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Hourly FWI Tutorial - R

Last updated: February 27th, 2026

R files

The following files can be found on the cffdrs-ng GitHub repository:

If you are unfamiliar with GitHub, there are many options for you to retrieve the code and data files:

  1. Download each file to your computer individually. On the file's GitHub page (e.g. PRF2007_hourly_wx.csv), find the "More file actions" button at the top-right side of the website (denoted "..."), and select "Download". This will begin downloading based on your browser settings. Repeat this for every file.
  2. Download the whole repository to your computer. See the GitHub documentation for more information.
  3. Clone the whole repository to your computer with Git. See the GitHub documentation for more information.
  4. Fork the repository to create a new repository on GitHub. See the GitHub documentation for more information.

Besides the files, ensure you have the required R packages, which are listed in the code documentation.

Data

PRF2007_hourly_wx.csv contains hourly weather recorded from a Petawawa Research Forest (PRF) weather station during the 2007 field season. The data has no gaps and is sorted sequentially by time, which is a requirement for FWI2025 input data.

The column headers are those required for hourly FWI calculations, details of which can be found in the code documentation. Grassland fuel load (grass_fuel_load), grassland curing (percent_cured) and solar radiation (solrad) are not included, but these are optional inputs and they will be automatically calculated if not provided. There is a column for UTC offset (timezone) which will be used by default in the hFWI() function.

Steps

Open the tutorial_hourly_FWI.r code file. You can either follow the code and comments in the file or continue on this page (both include the same code and content).

Load libraries

Run install.packages() to install any you are missing

library(lubridate)
library(data.table)

Load functions and data

Check your current working directory

getwd()

If the working directory is different from where you saved the FWI2025 scripts, change the working directory with setwd() to that folder.

Load the files containing the functions and variables to calculate FWI2025.

source("NG_FWI.r")
source("util.r")
source("daily_summaries.r")

Load the input weather station data file. The path below is specified based on the layout in the GitHub repository. Change the file path for PRF2007_hourly_wx.csv if your structure is different.

data <- read.csv("../../data/PRF2007_hourly_wx.csv")

Print the column names, data should contain 12 columns

print(names(data))

 [1] "id"       "lat"      "long"     "timezone" "yr"       "mon"
 [7] "day"      "hr"       "temp"     "rh"       "ws"       "prec"

Run FWI2025

hFWI() is the function that calculates hourly FWI codes in FWI2025. Details about it can be found in the code documentation. It can handle multiple stations and years/fire seasons (not shown in this tutorial). For the arguments you can run args().

args(hFWI)

function (df_wx, timezone = NA, ffmc_old = FFMC_DEFAULT, mcffmc_old = NA, 
    dmc_old = DMC_DEFAULT, dc_old = DC_DEFAULT, mcgfmc_matted_old = ffmc_to_mcffmc(FFMC_DEFAULT),
    mcgfmc_standing_old = ffmc_to_mcffmc(FFMC_DEFAULT), prec_cumulative = 0,
    canopy_drying = 0, silent = FALSE, round_out = 4)

For this tutorial, we will leave all the optional parameters to default.

data_fwi <- hFWI(data)

########
FWI2025 (YYYY-MM-DD)

Startup values used:
FFMC = 85.0 or mcffmc = NA %
DMC = 6.0 and DC = 15.0
mcgfmc matted = 16.3075 % and standing = 16.3075 %
cumulative precipitation = 0.0 mm and canopy drying = 0

Running PRF for 2007
########

Output is a data.frame (matching input class), with FWI calculations appended after the input columns. Save the output as a CSV file (overrides any preexisting file).

write.csv(data_fwi, "PRF2007_hourly_FWI.csv", row.names = FALSE)

Print the last two rows of the standard moisture codes and fire behaviour indices.

standard_components <- c("ffmc", "dmc", "dc", "isi", "bui", "fwi")
print(tail(data_fwi[standard_components], 2))

        ffmc    dmc       dc    isi    bui    fwi
2622 80.2669 3.0924 219.5169 1.6423 5.9745 0.7650
2623 82.1759 3.3503 220.0533 2.1462 6.4550 1.2023

