Articles | Volume 21, issue 1
https://doi.org/10.5194/bg-21-279-2024
https://doi.org/10.5194/bg-21-279-2024
Research article
 | 
17 Jan 2024
Research article |  | 17 Jan 2024

A global fuel characteristic model and dataset for wildfire prediction

Joe R. McNorton and Francesca Di Giuseppe

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Cited articles

Agustí-Panareda, A., Massart, S., Chevallier, F., Balsamo, G., Boussetta, S., Dutra, E., and Beljaars, A.: A biogenic CO2 flux adjustment scheme for the mitigation of large-scale biases in global atmospheric CO2 analyses and forecasts, Atmos. Chem. Phys., 16, 10399–10418, https://doi.org/10.5194/acp-16-10399-2016, 2016. 
Badlan, R. L., Sharples, J. J., Evans, J. P., and McRae, R. H. D.: Factors influencing the development of violent pyroconvection. Part I: fire size and stability, Int. J. Wildland Fire, 30, 484–497, https://doi.org/10.1071/WF20040, 2021. 
Boer, M. M., Nolan, R. H., Resco De Dios, V., Clarke, H., Price, O. F., and Bradstock, R. A.: Changing Weather Extremes Call for Early Warning of Potential for Catastrophic Fire, Earths Future, 5, 1196–1202, https://doi.org/10.1002/2017EF000657, 2017. 
Boussetta, S. and Balsamo, G.: Vegetation dataset of Land Use/Land Cover and Leaf Area Index. CONFESS-H2020, https://confessh2020.files.wordpress.com/2021/08/confess-d1-1-v1-0-.pdf (last access: 21 August 2023), 2021. 
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Short summary
Wildfires have wide-ranging consequences for local communities, air quality and ecosystems. Vegetation amount and moisture state are key components to forecast wildfires. We developed a combined model and satellite framework to characterise vegetation, including the type of fuel, whether it is alive or dead, and its moisture content. The daily data is at high resolution globally (~9 km). Our characteristics correlate with active fire data and can inform fire danger and spread modelling efforts.
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