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

Dataset DOI: [10.5061/dryad.63xsj3vdb](10.5061/dryad.63xsj3vdb)

## Description of the data and file structure

This repository contains the data and code used to produce version 1.0 of the Western United States Large Forest-Fire Stochastic Simulator (WULFFSS), as well as the equations that comprise the model and code to run the model. The WULFFSS simulates the probabilities and sizes of forest fires at least 1 km2 in size every month across forested areas of the western US on a 12-km resolution grid. 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. Fire probability and si...,