Agricultural risks from changing snowmelt

Abstract

Snowpack stores cold-season precipitation to meet warm-season water demand. Climate change threatens to disturb this balance by altering the fraction of precipitation falling as snow and the timing of snowmelt, which may have profound effects on food production in basins where irrigated agriculture relies heavily on snowmelt runoff. Here, we analyse global patterns of snowmelt and agricultural water uses to identify regions and crops that are most dependent on snowmelt water resources. We find hotspots primarily in high-mountain Asia (the Tibetan Plateau), Central Asia, western Russia, western US and the southern Andes. Using projections of sub-annual runoff under warming scenarios, we identify the basins most at risk from changing snowmelt patterns, where up to 40% of irrigation demand must be met by new alternative water supplies under a 4 °C warming scenario. Our results highlight basins and crops where adaptation of water management and agricultural systems may be especially critical in a changing climate.

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Fig. 1: Geographical pattern of snowmelt runoff during recent decades (1985–2015) and under future warming scenarios.
Fig. 2: Average monthly runoff and surface water demand during recent decades (1985–2015) and runoff under future warming scenarios.
Fig. 3: Hotspots of snow-dependent irrigated agriculture.
Fig. 4: Seasonal snowmelt dependence by crop type (1985–2015).
Fig. 5: Basins at risk from changes in snowmelt under 4 °C warming.

Data availability

The numerical results plotted in Fig. 1 is available from figshare: https://doi.org/10.6084/m9.figshare.12016254.v1; numerical results for Figs. 25 and the Extended Figures are provided with this paper. TerraClimate data is available from: http://www.climatologylab.org/terraclimate.html and GCWM outputs are available from: https://www.uni-frankfurt.de/45217988/Global_Crop_Water_Model__GCWM. All other data that support the findings of this study are available in the main text or the Supplementary Information.

Code availability

Computer code or algorithm used to generate results that are reported in the paper and central to the main claims are available from the corresponding authors on reasonable request.

References

  1. 1.

    Brauman, K. A., Richter, B. D., Postel, S., Malsy, M. & Florke, M. Water depletion: an improved metric for incorporating seasonal and dry-year water scarcity into water risk assessments. Elem. Sci. Anth. 4, 000083 (2016).

    Google Scholar 

  2. 2.

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

    CAS  Google Scholar 

  3. 3.

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

    Google Scholar 

  4. 4.

    Bruinsma, J. (ed.) World Agriculture: Towards 2015/2030. An FAO Perspective (Earthscan, 2003).

  5. 5.

    Alexandratos, N. & Bruinsma, J. World Agriculture Towards 2030/2050: The 2012 Revision. ESA Working Paper No. 12-03 (FAO, 2012).

  6. 6.

    Siebert, S. et al. Groundwater use for irrigation—a global inventory. Hydrol. Earth Syst. Sci. 14, 1863–1880 (2010).

    Google Scholar 

  7. 7.

    Jiménez Cisneros, B. E. et al. in Climate Change 2014: Impacts, Adaptation, and Vulnerability (eds, Field, C. B. et al) 229–269 (IPCC, Cambridge Univ. Press, 2014).

  8. 8.

    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 

  9. 9.

    Yu, C. Q. et al. Assessing the impacts of extreme agricultural droughts in China under climate and socioeconomic changes. Earths Future 6, 689–703 (2018).

    Google Scholar 

  10. 10.

    Vano, J. A. et al. Climate change impacts on water management and irrigated agriculture in the Yakima River Basin, Washington, USA. Clim. Change 102, 287–317 (2010).

    Google Scholar 

  11. 11.

    Portmann, F., Siebert, S., Bauer, C. & Döll, P. Global Dataset of Monthly Growing Areas of 26 Irrigated Crops. Frankfurt Hydrology Paper 06. Institute of Physical Geography 400 (University of Frankfurt, 2008).

  12. 12.

    Waliser, D. et al. Simulating cold season snowpack: impacts of snow albedo and multi-layer snow physics. Clim. Change 109, 95–117 (2011).

    Google Scholar 

  13. 13.

