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Crop water origins and hydroclimate vulnerability of global croplands

Nature Sustainability volume 8, pages 1491–1504 (2025)Cite this article

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Abstract

Water availability critically influences crop phenology and agricultural productivity. Here we use satellite-derived water isotope observations and physical models to trace atmospheric moisture origins for major global rain-fed crops from 2003 to 2019, distinguishing between oceanic and terrestrial sources. Our analysis shows that the fraction of rainwater originating from land (f) varies both geographically and seasonally, with an important threshold at ~36%. Regions with higher f, that is, more dependent on land-originating water, are more prone to insufficient rainwater supply and soil moisture deficits during the main growing season. Crops in these regions show higher sensitivity to hydroclimate—with reduced productivity in lower-rainfall years—and a higher likelihood of drought. Notably, more than 40% of global maize and 60% of winter wheat is grown in regions where rainfall depends heavily on land-originating moisture (f ≥ 36%), underscoring the vulnerability of key staple crops to hydroclimate stress. Our results highlight the importance of managing local land moisture sources and reveal where targeted water management strategies would be most expected to enhance agricultural resilience.

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Fig. 1: Spatial and vertical patterns of δD and q during 2003–2019 from AIRS observations.
Fig. 2: Spatial patterns and annual cycle of the fraction of land-based moisture during 2003–2019 from AIRS observations.
Fig. 3: Sub-seasonal water dynamics encoded by atmospheric moisture sources f.
Fig. 4: f as an indicator for crop hydrological sensitivity.
Fig. 5: f as an indicator for crops sensitivity to droughts.

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Data availability

The cropland maps are available at https://glad.umd.edu/dataset/croplands. Crop calendar datasets are available at https://sage.nelson.wisc.edu/data-and-models/datasets/crop-calendar-dataset/. The TES products are available at https://asdc.larc.nasa.gov/project/TES/TL2HDOLN_6. The AIRS products are available via GES DISC at https://disc.gsfc.nasa.gov/datasets?keywords=tropess%20%2B%20airs%20%2B%20reanalysis&page=1 and EarthData at https://www.earthdata.nasa.gov/data/catalog?keyword=TROPESS%20AIRS-Aqua%20Reanalysis. The NIRv data calculated from the MCD43A4 are available at https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD43A4. The CSIF product is available via figshare at https://doi.org/10.6084/m9.figshare.6387494.v2 (ref. 76). The GLEAM datasets can be obtained from https://www.gleam.eu/. The ESI record is available at https://gee-community-catalog.org/projects/global_esi/?h=esi. Processed data are available via Zenodo at https://doi.org/10.5281/zenodo.15041289 (ref. 77).

Code availability

All analyses were conducted in Python (version 3.9,12); code is available via Github at https://github.com/yjiangc/water_isotope.

References

  1. The State of Food Security and Nutrition in the World 2023 (FAO, IFAD, UNICEF, WFP and WHO, 2023).

  2. Potapov, P. et al. Global maps of cropland extent and change show accelerated cropland expansion in the twenty-first century. Nat. Food 3, 19–28 (2022).

    Article  Google Scholar 

  3. Proctor, J., Rigden, A., Chan, D. & Huybers, P. More accurate specification of water supply shows its importance for global crop production. Nat. Food 3, 753–763 (2022).

    Article  Google Scholar 

  4. Brown, M. E. Famine Early Warning Systems and Remote Sensing Data (Springer Science & Business Media, 2008).

  5. Brubaker, K. L., Dirmeyer, P. A., Sudradjat, A., Levy, B. S. & Bernal, F. A 36-yr climatological description of the evaporative sources of warm-season precipitation in the Mississippi River basin. J. Hydrometeorol. 2, 537–557 (2001).

    Article  Google Scholar 

  6. Marvel, K. & Bonfils, C. Identifying external influences on global precipitation. Proc. Natl Acad. Sci. USA 110, 19301–19306 (2013).

    Article  CAS  Google Scholar 

  7. Shukla, J. & Mintz, Y. Influence of land-surface evapotranspiration on the Earth’s climate. Science 215, 1498–1501 (1982).

    Article  CAS  Google Scholar 

  8. Cui, J. et al. Global water availability boosted by vegetation-driven changes in atmospheric moisture transport. Nat. Geosci. 15, 982–988 (2022).

