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Projected increase in global runoff dominated by land surface changes

Nature Climate Change volume 13, pages 442–449 (2023)Cite this article

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Abstract

Increases in atmospheric CO2 concentration affect continental runoff through radiative and physiological forcing. However, how climate and land surface changes, and their interactions in particular, regulate changes in global runoff remains largely unresolved. Here we develop an attribution framework that integrates top-down empirical and bottom-up modelling approaches to show that land surface changes account for 73–81% of projected global runoff increases. This arises from synergistic effects of physiological responses of vegetation to rising CO2 concentration and responses of land surface—for example, vegetation cover and soil moisture—to radiatively driven climate change. Although climate change strongly affects regional runoff changes, it plays a minor role (19–27%) in the global runoff increase, due to cancellation of positive and negative contributions from different regions. Our findings highlight the importance of accurate model representation of land surface processes for reliable projections of global runoff to support sustainable management of water resources.

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Fig. 1: Illustration of the direct and secondary effects of CO2 radiative and physiological forcings in CMIP6 simulations.
Fig. 2: Projected changes in terrestrial water balance and drivers based on CMIP6 model outputs.
Fig. 3: Global patterns of projected runoff changes and drivers in CMIP6 models.
Fig. 4: Integration and reconciliation of the top-down and bottom-up approaches in CMIP6 simulations.
Fig. 5: Projected changes in land surface characteristics in physiology and radiation simulations.

Data availability

The CMIP6 model simulations are publicly available from https://esgf-node.llnl.gov/search/cmip6/. Earth system models and simulations used in this study are listed in Supplementary Table 1.

Code availability

The R code used for runoff attribution is available from https://doi.org/10.5281/zenodo.7733618 (ref. 54).

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Acknowledgements

We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modelling groups (listed in Supplementary Table 1) for producing and making available their model output. For CMIP, the US Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. This work was supported by the National Natural Science Foundation of China under grant agreement no. 41991235 (S.Z.), the NSFC Excellent Young Scientists Fund (overseas, S.Z. and Y.Z.) and the Fundamental Research Funds for the Central Universities (S.Z.).

Author information

Authors and Affiliations

  1. State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, China

    Sha Zhou

  2. Institute of Land Surface System and Sustainable Development, Faculty of Geographical Science, Beijing Normal University, Beijing, China

    Sha Zhou

  3. School of Engineering and Built Environment, Griffith University, Nathan, Queensland, Australia

    Bofu Yu

  4. Department of Environmental Sciences, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA

    Benjamin R. Lintner

  5. NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, NJ, USA

    Kirsten L. Findell

  6. Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China

    Yao Zhang

  7. Institute of Carbon Neutrality, Peking University, Beijing, China

    Yao Zhang

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  1. Sha Zhou
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Contributions

S.Z. and Y.Z. conceived and designed the study. S.Z. processed model simulations. S.Z., B.Y., B.R.L., K.L.F. and Y.Z. contributed to data analysis and interpretation. S.Z. drafted the manuscript. All authors edited the manuscript.

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Correspondence to Sha Zhou or Yao Zhang.

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Nature Climate Change thanks Rene Orth, Adriaan J. Teuling and Guiling Wang for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Illustration of the attribution framework to integrate the top-down Budyko approach and the bottom-up experiment approach.

a, Change in runoff (ΔR) from ESMs is attributed to climate change effect (ΔRcc) and land surface effect (ΔRn) using the Budyko attribution method. b, Runoff change simulated in the 1pctCO2 experiment (ΔR), which is decomposed as a radiative term simulated by 1pctCO2-rad (ΔRrad), a physiological term by 1pctCO2-bgc (ΔRphy), and a residual term for the interactions between the CO2 radiative and physiological effects (ΔRint). c, Attribution of the change in runoff by applying the Budyko attribution approach to each of the three 1pctCO2 experiments to decompose the direct radiative and physiological effects and indirect effects through land-atmosphere interactions. In 1pctCO2-rad, rising CO2 impacts runoff directly through climate change (ΔRrad_cc) and indirectly from land surface responses to climate change (ΔRrad_n), while in 1pctCO2-bgc, rising CO2 affects runoff directly through the plant physiological effect (ΔRphy_n) and indirectly via the feedback of the land surface on the climate (ΔRphy_cc).

Extended Data Fig. 2 Global patterns of projected runoff changes and drivers in CMIP6 models using the Budyko attribution Method II.

a–c, Multi-model mean changes in precipitation (ΔP), potential evapotranspiration (ΔPET), and the region-specific parameter (Δn) between historical (1971–2000) and future (2071–2100, SSP5–8.5) periods (future minus historical). d–f, Multi-model mean changes in runoff (ΔR) between historical and future periods and separately, the climate change effect (ΔRcc) and the land surface effect (ΔRn) using the Budyko attribution Method II to take into account potential changes in precipitation characteristics other than the mean. The dotted area denotes regions where ΔR is dominated by ΔRcc (e) or ΔRn (f). g–o, the same as d–f, but for runoff changes between the first (Year 1 to 30) and last (Year 111 to 140) 30-year periods in the 1pctCO2, 1pctCO2-bgc, 1pctCO2-rad experiments and the decomposed climate change effect (ΔRcc) and land surface effect (ΔRn) by applying the Budyko attribution Method II to each experiment (Methods). The dotted area denotes regions where ΔR is dominated by ΔRcc or ΔRn in each experiment. p–r, Variations in the global mean ΔR, ΔRcc, and ΔRn among the 16 ESMs and the 7 ESMs that participated in the three 1pctCO2 experiments. The top and bottom of each box represent the first and third quartiles, the center line the median, the whiskers the data range except for outliers shown as a circle, and the cross indicates the mean runoff change based on all ESMs.

Extended Data Fig. 3 Comparison of attribution results using different PET equations.

a,b, Contributions of the climate change effect (ΔRcc, a) and the land surface effect (ΔRn, b) using the open-water Penman (Penman-OW) equation, the reference crop Penman-Monteith (PM-RC) equation, and the PM-RC equation that incorporates the CO2 effect (PM-CO2) and their comparison with the Priestley-Taylor (PT) equation. The top and bottom of each box represent the first and third quartiles, the center line the median, the whiskers the data range except for outliers shown as a circle, and the cross indicates the mean runoff change based on all ESMs. c,d, Comparison between different PET equations in terms of the climate change effect (ΔRcc, c) and the land surface effect (ΔRn, d) among the 16 ESMs. The 1:1 line and linear correlation coefficients (r) for ΔRcc (c) and ΔRn (d) using the three Penman-type equations and the PT equation are shown.

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Supplementary tables 1 and 2.

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Zhou, S., Yu, B., Lintner, B.R. et al. Projected increase in global runoff dominated by land surface changes. Nat. Clim. Chang. 13, 442–449 (2023). https://doi.org/10.1038/s41558-023-01659-8

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