Title: Prediction of Wildland Fire Behavior and Hazard
Version: 0.1.2
Description: Fire behavior prediction models, including the Scott & Reinhardt's (2001) Rothermel Wildland Fire Modelling System <doi:10.2737/RMRS-RP-29> and Alexander et al.'s (2006) Crown Fire Initiation & Spread model <doi:10.1016/j.foreco.2006.08.174>. Also contains sample datasets, estimation of fire behavior prediction model inputs (e.g., fuel moisture, canopy characteristics, wind adjustment factor), results visualization, and methods to estimate fire weather hazard.
Depends: R (≥ 3.4.0)
Imports: ggplot2, utils
License: GPL-2
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.0
Suggests: knitr, rmarkdown, reshape2, truncnorm, xtable
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-02-28 19:37:18 UTC; User
Author: Justin Ziegler [aut, cre]
Maintainer: Justin Ziegler <Justin.Ziegler@colostate.edu>
Repository: CRAN
Date/Publication: 2019-03-05 20:30:03 UTC

Bitmap of bulldozer Bitmap image of bulldozer

Description

Bitmap of bulldozer Bitmap image of bulldozer

Usage

bulldozer

Format

An object of class rastergrob (inherits from grob, gDesc) of length 12.

See Also

fireChart

Canopy Fuel Stratum Characteristics Calculator

Description

Canopy parameters estimated by Cruz, Alexander & Wakimoto (2003).

Usage

canFuel(ba, ht, tph, type)

Arguments

ba

a numeric vector of stand basal areas (m2/ha)

ht

a numeric vector of average stand tree heights (m)

tph

a numeric vector of stand densities (trees/ha)

type

a character vector of forest cover types, either: "df" for Douglas-fir (Pseudotsuga menziesii); "pp" for ponderosa pine (Pinus ponderosa); "lp" for lodgepole pine (Pinus contorta); "mc" for mixed conifer

Value

a data frame with canopy base height (m), canopy fuel load (kg/m2), and canopy bulk density (kg/m3)

Author(s)

Justin P Ziegler, justin.ziegler@colostate.edu

References

Cruz M.G., Alexander M.E., Wakimoto R.H. 2003. Assessing canopy fuel stratum characteristics in crown fire prone fuel types of western North America. International Journal of Wildland Fire. 12(1):39-50.

See Also

This function provides values for rothermel and cfis inputs.

Examples

#Two hypothetical forest stands
ba = c(10, 15)
ht = c(12, 20)
tph = c(100, 300)
type = c('df', 'lp')
canFuel(ba, ht, tph, type)

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)

Colorado dry forest inventory summary.

Description

Fuels inventory summary of seven sampled forests in the southern Rocky Mountains and Colorado Plateau. Each forest was sampled before and after tree thinnings.

Usage

data("coForest")

Format

A data frame with 14 observations of 10 variables:

site

name of forest location

status

either before (pre) or after (post) forest thinning

trees_perha

tree density (trees/ha)

basalArea_m2ha

basal area (m2/ha)

qmd_cm

quadratic mean diameter (cm)

height_m

mean tree height (m)

sfl_kgm2

surface fuel load (kg/m2)

cbd_kgm3

canopy bulk density (kg/m3)

cbh_m

canopy base height (m)

cfl_kgm2

canopy fuel load (kg/m2)

Source

Ziegler, J.P., Hoffman, C., Battaglia, M., Mell, W., 2017. Spatially explicit measurements of forest structure and fire behavior following restoration treatments in dry forests. Forest Ecology & Management 386, 1–12. doi:10.1016/j.foreco.2016.12.002

Fire Behavior Officer's table

Description

Look up charts in tabular form to determine fine fuel moisture

Usage

data("fboTable")

Format

A list with two data frames:

1

a list of reference fine fuel moistures (%) by temperature (deg. C) and relative humidity (%)

2

a list of correction fine fuel moistures (%) by month (0-12), hourly time (0-23), shading, cardinal aspect, slope cateory, and elevation level category

Source

Rothermel, R.C., Wilson, R.A., Morris, G.A., Sackett, S.S. 1986. Modeling moisture content of dead wildland fuels: input to the BEHAVE fire prediction system INT-RP-359. US Department of Agriculture, Forest Service, Intermountain Forest and Range Experimental Station.

Estimate fine fuel moisture

Description

Methods to estimate fine fuel moisture based on meteorological data.

