We present a global high-resolution calculation of the Canadian Fire Weather
Index (FWI) System indices using surface meteorology from the ERA5 HRES
reanalysis for 1979–2018. ERA5 HRES represents an improved dataset compared
to several other reanalyses in terms of accuracy, as well as spatial and
temporal coverage. The FWI calculation is performed using two different
procedures for setting the start-up value of the Drought Code (DC) at the
beginning of the fire season. The first procedure, which accounts for the
effects of inter-seasonal drought, overwinters the DC by adjusting the fall
DC value with a fraction of accumulated overwinter precipitation. The second
procedure sets the DC to its default start-up value (i.e. 15) at the start
of each fire season. We validate the FWI values over Canada using station
observations from Environment and Climate Change Canada and find generally
good agreement (mean Spearman correlation of 0.77). We also show that
significant differences in early season DC and FWI values can occur when the
FWI System calculation is started using the overwintered versus default DC
values, as is highlighted by an example from 2016 over North America. The
FWI System moisture codes and fire behaviour indices are made available for
both versions of the calculation at 10.5281/zenodo.3626193 (McElhinny et al., 2020), although
we recommend using codes and indices calculated with the overwintered DC,
unless specific research requirements dictate otherwise.
Introduction
Climate reanalyses provide a numerical and geospatial description of past
and present climate (Bengtsson et al., 2007). This method of climate simulation
assimilates weather observations into dynamic climate models of the
atmosphere and relevant Earth systems to represent the atmospheric and
surface states at a given time, usually for a historical period of multiple
decades to the near-present. The gridded product of reanalysis is spatially and
temporally continuous for the duration of the model simulation and has the
added benefit of producing data in remote areas that are sometimes
inaccessible to direct monitoring (Bengtsson et al., 2007). The best climate
reanalyses use the same model configuration for the duration of the
simulation, thus eliminating inhomogeneities that may occur through other
modes of climate tracking and providing a useful tool for studying
weather-related phenomena.
Past research in the field of reanalysis and fire weather has analysed the
correlation between metrics of fire danger produced by reanalyses and those
produced from weather stations at local to continental scales. In comparing
observed and reanalysis-derived indices of fire weather, reanalyses have
been found to be an effective tool for indicating fire danger (Bedia et al., 2012;
Venäläinen et al., 2014; Field et al., 2015). Other studies have investigated the
relationship between fire weather indices calculated from reanalyses and
measures of the fire regime, such as annual area burned (Bedia et al., 2014),
trends in fire season length (Jain et al., 2017), and quantification of global
seasonal fire danger (Vitolo et al., 2019). Reanalyses have also been used to
investigate the spatio-temporal variation in fire danger indices across
continents (Lu et al., 2011) and to develop new indices that investigate the
validity of incorporating synoptic and meso-scale weather processes into
metrics of fire behaviour (Srock et al., 2018). Recently, research has begun to
investigate the application of reanalyses in prediction of future fire
weather and fire behaviour patterns by evaluating how they can supplement the
coarse resolution of global climate models on local scales through
statistical downscaling (Bedia et al., 2013). Although climate reanalysis has been
found to be a useful and reliable tool for calculating indices of fire
behaviour, some metrics of fire danger require specific temporal weather
measurements, such as noon local standard time measurements, that many
reanalyses cannot directly provide (Herrera et al., 2013). However, the concerns
raised around this shortcoming have been addressed in recent years by new
reanalysis products with better temporal resolution, among other
improvements.
Many countries, including Canada, use the Canadian Fire Weather Index (FWI)
System to determine the effects of weather on forest fuel moisture and
subsequently fire behaviour (Lawson and Armitage, 2008). The FWI System
considers surface temperature, relative humidity, 24 h accumulated
precipitation, and wind speed at 10 m to calculate moisture in three fuel
layers represented by three moisture codes respectively: the Fine Fuel
Moisture Code (FFMC), the Duff Moisture Code (DMC), and the Drought Code
(DC). These values, plus wind speed, are then used to calculate indices of
potential fire behaviour; the Initial Spread Index (ISI) and Buildup Index
(BUI) from which the Fire Weather Index (FWI) and Daily Severity Rating
(DSR) are produced.
