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:

Dominant role of soil moisture in mediating carbon and water fluxes in dryland ecosystems

Nature Geoscience volume 17, pages 38–43 (2024)Cite this article

Subjects

Abstract

Drylands exert a strong influence over global interannual variability in carbon and water cycling due to their substantial heterogeneity over space and time. This variability in ecosystem fluxes presents challenges for understanding their primary drivers. Here we quantify the sensitivity of dryland gross primary productivity and evapotranspiration to various hydrometeorological drivers by synthesizing eddy covariance data, remote sensing products and land surface model output across the western United States. We find that gross primary productivity and evapotranspiration derived from eddy covariance are most sensitive to soil moisture fluctuations, with lesser sensitivity to vapour pressure deficit and little to no sensitivity to air temperature or light. We find that remote sensing data accurately capture the sensitivity of eddy covariance fluxes to soil moisture but largely over-predict sensitivity to atmospheric drivers. In contrast, land surface models underestimate sensitivity of gross primary productivity to soil moisture fluctuations by approximately 45%. Amid debates about the role of increasing vapour pressure deficit in a changing climate, we conclude that soil moisture is the primary driver of US dryland carbon–water fluxes. It is thus imperative to both improve model representation of soil water limitation and more realistically represent how atmospheric drivers affect dryland vegetation in remotely sensed flux products.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Fig. 1: Correlation coefficients between daily GPP/ET and environmental drivers.
Fig. 2: Correlation coefficients between GPP/ET and environmental drivers at half-hourly, weekly and monthly timescales.
Fig. 3: Differences between hydrometeorological flux coefficients derived from remotely sensed versus EC approaches.
Fig. 4: EC versus land surface model soil moisture correlation coefficients.

Similar content being viewed by others

Data availability

All data used for our analyses are publicly available. Eddy covariance tower data are available at ameriflux.lbl.gov, CMIP6 model output are accessible from esgf-node.llnl.gov/search/cmip6/. SMAP L4C, MOD16 and MOD17 data were all obtained using the AppEEARS subsetting tool (https://appeears.earthdatacloud.nasa.gov/). FluxSat data were obtained from the Oak Ridge National Lab Distributed Active Archive Center (https://daac.ornl.gov/VEGETATION/guides/FluxSat_GPP_FPAR.html), and GLEAM data were obtained from https://www.gleam.eu/.

References

  1. Ahlstrom, A. et al. The dominant role of semi-arid ecosystems in the tren and variability of the land CO2 sink. Science 348, 4503–4518 (2015).

    Article  Google Scholar 

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

    Article  Google Scholar 

  3. Zhou, L., Chen, H. & Dai, Y. Stronger warming amplification over drier ecoregions observed since 1979. Environ. Res. Lett. 10, 064012 (2015).

    Article  Google Scholar 

  4. Cook, B. I., Ault, T. R. & Smerdon, J. E. Unprecedented 21st century drought risk in the American Southwest and Central Plains. Sci. Adv. 1, e1400082 (2015).

    Article  Google Scholar 

  5. Cook, B. et al. Uncertainties, limits, and benefits of climate change mitigation for soil moisture drought in southwestern North America. Earthʼs Future 9, e2021EF002014 (2021).

    Article  Google Scholar 

  6. Barnes, M. L. et al. Improved dryland carbon flux predictions with explicit consideration of water–carbon coupling. Commun. Earth Environ. 2, 248 (2021).

    Article  Google Scholar 

  7. MacBean, N. et al. Dynamic global vegetation models underestimate net CO2 flux mean and inter-annual variability in dryland ecosystems. Environ. Res. Lett. 16, 094023 (2021).

    Article  Google Scholar 

  8. Smith, W. K. et al. Remote sensing of dryland ecosystem structure and function: progress, challenges, and opportunities. Remote Sens. Environ. 233, 111401 (2019).

    Article  Google Scholar 

  9. Ficklin, D. L. & Novick, K. A. Historic and projected changes in vapor pressure deficit suggest a continental-scale drying of the United States atmosphere. J. Geophys. Res. 122, 2061–2079 (2017).

    Article  Google Scholar 

  10. Cook, B. et al. Twenty-first century drought projections in the CMIP6 forcing scenarios. Earthʼs Future 8, e2019EF001461 (2020).

    Article  Google Scholar 

  11. Lian, X. et al. Multifaceted characteristics of dryland aridity changes in a warming world. Nat. Rev. Earth Environ. 2, 232–250 (2021).

    Article  Google Scholar 

  12. Thompson, S. E. et al. Comparative hydrology across AmeriFlux sites: the variable roles of climate, vegetation, and groundwater. Water Resour. Res. 47, 1–17 (2011).

