Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Large global-scale vegetation sensitivity to daily rainfall variability

Nature volume 636, pages 380–384 (2024)Cite this article

Subjects

Abstract

Rainfall events are globally becoming less frequent but more intense under a changing climate, thereby shifting climatic conditions for terrestrial vegetation independent of annual rainfall totals1,2,3. However, it remains uncertain how changes in daily rainfall variability are affecting global vegetation photosynthesis and growth3,4,5,6,7,8,9,10,11,12,13,14,15,16,17. Here we use several satellite-based vegetation indices and field observations indicative of photosynthesis and growth, and find that global annual-scale vegetation indices are sensitive to the daily frequency and intensity of rainfall, independent of the total amount of rainfall per year. Specifically, we find that satellite-based vegetation indices are sensitive to daily rainfall variability across 42 per cent of the vegetated land surfaces. On average, the sensitivity of vegetation to daily rainfall variability is almost as large (95 per cent) as the sensitivity of vegetation to annual rainfall totals. Moreover, we find that wet-day frequency and intensity are projected to change with similar magnitudes and spatial extents as annual rainfall changes. Overall, our findings suggest that daily rainfall variability and its trends are affecting global vegetation photosynthesis, with potential implications for the carbon cycle and food security.

Access through your institution
Buy or subscribe

This is a preview of subscription content, access via your institution

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Sensitivity of vegetation function.
Fig. 2: Example time series of dry savannah in Botswana.
Fig. 3: Vegetation indices in years with less frequent, more intense wet days tend to increase in drier ecosystems and decrease in wetter ecosystems.
Fig. 4: Daily rainfall variability trends are of similar absolute magnitude and spatial extent as shifts due to annual rainfall total, which consequently shifts annual mean vegetation function.

Similar content being viewed by others

Data availability

The data used and created in the study are available in two repositories. The processed data inputs are available on Zenodo at https://doi.org/10.5281/zenodo.10947071 (ref. 97). The output data and reduced-size example input data are available on Zenodo at https://doi.org/10.5281/zenodo.13551521 (ref. 98). All datasets used in the study are freely available and were obtained as follows. The MODIS NDVI product can be obtained from https://modis.gsfc.nasa.gov/data/dataprod/mod13.php. AVHRR NDVI can be obtained from https://www.ncei.noaa.gov/data/land-normalized-difference-vegetation-index/access/. GOME-2 SIF can be downloaded from https://daac.ornl.gov/SIF-ESDR/guides/MetOpA_GOME2_SIF.html. OCO-2 SIF can be obtained from https://disc.gsfc.nasa.gov/datasets/OCO2_L2_Lite_SIF_10r/summary. The MT-DCA vegetation optical depth dataset retrieved from SMAP is freely available at https://doi.org/10.5281/zenodo.5579549. AIRS humidity and air temperature data are available at https://airs.jpl.nasa.gov/data/get-data/standard-data/. The MODIS land surface temperature product can obtained from https://lpdaac.usgs.gov/products/myd11c2v006/. MERRA-2 precipitation data can be accessed at https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/data_access/. CERES radiation can be accessed at https://asdc.larc.nasa.gov/project/CERES/CER_SYN1deg-Day_Terra-Aqua-MODIS_Edition4A. SMAP soil moisture can be obtained from https://nsidc.org/data/smap/data. GPM precipitation outputs are available at https://gpm.nasa.gov/data/directory. CPC precipitation data are available at https://psl.noaa.gov/data/gridded/data.cpc.globalprecip.html. REGEN precipitation data are available at https://thredds-x.ipsl.fr/thredds/catalog/FROGs/REGEN_ALL_V1-2019/catalog.html. FLUXNET gross primary production observations can be obtained from https://fluxnet.org. CMIP6 rainfall projections can be obtained from https://cds.climate.copernicus.eu.

Code availability

The code is available on Zenodo at https://doi.org/10.5281/zenodo.13551521 (ref. 98) to both create the figures and conduct the analysis. This repository includes the main analysis outputs and example input data. The full processed data inputs are available on Zenodo at https://doi.org/10.5281/zenodo.10947071 (ref. 97).

