Wildfire Danger Assessment Over Southwest China Based on Short-Term Features of Weather and Fuel Variables

Wildfire is a typical complex nonlinear process, which has various interactions with weather, fuel, topography, and human variables. Previous studies on wildfire danger modeling mainly focused on the current climate or future scenarios of these variables. However, the outbreaks of wildfire are not only related to various factors at the current moment but also related to the change and trend of factors before the outbreak time point. The short-term features of weather and fuel dynamics
$\text{(weather}\&\text{fuel}_{\text{short-term}_{-}\text{features}})$
were therefore assumed to be of great importance in wildfire danger modeling within this context. To verify this hypothesis, the wildfire danger was modeled across the fire-prone area over southwest China from 2001 to 2020. The state-of-the-art eXtreme Gradient Boosting (XGBoost) model was applied to explore the relations between this topography, weather, fuel, and human variables and the wildfire occurrence, as well as to identify the role of five categories of
$\text{weather}\&\text{fuel}_{\text{short}-} \text{term}_{-}\text{features}$
, i.e., the
$t^{C3}, t^{Entropy}$
, Ricker's wavelet transformation
$(f^{cwt})$
, regular statistic description
$(t^{RSD})$
, and fast Fourier transform
$(t^{FFT})$
. Results showed that wildfire danger modeling can be improved by consideration of the
$\text{weather}\&\text{fuel}_{\text{short-term}_{-}\text{features}}$
which is proved to play an important role in driving the wildfire occurrence by factor importance analysis.