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Background: Wildfires are thought to become more prevalent during periods of extended dry weather. This issue is examined using two dryness metrics commonly applied in Australian fire management agencies. Aims: This paper investigated links between wildfire incidence and the Keetch–Byram Drought Index (KBDI) and McArthur's Drought Factor (DF) across the state of Victoria, Australia. Methods: Weather records and data from 41 418 wildfires that occurred across the State over a 17-year period were compiled to examine the distributions of KBDI and DF on days with fires smaller and larger than 5 ha in area and all days, using kernel density plots. Key results: Days with fires, particularly days with fires that escaped initial attack, have higher DFs and KBDIs compared with all days. These differences vary between regions and are greatest in areas with moist climates. Conclusions and implications: An appreciation of dryness conditions using tools such as KBDI and DF is useful for understanding fire potential, particularly in areas that experience higher and more regular rainfall. McArthur's Drought Factor and the Keetch–Byram Drought Index are higher than normal on days with fires in Victoria, Australia. These metrics provide a more reliable indication of fire potential in high-rainfall areas than in lower-rainfall areas. This article belongs to the Collection Fire and Climate. [ABSTRACT FROM AUTHOR]

Background. The small portion of fires that escape initial attack (IA) have the greatest impacts on communities and incur most suppression costs. Early identification of fires with potential for escaping IA can prompt fire managers to order additional suppression resources, issue timely public warnings and plan longer-term containment strategies when they have the greatest potential for reducing a fire's impact. Aims. To develop IA models from a state-wide incident dataset containing novel variables that can be used to estimate the probability of IA when a new fire has been reported. Methods. A large dataset was compiled from bushfire incident records, geographical data and weather observations across the state of Victoria (n = 35 154) and was used to develop logistic regression models predicting the probability of initial attack success in grassland-, forest- and shrubland-dominated vegetation types. Key results. Models including input variables describing weather conditions, travel delay, slope and distance from roads were able to reasonably discriminate fires contained to 5 ha. Conclusions and implications. The models can be used to estimate IA success - using information available when the location of a new fire can be estimated - and they can be used to prompt planning for larger fires. [ABSTRACT FROM AUTHOR]