References

  1. Pendergrass, A. G., Knutti, R., Lehner, F., Deser, C. & Sanderson, B. M. Precipitation variability increases in a warmer climate. Sci. Rep. 7, 17966 (2017).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  2. Pendergrass, A. G. & Knutti, R. The uneven nature of daily precipitation and its change. Geophys. Res. Lett. 45, 11,980–11,988 (2018).

    Article  Google Scholar 

  3. Feldman, A. F. et al. Plant responses to changing rainfall frequency and intensity. Nat. Rev. Earth Environ. https://doi.org/10.1038/s43017-024-00534-0 (2024).

    Article  Google Scholar 

  4. Thomey, M. L. et al. Effect of precipitation variability on net primary production and soil respiration in a Chihuahuan Desert grassland. Glob. Change Biol. 17, 1505–1515 (2011).

    Article  ADS  Google Scholar 

  5. Fay, P. A. et al. Relative effects of precipitation variability and warming on tallgrass prairie ecosystem function. Biogeosciences 8, 3053–3068 (2011).

    Article  ADS  CAS  Google Scholar 

  6. Liu, J. et al. Impact of temporal precipitation variability on ecosystem productivity. Wiley Interdiscip. Rev. Water 7, e1481 (2020).

    Article  Google Scholar 

  7. Sloat, L. L. et al. Increasing importance of precipitation variability on global livestock grazing lands. Nat. Clim. Change 8, 214–218 (2018).

    Article  ADS  Google Scholar 

  8. Ritter, F., Berkelhammer, M. & Garcia-Eidell, C. Distinct response of gross primary productivity in five terrestrial biomes to precipitation variability. Commun. Earth Environ. 1, 34 (2020).

    Article  ADS  Google Scholar 

  9. Guan, K. et al. Continental-scale impacts of intra-seasonal rainfall variability on simulated ecosystem responses in Africa. Biogeosciences 11, 6939–6954 (2014).

    Article  ADS  Google Scholar 

  10. Knapp, A. K. et al. Rainfall variability, carbon cycling, and plant species diversity in a mesic grassland. Science 298, 2202–2205 (2002).

    Article  ADS  CAS  PubMed  Google Scholar 

  11. Ross, I. et al. How do variations in the temporal distribution of rainfall events affect ecosystem fluxes in seasonally water-limited Northern Hemisphere shrublands and forests? Biogeosciences 9, 1007–1024 (2012).

    Article  ADS  Google Scholar 

  12. Su, J., Zhang, Y. & Xu, F. Divergent responses of grassland productivity and plant diversity to intra-annual precipitation variability across climate regions: a global synthesis. J. Ecol. 111, 1921–1934 (2023).

    Article  Google Scholar 

  13. Good, S. P. & Caylor, K. K. Climatological determinants of woody cover in Africa. Proc. Natl Acad. Sci. USA 108, 4902–4907 (2011).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  14. Zhang, F. et al. Precipitation temporal repackaging into fewer, larger storms delayed seasonal timing of peak photosynthesis in a semi‐arid grassland. Funct. Ecol. 36, 646–658 (2021).

    Article  Google Scholar 

  15. Xu, X., Medvigy, D. & Rodriguez-Iturbe, I. Relation between rainfall intensity and savanna tree abundance explained by water use strategies. Proc. Natl Acad. Sci USA. 112, 12992–12996 (2015).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  16. Case, M. F. & Staver, A. C. Soil texture mediates tree responses to rainfall intensity in African savannas. New Phytol. 219, 1363–1372 (2018).

    Article  PubMed  Google Scholar 

  17. Heisler-White, J. L., Blair, J. M., Kelly, E. F., Harmoney, K. & Knapp, A. K. Contingent productivity responses to more extreme rainfall regimes across a grassland biome. Glob. Change Biol. 15, 2894–2904 (2009).

