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Water limitation regulates positive feedback of increased ecosystem respiration

Nature Ecology & Evolution (2024)Cite this article

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Abstract

Terrestrial ecosystem respiration increases exponentially with temperature, constituting a positive feedback loop accelerating global warming. However, the response of ecosystem respiration to temperature strongly depends on water availability, yet where and when the water effects are important, is presently poorly constrained, introducing uncertainties in climate–carbon cycle feedback projections. Here, we disentangle the effects of temperature and precipitation (a proxy for water availability) on ecosystem respiration by analysing eddy covariance CO2 flux measurements across 212 globally distributed sites. We reveal a threshold precipitation function, determined by the balance between precipitation and ecosystem water demand, which separates temperature-limited and water-limited respiration. Respiration is temperature limited for precipitation above that threshold function, whereas in drier areas water limitation reduces the temperature sensitivity of respiration and its positive feedback to global warming. If the trend of expansion of water-limited areas with warming climate over the last decades continues, the positive feedback of ecosystem respiration is likely to be weakened and counteracted by the increasing water limitation.

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Fig. 1: The statistical performance of models of ecosystem respiration (Re) as a function of mean annual temperature (T) and precipitation (P), Re(T) and Re(P).
Fig. 2: Comparative threshold functions for mean temperature (T) or mean precipitation (P) regulation of ecosystem respiration (Re).

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Data availability

All data used for this study are publicly accessible and downloadable and all results of this study can be reproduced according to the methods provided. The FLUXNET2015 dataset used here are publicly available at https://fluxnet.org/data/fluxnet2015-dataset/. The CMIP6 data are publicly available at https://esgf-node.llnl.gov/projects/cmip6/. Information on the 212 sites used in this paper and their groupings are available on GitHub (https://github.com/chuixiangyi/Water-limitation).

Code availability

The Matlab code used for the analysis is available on GitHub (https://github.com/chuixiangyi/).

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Acknowledgements

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 were 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. C.Y. completed this research when he was a Fulbright Visiting Professor at University of Innsbruck with support from the US-Austria Fulbright Program. D.C. is supported by the Swedish national strategic research area BECC. G.D. has received support for this research from the Swedish Research Council (VR; grant number 2022-04672). The research of M.R. is supported by the European Research Council (ERC-Synergy project RESILIENCE, proposal no. 101071417) and by the Dutch Research Council (NWO ‘Resilience in complex systems through adaptive spatial pattern formation’, project no. OCENW.M20.169). S.M. was supported by ERC grant 101001608. G.W. acknowledges support from Austrian Research Promotion Agency grant 878893 and the Austrian Science Fund (10.55776/PAT2661823). N.K. acknowledges funding from National Oceanic and Atmospheric Administration Educational Partnership Program with Minority-Serving Institutions—Cooperative Science Center for Earth System Sciences and Remote Sensing Technologies cooperative agreement grant NA22SEC4810016.

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Authors and Affiliations

  1. Institution of Water and Environment Research, Dalian University of Technology, Dalian, China

    Qin Zhang, Jianxu Han & Shiguo Xu

  2. Department of Physical Geography, Stockholm University, Stockholm, Sweden

    Qin Zhang, Georgia Destouni, Stefano Manzoni, Gustaf Hugelius, Jerker Jarsjo & Jianxu Han

  3. Universität Innsbruck, Institut für Ökologie, Innsbruck, Austria

    Chuixiang Yi & Georg Wohlfahrt

  4. School of Earth and Environmental Sciences, Queens College, City University of New York, Flushing, NY, USA

    Chuixiang Yi & George Hendrey

  5. Earth and Environmental Sciences Department, Graduate Center, City University of New York, New York, NY, USA

    Chuixiang Yi, George Hendrey & Nir Krakauer

  6. Department of Sustainable Development, Environmental Science and Engineering, KTH Royal Institute of Technology, Stockholm, Sweden

    Georgia Destouni

  7. Department of Soil Science of Temperate Ecosystems, Department of Agricultural Soil Science, University of Goettingen, Göttingen, Germany

