| cfis {firebehavioR} | R Documentation |
Canopy Fire Initiation & Spread model
Description
Prediction of crown fire probability, crown fire rate of spread and separation distance (Alexander and Cruz 2006). Separation distance is distance ahead of main fire front required for a spot fire to form, separate of a main fire.
Usage
cfis(fsg, u10, effm, sfc, cbd, id)
Arguments
fsg |
a numeric vector of fuel stratum gaps (m) |
u10 |
a numeric vector of 10-m open wind speeds (km/hr) |
effm |
a numeric vector of effective fine fuel moistures (%) |
sfc |
a numeric vector of surface fuel consumed (Mg/ha) |
cbd |
a numeric vector of canopy bulk densities (kg/m3) |
id |
a numeric vector of spot ignition delays, the time during which a given firebrand generates, is transported aloft, and ignites a receptive fuelbed (min) |
Value
a data frame with type of fire, probability of crown fire occurrences (%), crown fire rate of spread (m/min), and critical spotting distance (m)
Author(s)
Justin P Ziegler, justin.ziegler@colostate.edu
References
Alexander M.E., Cruz M.G. 2006. Evaluating a model for predicting active crown fire rate of spread using wildfire observations. Canadian Journal of Forest Research. 36:2015-3028.
Examples
data("coForest")
# show the data format:
head(coForest)
# Predict crown fire, using coForest
# measurements and assumed weather
# parameters
df.cfis = cfis(fsg = coForest$cbh_m, u10 = 20,
effm = 6, sfc = coForest$sfl_kgm2*10, cbd = coForest$cbd_kgm3,
id = 1)
print(df.cfis)
# Examine differences between treatment
# statuses
aggregate(x = df.cfis$cROS, by = list(treatmentStatus = coForest$status),
FUN = mean)
# Now, examine the sensitivity of fire
# type designations to wind speed by
# treatment status
coForest = coForest[rep(seq_len(nrow(coForest)),
11), ]
coForest$u10 = sort(rep(10:20, 14))
coForest$type = cfis(coForest$cbh_m, coForest$u10,
6, coForest$sfl_kgm2*10, coForest$cbd_kgm3,
1)$type
table(u10 = coForest$u10, coForest$type,
coForest$status)