Title: A project in two parts: Developing fire histories for the eastern U.S. and creating a climate-based continental fire frequency model to fill data gaps

Author: Guyette, Richard; Stambaugh, Michael; Dey, Daniel.

Year: 2011

Publication: JFSP Fire Science Brief. 142: 1-6.

Abstract: Tree-ring dated fire scars provide long-term records of fire frequency, giving land managers valuable baseline information about the fire regimes that existed prior to Euro-American settlement. However, for the East, fire history data prove difficult to acquire because the generally moister climate of the region causes rapid decay of wood. In an endeavor to fill data gaps, the research team collected fire scar data in the states of Alabama, Louisiana, Kentucky, Tennessee, Iowa, Wisconsin, and Michigan. The second part of the project used this newly collected fire history data combined with previously collected records to parameterize and calibrate a continental fire frequency model based on climate. The purpose of this model is to aid in understanding how climate constrains and drives fire regimes across the U.S. Large temporal and spatial gaps exist in our knowledge of continental fire regimes, but the new Physical Chemistry Fire Frequency Model (PC2FM) can assign a fire frequency to any square kilometer in North America. Even in places where there are no fire history data, the model can estimate with high precision how frequently fires occurred on average and what the upper and lower fire frequency limits were. The model's predictor variables were selected in part based on physical chemistry because, in its most basic form, fire is a chemical reaction. This model addresses how chemical reactions are controlled by temperature and precipitation, and how these variables combine to control combustion reactions. While previously developed fire regime models are typically based on specific vegetation communities, this model relies on climate variables as predictors. A benefit of a vegetation-free model is the applicability of the model to make predictions of fire frequency in situations where vegetation data are unavailable, not of primary interest, or when current vegetation might differ from historical or future. The research team's goal was to develop a climate-based model that can bring together and analyze disparate fire history data for: (1) a broad-scale characterization of past and future fire regimes and (2) assessing fire regime sensitivity to changes in climate.