Risk Assessment and Analysis

Background

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.

Background

During periods of heightened wildland fire activity in the United States, multiagency coordinating groups must prioritize among multiple on-going fires to allocate scarce suppression resources.

Multi-stakeholder planning and prioritization for ecosystem management and wildfire risk mitigation are complicated by the need to balance a multitude of values, goals, viewpoints, and interests across large landscapes.

Mature and old-growth forests (MOG) provide essential ecosystem services, yet they face increasing threats. Currently, high-intensity, high-severity wildfires are the main driver for loss of MOG on federally managed forests across the United States. Quantifying MOG forests with greatest exposure to stand-replacing wildfires provides essential information for land managers.

Background

Post-wildfire debris flows (PFDFs) frequently threaten life, property and infrastructure in California. To date, there is no comprehensive assessment of their spatial distribution, seasonality, atmospheric drivers and interannual variability across the state.

Aims