    Article  Google Scholar 

  13. 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  Google Scholar 

  14. Kannenberg, S. A. et al. Quantifying the drivers of ecosystem fluxes and water potential across the soil–plant–atmosphere continuum in an arid woodland. Agric. For. Meteorol. 329, 109269 (2023).

    Article  Google Scholar 

  15. Sun, X., Wilcox, B. P. & Zou, C. B. Evapotranspiration partitioning in dryland ecosystems: a global meta-analysis of in situ studies. J. Hydrol. 576, 123–136 (2019).

    Article  Google Scholar 

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

    Article  Google Scholar 

  17. Grossiord, C. et al. Plant responses to rising vapor pressure deficit. N. Phytol. 226, 1550–1566 (2020).

    Article  Google Scholar 

  18. Huxman, T. E. et al. Precipitation pulses and carbon fluxes in semiarid and arid ecosystems. Oecologia 141, 254–268 (2004).

    Article  Google Scholar 

  19. Schwinning, S. & Sala, O. E. Hierarchy of responses to resource pulses in arid and semi-arid ecosystems. Oecologia 141, 211–220 (2004).

    Article  Google Scholar 

  20. Zhang, F. et al. Five decades of observed daily precipitation reveal longer and more variable drought events across much of the western United States. Geophys. Res. Lett. 48, 1–11 (2021).

    Google Scholar 

  21. Brust, C. et al. Using SMAP Level-4 soil moisture to constrain MOD16 evapotranspiration over the contiguous USA. Remote Sens. Environ. 255, 112277 (2021).

    Article  Google Scholar 

  22. Purdy, A. J. et al. SMAP soil moisture improves global evapotranspiration. Remote Sens. Environ. 219, 1–14 (2018).

    Article  Google Scholar 

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

    Article  Google Scholar 

  24. Mladenova, I. E., Bolten, J. D., Crow, W., Sazib, N. & Reynolds, C. Agricultural drought monitoring via the assimilation of SMAP soil moisture retrievals into a global soil water balance model. Front. Big Data 3, 10 (2020).

    Article  Google Scholar 

  25. Williams, A. P., Cook, B. I. & Smerdon, J. E. Rapid intensification of the emerging southwestern North American megadrought in 2020–2021. Nat. Clim. Change 12, 232–234 (2022).

    Article  Google Scholar 

  26. Miguez-Macho, G. & Fan, Y. Spatiotemporal origin of soil water taken up by vegetation. Nature 598, 624–628 (2021).

    Article  Google Scholar 

  27. McCormick, E. L. et al. Widespread woody plant use of water stored in bedrock. Nature 597, 225–229 (2021).

    Article  Google Scholar 

  28. MacBean, N. et al. Testing water fluxes and storage from two hydrology configurations within the ORCHIDEE land surface model across US semi-arid sites. Hydrol. Earth Syst. Sci. 24, 5203–5230 (2020).

    Article  Google Scholar 

  29. Qiao, L., Zuo, Z. & Xiao, D. Evaluation of soil moisture in CMIP6 simulations. J. Clim. 35, 779–800 (2022).

    Article  Google Scholar 

  30. 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  Google Scholar 

  31. Collier, N. et al. The International Land Model Benchmarking (ILAMB) system: design, theory, and implementation. J. Adv. Model. Earth Syst. 10, 2731–2754 (2018).

    Article  Google Scholar 

  32. Stoy, P. C. et al. Reviews and syntheses: turning the challenges of partitioning ecosystem evaporation and transpiration into opportunities. Biogeosciences 16, 3747–3775 (2019).

    Article  Google Scholar 

  33. Kennedy, D. et al. Implementing plant hydraulics in the Community Land Model, version 5. J. Adv. Model. Earth Syst. 11, 485–513 (2019).

    Article  Google Scholar 

  34. 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).

    Article  Google Scholar 

  35. Samuels-Crow, K. E., Ogle, K. & Litvak, M. E. Atmosphere–soil interactions govern ecosystem flux sensitivity to environmental conditions in semiarid woody ecosystems over varying timescales. J. Geophys. Res. Biogeosci. 125, 1–16 (2020).

    Article  Google Scholar 

  36. Ripullone, F., Camarero, J. J., Colangelo, M. & Voltas, J. Variation in the access to deep soil water pools explains tree-to-tree differences in drought-triggered dieback of Mediterranean oaks. Tree Physiol. 40, 591–604 (2020).

    Article  Google Scholar 

  37. Mote, P. W., Li, S., Lettenmaier, D. P., Xiao, M. & Engel, R. Dramatic declines in snowpack in the western US. npj Clim. Atmos. Sci. 1, 2 (2018).