    Article  ADS  Google Scholar 

  18. Jasechko, S. et al. Terrestrial water fluxes dominated by transpiration. Nature 496, 347–350 (2013).

    Article  ADS  CAS  PubMed  Google Scholar 

  19. Green, J. K. et al. Large influence of soil moisture on long-term terrestrial carbon uptake. Nature 565, 476–479 (2019).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  20. Rigden, A. J., Mueller, N. D., Holbrook, N. M., Pillai, N. & Huybers, P. Combined influence of soil moisture and atmospheric evaporative demand is important for accurately predicting US maize yields. Nat. Food 1, 127–133 (2020).

    Article  CAS  PubMed  Google Scholar 

  21. Wang, L. et al. Dryland productivity under a changing climate. Nat. Clim. Change 12, 981–994 (2022).

    Article  ADS  Google Scholar 

  22. Isbell, F. et al. High plant diversity is needed to maintain ecosystem services. Nature 477, 199–202 (2011).

    Article  ADS  CAS  PubMed  Google Scholar 

  23. Gherardi, L. A. & Sala, O. E. Effect of interannual precipitation variability on dryland productivity: a global synthesis. Glob. Change Biol. 25, 269–276 (2019).

    Article  ADS  Google Scholar 

  24. Nemani, R. R. et al. Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science 300, 1560–1563 (2003).

    Article  ADS  CAS  PubMed  Google Scholar 

  25. Maurer, G. E., Hallmark, A. J., Brown, R. F., Sala, O. E. & Collins, S. L. Sensitivity of primary production to precipitation across the United States. Ecol. Lett. 23, 527–536 (2020).

    Article  PubMed  Google Scholar 

  26. Sala, O. E., Parton, W. J., Joyce, L. A. & Lauenroth, W. K. Primary production of the central grassland region of the United States. Ecology 69, 40–45 (1988).

    Article  Google Scholar 

  27. Biederman, J. A. et al. CO2 exchange and evapotranspiration across dryland ecosystems of southwestern North America. Glob. Change Biol. 23, 4204–4221 (2017).

    Article  ADS  Google Scholar 

  28. Ukkola, A. M. et al. Annual precipitation explains variability in dryland vegetation greenness globally but not locally. Glob. Change Biol. 27, 4367–4380 (2021).

    Article  CAS  Google Scholar 

  29. Trugman, A. T., Medvigy, D., Mankin, J. S. & Anderegg, W. R. L. Soil moisture stress as a major driver of carbon cycle uncertainty. Geophys. Res. Lett. 45, 6495–6503 (2018).

    Article  ADS  Google Scholar 

  30. Denissen, J. M. C. et al. Widespread shift from ecosystem energy to water limitation with climate change. Nat. Clim. Change 12, 677–684 (2022).

    Article  ADS  Google Scholar 

  31. Zhu, Z. et al. Greening of the Earth and its drivers. Nat. Clim. Change 6, 791–795 (2016).

    Article  ADS  CAS  Google Scholar 

  32. Li, F. et al. Global water use efficiency saturation due to increased vapor pressure deficit. Science 381, 672–677 (2023).

    Article  ADS  CAS  PubMed  Google Scholar 

  33. Smith, W. K. et al. Large divergence of satellite and Earth system model estimates of global terrestrial CO2 fertilization. Nat. Clim. Change 6, 306–310 (2016).

    Article  ADS  Google Scholar 

  34. Trenberth, K. E. Changes in precipitation with climate change. Clim. Res. 47, 123–138 (2011).

    Article  Google Scholar 

  35. Lian, X., Zhao, W. & Gentine, P. Recent global decline in rainfall interception loss due to altered rainfall regimes. Nat. Commun. 13, 7642 (2022).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  36. Feldman, A. F., Short Gianotti, D. J., Trigo, I. F., Salvucci, G. D. & Entekhabi, D. Land–atmosphere drivers of landscape-scale plant water content loss. Geophys. Res. Lett. 47, e2020GL090331 (2020).

