Plant water storage is fundamental to the functioning of terrestrial ecosystems by participating in plant metabolism, nutrient and sugar transport, and maintenance of the integrity of the hydraulic system of the plant. However, a global view of the size and dynamics of the water pools stored in plant tissues is still lacking. Here, we report global patterns of seasonal variations in ecosystem-scale plant water storage and their relationship with leaf phenology, based on space-borne measurements of L-band vegetation optical depth. We find that seasonal variations in plant water storage are highly synchronous with leaf phenology for the boreal and temperate forests, but asynchronous for the tropical woodlands, where the seasonal development of plant water storage lags behind leaf area by up to 180 days. Contrasting patterns of the time lag between plant water storage and terrestrial groundwater storage are also evident in these ecosystems. A comparison of the water cycle components in seasonally dry tropical woodlands highlights the buffering effect of plant water storage on the seasonal dynamics of water supply and demand. Our results offer insights into ecosystem-scale plant water relations globally and provide a basis for an improved parameterization of eco-hydrological and Earth system models.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Additional information

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


  1. 1.

    Street, H. E. & Cockburn, W. Plant Metabolism 2nd edn (Elsevier, Oxford, 1972).

  2. 2.

    Sack, L. et al. Plant hydraulics as a central hub integrating plant and ecosystem function: meeting report for ‘Emerging Frontiers in Plant Hydraulics’ (Washington, DC, May 2015). Plant Cell Environ. 39, 2085–2094 (2016).

  3. 3.

    Lin, Y.-S. et al. Optimal stomatal behaviour around the world. Nat. Clim. Change 5, 459–464 (2015).

  4. 4.

    De Boer, A. H. & Volkov, V. Logistics of water and salt transport through the plant: structure and functioning of the xylem. Plant Cell Environ. 26, 87–101 (2003).

  5. 5.

    Chave, J. et al. Towards a worldwide wood economics spectrum. Ecol. Lett. 12, 351–366 (2009).

  6. 6.

    Hartzell, S., Bartlett, M. S. & Porporato, A. The role of plant water storage and hydraulic strategies in relation to soil moisture availability. Plant Soil 419, 503–521 (2017).

  7. 7.

    Matheny, A. M. et al. Observations of stem water storage in trees of opposing hydraulic strategies. Ecosphere 6, 1–13 (2015).

  8. 8.

    Jones, H. G. Monitoring plant and soil water status: established and novel methods revisited and their relevance to studies of drought tolerance. J. Exp. Bot. 58, 119–130 (2007).

  9. 9.

    Wullschleger, S. D., Meinzer, F. C. & Vertessy, R. A. A review of whole-plant water use studies in tree. Tree Physiol. 18, 499–512 (1998).

  10. 10.

    Goldstein, G. et al. Stem water storage and diurnal patterns of water use in tropical forest canopy trees. Plant Cell Environ. 21, 397–406 (1998).

  11. 11.

    Jackson, T. J. & Schmugge, T. J. Vegetation effects on the microwave emission of soils. Remote Sens. Environ. 36, 203–212 (1991).

  12. 12.

    Van de Griend, A. A. & Wigneron, J. P. The b-factor as a function of frequency and canopy type at H-polarization. IEEE Trans. Geosci. Remote Sens. 42, 786–794 (2004).

  13. 13.

    Guglielmetti, M. et al. Measured microwave radiative transfer properties of a deciduous forest canopy. Remote Sens. Environ. 109, 523–532 (2007).

  14. 14.

    Santi, E., Paloscia, S., Pampaloni, P. & Pettinato, S. Ground-based microwave investigations of forest plots in Italy. IEEE Trans. Geosci. Remote Sens. 47, 3016–3025 (2009).

  15. 15.

    Ferrazzoli, P., Guerriero, L. & Wigneron, J. P. Simulating L-band emission of forests in view of future satellite applications. IEEE Trans. Geosci. Remote Sens. 40, 2700–2708 (2002).

  16. 16.

    Sternberg, M. & Shoshany, M. Aboveground biomass allocation and water content relationships in Mediterranean trees and shrubs in two climatological regions in Israel. Plant Ecol. 157, 173–181 (2001).

  17. 17.

    Jones, M. O., Jones, L. A., Kimball, J. S. & McDonald, K. C. Satellite passive microwave remote sensing for monitoring global land surface phenology. Remote Sens. Environ. 115, 1102–1114 (2011).

  18. 18.

    Tian, F. et al. Remote sensing of vegetation dynamics in drylands: evaluating vegetation optical depth (VOD) using AVHRR NDVI and in situ green biomass data over West African Sahel. Remote Sens. Environ. 177, 265–276 (2016).

  19. 19.

    Momen, M. et al. Interacting effects of leaf water potential and biomass on vegetation optical depth. J. Geophys. Res. Biogeosci. 122, 3031–3046 (2017).

  20. 20.

    Konings, A. G. & Gentine, P. Global variations in ecosystem-scale isohydricity. Glob. Change Biol. 23, 891–905 (2016).

  21. 21.

    Konings, A. G., Williams, A. P. & Gentine, P. Sensitivity of grassland productivity to aridity controlled by stomatal and xylem regulation. Nat. Geosci. 10, 284–288 (2017).

  22. 22.

    Kerr, Y. H. et al. The SMOS mission: new tool for monitoring key elements of the global water cycle. Proc. IEEE 98, 666–687 (2010).

  23. 23.

    Fernandez-Moran, R. et al. SMOS-IC: an alternative SMOS soil moisture and vegetation optical depth product. Remote Sens. 9, 457 (2017).

  24. 24.

    Wigneron, J. P. et al. L-band microwave emission of the biosphere (L-MEB) model: description and calibration against experimental data sets over crop fields. Remote Sens. Environ. 107, 639–655 (2007).

  25. 25.

    Oliva, R. et al. SMOS radio frequency interference scenario: status and actions taken to improve the RFI environment in the 1400–1427-MHz passive band. IEEE Trans. Geosci. Remote Sens. 50, 1427–1439 (2012).

  26. 26.

    Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on Earth. BioScience 51, 933–938 (2001).

  27. 27.

    Kobayashi, T., Tsend-Ayush, J. & Tateishi, R. A new tree cover percentage map in Eurasia at 500 m resolution using MODIS data. Remote Sens. 6, 209–232 (2014).

  28. 28.

    Fluet-Chouinard, E., Lehner, B., Rebelo, L.-M., Papa, F. & Hamilton, S. K. Development of a global inundation map at high spatial resolution from topographic downscaling of coarse-scale remote sensing data. Remote Sens. Environ. 158, 348–361 (2015).

  29. 29.

    Rahmoune, R. et al. SMOS retrieval results over forests: comparisons with independent measurements. IEEE J. Sel. Topics Appl. Earth Observ. 7, 3858–3866 (2014).

  30. 30.

    Konings, A. G., Piles, M., Das, N. & Entekhabi, D. L-band vegetation optical depth and effective scattering albedo estimation from SMAP. Remote Sens. Environ. 198, 460–470 (2017).

  31. 31.

    Morris, H. et al. A global analysis of parenchyma tissue fractions in secondary xylem of seed plants. New Phytol. 209, 1553–1565 (2016).

  32. 32.

    Meinzer, F. C., Johnson, D. M., Lachenbruch, B., McCulloh, K. A. & Woodruff, D. R. Xylem hydraulic safety margins in woody plants: coordination of stomatal control of xylem tension with hydraulic capacitance. Funct. Ecol. 23, 922–930 (2009).

  33. 33.

    Tyree, M. T. & Sperry, J. S. Vulnerability of xylem to cavitation and embolism. Annu. Rev. Plant Physiol. Plant Mol. Biol. 40, 19–36 (1989).

  34. 34.

    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).

  35. 35.

    Du, J. et al. A global satellite environmental data record derived from AMSR-E and AMSR2 microwave Earth observations. Earth Syst. Sci. Data 9, 791–808 (2017).

  36. 36.

    Tapley, B. D., Bettadpur, S., Ries, J. C., Thompson, P. F. & Watkins, M. M. GRACE measurements of mass variability in the Earth System. Science 305, 503–505 (2004).

  37. 37.

    Pokhrel, Y. N., Fan, Y., Miguez‐Macho, G., Yeh, P. J. F. & Han, S. C. The role of groundwater in the Amazon water cycle: 3. Influence on terrestrial water storage computations and comparison with GRACE. J. Geophys. Res. Atmos. 118, 3233–3244 (2013).

  38. 38.

    Köcher, P., Horna, V. & Leuschner, C. Stem water storage in five coexisting temperate broad-leaved tree species: significance, temporal dynamics and dependence on tree functional traits. Tree Physiol. 33, 817–832 (2013).

  39. 39.

    Ryan, C. M., Williams, M., Grace, J., Woollen, E. & Lehmann, C. E. R. Pre-rain green-up is ubiquitous across southern tropical Africa: implications for temporal niche separation and model representation. New Phytol. 213, 625–633 (2017).

  40. 40.

    Guan, K. et al. Terrestrial hydrological controls on land surface phenology of African savannas and woodlands. J. Geophys. Res. Biogeosci. 119, 1652–1669 (2014).

  41. 41.

    Reich, P. B. Phenology of tropical forests: patterns, causes, and consequences. Can. J. Bot. 73, 164–174 (1995).

  42. 42.

    Williams, R. J., Myers, B. A., Muller, W. J., Duff, G. A. & Eamus, D. Leaf phenology of woody species in a north Australian tropical savanna. Ecology 78, 2542–2558 (1997).

  43. 43.

    Borchert, R. Soil and stem water storage determine phenology and distribution of tropical dry forest trees. Ecology 75, 1437–1449 (1994).

  44. 44.

    Chapotin, S. M., Razanameharizaka, J. H. & Holbrook, N. M. Baobab trees (Adansonia) in Madagascar use stored water to flush new leaves but not to support stomatal opening before the rainy season. New Phytol. 169, 549–559 (2006).

  45. 45.

    Trouet, V., Mukelabai, M., Verheyden, A. & Beeckman, H. Cambial growth season of brevi-deciduous Brachystegia spiciformis trees from South Central Africa restricted to less than four months. PLoS ONE 7, e47364 (2012).

  46. 46.

    Elifuraha, E., Nöjd, P. & Mbwambo, L. Short term growth of Miombo tree species at Kitulangalo. Work. Pap. Finn. For. Res. Inst. 98, 37–45 (2008).

  47. 47.

    Domec, J. C. et al. Hydraulic redistribution of soil water by roots affects whole‐stand evapotranspiration and net ecosystem carbon exchange. New Phytol. 187, 171–183 (2010).

  48. 48.

    Yu, K. & D’Odorico, P. Hydraulic lift as a determinant of tree–grass coexistence on savannas. New Phytol. 207, 1038–1051 (2015).

  49. 49.

    Huang, C.-W. et al. The effect of plant water storage on water fluxes within the coupled soil–plant system. New Phytol. 213, 1093–1106 (2017).

  50. 50.

    Kerr, Y. H. et al. The SMOS soil moisture retrieval algorithm. IEEE Trans. Geosci. Remote Sens. 50, 1384–1403 (2012).

  51. 51.

    Myneni, R. B. et al. Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data. Remote Sens. Environ. 83, 214–231 (2002).

  52. 52.

    Strassberg, G., Scanlon, B. R. & Chambers, D. Evaluation of groundwater storage monitoring with the GRACE satellite: case study of the High Plains aquifer, central United States. Water Resour. Res. 45, W05410 (2009).

