Abstract
Tropical forest restoration is increasingly promoted as a nature-based solution for climate change mitigation, yet its capacity to reverse the hydrological impacts of deforestation remains unclear. Here we show forest gain increases evapotranspiration and precipitation more than forest loss reduces them. This hydrological asymmetry, driven by the rapid growth of young forests and enhanced moisture recycling, indicates that compensating for hydrological effects of deforestation requires restoring 43–63% and 53–83% of lost forest area in South America and Africa, respectively. In contrast, climatic conditions in Southeast Asia limit the potential of reforestation, underscoring the importance of forest conservation. Current climate models fail to capture these asymmetric responses, largely owing to poor representation of vegetation dynamics and age-dependent traits. Our findings call for reframing forest-based climate solutions through the lens of hydrological resilience, emphasizing the importance of spatially targeted, mechanism-informed restoration strategies to maximize ecosystem service co-benefits.
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Data availability
All datasets used in this study are publicly available. GLC_FCS30D is available via Zenodo at https://doi.org/10.5281/zenodo.15063683 (ref. 95). GLCLU and forest height are from https://glad.earthengine.app/view/glcluc-2000-2020. ESA CCI is from http://maps.elie.ucl.ac.be/CCI/viewer/download.php. MODIS MOD44B is from https://lpdaac.usgs.gov/products/mod44bv061. GLOBMAP FTC is available via Zenodo at https://doi.org/10.5281/zenodo.10589730 (ref. 96). CHIRPS is from https://data.chc.ucsb.edu/products/CHIRPS-2.0. PERSIANN, PERSIANN-CDR, PDIR-NOW and PERSIANN-CCS-CDR are from https://chrsdata.eng.uci.edu. GPM is from https://gpm1.gesdisc.eosdis.nasa.gov/data/GPM_L3. CMORPH is from https://www.ncei.noaa.gov/data/cmorph-high-resolution-global-precipitation-estimates/access/daily/0.25deg/. GPCP is from https://disc.gsfc.nasa.gov/datasets/GPCPMON_3.2/summary?keywords=GPCPMON. MOD16A2GF is from https://lpdaac.usgs.gov/products/mod16a2v061. PML_V2 is from https://data.tpdc.ac.cn/zh-hans/data/48c16a8d-d307-4973-abab-972e9449627c. SNRT is from https://data.tpdc.ac.cn/zh-hans/data/236e33bf-e66b-4682-bbc1-274de1dcbcd3. GLEAM 4.2a is from https://www.gleam.eu. FLUXCOM-X is from https://doi.org/10.18160/5NZG-JMJE.MOD15A2H is from https://doi.org/10.5067/MODIS/MOD15A2H.006. CMIP6 model simulations are from https://esgf-node.llnl.gov/search/cmip6. The global forest management data are available via Zenodo at https://doi.org/10.5281/zenodo.5879022 (ref. 97). A global map of planting years of plantations is available via Figshare at https://doi.org/10.6084/m9.figshare.19070084.v2 (ref. 98). The regenerating tropical moist forest age is available via Zenodo at https://doi.org/10.5281/zenodo.15120870 (ref. 99). Amazon boundary is from https://amazonia.mapbiomas.org/colecciones-de-mapbiomas-amazonia. Terrestrial ecoregions are from https://ecoregions.appspot.com. SRTM elevation data are from https://srtm.csi.cgiar.org. Distance to nearest coast is from https://catalog.data.gov/dataset/distance-to-nearest-coastline-0-01-degree-grid-ocean. TerraClimate data are from https://www.climatologylab.org/terraclimate.html.
Code availability
All relevant R functions used in this study are described in the Methods section. The code used for this study is available
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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 for producing and making available their model output. For CMIP the US Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provided coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. We thank H. Ma for providing the CWC product. We thank T. Alemayehu Abera for his useful feedback on the manuscript.
Funding
This work was supported by the National Key Research and Development Program of China (2022YFF0802401), the National Natural Science Foundation of China (42521001) and the 111 Project of China (B23027). T.S. acknowledges support from the DFG STRIVE project (SM 710/2-1).
Authors and Affiliations
Contributions
S.Z. conceived and designed the study. S.M. developed the methodology and performed data analysis. S.M. and S.Z. wrote the manuscript. D.E., A.S., T.S., C.S. and A.J.T. discussed the interpretation of the results. D.E., A.S., T.S., C.S. and A.J.T. edited the manuscript.
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Extended data
Extended Data Fig. 1 Hydrological impacts of tropical forest loss and gain on evapotranspiration and precipitation across tropical regions from 2001 to 2020.
a–d, Spatial patterns of evapotranspiration (ΔET) and precipitation (ΔP) changes associated with forest loss (a, b) and gain (c, d) across tropical regions from 2001 to 2020. ΔP and ΔET are calculated as the multi-year mean differences between 2001–2003 and 2020–2022, comparing forest change grid cells to neighbouring undisturbed grid cells. Analyses are conducted at 0.05° resolution and aggregated to 1° for visualization. Basemap data from Natural Earth (https://www.naturalearthdata.com).