Print a simple summary of the standard FWI components.

summary(data_fwi[standard_components])

      ffmc            dmc               dc              isi
 Min.   : 0.00   Min.   : 0.000   Min.   : 15.41   Min.   : 0.0000
 1st Qu.:67.87   1st Qu.: 5.925   1st Qu.: 72.28   1st Qu.: 0.7877
 Median :79.38   Median :16.461   Median :133.06   Median : 1.3958
 Mean   :70.62   Mean   :17.487   Mean   :136.12   Mean   : 2.1582
 3rd Qu.:85.18   3rd Qu.:26.511   3rd Qu.:188.64   3rd Qu.: 2.9785
 Max.   :94.40   Max.   :50.174   Max.   :292.95   Max.   :17.5958
      bui              fwi
 Min.   : 0.000   Min.   : 0.000
 1st Qu.: 8.408   1st Qu.: 0.422
 Median :22.623   Median : 2.408
 Mean   :23.972   Mean   : 3.877
 3rd Qu.:34.605   3rd Qu.: 5.930
 Max.   :66.148   Max.   :25.135

Compare your outputs with our standard outputs in PRF2007_standard_hourly_FWI.csv.

Calculate daily summaries

Calculate outputs like peak burn time and number of hours of spread potential. Details about the daily summaries function can be found in the code documentation.

report <- generate_daily_summaries(data_fwi)

########
FWI2025: Daily Summaries (YYYY-MM-DD)

Summarizing PRF to daily
########

Print a simple summary of the daily report.

daily_components <- c("peak_hr", "duration", "isi_smooth", "dsr")
summary(report[daily_components])

    peak_hr         duration        isi_smooth          dsr
 Min.   :13.00   Min.   : 0.000   Min.   : 0.000   Min.   :0.0000
 1st Qu.:17.00   1st Qu.: 0.000   1st Qu.: 1.234   1st Qu.:0.0338
 Median :17.00   Median : 0.000   Median : 4.032   Median :0.9913
 Mean   :17.29   Mean   : 2.697   Mean   : 4.324   Mean   :1.5533
 3rd Qu.:18.00   3rd Qu.: 5.000   3rd Qu.: 6.495   3rd Qu.:2.5940
 Max.   :23.00   Max.   :14.000   Max.   :15.771   Max.   :6.9486

From here, the outputs can be converted to any datatype for further analysis or plotted for visualization.

Appendix: Timezone

This section will cover how to determine the UTC offset of a Local Standard Time (LST) based on a date and location (latitude and longitude). This only applies to datasets that are known to be recorded using LST, and is not a substitute in cases where a dataset's UTC offset is unknown. See the FAQ for an explanation.

The 'lutz' library has functions to get the timezone of the weather station based on latitude and longitude.

library(lutz)

First, make a dataframe of stations with unique ID, latitude, and longitude.

stations <- unique(data[c("id", "lat", "long")])

Print the unique station IDs and coordinates. For this dataset the only station is at Petawawa Research Forest (PRF).

print(stations)

   id    lat    long
1 PRF 45.996 -77.427

Find the timezone based on latitude and longitude, this can take some time. You may need to download the package 'sf' for method = "accurate".

tz_loc <- tz_lookup_coords(stations$lat, stations$long, method = "accurate")

Print the timezone location. PRF is equivalent to "America/Toronto".

print(tz_loc)

[1] "America/Toronto"

The UTC offset can then be determined from the timezone location and a date. To guarantee the UTC offset for LST, use a date in winter (e.g. January 1st in the Northern hemisphere or July 1st in the Southern hemisphere).

utc <- tz_offset("2007-01-01", tz_loc)[[5]]

Print the UTC offset.

print(utc)

[1] -5

The provided PRF dataset is actually not recorded in LST, but Local Daylight Time (Eastern Daylight Time). This is why the timezone parameter is set to -4, and why this process is not a substitute for actual information about a dataset.