    Article  Google Scholar 

  38. Siirila-Woodburn, E. et al. A low-to-no snow future and its impacts on water resources in the western United States. Nat. Rev. Earth Environ. 2, 800–819 (2021).

    Article  Google Scholar 

  39. Breshears, D. D. et al. Regional vegetation die-off in response to global-change-type drought. Proc. Natl Acad. Sci. USA 102, 15144–15148 (2005).

    Article  Google Scholar 

  40. Kannenberg, S. A., Driscoll, A. W., Malesky, D. & Anderegg, W. R. L. Rapid and surprising dieback of Utah juniper in the southwestern USA due to acute drought stress. For. Ecol. Manag. 480, 118639 (2021).

    Article  Google Scholar 

  41. Ganey, J. L. & Vojta, S. C. Tree mortality in drought-stressed mixed-conifer and ponderosa pine forests, Arizona, USA. For. Ecol. Manag. 261, 162–168 (2011).

    Article  Google Scholar 

  42. Hantson, S., Huxman, T. E., Kimball, S., Randerson, J. T. & Goulden, M. L. Warming as a driver of vegetation loss in the Sonoran Desert of California. J. Geophys. Res. Biogeosci. 126, 285–295 (2021).

    Article  Google Scholar 

  43. Lal, P., Shekhar, A., Gharun, M. & Das, N. N. Spatiotemporal evolution of global long-term patterns of soil moisture. Sci. Total Environ. 867, 161470 (2023).

    Article  Google Scholar 

  44. Hajek, O. L. & Knapp, A. K. Shifting seasonal patterns of water availability: ecosystem responses to an unappreciated dimension of climate change. N. Phytol. 233, 119–125 (2022).

    Article  Google Scholar 

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

    Article  Google Scholar 

  46. Li, H. et al. Drylands face potential threat of robust drought in the CMIP6 SSPs scenarios. Environ. Res. Lett. 16, 114004 (2021).

    Article  Google Scholar 

  47. Britton, C. & Dodd, J. Relationships of photosynthetically active radiation and shortwave irradiance. Agric. Meteorol. 17, 1–7 (1976).

    Article  Google Scholar 

  48. 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).

    Article  Google Scholar 

  49. Wutzler, T. et al. Basic and extensible post-processing of eddy covariance flux data with REddyProc. Biogeosciences 15, 5015–5030 (2018).

    Article  Google Scholar 

  50. Keenan, T. F. et al. Net carbon uptake has increased through warming-induced changes in temperate forest phenology. Nat. Clim. Change 4, 598–604 (2014).

    Article  Google Scholar 

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

    Article  Google Scholar 

  52. Jones, L. A. et al. The SMAP level 4 carbon product for monitoring ecosystem land–atmosphere CO2 exchange. IEEE Trans. Geosci. Remote Sens. 55, 6517–6532 (2017).

    Article  Google Scholar 

  53. Joiner, J. & Yoshida, Y. Global MODIS and FLUXNET-derived daily gross primary production, V2. ORNL DAAC https://doi.org/10.3334/ORNLDAAC/1835 (2021).

  54. Mu, Q., Zhao, M. & Running, S. W. Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sens. Environ. 115, 1781–1800 (2011).

    Article  Google Scholar 

  55. 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 

  56. Tonidandel, S. & LeBreton, J. M. Relative importance analysis: a useful supplement to regression analysis. J. Bus. Psychol. 26, 1–9 (2011).

    Article  Google Scholar 

  57. R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2022).

Download references

Acknowledgements

We sincerely thank all flux tower site principal investigators for contributing flux data and the AmeriFlux Management Project team for making these data openly available. Funding for the AmeriFlux data portal was provided by the US Department of Energy. S.A.K. and M.L.B. were supported by the US Department of Energy Environmental System Science program grant number DE-SC0022052. W.R.L.A. acknowledges support from the David and Lucille Packard Foundation, US National Science Foundation grants 1802880, 2003017, 2044937 and IOS-2325700 from the Alan T. Waterman Award. M.P.D. and M.L.B. were supported by NASA SMAP Science Team grant number 80NSSC20K1805.

