Predicting Fire Intensity Using Vegetation, Fuel, and Weather Variables

Abstract

When applying prescribed fire to achieve various management objectives, forest managers benefit from being able to accurately predict fire behavior with site- and time-specific data. At the William B. Bankhead National Forest in northcentral Alabama, we used three levels of thinning (none, light, heavy) and three fire return intervals (no fire, 9-year return, 3-year return) in mixed pine-oak (Pinus spp.-Quercus spp.) stands to move stands toward more desirable structures. After 38 fires over 12 years, we amassed a substantial store of thermocouple temperatures and burn condition data. We modeled maximum thermocouple temperature as a surrogate for fire intensity by using a mixed effects model that incorporated stand and vegetation variables, fuel loading measurements, and weather data. The random portion of our model estimated low correlation of maximum temperatures within stands, but moderate correlation within plots (plots were nested within stands). The fixed effects portion of our model revealed associations of maximum temperature with several vegetation (importance values of oaks and pines, seedling counts of pines and other species), fuels (loadings of both bark and duff), and weather variables (average ambient temperature and relative humidity over the 24 hours prior to ignition, fuel temperature, and fuel moisture).

Parent Publication

• Proceedings of the 22nd Biennial Southern Silvicultural Research Conference

Citation

Craycroft, John; Schweitzer, Callie. 2024. Predicting Fire Intensity Using Vegetation, Fuel, and Weather Variables. In: Bragg, Don C.; Oswald, Brian P.; Koerth, Nancy E., eds. Proceedings of the 22nd biennial southern silvicultural research conference. Gen. Tech. Rep. SRS-274. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station: 59–67. https://doi.org/10.2737/SRS-GTR-274-Pap9.