    Huning, L. S. & AghaKouchak, A. Mountain snowpack response to different levels of warming. Proc. Natl Acad. Sci. USA 115, 10932–10937 (2018).

    CAS  Google Scholar 

  14. 14.

    Li, D. Y., Wrzesien, M. L., Durand, M., Adam, J. & Lettenmaier, D. P. How much runoff originates as snow in the western United States, and how will that change in the future? Geophys. Res. Lett. 44, 6163–6172 (2017).

    Google Scholar 

  15. 15.

    Vicuna, S., McPhee, J. & Garreaud, R. D. Agriculture vulnerability to climate change in a snowmelt-driven basin in semiarid chile. J Water Resour. Plan. Manag. 138, 431–441 (2012).

    Google Scholar 

  16. 16.

    Easterling, W. E. et al. in Climate Change 2007: Impacts, Adaptation and Vulnerability (Parry, M. L. et al.) 273–313 (IPCC, Cambridge Univ. Press, 2007).

  17. 17.

    Kapnick, S. B. & Delworth, T. L. Controls of global snow under a changed climate. J. Clim. 26, 5537–5562 (2013).

    Google Scholar 

  18. 18.

    Barnett, T. P., Adam, J. C. & Lettenmaier, D. P. Potential impacts of a warming climate on water availability in snow-dominated regions. Nature 438, 303–309 (2005).

    CAS  Google Scholar 

  19. 19.

    Adam, J. C., Hamlet, A. F. & Lettenmaier, D. P. Implications of global climate change for snowmelt hydrology in the twenty-first century. Hydrol. Process. 23, 962–972 (2009).

    Google Scholar 

  20. 20.

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

    CAS  Google Scholar 

  21. 21.

    Mankin, J. S., Viviroli, D., Singh, D., Hoekstra, A. Y. & Diffenbaugh, N. S. The potential for snow to supply human water demand in the present and future. Environ. Res. Lett. 10, 114016 (2015).

  22. 22.

    Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A. & Hegewisch, K. C. Terraclimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015. Sci. Data 5, 170191 (2018).

    Google Scholar 

  23. 23.

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

    CAS  Google Scholar 

  24. 24.

    Siebert, S. & Doll, P. Quantifying blue and green virtual water contents in global crop production as well as potential production losses without irrigation. J. Hydrol. 384, 198–217 (2010).

    Google Scholar 

  25. 25.

    Food and Agriculture Data (Food and Agriculture Organization of the United Nations, 2018); http://www.fao.org/faostat/en/#data

  26. 26.

    Hoekstra, A. Y., Mekonnen, M. M., Chapagain, A. K., Mathews, R. E. & Richter, B. D. Global monthly water scarcity: blue water footprints versus blue water availability. PLoS ONE 7, e32688 (2012).

    CAS  Google Scholar 

  27. 27.

    Goldewijk, K. K., Beusen, A., Doelman, J. & Stehfest, E. Anthropogenic land use estimates for the Holocene – HYDE 3.2. Earth Syst. Sci. Data 9, 927–953 (2017).

    Google Scholar 

  28. 28.

    IPCC: Summary for Policymakers. In Climate Change 2014: Impacts, Adaptation, and Vulnerability (eds Field, C. B. et al.) 1–32 (Cambridge Univ. Press, 2014).

  29. 29.

    Sharma, J. & Ravindranath, N. Applying IPCC 2014 framework for hazard-specific vulnerability assessment under climate change. Environ. Res. Commun. 1, 051004 (2019).

    Google Scholar 

  30. 30.

    Hagenlocher, M. et al. Drought vulnerability and risk assessments: state of the art, persistent gaps, and research agenda. Environ. Res. Lett. 14, 083002 (2019).

    Google Scholar 

  31. 31.

    Ben Fraj, W., Elloumi, M. & Molle, F. The politics of interbasin transfers: socio-environmental impacts and actor strategies in Tunisia. Nat. Resour. Forum 43, 17–30 (2019).

    Google Scholar 

  32. 32.

    Liu, L. et al. Quantifying the potential for reservoirs to secure future surface water yields in the world’s largest river basins. Environ. Res. Lett. 13, 044026 (2018).

    Google Scholar 

  33. 33.

