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Global water availability boosted by vegetation-driven changes in atmospheric moisture transport

Nature Geoscience volume 15, pages 982–988 (2022)Cite this article

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Abstract

Surface-water availability, defined as precipitation minus evapotranspiration, can be affected by changes in vegetation. These impacts can be local, due to the modification of evapotranspiration and precipitation, or non-local, due to changes in atmospheric moisture transport. However, the teleconnections of vegetation changes on water availability in downwind regions remain poorly constrained by observations. By linking measurements of local precipitation to a new hydrologically weighted leaf area index that accounts for both local and upwind vegetation contributions, we demonstrate that vegetation changes have increased global water availability at a rate of 0.26 mm yr−2 for the 2001–2018 period. Critically, this increase has attenuated about 15% of the recently observed decline in global water availability. The water availability increase is due to a greater rise in precipitation relative to evapotranspiration for over 53% of the global land surface. We also quantify the potential hydrological impacts of regional vegetation increases at any given location across global land areas. We find that enhanced vegetation is beneficial to both local and downwind water availability for ~45% of the land surface, whereas it is adverse elsewhere, primarily in water-limited or high-elevation regions. Our results highlight the potential strong effects of deliberate vegetation changes, such as afforestation programmes, on water resources beyond local and regional scales.

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Fig. 1: Contribution of terrestrial evapotranspiration to recycled land precipitation at different spatial scales.
Fig. 2: Comparison of precipitation sensitivity to interannual variations of LAI or LAI_w based on observations for the period 2001–2018.
Fig. 3: Trends in LAI and its net effect on terrestrial water availability.
Fig. 4: Global map of Svh.

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Data availability

The dataset on global land precipitation source and evapotranspiration sink is available at https://doi.org/10.1594/PANGAEA.908705. The MODIS LAI C6 product is available at https://doi.org/10.5067/MODIS/MOD15A2H.006. GPCP v.2.3 precipitation data are available at https://psl.noaa.gov/data/gridded/data.gpcp.html. GLEAM v.3.3a evapotranspiration data are available at https://www.gleam.eu/. Air temperature and wind speed from ERA5 are available at https://cds.climate.copernicus.eu. Surface radiation (CERES_SYN1deg_Ed4.1) data are available at https://ceres.larc.nasa.gov/. SST from NOAA Optimum Interpolation v.2 is available at https://psl.noaa.gov/data/gridded/data.noaa.oisst.v2.html. Snow-cover product is available at https://nsidc.org/data/NSIDC-0046/versions/4. Elevation data are available at https://www.ngdc.noaa.gov/mgg/global/global.html.

Code availability

The processing MATLAB codes are available from the corresponding author upon request.

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Acknowledgements

This study was supported by the Second Tibetan Plateau Scientific Expedition and Research (STEP) programme (grant no. 2019QZKK0208), the National Natural Science Foundation of China (41988101) and the VESRI LEMONTREE project (P-1-00381). C.H. was supported by the NERC National Capability Fund to UKCEH.

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Authors and Affiliations

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

    Jiangpeng Cui, Xu Lian, Mingzhu He, Hao Xu & Shilong Piao

  2. State Key Laboratory of Tibetan Plateau Earth System and Resources Environment (TPESRE), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China

    Jiangpeng Cui, Tao Wang, Jinzhi Ding & Shilong Piao

  3. Department of Earth and Environmental Engineering, Columbia University, New York, NY, USA

    Xu Lian & Pierre Gentine

  4. UK Centre for Ecology and Hydrology, Wallingford, UK

    Chris Huntingford

  5. Environmental Physics Laboratory (EPhysLab), CIM-UVigo, Universidade de Vigo, Ourense, Spain

    Luis Gimeno

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

    Anping Chen

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Contributions

J.C. and X.L. designed the research; J.C. performed analysis; and J.C., X.L. and C.H. drafted the paper. J.C., X.L., C.H., L.G., T.W., J.D., M.H., H.X., A.C., P.G. and S.P. contributed to the interpretation of the results and to the text.

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Correspondence to Xu Lian.

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Nature Geoscience thanks Arie Staal and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Tom Richardson, in collaboration with the Nature Geoscience team.

