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.
This is a preview of subscription content, access via your institution
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Rent or buy this article
Prices vary by article type
Prices may be subject to local taxes which are calculated during checkout
Allan, R. P. et al. Advances in understanding large‐scale responses of the water cycle to climate change. Ann. N. Y. Acad. Sci. 1472, 49–75 (2020).
Milly, P. C. D. & Dunne, K. A. Potential evapotranspiration and continental drying. Nat. Clim. Change 6, 946–949 (2016).
Milly, P. C. D. & Dunne, K. A. A hydrologic drying bias in water-resource impact analyses of anthropogenic climate change. J. Am. Water Resour. Assoc. 53, 822–838 (2017).
Fischer, E. M. & Knutti, R. Anthropogenic contribution to global occurrence of heavy-precipitation and high-temperature extremes. Nat. Clim. Change 5, 560–564 (2015).
Bintanja, R. & Andry, O. Towards a rain-dominated Arctic. Nat. Clim. Change 7, 263–267 (2017).
Padrón, R. S., Gudmundsson, L., Greve, P. & Seneviratne, S. I. Large-scale controls of the surface water balance over land: insights from a systematic review and meta-analysis. Water Resour. Res. 53, 9659–9678 (2017).
Zhu, Z. et al. Greening of the Earth and its drivers. Nat. Clim. Change 6, 791–795 (2016).
Berg, A., Sheffield, J. & Milly, P. C. D. Divergent surface and total soil moisture projections under global warming. Geophys. Res. Lett. 44, 236–244 (2017).
Mankin, J. S., Seager, R., Smerdon, J. E., Cook, B. I. & Williams, A. P. Mid-latitude freshwater availability reduced by projected vegetation responses to climate change. Nat. Geosci. 12, 983–988 (2019).
Gedney, N. et al. Detection of a direct carbon dioxide effect in continental river runoff records. Nature 439, 835–838 (2006).
Betts, R. A. et al. Projected increase in continental runoff due to plant responses to increasing carbon dioxide. Nature 448, 1037–1041 (2007).
Lemordant, L., Gentine, P., Swann, A. S., Cook, B. I. & Scheff, J. Critical impact of vegetation physiology on the continental hydrologic cycle in response to increasing CO2. Proc. Natl Acad. Sci. USA 115, 4093–4098 (2018).
Fowler, M. D., Kooperman, G. J., Randerson, J. T. & Pritchard, M. S. The effect of plant physiological responses to rising CO2 on global streamflow. Nat. Clim. Change 9, 873–879 (2019).
Betts, R. A., Cox, P. M., Lee, S. E. & Woodward, F. I. Contrasting physiological and structural vegetation feedbacks in climate change simulations. Nature 387, 796–799 (1997).
Piao, S. et al. Changes in climate and land use have a larger direct impact than rising CO2 on global river runoff trends. Proc. Natl Acad. Sci. USA 104, 15242–15247 (2007).
Zhan, C. et al. Emergence of the physiological effects of elevated CO2 on land–atmosphere exchange of carbon and water. Glob. Change Biol. 28, 7313–7326 (2022).
Kooperman, G. J. et al. Forest response to rising CO2 drives zonally asymmetric rainfall change over tropical land. Nat. Clim. Change 8, 434–440 (2018).
Cui, J. et al. Vegetation forcing modulates global land monsoon and water resources in a CO2-enriched climate. Nat. Commun. 11, 5184 (2020).
Zhou, S. et al. Large divergence in tropical hydrological projections caused by model spread in vegetation responses to elevated CO2. Earth’s Future 10, e2021EF002457 (2022).
Cao, L., Bala, G., Caldeira, K., Nemani, R. & Ban-Weiss, G. Importance of carbon dioxide physiological forcing to future climate change. Proc. Natl Acad. Sci. USA 107, 9513–9518 (2010).
Sterling, S. M., Ducharne, A. & Polcher, J. The impact of global land-cover change on the terrestrial water cycle. Nat. Clim. Change 3, 385–390 (2013).
Yang, Y., Roderick, M. L., Zhang, S., McVicar, T. R. & Donohue, R. J. Hydrologic implications of vegetation response to elevated CO2 in climate projections. Nat. Clim. Change 9, 44–48 (2019).
Zhou, S. et al. Soil moisture–atmosphere feedbacks mitigate declining water availability in drylands. Nat. Clim. Change 11, 38–44 (2021).
Hrachowitz, M. & Clark, M. P. The complementary merits of competing modelling philosophies in hydrology. Hydrol. Earth Syst. Sci. 21, 3953–3973 (2017).
Zhou, G. et al. Global pattern for the effect of climate and land cover on water yield. Nat. Commun. 6, 5918 (2015).
Hoek van Dijke, A. J. et al. Shifts in regional water availability due to global tree restoration. Nat. Geosci. 15, 363–368 (2022).
Yang, H. & Yang, D. Derivation of climate elasticity of runoff to assess the effects of climate change on annual runoff. Water Resour. Res. 47, W07526 (2011).
