Preprints
https://doi.org/10.5194/nhess-2022-52
https://doi.org/10.5194/nhess-2022-52
 
08 Mar 2022
08 Mar 2022
Status: a revised version of this preprint was accepted for the journal NHESS and is expected to appear here in due course.

Modelling ignition probability for human- and lightning-caused wildfires in Victoria, Australia

Annalie Dorph1,2, Erica Marshall1, Kate A. Parkins1, and Trent D. Penman1 Annalie Dorph et al.
  • 1FLARE Wildfire Research, School of Ecosystem and Forest Sciences, University of Melbourne, Creswick, Victoria, Australia
  • 2School of Environmental and Rural Science, University of New England, Armidale, NSW, Australia

Abstract. Wildfires pose a significant risk to people and property which is expected to grow with urban expansion into fire-prone landscapes and climate change causing increases in fire extent, severity and frequency. Identifying spatial patterns associated with wildfire activity is important for assessing the potential impacts of wildfires on human life, property and other values. Here, we model the probability of fire ignitions in vegetation across Victoria, Australia to determine the key drivers of human- and lightning-caused wildfire ignitions. In particular, we extend previous research to consider the role fuel moisture has in predicting ignition probability while accounting for environmental and local conditions previously identified as important. We used Random Forests to test the effect of variables measuring infrastructure, topography, climate, fuel and soil moisture, fire history, and local weather conditions to investigate what factors drove ignition probability for human- and lightning-caused ignitions. Human-caused ignitions were predominantly influenced by measures of infrastructure and local weather. Lightning-sourced ignitions were driven by fuel moisture, average annual rainfall and local weather. Both human- and lightning-caused ignitions were influenced by dead fuel moisture with ignitions more likely to occur when dead fuel moisture dropped below 20 %. In future, these models of ignition probability may be used to produce spatial likelihood maps which will improve our models of future wildfire risk and enable land managers to better allocate resources to areas of increased fire risk during the fire season.

Annalie Dorph et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2022-52', Tomàs Artés Vivancos, 30 Mar 2022
    • AC1: 'Reply on RC1', Annalie Dorph, 19 May 2022
  • RC2: 'Comment on nhess-2022-52', Anonymous Referee #2, 10 Apr 2022
    • AC2: 'Reply on RC2', Annalie Dorph, 19 May 2022

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2022-52', Tomàs Artés Vivancos, 30 Mar 2022
    • AC1: 'Reply on RC1', Annalie Dorph, 19 May 2022
  • RC2: 'Comment on nhess-2022-52', Anonymous Referee #2, 10 Apr 2022
    • AC2: 'Reply on RC2', Annalie Dorph, 19 May 2022

Annalie Dorph et al.

Annalie Dorph et al.

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Short summary
Wildfire spatial patterns are determined by fire ignition sources and vegetation fuel moisture. Fire ignitions can be: (1) human-mediated – due to proximity to human infrastructure, or (2) lightning-caused – due to fuel moisture, average annual rainfall and local weather. When moisture in dead vegetation is below 20 % the probability of a wildfire increases. The results of this research enable accurate spatial mapping of ignition probability to aid in fire suppression efforts and future research.
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