Usage

ffm(method, rh, temp, month, hour, asp, slp, bla, shade)

Arguments

method

a character vector of specifying the method ("pech", "simard", "wagner", "anderson", "mcarthur", "fbo")

rh

a numeric vector of relative humidities (%)

temp

a numeric vector of dry bulb temperatures (deg. C)

month

a numeric vector of Gregorian month numbers (1-12)

hour

a numeric vector of hours (1-24)

asp

a character vector of aspects specified as cardinal directions, either "N", "S", "E", or "W"

slp

a numeric vector of topographic slopes (%)

bla

a character vector specifying the difference in elevation between the fine fuel's location and that of the meteorological data; either within 305 m ('l', the default), or the meteorological data are > 305m below ("b"), or above ("a") the fine fuel's location

shade

a character vector specifying whether fine fuels are shaded, "y" or "n"

Details

This function has six methods to estimate fine fuel moisture. If method = "fbo", all arguments must be specified, otherwise, only method, rh and temp are required.

Value

a data frame of litter, 1-hr, 10-hr, and 100-hr fuel moistures

Author(s)

Justin P Ziegler, justin.ziegler@colostate.edu

References

Viney, N.R. 1991. A review of fine fuel moisture modelling. International Journal of Wildland Fire. 1(4):215–234.

Examples

#Example using RAWS meteorological station data
data(rrRAWS)
wx = rrRAWS[2000:3000,]
ff = rbind(
data.frame(ffm = ffm('simard',wx$rh, wx$temp_c)$fm1hr,method='simard'),
data.frame(ffm = ffm('wagner',wx$rh, wx$temp_c)$fm1hr,method='wagner'),
data.frame(ffm = ffm('anderson',wx$rh, wx$temp_c)$fm1hr,method='anderson')
)
ff$dateTime = rep(wx$dateTime,3)
par(mfrow=c(3,1))
hist(ff$ffm[ff$method=="simard"])
hist(ff$ffm[ff$method=="wagner"])
hist(ff$ffm[ff$method=="anderson"])

#The FBO method requires more inputs
rh = wx$rh
temp =wx$temp_c
month = as.numeric(format(strptime(wx$dateTime,"%m/%d/%Y %H:%M"),'%m'))
hour = as.numeric(format(strptime(wx$dateTime,"%m/%d/%Y %H:%M"),'%H'))
ffm(method = 'fbo', rh, temp, month, hour, asp = 'N', slp = 10, bla = 'l', shade = 'n')

Fire Characteristics Chart

Description

Visualization of predicted fire behavior using the Fire Characteristics Chart.

Usage

fireChart(name, hpua, ros)

Arguments

name

a character vector identifying names of predictions

hpua

a numeric vector of heat per unit area (kJ/m2)

ros

a numeric vector of rate of spread (m/min)

Value

an object of class ggplot

Author(s)

Justin P Ziegler, justin.ziegler@colostate.edu

References

Andrews, P.L. & Rothermel, R.C. 1982. Charts for interpreting wildland fire behavior characteristics. INT-GTR-131. USDA Forest Service Intermountain Forest & Range Experimental Station.

Examples

fc = fireChart('fire',hpua = 15000, ros = 50)
print(fc)

Template data for fire characteristics chart

Description

Data of heat per unit area (kJ/m2), rate of spread (m/min), and flame length (m) for creating the template of the fire characteristics chart

Usage

data("fireChartData")

Format

An object of class data.frame with 9180 rows and 3 columns.

Source

Andrews, P.L., & Rothermel, R.C. 1982. Charts for interpreting wildland fire behavior characteristics. INT-GTR-131. US Department of Agriculture, Forest Service, Intermountain Forest and Range Experimental Station.

Fire weather indices based on static weather observations

Description

Methods to estimate fire weather indices using static weather observations.

Usage

fireIndex(temp, u, rh, fuel = 4.5, cure = 100)

Arguments

temp

a numeric vector of air temperatures (C)

u

a numeric vector of wind speeds (km/hr)

rh

a numeric vector of relative humidities (%)

fuel

a numeric vector of available fuel load (Mg/ha), defaults to 4.5

cure

a numeric vector for proportion of cured grass (%), defaults to 100

Details

This function computes seven methods to estimate static fire weather indices: the Angstrom Index, the Chandler Burning Index, the Hot Dry Windy Index, the Fuel Moisture Index, the Fosberg Fire Weather Index, the MacArthur Grassland Mark 4 Index, and the MacArthur Grassland Mark 5 Index. Each of these are static in that values are derived using a daily weather summary and do not consider weather during prior days. temp, rh and u are required for all methods. The latter two indices also use fuel, and the Grassland Mark 4 Index uses cure. Defaults for fuel and cure are provided, but can be specified by the user. Sharples (2009a, b) review all of the methods.