    Article  CAS  Google Scholar 

  9. Chahine, M. T. The hydrological cycle and its influence on climate. Nature 359, 373–380 (1992).

    Article  Google Scholar 

  10. Makarieva, A. M. & Gorshkov, V. G. Biotic pump of atmospheric moisture as driver of the hydrological cycle on land. Hydrol. Earth Syst. Sci. 11, 1013–1033 (2007).

    Article  Google Scholar 

  11. Wright, J. S. et al. Rainforest-initiated wet season onset over the southern Amazon. Proc. Natl Acad. Sci. USA 114, 8481–8486 (2017).

    Article  CAS  Google Scholar 

  12. Worden, S., Fu, R., Chakraborty, S., Liu, J. & Worden, J. Where does moisture come from over the Congo Basin?. J. Geophys. Res. Biogeosci. 126, e2020JG006024 (2021).

    Article  Google Scholar 

  13. Zhu, P. et al. The critical benefits of snowpack insulation and snowmelt for winter wheat productivity. Nat. Clim. Change 12, 485–490 (2022).

    Article  Google Scholar 

  14. Entekhabi, D., Rodriguez-Iturbe, I. & Bras, R. L. Variability in large-scale water balance with land surface-atmosphere interaction. J. Clim. 5, 798–813 (1992).

    Article  Google Scholar 

  15. Worden, J., Noone, D. & Bowman, K. Importance of rain evaporation and continental convection in the tropical water cycle. Nature 445, 528–532 (2007).

    Article  CAS  Google Scholar 

  16. Noone, D. Pairing measurements of the water vapor isotope ratio with humidity to deduce atmospheric moistening and dehydration in the tropical midtroposphere. J. Clim. 25, 4476–4494 (2012).

    Article  Google Scholar 

  17. Konecky, B. L. et al. Globally coherent water cycle response to temperature change during the past two millennia. Nat. Geosci. 16, 997–1004 (2023).

    Article  CAS  Google Scholar 

  18. Galewsky, J. et al. Stable isotopes in atmospheric water vapor and applications to the hydrologic cycle. Rev. Geophys. 54, 809–865 (2016).

    Article  Google Scholar 

  19. Yang, X. & Yao, T. Seasonality of moisture supplies to precipitation over the Third Pole: a stable water isotopic perspective. Sci. Rep. 10, 15020 (2020).

    Article  CAS  Google Scholar 

  20. Diao, C. Remote sensing phenological monitoring framework to characterize corn and soybean physiological growing stages. Remote Sens. Environ. 248, 111960 (2020).

    Article  Google Scholar 

  21. Brown, M., De Beurs, K. & Marshall, M. Global phenological response to climate change in crop areas using satellite remote sensing of vegetation, humidity and temperature over 26 years. Remote Sens. Environ. 126, 174–183 (2012).

    Article  Google Scholar 

  22. Sakamoto, T., Wardlow, B. D. & Gitelson, A. A. Detecting spatiotemporal changes of corn developmental stages in the US corn belt using MODIS WDRVI data. IEEE Trans. Geosci. Remote Sens. 49, 1926–1936 (2011).

    Article  Google Scholar 

  23. Lokupitiya, E. et al. Incorporation of crop phenology in Simple Biosphere Model (SiBcrop) to improve land-atmosphere carbon exchanges from croplands. Biogeosciences 6, 969–986 (2009).

    Article  CAS  Google Scholar 

  24. Bolton, D. K. & Friedl, M. A. Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics. Agric. For. Meteorol. 173, 74–84 (2013).

    Article  Google Scholar 

  25. Zhu, P. & Burney, J. Untangling irrigation effects on maize water and heat stress alleviation using satellite data. Hydrol. Earth Syst. Sci. 26, 827–840 (2022).

    Article  Google Scholar 

  26. Lobell, D. B., Schlenker, W. & Costa-Roberts, J. Climate trends and global crop production since 1980. Science 333, 616–620 (2011).

    Article  CAS  Google Scholar 

  27. Guan, K. et al. Photosynthetic seasonality of global tropical forests constrained by hydroclimate. Nat. Geosci. 8, 284–289 (2015).

    Article  CAS  Google Scholar 

  28. Worden, J. R. et al. Characterization and evaluation of AIRS-based estimates of the deuterium content of water vapor. Atmos. Meas. Tech. 12, 2331–2339 (2019).