    Article  ADS  Google Scholar 

  37. Feldman, A. F. et al. Moisture pulse-reserve in the soil–plant continuum observed across biomes. Nat. Plants 4, 1026–1033 (2018).

    Article  PubMed  Google Scholar 

  38. Williams, C. A., Hanan, N., Scholes, R. J. & Kutsch, W. Complexity in water and carbon dioxide fluxes following rain pulses in an African savanna. Oecologia 161, 469–480 (2009).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  39. Humphrey, V. et al. Sensitivity of atmospheric CO2 growth rate to observed changes in terrestrial water storage. Nature 560, 628–631 (2018).

    Article  ADS  CAS  PubMed  Google Scholar 

  40. Sun, Y. et al. From remotely sensed solar-induced chlorophyll fluorescence to ecosystem structure, function, and service: Part I—Harnessing theory. Glob. Change Biol. 29, 2926–2952 (2023).

    Article  CAS  Google Scholar 

  41. Smith, W. K., Fox, A. M., MacBean, N., Moore, D. J. P. & Parazoo, N. C. Constraining estimates of terrestrial carbon uptake: new opportunities using long-term satellite observations and data assimilation. New Phytol. 225, 105–112 (2020).

    Article  PubMed  Google Scholar 

  42. Fatichi, S., Ivanov, V. Y. & Caporali, E. Investigating interannual variability of precipitation at the global scale: is there a connection with seasonality? J. Clim. 25, 5512–5523 (2012).

    Article  ADS  Google Scholar 

  43. Knapp, A. K. et al. Consequences of more extreme precipitation regimes for terrestrial ecosystems. Bioscience 58, 811–821 (2008).

    Article  Google Scholar 

  44. Green, J. K., Berry, J., Ciais, P., Zhang, Y. & Gentine, P. Amazon rainforest photosynthesis increases in response to atmospheric dryness. Sci. Adv. 6, eabb7232 (2020).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  45. Post, A. K. & Knapp, A. K. Plant growth and aboveground production respond differently to late-season deluges in a semi-arid grassland. Oecologia 191, 673–683 (2019).

    Article  ADS  PubMed  Google Scholar 

  46. Feldman, A. F., Chulakadabba, A., Short Gianotti, D. J. & Entekhabi, D. Landscape-scale plant water content and carbon flux behavior following moisture pulses: from dryland to mesic environments. Water Resour. Res. 57, e2020WR027592 (2021).

    Article  ADS  Google Scholar 

  47. Huxman, T. E. et al. Convergence across biomes to a common rain-use efficiency. Nature 429, 651–654 (2004).

    Article  ADS  CAS  PubMed  Google Scholar 

  48. Poulter, B. et al. Contribution of semi-arid ecosystems to interannual variability of the global carbon cycle. Nature 509, 600–603 (2014).

    Article  ADS  CAS  PubMed  Google Scholar 

  49. Ahlström, A. et al. The dominant role of semi-arid ecosystems in the trend and variability of the land CO2 sink. Science 348, 895–900 (2015).

    Article  ADS  PubMed  Google Scholar 

  50. Pendergrass, A. G. What precipitation is extreme? Science 360, 1072–1073 (2018).

    Article  ADS  CAS  PubMed  Google Scholar 

  51. Kannenberg, S. A., Bowling, D. R. & Anderegg, W. R. L. Hot moments in ecosystem fluxes: high GPP anomalies exert outsized influence on the carbon cycle and are differentially driven by moisture availability across biomes. Environ. Res. Lett. 15, 054004 (2020).

    Article  ADS  CAS  Google Scholar 

  52. Wainwright, C. M., Allan, R. P. & Black, E. Consistent trends in dry spell length in recent observations and future projections. Geophys. Res. Lett. 49, e2021GL097231 (2022).

  53. Piao, S. et al. Characteristics, drivers and feedbacks of global greening. Nat. Rev. Earth Environ. 1, 14–27 (2020).

    Article  ADS  Google Scholar 

  54. Higgins, S. I., Conradi, T. & Muhoko, E. Shifts in vegetation activity of terrestrial ecosystems attributable to climate trends. Nat. Geosci. 16, 147–153 (2023).

