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Agricultural risks from changing snowmelt

Nature Climate Change volume 10, pages 459–465 (2020)Cite this article

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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. 2–5 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.

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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.

Author information

Authors and Affiliations

  1. Department of Geography, The Ohio State University, Columbus, OH, USA

    Yue Qin

  2. Sustainability Institute, The Ohio State University, Columbus, OH, USA

    Yue Qin

  3. Department of Earth System Science, University of California, Irvine, Irvine, CA, USA

    Yue Qin, Amir AghaKouchak, Chaopeng Hong, Dan Tong & Steven J. Davis

  4. Management of Complex Systems Department, University of California, Merced, Merced, CA, USA

    John T. Abatzoglou

  5. Department of Geography, University of Idaho, Moscow, ID, USA

    John T. Abatzoglou

  6. Department of Crop Sciences, University of Göttingen, Göttingen, Germany

    Stefan Siebert

  7. Department of Civil and Environmental Engineering, University of California, Irvine, Irvine, CA, USA

    Laurie S. Huning, Amir AghaKouchak & Steven J. Davis

  8. Department of Geography, Dartmouth College, Hanover, NH, USA

    Justin S. Mankin

  9. Department of Earth Sciences, Dartmouth College, Hanover, NH, USA

    Justin S. Mankin

  10. Lamont–Doherty Earth Observatory of Columbia University, Palisades, New York, NY, USA

    Justin S. Mankin

  11. Department of Ecosystem Science and Sustainability, Colorado State University, Fort Collins, CO, USA

    Nathaniel D. Mueller

  12. Department of Soil and Crop Sciences, Colorado State University, Fort Collins, CO, USA

    Nathaniel D. Mueller

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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.

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

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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