Drought impacts on terrestrial primary production underestimated by satellite monitoring

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Satellite retrievals of information about the Earth’s surface are widely used to monitor global terrestrial photosynthesis and primary production and to examine the ecological impacts of droughts. Methods for estimating photosynthesis from space commonly combine information on vegetation greenness, incoming radiation, temperature and atmospheric demand for water (vapour-pressure deficit), but do not account for the direct effects of low soil moisture. They instead rely on vapour-pressure deficit as a proxy for dryness, despite widespread evidence that soil moisture deficits have a direct impact on vegetation, independent of vapour-pressure deficit. Here, we use a globally distributed measurement network to assess the effect of soil moisture on photosynthesis, and identify a common bias in an ensemble of satellite-based estimates of photosynthesis that is governed by the magnitude of soil moisture effects on photosynthetic light-use efficiency. We develop methods to account for the influence of soil moisture and estimate that soil moisture effects reduce global annual photosynthesis by ~15%, increase interannual variability by more than 100% across 25% of the global vegetated land surface, and amplify the impacts of extreme events on primary production. These results demonstrate the importance of soil moisture effects for monitoring carbon-cycle variability and drought impacts on vegetation productivity from space.

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Fig. 1: Bias in GPP estimates.
Fig. 2: Effect of soil moisture limitation on GPP.
Fig. 3: Amplification of GPP IAV due to the effects of soil moisture.
Fig. 4: Contributing regions where the effects of soil moisture increase and reduce global GPP IAV.
Fig. 5: Soil moisture effects on GPP extreme events.

Code availability

Reproducible code is available via github (https://github.com/stineb/soilm_global) and published on Zenodo at https://doi.org/10.5281/zenodo.2543324.

Data availability

P-model outputs from site-scale and global simulations are available on Zenodo at: https://doi.org/10.5281/zenodo.1423484.


  1. 1.

    Ciais, P. et al. Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Nature 437, 529–533 (2005).

  2. 2.

    Zhang, Y. et al. Development of a coupled carbon and water model for estimating global gross primary productivity and evapotranspiration based on eddy flux and remote sensing data. Agric. For. Meteorol. 223, 116–131 (2016).

  3. 3.

    Running, S. W. et al. A continuous satellite-derived measure of global terrestrial primary production. BioScience 54, 547–560 (2004).

  4. 4.

    Zhao, M. & Running, S. W. Drought-induced reduction in global terrestrial net primary production from 2000 through 2009. Science 329, 940–943 (2010).

  5. 5.

    Zhang, Y. et al. Canopy and physiological controls of GPP during drought and heat wave. Geophys. Res. Lett. 43, 3325–3333 (2016).

  6. 6.

    Schwalm, C. R. et al. Global patterns of drought recovery. Nature 548, 202–205 (2017).

  7. 7.

    Ballantyne, A. et al. Accelerating net terrestrial carbon uptake during the warming hiatus due to reduced respiration. Nat. Clim. Change 7, 148–152 (2017).

  8. 8.

    Liu, Z. et al. Precipitation thresholds regulate net carbon exchange at the continental scale. Nat. Commun. 9, 3596 (2018).

  9. 9.

    Monteith, J. L. Solar radiation and productivity in tropical ecosystems. J. Appl. Ecol. 9, 747–766 (1972).

  10. 10.

    Cowan, I. R. & Farquhar, G. D. Stomatal function in relation to leaf metabolism and environment. Symp. Soc. Exp. Biol. 31, 471–505 (1977).

  11. 11.

    Martínez-Vilalta, J., Poyatos, R., Aguadé, D., Retana, J. & Mencuccini, M. A new look at water transport regulation in plants. New Phytol. 204, 105–115 (2014).

  12. 12.

    Sperry, J. S. et al. Predicting stomatal responses to the environment from the optimization of photosynthetic gain and hydraulic cost. Plant Cell Environ. 40, 816–830 (2017).

  13. 13.