The DC is one of three moisture codes that impacts fire behaviour and is the
metric that tracks moisture in the deepest layer of forest floor fuels as
well as in large, dead, woody debris (Wotton, 2009). Due to its depth, the DC
is the slowest-changing moisture code with a time lag of 52 d (Van
Wagner, 1987). Essentially, the DC value decreases with effective rainfall
and increases with evapotranspiration so that higher values indicate a
higher likelihood that a wildfire will persist and smoulder (Van Wagner,
1987).
In areas where winter precipitation is sufficient (i.e. greater than 200 mm
rain or snow equivalent), moisture reserves are typically recharged in the
spring so that the default DC value of 15 represents near saturation of deep
organic layers (Alexander, 1982; Lawson and Armitage, 2008). However, when
this is not the case, an alternative method to start up the FWI calculation
uses an overwintered value of the DC. This value is determined from the final DC
of the preceding fire season, representing any potential fall moisture
deficit and a percentage of overwinter precipitation assumed available to
recharge that deficit (Lawson and Armitage, 2008). The main body of thought
behind using the overwintered DC is that it accounts for fall drought
conditions and/or dry winter conditions and thus may indicate drier
moisture conditions, leading to more severe fire weather risk at the
beginning of the fire season than is suggested by the default DC.
A number of empirical field studies further support the need for
overwintering the DC when calculating FWI System indices. Lawson and
Dalrymple (1996) describe a method for ground-truthing (i.e. the process of
calibrating from and/or validating against field sample measurements) the DC
with destructive sampling, which can be executed at any time during the fire
season. They concluded that overwintering the DC was adequate for broad
areas but that site-specific calibration may still be necessary. Confirming
this finding, Bourgeau-Chavez et al. (2007) demonstrated that the default
start-up value of DC was not sufficient for describing spring fuel moisture
in Alaska and Girardin et al. (2006) showed that both area burned and number
of large fires were correlated with the previous season's DC. Furthermore,
Wilmore (2001) showed that default DC values overpredicted spring fuel
moisture and that overwintering the DC led to improvements in estimates of
drought conditions, although this could be further improved upon using an
alternative site-specific overwintering equation.
Although these papers largely indicate that the overwintered DC is more
representative of actual conditions than the default DC, many note that
regional adjustments for the carry-over fraction from the previous season's
fall moisture and the coefficient for effectiveness of winter precipitation
in recharging moisture reserves in the spring are necessary when calculating
the overwintered DC (Lawson and Armitage, 2008; Anderson and Otway, 2003).
In analysing the conditions leading up to the Fort McMurray wildfire in
Alberta, Canada, it was found that accounting for observations of fuel
moisture when calculating the start-up DC value can additionally improve the
accuracy of fire danger detection (Elmes et al., 2018).
ERA5 HRES is a high-resolution reanalysis dataset named for and produced by
the European Centre for Medium-Range Weather Forecasts (ECMWF)
reanalysis high-resolution product, of which the dataset is the fifth
generation (after FGGE, ERA-15, ERA-40, and ERA-Interim; Hennerman and
Berrisford, 2019). The spatial and temporal continuity and resolution of
this dataset makes it a useful tool for analysing past weather and
associated phenomenon. The main purpose of this paper is to document the
calculation of FWI System indices using the global ERA5 HRES reanalysis
(hereafter known as ERA5). We perform the calculation using both default and
overwintered DC start-up values, the latter being important for some regions
with snow cover or ground freeze over winter.
Data
The ERA5 reanalysis product is produced from the CY41R2 global ensemble
system of the ECMWF Integrated Forecast System (Copernicus Climate Change Service (C3S), 2017). Weather observations from
satellites and in situ data from the World Meteorological Organization are
integrated into the global ensemble using 4-dimensional variational analysis
data assimilation (Hennerman and Berrisford, 2019).
