Articles | Volume 18, issue 12
https://doi.org/10.5194/bg-18-3861-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-18-3861-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
29 Jun 2021
Research article |
| 29 Jun 2021
| 29 Jun 2021
The importance of antecedent vegetation and drought conditions as global drivers of burnt area
Alexander Kuhn-Régnier
CORRESPONDING AUTHOR
Leverhulme Centre for Wildfires, Environment, and Society, London, SW7 2AZ, UK
Department of Physics, Imperial College London, London, SW7 2AZ, UK
Apostolos Voulgarakis
Leverhulme Centre for Wildfires, Environment, and Society, London, SW7 2AZ, UK
Department of Physics, Imperial College London, London, SW7 2AZ, UK
School of Environmental Engineering, Technical University of Crete, Chania, Kounoupidiana,
Akrotiri, 73100 Chania, Greece
Peer Nowack
Department of Physics, Imperial College London, London, SW7 2AZ, UK
Grantham Institute and the Data Science Institute, Imperial College London, London, SW7 2AZ, UK
Climatic Research Unit, School of Environmental Sciences, University of East Anglia, Norwich, NR4 7TJ, UK
Matthias Forkel
Environmental Remote Sensing Group, TU Dresden, Dresden, Germany
I. Colin Prentice
Leverhulme Centre for Wildfires, Environment, and Society, London, SW7 2AZ, UK
Department of Life Sciences, Imperial College London, London, SW7 2AZ, UK
Sandy P. Harrison
Leverhulme Centre for Wildfires, Environment, and Society, London, SW7 2AZ, UK
Geography and Environmental Science, University of Reading, Reading, RG6 6AB, UK
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Cited
32 citations as recorded by crossref.
- Estimating leaf moisture content at global scale from passive microwave satellite observations of vegetation optical depth M. Forkel et al.
- Deep Learning Surrogate Models of JULES-INFERNO for Wildfire Prediction on a Global Scale S. Cheng et al.
- Multifactorial interactions contribute to contrasting wildfire trends at mid–high latitudes of the Northern Hemisphere H. Zhao et al.
- Land and Atmosphere Precursors to Fuel Loading, Wildfire Ignition and Post‐Fire Recovery M. Alizadeh et al.
- Climate-corrected modeling of biomass burning in Equatorial Asia: the roles of climate change and anthropogenic control S. Yin et al.
- High Resolution Forecasting of Summer Drought in the Western United States R. Abolafia‐Rosenzweig et al.
- SMLFire1.0: a stochastic machine learning (SML) model for wildfire activity in the western United States J. Buch et al.
- The response of wildfire regimes to Last Glacial Maximum carbon dioxide and climate O. Haas et al.
- Assessing and explaining temporal deep learning models for wildfire danger prediction P. Becker et al.
- Understanding and simulating cropland and non-cropland burning in Europe using the BASE (Burnt Area Simulator for Europe) model M. Forrest et al.
- Winter and spring climate explains a large portion of interannual variability and trend in western U.S. summer fire burned area R. Abolafia-Rosenzweig et al.
- Fire seasonality drives flowering patterns in the campo rupestre: implications for sempre-vivas management M. de Azevedo et al.
- Monitoring post-fire recovery of various vegetation biomes using multi-wavelength satellite remote sensing E. Bousquet et al.
- Assessing the sensitivity of multi-frequency passive microwave vegetation optical depth to vegetation properties L. Schmidt et al.
- A machine learning approach to quantify meteorological drivers of ozone pollution in China from 2015 to 2019 X. Weng et al.
- Global environmental controls on wildfire burnt area, size, and intensity O. Haas et al.
- Climate drivers of global wildfire burned area M. Grillakis et al.
- The global drivers of wildfire O. Haas et al.
- Compounding preconditions of wildfires vary in time and space within Europe J. Miller et al.
- Fuel availability versus flammability: divergent fire controls across Eurasian drylands H. Yu et al.
- Advancing causal inference in ecology: Pathways for biodiversity change detection and attribution F. Schrodt et al.
- Development of a statistical model for global burned area simulation within a DGVM-compatible framework B. Kavhu et al.
- Global and Regional Trends and Drivers of Fire Under Climate Change M. Jones et al.
- High-resolution mapping of wildfire drivers in California based on machine learning L. Qiu et al.
