Rothermel (version 1.2)

bestFM: Function to select best standard fuel model for observed fire rate of spread

Description

The function preloads the 13 fire behavior fuel models by Albini (1976) and the 40 fuel models by Scott & Burgan (2005), computes rate of spread using Rothermel's (1972) model for a vector or data frame of fire experiment data, and computes root mean square error and mean bias of each fuel model to observed rate of spread.

Usage

bestFM(obs, m, u, slope)

Arguments

obs

a single value, or vector, of observed ROS [m/min].

m

a vector, or data frame, of percent moisture on a dry weight basis [percent] for fuel classes 1-hour, 10-hour, 100-hour, live herbs and live woody, respectively (5 values or columns; 0 if fuel class is absent).

u

a single value, or vector, of midflame windspeed [km/h].

slope

a single value, or vector, of percent site slope [percent].

Value

If a single observation of rate of spread is analyzed (length of obs =1), a vector of bias values, computed as (predicted - observed ROS) for every standard fire behavior fuel model. If more observations are analyzed (length of obs >1), a data.frame of root mean square error and mean bias computed from observed and predicted ROS using every standard fire behavior fuel model.

References

Albini, F. A. (1976). Estimating wildfire behavior and effects. Gen. Tech. Rep. INT-30. Ogden, UT: US Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station.

Rothermel, R. C. (1972). A mathematical model for fire spread predictions in wildland fires. Research Paper INT-115. Ogden, UT: US Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station.

Scott, J., Burgan, R. E. (2005). A new set of standard fire behavior fuel models for use with Rothermel's surface fire spread model. Gen. Tech. Rep. RMRS-GTR-153. Fort Collins, CO: US Department of Agriculture, Forest Service, Rocky Mountain Research Station.

See Also

ros, SFM_metric

Examples

data ("firexp")
a <- bestFM (obs = firexp$ros,
            m = firexp[,18:22],
            u = firexp[,"u"],
            slope = firexp[,"slope"])  
a      

# plot results
barplot (a$error,
        cex.names = 0.7,
        las = 2,
        ylab = "rmse",
        col = sign (a$bias) + 3,
        names.arg = rownames (a))
          
legend (1, max (a [, 1]), 
        c ("Overprediction", "Underprediction"),
        col = c ("blue", "red"),
        pch = 15)