    Article  CAS  Google Scholar 

  29. Bowman, K. TROPESS AIRS-Aqua, AIRS only, L2 deuterated water reanalysis processing, summary product V1. NASA Goddard Earth Science Data Information Service Center DAAC https://cmr.earthdata.nasa.gov/search/concepts/C2756762233-GES_DISC.html (2023).

  30. Worden, J. et al. Profiles of CH4, HDO, H2O and N2O with improved lower tropospheric vertical resolution from Aura TES radiances. Atmos. Meas. Tech. 5, 397–411 (2012).

    Article  CAS  Google Scholar 

  31. Lobell, D. B., Di Tommaso, S. & Burney, J. A. Globally ubiquitous negative effects of nitrogen dioxide on crop growth. Sci. Adv. 8, eabm9909 (2022).

    Article  CAS  Google Scholar 

  32. Guanter, L. et al. Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence. Proc. Natl Acad. Sci. USA 111, E1327–E1333 (2014).

    Article  CAS  Google Scholar 

  33. Anderson, M. C. et al. Evaluation of drought indices based on thermal remote sensing of evapotranspiration over the continental United States. J. Clim. 24, 2025–2044 (2011).

    Article  Google Scholar 

  34. Schumacher, D. L., Keune, J., Dirmeyer, P. & Miralles, D. G. Drought self-propagation in drylands due to land–atmosphere feedbacks. Nat. Geosci. 15, 262–268 (2022).

    Article  CAS  Google Scholar 

  35. Chaves, M. M. et al. How plants cope with water stress in the field? Photosynthesis and growth. Ann. Bot. 89, 907 (2002).

    Article  CAS  Google Scholar 

  36. Lobell, D. B. & Asseng, S. Comparing estimates of climate change impacts from process-based and statistical crop models. Environ. Res. Lett. 12, 015001 (2017).

    Article  Google Scholar 

  37. Buckley, T. N. How do stomata respond to water status?. N. Phytol. 224, 21–36 (2019).

    Article  Google Scholar 

  38. Ortiz-Bobea, A., Ault, T. R., Carrillo, C. M., Chambers, R. G. & Lobell, D. B. Anthropogenic climate change has slowed global agricultural productivity growth. Nat. Clim. Change 11, 306–312 (2021).

    Article  Google Scholar 

  39. Seneviratne, S. I. et al. Investigating soil moisture–climate interactions in a changing climate: a review. Earth-Sci. Rev. 99, 125–161 (2010).

    Article  CAS  Google Scholar 

  40. Zeppetello, L. V., Tétreault-Pinard, É., Battisti, D. & Baker, M. Identifying the sources of continental summertime temperature variance using a diagnostic model of land–atmosphere interactions. J. Clim. 33, 3547–3564 (2020).

  41. Webber, H. et al. Canopy temperature for simulation of heat stress in irrigated wheat in a semi-arid environment: a multi-model comparison. Field Crops Res. 202, 21–35 (2017).

    Article  Google Scholar 

  42. Koster, R. D. et al. Regions of strong coupling between soil moisture and precipitation. Science 305, 1138–1140 (2004).

    Article  CAS  Google Scholar 

  43. Risi, C., Noone, D., Frankenberg, C. & Worden, J. Role of continental recycling in intraseasonal variations of continental moisture as deduced from model simulations and water vapor isotopic measurements. Water Resour. Res. 49, 4136–4156 (2013).

    Article  CAS  Google Scholar 

  44. Jana, S., Rajagopalan, B., Alexander, M. A. & Ray, A. J. Understanding the dominant sources and tracks of moisture for summer rainfall in the southwest United States. J. Geophys. Res. Atmos. 123, 4850–4870 (2018).

    Article  Google Scholar 

  45. Cheng, T. F. & Lu, M. Global Lagrangian tracking of continental precipitation recycling, footprints, and cascades. J. Clim. 36, 1923–1941 (2023).

    Article  Google Scholar 

  46. Lu, Y., Harding, K. & Kueppers, L. Irrigation effects on land–atmosphere coupling strength in the United States. J. Clim. 30, 3671–3685 (2017).

    Article  Google Scholar 

  47. Nikiel, C. A. & Eltahir, E. A. Summer climate change in the Midwest and Great Plains due to agricultural development during the twentieth century. J. Clim. 32, 5583–5599 (2019).