Value

a data frame of static fire weather index values

Author(s)

Justin P Ziegler, justin.ziegler@colostate.edu

References

Sharples, J.J., McRae, R.H.D., Weber, R.O. and Gill, A.M., 2009a. A simple index for assessing fuel moisture content. Environmental Modelling & Software, 24(5):637-646.
Sharples, J.J., McRae, R.H.D., Weber, R.O. and Gill, A.M., 2009b. A simple index for assessing fire danger rating. Environmental Modelling & Software. 24(6):764-774.

Examples

#Example using RAWS meteorological station data
data(rrRAWS)
rrRAWS.daily =   rrRAWS[format(strptime(rrRAWS$dateTime, "%m/%d/%Y %H:%M"), "%H:%M")=="14:35",]
fireIndex(temp=rrRAWS.daily$temp_c, u= rrRAWS.daily$windSpeed_kmh, rh = rrRAWS.daily$rh)

Fire weather indices based on cumulative weather observations

Description

Methods to estimate daily fire weather indices using dynamic weather observations.

Usage

fireIndexKBDI(temp, precip, map, rh, u)

Arguments

temp

a numeric vector of daily air temperatures (C)

precip

a numeric vector of daily precipitations (mm)

map

a single numeric value of mean annual precipitation (mm)

rh

a numeric vector of relative humidities (%)

u

a numeric vector of daily wind speeds (km/hr)

Details

This function computes up to 8 methods to estimate dynamic fire weather indices. These methods are dynamic in that they take prior days' weather into consideration. Therefore the inputs must be ordered by day (i.e., weather observations for a given day are followed by weather observations for the next day.) The number of computed methods depends on the supplied arguments. If requisite arguments for specific methods are not supplied, fireIndexKBDI will not output results for those specific methods (i.e., there will be fewer than 8 columns). The requisite arguments for each method:

kbdi

'temp', 'precip', 'map'

drought factor

'temp', 'precip', 'map'

forestMark5

'temp', 'precip', 'map','u', 'rh'

fosbergKBDI

'temp', 'precip', 'map','u', 'rh'

fuelMoistureKBDI

'temp', 'precip', 'map','u', 'rh'

nesterov

'temp', 'precip', 'rh'

nesterovMod

'temp', 'precip', 'rh'

zdenko

'temp', 'precip', 'rh'

Value

a data frame of fire weather index values with a column for each valid method

Author(s)

Justin P Ziegler, justin.ziegler@colostate.edu

References

Sharples, J.J., McRae, R.H.D., Weber, R.O. and Gill, A.M., 2009. A simple index for assessing fuel moisture content. Environmental Modelling & Software, 24(5):637-646.
Goodrick, S.L., 2002. Modification of the Fosberg fire weather index to include drought. International Journal of Wildland Fire, 11(4), pp.205-211.
Sharples, J.J., McRae, R.H.D., Weber, R.O. and Gill, A.M., 2009. A simple index for assessing fire danger rating. Environmental Modelling & Software. 24(6):764-774.
Keetch, J.J., Byram, G.M., 1968. A drought index for forest fire control. RP-SE-68, US Department of Agriculture, Forest Service, Southeastern Forest Experiment Station.
Groisman, P.Y., Sherstyukov, B.G., Razuvaev, V.N., Knight, R.W., Enloe, J.G., Stroumentova, N.S., Whitfield, P.H., Førland, E., Hannsen-Bauer, I., Tuomenvirta, H. and Aleksandersson, H., 2007. Potential forest fire danger over Northern Eurasia: changes during the 20th century. Global and Planetary Change, 56(3-4):371-386.
Skvarenina, J., Mindas, J., Holecy, J. and Tucek, J., 2003, May. Analysis of the natural and meteorological conditions during two largest forest fire events in the Slovak Paradise National Park. In Proceedings of the International Scientific Workshop on Forest Fires in the Wildland–Urban Interface and Rural Areas in Europe: an integral planning and management challenge.