    Article  ADS  CAS  Google Scholar 

  55. Didan, K. MODIS/Terra Vegetation Indices 16-Day L3 Global 0.05 Deg CMG V061 EarthData https://doi.org/10.5067/MODIS/MOD13C1.061 (2021).

  56. Vermote, E. et al. NOAA Climate Data Record (CDR) of Normalized Difference Vegetation Index (NDVI), Version 4. AVH13C1 (NOAA National Centers for Environmental Information, 2014); https://doi.org/10.7289/V5PZ56R6.

  57. OCO-2-Science-Team, Gunson, M. & Eldering, A. OCO-2 Level 2 Bias-corrected Solar-induced Fluorescence and Other Select Fields from the IMAP-DOAS Algorithm Aggregated as Daily Files, Retrospective Processing V10r (Goddard Earth Sciences Data and Information Services Center, 2020).

  58. Joiner, J. et al. Global monitoring of terrestrial chlorophyll fluorescence from moderate-spectral-resolution near-infrared satellite measurements: methodology, simulations, and application to GOME-2. Atmos. Meas. Tech. 6, 2803–2823 (2013).

    Article  Google Scholar 

  59. Huffman, G., Stocker, E. F., Bolvin, D. T., Nelkin, E. J. & Tan, J. GPM IMERG Final Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V06 (Goddard Earth Sciences Data and Information Services Center, 2019).

  60. Xie, P. et al. A gauge-based analysis of daily precipitation over East Asia. J. Hydrometeorol. 8, 607–626 (2007).

    Article  ADS  Google Scholar 

  61. Contractor, S. et al. Rainfall Estimates on a Gridded Network (REGEN)—a global land-based gridded dataset of daily precipitation from 1950 to 2016. Hydrol. Earth Syst. Sci. 24, 919–943 (2020).

    Article  ADS  Google Scholar 

  62. Roca, R. et al. FROGS: a daily 1° × 1° gridded precipitation database of rain gauge, satellite and reanalysis products. Earth Syst. Sci. Data 11, 1017–1035 (2019).

    Article  ADS  Google Scholar 

  63. Reichle, R. H. et al. Land surface precipitation in MERRA-2. J. Clim. 30, 1643–1664 (2017).

    Article  ADS  Google Scholar 

  64. Eyring, V. et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958 (2016).

    Article  ADS  Google Scholar 

  65. Copernicus Climate Change Service Climate Data Store. CMIP6 climate projections. Climate Data Store https://doi.org/10.24381/cds.c866074c (2021).

  66. Joiner, J. et al. Estimation of terrestrial global gross primary production (GPP) with satellite data-driven models and eddy covariance flux data. Remote Sens. 10, 1346 (2018).

    Article  ADS  Google Scholar 

  67. NASA/LARC/SD/ASDC. CERES and GEO-Enhanced TOA, Within-Atmosphere and Surface Fluxes, Clouds and Aerosols Daily Terra-Aqua Edition4A [Data set]. EarthData https://doi.org/10.5067/Terra+Aqua/CERES/SYN1degDay_L3.004A (2017).

  68. Gelaro, R. et al. The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). J. Clim. 30, 5419–5454 (2017).

    Article  ADS  Google Scholar 

  69. Wan, Z., Hook, S. & Hulley, G. MYD11C2 MODIS/Aqua Land Surface Temperature/Emissivity 8-Day L3 Global 0.05 Deg CMG V006. EarthData https://doi.org/10.5067/MODIS/MYD11C2.006 (2015).

  70. O’Neill, P. E. et al. SMAP Enhanced L3 Radiometer Global Daily 9 km EASE-Grid Soil Moisture, Version 3 (NASA National Snow and Ice Data Center, 2019).