    Article  Google Scholar 

  48. McDermid, S. et al. Irrigation in the Earth system. Nat. Rev. Earth Environ. 4, 435–453 (2023).

    Article  Google Scholar 

  49. Li, H. et al. Land–atmosphere feedbacks contribute to crop failure in global rainfed breadbaskets. npj Clim. Atmos. Sci. 6, 51 (2023).

    Article  CAS  Google Scholar 

  50. Vergopolan, N. et al. Field-scale soil moisture bridges the spatial-scale gap between drought monitoring and agricultural yields. Hydrol. Earth Syst. Sci. 25, 1827–1847 (2021).

    Article  Google Scholar 

  51. Lesk, C. et al. Stronger temperature–moisture couplings exacerbate the impact of climate warming on global crop yields. Nat. Food 2, 683–691 (2021).

    Article  Google Scholar 

  52. Seo, Y.-W. & Ha, K.-J. Changes in land-atmosphere coupling increase compound drought and heatwaves over northern East Asia. npj Clim. Atmos. Sci. 5, 100 (2022).

    Article  Google Scholar 

  53. Potapov, P. et al. Landsat analysis ready data for global land cover and land cover change mapping. Remote Sens. 12, 426 (2020).

    Article  Google Scholar 

  54. Sacks, W. J., Deryng, D., Foley, J. A. & Ramankutty, N. Crop planting dates: an analysis of global patterns. Glob. Ecol. Biogeogr. 19, 607–620 (2010).

    Article  Google Scholar 

  55. Becker-Reshef, I. et al. Crop type maps for operational global agricultural monitoring. Sci. Data 10, 172 (2023).

    Article  Google Scholar 

  56. Teluguntla, P. et al. NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security Support Analysis Data (GFSAD) Crop Mask 2010 Global 1 km V001. NASA Land Processes Distributed Active Archive Center https://doi.org/10.5067/MEASURES/GFSAD/GFSAD1KCM.001 (2016).

  57. Shi, M. et al. Amazonian terrestrial water balance inferred from satellite-observed water vapor isotopes. Nat. Commun. 13, 2686 (2022).

    Article  CAS  Google Scholar 

  58. Rozanski, K., Araguás-Araguás, L. & Gonfiantini, R. Isotopic patterns in modern global precipitation. Clim. Change Cont. Isot. Rec. 78, 1–36 (1993).

    Google Scholar 

  59. Jiang, Y. et al. Widespread increase of boreal summer dry season length over the Congo rainforest. Nat. Clim. Change 9, 617–622 (2019).

    Article  Google Scholar 

  60. Badgley, G., Field, C. B. & Berry, J. A. Canopy near-infrared reflectance and terrestrial photosynthesis. Sci. Adv. 3, e1602244 (2017).

    Article  Google Scholar 

  61. Schaaf, C. & Wang, Z. MODIS/terra+ aqua BRDF/albedo daily L3 global—500m V061. NASA EOSDIS Land Process https://doi.org/10.5067/MODIS/MCD43A4.061 (2021).

  62. Badgley, G., Anderegg, L. D., Berry, J. A. & Field, C. B. Terrestrial gross primary production: using NIRV to scale from site to globe. Glob. Change Biol. 25, 3731–3740 (2019).

    Article  Google Scholar 

  63. Zhang, Y., Joiner, J., Alemohammad, S. H., Zhou, S. & Gentine, P. A global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networks. Biogeosciences 15, 5779–5800 (2018).

    Article  CAS  Google Scholar 

  64. Sun, Y. et al. Overview of Solar-Induced chlorophyll Fluorescence (SIF) from the orbiting carbon observatory-2: retrieval, cross-mission comparison, and global monitoring for GPP. Remote Sens. Environ. 209, 808–823 (2018).

    Article  Google Scholar 

  65. Zeng, Y. et al. Combining near-infrared radiance of vegetation and fluorescence spectroscopy to detect effects of abiotic changes and stresses. Remote Sens. Environ. 270, 112856 (2022).

    Article  Google Scholar 

  66. Markus, Z. et al. GPCC Full Data Daily Version 2022 at 1.0°: Daily Land-Surface Precipitation from Rain-Gauges Built on GTS-Based and Historic Data (Global Precipitation Climatology Centre (GPCC) at Deutscher Wetterdienst, 2022); https://doi.org/10.5676/DWD_GPCC/FD_D_V2022_100

  67. Martens, B. et al. GLEAM v3: satellite-based land evaporation and root-zone soil moisture. Geosci. Model Dev. 10, 1903–1925 (2017).

