Preprints
https://doi.org/10.5194/essd-2023-430
https://doi.org/10.5194/essd-2023-430
09 Nov 2023
 | 09 Nov 2023
Status: this preprint is currently under review for the journal ESSD.

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

Abstract. Wildfires are increasingly impacting social and environmental systems in the United States. The ability to mitigate the adverse effects of wildfires increases with understanding of the social, physical, and biological conditions that co-occurred with or caused the wildfire ignitions and contributed to the wildfire impacts. To this end, we developed the FPA FOD-Attributes dataset, which augments the sixth version of the Fire Program Analysis-Fire Occurrence Database (FPA FOD v6) with nearly 270 attributes that coincide with the date and location of each wildfire ignition in the United States. FPA FOD v6 contains information on location, jurisdiction, discovery time, cause, and final size of >2.3 million wildfires from 1992–2020 in the United States. For each wildfire, we added physical (e.g., weather, climate, topography, infrastructure), biological (e.g., land cover, normalized difference vegetation index), social (e.g., population density, social vulnerability index), and administrative (e.g., national and regional preparedness level, jurisdiction) attributes. This publicly available dataset can be used to answer numerous questions about the covariates associated with human- and lightning-caused wildfires. Furthermore, the FPA FOD-Attributes dataset can support descriptive, diagnostic, predictive, and prescriptive wildfire analytics, including development of machine learning models. The FPA FOD-Attributes dataset is available at https://zenodo.org/record/8381129 (Pourmohamad et al. 2023).

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

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2023-430', Anonymous Referee #1, 05 Jan 2024
  • RC2: 'Comment on essd-2023-430', Anonymous Referee #2, 28 Feb 2024
  • RC3: 'Comment on essd-2023-430', Anonymous Referee #3, 11 Mar 2024
  • RC4: 'Comment on essd-2023-430', Anonymous Referee #4, 04 Apr 2024
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

Data sets

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, Mojtaba Sadegh https://zenodo.org/record/8381129

Model code and software

FPA FOD-Attributes Python Codes Yavar Pourmohamad https://github.com/YavarPourmohamad/FPA-FOD.git

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

Viewed

Total article views: 416 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
305 83 28 416 20 19 17
  • HTML: 305
  • PDF: 83
  • XML: 28
  • Total: 416
  • Supplement: 20
  • BibTeX: 19
  • EndNote: 17
Views and downloads (calculated since 09 Nov 2023)
Cumulative views and downloads (calculated since 09 Nov 2023)

Viewed (geographical distribution)

Total article views: 404 (including HTML, PDF, and XML) Thereof 404 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 28 Apr 2024
Download
Short summary
This dataset provides >300 biological, physical, social and administrative attributes associated with >2.3 million wildfire incidents across the US from 1992–2020.
Altmetrics