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Snowmelt risk telecouplings for irrigated agriculture

Nature Climate Change volume 12, pages 1007–1015 (2022)Cite this article

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Abstract

Climate change is altering the timing and magnitude of snowmelt, which may either directly or indirectly via global trade affect agriculture and livelihoods dependent on snowmelt. Here, we integrate subannual irrigation and snowmelt dynamics and a model of international trade to assess the global redistribution of snowmelt dependencies and risks under climate change. We estimate that 16% of snowmelt used for irrigation is for agricultural products traded globally, of which over 70% is from five countries. Globally, we observe a prodigious snowmelt dependence and risk diffusion, with particularly evident importing of products at risk in western Europe. In Germany and the UK, local fraction of surface-water-irrigated agriculture supply exposed to snowmelt risks could increase from negligible to 16% and 10%, respectively, under a 2 °C warming. Our results reveal the trade-exposure of agricultural supplies, highlighting regions and crops whose consumption may be vulnerable to changing snowmelt even if their domestic production is not.

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Fig. 1: GTAP region-level surface irrigation water demand met by different water sources (1985–2015).
Fig. 2: Source-specific irrigation surface-water transfer embodied in international trade.
Fig. 3: Global production-based and consumption-based hotspots of snowmelt-dependence for irrigated agriculture.
Fig. 4: Virtual transfer of agricultural products at risk embodied in international trade.
Fig. 5: Virtual transfer of agricultural production at risk detailed across trading partners.
Fig. 6: Crop-specific production at risk for top importers and exporters.

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Data availability

GTAP is available from: https://www.gtap.agecon.purdue.edu/. GCWM outputs are available from: https://www.uni-frankfurt.de/45217988/Global_Crop_Water_Model__GCWM. TerraClimate data are available from: http://www.climatologylab.org/terraclimate.html. FAO data are available from: https://www.fao.org/faostat/en/#data. All other data that support the findings of this study are available in the main text or the supplementary materials. Source data are provided with this paper.

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 figshare79.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China grant no. 42277482 to Y.Q., the Foundation for Food and Agriculture Research through a New Innovator Award to N.D.M., the US National Science Foundation INFEWS grant EAR 1639318 to S.J.D., the German Federal Ministry of Education and Research (BMBF; grant no. 02WGR1642A) through its Global Resource Water (GRoW) funding initiative and the German Research Foundation SFB 1502/1-2022-Project no. 450058266 to S.S., the University of California, Division of Agriculture and Natural Resources California Institute for Water Resources and US Geological Survey grant G21AP10611-00 and a California State University Water Resources and Policy Initiatives grant to L.S.H., the Scientific Research Start-up Funds (QD2021030C) from Tsinghua Shenzhen International Graduate School to C.H., the USDA-NIFA award (2021-69012-35916) to J.T.A. and National Natural Science Foundation of China grant no. 71904097 to H.Z. We acknowledge helpful discussions with D. Li and P. Lin.

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Authors and Affiliations

  1. College of Environmental Science and Engineering, Peking University, Beijing, China

    Yue Qin, Shiyu Li & Tong Zhu

  2. Institute of Environment and Ecology, Shenzhen International Graduate School, Tsinghua University, Shenzhen, China

    Chaopeng Hong

  3. School of Environment, Beijing Normal University, Beijing, China

    Hongyan Zhao

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

    Stefan Siebert

  5. Centre of Biodiversity and Sustainable Land Use (CBL), University of Göttingen, Göttingen, Germany

    Stefan Siebert

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

    John T. Abatzoglou

  7. Department of Civil Engineering and Construction Engineering Management, California State University, Long Beach, CA, USA

    Laurie S. Huning

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

    Laurie S. Huning & Steven J. Davis

  9. World Resources Institute, Washington, DC, USA

    Lindsey L. Sloat

  10. Computational and Data Sciences, George Mason University, Incheon, South Korea

    Sohyun Park

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

    Sohyun Park

  12. Lincoln Institute of Land Policy, Cambridge, MA, USA

    Darla K. Munroe

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

    Steven J. Davis

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

    Nathaniel D. Mueller

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

    Nathaniel D. Mueller

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Contributions

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

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Correspondence to Yue Qin or Nathaniel D. Mueller.

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Extended data

Extended Data Fig. 1 Source-specific water demand for major countries under a 2 °C warming scenario.

GTAP region-level surface-water demand met by different water sources under the +2 °C warming scenario. Monthly runoff from snowmelt runoff, rainfall runoff, and alternative water demand for (a) China, (b) U.S., (c) Pakistan, and (d) India are shown in stacked bars inside the box, where the shaded red, blue and grey bars denote the corresponding contributions from rainfall, snowmelt, and alternative surface-water sources (that is, reservoirs storage and interbasin transfer), respectively.

Source data

Extended Data Fig. 2 Source-specific water demand for major countries under a 4 °C warming scenario.

GTAP region-level surface-water demand met by different water sources under the +4 °C warming scenario. Monthly runoff from snowmelt runoff, rainfall runoff, and alternative water demand for (a) China, (b) U.S., (c) Pakistan, and (d) India are shown in stacked bars inside the box, where the shaded red, blue and grey bars denote the corresponding contributions from rainfall, snowmelt, and alternative surface-water sources (that is, reservoirs storage and interbasin transfer), respectively.

Source data

Extended Data Fig. 3 Virtual transfer of agricultural production at risk under the 2 °C warming scenario.

GTAP-level agricultural production at risk under the 2 °C warming scenario and virtual transfer throughout the whole global supply chains. GTAP-level (a) surface-water-irrigated agricultural products exposed to snowmelt risks under production-based accounting, (b) imports of surface-water-irrigated agricultural products at risk embodied in trade, (c) exports of surface-water-irrigated agricultural products at risk embodied in trade, and (d) surface-water-irrigated agricultural products exposed to snowmelt risks under consumption-based accounting.

Source data

Extended Data Fig. 4 Virtual transfer of agricultural production at risk under the 4 °C warming scenario.

GTAP-level agricultural production at risk under the 4 °C warming scenario and virtual transfer throughout the whole global supply chains. GTAP-level (a) surface-water-irrigated agricultural products exposed to snowmelt risks under production-based accounting, (b) imports of surface-water-irrigated agricultural products at risk embodied in trade, (c) exports of surface-water-irrigated agricultural products at risk embodied in trade, and (d) surface-water-irrigated agricultural products exposed to snowmelt risks under consumption-based accounting.

Source data

Supplementary information

Supplementary Information

Supplementary notes, Figs. 1–28 and Tables 1 and 2.

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Source Data Fig. 1

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Qin, Y., Hong, C., Zhao, H. et al. Snowmelt risk telecouplings for irrigated agriculture. Nat. Clim. Chang. 12, 1007–1015 (2022). https://doi.org/10.1038/s41558-022-01509-z

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