    Yakov Kuzyakov

  8. Peoples Friendship University of Russia (RUDN University), Moscow, Russia

    Yakov Kuzyakov

  9. Department of Statistics, Pennsylvania State University, University Park, PA, USA

    Runze Li

  10. Barry Commoner Center for Health & the Environment, Queens College, City University of New York, Flushing, NY, USA

    Eric Kutter

  11. Department of Earth Sciences, University of Gothenburg, Gothenburg, Sweden

    Deliang Chen

  12. Copernicus Institute of Sustainable Development, Utrecht University, TC Utrecht, the Netherlands

    Max Rietkerk

  13. Department of Mathematics and Computer, China University of Labor Relations, Beijing, China

    Zhenkun Tian

  14. Department of Biology, Pace University, New York, NY, USA

    Wei Fang

  15. Department of Civil Engineering, The City College of New York, City University of New York, New York, NY, USA

    Nir Krakauer

  16. Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden

    Gustaf Hugelius

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Contributions

C.Y., Q.Z. and G.D. conceived the project. Q.Z. performed data processing with R.L., Z.T. and J.H. Q.Z. and C.Y. conducted data analysis. C.Y. wrote the first version of the manuscript with Q.Z., E.K., R.L., D.C., G.W., Y.K., G.D., S.M., M.R., G.H. and W.F. All authors discussed the results and contributed to the writing and to the final manuscript.

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Correspondence to Chuixiang Yi.

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Extended data

Extended Data Fig. 1 The statistics of the empirical threshold model \(\tilde{P}(T)\).

The blue filled circles are the data of mean annual temperature and mean annual precipitation in B-group. The threshold curve (black line) is the exponential regression line with 95% confidence interval (shaded area), R2 = 0.53 and p < 0.01 for One-Tailed Test.

Extended Data Fig. 2 Differential sensitivity of Q10 to Dryness (DI).

212 sites of FLUXNET2015 were divided into six groups with six DI intervals: 0 < DI < 0.4 (9 sites); 0.4 < DI < 0.7 (61 sites); 0.7 < DI < 1.0 (58 sites); 1.0 < DI < 1.4 (38 sites); 1.4 < DI < 2.2 (23 sites); and DI > 2.2 (23 sites). The apparent group average Q10 were estimated by Q10 models (see Methods).

Extended Data Fig. 3 Differential sensitivity of Q10 to temperature revealed by Dryness (DI).

212 sites of FLUXNET2015 were divided into six groups with six DI intervals: 0 < DI < 0.4 (9 sites); 0.4 < DI < 0.7 (61 sites); 0.7 < DI < 1.0 (58 sites); 1.0 < DI < 1.4 (38 sites); 1.4 < DI < 2.2 (23 sites); and DI > 2.2 (23 sites). The filled circles and error bars represent DI-group means and their standard deviations, respectively. The blue line is the regression (y = -0.1719x + 3.2197, R2 = 0.63 for the data DI < 1, while the red line is the regression (y = -0.047x + 1.97, R2 = 0.93) for the data DI > 1.

Extended Data Fig. 4 The distribution and statistics of Q10 for each DI group.

212 sites of FLUXNET2015 were divided into six groups with six DI intervals: (a) 0 < DI < 0.4 (9 sites); (b) 0.4 < DI < 0.7 (61 sites); (c) 0.7 < DI < 1.0 (58 sites); (d) 1.0 < DI < 1.4 (38 sites); (e) 1.4 < DI < 2.2 (23 sites); and (f) DI > 2.2 (23 sites). Q10 was calculated with Q10 model (equation (14)) based on FLUXNET2015 ecosystem respiration \({R}_{e}^{{EC}}\) data and temperature data (see Methods).

Extended Data Fig. 5 The impact of precipitation (P) on ecosystem respiration (Re) across various time scales based on the data of Re and P from the same P-group sites.

(a) half-hourly; (b) daily; (c) weekly; (d) monthly; and (e) yearly. n is the number of scatter points.

Extended Data Fig. 6 The asymmetric effect of precipitation (P) on ecosystem respiration (Re).

(a) all sites; (b) T-group; (c) P-group; and (d) B-group. The y-axis represents the residuals of model Re(P) (equation(12)). The x-axis is coefficient of variation of monthly precipitation.

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Zhang, Q., Yi, C., Destouni, G. et al. Water limitation regulates positive feedback of increased ecosystem respiration. Nat Ecol Evol (2024). https://doi.org/10.1038/s41559-024-02501-w

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