Research ArticleAGRICULTURE

Mitigation efforts will not fully alleviate the increase in water scarcity occurrence probability in wheat-producing areas

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Science Advances  25 Sep 2019:
Vol. 5, no. 9, eaau2406
DOI: 10.1126/sciadv.aau2406
  • Fig. 1 Most important wheat-growing areas and the effect of SWS on wheat yields.

    (A) Colors mark the spatial distributions of the wheat-growing area and the top 10 wheat exporters during 2009–2012 in descending order, with light gray showing arable land without wheat cultivation. (B and C) Comparison of wheat yield deviations during years with and without severe water scarcity (SWS) occurrence, combining the 10 main wheat exporters [European Union (EU), Russia, Canada, United States, Ukraine, Australia, Kazakhstan, Argentina, Turkey, and Brazil]. SWS and yield data over the period 1991–2016 were used. (B) Frequency of yield deviation expressed as the Z score and smoothed by Gaussian filter for years with no SWS occurrence (n = 136) versus years when at least 1% of the exporter’s wheat-growing area was affected by SWS (n = 78) during the year of harvest. (C) Yield differences at the exporter entity level relative to the previous year for years with no SWS occurrence (n = 136) versus years when at least 10% of the exporter’s wheat-growing area was affected by SWS (n = 5).

  • Fig. 2 Areas that are most and least at risk of an increased probability of SWS during the wheat season.

    The hot spots depict the 10% (or 25%) of grids with the highest SWS occurrence, which are also important wheat-producing areas. The good spots represent the 10% or 25% of wheat-producing grids with the lowest probability of SWS. The estimates are based on the analysis of the entire set of projections of 27 GCMs (table S2) for RCP 2.6, RCP 4.5, and RCP 8.5 for the period 2041–2070, which was compared with the SWS occurrence from 1961 to 1990, based on the control run using the same set of GCMs. The grids where wheat is being grown outside hot/good spots are depicted by light yellow, and light gray depicts the remaining agricultural land.

  • Fig. 3 Estimated proportion of global wheat-growing area affected by SWS between 1861 and 2100.

    (A) Box plots of the proportions of the global WhA affected by SWS during the harvest year or in one of the two preceding seasons, based on observed data (12) and GCM data (table S2) for two controls and three future time slices. (B) Annual values of areas affected by SWS during the harvest year or two preceding seasons using CRU-based observed data (1911–2016) and control run data (i.e., 1860–2010) (24) and GCM data for three RCP scenarios during the period 2011–2100.

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/5/9/eaau2406/DC1

    Table S1. Definitions of SWS and EWS for the present study.

    Table S2. Return period of SWS and EWS (as defined in table S1), calculated as the average ± SD across all grids included in the global wheat-growing area (fig. S1) for the year of harvest and as the cumulative value for the harvest year and the preceding year.

    Table S3. A list of the CMIP5 GCMs used in this study (with brief descriptions).

    Table S4. Symbols for weighting schemes used in the paper.

    Fig. S1. Weights of grids as used in the study.

    Fig. S2. Relationship between the proportion of the global arable land affected by severe water scarcity (SWS) and the cereal price index.

    Fig. S3. Relationship between the proportion of the global wheat growing area affected by severe water scarcity (SWS) and the cereal price index.

    Fig. S4. Relationship between the proportion of the wheat growing area of the top ten wheat exporters affected by severe water scarcity (SWS) and the cereal price index.

    Fig. S5. Change in the wheat production area (WhA) affected by severe/extreme water scarcity for the major world areas and the five main producers.

    Fig. S6. Extent of wheat production affected by severe water scarcity for the harvest year (a) and the harvest and preceding year(s) (b) for the period from 1861 to 2100.

    Fig. S7. Area affected by severe water scarcity for two different definitions of wheat sensitive period.

    Fig. S8. Changes in median/maximum wheat area affected by severe and extreme water scarcity expressed as absolute change and change per 1°C global temperature increase for period 2041–2070.

    Fig. S9. Changes in median/maximum wheat area affected by severe and extreme water scarcity occurring expressed as absolute change and change per 1°C global temperature increase for period 2071–2099.

    Fig. S10. Comparison of area affected by severe and extreme water scarcity when future harvest is shifted by ±1 month.

    Movie S1. The annual extent of SWS events for the period 1901–2016 based on the CRU data and then projections for six GCM models under the RCP 2.6 scenario.

    Movie S2. The annual extent of SWS events for the period 1901–2016 based on the CRU data and then projections for six GCM models under the RCP 4.5 scenario.

    Movie S3. The annual extent of SWS events for the period 1901–2016 based on the CRU data and then projections for six GCM models under the RCP 8.5 scenario.

  • Supplementary Materials

    The PDF file includes:

    • Table S1. Definitions of SWS and EWS for the present study.
    • Table S2. Return period of SWS and EWS (as defined in table S1), calculated as the average ± SD across all grids included in the global wheat-growing area (fig. S1) for the year of harvest and as the cumulative value for the harvest year and the preceding year.
    • Table S3. A list of the CMIP5 GCMs used in this study (with brief descriptions).
    • Table S4. Symbols for weighting schemes used in the paper.
    • Fig. S1. Weights of grids as used in the study.
    • Fig. S2. Relationship between the proportion of the global arable land affected by severe water scarcity (SWS) and the cereal price index.
    • Fig. S3. Relationship between the proportion of the global wheat growing area affected by severe water scarcity (SWS) and the cereal price index.
    • Fig. S4. Relationship between the proportion of the wheat growing area of the top ten wheat exporters affected by severe water scarcity (SWS) and the cereal price index.
    • Fig. S5. Change in the wheat production area (WhA) affected by severe/extreme water scarcity for the major world areas and the five main producers.
    • Fig. S6. Extent of wheat production affected by severe water scarcity for the harvest year (a) and the harvest and preceding year(s) (b) for the period from 1861 to 2100.
    • Fig. S7. Area affected by severe water scarcity for two different definitions of wheat sensitive period.
    • Fig. S8. Changes in median/maximum wheat area affected by severe and extreme water scarcity expressed as absolute change and change per 1°C global temperature increase for period 2041–2070.
    • Fig. S9. Changes in median/maximum wheat area affected by severe and extreme water scarcity occurring expressed as absolute change and change per 1°C global temperature increase for period 2071–2099.
    • Fig. S10. Comparison of area affected by severe and extreme water scarcity when future harvest is shifted by ±1 month.
    • Legends for movies S1 to S3

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    Other Supplementary Material for this manuscript includes the following:

    • Movie S1 (.mp4 format). The annual extent of SWS events for the period 1901–2016 based on the CRU data and then projections for six GCM models under the RCP 2.6 scenario.
    • Movie S2 (.mp4 format). The annual extent of SWS events for the period 1901–2016 based on the CRU data and then projections for six GCM models under the RCP 4.5 scenario.
    • Movie S3 (.mp4 format). The annual extent of SWS events for the period 1901–2016 based on the CRU data and then projections for six GCM models under the RCP 8.5 scenario.

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