  71. Harmonized World Soil Database v2.0 (Food and Agriculture Organization of the United Nations, 2024); https://www.fao.org/soils-portal/data-hub/soil-maps-and-databases/harmonized-world-soil-database-v20/en/.

  72. Fan, Y., Miguez-Macho, G., Jobbágy, E. G., Jackson, R. B. & Otero-Casal, C. Hydrologic regulation of plant rooting depth. Proc. Natl Acad. Sci. USA 114, 10572–10577 (2017).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  73. Feldman, A. F., Konings, A., Piles, M. & Entekhabi, D. The Multi-Temporal Dual Channel Algorithm (MT-DCA) (Version 5) [Data set]. Zenodo https://doi.org/10.5281/zenodo.5619583 (2021).

  74. Kim, S. Ancillary Data Report: Landcover Classification JPL D-53057 (Jet Propulsion Laboratory, California Institute of Technology, 2013).

  75. Sala, O. E. & Lauenroth, W. K. Small rainfall events: an ecological role in semiarid regions. Oecologia 53, 301–304 (1982).

    Article  ADS  CAS  PubMed  Google Scholar 

  76. Giorgi, F., Raffaele, F. & Coppola, E. The response of precipitation characteristics to global warming from climate projections. Earth Syst. Dyn. 10, 73–89 (2019).

    Article  ADS  Google Scholar 

  77. Grömping, U. Estimators of relative importance in linear regression based on variance decomposition. Am. Stat. 61, 139–147 (2007).

    Article  MathSciNet  Google Scholar 

  78. Pastorello, G. et al. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. Sci. Data 7, 225 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  79. Ploton, P. et al. Spatial validation reveals poor predictive performance of large-scale ecological mapping models. Nat. Commun. 11, 4540 (2020).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  80. Lewińska, K. E. et al. Beyond “greening” and “browning”: trends in grassland ground cover fractions across Eurasia that account for spatial and temporal autocorrelation. Glob. Change Biol. 29, 4620–4637 (2023).

    Article  Google Scholar 

  81. Ludwig, M., Moreno-Martinez, A., Hölzel, N., Pebesma, E. & Meyer, H. Assessing and improving the transferability of current global spatial prediction models. Glob. Ecol. Biogeogr. 32, 356–368 (2023).

    Article  Google Scholar 

  82. James, G. M., Witten, D., Hastie, T. & Tibshirani, R. An Introduction to Statistical Learning: With Applications in R (Springer, 2014).

  83. Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).

    MathSciNet  Google Scholar 

  84. Brunsdon, C., Fotheringham, A. S. & Charlton, M. E. Geographically weighted regression: a method for exploring spatial nonstationarity. Geogr. Anal. 28, 281–298 (1996).

    Article  Google Scholar 

  85. Li, Y. et al. Widespread spring phenology effects on drought recovery of Northern Hemisphere ecosystems. Nat. Clim. Change 13, 182–188 (2023).

    Article  ADS  Google Scholar 

  86. Greene, W. H. Econometric Analysis (Prentice Hall, 2003).

  87. Griffin-Nolan, R. J., Slette, I. J. & Knapp, A. K. Deconstructing precipitation variability: rainfall event size and timing uniquely alter ecosystem dynamics. J. Ecol. https://doi.org/10.1080/10643389.2012.728825 (2021).

  88. Green, J. K. et al. Regionally strong feedbacks between the atmosphere and terrestrial biosphere. Nat. Geosci. 10, 410–414 (2017).

    Article  ADS  CAS  Google Scholar 

  89. Madani, N., Kimball, J. S., Jones, L. A., Parazoo, N. C. & Guan, K. Global analysis of bioclimatic controls on ecosystem productivity using satellite observations of solar-induced chlorophyll fluorescence. Remote Sens. 9, 530 (2017).

    Article  ADS  Google Scholar 

  90. Lundberg, S. M. & Lee, S.-I. A unified approach to interpreting model predictions. In 31st Conference on Neural Information Processing System (NeurIPS, 2017); https://papers.nips.cc/paper_files/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html.