    Stocker, B. sofun v1.1.0 (2018); https://doi.org/10.5281/zenodo.1213758.

  14. 14.

    Ruddell, B. L. & Kumar, P. Ecohydrologic process networks: 1. Identification. Water Resour. Res. https://doi.org/10.1029/2008WR007279 (2009).

  15. 15.

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

  16. 16.

    Goerner, A., Reichstein, M. & Rambal, S. Tracking seasonal drought effects on ecosystem light use efficiency with satellite-based PRI in a Mediterranean forest. Remote. Sens. Environ. 113, 1101–1111 (2009).

  17. 17.

    Novick, K. A. et al. The increasing importance of atmospheric demand for ecosystem water and carbon fluxes. Nat. Clim. Change 6, 1023–1027 (2016).

  18. 18.

    Sulman, B. N. et al. High atmospheric demand for water can limit forest carbon uptake and transpiration as severely as dry soil. Geophys. Res. Lett. 43, 2016GL069416 (2016).

  19. 19.

    Rogers, A. et al. A roadmap for improving the representation of photosynthesis in Earth system models. New Phytol. 213, 22–42 (2017).

  20. 20.

    Stocker, B. D. et al. Quantifying soil moisture impacts on light use efficiency across biomes. New Phytol. 218, 1430–1449 (2018).

  21. 21.

    Egea, G., Verhoef, A. & Vidale, P. L. Towards an improved and more flexible representation of water stress in coupled photosynthesis–stomatal conductance models. Agric. For. Meteorol. 151, 1370–1384 (2011).

  22. 22.

    Wang, H. et al. Towards a universal model for carbon dioxide uptake by plants. Nat. Plants 3, 734–741 (2017).

  23. 23.

    Davis, T. W. et al. Simple process-led algorithms for simulating habitats (SPLASH v.1.0): robust indices of radiation, evapotranspiration and plant-available moisture. Geosci. Model Dev. 10, 689–708 (2017).

  24. 24.

    Zhang, Y. et al. A global moderate resolution dataset of gross primary production of vegetation for 2000–2016. Sci. Data 4, 170165 (2017).

  25. 25.

    Jiang, C. & Ryu, Y. Multi-scale evaluation of global gross primary productivity and evapotranspiration products derived from breathing earth system simulator (BESS). Remote. Sens. Environ. 186, 528–547 (2016).

  26. 26.

    Hengl, T. et al. SoilGrids1km–global soil information based on automated mapping. PLoS ONE 9, e105992 (2014).

  27. 27.

    Zhu, Z. et al. Global data sets of vegetation leaf area index (LAI)3g and fraction of photosynthetically active radiation (FPAR)3g derived from global inventory modeling and mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) for the Period 1981 to 2011. Remote Sensing 5, 927–948 (2013).

  28. 28.

    Reichstein, M. et al. Climate extremes and the carbon cycle. Nature 500, 287–295 (2013).

  29. 29.

    Zscheischler, J. et al. A few extreme events dominate global interannual variability in gross primary production. Environ. Res. Lett. 9, 035001 (2014).

  30. 30.

    Turner, D. P. et al. Site-level evaluation of satellite-based global terrestrial gross primary production and net primary production monitoring. Glob. Change Biol. 11, 666–684 (2005).

  31. 31.

    Leuning, R., Cleugh, H. A., Zegelin, S. J. & Hughes, D. Carbon and water fluxes over a temperate Eucalyptus forest and a tropical wet/dry savanna in Australia: measurements and comparison with MODIS remote sensing estimates. Agric. For. Meteorol. 129, 151–173 (2005).

  32. 32.

    Mu, Q. et al. Evaluating water stress controls on primary production in biogeochemical and remote sensing based models. J. Geophys. Res. Biogeosci. 112, G01002 (2007).

  33. 33.

    Sims, D. A., Brzostek, E. R., Rahman, A. F., Dragoni, D. & Phillips, R. P. An improved approach for remotely sensing water stress impacts on forest C uptake. Glob. Change Biol. 20, 2856–2866 (2014).

