Data for the western United States large forest-fire stochastic simulator (WULFFSS) 1.0: A monthly gridded forest-fire model using interpretable statistics

This archive contains the data and code used to produce the Western United
States Large Forest-Fire Stochastic Simulator (WULFFSS), version 1.0,
which is a monthly gridded forest-fire model using interpretable
statistics. The WULFFSS operates at 12-km resolution and calculates
monthly probabilities of forest fires ≥100 ha as well as the area burned
per fire. The model is forced by variables related to vegetation,
topographic, anthropogenic, and climate factors, organized into three
indices representing spatial, annual-cycle, and lower frequency temporal
domains. These indices can interact, so variables promoting fire in one
domain amplify fire-promoting effects in another. The fire probability and
size modules use multiple logistic and linear regression, respectively,
and can be easily updated as new data or ideas emerge. During its training
period of 1985–2024, WULFFSS captures >70% and >80% of
observed interannual variability in western US forest-fire frequency and
area, respectively. It reproduces regional differences in seasonal timing,
frequencies, and sizes of fires, and performs well in cross-validation
exercises that test the model’s accuracy in years or regions not
considered during model training. While lacking fine-scale fire dynamics,
the model's use of classic statistics promotes interpretability and
efficient ensemble generation. An important feature of the WULFFSS is that
it was designed to run within a vegetation ecosystem model, allowing for
simulations of bidirectional feedbacks between vegetation and fire such
that simulations can be used to assess how ecosystem changes have altered
or will alter fire-climate relationships across the western US. The
model's predictive power should improve with increasingly accurate
and extensive observational data, and its approach can be extended to
other regions.