  91. Andrews, T. et al. On the effect of historical SST patterns on radiative feedback. J. Geophys. Res. Atmos. 127, e2022JD036675 (2022).

  92. Bueso, D. et al. Soil and vegetation water content identify the main terrestrial ecosystem changes. Natl. Sci. Rev. 10, nwad026 (2023).

  93. Ives, A. R. et al. Statistical inference for trends in spatiotemporal data. Remote Sens. Environ. 266, 112678 (2021).

    Article  Google Scholar 

  94. Cortés, J. et al. Where are global vegetation greening and browning trends significant? Geophys. Res. Lett. 48, 1–9 (2021).

    Article  Google Scholar 

  95. Cortés, J., Mahecha, M., Reichstein, M. & Brenning, A. Accounting for multiple testing in the analysis of spatio-temporal environmental data. Environ. Ecol. Stat. 27, 293–318 (2020).

    Article  Google Scholar 

  96. Keenan, T. F. et al. Increase in forest water-use efficiency as atmospheric carbon dioxide concentrations rise. Nature 499, 324–327 (2013).

    Article  ADS  CAS  PubMed  Google Scholar 

  97. Feldman, A. Feldman et al. Global one degree datasets. Zenodo https://doi.org/10.5281/zenodo.10947071 (2024).

  98. Feldman, A. et al. Feldman et al. 2024 “Large global scale vegetation sensitivity to daily rainfall variability”. Zenodo https://doi.org/10.5281/zenodo.13551521 (2024).

Download references

Acknowledgements

A.F.F. was supported by an appointment to the NASA Postdoctoral Program at the NASA Goddard Space Flight Center, administered by Oak Ridge Associated Universities under contract with NASA. A.F.F. was also partly supported by a NASA Terrestrial Ecology Program scoping study for dryland ecosystems. A.G.K. was supported by the Alfred P. Sloan Foundation and by NSF DEB 1942133. W.K.S. and B.P. acknowledge support from the NASA Carbon Cycle Science grant number 80NSSC23K0109. M.A. acknowledges Swiss National Science Foundation grant number 206603. L.W. acknowledges partial support from the US National Science Foundation (DEB-2307257 and DEB-2406931). USDA is an equal-opportunity employer and provider. We acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modelling, coordinated and promoted CMIP6. We thank the climate modelling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the data and providing access, and the multiple funding agencies that support CMIP6 and ESGF. This work used eddy covariance data acquired and shared by the FLUXNET community, including these networks: AmeriFlux, AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet-Canada, GreenGrass, ICOS, KoFlux, LBA, NECC, OzFlux-TERN, TCOS-Siberia and USCCC. The FLUXNET eddy covariance data processing and harmonization was carried out by the ICOS Ecosystem Thematic Center, AmeriFlux Management Project and Fluxdata project of FLUXNET, with the support of CDIAC, and the OzFlux, ChinaFlux and AsiaFlux offices.