    Wada, Y. et al. Global monthly water stress: 2. Water demand and severity of water stress.Water Resour. Res. 47, W07518 (2011).

    Google Scholar 

  34. 34.

    Nelson, K. S. & Burchfield, E. K. Effects of the structure of water rights on agricultural production during drought: a spatiotemporal analysis of California’s central valley. Water Resour. Res. 53, 8293–8309 (2017).

    Google Scholar 

  35. 35.

    Lehner, B. C. et al. High-resolution mapping of the world’s reservoirs and dams for sustainable river-flow management. Front. Ecol. Environ. 9, 494–502 (2011).

    Google Scholar 

  36. 36.

    Qin, Y. et al. Flexibility and intensity of global water use. Nat. Sustain. 2, 515–523 (2019).

  37. 37.

    Sneed M. & Brandt J. M. S. Land Subsidence Along the DeltaMendota Canal in the Northern Part of the San Joaquin Valley, California, 2003–2010 Scientific Investigations Report 2013-5142 (US Geological Survey, 2013); http://pubs.usgs.gov/sir/2013/5142/

  38. 38.

    Godfray, H. C. J. et al. Food security: the challenge of feeding 9 billion people. Science 327, 812–818 (2010).

    CAS  Google Scholar 

  39. 39.

    Myers, S. S. et al. Climate change and global food systems: potential impacts on food security and undernutrition. Annu. Rev. Publ. Health 38, 259–277 (2017).

    Google Scholar 

  40. 40.

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

    Google Scholar 

  41. 41.

    Mankin, J. S. & Diffenbaugh, N. S. Influence of temperature and precipitation variability on near-term snow trends. Clim. Dynam. 45, 1099–1116 (2015).

    Google Scholar 

  42. 42.

    Rothausen, S. G. S. A. & Conway, D. Greenhouse-gas emissions from energy use in the water sector. Nat. Clim. Change 1, 210–219 (2011).

    CAS  Google Scholar 

  43. 43.

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

    CAS  Google Scholar 

  44. 44.

    Bormann, K. J., Brown, R. D., Derksen, C. & Painter, T. H. Estimating snow-cover trends from space. Nat. Clim. Change 8, 923–927 (2018).

    Google Scholar 

  45. 45.

    Knowles, N., Dettinger, M. D. & Cayan, D. R. Trends in snowfall versus rainfall in the Western United States. J. Clim. 19, 4545–4559 (2006).

    Google Scholar 

  46. 46.

    Mote, P. W. Trends in snow water equivalent in the Pacific Northwest and their climatic causes. Geophys. Res. Lett. 30, 12 (2003).

    Google Scholar 

  47. 47.

    Westerling, A. L., Hidalgo, H. G., Cayan, D. R. & Swetnam, T. W. Warming and earlier spring increase western US forest wildfire activity. Science 313, 940–943 (2006).

    CAS  Google Scholar 

  48. 48.

    Davis, S. J. et al. Net-zero emissions energy systems. Science 360, eaas9793 (2018).

    Google Scholar 

  49. 49.

    Guan, D. B. et al. Structural decline in China’s CO2 emissions through transitions in industry and energy systems. Nat. Geosci. 11, 551–555 (2018).

    Google Scholar 

  50. 50.

    Davies, D. M. et al. Combined economic and technological evaluation of battery energy storage for grid applications. Nat. Energy 4, 42–50 (2019).

    Google Scholar 

  51. 51.

    Clow, D. W. Changes in the timing of snowmelt and streamflow in colorado: A response to recent warming. J Clim. 23, 2293–2306 (2010).

    Google Scholar 

  52. 52.

    Rasmussen, R. et al. Climate change impacts on the water balance of the colorado headwaters: high-resolution regional climate model simulations. J. Hydrometeorol. 15, 1091–1116 (2014).

    Google Scholar 

  53. 53.

    The NCAR Command Language v.6. 6.2 (NCAR, 2019); https://doi.org/10.5065/D6WD3XH5

  54. 54.

    Snow Data Assimilation System (SNODAS) Data Products at NSIDC, Version 1 (NSIDC, accessed December 2018); https://doi.org/10.7265/N5TB14TC

  55. 55.