Examples

#Example using RAWS meteorological station data
data(rrRAWS)
ff = rbind(
data.frame(ffm = ffm('simard', rrRAWS$rh, rrRAWS$temp_c)$fm1hr, method = 'simard'),
data.frame(ffm = ffm('wagner', rrRAWS$rh, rrRAWS$temp_c)$fm1hr, method = 'wagner'),
data.frame(ffm = ffm('anderson', rrRAWS$rh, rrRAWS$temp_c)$fm1hr, method = 'anderson')
)
ff$dateTime = rep(rrRAWS$dateTime, 3)

#NOT RUN

#par(mfrow=c(3,1))
#hist(ff$ffm[ff$method=="simard"])
#hist(ff$ffm[ff$method=="wagner"])
#hist(ff$ffm[ff$method=="anderson"])

Bitmap of firefighter Bitmap image of bulldozer

Description

Bitmap of firefighter Bitmap image of bulldozer

Usage

firefighter

Format

An object of class rastergrob (inherits from grob, gDesc) of length 12.

See Also

fireChart

Surface fuel models.

Description

Fuel models developed by Anderson (1982), Scott (1999), and Scott & Burgan (2005) for prediction of surface fire behavior.

Usage

data("fuelModels")

Format

A data frame with 60 observations of 18 variables:

fuelModelType

"S"tatic or "D"ynamic fuel load transfer

loadLitter

load of litter fuel (Mg/ha)

load1hr

load of 1-hr fuel (Mg/ha)

load10hr

load of 10-hr fuel (Mg/ha)

load100hr

load of 100-hr fuel (Mg/ha)

loadLiveHerb

load of herbaceous fuel (Mg/ha)

loadLiveWoody

load of woody fuel(Mg/ha)

savLitter

surface area to volume ratio of litter fuel (m2/m3)

sav1hr

surface area to volume ratio of 1-hr fuel (m2/m3)

sav10hr

surface area to volume ratio of 10-hr fuel (m2/m3)

sav100hr

surface area to volume ratio of 100-hr fuel (m2/m3)

savLiveHerb

surface area to volume ratio of herbaceous fuel (m2/m3)

savLiveWoody

surface area to volume ratio of woody fuel (m2/m3)

fuelBedDepth

depth of woody fuel (cm)

mxDead

dead fuel moisture of extinction (%)

heat

heat content (J/g)

description

fuel model description

source

scientific source

References

Anderson, H.E. 1982. Aids to determining fuel models for estimating fire behavior. INT-GTR-122. US Department of Agriculture, Forest Service, Intermountain Forest and Range Experimental Station.
Scott, J.H. 1999. NEXUS: A system for assessing crown fire hazard. Fire Management Notes 59(2):20 –24.
Scott, J.H., & Burgan, R. E. 2005. A new set of standard fire behavior fuel models for use with Rothermel’s surface fire spread model. RMRS-GTR-153. US Department of Agriculture, Forest Service, Rocky Mountain Research Station.

See Also

rothermel

Modified Scott & Burgan (2005) moisture scenarios.

Description

Moisture scenarios are a set of fuel moistures of surface fuels, on a dry-weight basis, for each of the surface fuel classes. Adapted from Scott & Burgan (2005), this dataset includes fuel moistures of litter.

Usage

data("fuelMoisture")

Format

A data frame with 16 observations of 7 variables:

fmLitter

moisture of litter (%)

fm1hr

moisture of 1-hr fuel (%)

fm10hr

moisture of 10-hr fuel (%)

fm100hr

moisture of 100-hr fuel (%)

fmLiveHerb

moisture of herbaceous fuel (%)

fmLiveWoody

moisture of woody fuel (%)

description

scenario description

Source

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

See Also

rothermel

KBDI Lookup table

Description

Data of drought factor by drought index, temperature and mean annual precipitation.

Usage

data("kbdiTable")

Format

An object of class data.frame with 1680 rows and 7 columns.

Bitmap of fire plume Bitmap image of fire plume

Description

Bitmap of fire plume Bitmap image of fire plume

Usage

plume

Format

An object of class rastergrob (inherits from grob, gDesc) of length 12.

See Also

fireChart

Rothermel Fire Behavior Modeling System

Description

Potential surface and crown fire behavior predicted by Scott and Reinhardt (2001).