  53. 53.

    Reager, J. T. et al. A decade of sea level rise slowed by climate-driven hydrology. Science 351, 699–703 (2016).

  54. 54.

    Sakumura, C., Bettadpur, S. & Bruinsma, S. Ensemble prediction and intercomparison analysis of GRACE time-variable gravity field models. Geophys. Res. Lett. 41, 1389–1397 (2014).

  55. 55.

    Brandt, M. et al. Human population growth offsets climate-driven increase in woody vegetation in sub-Saharan Africa. Nat. Ecol. Evol. 1, 0081 (2017).

  56. 56.

    Brandt, M. et al. Woody plant cover estimation in drylands from Earth Observation based seasonal metrics. Remote Sens. Environ. 172, 28–38 (2016).

  57. 57.

    Simard, M., Pinto, N., Fisher, J. B. & Baccini, A. Mapping forest canopy height globally with spaceborne LIDAR. J. Geophys. Res. Biogeosci. 116, G04021 (2011).

  58. 58.

    Huffman, G. J. et al. The TRMM multisatellite precipitation analysis (TMPA): quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J. Hydrometeorol. 8, 38–55 (2007).

  59. 59.

    Martens, B. et al. GLEAMv3: satellite-based land evaporation and root-zone soil moisture. Geosci. Model Dev. 10, 1903–1925 (2017).

  60. 60.

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

  61. 61.

    Garonna, I. et al. Strong contribution of autumn phenology to changes in satellite-derived growing season length estimates across Europe (1982–2011). Glob. Change Biol. 20, 3457–3470 (2014).

  62. 62.

    Parrens, M. et al. Global-scale surface roughness effects at L-band as estimated from SMOS observations. Remote Sens. Environ. 181, 122–136 (2016).

  63. 63.

    Fernandez-Moran, R. et al. A new calibration of the effective scattering albedo and soil roughness parameters in the SMOS SM retrieval algorithm. Int. J. Appl. Earth Obs. Geoinf. 62, 27–38 (2017).

  64. 64.

    Vittucci, C., Ferrazzoli, P., Richaume, P. & Kerr, Y. Effective scattering albedo of forests retrieved by SMOS and a three-parameter algorithm. IEEE Geosci. Remote Sens. Lett. 14, 2260–2264 (2017).

  65. 65.

    Parrens, M. et al. Estimation of the L-band effective scattering albedo of tropical forests using SMOS observations. IEEE Geosci. Remote Sens. Lett. 14, 1223–1227 (2017).

  66. 66.

    Du, J., Kimball, J. S. & Jones, L. A. Passive microwave remote sensing of soil moisture based on dynamic vegetation scattering properties for AMSR-E. IEEE Trans. Geosci. Remote Sens. 54, 597–608 (2016).

  67. 67.

    Wigneron, J. P. et al. Modelling the passive microwave signature from land surfaces: a review of recent results and application to the L-band SMOS & SMAP soil moisture retrieval algorithms. Remote Sens. Environ. 192, 238–262 (2017).

  68. 68.

    Holmes, T. R. H., Jackson, T. J., Reichle, R. H. & Basara, J. B. An assessment of surface soil temperature products from numerical weather prediction models using ground‐based measurements. Water Resour. Res. 48, W02531 (2012).

  69. 69.

    Albergel, C. et al. Soil temperature at ECMWF: an assessment using ground‐based observations. J. Geophys. Res. Atmos. 120, 1361–1373 (2015).

