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Drought legacies delay spring green-up in northern ecosystems

Nature Climate Change volume 15, pages 444–451 (2025)Cite this article

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Abstract

Global warming has increased the frequency and intensity of droughts, causing large impacts on the structure and functioning of terrestrial ecosystems. The direct effect of droughts on autumn senescence is well-documented, but the extent to which the legacy effects influence plant phenology of the following year remains unclear. Using satellite greenness data and long-term in situ observations, we demonstrate that droughts substantially delay the green-up and leaf unfolding of the next spring, particularly following prolonged events with delayed soil moisture recovery. These delays cannot be explained by state-of-the-art phenology models and are strongly linked to postdrought temperature, local climate, drought characteristics and reductions in photosynthesis. Compared to the endogenous memory effects within plants themselves, the exogenous memory effects through changes in environment are five times stronger in drylands and twice as strong in non-drylands. Given projections of increased drought frequency and severity, future advances in spring phenology may be less pronounced than previously anticipated.

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Fig. 1: Effects of drought on the SOS of the next year from GIMMS NDVI 3g dataset.
Fig. 2: Effects of drought on LUD of next year using ground-based observations from three phenological networks.
Fig. 3: Effects of three types of drought on the SOS of the next year.
Fig. 4: Response functions for SOS changes (SOSnext − SOScurrent) following three types of drought.
Fig. 5: Path diagrams illustrating the underlying mechanisms of legacy effect of drought.

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Data availability

The data that support the findings of this study are derived from the following resources. The PEP725 dataset can be downloaded from www.pep725.eu. The RCNN dataset can be downloaded from https://doi.org/10.1038/s41597-020-0376-z. The CPON dataset can be downloaded from https://data.casearth.cn/dataset/5c19a5650600cf2a3c557ab1. The GIMMS NDVI 3g v.1 is available at https://data.tpdc.ac.cn/zh-hans/data/9775f2b4-7370-4e5e-a537-3482c9a83d88. The SM data are available at https://www.gleam.eu/. The SPEI dataset is available at https://spei.csic.es/database.html. The CRU climate dataset is available at https://crudata.uea.ac.uk/cru/data/hrg/. FLUXCOM GPP dataset can be downloaded from https://www.fluxcom.org/. The maximum root-depth data are available at https://wci.earth2observe.eu/thredds/catalog/usc/root-depth/catalog.html. The plant biodiversity data are available at http://ecotope.org/anthromes/biodiversity/plants/data/. The mean above-ground biomass data are available at https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1763. The iso/anisohydry data are available via figshare at https://doi.org/10.6084/m9.figshare.5323987.v1 (ref. 94). The biomes data can be downloaded from https://www.worldwildlife.org/publications/terrestrial-ecoregions-of-the-world. The land cover data are available at https://lpdaac.usgs.gov/products/mcd12q1v006/. The soil properties data can be downloaded from https://daac.ornl.gov/SOILS/guides/HWSD.html. The soil total phosphorus concentration is available via figshare at https://doi.org/10.6084/m9.figshare.14583375 (ref. 95). The soil total nitrogen concentration is available at https://www.isric.org/explore/soilgrids/. Source data are provided with this paper.

Code availability

Main codes used for data processing in this study are available via figshare at https://doi.org/10.6084/m9.figshare.26130907 (ref. 96).

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Acknowledgements

This study is supported by the National Key R&D Program of China (2023YFF0805702) and the National Natural Science Foundation of China (42141005). Y.L. acknowledges additional support from the National Natural Science Foundation of China (42301016). S.A.K. was supported by the US National Science Foundation Division of Environmental Biology award no. 2331162. J.P. was supported by the Catalan Government grant AGAUR2023 CLIMA 00118. This work is supported by High-performance Computing Platform of Peking University.

Author information

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

    Ying Liu, Yao Zhang, Haibo Gong, Wenping Yuan & Shilong Piao

  2. CREAF, Barcelona, Spain

    Josep Peñuelas

  3. CSIC, Global Ecology Unit CREAF-CSIC-UAB, Barcelona, Spain

    Josep Peñuelas

  4. Department of Biology, West Virginia University, Morgantown, WV, USA

    Steven A. Kannenberg

  5. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China

    Chaoyang Wu

  6. Faculty of Geographical Science, Beijing Normal University, Beijing, China

    Sha Zhou

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Contributions

Y.Z. conceived the idea. Y.Z. and Y.L. designed the study. Y.L. performed the analysis. Y.L. and Y.Z. prepared the figures and wrote the first draft of the manuscript. All authors contributed to the interpretation of the results and the revisions of the text.

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Correspondence to Yao Zhang.

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Nature Climate Change thanks Cyrille Rathgeber and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 The schematic diagram of growing season drought identification using soil moisture (SM) and Normalized Difference Vegetation Index (NDVI).

