The importance of antecedent vegetation and drought conditions as global drivers of burnt area

Alexander Kuhn-Régnier, Apostolos Voulgarakis, Peer Nowack, Matthias Forkel, I. Colin Prentice, Sandy P. Harrison

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Abstract

The seasonal and longer-term dynamics of fuel accumulation affect fire seasonality and the occurrence of extreme wildfires. Failure to account for their influence may help to explain why state-of-the-art fire models do not simulate the length and timing of the fire season or interannual variability in burnt area well. We investigated the impact of accounting for different timescales of fuel production and accumulation on burnt area using a suite of random forest regression models that included the immediate impact of climate, vegetation, and human influences in a given month and tested the impact of various combinations of antecedent conditions in four productivity-related vegetation indices and in antecedent moisture conditions. Analyses were conducted for the period from 2010 to 2015 inclusive. Inclusion of antecedent vegetation conditions representing fuel build-up led to an improvement of the global, climatological out-of-sample R2 from 0.579 to 0.701, but the inclusion of antecedent vegetation conditions on timescales ≥1 year had no impact on simulated burnt area. Current moisture levels were the dominant influence on fuel drying. Additionally, antecedent moisture levels were important for fuel build-up. The models also enabled the visualisation of interactions between variables, such as the importance of antecedent productivity coupled with instantaneous drying. The length of the period which needs to be considered varies across biomes; fuel-limited regions are sensitive to antecedent conditions that determine fuel build-up over longer time periods (g1/44 months), while moisture-limited regions are more sensitive to current conditions that regulate fuel drying.

Original languageEnglish
Pages (from-to)3861-3879
Number of pages19
JournalBiogeosciences
Volume18
Issue number12
DOIs
Publication statusPublished - 29 Jun 2021

Keywords

  • Wildfires
  • Remote sensing
  • Climate change
  • Vegetation
  • Burnt Area
  • Machine Learning
  • Droughts
  • Earth observations

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