rosunc: Function to carry out uncertainty propagation analysis on...

In Rothermel: Rothermel fire spread model for R

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.

Author(s)

Giorgio Vacchiano, Davide Ascoli (DISAFA, University of Torino, Italy)

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)

Example output

Loading required package: GA
Loading required package: foreach
Loading required package: iterators
Package 'GA' version 3.2
Type 'citation("GA")' for citing this R package in publications.

Attaching package: 'GA'

The following object is masked from 'package:utils':

    de

Loading required package: ftsa
Loading required package: forecast
Loading required package: rainbow
Loading required package: MASS
Loading required package: pcaPP
Loading required package: sde
Loading required package: stats4
Loading required package: fda
Loading required package: splines
Loading required package: Matrix

Attaching package: 'fda'

The following object is masked from 'package:forecast':

    fourier

The following object is masked from 'package:graphics':

    matplot

Loading required package: zoo

Attaching package: 'zoo'

The following objects are masked from 'package:base':

    as.Date, as.Date.numeric

sde 2.0.15
Companion package to the book
'Simulation and Inference for Stochastic Differential Equations With R Examples'
Iacus, Springer NY, (2008)
To check the errata corrige of the book, type vignette("sde.errata")

Attaching package: 'ftsa'

The following objects are masked from 'package:stats':

    sd, var

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   7.26   10.72   12.48   13.23   14.54   30.96 

Rothermel documentation built on May 2, 2019, 7:23 a.m.