A growing season effective drought occurred when deseasonalized soil moisture is below 0.5 s.d. for consecutive two months within growing season, and deseasonalized NDVI is below −0.5 s.d. east one month simultaneously. Type 1 drought: SM has not been recovered before the next growing season. Type 2 drought: SM recovered after the current growing season. Type 3 drought: SM recovered (SM anomaly higher than 0) within the current growing season. The blue background indicates the growing season, and the red background represents drought events.

Extended Data Fig. 2 The long-term effects of drought on the start of growing season (SOS) during 1982-2015.

Spatial distribution of cumulative changes between next year’s SOS (SOSnext) and current year’s SOS (SOScurrent), normalized by the average over 34 years, when droughts occurred (a) and not occurred (b). Insets show the area fraction of SOS delayed (orange) or advanced (blue) by drought.

Extended Data Fig. 3 Effects of drought on next year’s leaf unfolding date (LUD) using ground-based observations of eight species.

Changes in LUDnext − LUDcurrent when drought occurred or not for Aesculus hippocastanum L. (a), Betula pendula Roth (b), Fagus sylvatica L. (c), Larix decidua Mill. (d), Quercus robur L. (e), Sorbus aucuparia L. (f), Tilia cordata Mill. (g) and Tilia platyphyllos Scop. (h).

Extended Data Fig. 4 The spatial patterns of the correlation between current year’s EOS and next year’s SOS.

The results for non-drought years (a, d, g, j), all years (b), Type 1 and non-drought years (e), Type 2 and non-drought years (h), Type 3 and non-drought years (k). The percentage of negative correlation between EOS and next year’s SOS along latitudes (c, f, i and l).

Extended Data Fig. 5 Response functions for start of the growing season (SOS) changes (SOSobs−SOSpred) following three types of droughts.

Results from three random forest models for Type 1 (a), Type 2 (b), and Type 3 (c) droughts. Left panels show response functions with lower and upper bounds of independent variables. Bars on the right indicate variable importance. Blue denotes climatic factors, yellow represents drought characteristics, green shows biological variables, and red indicates soil composition variables. Variables with black borders are time-varying for each drought event; others are static. Code for biome types 1. Temperate Broadleaf and Mixed Forests; 2. Temperate Coniferous Forests; 3. Boreal Forests/Taiga; 4. Temperate Grasslands, Savannas, and shrublands; 5. Montane Grasslands and shrublands; 6. Tundra; 7. Mediterranean Forests; 8. Xeric shrublands.

Extended Data Fig. 6 The path diagrams and path effects of the underlying mechanisms for the relationship between the soil moisture loss (SM loss) and the anomaly of the start of the growing season (SOS) for different arid types.

a-d, The results for arid (a), semi-arid (b), dry sub-humid (c) and humid (d) regions. The numbers represent the mean of standardized path coefficients, with asterisks denote the significance (**P < 0.01; *P < 0.05). The colors and widths of the arrows represent the signs (blue for negative, red for positive) and magnitudes of the path coefficients, respectively. The significance was based on a two-tailed Student’s t-test. En1, En2, Ex1, Ex2, Ex3, Ex32 indicate the effect of six major paths; EnT is the total endogenous effect, and ExT is the total exogenous effect.

Extended Data Fig. 7 The path diagrams and path effects of the underlying mechanisms for the relationship between the soil moisture loss (SM loss) and the anomaly of the start of the growing season (SOS) for eight biomes.

a-h, The results for temperate broadleaf and mixed forests (a), temperate coniferous forests (b), boreal forests/taiga (c), tundra (d), montane grasslands and shrublands (e), temperate grasslands, savannas and shrublands (f), Mediterranean forests (g) and xeric shrublands (h). The numbers represent the mean of standardized path coefficients, with asterisks denote the significance (**P < 0.01; *P < 0.05). The colors and widths of the arrows represent the signs (blue for negative, red for positive) and magnitudes of the path coefficients, respectively. The significance was based on a two-tailed Student’s t-test. En1, En2, Ex1, Ex2, Ex3, Ex32 indicate the effect of six major paths; EnT is the total endogenous effect, and ExT is the total exogenous effect.

Extended Data Fig. 8 The anomaly of GPP and EOS of drought years across three drought types.

The GPP (a) and EOS (b) anomalies of drought years compared to the multi-year average. Length of each box indicates the interquartile range, the horizontal line inside each box the median, and the bottom and top of the box the first and third quartiles, respectively.

Extended Data Fig. 9 The legacy effect of drought along soil nutrient gradient.

The total endogenous effect and total exogenous effect of drought along soil nitrogen content (a) and soil phosphorus content (b) at 0-30 cm depth. Each dot represents the average path effect for regions within each bin along nitrogen content or phosphorous content. Shades represent the 95% confidence interval.

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Liu, Y., Zhang, Y., Peñuelas, J. et al. Drought legacies delay spring green-up in northern ecosystems. Nat. Clim. Chang. 15, 444–451 (2025). https://doi.org/10.1038/s41558-025-02273-6

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