modelling

Wildfires have increasingly affected human and natural systems across the western United States (WUS) in recent decades. Given that the majority of ignitions are human-caused and potentially preventable, improving the ability to predict fire occurrence is critical for effective wildfire prevention and risk mitigation.

Aim

Background

We developed the WULFFSS, a stochastic monthly gridded forest-fire model for the western United States (US). Operating at 12 km resolution, WULFFSS calculates monthly probabilities of fires that burn at least 100 ha of forest area as well as the forest area burned per fire.