Roderick, M., Sun, F., Lim, W. H. & Farquhar, G. A general framework for understanding the response of the water cycle to global warming over land and ocean. Hydrol. Earth Syst. Sci. 18, 1575–1589 (2014).
Zhou, S. et al. A new method to partition climate and catchment effect on the mean annual runoff based on the Budyko complementary relationship. Water Resour. Res. 52, 7163–7177 (2016).
Kooperman, G. J. et al. Plant physiological responses to rising CO2 modify simulated daily runoff intensity with implications for global-scale flood risk assessment. Geophys. Res. Lett. 45, 12457–12466 (2018).
Eyring, V. et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958 (2016).
Huang, M. et al. Air temperature optima of vegetation productivity across global biomes. Nat. Ecol. Evol. 3, 772–779 (2019).
Roderick, M. L., Greve, P. & Farquhar, G. D. On the assessment of aridity with changes in atmospheric CO2. Water Resour. Res. 51, 5450–5463 (2015).
Lian, X. et al. Multifaceted characteristics of dryland aridity changes in a warming world. Nat. Rev. Earth Environ. 2, 232–250 (2021).
Sheffield, J., Wood, E. F. & Roderick, M. L. Little change in global drought over the past 60 years. Nature 491, 435–438 (2012).
Yang, Y. et al. Comparing Palmer Drought Severity Index drought assessments using the traditional offline approach with direct climate model outputs. Hydrol. Earth Syst. Sci. 24, 2921–2930 (2020).
Berg, A. & McColl, K. A. No projected global drylands expansion under greenhouse warming. Nat. Clim. Change 11, 331–337 (2021).
Huang, J., Yu, H., Guan, X., Wang, G. & Guo, R. Accelerated dryland expansion under climate change. Nat. Clim. Change 6, 166–171 (2016).
Scheff, J., Coats, S. & Laguë, M. M. Why do the global warming responses of land‐surface models and climatic dryness metrics disagree? Earth’s Future 10, e2022EF002814 (2022).
Zhou, S. et al. Diminishing seasonality of subtropical water availability in a warmer world dominated by soil moisture–atmosphere feedbacks. Nat. Commun. 13, 5756 (2022).
Priestley, C. H. B. & Taylor, R. J. On the assessment of surface heat flux and evaporation using large-scale parameters. Mon. Weather Rev. 100, 81–92 (1972).
Shuttleworth, W. J. in Handbook of Hydrology (ed. Maidment, D. R.) Ch. 4 (McGraw-Hill Education, New York, 1993).
Allen, R. G., Pereira, L. S., Raes, D. & Smith, M. Crop Evapotranspiration: Guidelines for Computing Crop Water Requirements (FAO, 1998).
Budyko, M. I. Climate and Life (Academic Press, 1974).
Xu, X., Liu, W., Scanlon, B. R., Zhang, L. & Pan, M. Local and global factors controlling water–energy balances within the Budyko framework. Geophys. Res. Lett. 40, 6123–6129 (2013).
Zhang, L. et al. A rational function approach for estimating mean annual evapotranspiration. Water Resour. Res. 40, W02502 (2004).
Yang, H., Yang, D., Lei, Z. & Sun, F. New analytical derivation of the mean annual water–energy balance equation. Water Resour. Res. 44, W03410 (2008).
Zhou, S., Yu, B., Huang, Y. & Wang, G. The complementary relationship and generation of the Budyko functions. Geophys. Res. Lett. 42, 1781–1790 (2015).
Roderick, M. L. & Farquhar, G. D. A simple framework for relating variations in runoff to variations in climatic conditions and catchment properties. Water Resour. Res. 47, W00G07 (2011).
Zhang, L., Dawes, W. R. & Walker, G. R. Response of mean annual evapotranspiration to vegetation changes at catchment scale. Water Resour. Res. 37, 701–708 (2001).
Zhang, S., Yang, H., Yang, D. & Jayawardena, A. W. Quantifying the effect of vegetation change on the regional water balance within the Budyko framework. Geophys. Res. Lett. 43, 1140–1148 (2016).
Gan, G., Liu, Y. & Sun, G. Understanding interactions among climate, water, and vegetation with the Budyko framework. Earth Sci. Rev. 212, 103451 (2021).
Ning, T. et al. Interaction of vegetation, climate and topography on evapotranspiration modelling at different time scales within the Budyko framework. Agric. For. Meteorol. 275, 59–68 (2019).
Zhou, S., Yu, B., Lintner, B. R., Findell, K. L. & Zhang. Y. A new method to partition the effects of climate change and land surface changes on mean annual runoff. Zenodo https://doi.org/10.5281/zenodo.7733618 (2023).
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.).
The authors declare no competing interests.
Peer review information
Nature Climate Change thanks Rene Orth, Adriaan J. Teuling and Guiling Wang for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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.
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.
About this article
Cite this article
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
This article is cited by
Nature Water (2023)