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

Williams, Park1

Published Aug 02, 2025 on Dryad. https://doi.org/10.5061/dryad.63xsj3vdb

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Abstract

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.