The high-resolution realization is 31 km globally or 0.28125∘ on a
reduced Gaussian grid (output at 0.25∘ on a regular geographic grid),
which provides an improvement in precision over its predecessor,
ERA-Interim, for which the resolution was 79 km globally (Hennerman and
Berrisford, 2019). Additionally, ERA5 has a finer resolution compared to
other global reanalysis products including the NCEP North American Regional
Reanalysis (NARR), NCEP-DOE Reanalysis 2, and NASA's Modern-Era Retrospective analysis for Research and Applications (MERRA) as well as
MERRA-2. The ERA5 dataset covers 1979 to 2–3 months before present (our
calculation only used data up to 2018 to obtain a full final year), on an
hourly scale producing numerous global climatological variables including
surface and upper-atmosphere quantities. For this study we obtained surface
variables including temperature (K), dew point temperature (K), U and
V components of wind (m s-1), precipitation (m), and a land–sea mask,
available from the ECMWF Climate Data Store (CDS) website
(https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=form,
last access: 17 May 2019).
Compared with the ERA-Interim product, ERA5 has been shown to perform better
with respect to variation in data quality over space and time, tropospheric
representation, representation of tropical cyclones, soil moisture accuracy,
sea surface temperature and sea ice cover detection, the global
precipitation and evaporation balance, and precipitation over land
especially in the deep tropics (Hennerman and Guillory, 2019). One of the
most impressive improvements to the ERA5 dataset is with respect to
precipitation modelling. Multiple studies have been conducted to determine
the accuracy with which ERA5 detects various aspects of past precipitation
events. One study, conducted in the Assiniboine River basin of the northern
Great Plains, found that of six reanalysis products, including CaPA,
ERA-Interim, ERA5, JRA-55, MERRA-2, and NLDAS-2, the ERA5 dataset
consistently performed in the top three regarding precipitation detection,
correlation to observed precipitation events, mean absolute error, and root
mean square error (RMSE; Xu et al., 2019). A study in the Fram Strait, off the
coast of Greenland, found that of ERA5, ERA-Interim, JRA-55, CFSv2, and
MERRA-2, the ERA5 reanalysis produced the most accurate simulation of
radiosonde profiles over the strait and showed the lowest vertically
averaged absolute biases for every variable except relative humidity, which
was best simulated by JRA-55 (Graham et al., 2019). Over North America, ERA5 was
consistently found to have lower precipitation (and temperature) biases than
ERA-Interim, reducing the median gap between observations by 40 % compared
to its predecessor (Tarek et al., 2020). ERA5 performance was found to be
equivalent to directly observed data for most of the region, excluding the
eastern USA where observations were more accurate. Finally, a study
conducted over the contiguous United States found that of 26 (sub)daily
precipitation datasets, ERA5 had the best performance of the compared
uncorrected precipitation products, meaning those using only satellite
and/or reanalysis data (Beck et al., 2019). The significant capabilities of the
ERA5 reanalysis product indicate the impressive advances in Earth system
modelling that have been made in recent years by the ECMWF. With the
improved representation of surface weather from ERA5 in mind, we used the
product to calculate indices of fire weather for the entire globe.
MethodsFire weather input variables
The requirements of the FWI calculation stipulate that 2 m temperature
(∘C), 10 m wind speed (km h-1), and 2 m relative humidity (%)
measurements be taken at noon LST for each global time zone and that
precipitation (mm) be accumulated over the previous 24 h ending at noon LST of each day (Van Wagner, 1987; see Lawson and Armitage, 2008, for details on
weather station sensors). To facilitate subsetting the data to noon for each
time zone, we created a land index from the ECMWF land–sea mask and a
shapefile of global time zones (retrieved from the Natural Earth website,
https://www.naturalearthdata.com/downloads/10m-cultural-vectors/timezones/,
last access: 18 May 2019). The land index contained information regarding the
location of each ECMWF grid cell over land or water as well as the time zone
(as an offset from UTC±00:00) the grid cell covered. It should be
noted that cells containing any amount of land were considered to be
completely over land and that cells entirely over water were not processed
in subsequent steps of the project.