Usage

rothermel(surfFuel, moisture, crownFuel, enviro, rosMult = 1,
  cfbForm = "f", folMoist = "y")

Arguments

surfFuel

a data frame of surface fuel attributes. Variable names are not important but the order is important. The first variable is a character type whereas the rest are numeric

  1. the fuel model type, either 'S'tatic or D'ynamic herb load transfer

  2. litter load (Mg/ha)

  3. 1-hr load (Mg/ha)

  4. 10-hr load (Mg/ha)

  5. 100-hr load (Mg/ha)

  6. herb load (Mg/ha)

  7. woody load (Mg/ha)

  8. litter SAV (m2/m3)

  9. 1-hr SAV (m2/m3)

  10. 10-hr SAV (m2/m3)

  11. 100-hr SAV (m2/m3)

  12. herb SAV (m2/m3)

  13. woody SAV (m2/m3)

  14. fuel bed depth (cm)

  15. moisture of extinction (%)

  16. heat content (J/g)

moisture

a numeric-only data frame of surface fuel moistures on a dry-weight basis (%). Variable names are not important but the following order of surface fuel components is important: litter, 1-hr woody fuels, 10-hr woody fuels, 100-hr woody fuels, live herbaceous, and live woody vegetation (6 values or columns)

crownFuel

a vector or data frame with canopy fuel attributes, in order:

  1. canopy bulk density (kg/m3)

  2. foliar moisture content (%)

  3. canopy base height (m)

  4. canopy fuel load (kg/m2)

enviro

a numeric-only data frame with environmental attributes, in order:

  1. topographic slope (%)

  2. open windspeed (km/hr)

  3. wind direction, from uphill (deg.)

  4. wind adjustment factor (0-1)

rosMult

a numeric value for multiplying crown fire rate of spread, defaults to 1 (see details)

cfbForm

a character specifying how crown fraction burned is calculated. Options are "sr", "w", or "f" (default); see details.

folMoist

either "y" (default) or "n", specifying if foliar moisture effect is calculated (see details)

Details

This in an R build of the Rothermel fire behavior modelling system (Scott & Reinhardt 2001) which links sub-models of surface fire rate of spread (Rothermel 1972), crown fire initiation (Van Wagner 1977), and Rothermel's (1991) crown fire rate of spread.
rosMult multiples the rate of spread for active or passive crown fires and is recommended a value of 1.7 when a user desires a maximum crown fire rate of spread (Rothermel 1991).
cfbForm selects the method to estimate crown fraction burned. This selection impacts estimates of passive crown fraction burned, fireline intensity, and heat per unit area. Use "sr" for Scott and Reinhardt (2001), "w" for van Wagner (1993), and "f" for Finney (1998).
folMoist, if invoked with a "y", calculates the foliar moisture effect to scale crown fire rate of spread (see Scott & Reinhardt 2001). If "n", no foliar moisture effect is determined.

Value

a list with 6 data frames

fireBehavior

a data frame with fire behavior estimates including fire type, crown fraction burned (%), rate of spread (m/min), heat per unit area (kJ/m2), fireline intensity (kW/m), flame length (m), direction of max spread (deg), scorch height (m), torching index (km/hr), crowning index (km/hr), surfacing index (km/hr), effective midflame wind (km/hr), flame residence time (min)

detailSurface

a data frame with some intermediate variables of surface fire behavior including: potential ROS (m/min); no wind, no slope ROS (m/min); slope factor (-); wind factor (-); characteristic dead fuel moisture (%); characteristic live fuel moisture (%); characteristic SAV (m2/m3); bulk density (kg/m3); packing ratio (-); relative packing ratio (-); reaction intensity (kW/m2); heat source (kW/m2); heat sink (kJ/m3)

detailCrown

a data frame with some intermediate variables of crown fire behavior including: potential ROS (m/min); no wind, no slope ROS (m/min); slope factor (-); wind factor (-); characteristic dead fuel moisture (%); characteristic live fuel moisture (%); characteristic SAV (m2/m3); bulk density (kg/m3); packing ratio (-); relative packing ratio (-); reaction intensity (kW/m2); heat source (kW/m2); heat sink (kJ/m3)

critInit

a data frame of critical values for crown fire initiation including: fireline intensity (kW/m), flame length (m), surface ROS (m/min), Canopy base height (m)

critActive

a data frame of critical values for active crown fire including: canopy bulk density (kg/m3)", "ROS, crown (R'active) (m/min)

critCess

a data frame of critical values for cessation of crown fire including: canopy base height (m), O'cessation (km/hr)

Author(s)