Author information

Authors and Affiliations

  1. Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA

    Andrew F. Feldman & Benjamin Poulter

  2. Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA

    Andrew F. Feldman

  3. Department of Earth System Science, Stanford University, Stanford, CA, USA

    Alexandra G. Konings

  4. Department of Earth and Environmental Engineering, Columbia University, New York, NY, USA

    Pierre Gentine & Mitra Cattry

  5. Department of Earth and Environmental Sciences, Indiana University Indianapolis, Indianapolis, IN, USA

    Lixin Wang

  6. School of Natural Resources and the Environment, University of Arizona, Tucson, AZ, USA

    William K. Smith

  7. Agricultural Research Service, US Department of Agriculture, Tucson, AZ, USA

    Joel A. Biederman

  8. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA

    Abhishek Chatterjee

  9. Atmospheric Chemistry and Dynamics Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA

    Joanna Joiner

Authors
  1. Andrew F. Feldman
    View author publications

    Search author on:PubMed Google Scholar

  2. Alexandra G. Konings
    View author publications

    Search author on:PubMed Google Scholar

  3. Pierre Gentine
    View author publications

    Search author on:PubMed Google Scholar

  4. Mitra Cattry
    View author publications

    Search author on:PubMed Google Scholar

  5. Lixin Wang
    View author publications

    Search author on:PubMed Google Scholar

  6. William K. Smith
    View author publications

    Search author on:PubMed Google Scholar

  7. Joel A. Biederman
    View author publications

    Search author on:PubMed Google Scholar

  8. Abhishek Chatterjee
    View author publications

    Search author on:PubMed Google Scholar

  9. Joanna Joiner
    View author publications

    Search author on:PubMed Google Scholar

  10. Benjamin Poulter
    View author publications

    Search author on:PubMed Google Scholar

Contributions

A.F.F. conceived the study with input from B.P. A.F.F. conducted the analysis and wrote the initial paper. A.G.K., P.G., J.J., A.C. and B.P. provided guidance on the methods throughout the analysis. M.A., L.W., W.K.S. and J.A.B. provided guidance in part on methods and mainly on the interpretation of results. All authors contributed substantial revisions to the text and figures.

Corresponding author

Correspondence to Andrew F. Feldman.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature thanks Richard Allan and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

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

Extended data figures and tables

Extended Data Fig. 1 Across historical simulations and projections, rainfall is becoming less frequent, but more intense.

Historical and projected rainfall trends of (a, b) wet day frequency, and (c, d) wet day intensity, and (e, f) annual rainfall total using CMIP6 historical simulations (1940–2020) and CMIP6 RCP8.5 models (2020–2099).

Extended Data Fig. 2 Across observation-based rainfall datasets, rainfall is becoming less frequent, but more intense.

Rainfall trends of (a, b) wet day frequency, and (c, d) wet day intensity, and (e, f) annual rainfall total using CPC gridded observations (1980–2020) and MERRA2 model reanalysis (1980–2020).

Extended Data Fig. 3 Wet day frequency and annual rainfall amount have enough uncorrelated information to be included together and partitioned in a regression.

(a) Variance inflation factor of wet day frequency and intensity. Higher values (especially much over 5) indicate multi-collinearity with annual rainfall mean and thus higher uncertainty partitioning effects between the variables. (b) Interannual coefficient of variation computed as the interannual standard deviation divided by interannual mean for each respective rainfall characteristic. Similar magnitudes between variables suggest variability of one variable is not dominating the regression.

Extended Data Fig. 4 Mechanistic explanation of vegetation sensitivity to more intense, less frequent wet days across the global mean rainfall gradient (in Fig. 3).

(a) Effect of soil, plant, and atmospheric factors on vegetation sensitivity to more intense, less frequent wet days. ** indicates significance (p < 0.05). Positive values suggest that increasing the respective driver promotes higher vegetation behavior in years with more intense, less frequent wet days. Computation of individual mechanistic factors is discussed in the Methods and their relationships with mean annual rainfall are shown in Fig. S15. Mean VPD, Soil Moisture, and Solar Radiation “Sensitivity” refers to the response of these climate variables to more intense, less frequent wet days (see text and Methods). (b) Variance explained of factors in (a).

Extended Data Fig. 5 Empirically estimated vegetation trends due to daily rainfall variability trends.

Spatial maps of empirically estimated vegetation trends due to trends in daily rainfall variability from (a) CPC, (b) MERRA2, (c) CMIP6 historical simulations, and (d) CMIP6 RCP8.5 projections.

Supplementary information

Supplementary Information

This file includes one notes section that covers several details about robustness of the results in the main text. Supplementary Figs. 1–20 provide robustness tests and context to Figs. 1, 3 and 4. Supplementary Tables 1 and 2 include details about the CMIP6 models and FLUXNET sites.

Peer Review File

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Feldman, A.F., Konings, A.G., Gentine, P. et al. Large global-scale vegetation sensitivity to daily rainfall variability. Nature 636, 380–384 (2024). https://doi.org/10.1038/s41586-024-08232-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41586-024-08232-z

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing