Articles | Volume 21, issue 1
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


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-1984', Anonymous Referee #1, 25 Sep 2023
  • RC2: 'Comment on egusphere-2023-1984', Anonymous Referee #2, 02 Nov 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Reconsider after major revisions (18 Nov 2023) by Paul Stoy
AR by Joe McNorton on behalf of the Authors (20 Nov 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (20 Nov 2023) by Paul Stoy
RR by Anonymous Referee #2 (27 Nov 2023)
ED: Publish as is (27 Nov 2023) by Paul Stoy
AR by Joe McNorton on behalf of the Authors (28 Nov 2023)  Manuscript 
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.
Final-revised paper