Fire management decision support
The modelling framework presented here has several features that are
useful at regional scale for fire managers. It can incorporate long-term
field-based fuel hazard surveys. It can use a comprehensive selection of
predictors including climate, terrain, soil and vegetation. It can
produce output that is interpretable and useful to fire managers and at
a resolution that is relevant for operational management (Penman et al.,
2022). Despite the high resolution, it has relatively low computational
demand for regional projections (i.e., does not require high-performance
computing). The comprehensiveness separated this approach from previous
empirical models that only consider a subset of predictors (e.g., Pierce
et al., 2012; Jenkins et al., 2017; McCollâGausden et al., 2020) while
the high resolution (90 m) is what current process-based modelling
cannot provide (Rabin et al., 2017).
The random forest model predicted increased P4_5 in the
future for areas currently under semiarid climates (Figure 4). This
prediction is consistent with a previous analysis on historical burnt
area and aridity (Kelley et al., 2019). Combining the finding with
current knowledge on key limiting factors in different regions
(Archibald et al., 2013; Bradstock et al., 2014; Boer et al., 2016), our
predictions indicate changes in future fire regime as current semiarid
regions showing increasing fuel load in elevated stratum. Specific fire
management measures should be thus based on the actual fuel hazard and
its change in all fuel strata (Figure 3; Figure S4 and S5).