Author information

Authors and Affiliations

  1. Department of Biology, West Virginia University, Morgantown, WV, USA

    Steven A. Kannenberg

  2. Department of Biology and Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO, USA

    Steven A. Kannenberg & Alan K. Knapp

  3. School of Biological Sciences, University of Utah, Salt Lake City, UT, USA

    William R. L. Anderegg

  4. Wilkes Center for Climate Science and Policy, University of Utah, Salt Lake City, UT, USA

    William R. L. Anderegg

  5. O’Neill School of Public and Environmental Affairs, Indiana University, Bloomington, IN, USA

    Mallory L. Barnes

  6. Department of Geographical and Sustainability Sciences, University of Iowa, Iowa City, IA, USA

    Matthew P. Dannenberg

Authors
  1. Steven A. Kannenberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. William R. L. Anderegg
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mallory L. Barnes
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Matthew P. Dannenberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Alan K. Knapp
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

S.A.K. initially conceived of the research, with subsequent contributions from all authors. W.R.L.A., M.L.B. and M.P.D. assisted with data extraction. S.A.K. performed all data analysis and wrote the first draft of the manuscript. All authors contributed to subsequent manuscript revisions.

Corresponding author

Correspondence to Steven A. Kannenberg.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Geoscience thanks Stefan Hagemann, Andrew F. Feldman and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editors: Alireza Bahadori, Xujia Jiang, in collaboration with the Nature Geoscience team.

Additional information

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

Extended data

Extended Data Fig. 1 Map of the eddy covariance tower sites used in this analysis.

Color corresponds to the IGBP vegetation type.

Extended Data Fig. 2 Correlation plot representing the coefficient of daily GPP to various environmental drivers.

Each row represents a different flux tower site, while the last row represents the mean across sites. Sites are arranged (top to bottom) in order of decreasing aridity (mean climatic water deficit). Color, point size, and number indicates the Pearson’s R correlation coefficient. Missing values indicate a lack of data for that particular variable. ‘n.s.’ indicates a non-significant relationship (α = 0.05). Deep VWC correlations for US-Whs were excluded due to their limited temporal record.

Extended Data Fig. 3 Correlation plot representing the coefficient of daily ET to various environmental drivers.

Each row represents a different flux tower site, while the last row represents the mean across sites. Sites are arranged (top to bottom) in order of decreasing aridity (mean climatic water deficit). Color, point size, and number indicates the Pearson’s R correlation coefficient. Missing values indicate a lack of data for that variable. ‘n.s.’ indicates a non-significant relationship (α = 0.05). Deep VWC correlations for US-Whs were excluded due to their limited temporal record.

Extended Data Fig. 4 Relative weights for models between GPP/ET and hydrometeorological drivers.

Only a subset of sites were used for this analysis in order to keep relative weights comparable (see Methods). Box plot lines represent the interquartile range and median, while the whiskers represent 1.5 times the interquartile range.

Extended Data Fig. 5 Spearman’s correlation coefficients between daily GPP/ET and environmental drivers.

Environmental drivers include air temperature (TA), vapor pressure deficit (VPD), photosynthetic photon flux density (PPFD), and various layers of volumetric water content (VWC, see Methods for the depths included in each layer). Asterisks indicate where coefficients are significantly different from zero (α = 0.05). Box plot lines represent the interquartile range and median, while the whiskers represent 1.5 times the interquartile range.

Extended Data Fig. 6 Remotely-sensed correlation coefficients.

Pearsons’ R coefficient between various data products (boxplots and points) and a given environmental driver (panels) at each site. Box plot lines represent the interquartile range and median, while the whiskers represent 1.5 times the interquartile range.

Extended Data Fig. 7 Eddy covariance (EC) versus remotely-sensed (RS) coefficients for various environmental drivers.

R2 values are those from a linear model containing all remotely-sensed data products (all models statistically significant at α = 0.05), and the dashed black lines are 1:1 lines. Gray shading indicates the 95% confidence interval.

Extended Data Table 1 Basic site information for all eddy covariance towers
Full size table
Extended Data Table 2 Remote sensing products used and details regarding the basic method for flux estimation, the frequency of flux estimates, and the spatial resolution of the gridded products
Full size table
Extended Data Table 3 R2 values between daily SMAP surface and root-zone VWC and in situ daily mean VWC across depths at each flux tower site. The final row indicates the mean R2 across all sites. Empty values indicate either that no in situ soil moisture data were present at that depth for that site, or that the flux tower record did not overlap the SMAP record (2015 – present)
Full size table

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

Kannenberg, S.A., Anderegg, W.R.L., Barnes, M.L. et al. Dominant role of soil moisture in mediating carbon and water fluxes in dryland ecosystems. Nat. Geosci. 17, 38–43 (2024). https://doi.org/10.1038/s41561-023-01351-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41561-023-01351-8

Search

Advanced search

Quick links

Nature Briefing Anthropocene

Sign up for the Nature Briefing: Anthropocene newsletter — what matters in anthropocene research, free to your inbox weekly.

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