  34. 34.

    Migliavacca, M. et al. Seasonal and interannual patterns of carbon and water fluxes of a poplar plantation under peculiar eco-climatic conditions. Agric. For. Meteorol. 149, 1460–1476 (2009).

  35. 35.

    Koirala, S. et al. Global distribution of groundwater-vegetation spatial covariation. Geophys. Res. Lett. 44, 4134–4142 (2017).

  36. 36.

    Sperry, J. S. & Love, D. M. What plant hydraulics can tell us about responses to climate-change droughts. New Phytol. 207, 14–27 (2015).

  37. 37.

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

  38. 38.

    Quéré, C. L. et al. Global carbon budget 2017. earth system science. Data 10, 405–448 (2018).

  39. 39.

    Xiao, X. et al. Satellite-based modeling of gross primary production in an evergreen needleleaf forest. Remote Sens. Environ. 89, 519–534 (2004).

  40. 40.

    Mahadevan, P. et al. A satellite-based biosphere parameterization for net ecosystem CO2 exchange: Vegetation Photosynthesis and Respiration Model (VPRM). Glob. Biogeochem. Cycles https://doi.org/10.1029/2006GB002735 (2008).

  41. 41.

    Gamon, J. A., Peñuelas, J. & Field, C. B. A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sens. Environ. 41, 35–44 (1992).

  42. 42.

    Penuelas, J., Filella, I. & Gamon, J. A. Assessment of photosynthetic radiation-use efficiency with spectral reflectance. New Phytol. 131, 291–296 (1995).

  43. 43.

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

  44. 44.

    Porcar-Castell, A. et al. Linking chlorophyll a fluorescence to photosynthesis for remote sensing applications: mechanisms and challenges. J. Exp. Bot. 65, 4065 (2014).

  45. 45.

    Vicca, S. et al. Remotely-sensed detection of effects of extreme droughts on gross primary production. Sci. Rep. 6, 28269 (2016).

  46. 46.

    He, M. et al. Satellite detection of soil moisture related water stress impacts on ecosystem productivity using the MODIS-based photochemical reflectance index. Remote Sens. Environ. 186, 173–183 (2016).

  47. 47.

    Dorigo, W. et al. ESA CCI soil moisture for improved earth system understanding: state-of-the art and future directions. Remote Sens. Environ. 203, 185–215 (2017).

  48. 48.

    Mohanty, B. P., Cosh, M. H., Lakshmi, V. & Montzka, C. Soil moisture remote sensing: state-of-the-science. Vadose Zone J. 16, 0 (2017).

  49. 49.

    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).

  50. 50.

    Keeling, R. F. et al. Atmospheric evidence for a global secular increase in carbon isotopic discrimination of land photosynthesis. Proc. Natl Acad. Sci. USA 114, 10361–10366 (2017).

  51. 51.

    Sheffield, J., Wood, E. F. & Roderick, M. L. Little change in global drought over the past 60 years. Nature 491, 435–438 (2012).

  52. 52.

    Berg, A. & Sheffield, J. Climate change and drought: the soil moisture perspective. Curr. Clim. Change Rep. https://doi.org/10.1007/s40641-018-0095-0 (2018).

  53. 53.

    Hao, Z., AghaKouchak, A., Nakhjiri, N. & Farahmand, A. Global integrated drought monitoring and prediction system. Sci. Data 1, 140001 (2014).

  54. 54.

    Jung, M. et al. Compensatory water effects link yearly global land CO2 sink changes to temperature. Nature 541, 516–520 (2017).

  55. 55.

    Ahlström, A. et al. The dominant role of semi-arid ecosystems in the trend and variability of the land CO2 sink. Science 348, 895–899 (2015).

  56. 56.

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

  57. 57.

    Zscheischler, J., Mahecha, M. D., Harmeling, S. & Reichstein, M. Detection and attribution of large spatiotemporal extreme events in earth observation data. Ecol. Inform. 15, 66–73 (2013).

