The global distribution and dynamics of surface soil moisture

Journal name:
Nature Geoscience
Year published:
Published online


Surface soil moisture has a direct impact on food security, human health and ecosystem function. It also plays a key role in the climate system, and the development and persistence of extreme weather events such as droughts, floods and heatwaves. However, sparse and uneven observations have made it difficult to quantify the global distribution and dynamics of surface soil moisture. Here we introduce a metric of soil moisture memory and use a full year of global observations from NASA’s Soil Moisture Active Passive mission to show that surface soil moisture—a storage believed to make up less than 0.001% of the global freshwater budget by volume, and equivalent to an, on average, 8-mm thin layer of water covering all land surfaces—plays a significant role in the water cycle. Specifically, we find that surface soil moisture retains a median 14% of precipitation falling on land after three days. Furthermore, the retained fraction of the surface soil moisture storage after three days is highest over arid regions, and in regions where drainage to groundwater storage is lowest. We conclude that lower groundwater storage in these regions is due not only to lower precipitation, but also to the complex partitioning of the water cycle by the surface soil moisture storage layer at the land surface.

At a glance


  1. The stored precipitation fraction.
    Figure 1: The stored precipitation fraction.

    Top left and right: the stored precipitation fraction FP(f) is a dimensionless measure of the degree to which a soil layer (of depth Δz) retains precipitation inputs (P) over a given timescale 1/f, given losses due to evapotranspiration (E), drainage (D) and runoff (R). Bottom left and right: two example soil moisture time series from in situ observations30, sampled at two different sampling frequencies — f = 12 d−1 (black, crosses) and f = 1/3 d−1 (red, circles). Inset: the sum of positive increments in the soil water time series, for the two different sampling frequencies.

  2. Global distribution and memory of surface soil moisture.
    Figure 2: Global distribution and memory of surface soil moisture.

    a, Global map of annual mean SSM (1 April 2015–31 March 2016), with PDF (inset) and zonal mean (right panel). Marker sizes in all zonal plots are proportional to zonal land area; shaded region shows ±1 standard deviation. White regions in map are missing or masked (see Methods). b, The same as in a, except for FP(1/3), and the marker colours in the zonal plot indicate the zonal mean SSM. c, The same as in a, for mean daily precipitation.

  3. Global relations between stored precipitation fraction and soil moisture content and texture.
    Figure 3: Global relations between stored precipitation fraction and soil moisture content and texture.

    a, Global relation between FP(1/3) and annual mean SSM, estimated using one year of observations (1 April 2015–31 March 2016). Boxplots show the median (red horizontal line), 25th and 75th percentiles (top and bottom of the grey shaded box, respectively), and maximum and minimum observed values (edges of the top and bottom whiskers, respectively). b, Global relation between FP(1/3) and sand fraction. c, Global relation between FP(1/3) and clay fraction.