To account for the noon LST requirement, the UTC offset values contained in
each grid cell of the land index were used to select the first noon LST
layer of each ECMWF monthly surface weather variable. A sequence of 24 h
increments was then applied to each cell, starting at the first noon LST
layer, to select (or sum in the case of precipitation) 24 h increments
between the noons LST of each day.
To account for the correct FWI units, the temperature (K) and accumulated
precipitation (m) data were converted to degrees Celsius and millimetres respectively.
Relative humidity was calculated from the subsetted datasets for temperature
and dew point temperature according to a derivation of the Rothfusz
regression (Rothfusz, 1990), Eqs. (1) and (2):
1a=112.0-0.1T+Td112.0+09.T,2RH=100a8,
where T is temperature (∘C), Td is
dew point temperature (∘C), and RH is relative humidity (%). For
wind speed, the subsetted datasets for the U component and V component of
wind were converted to wind speed according to Eq. (3):
WS=3.6u2+v2,
where u is the U component of wind (m s-1), v is the V component of wind
(m s-1), and WS is wind speed (km h-1). Once each weather variable was subset
into daily noon LST values and converted into the correct units, all monthly
datasets for a year were bound together to produce annual datasets of daily
values for each element of fire weather.
Overwintering masksOverwintered Drought Code
In regions covered by snow over winter, the fire season is considered to be
active on the third day after snow has disappeared and the fire season is
considered to be over when snow covers the ground. Alternatively, as a proxy
for the snow condition, the fire season is considered to be active on the
fourth day following 3 consecutive days with a maximum temperature of
12 ∘C or higher and the fire season is considered to be over after
3 consecutive days with a maximum temperature of 5 ∘C or lower
(Wotton and Flannigan, 1993; Lawson and Armitage, 2008). Using this
definition of overwintering means that the fire season can switch on and off
throughout the year. For example, the upper and lower maximum temperature
thresholds may be met multiple times throughout the year resulting in short
periods of fire season in the shoulder seasons (where the maximum daily
temperature condition is met for short periods before and/or after the main
fire season), in addition to a longer fire season period. The 24 h
maximum temperature of each day (using local standard time) were calculated
from the ECMWF data using the hourly 2 m temperature data. Accounting for the
maximum temperature thresholds, we created binary masks of overwintering for
each year from the annual datasets of midnight LST 24 h maximum
temperature, with the process of overwintering the DC in mind.
Default Drought Code
When the default DC is used to start up the FWI calculation, it is not
desirable to include short shoulder fire seasons since the DC value is reset
at the beginning of each fire season period, which leads to discontinuities
in the calculated codes and indices. Thus, we only used the longest
continuous fire season period from the Drought Code overwintering masks, by
creating a mask for default DC with a single fire season start and end date
per year in each grid cell of the Northern Hemisphere (NH) and Southern
Hemisphere (SH). For NH grid cells, we saved the longest continuous fire
season between 1 January and 31 December of the same calendar year, and for
SH cells, we saved the longest fire season between 1 July of 1 calendar
year and 30 June of the next calendar year, with each time period chosen to
contain the boreal and astral summers respectively. Although SH cells were
processed across years, the final product was organized to contain the
overwintering status values for each cell on the globe for a calendar year
in both the NH and SH. It is important to note that in some cases, a fire
season run would extend beyond the defined end of year. Here, we added the
length of days for which the fire season extended into the new year to the
length of the fire season in the preceding year. Nevertheless, the main fire
season was still defined as the longest run of fire season days within the
defined year (corresponding to each hemisphere) for each calendar year.
Monthly climatologies of mean FWI values for January (a),
April (b), July (c), and October (d). Barren areas (e.g.
the Sahara) and any locations where overwintering is active for
greater than 50 % of the temporal record are masked out. Barren areas are
further masked using land cover data available from Li et al. (2018), part of
the ESA Climate Change Initiative – Land Cover led by UCLouvain (2017). Map
displayed in World Geodetic System 1984 projection (WGS84, EPSG:4326).