    Mitchell, T. D. Pattern scaling—an examination of the accuracy of the technique for describing future climates. Clim. Change 60, 217–242 (2003).

    CAS  Google Scholar 

  56. 56.

    Huntingford, C. & Cox, P. M. An analogue model to derive additional climate change scenarios from existing GCM simulations. Clim. Dynam. 16, 575–586 (2000).

    Google Scholar 

  57. 57.

    Kirtman, B. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) Ch. 11 (IPCC, Cambridge Univ. Press, 2013).

  58. 58.

    Hawkins, E. et al. Estimating changes in global temperature since the preindustrial period. Bull. Am. Meteorol. Soc. 98, 1841–1856 (2017).

    Google Scholar 

  59. 59.

    James, R., Washington, R., Schleussner, C. F., Rogelj, J. & Conway, D. Characterizing half-a-degree difference: a review of methods for identifying regional climate responses to global warming targets. WIRES Clim. Change 8, e457 (2017).

  60. 60.

    Kruijt, B., Witte, J. P. M., Jacobs, C. M. J. & Kroon, T. Effects of rising atmospheric CO2 on evapotranspiration and soil moisture: a practical approach for the Netherlands. J. Hydrol. 349, 257–267 (2008).

    Google Scholar 

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Acknowledgements

This work was supported by the Foundation for Food and Agriculture Research through a New Innovator Award to N.D.M., by the US National Science Foundation INFEWS grant EAR 1639318 to S.J.D., and by the German Federal Ministry of Education and Research (BMBF; grant no. 02WGR1457F) through its Global Resource Water (GRoW) funding initiative to S.S.

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Affiliations

Authors

Contributions

N.D.M., S.J.D. and Y.Q. designed the study. Y.Q. performed the analyses, with additional support from J.T.A., S.S., L.S.H., A.A., J.S.M. on datasets and S.S., J.T.A., J.S.M., C.H. and D.T. on analytical approaches. Y.Q., N.D.M., S.J.D. and J.T.A. led the writing with input from all co-authors.

Corresponding authors

Correspondence to Yue Qin or Nathaniel D. Mueller.

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The authors declare no competing interests.

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Peer review information Nature Climate Change thanks Tobias Siegfried and Julie Vano for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Share of annual average irrigation surface water consumption 1985–2015 met by different sources.

Share of annual average irrigation surface water consumption 1985–2015 met by (a) snowmelt runoff, (b) rainfall runoff, and (c) alternative sources (for example, water stored in reservoirs and inter-basin transfers). Shares from all three sources are zero for basins without irrigation surface water consumption. Source data

Extended Data Fig. 2 Monthly and crop-specific irrigation share and snow consumption.

Crop-specific (a) monthly ratio of irrigation surface water consumption to corresponding annual total, and (b) monthly snowmelt runoff consumption for the period of 1985–2015. Crops are ordered by their annual total irrigation surface water consumption, with higher total consumption to the right. Source data

Extended Data Fig. 3 Changes in irrigation surface water from snowmelt and alternative sources under 2 °C warming for snow-dependent basins.

Details of this figure are identical to Fig. 5, except displayed for 2 °C warming. Source data

Supplementary information

Supplementary Information

Supplementary Figs. 1–7 and Tables 1–4.

Source data

Source Data Fig. 2

Numerical data used to generate graphs in Fig. 2.

Source Data Fig. 3

Numerical data used to generate graphs in Fig. 3.

Source Data Fig. 4

Numerical data used to generate graphs in Fig. 4.

Source Data Fig. 5

Numerical data used to generate graphs in Fig. 5.

Source Data Extended Data Fig. 1

Numerical data used to generate graphs in Extended Data Fig. 1.

Source Data Extended Data Fig. 2

Numerical data used to generate graphs in Extended Data Fig. 2.

Source Data Extended Data Fig. 3

Numerical data used to generate graphs in Extended Data Fig. 3.

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Qin, Y., Abatzoglou, J.T., Siebert, S. et al. Agricultural risks from changing snowmelt. Nat. Clim. Chang. 10, 459–465 (2020). https://doi.org/10.1038/s41558-020-0746-8

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