Justin P Ziegler, justin.ziegler@colostate.edu

References

Rothermel, R.C. 1972. A mathematical model for predicting fire spread in wildland fuels. INT-RP-115. USDA Forest Service Intermountain Forest & Range Experimental Station.
Van Wagner, C.E. 1977. Conditions for the start and spread of crown fire. Canadian Journal of Forest Research 7:23–34.
Rothermel, R.C., 1991. Predicting behavior and size of crown fires in the northern Rocky Mountains. INT-RP-438. USDA Forest Service Intermountain Research Station.
Van Wagner, C.E. 1993. Prediction of crown fire behavior in two stands of jack pine. Canadian Journal of Forest Research 23:442–449.
Finney, M.A. 1998. FARSITE: Fire area simulator — model development and evaluation. RMRS-RP-47. USDA Forest Service Rocky Mountain Research Station.
Scott, J.H., Reinhardt, E.D. 2001. Assessing crown fire potential by linking models of surface and crown fire behavior. RMRS-RP-29. USDA Forest Service Rocky Mountain Research Station.

Examples

data(fuelModels, fuelMoisture)
#fuelModels['A10',]
exampSurfFuel = fuelModels['A10',]

#fuelMoisture['D1L1',]
exampFuelMoisture = fuelMoisture['D1L1',]

exampCrownFuel = data.frame(
 CBD = coForest$cbd_kgm3[1],
 FMC = 100,
 CBH = coForest$cbh_m[1],
 CFL = coForest$cfl_kgm2[1]
)

exampEnviro = data.frame(
 slope = 10,
 windspeed = 40,
 direction = 0,
 waf = 0.2
)
rothermel(exampSurfFuel, exampFuelMoisture, exampCrownFuel, exampEnviro)

Rampart Range RAWS meteorological data

Description

Hourly meteorological data from April 2017 through September 2017 from the Rampart Range Remote Automated Weather Station (RAWS; Station ID: RRAC2), maintained by the United States Forest Service.

Usage

data("rrRAWS")

Format

A data frame with 4392 observations of 4 variables:

dateTime

date and time of individual observation formatted as "%m/%d/%Y %H:%M"

temp_c

air temperature (deg. C)

rh

relative humidity (%)

windSpeed_kmh

wind speed (km/hr)

precip_mm

precipitation (mm)

Source

RAWS USA Climate Archive https://raws.dri.edu/

Bitmap of burning tree Bitmap image of burning tree

Description

Bitmap of burning tree Bitmap image of burning tree

Usage

tree

Format

An object of class rastergrob (inherits from grob, gDesc) of length 12.

See Also

fireChart

Calculated wind adjustment factor

Description

Prediction of wind adjustment factor for sheltered and unsheltered fuels.

Usage

waf(fuelDepth, forestHt, cr, cc, sheltered = "n")

Arguments

fuelDepth

a numeric vector of surface fuel bed depths (cm)

forestHt

a numeric vector of average stand tree heights (m)

cr

a numeric vector of crown ratios (%)

cc

a numeric vector of canopy cover (%)

sheltered

a character vector of either "y" or "n" (default) to use sheltered or not (unsheltered) equations

Details

This calculates the wind adjustment factor (ratio of 20-ft open wind speed to wind speed at midflame height of a surface fire). One of two equations are used, depending on user input: by default, this function assumes the surface fuel bed is unsheltered. fuelDepth must be a positive value if the unsheltered variant is invoked.
There are two conditions to enable calculation for a sheltered fuelbed. One, the product of cr and cc must exceed 5%. Alternatively, if cr and cc are not supplied, the user may enter "sheltered = y". The former method is recommended when cr and cc are known. In addition, either means of invoking the sheltered equation must also have forestHt provided.

Value

a vector of wind adjustment factors

Author(s)

Justin P Ziegler, justin.ziegler@colostate.edu

References

Andrews, P.L. 2012. Modeling wind adjustment factor and midflame wind speed for Rothermel’s surface fire spread model. RMRS-GTR-266. USDA Forest Service Rocky Mountain Research Station.

Examples

#Sheltered fuelbed with a 10 m tall forest with unknown crown ratio and canopy cover
waf(forestHt = 10, sheltered = 'y')
#Sheltered fuelbed with known high crown ratio and canopy cover
waf(forestHt = 10, cr = 40, cc = 40)
#Sheltered fuelbed with known low crown ratio and canopy cover
waf(fuelDepth = 1, forestHt = 10, cr = 10, cc = 10)
#Because cr and cc are low, the previous solution is equivalent to an unsheltered fuelbed
waf(fuelDepth = 1)