Feedback between drought and deforestation in the Amazon

Arie Staal*, Bernardo M. Flores, Ana Paula D. Aguiar, Joyce H.C. Bosmans, Ingo Fetzer, Obbe A. Tuinenburg

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

15 Citations (Scopus)

Abstract

Deforestation and drought are among the greatest environmental pressures on the Amazon rainforest, possibly destabilizing the forest-climate system. Deforestation in the Amazon reduces rainfall regionally, while this deforestation itself has been reported to be facilitated by droughts. Here we quantify the interactions between drought and deforestation spatially across the Amazon during the early 21st century. First, we relate observed fluctuations in deforestation rates to dry-season intensity; second, we determine the effect of conversion of forest to cropland on evapotranspiration; and third, we simulate the subsequent downwind reductions in rainfall due to decreased atmospheric water input. We find large variability in the response of deforestation to dry-season intensity, with a significant but small average increase in deforestation rates with a more intense dry season: With every mm of water deficit, deforestation tends to increase by 0.13% per year. Deforestation, in turn, has caused an estimated 4% of the recent observed drying, with the south-western part of the Amazon being most strongly affected. Combining both effects, we quantify a reinforcing drought-deforestation feedback that is currently small, but becomes gradually stronger with cumulative deforestation. Our results suggest that global climate change, not deforestation, is the main driver of recent drying in the Amazon. However, a feedback between drought and deforestation implies that increases in either of them will impede efforts to curb both.

Original languageEnglish
Article number044024
JournalEnvironmental Research Letters
Volume15
Issue number4
DOIs
Publication statusPublished - Apr 2020

Keywords

  • Agriculture
  • Fire
  • Forest clearing
  • Land use change
  • Modeling
  • Moisture recycling
  • Remote sensing

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