (panels c, d) and RCP8.5-SSP3 (panels e,f).

Extended Data Fig. 4 Shifting sources of irrigation withdrawal in the Indus basin during the month of May.

Dots indicate annual fractional basin-averaged contribution to irrigation withdrawal from groundwater (x-axis), meltwater (y-axis) and rainfall and baseflow (indicated by grey lines), for inidvidual years 1981–2100. Convex hulls indicate the range of years 1981–2010, 2036–2065, and 2071–2100). The color scale shows the 30 year moving average starting at 1981–2010 and ending at 2071–2100. Separate plots are shown for cold/wet (a), cold/dry (b), warm/dry (c), and warm/wet (d) future scenarios. In each panel results are shown for RCP4.5-SSP1 (lower left part of panel) and RCP8.5-SSP3 (upper right part of panel, with flipped axis direction).

Extended Data Fig. 5 Shifting sources of irrigation withdrawal in the Indus basin during the month of August.

Dots indicate annual fractional basin-averaged contribution to irrigation withdrawal from groundwater (x-axis), meltwater (y-axis) and rainfall and baseflow (indicated by grey lines), for inidvidual years 1981–2100. Convex hulls indicate the range of years 1981–2010, 2036–2065, and 2071–2100). The color scale shows the 30 year moving average starting at 1981–2010 and ending at 2071–2100. Separate plots are shown for cold/wet (a), cold/dry (b), warm/dry (c), and warm/wet (d) future scenarios. In each panel results are shown for RCP4.5-SSP1 (lower left part of panel) and RCP8.5-SSP3 (upper right part of panel, with flipped axis direction).

Extended Data Fig. 6 Contribution of meltwater and groundwater to irrigation for key crop types in the Ganges river basin.

Panels a-c show the 30 year daily mean contribution of meltwater to irrigation during the reference period (1981–2010) and end of century period (2071–2010) for each ensemble member in the RCP4.5-SSP1 and RCP8.5-SSP3 ensembles, for rice (a), wheat (b), and sugarcane (c). Panels d-f show the same for the groundwater contribution to irrigation for rice (d), wheat (e), and sugarcane (f).

Supplementary information

Supplementary Information

Supplementary text, Fig. 1 and Tables 1–5.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Lutz, A.F., Immerzeel, W.W., Siderius, C. et al. South Asian agriculture increasingly dependent on meltwater and groundwater. Nat. Clim. Chang. 12, 566–573 (2022). https://doi.org/10.1038/s41558-022-01355-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41558-022-01355-z

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

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