The national fire-danger rating system: basic equations

Abstract

The work reported herein was done while we were assigned to the Intermountain Forest and Range Experiment Station's Northern Forest Fire Laboratory at Missoula, Montana. We were aided materially by Robert E. Burgan. Other researchers who contributed to the updating of the National Fire-Danger Rating System and their contributions were from the Intermountain Station, Missoula, Montana-Richard C. Rothermel, Frank A. Albini, and Patricia L. Andrews, who assisted in adapting the current fire modeling technology, including the addition of 1000-hour fuels and herbaceous to 1-hour transfer; Hal E. Anderson and James K. Brown, who worked on fuels and fuel models; Donald F Fuquay, and Donald J. Latham, who worked on the lightning-caused fire occurrence index; North Central Forest Experiment Station, East Lansing, Michigan-Von J. Johnson, William A. Main, and Craig A. Johnson, who worked on human-caused fire occurrence index; and the Rocky Mountain Forest and Range Experiment Station, Fort Collins, Colorado-Michael A. Fosberg, who worked on fuel moisture models; and R. William Furman and Glen F. Brink, who collected fire weather data needed to develop the system. Cover: Four climate classes in the United States are associated with different vegetation moisture contents, which affect fire spread and fire danger rating.

Generation of a global fuel data set using the Fuel Characteristic Classification System

“…FCCS, meanwhile, allows creating fuelbeds for environments not contemplated by other models, such as moist ecosystems that are found in several parts of the world. Also, the parameters included in the FCCS fuelbeds also provide information on the crown and ground fuels, not included in most models only developed for surface fuels (Cohen and Deeming, 1985;Scott and Burgan, 2005). This extends its use to other applications beyond fire behavior estimations, allowing also estimating crown fire potentials, the amount of available fuel or predicting fuel consumption.…”

“…When fuel types are used as input to fire behavior models they are converted to fuel models, which include the specific parameters necessary to run fire simulation programs. Such is the case of the 13 fuel models of the Northern Forest Fire Laboratory (NFFL) (Rothermel, 1972), the 20 fuel models of the National Fire Danger Rating System (NF-DRS) (Cohen and Deeming, 1985), or the 17 fuel types of the Canadian Fire Behavior Prediction System (FBP) (Stocks et al, 1989). Other fuel type classifications were created with a broader scope.…”

“…Variables such as fuel loading, fuel depth, stand structure, fuel moisture, etc., will determine fire behavior parameters such as rate of spread, fire intensity, or fuel consumption, amongst others (Cohen and Deeming, 1985). Fuel variables are commonly grouped in fuel types, following different classification systems.…”

Independence Day holiday weekend 2010 flood on the southern plains of West Texas

“…The ERC is a quantity directly related to the total energy per unit area of vegetative fuel, or the potential heat release available for burning in the flaming zone of a fire for a specific fuel model. Variables of ERC include weighted fuel loading (surface area-to-volume ratio) as well as a composite of live and dead large-fuel moistures (Cohen and Deeming 1985). The ERC is a cumulative index, and applies values from each of the previous seven days to successive calculations.…”

Assessing the expected effects of wildfire on vegetation condition on the Bridger-Teton National Forest, Wyoming, USA

“…ERC is calculated from historical weather data (Cohen and Deeming 1985). The simulated ERC is used in two ways: first, to determine the probability of a wildfire start for each day, and second, to determine which of three fuel moisture scenarios to use for the day.…”