Download references


We thank E. Fluet-Chouinard, F. Aires and C. Prigent for providing the global inundation map. This work was funded by the CNES through the Science Terre Environment et Climat programme, European Space Agency, Support to Science Element programme and SMOS Expert Support Laboratory. F.T. and R.F. acknowledge funding from the Danish Council for Independent Research (grant ID: DFF–6111-00258). F.T. is also the recipient of the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement (project number 746347). P.C. and J.P. acknowledge funding from the European Research Council Synergy grant ERC-2013-SyG-610028 IMBALANCE-P. T.T. was funded by the Swedish National Space Board (Dnr: 95/16). P.C. acknowledges additional support from the ANR ICONV CLAND grant. J.C. has benefited from ‘Investissement d’Avenir’ grants managed by the French Agence Nationale de la Recherche (CEBA (ref. ANR-10-LABX-25-01) and TULIP (ref. ANR-10-LABX-0041)), as well as TOSCA funds from the CNES.

Author information


  1. Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden

    • Feng Tian
    • , Torbern Tagesson
    •  & Jonas Ardö
  2. Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark

    • Feng Tian
    • , Anders Ræbild
    • , Xiaoye Tong
    • , Martin Brandt
    • , Torbern Tagesson
    • , Wenmin Zhang
    •  & Rasmus Fensholt
  3. INRA, UMR 1391 ISPA, Bordeaux, France

    • Jean-Pierre Wigneron
    • , Jérôme Ogée
    •  & Amen Al-Yaari
  4. Laboratoire des Sciences du Climat et de l’Environnement, CEA/CNRS/UVSQ, Gif-sur-Yvette, France

    • Philippe Ciais
  5. UMR 5174 Laboratoire Evolution et Diversité Biologique, Université Paul Sabatier, CNRS, Toulouse, France

    • Jérôme Chave
  6. CSIC, Global Ecology Unit CREAF-CSIC-UAB, Bellaterra, Spain

    • Josep Peñuelas
  7. CREAF, Cerdanyola del Vallès, Spain

    • Josep Peñuelas
  8. Bordeaux Sciences Agro, UMR 1391 INRA-ISPA, Gradignan, France

    • Jean-Christophe Domec
  9. CESBIO, Université de Toulouse, CNES/CNRS/IRD/UPS, Toulouse, France

    • Arnaud Mialon
    • , Nemesio Rodriguez-Fernandez
    •  & Yann Kerr
  10. Department of Earth and Environment, Boston University, Boston, MA, USA

    • Chi Chen
    •  & Ranga B. Myneni


  1. Search for Feng Tian in:

  2. Search for Jean-Pierre Wigneron in:

  3. Search for Philippe Ciais in:

  4. Search for Jérôme Chave in:

  5. Search for Jérôme Ogée in:

  6. Search for Josep Peñuelas in:

  7. Search for Anders Ræbild in:

  8. Search for Jean-Christophe Domec in:

  9. Search for Xiaoye Tong in:

  10. Search for Martin Brandt in:

  11. Search for Arnaud Mialon in:

  12. Search for Nemesio Rodriguez-Fernandez in:

  13. Search for Torbern Tagesson in:

  14. Search for Amen Al-Yaari in:

  15. Search for Yann Kerr in:

  16. Search for Chi Chen in:

  17. Search for Ranga B. Myneni in:

  18. Search for Wenmin Zhang in:

  19. Search for Jonas Ardö in:

  20. Search for Rasmus Fensholt in:


F.T., J.-P.W., M.B. and R.F. designed the study with inputs from P.C., J.C., J.P. and A.R. J.-P.W., Y.K., A.M., N.R.-F. and A.A.-Y. prepared the SMOS-IC data and performed the sensitivity analyses. C.C. and R.B.M. prepared the MODIS LAI data. F.T. performed the data analyses. The results were interpreted by J.-P.W., A.R., J.C., F.T., P.C., J.P., J.O., J.-C.D., X.T., N.R.-F., A.M., T.T., A.A.-Y. and R.F. F.T. drafted the manuscript with editing by P.C., J.P., J.O. and J.C., and contributions from all co-authors.

Competing interests

The authors declare no competing interests.

Corresponding authors

Correspondence to Feng Tian or Jean-Pierre Wigneron.

Supplementary Information

  1. Supplementary Information

    Supplementary figures and references.

  2. Reporting Summary

About this article

Publication history