Annual FWI System indices
Daily FWI System outputs, including FFMC, DMC, DC, ISI, BUI, FWI, and DSR
were calculated using the cffdrs package in R (Version 1.8.5; Wang et al., 2017)
with the elements of fire weather data as inputs (for a full listing of R packages and versions used for the calculation see Appendix B). When
accounting for the overwintered DC, we programmed the fwiRaster function
according to a delta mask. The delta value for each day was calculated by
subtracting the previous day's overwintered DC mask value from the current day's
mask value for each grid cell. This created four cases:
Case 1. The delta mask was equivalent to 1. This indicated that it was the
first day of overwintering (the fire season was inactive) at that location,
and thus we saved the DC of the previous day and the 24 h accumulated
precipitation of the current day.
Case 2. The delta mask was equivalent to 0, and the current day's
overwintering mask was equivalent to 1. This indicated that the
overwintering status of the location was active (but it was not the first
day of overwintering), and thus we saved the sum of the current day's
precipitation and all precipitation since overwintering began.
Case 3. The delta mask was equivalent to -1. This indicated that it was the
first day of the fire season at that location, and thus we calculated the
start-up DC (a.k.a. the overwintered DC) from the saved DC value when
overwintering began and the precipitation that accumulated through the
overwintering period, using the overwintering Drought Code function of the
cffdrs package (Wang et al., 2017). The final value of accumulated precipitation
represents the total value of precipitation that fell during the period
defined by the maximum temperature threshold criteria. Additionally, we set
the FFMC and DMC to the default values of 85 and 6 respectively and stopped
accumulating precipitation.
Case 4. The delta mask was equivalent to 0, and the current day's
overwintering mask was equivalent to 0. This indicated that the fire season
status of the location was active (but it was not the first day of the fire
season), and thus the FWI calculation was reliant on the current day's
weather variables and the previous day's FWI moisture code outputs.
When accounting for the default DC, the delta mask was produced from the
default Drought Code overwintering masks as above. However, this resulted in
only two relevant cases: Case 3 and 4. Case 4 was the same for the
overwintered DC situation, but Case 3 was different in that the start-up DC
was set to 15, rather than calculating the overwintered DC value.
In the case of the overwintered DC, the adjusted start-up values of the DC were
calculated using the wDC function in the cffdrs R package. In particular, we
set the two required coefficients for this function as a=1 (representing
carry-over fraction of last fall's moisture) and b=0.75 (default value
of effectiveness of winter precipitation in recharging moisture reserves in
spring). As noted by Lawson and Armitage (2008) and Anderson and Otway (2003), the overwintered DC is most accurately represented when regional
conditions are analysed and the coefficients of the wDC function are
adjusted accordingly. However, the ERA5 dataset did not contain information
that allowed us to vary these coefficients, and thus we chose the default
values.
FWI indices in the overwintered and default DC situations were calculated
for Case 3 and 4. When Case 3 was identified, FWI indices were calculated
from the default FFMC and DMC, that day's values for the elements of fire
weather, and the overwintered DC or default DC value depending on which
overwintering mask was used. When Case 4 was identified, FWI indices were
calculated from that day's elements of fire weather and the previous day's
moisture codes in both Drought Code situations. We produced two final
datasets of daily FWI System indices for 1979 to 2018: the first used the
overwintered DC value at fire season start-up and calculated FWI values each
time the maximum temperature thresholds were met, and the second used the
default DC value at fire season start-up and only produced FWI values for
the longest annual fire season in each hemisphere.
AnalysisClimatologies
Mean FWI values vary spatially and temporally based on climatological
conditions and surface topography. Figure 1 shows monthly climatologies of
mean FWI values for January, April, July, and October, which are indicative
of global seasonal changes in FWI values. Note that the absence of values in
the northern latitudes for January, April, and October represents grid cells
where the FWI System calculation is suspended because it is outside of the
fire season period.
