Predictors of fuel hazard
Based on our analysis we recommend using a combination of climate, fine
scale topographic attributes and plant response to climate change for
fuel projections. We found CO2 fertilisation to
contribute up to 12% of P4_5 in Victoria (Figure 4).
Empirical models that do not include rainfall seasonality are unlikely
to capture the actual change of fuel hazard score under future climate
in Victoria where rainfall seasonality is a key driver of plant
productivity (Figure 5) despite the optimal LAI ranked only in the
middle in the importance list (Figure 2). Although this finding does not
directly apply to other regions, the approach is generalisable and could
help extract knowledge from field-based observations across the world.
In contrast, land surface models running on coarse resolution
(>5 km) generally cannot resolve terrain-driven variation
in plant growth (e.g., Clark et al., 2016; Wu et al. 2022; but see
Fiddes et al., 2022) and are unable to capture fine-scale variations in
fuel hazard scores (Figure 6). Although spatial resolution might not be
critical in capturing global trends in fire risk, fine scale predictions
are crucial for operational fire management on regional scales (Bale et
al., 1998; Nyman et al., 2015; Inbar et al., 2018).
Despite the good overall predictive performance, this machine learning
approach has four major limitations: 1. It cannot provide information
about the transient response of vegetation structural change due to
gradual climate change; 2. It does not mechanistically model the impacts
of plant composition and range shifts on fuel structure; 3. The ML
models do not explicitly consider climate driven changes in fire regimes
and the associated feedbacks with vegetation, which potentially affects
vegetation distribution and fuel accumulation (e.g., Murphy and Bowman,
2012). 4. Human activities (e.g., land use change and fire hazard
reduction efforts) are not included in the models but could result in
substantial changes in future fire characteristics (e.g., Wu et al.,
2022). Our approach aimed to quantify the potential for changes in fuel
hazard as set by environmental and biological constrains. The
shortcomings of this study could be addressed by process-based models
which require significant developments in the computational capacity and
the understanding of climate-vegetation-fire interactions.