- Examining wildfire dynamics using ECOSTRESS data with machine learning approaches: the case of South‐Eastern Australia's black summer Y. Zhu et al.
- Modelling the daily probability of wildfire occurrence in the contiguous United States T. Keeping et al.
- Reconstructing burnt area during the Holocene: an Iberian case study Y. Shen et al.
- Divergent burned-area trends in Southern and Northern-Hemisphere Africa over the first half of the 21st century S. Mohammed et al.
- Forest fire threatens global carbon sinks and population centres under rising atmospheric water demand H. Clarke et al.
- A data-driven model for Fennoscandian wildfire danger S. Bakke et al.
- Wildfires on a changing planet O. Haas et al.
- Wildfire risk in a changing climate: Evaluating fire weather indices and their global patterns with CMIP6 multi-model projections Y. He et al.
32 citations as recorded by crossref.
- Estimating leaf moisture content at global scale from passive microwave satellite observations of vegetation optical depth M. Forkel et al.
- Deep Learning Surrogate Models of JULES-INFERNO for Wildfire Prediction on a Global Scale S. Cheng et al.
- Multifactorial interactions contribute to contrasting wildfire trends at mid–high latitudes of the Northern Hemisphere H. Zhao et al.
- Land and Atmosphere Precursors to Fuel Loading, Wildfire Ignition and Post‐Fire Recovery M. Alizadeh et al.
- Climate-corrected modeling of biomass burning in Equatorial Asia: the roles of climate change and anthropogenic control S. Yin et al.
- High Resolution Forecasting of Summer Drought in the Western United States R. Abolafia‐Rosenzweig et al.
- SMLFire1.0: a stochastic machine learning (SML) model for wildfire activity in the western United States J. Buch et al.
- The response of wildfire regimes to Last Glacial Maximum carbon dioxide and climate O. Haas et al.
- Assessing and explaining temporal deep learning models for wildfire danger prediction P. Becker et al.
- Understanding and simulating cropland and non-cropland burning in Europe using the BASE (Burnt Area Simulator for Europe) model M. Forrest et al.
- Winter and spring climate explains a large portion of interannual variability and trend in western U.S. summer fire burned area R. Abolafia-Rosenzweig et al.
- Fire seasonality drives flowering patterns in the campo rupestre: implications for sempre-vivas management M. de Azevedo et al.
- Monitoring post-fire recovery of various vegetation biomes using multi-wavelength satellite remote sensing E. Bousquet et al.
- Assessing the sensitivity of multi-frequency passive microwave vegetation optical depth to vegetation properties L. Schmidt et al.
- A machine learning approach to quantify meteorological drivers of ozone pollution in China from 2015 to 2019 X. Weng et al.
- Global environmental controls on wildfire burnt area, size, and intensity O. Haas et al.
- Climate drivers of global wildfire burned area M. Grillakis et al.
- The global drivers of wildfire O. Haas et al.
- Compounding preconditions of wildfires vary in time and space within Europe J. Miller et al.
- Fuel availability versus flammability: divergent fire controls across Eurasian drylands H. Yu et al.
- Advancing causal inference in ecology: Pathways for biodiversity change detection and attribution F. Schrodt et al.
- Development of a statistical model for global burned area simulation within a DGVM-compatible framework B. Kavhu et al.
- Global and Regional Trends and Drivers of Fire Under Climate Change M. Jones et al.
- High-resolution mapping of wildfire drivers in California based on machine learning L. Qiu et al.
- Examining wildfire dynamics using ECOSTRESS data with machine learning approaches: the case of South‐Eastern Australia's black summer Y. Zhu et al.
- Modelling the daily probability of wildfire occurrence in the contiguous United States T. Keeping et al.
- Reconstructing burnt area during the Holocene: an Iberian case study Y. Shen et al.
- Divergent burned-area trends in Southern and Northern-Hemisphere Africa over the first half of the 21st century S. Mohammed et al.
- Forest fire threatens global carbon sinks and population centres under rising atmospheric water demand H. Clarke et al.
- A data-driven model for Fennoscandian wildfire danger S. Bakke et al.
- Wildfires on a changing planet O. Haas et al.
- Wildfire risk in a changing climate: Evaluating fire weather indices and their global patterns with CMIP6 multi-model projections Y. He et al.
Saved (final revised paper)
Latest update: 14 May 2026
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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