Extended Data Fig. 2 Scale-dependent sensitivities of precipitation and evapotranspiration to tropical forest loss and gain from 2001 to 2020.
a–c, Mean changes in precipitation (ΔP) and evapotranspiration (ΔET) per 1% forest change at spatial resolutions of 0.05°, 0.25°, 0.5°, and 1.0° in South America (a), Africa (b), and Southeast Asia (SEA; c). Sensitivities are computed as the mean annual precipitation and ET change per 1% forest change, based on differences between 2001–2003 and 2020–2022 relative to nearby undisturbed controls. Bars indicate regional means, with error bars denoting standard errors across n product combinations: n = 9 for both precipitation and ET at 0.05°; n = 21 for precipitation and n = 12 for ET at 0.25°; n = 24 for precipitation and n = 12 for ET at 0.5° and 1.0°. Precipitation and ET values are derived from multiple satellite-based datasets (Supplementary Tables 3 and 4), enabling robust comparison across spatial resolutions. The limited number of forest-gain samples at 1.0° resolution precluded robust conclusions, and our analysis at this scale therefore focused exclusively on forest loss.
Extended Data Fig. 3 Asymmetric effects of tropical forest gain and loss on evapotranspiration and precipitation at larger spatial scales.
a–d, Sensitivity of evapotranspiration (ΔET) and precipitation (ΔP) to forest loss (a, b) and gain (c, d) computed at 0.25° resolution and aggregated to 1° for visualization. Insets show regional means ± standard errors across product-combination estimates. e–g, Hydrological responses to forest gain and loss at 0.25° and 0.5° resolution in South America (e), Africa (f), and SEA (g). At 0.25°, paired comparisons are conducted in bins in which sensitivities to both forest gain and loss are positive, enabling evaluation of asymmetric responses to equal-magnitude forest changes. Due to limited sample size, forest gain and loss effects at 0.5° are summarized across all regions where sensitivities for each variable (ΔET and ΔP) are positive. Bars indicate the mean ΔET and ΔP, with error bars denoting standard errors across n product combinations: n = 21 for precipitation and n = 12 for ET. Basemap data in a–d from Natural Earth (https://www.naturalearthdata.com).
Extended Data Fig. 4 Hydrological reversibility along idealized forest–non-forest–forest trajectories.
a–f, Idealized changes in evapotranspiration (∆ET) and precipitation (∆P) along forest transition trajectories in South America (a, b), Africa (c, d), and Southeast Asia (SEA; e, f). Red curves represent the effects of progressive forest loss from intact forest (initial proportion ≈100%) to open non-forest state ( ≈ 0%), whereas blue curves represent the effects of subsequent forest gain from non-forest to restored forest, and shaded areas denote standard errors across nine product combinations. For precipitation and ET, nine product combinations are used: three land-cover datasets combined with three precipitation products for precipitation, and the same three land-cover datasets combined with three ET products for ET. The vertical dashed line marks the transition from forest loss to subsequent forest gain. All values are expressed relative to intact forest (initial proportion ≈100%), serving as a baseline to assess the reversibility of hydrological impacts. ∆P and ∆ET are aggregated by 10% intervals of forest change, integrating ET and precipitation responses with both the magnitude of forest change and the initial forest proportion. Because of sample-size limitations, we accumulate the results of ~10% forest gain and loss under different initial forest proportions to derive the idealized hydrological trajectory under forest transition.
Extended Data Fig. 5 Evapotranspiration response to planted forest across tropical regions.
a–c, Sensitivities of evapotranspiration (ΔET) to forest age across three tropical regions: (a) South America, (b) Africa, and (c) Southeast Asia (SEA). Sensitivities are computed as the mean annual ET change per 1% forest gain. Solid lines show the mean response and shaded areas indicate ±1 standard error across three ET products.
Extended Data Fig. 6 Evapotranspiration response to tropical moist forest regeneration across tropical regions.
a–c, Sensitivities of evapotranspiration (ΔET) to forest age across three tropical regions: (a) South America, (b) Africa, and (c) Southeast Asia (SEA). Sensitivities are computed as the mean annual ET change per 1% forest gain. Solid lines show the mean response and shaded areas indicate ±1 standard error across three ET products.
Extended Data Fig. 7 Consistency between precipitation and evapotranspiration sensitivities in regions with positive precipitation sensitivity.
Consistency of precipitation sensitivity to forest gain (ΔPgain) and loss (ΔPloss) with corresponding evapotranspiration sensitivity (ΔET). Bars indicate mean consistency, with error bars denoting standard errors across nine product combinations. For precipitation and ET, nine product combinations are used: three land-cover datasets combined with three precipitation products and three ET products, respectively.
Extended Data Fig. 8 Modelled impacts of forest gain and loss on leaf area index (LAI) in CMIP6 simulations.
a,b, Comparison of the impacts of forest gain and loss on LAI for all regions (a) and for regions with positive sensitivity (b) across different models. Bars show multi-model mean responses across seven climate models and symbols indicate individual climate models.
Supplementary information
Supplementary Text 1, Figs. 1–16 and Tables 1–5.
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Ma, S., Zhou, S., Ellison, D. et al. Targeted tropical forest restoration can offset deforestation-induced water flux losses.
Nat. Clim. Chang. (2026). https://doi.org/10.1038/s41558-026-02709-7
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Version of record:15 July 2026
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DOI
:https://doi.org/10.1038/s41558-026-02709-7