    Article  Google Scholar 

  68. Miralles, D. G. et al. Global land-surface evaporation estimated from satellite-based observations. Hydrol. Earth Syst. Sci. 15, 453–469 (2011).

    Article  Google Scholar 

  69. Allen, R. G. Crop evapotranspiration. FAO Irrig. Drain. Pap. 56, 60–64 (1998).

    Google Scholar 

  70. Dai, A., Zhao, T. & Chen, J. Climate change and drought: a precipitation and evaporation perspective. Curr. Clim. Change Rep. 4, 301–312 (2018).

    Article  Google Scholar 

  71. Migliavacca, M. et al. Using digital repeat photography and eddy covariance data to model grassland phenology and photosynthetic CO2 uptake. Agric. For. Meteorol. 151, 1325–1337 (2011).

  72. Beck, P. S., Atzberger, C., Høgda, K. A., Johansen, B. & Skidmore, A. K. Improved monitoring of vegetation dynamics at very high latitudes: a new method using MODIS NDVI. Remote Sens. Environ. 100, 321–334 (2006).

    Article  Google Scholar 

  73. Vautard, R., Yiou, P. & Ghil, M. Singular-spectrum analysis: a toolkit for short, noisy chaotic signals. Phys. Nonlinear Phenom. 58, 95–126 (1992).

    Article  Google Scholar 

  74. Nguyen, H. et al. Climatology and variability of the evaporative stress index and its suitability as a tool to monitor Australian drought. J. Hydrometeorol. 21, 2309–2324 (2020).

    Article  Google Scholar 

  75. Nguyen, H., Thompson, A. & Costello, C. Impacts of historical droughts on maize and soybean production in the southeastern United States. Agric. Water Manage. 281, 108237 (2023).

    Article  Google Scholar 

  76. Zhang, Y. CSIF. figshare https://doi.org/10.6084/m9.figshare.6387494.v2 (2018).

  77. Jiang, Y. Analyses of water origins for hydroclimate vulnerability of global croplands. Zenodo https://doi.org/10.5281/zenodo.15041289 (2025).

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Acknowledgements

This study was supported by the US National Science Foundation (NSF INFEWS #1639318) and the Center for Global Transformation at UC San Diego’s School of Global Policy and Strategy.

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Authors and Affiliations

  1. School of Global Policy and Strategy, University of California San Diego, La Jolla, CA, USA

    Yan Jiang

  2. Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA

    Yan Jiang

  3. Doerr School of Sustainability, Stanford University, Stanford, CA, USA

    Jennifer A. Burney

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Y.J. and J.A.B. designed the research. Y.J. analysed the data. Both authors wrote and revised the paper.

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Correspondence to Yan Jiang.

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Extended data

Extended Data Fig. 1 Multiple year (2003-2019) mean annual vertical profiles of δD (‰).

Multiple year (2003-2019) mean annual vertical profiles of \({\boldsymbol{\delta }}{\boldsymbol{D}}\) (‰) over a, Eurasia, c, Asia, e, East Africa, g, Australia estimated from the AIRS. Multiple year mean \({\boldsymbol{q}}\) (g/kg) over b, Eurasia, d, Asia, f, East Africa, h, Australia. These complement Fig. 1 in the main manuscript to show the remaining regions of study not presented there.

Extended Data Fig. 2 Multiple year (2003-2019) mean seasonal cycle of precipitation (P, mm/day) and terrestrial moisture-contributed precipitation (P*f).

Multiple year (2003-2019) mean seasonal cycle of precipitation (P, mm/day) and terrestrial moisture-contributed precipitation (P*f) over a, North America, b, Eurasia, c, India, d, Asia, e, South America, f, Sahel, g, Southeastern Africa and h, Australia. Blue dots mark the peak rainfall amount. Shaded yellow areas present the general local growing season, where solid black lines are the start of the growing season and dashed black lines are the end of the growing season.

Extended Data Fig. 3 Multiple year (2003-2019) mean seasonal cycle of precipitation (P), potential evapotranspiration (PET), and RZSM anomaly.

