Rothermel (version 1.2)

rosunc: Function to carry out uncertainty propagation analysis on Rothermel's (1972) fire spread model

Description

This function offers the user the possibility to perturb inputs to Rothermel's (1972) fire behavior model and propagate the uncertainty to the resulting estimate of Rate of spread [m/min] by means of Monte Carlo iterative sampling. Random values are extracted from Gaussian distributions with mean = observed values, and spread defined by a custom ratio of standard deviation to the mean defined by the user.

Usage

rosunc (modeltype, w, s, delta, mx.dead, h, m, u, slope,  sdu = 0, sdm = 0, sds = 0, sdw = 0, sdd = 0,  nsim = 1000)

Arguments

modeltype

S(tatic), D(ynamic)

w

a vector of fuel load [t/ha] for fuel classes 1-hour, 10-hour, 100-hour, live herbs and live woody, respectively (5 values; 0 if fuel class is absent).

s

a vector of surface-to-volume ratio [m2/m3] for fuel classes 1-hour, 10-hour, 100-hour, live herbs and live woody, respectively (5 values; 0 if fuel class is absent).

delta

atomic vector, fuel bed depth [cm]

mx.dead

atomic vector, dead fuel moisture of extinction [percent]

h

a vector of heat content [kJ/kg] for fuel classes 1-hour, 10-hour, 100-hour, live herbs and live woody, respectively (5 values; 0 if fuel class is absent).

m

a vector 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; 0 if fuel class is absent).

u

atomic vector, midflame windspeed [km/h]

slope

atomic vector, site slope [percent]

sdu

coefficient of variation for wind speed (ratio of standard deviation to the mean; default = no perturbation)

sdm

coefficient of variation for fuel moistures (ratio of standard deviation to the mean; default = no perturbation)

sds

coefficient of variation for slope (ratio of standard deviation to the mean; default = no perturbation)

sdw

coefficient of variation for fuel loadings (ratio of standard deviation to the mean; default = no perturbation)

sdd

coefficient of variation for fuel bed depth (ratio of standard deviation to the mean; default = no perturbation)

nsim

number of Monte Carlo iterations (default =1000)

Value

A vector of predicted ROS [m/min] from Monte Carlo simulations.

References

Cruz M. G. (2010). Monte Carlo-based ensemble method for prediction of grassland fire spread. International Journal of Wildland Fire 19: 521-530.

Jimenez E., Hussaini M. Y., Goodrick S. (2008). Quantifying parametric uncertainty in the Rothermel model. International Journal of Wildland Fire, 17: 638-649.

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.

See Also

ros, SFM_metric, firexp

Examples

data ("firexp")
varnames <- names (firexp)

# select only one observation and create a numeric vector for function input
firexp <- as.numeric (firexp [5,])
names (firexp) <- varnames

pred <- rosunc (
        modeltype = "D",
        w = firexp [1:5],
        s = firexp [6:10],
        delta = firexp ["Fuel_Bed_Depth"],
        mx.dead = firexp ["Mx_dead"],
        h = firexp [13:17],
        m = firexp [18:22],
        u = firexp ["u"],
        slope = firexp ["slope"],
        sdm = 0.3,
        nsim = 100)

summary (pred)

# Figure
hist (pred, 
      xlab = "ROS [m/min]",
      freq = FALSE,
      xlim = c (0, max (pred)),
      breaks = 20,
      main = "")
lines (density (pred), lty=2, lwd=2)
abline (v = firexp ["ros"],col = "red")
text (firexp ["ros"], 
      max (density (pred)$y), 
      labels = "obs", 
      pos = 4)