Deforestation Increases Vegetation Vulnerability to Drought Across Biomes

Chenwei Xiao, Sönke Zaehle, Stephen Sitch, Gregory Duveiller, Daniel E. Pabon-Moreno, Anthony P. Walker, Jürgen Knauer, Fabienne Maignan, Christiane Schmullius, Ana Bastos

Research output: Contribution to journalArticlepeer-review

Abstract

Land use and land cover changes have altered terrestrial ecosystem carbon storage, but their impacts on ecosystem sensitivity to drought and temperature fluctuations have not been evaluated spatially over the globe. We estimate drought and temperature sensitivities of ecosystems using vegetation greenness from satellite observations and vegetation biomass from dynamic global vegetation model (DGVM) simulations. Using a space-for-time substitution with satellite data, we first illustrate the effects of vegetation cover changes on drought and temperature sensitivity and compare them with the effects estimated from DGVMs. We also compare simulations forced by scenarios with and without land cover changes to estimate the historical land cover change effects. Satellite data and vegetation models both show that converting forests to grasslands results in a more negative or decreased positive sensitivity of vegetation greenness or biomass to drought. Significant variability exists among models for other types of land cover transitions. We identify substantial effects of historical land cover changes on drought sensitivity from model simulations with a generally positive direction globally. Deforestation can lead to either an increased negative sensitivity, as drought-tolerant forests are replaced by grasslands based on model ensemble mean, or a decreased negative sensitivity, since forests under current land cover are predicted to exhibit greater drought resistance compared to those under pre-industrial land cover. Overall, our findings emphasize the critical role of forests in maintaining ecosystem stability and resistance to drought and temperature fluctuations, thereby implying their importance in stabilizing the carbon stock under increasingly extreme climate conditions.

Original languageEnglish
Article numbere2024GB008378
JournalGlobal Biogeochemical Cycles
Volume39
Issue number5
DOIs
StatePublished - May 2025

Funding

Chenwei Xiao acknowledges support from the International Max Planck Research School for Global Biogeochemical Cycles. The authors would like to thank Ulrich Weber for the preparation of TerraClimate data, MODIS land cover, MODIS NDVI, and ESA CCI PFT maps. The authors thank Dr. Peter Anthoni for the insightful discussions related to LPJ‐GUESS simulations. ORNL is managed by UT‐Battelle LLC for the DOE under contract DE‐AC05‐1008 00OR22725. Sönke Zaehle acknowledges funding by the European Commission H2020 research project Carbon–Climate Interaction in the Coming Century (4C) under Grant 821003. Ana Bastos acknowledges funding by the European Union (ERC StG, ForExD, Grant 101039567). Gregory Duveiller acknowledges funding by the European Research Council (ERC) Synergy Grant 'Understanding and modelling the Earth System with Machine Learning (USMILE)' under the Horizon 2020 research and innovation programme (Grant Agreement No. 855187). Gregory Duveiller and Daniel E. Pabon‐Moreno acknowledge funding from the European Union’s Horizon Europe Research and Innovation programme under grant agreement no. 101059548 (OEMC project). Open Access funding enabled and organized by Projekt DEAL. Chenwei Xiao acknowledges support from the International Max Planck Research School for Global Biogeochemical Cycles. The authors would like to thank Ulrich Weber for the preparation of TerraClimate data, MODIS land cover, MODIS NDVI, and ESA CCI PFT maps. The authors thank Dr. Peter Anthoni for the insightful discussions related to LPJ-GUESS simulations. ORNL is managed by UT-Battelle LLC for the DOE under contract DE-AC05-1008 00OR22725. Sönke Zaehle acknowledges funding by the European Commission H2020 research project Carbon–Climate Interaction in the Coming Century (4C) under Grant 821003. Ana Bastos acknowledges funding by the European Union (ERC StG, ForExD, Grant 101039567). Gregory Duveiller acknowledges funding by the European Research Council (ERC) Synergy Grant 'Understanding and modelling the Earth System with Machine Learning (USMILE)' under the Horizon 2020 research and innovation programme (Grant Agreement No. 855187). Gregory Duveiller and Daniel E. Pabon-Moreno acknowledge funding from the European Union’s Horizon Europe Research and Innovation programme under grant agreement no. 101059548 (OEMC project). Open Access funding enabled and organized by Projekt DEAL.

Keywords

  • TRENDY
  • drought stress
  • dynamic global vegetation models
  • ecosystem sensitivity
  • land cover change
  • satellite data

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