This report describes a new set of standard fire behavior fuel models for use with Rothermel's surface fire spread model and the relationship of the new set to the original set of 13 fire behavior fuel models. To assist with transition to using the new fuel models, a fuel model selection guide,…
Keywords: fire behavior prediction, fire modeling, surface fuel, dynamic fuel model
Source: Gen. Tech. Rep. RMRS-GTR-153. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 72 p.
This manual documents procedures for estimating the rate of forward spread, intensity, flame length, and size of fires burning in forests and rangelands. Contains instructions for obtaining fuel and weather data, calculating fire behavior, and interpreting the results for application to actual fire…
Keywords: fire behavior prediction, fire spread, fire intensity, fire growth
Source: Gen. Tech. Rep. INT-143. Ogden, UT: U.S. Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station. 161 p.
Mathematical modeling of fire behavior prediction is only possible if the models are supplied with an information database that provides spatially explicit input parameters for modeled area. Mathematical models can be of three kinds: 1) physical; 2) empirical; and 3) quasi-empirical (Sullivan, 2009…
Keywords: forest fires, fire growth models, fire behavior prediction, information database, GIS
Source: In: Viegas, D. X., ed. Proceedings of the VI International Conference on Forest Fire Research; 15-18 November 2010; Coimbra, Portugal. Coimbra, Portugal: University of Coimbra. 7 p.
Land management agencies need to understand and monitor the consequences of their fire suppression decisions. We developed a framework for retrospective fire behavior modeling and impact assessment to determine where ignitions would have spread had they not been suppressed and to assess the…
Keywords: fire behavior prediction, fire effects, fire modeling, retrospective, impact assessment
Source: Gen. Tech. Rep. RMRS-GTR-236. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 40 p.
Describes BURN Subsystem, Part 1, the operational fire behavior prediction subsystem of the BEHAVE fire behavior prediction and fuel modeling system. The manual covers operation of the computer program, assumptions of the mathematical models used in the calculations, and application of the…
Keywords: fire, fire behavior prediction, fire spread, fire intensity
Source: General Technical Report INT-194. Ogden, UT: U.S. Department of Agriculture, Forest Service, Intermountain Research Station. 130 p.
This manual documents the fuel modeling procedures of BEHAVE--a state-of-the-art wildland fire behavior prediction system. Described are procedures for collecting fuel data, using the data with the program, and testing and adjusting the fuel model.
Keywords: fire, fuels, fire behavior prediction
Source: General Technical Report INT-167. Ogden, UT: U. S. Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station. 126 p.
A complete set of nomographs for estimating surface fire rate of spread and flame length for the original 13 and new 40 fire behavior fuel models is presented. The nomographs allow calculation of spread rate and flame length for wind in any direction with respect to slope and allow for nonheading…
Keywords: rate of spread, flame length, fire behavior prediction, fuel model
Source: Gen. Tech. Rep. RMRS-GTR-192. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 119 p.
Computer models used to predict forest and fuels dynamics and wildfire behavior inform decisionmaking in contexts such as postdisturbance management. It is imperative to understand possible uncertainty in model predictions. We evaluated sensitivity of the Fire and Fuels Extension to the Forest…
Keywords: Salvage logging, sensitivity and uncertainty analysis, fuel succession, Fuel and Fire Effects extension to Forest Vegetation Simulator, FFE-FVS, fire behavior prediction
Source: Forest Science. 67(1): 30-42.