Introduction
Recent catastrophic fires around the world have drawn attention to the
need for improved fire risk assessments (Bowman et al., 2020; Duane et
al., 2021). The likelihood of fire in terrestrial ecosystems is a
function of: (i) the fuel hazard (i.e., the amount, density and
three-dimensional distribution of plant biomass, both dead and alive),
(ii) fuel dryness, (iii) the weather conditions, and (iv) the
availability of ignition sources (Bradstock, 2010; Boer et al., 2017).
Existing fire behaviour models can capture the impacts of weather on
fire spread rate and intensity but require spatially explicit
information about a range of fuel attributes as input (Tolhurst et al.,
2008). Although current fuel hazard can be mapped using a combination of
ground-based and remote-sensed observations (Pierce et al., 2012),
quantification of future changes in these patterns in response to
climate change requires predictive models.
Fuel hazard is closely related to variation in the composition and
structure of the vegetation, which in turn are shaped by plant responses
to long-term environmental conditions and disturbance regimes (Kelley,
et al., 2019). Consequently, predictions of future fuel hazard need to
incorporate the potential impacts of climate change. There are two major
ways that climate change affects fuel hazard. First, the rising
atmospheric CO2 concentration (Ca)
fertilises plants via an enhancement of photosynthesis (Ainsworth and
Rogers, 2007), potentially resulting in an increase in plant biomass
(Ainsworth and Long, 2005; Norby et al., 2005; Zhu et al., 2016; Walker
et al., 2019). Any increase in plant biomass is likely to result in
higher fuel loads, but the magnitude of change and how it will interact
with other environmental factors remains uncertain (Bradstock, 2010).
Second, rising air temperatures and altered rainfall patterns have
distinct effects on plant productivity and species composition, both of
which could lead to altered fuel hazard (Archibald et al., 2013). It is
thus critical to account for plant responses to climate change when
projecting future fuel hazard.
Changes in plant biomass under future climate can be predicted with a
range of modelling approaches, which have been used to estimate fuel
loads. For example, Clarke et al. (2016) projected future fuel loads
using the net primary productivity (NPP) predictions from a land surface
model, assuming a linear relationship between NPP and fuel load.
However, existing evidence suggests that fire regimes (i.e., fire
frequency, intensity, season, type, and extent) could varied within a
biome with similar NPP - a single biome could have more than one fire
regime while the same fire regime can be observed in different biomes
(Archibald et al., 2013). Consequently, predictions of the spatial
variation of fuel hazard under climate change need to be constrained by
and evaluated against the fuel hazard observations under different
environmental controls (i.e., climate, soil and topography).
Ground-based fuel observations have been routinely collected by fire
management agencies in Victoria, Australia since 1995 (e.g., Hines et
al. 2010), and have provided valuable insight on the spatial variation
of fuel hazard at landscape to regional scales (e.g., Jenkins et al.,
2020; McColl‐Gausden et al., 2020). At each survey site, an ordinal
score is assigned to each fuel stratum based on visual estimates of fuel
hazard. The potential of these fine-resolution data to help inform
process-based model predictions of future fuel hazard remains
under-utilised. Assuming the spatial variation of fuel hazard along
climatic gradients is indicative of how fuel hazard may change with
climate over time (i.e., space-for-time substitution; Picket, 1989),
these ground-based fuel surveys contain possibly the best information
about the potential for changes in fuel hazard across Victoria in
response to projected climate conditions.
Random forest models have been used to synthesise field-based fire
observations and environmental drivers with demonstrated success (Pierce
et al., 2012; Jenkins et al., 2017; McColl‐Gausden et al., 2020).
However, previous machine learning approaches have generally ignored
plant responses to climate change (e.g., McColl‐Gausden et al., 2020),
due to the this ‘space-for-time’ approach needing additional
process-based information on vegetation responses to novel climate.
The past developments of empirical approaches thus exposed the
limitation of pure statistical analysis and advocate novel ways to
combine strengths of process-based plant biomass predictions with
data-driven approaches (e.g., Jenkins et al., 2020). Yang et al. (2018)
modelled the change of leaf area index (LAI; an indicator of plant
foliage biomass) under changing climate and rising Ca.
Incorporating this LAI model and random forest models could help address
the lack of plant responses to future climate in current machine
learning frameworks. This combined framework could unite the strength of
fine-resolution empirical observations and the process-based plant
responses to climate changes, addressing the weakness of previous
regression and process-based models.
Here, we used random forest models to predict spatial variation in fuel
hazard at fine spatial resolution across the state of Victoria as a
function of climate, soil and topographic attributes as well as modelled
plant responses to climate change. The goal was to assess the potential
of change in fuel hazard in response to projected future climate
conditions and Ca. Although the training and evaluation
of the models focused on a specific region, the methods and conclusions
built a quantitative understanding of anthropogenic impacts on future
fuel hazard, which is applicable to similar regions across the world.