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Extended data

Extended Data Fig. 1 Schematic of the calculations of (a) LAI_w and (b) ∂P/∂LAI_w.

a, The shaded area in upper left panel indicates the precipitationshed of the grid in the center of the red box. Blue (green) color indicates the grid with a low (high) contribution to precipitation of the grid in the red box. By integrating LAI within precipitaitonshed, we got a new dataset of hydrologically weighted LAI (see Methods). b, Sensitivity of precipitation to interannual variations of weighted LAI, ∂P/∂LAI_w, was calculated as the partial derivative (that is, a in the equation) in a multiple regression of precipitation (P) against LAI_w (LAI_weighted), surface air temperature (Ta), net radiation (Rn) as well the first canonical variable obtained from CCA above that represents the effect of SST on precipitation (SSTCCA). ε denotes the difference between the observed and predicted precipitation. Because precipitation can only influence vegetation locally, but not vegetation in remote upwind land areas, while LAI_w aggregated vegetation signals in both local and upwind areas. Therefore, the regression with LAI_w as input naturally forms a unidirectional system, that is, only from LAI_w to precipitation (the green arrow; see Methods).

Extended Data Fig. 2 Annual mean (a) LAI and (b) LAI_w.

The value is averaged over the 2001–2018 period.

Extended Data Fig. 3 Spatial pattern and features of model-based metric ∂P/∂LAI_w and comparison with its calculation from observations.

a, Spatial pattern of ∂P/∂LAI_w derived from single-forcing experiments (in which only LAI varies) conducted with a coupled land-atmosphere model (IPSLCM)14. Stipping indicates regions where ∂P/∂LAI_w is statistically significant (P < 0.05). b, Zonal average of modelled ∂P/∂LAI_w, with shaded areas representing one standard deviation. c, Mean modelled ∂P/∂LAI_w as a function of mean annual temperature and precipitation, depending on location. d, Regions where model and observation-based ∂P/∂LAI_w agree on sign (coloured blue). e, Histogram of ∂P/∂LAI_w values derived from the IPCSLCM model and from observations.

Extended Data Fig. 4 Comparison of mean maximum distance of significant spatial autocorrelation versus average distance of precipitation recycling.

a, Schematic of the autocorrelation distance calculation and its comparison with average distance of precipitation recycling. The yellow area indicates the grids that are significantly correlated with the dark blue single grid point in the center of yellow area. The average distance between the small red point grids on the boundary of yellow area and the inner dark blue grid point defines the mean maximum distance of significant spatial autocorrelation for current grid. The green and big red points present an example of grid with a distance of mean rainfall recycling which is larger or less than the mean maximum distance of significant spatial autocorrelation of current grid, respectively (see Methods for details). b-c, Spatial pattern of mean maximum distance of significant spatial autocorrelation for (b) precipitation and (c) LAI. d, Spatial pattern of average distance of precipitation recycling. The small arrows represent the direction of local evapotranspiration transported to downwind regions. e, Histogram of distance difference between precipitation recycling versus precipitation autocorrelation, or precipitation recycling versus LAI autocorrelation. f, Global mean and median distances of significant spatial autocorrelation and precipitation recycling.

Extended Data Fig. 5 Schematic of the impact of vegetation on downwind precipitation in non-LLJ/monsoon versus LLJ/monsoon regions.

The blue and green arrows indicate the oceanic- and vegetation-sourced moisture, respectively. The atmospheric horizontal arrows with two colors represent the extent to which moisture transport consist of the two sources. That is, the portion of green indicates the relative strength of vegetation feedbacks on downwind precipitation, while the size of the arrows represents the absolute magnitude of moisture transport. In non-LLJ/monsoon regions a, the large-scale moisture transport from ocean to land is weak and thus moisture travels a relatively short distance inland. Accordingly, the vegetation is often sparse and recycles less moisture to fuel precipitation (Fig. R4a). In LLJ/monsoons regions b, however, the strong LLJ/monsoon transports substantial moisture from ocean to land. The vegetation flourishes at large spatial scale or distance due to abundant precipitation, but simultaneously can recycle more water back to the atmosphere that is further transported downwind.

Extended Data Fig. 6 Relative interannual variability of (a) LAI, (c) LAI_w and (e) precipitation, and seasonal variations of (b) LAI, (d) LAI_w and (f) precipitation.

The relative value is calculated as standard deviation of interannual variability divided by annual mean for the 2001–2018 period. The red and blue lines in right panels correspond to the grids with negative (black point in western Amazon in left panels) and positive (black point in southern Amazon in left panels) ∂P/∂LAI_w values, respectively. The shaded area indicates one standard deviation of interannual variability.

Extended Data Fig. 7

(a) Altitude and (b) snow cover duration in the northern hemisphere (0o-90oN).

Extended Data Fig. 8 Sensitivity of precipitation variance explained by SST to number of SST EOF modes in CCA.

The blue and red lines represent mean and median of precipitation variance explained by SST for global land grids, respectively.

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Supplementary Figs. 1–5 and Text 1 and 2.

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Cui, J., Lian, X., Huntingford, C. et al. Global water availability boosted by vegetation-driven changes in atmospheric moisture transport. Nat. Geosci. 15, 982–988 (2022). https://doi.org/10.1038/s41561-022-01061-7

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