Articles | Volume 16, issue 6
https://doi.org/10.5194/essd-16-3045-2024
https://doi.org/10.5194/essd-16-3045-2024
Data description paper
 | 
28 Jun 2024
Data description paper |  | 28 Jun 2024

Physical, social, and biological attributes for improved understanding and prediction of wildfires: FPA FOD-Attributes dataset

Yavar Pourmohamad, John T. Abatzoglou, Erin J. Belval, Erica Fleishman, Karen Short, Matthew C. Reeves, Nicholas Nauslar, Philip E. Higuera, Eric Henderson, Sawyer Ball, Amir AghaKouchak, Jeffrey P. Prestemon, Julia Olszewski, and Mojtaba Sadegh

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Cited articles

Abatzoglou, J. T.: Development of gridded surface meteorological data for ecological applications and modelling, Int. J. Climatol., 33, 121–131, 2013. 
Abt, K. L., Butry, D. T., Prestemon, J. P., and Scranton, S.: Effect of fire prevention programs on accidental and incendiary wildfires on tribal lands in the United States, Int. J. Wildl. Fire, 24, 749–762, 2015. 
Aldersley, A., Murray, S. J., and Cornell, S. E.: Global and regional analysis of climate and human drivers of wildfire, Sci. Total Environ., 409, 3472–3481, 2011. 
Alizadeh, M. R., Abatzoglou, J. T., Luce, C. H., Adamowski, J. F., Farid, A., and Sadegh, M.: Warming enabled upslope advance in western US forest fires, P. Natl. Acad. Sci. USA, 118, e2009717118, https://doi.org/10.1073/pnas.2009717118, 2021. 
Alizadeh, M. R., Abatzoglou, J. T., Adamowski, J., Modaresi Rad, A., AghaKouchak, A., Pausata, F. S. R., and Sadegh, M.: Elevation-dependent intensification of fire danger in the western United States, Nat. Commun., 14, 1773, https://doi.org/10.1038/s41467-023-37311-4, 2023. 
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The FPA FOD-Attributes dataset provides > 300 biological, physical, social, and administrative attributes associated with > 2.3×106 wildfire incidents across the US from 1992 to 2020. The dataset can be used to (1) answer numerous questions about the covariates associated with human- and lightning-caused wildfires and (2) support descriptive, diagnostic, predictive, and prescriptive wildfire analytics, including the development of machine learning models.
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