Table 2 Three cffdrs example application codes: (a) standard FWI System with wintering DC adjustment, (b) raster-based hourly FFMC, and (c) fire behavior prediction based on results from the previous example application

#Load test data for wDC:

data ("test_wDC")

#Keep the first weather station, which

#is from Penticton, BC, Canada. The

#data contains two complete years’

#weather records between 2013 and

#2014.

dat <- test_wDC[test_wDC$id==1,]

#Calculate the fire season start

#and end dates:

fs <- fireSeason (dat)

#Calculate the FWI System

#variables for the first year with

#standard initial start values.

#First, we need to subset the

#input data by the start and end

#of the fire season in the first year:

dat$date <- as.Date (as.POSIXlt (paste (dat$yr,dat$mon,dat$day,sep="-")))

fs$date<- as.Date (as.POSIXlt (paste (fs$yr,fs$mon,fs$day,sep="-")))

curYr <- subset(dat,date>=fs$date[1]&date<=fs$date[2])

#Use of the fwi function is very

#straightforward:

curYr.fwi<-fwi (curYr)

#Calculate the FWI System

#variables for the second year

#with overwintering modified DC.

#This may take a few steps to

#accomplish. First, we need to

#subset the overwinter period from

#this two year weather #observations:

winT<-subset (dat,date>fs$date[2]&date<fs$date[3])

#Overwintering DC can then be

#calculated easily with wDC function:

init.DC<-wDC (curYr.fwi$DC[nrow (curYr.fwi)],sum (winT$prec))

#Second, extract the second fire

#season data:

secYr<-subset (dat,date>=fs$date[3]&date<=fs$date[4])

#The second year FWI System could

#be calculated with the overwintering

#DC as the following:

secYr.fwi<-fwi (secYr,c (85,6,init.DC))

#To show the difference, we also

#calculated the FWI System variables

#based on the default initial values:

secYr.fwi.default<-fwi (secYr)

#We then plotted the 3 outputs that

#are affected by the overwintering DC

#adjustment, i.e. DC, BUI, and FWI, to

#show the difference it makes:

x11 (width=12, height=4)

par (mfrow=c (1,3), mar=c (5,5,2,1))

plot(secYr.fwi.default$DC,secYr.fwi$DC,pch=19,col='grey50',ylim=c (0,850),xlab="",ylab="With wintering DC",cex.lab=2.0,cex.axis=1.5)

abline (0,1,lwd=1.5,lty=2)

text (100,800,"DC",cex=2)

plot (secYr.fwi.default$BUI,secYr.fwi$BUI,pch=19,col='grey50',xlab="Without wintering DC",ylab="",cex.lab=2.0,cex.axis=1.5)

abline (0,1,lwd=1.5,lty=2)

text (28,185,"BUI",cex=2)

plot (secYr.fwi.default$FWI,secYr.fwi$FWI,pch=19,col='grey50',xlab="",ylab="",cex.lab=2.0,cex.axis=1.5)

abline (0,1,lwd=1.5,lty=2)

text (8,59,"FWI",cex=2)

#Load the test data for the first

#hour, namely hour01 (See Fig. 4):

data(hour01) #or

hour01src <- system.file("extdata","test_rast_hour01.tif",package="cffdrs")

hour01 <- stack (hour01src)

#Assign names to the layers:

names (hour01)<-c ("temp","rh","ws","prec")

#We calculate the first hour’s

#FFMC with the default initial

#FFMC value of 85:

hffmc.hour01<-hffmcRaster (hour01)

#The FFMC for the second hour is

#then calculated based on

#the previous hour's FFMC. First,

#we load the test data for the

#second hour, namely hour02, to R:

data ("hour02")

hour02src <- system.file("extdata","test_rast_hour02.tif",package="cffdrs")

hour02 <- stack (hour02src)

#Assign variable names to the

#layers:

names (hour02)<-c ("temp","rh","ws","prec")

