Providing a benchmark for process-based models
Although many process-based models have a fire component, the evaluation
of those models focused on the carbon and water components (Luo et al.,
2012; Hantson et al., 2020). Existing evaluations of the fire modules
relied on satellite derived burned area (e.g., Arora and Boer 2005;
Hantson et al., 2020). Our predicted fuel hazard represented the
potential to use machine learning approaches to upscale field-based
observations to the resolution of land surface models and thus could be
used as a reference for benchmarking process-based models alternative to
satellite-derived burned areas.
Current process-based models run on coarse horizontal grids (Eyring et
al., 2016; Friedlingstein et al., 2022) which cannot capture the fine
scale variation of fuel shown in field observations. Although fine
spatial resolution simulations accounting for fuel variation are
possible at regional scales (Fiddes et al., 2022), the computational
demand prevents such implementation at the regional and global scales.
Our machine learning approach could be used in hybrid modelling
framework to improve the model behaviour at fine spatial resolution
(Reichstein et al., 2019).