Abstract
Bushfire fuel hazard is determined by fuel hazard that represents the
type, amount, density, and three-dimensional distribution of plant
biomass and litter. The fuel hazard represents a biological control on
fire danger and may change in future with plant growth patterns. Rising
atmospheric CO2 concentration (Ca) tends to increase plant productivity
(‘fertilisation effect’) but also alters climate, leading to a ‘climatic
effect’. Both effects will impact on future vegetation and thus fuel
hazard. Quantifying these effects is an important component of
predicting future fire regimes and evaluating fire management options.
Here, by combining a machine learning algorithm that incorporates the
power of large fine-resolution datasets with a novel optimality model
that accounts for the climatic and fertilisation effects on vegetation
cover, we developed a random forest model to predict fuel hazard at fine
spatial resolution across the state of Victoria in Australia. We fitted
and evaluated model performance with long-term (i.e., 20 years),
ground-based fuel observations. The model achieved strong agreement with
observations across the fuel hazard range (accuracy >65%).
We found fuel hazard increased more in dry environments to future
climate and Ca. The contribution of the ‘fertilisation effect’ to future
fuel hazard varied spatially by up to 12%. The predictions of future
fuel hazard are directly useful to inform fire mitigation policies and
as a reference for climate model projections to account for fire
impacts.