Validation for Canada
It is instructive to examine the accuracy of the ERA5 FWI System calculation
when compared with station observations, particularly given its intended use
as a proxy for observed data. We perform a simple validation for Canada, for
which the FWI System was initially developed and calibrated. We used FWI
values calculated from the historical Environment and Climate Change Canada
(ECCC) archive from 1979 to 2009, which represented the temporal period of
quality-controlled data overlapping the ERA5 reanalysis data (Natural
Resources Canada – Canadian Forest Service, Wildland Fire Information
Systems, 2016).
For the validation, we considered three simple metrics: (1) mean absolute
error (mean(|X1-X0|)), (2) mean bias error (MBE;
mean(X1-X0)), and (3) Spearman rank correlation (SRC; ρs(X0,X1)), denoting X1 as the FWI values calculated
from the ERA5 reanalysis dataset and X0 as the FWI values calculated
using ECCC station data. Figure 2 shows the spatial distribution of the three
metrics with histograms of their values. Overall, citing the mean values
with the 5th and 95th percentiles (in square brackets) gives
MAE = 5.005049, 90 % CI [1.58, 11.05]; MBE =-3.6745624, 90 % CI [-10.15,
0.26]; and SRC = 0.7764061, 90 % CI [0.63, 0.88]. These results suggest that
although there is a strong correlation between the reanalysis and observed
FWI values, the FWI values calculated from ERA5 exhibit a negative bias,
particularly in Alberta, Canada (see Fig. 2b). Note, the higher density of
stations in Alberta can be attributed to a greater number of ECCC stations
in the ECCC historical archive, including those from provincial weather
station networks including both Alberta Agriculture and Forestry and the
Alberta Wildfire management branch.
An investigation into the source of the model bias is outside the scope of
this paper. However, we note that FWI values from the ERA5 reanalysis may be
underestimated due to biases in wind speed and precipitation, as noted in
one recent study in Canada (Betts et al., 2019). With respect to non-gauge-corrected precipitation models, ERA5 performs well compared with other
datasets (Beck et al., 2019). Nevertheless, users of the dataset documented here
should be aware of any limitations in model bias and accuracy for their intended
study area and period of interest.
Validation of FWI values calculated from the ERA5 reanalysis
compared with observed FWI values calculated from ECCC station data for
1979–2009. Spatial distribution and histogram values are shown for the mean
absolute error (a), mean bias error (b), and Spearman rank
correlation (c). Map displayed in Atlas of Canada Lambert conformal
projection (EPSG:3978).
Differences in FWI calculation using the default DC start-up value
versus the overwintered DC start-up value for North America in 2016. (a) Start-up day of year for FWI calculation based on longest period satisfying
the meteorological fire season condition given by Wotton and Flannigan (1993). (b) The difference between overwintered DC and default DC
start-up values (i.e. DC = 15) on the day of year given by (a). (c) The difference in FWI values corresponding to (b). (d) The
corresponding difference in fire spread days (defined as FWI > 19
as per Podur and Wotton, 2011). Map displayed in Atlas of Canada
Lambert conformal projection (EPSG:3978).
Effect of overwintering the Drought Code
As discussed earlier, overwintering the Drought Code can modify the FWI
System indices, particularly in areas with low overwinter precipitation and
during spring (i.e. after snowmelt but before fuel moisture can be
recharged from precipitation events). To explore this possibility further,
we show differences between FWI calculations where the process of
overwintering the DC is performed and alternatively when the default DC
start-up value (DC = 15) is used. We focus on the case of calculated DC and
FWI values for 2016 over North America. Fuel moisture preceding the Fort McMurray wildfire (Alberta, Canada) in 2016 is widely considered to have
been anomalously low due to low overwinter precipitation and severe fall
drought conditions (Elmes et al., 2018). Figure 3 shows the day of year associated
with fire season start-up (panel a), the difference in DC values
(overwintered vs. default) on the corresponding start-up day of year (panel b), the corresponding difference in FWI values (panel c), and the difference
in spread day events for 2016 between the overwintered and default
calculations (panel d). These results show that even a modest difference in
FWI values at start-up can lead to important differences in the number and
spatial distribution of fire spread days between the overwintered and
default calculations; in general, the greater number of fire spread days
associated with the overwintered calculation may therefore account for
sizeable differences in modelling area burned where DC, BUI, or FWI metrics
are used as explanatory variables. Note that the results for other years
(not shown) show a similar spatial pattern to the 2016 results.