#The calculation of the second

#hour FFMC is slightly different:

hffmc.hour02<-hffmcRaster (hour02,ffmc_old=hffmc.hour01)

#Map both the inputs and outputs

#with a plot function from the

#raster package:

x11 (width=5, height=6.2)

plot (hour02)

#First, make a copy of the study area:

bui<-hour02$temp

#Assign the BUI value:

values (bui) <-50

#Rename the layer:

names(bui) <- "bui"

#Make it into the stack, which can

#be used as input in the #hffmcRaster function.

hour02<-stack (hour02,bui=bui)

#Now we can calculate all hourly

#FWI System variables and plot

#them:

hfwi.hour02<-hffmcRaster (hour02,ffmc_old=hffmc.hour01,hourlyFWI=TRUE)

x11 (width=5, height=6.2)

plot (hfwi.hour02)

#First, we calculate the hourly

#FFMC with hffmc function. We

#convert our raster inputs into a

#data.frame first:

hffmc.hour01<-as.data.frame (rasterToPoints (hour01))

#The fbp function requires the

#coordinates being labeled as

#"long" and "lat":

names (hffmc.hour01) [1:2]<-c ("long","lat")

#Hourly FFMC as inputs for fbp (note

#this is a 'multiple weather stations

#at one time step' situation):

hffmc.hour01$ffmc<-hffmc (hffmc.hour01,batch=FALSE)

#Alternatively, this could be done

#with hffmcRaster:

r<-hffmcRaster (hour01)

hffmc.hour01$ffmc<-rasterToPoints (r) [,3]

## Add fuel type and unique ID

hffmc.hour01$id<-1:nrow (hffmc.hour01)

hffmc.hour01$fuelType<-"C-2"

#The new variable that was added

#to hffmc.hour01 is named as

#'ffmc', which is required by the

#fbp function.

#Calculate the FBP System (primary

#outputs) with all other default

#factors:

FBP<-fbp (hffmc.hour01)

#We can then map a few key fire

#behavior measurements from the

#outputs: Head Fire Intensity

#(HFI), Equilibrium Head Fire Rate

#of Spread (ROS), and Total Fuel

#Consumption (TFC).

HFI<-rasterize (as.matrix(hffmc.hour01[,c ("long","lat")]),r,FBP$HFI)

ROS<-rasterize (as.matrix(hffmc.hour01[,c ("long","lat")]),r,FBP$ROS)

TFC<-rasterize (as.matrix(hffmc.hour01[,c ("long","lat")]),r,FBP$TFC)

#Plot them out:

x11 (width=10, height=5.3)

par (mfrow=c (1,3), mar=c (5,3.5,5,4))

brks <- seq (0, 1, by=0.1)

nb <- length (brks)-1

cols <- rev (heat.colors(nb))

plot (HFI,col=cols,main="Head Fire Intensity (HFI)",cex.main=1.8)

plot (ROS,col=cols,main="Rate of Spread (ROS)",cex.main=1.8)

plot (TFC,col=cols,main="Total Fuel Consumption (TFC)",cex.main=1.8)

# using fpbRaster function, it is

# much simplified:

ffmc<-hffmcRaster(hour01)

ws<-hour01$ws

fuel<-ffmc # create a fuel layer

fuel[]<-2 # for C-2 fuel type

r<-stack (ffmc,fuel,ws)

names(r)<-c ("ffmc","fueltype","ws")

FBP<-fbpRaster (r)

# Plot them:

x11 (width=10, height=5.3)

par(mfrow=c (1,3), mar=c (5,3.5,5,4))

brks <- seq (0, 1, by=0.1)

nb <- length (brks)-1

cols <- rev (heat.colors(nb))

plot (FBP$HFI,col=cols,main="Head Fire Intensity (HFI)",cex.main=1.8)

plot (FBP$ROS,col=cols,main="Rate of Spread (ROS)",cex.main=1.8)

plot (FBP$TFC,col=cols,main="Total Fuel Consumption (TFC)",cex.main=1.8)