  58. 58.

    Gillespie, C. S. Fitting heavy tailed distributions: the powerlaw package. J. Stat. Softw. https://doi.org/10.18637/jss.v064.i02 (2015).

  59. 59.

    Reichstein, M. et al. On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm. Glob. Change Biol. 11, 1424–1439 (2005).

  60. 60.

    Gorelick, N. et al. Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).

  61. 61.

    Hufkens, K. khufkens/gee_subset: Google Earth Engine Subset Script & Library (Zenodo, 2017); https://doi.org/10.5281/zenodo.833789

  62. 62.

    Farquhar, G. D., von Caemmerer, S. & Berry, J. A. A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta 149, 78–90 (1980).

  63. 63.

    Xiao, X. et al. Observation of flooding and rice transplanting of paddy rice fields at the site to landscape scales in China using VEGETATION sensor data. Int. J. Remote Sens. 23, 3009–3022 (2002).

  64. 64.

    Prentice, I. C., Dong, N., Gleason, S. M., Maire, V. & Wright, I. J. Balancing the costs of carbon gain and water transport: testing a new theoretical framework for plant functional ecology. Ecol. Lett. 17, 82–91 (2014).

  65. 65.

    Priestley, C. H. B. & Taylor, R. J. On the assessment of surface heat flux and evaporation using large-scale parameters. Mon. Weather Rev. 100, 81–92 (1972).

  66. 66.

    Stocker, B. sofun: v1.1. 0. (Zenodo, 2018); https://doi.org/10.5281/zenodo.1213758

  67. 67.

    Weedon, G. P. et al. The WFDEI meteorological forcing data set: WATCH Forcing Data methodology applied to ERA-Interim reanalysis data. Water Resour. Res. 50, 7505–7514 (2014).

  68. 68.

    Buchsbaum, B. R. neuroim: Data structures and handling for neuroimaging data. v.0.0.6 (2016).

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We thank C. Jian and Y. Ryu for sharing BESS model outputs, and Y. Zhang for sharing VPM model outputs and all for supporting the use and interpretation of their data. We thank Z. Zhu for sharing updated FPAR3g data, D. Sandoval Calle for preparing soil data, and W. Han, R. Thomas and T. Davis for their contributions to the development of the P-model. B.D.S. was funded by ERC Marie Sklodowska-Curie fellowship H2020-MSCA-IF-2015, project FIBER, grant no. 701329. J.P. was funded by ERC Synergy grant no. ERC-SyG-2013-610028 IMBALANCE-P, the Spanish Government grant no. CGL2016-79835-P and the Catalan Government grant SGR-2017-1005. T.F.K was supported by the NASA Terrestrial Ecology Program IDS Award No. NNH17AE86I. This work is a contribution to the AXA Chair Programme in Biosphere and Climate Impacts and the Imperial College initiative on Grand Challenges in Ecosystems and the Environment (I.C.P.). S.I.S acknowledges support from the EU FP7 programme, through the ERC DROUGHT-HEAT project (contract no. 617518). 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 processing and harmonization of the FLUXNET eddy covariance data was carried out by the European Fluxes Database Cluster, AmeriFlux Management Project and the Fluxdata project of FLUXNET, with the support of the CDIAC and ICOS Ecosystem Thematic Center, and the OzFlux, ChinaFlux and AsiaFlux offices.

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B.D.S. designed the research in collaboration with all co-authors, conducted the analysis and designed the figures. J.Z. and T.F.K. assisted in the analysis. Co-authors contributed to interpreting the results and writing the manuscript.

Correspondence to Benjamin D. Stocker.

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Supplementary Description, Supplementary Figures 1–13 and Tables 1,2

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Stocker, B.D., Zscheischler, J., Keenan, T.F. et al. Drought impacts on terrestrial primary production underestimated by satellite monitoring. Nat. Geosci. 12, 264–270 (2019) doi:10.1038/s41561-019-0318-6

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