Predicting fire severity using surface fuels and moisture

Fire severity classifications have been used extensively in fire management over the last 30 years to describe specific environmental or ecological impacts of fire on fuels, vegetation, wildlife, and soils in recently burned areas. New fire severity classifications need to be more objective, predictive, and ultimately more useful to fire management and planning. Our objectives were to (1) quantify the relationships between fuel loading and moisture characteristics of surface fuels and the temperature and energy produced during combustion, and (2) to produce a classification that summarized these relationships into unique, realistic classes of fire severity. Using computer simulation, we created 115,280 synthetic fuel beds with diverse compositions and moisture conditions and burned them using computer simulation with the First Order Fire Effects Model (FOFEM). Using average fire intensity, fire residence time, total fuel consumed, depth of soil heating, and temperature in the top 1 cm of soil, we created a nine-group classification that separated fire severity classes based first on soil heating, second on intensity and fire time, and third on fuel consumed. Fuel beds were correctly placed into the nine fire severity classes 98% of the time using subsets of the synthetic fuel beds.