  1. Oki, T., Entekhabi, D. & Harrold, T. I. in The State of the Planet: Frontiers and Challenges in Geophysics Vol. 19 (eds Stephan, R., Spark, J. & Hawkesworth, C. J.) 225237 (Geophysical Monograph 150, American Geophysical Union, 2004).
  2. Manzoni, S., Schimel, J. P. & Porporato, A. Responses of soil microbial communities to water stress: results from a meta-analysis. Ecology 93, 930938 (2012).
  3. D’Odorico, P., Laio, F., Porporato, A. & Rodriguez-Iturbe, I. Hydrologic controls on soil carbon and nitrogen cycles. II. A case study. Adv. Water Resour. 26, 5970 (2003).
  4. Botter, G., Peratoner, F., Porporato, A., Rodriguez-Iturbe, I. & Rinaldo, A. Signatures of large-scale soil moisture dynamics on streamflow statistics across US climate regimes. Wat. Resour. Res. 43, W11413 (2007).
  5. Rosenzweig, C., Tubiello, F. N., Goldberg, R., Mills, E. & Bloomfield, J. Increased crop damage in the US from excess precipitation under climate change. Glob. Environ. Change 12, 197202 (2002).
  6. Fécan, F., Marticorena, B. & Bergametti, G. Parametrization of the increase of the aeolian erosion threshold wind friction velocity due to soil moisture for arid and semi-arid areas. Ann. Geophys. 17, 149157 (1999).
  7. Bomblies, A. & Eltahir, E. A. B. Assessment of the impact of climate shifts on malaria transmission in the Sahel. EcoHealth 6, 426437 (2010).
  8. Hirschi, M., Mueller, B., Dorigo, W. & Seneviratne, S. I. Using remotely sensed soil moisture for land–atmosphere coupling diagnostics: the role of surface vs. root-zone soil moisture variability. Remote Sens. Environ. 154, 246252 (2014).
  9. Qiu, J., Crow, W. T. & Nearing, G. S. The impact of vertical measurement depth on the information content of soil moisture for latent heat flux estimation. J. Hydrometeorol. 19, 24192430 (2016).
  10. Entekhabi, D., Rodriguez-Iturbe, I. & Bras, R. L. Variability in large-scale water balance with land surface-atmosphere interaction. J. Clim. 5, 798813 (1992).
  11. Entin, J. K. et al. Temporal and spatial scales of observed soil moisture variations in the extratropics. J. Geophys. Res. 105, 1186511877 (2000).
  12. Seneviratne, S. I. et al. Soil moisture memory in AGCM simulations: analysis of global land–atmosphere coupling experiment (GLACE) data. J. Hydrometeorol. 7, 10901112 (2006).
  13. Katul, G. G. et al. On the spectrum of soil moisture from hourly to interannual scales. Wat. Resour. Res. 43, W05428 (2007).
  14. Orth, R. & Seneviratne, S. I. Analysis of soil moisture memory from observations in Europe. J. Geophys. Res. 117, D15115 (2012).
  15. Koster, R. D. & Suarez, M. J. Soil moisture memory in climate models. J. Hydrometeorol. 2, 558570 (2001).
  16. Koster, R. D. et al. On the nature of soil moisture in land surface models. J. Clim. 22, 43224335 (2009).
  17. Kerr, Y. H. et al. The SMOS mission: new tool for monitoring key elements of the global water cycle. Proc. IEEE 98, 666687 (2010).
  18. Njoku, E. G., Jackson, T. J., Lakshmi, V., Chan, T. K. & Nghiem, S. V. Soil moisture retrieval from AMSR-E. IEEE Trans. Geosci. Remote Sensing 41, 215229 (2003).
  19. Figa-Saldaña, J. et al. The advanced scatterometer (ASCAT) on the meteorological operational (MetOp) platform: a follow on for European wind scatterometers. Can. J. Remote Sensing 28, 404412 (2002).
  20. Entekhabi, D. et al. The Soil Moisture Active Passive (SMAP) mission. Proc. IEEE 98, 704716 (2010).
  21. Albergel, C. et al. Monitoring multi-decadal satellite earth observation of soil moisture products through land surface reanalyses. Remote Sens. Environ. 138, 7789 (2013).
  22. McColl, K. A., Entekhabi, D. & Piles, M. Uncertainty analysis of soil moisture and vegetation indices using Aquarius scatterometer observations. IEEE Trans. Geosci. Remote Sensing 52, 42594272 (2014).
  23. Koster, R. D., Brocca, L., Crow, W. T., Burgin, M. S. & De Lannoy, G. J. M. Precipitation estimation using L-band and C-band soil moisture retrievals: precipitation estimation from soil moisture retrievals. Wat. Resour. Res. 52, 72137225 (2016).
  24. Döll, P. & Fiedler, K. Global-scale modeling of groundwater recharge. Hydrol. Earth Syst. Sci. 12, 863885 (2008).
  25. Gleeson, T., Befus, K. M., Jasechko, S., Luijendijk, E. & Cardenas, M. B. The global volume and distribution of modern groundwater. Nat. Geosci. 9, 161167 (2015).
