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Biogeosciences An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/bg-2020-409
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-2020-409
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  11 Nov 2020

11 Nov 2020

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This preprint is currently under review for the journal BG.

Quantifying the Importance of Antecedent Fuel-Related Vegetation Properties for Burnt Area using Random Forests

Alexander Kuhn-Régnier1,2, Apostolos Voulgarakis1,2,3, Peer Nowack2,4,5, Matthias Forkel6, I. Colin Prentice1,7, and Sandy P. Harrison1,8 Alexander Kuhn-Régnier et al.
  • 1Leverhulme Centre for Wildfires, Environment, and Society
  • 2Department of Physics, Imperial College London, UK
  • 3School of Environmental Engineering, Technical University of Crete, Chania, Greece
  • 4Grantham Institute and the Data Science Institute, Imperial College London, UK
  • 5Climatic Research Unit, School of Environmental Sciences, University of East Anglia, UK
  • 6Environmental Remote Sensing Group, TU Dresden, Dresden, Germany
  • 7Department of Life Sciences, Imperial College London, UK
  • 8Geography and Environmental Science, University of Reading, UK

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. We showed that the inclusion of antecedent vegetation conditions on timescales > 1 yr had no impact on burnt area, but inclusion of antecedent vegetation conditions representing fuel build-up led to an improvement of the global, climatological out-of-sample R2 from 0.567 to 0.686. The inclusion of antecedent moisture conditions also improved the simulation of burnt area through its influence on fuel build-up, which is additional to the influence of current moisture levels on fuel drying. The length of the period which needs to be considered to account for fuel build-up varies across biomes; fuel-limited regions are sensitive to antecedent conditions over longer time periods (~4 months) and moisture-limited regions are more sensitive to current conditions.

Alexander Kuhn-Régnier et al.

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Alexander Kuhn-Régnier et al.

Model code and software

fuel-build-up (version 0.1.0) Alexander Kuhn-Regnier https://doi.org/10.5281/zenodo.4167733

wildfires (version 0.6.3) Alexander Kuhn-Regnier https://doi.org/10.5281/zenodo.4167172

era5analysis (version 0.2.1) Alexander Kuhn-Regnier https://doi.org/10.5281/zenodo.4173493

ALEPython (version 0.3.2) Alexander Kuhn-Regnier, Maxime Jumelle, and Sanjif Rajaratnam https://doi.org/10.5281/zenodo.4171102

Alexander Kuhn-Régnier et al.

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Short summary
Along with current climate, vegetation, and human influences, long-term accumulation of biomass affects fires. Here, we find that including the influence of antecedent vegetation and moisture improves our ability to predict global burnt area. Additionally, the length of the preceding period which needs to be considered for accurate predictions varies across regions.
Along with current climate, vegetation, and human influences, long-term accumulation of biomass...
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