Multiple year (2003-2019) mean seasonal cycle of precipitation (P), potential evapotranspiration (PET), and RZSM anomaly over a, North America, b, Eurasia, c, India, d, Asia, e, South America, f, Sahel, g, Southeastern Africa and h, Australia. Shades of each curve present the standard error of each observation. Shaded yellow areas present the general local growing season, where solid black lines are the start of the growing season and dashed black lines are the end of the growing season. The growing season is particularly long in Australia, for wheat is the main crop grown here (sowing starting in autumn and winter, harvesting occurring in spring and summer).

Extended Data Fig. 4 Subseasonal water dynamics indicated by the aridity index (AI).

a, Multiple-year mean seasonal cycles of precipitation (mm/day) and PET (mm/day) for the two groups seperated by the 0.65 AI threshold. b, The same as a, but for RZSM anomaly. Shaded areas present the standard error of each variable. c, Mean annual cycles of rainwater supply aggregated at different AI indices. d, The same as c, but for RZSM anomaly. The two solid lines indicate the general start and end of the merged crop growing season. The dashed line presents the general start of the harvest stage.

Extended Data Fig. 5 f as an indicator for different crops’ hydrological sensitivity.

a, Distribution of R2 of statistical model relating the maximum crop greenness (NIRvmax, a proxy for yield) to growing season hydrological parameters (rainwater supply and RZSM anomaly) for different major crops (Eq. 7). b, Mean R2 aggregated by different f values at 12% intervales for different crops. Shaded areas present the 25th and the 75th percentiles of R2.

Extended Data Fig. 6 Spatial patterns of R2 of the multiple linear regression model.

Spatial patterns of R2 of the multiple linear regression model for a, Maize. b, Rice. c, Soybean. d, Winter wheat. Grids marked with light orange are crop grids not included in the analysis. Girds marked with dots indicate statistical significance of the multiple linear regression model at the 90% confidence level and at least 90% accuracy using cross validation.

Extended Data Fig. 7 Global drought occurrence during 2003-2019.

a, Spatial pattern of the number of droughts during 2003-2019. Droughts are identified as a continuous period when the drought index (ESI) is under a threshold during the growing season (for example, ESI < -0.5; see Methods). b, Average length (black line) and strength (brown line) of droughts aggregated by different f values. Shaded areas present the 25th and the 75th percentiles of the variables.

Extended Data Fig. 8 Averaged year-to-year yield change in response to different drought categories.

Averaged year-to-year yield (measured by maximum greenness, NIRvmax) change in response to different drought categories for a, Maize. b, Rice. c, Soybean. d, Winter wheat during 2003-2019. The black whisker lines represent the 5-95th percentiles from 1000 bootstraps. Whisker lines not crossing 0 indicate that yield deviations during droughts are different than non-droughts at 90% confidence. Light purple and dark purple shades separate bars with f < 36% and f \(\ge\)36%. P-values shown are estimated by using a two-sided t-test comparing the yield deviations in drought vs non-drought years across croplands with f < 36% and f \(\ge\) 36% (that is, in panel a, Maize yields are statistically more sensitive to extremely dry conditions in areas with f \(\ge\) 36%).

Extended Data Fig. 9 Sensitivity of f to the choice of mixing value estimated by AIRS during 2003-2019.

Relative changes in f (%/σ) during the first pentad averaged over different continents when changing a single boundary condition by adding or subtracting standard deviation (denoted as ‘sd’ or ‘σ’) to the mean value for a, \({{\boldsymbol{\delta }}{\boldsymbol{D}}}_{{\boldsymbol{0}}}\) in the free troposphere (618-510hPa). b, \({{\boldsymbol{q}}}_{{\boldsymbol{0}}}\) in the free troposphere. c, \({{\boldsymbol{\delta }}{\boldsymbol{D}}}_{{\boldsymbol{F}}}\) as the surface boundary condition. The whiskers of each marker represent the standard deviation of the change within the aggregated region. GL—global (n = 1230), NA—North America (n = 228), SA—South America (n = 99), EU—Europe (n = 241), AF—Africa (n = 173), AS—Asia (n = 364), AU—Australia (n = 44).

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Jiang, Y., Burney, J.A. Crop water origins and hydroclimate vulnerability of global croplands. Nat Sustain 8, 1491–1504 (2025). https://doi.org/10.1038/s41893-025-01662-1

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