  26. Koster, R. D. & Suarez, M. J. Impact of land surface initialization on seasonal precipitation and temperature prediction. J. Hydrometeorol. 4, 408423 (2003).
  27. Gentine, P., Holtslag, A. A. M., D’Andrea, F. & Ek, M. Surface and atmospheric controls on the onset of moist convection over land. J. Hydrometeorol. 14, 14431462 (2013).
  28. Koster, R. D. et al. The second phase of the global land–atmosphere coupling experiment: soil moisture contributions to subseasonal forecast skill. J. Hydrometeorol. 12, 805822 (2011).
  29. Tuttle, S. & Salvucci, G. Empirical evidence of contrasting soil moisture-precipitation feedbacks across the United States. Science 352, 825828 (2016).
  30. Cuenca, R. H., Hagimoto, Y. & Moghaddam, M. Three-and-a-half decades of progress in monitoring soils and soil hydraulic properties. Proc. Environ. Sci. 19, 384393 (2013).
  31. Monerris, A. et al. IEEE MicroRad 2006 171175 (IEEE, 2006).
  32. O’ Neill, P. E., Chan, S., Njoku, E. G., Jackson, T. & Bindlish, R. SMAP L3 Radiometer Global Daily 36km EASE-Grid Soil Moisture, Version 2 (NASA National Snow and Ice Data Center Distributed Active Archive Center, 2016).
  33. Chan, S. K. et al. Assessment of the SMAP passive soil moisture product. IEEE Trans. Geosci. Remote Sensing 54, 49945007 (2016).
  34. Huffman, G. GPM Level 3 IMERG Half Hourly 0.1 × 0.1 Degree Precipitation, version 03 (Goddard Space Flight Center Distributed Active Archive Center (GSFC DAAC), 2015).
  35. Das, N. SMAP Ancillary Data Report: Soil Attributes (Jet Propulsion Laboratory, California Institute of Technology, accessed September 2016, 2013);
  36. Delworth, T. L. & Manabe, S. The influence of potential evaporation on the variabilities of simulated soil wetness and climate. J. Clim. 1, 523547 (1988).
  37. Delworth, T. & Manabe, S. The influence of soil wetness on near-surface atmospheric variability. J. Clim. 2, 14471462 (1989).
  38. Wang, A., Zeng, X., Shen, S. S. P., Zeng, Q.-C. & Dickinson, R. E. Time Scales of Land Surface Hydrology. J. Hydrometeorol. 7, 868879 (2006).
  39. Ghannam, K. et al. Persistence and memory timescales in root-zone soil moisture dynamics. Wat. Resour. Res. 52, 14271445 (2016).
  40. Nakai, T. et al. Radiative and precipitation controls on root zone soil moisture spectra. Geophys. Res. Lett. 41, 75467554 (2014).
  41. Vinnikov, K. Y. & Yeserkepova, I. B. Soil moisture: empirical data and model results. J. Clim. 4, 6679 (1991).
  42. Vinnikov, K. Y., Robock, A., Speranskaya, N. A. & Schlosser, C. A. Scales of temporal and spatial variability of midlatitude soil moisture. J. Geophys. Res. 101, 71637174 (1996).
  43. Wu, W., Geller, M. A. & Dickinson, R. E. The response of soil moisture to long-term variability of precipitation. J. Hydrometeorol. 3, 604613 (2002).
  44. McColl, K. A. et al. Extended triple collocation: Estimating errors and correlation coefficients with respect to an unknown target. Geophys. Res. Lett. 41, 2014GL061322 (2014).
  45. Crow, W. T. et al. Robust estimates of soil moisture and latent heat flux coupling strength obtained from triple collocation. Geophys. Res. Lett. 42, 2015GL065929 (2015).

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Author information


  1. Department of Civil and Environmental Engineering, MIT, Cambridge, Massachusetts 02139, USA

    • Kaighin A. McColl,
    • Seyed Hamed Alemohammad,
    • Ruzbeh Akbar,
    • Alexandra G. Konings &
    • Dara Entekhabi
  2. Department of Earth and Planetary Sciences, Harvard University, Cambridge, Massachusetts 02138, USA

    • Kaighin A. McColl
  3. Department of Earth System Science, Stanford University, Stanford, California 94305, USA

    • Alexandra G. Konings
  4. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California 91109, USA

    • Simon Yueh
  5. Department of Earth, Atmospheric and Planetary Sciences, MIT, Cambridge, Massachusetts 02139, USA

    • Dara Entekhabi


K.A.M. wrote the manuscript. R.A., K.A.M. and S.H.A. conducted analyses and produced figures. D.E. conceived and led the project, and developed the ‘stored precipitation fraction’ in discussions with K.A.M., S.H.A. and A.G.K. S.Y. contributed to interpretation of the results. All authors discussed and edited drafts of the manuscript.

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