We also found (not shown) that central and eastern Siberia displayed
differences between the two calculations, most likely due to the low
overwinter precipitation that occurs there (Stocks et al., 1996). In general, we
found that regions where overwintering leads to drier fuel moisture
conditions correspond to areas of low overwinter precipitation and were
largely confined to western North America and parts of Eurasia. For regions
where overwintering is likely to have an effect on spring fuel conditions,
we therefore recommend using the version of the FWI calculation that
overwinters the Drought Code.
Data availability
The FWI System indices calculated using both procedures (i.e. default and
overwintered start-up values of the DC) can be downloaded from Zenodo as
annual NetCDF files of daily values from https://doi.org/10.5281/zenodo.3626193 (McElhinny et al., 2020). Note that
this product is not intended to be updated annually.
Conclusions
The Global Fire Weather Indices dataset developed from the ECMWF ERA5 HRES
reanalysis product is a publicly available global dataset that presents
seven key variables representing fuel moisture (FFMC, DMC, DC) and potential
fire behaviour (ISI, BUI, FWI, and DSR). The dataset covers a period of 1979
to 2018 and accounts for the procedures of using the default DC or
alternatively the overwintered DC to calculate fire behaviour at fire season
start-up. This dataset shows that there can be a significant difference in
DC (and therefore also BUI, FWI, and DSR) values, particularly at the
beginning of the fire season, depending on which procedure is employed,
suggesting that fire danger in some regions may be more severe than what is
predicted by the default DC.
The FWI calculated from the ECMWF data shows generally strong
agreement with calculations based on Canadian weather station data (mean
Spearman correlation of 0.77, mean absolute error of 5.0, and mean bias of
-3.7). However, there are several caveats that are important to consider for
users of the data. First, it is important to note that the assumptions made
for the overwintering process include that (a) the carry-over fraction from the
previous season's fall moisture is always 1 and (b) the coefficient for
effectiveness of winter precipitation in recharging moisture reserves in the
spring is always 0.75. In reality these coefficients would vary
spatially and temporally to reflect variations in topography as well as
weather and climate. Second, as reanalyses represent modelled data, there are
biases associated with model and/or data uncertainty. For example ERA5 has
been shown to exhibit a negative daytime wind speed bias in the Canadian
Prairies (Betts et al., 2019). Lastly, although the resolution of the produced
dataset is considered fine in relation to other reanalysis products (e.g.
ERA-Interim), there may still be unresolved fine-scale variations in fire
behaviour indices due to topographic or microclimatic variations. Regardless
of these caveats, this dataset provides historical fire weather and
potential fire behaviour data that users should find useful for several
research applications including calibration of FWI-based fire prediction
models, historical relationships between fire weather and fire danger at
regional to global scales, baseline data for future fire danger projections
under climate change scenarios, and analysis of regional or global trends in
fire weather or behaviour.
UTC time zone adjustments.
UTC time zoneChanged toUTC-12:00UTC-11:00UTC-09:30UTC-09:00UTC-04:30UTC-04:00UTC-03:30UTC-04:00UTC+03:30UTC+04:00UTC+04:30UTC+05:00UTC+05:30UTC+06:00UTC+05:45UTC+06:00UTC+06:30UTC+07:00UTC+08:45UTC+09:00UTC+09:30UTC+09:00UTC+10:30UTC+11:00UTC+12:45UTC+12:00UTC+13:00UTC+12:00UTC+14:00UTC+12:00
R packages and versions used for the FWI calculation presented in this
paper.
MM, MF, and PJ devised the methodology. MM, JFB, and PJ wrote and optimized the R code for the FWI calculation. MM and JFB performed analysis and produced the final graphics. MM, JFB, CH, and PJ wrote the manuscript. All authors contributed to editing the manuscript.
Competing interests
The authors declare that they have no conflict of interest.
Review statement
This paper was edited by Jens Klump and reviewed by two anonymous referees.
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