Figure 3 . Boxplots of (a) air temperature, (b) RH, (c) FFMC,
and (d) DC for the “Fire” and “No fire” events across the three
tundra regions on fire-occurrence days.
4 Discussions
This study identifies the CG lightning probability as the key driver of
tundra fire occurrence. Though lightning is normally assumed to be the
primary ignition source in the tundra due to the remoteness of the
region and the limited human activities, we provide the first
quantitative piece of evidence that supports this assumption, as the
results from all models in this study point to CG lightning probability
as the most influential factor that predicts fire occurrence. This
finding is consistent with previous research conducted in the boreal
forests of North America (Veraverbeke et al., 2017). Yet, the role of
lightning is not always emphasized in other ecosystems (Díaz-avalos et
al., 2001; Liu et al., 2012; Vecín-Arias et al., 2016). Previous studies
have also established relationships between fires and lightning
characteristics observed from ground-based detection networks, such as
the count, polarity, and peak current of lightning strikes (Peterson et
al., 2010). This study, whereas, suggest that the probability of CG
lightning modeled purely with atmospheric variables is a powerful
indicator of tundra fire potential.
In addition to lightning, warmer and drier near-surface fire weather
conditions support burnings in the tundra. With generally low
temperatures and high water table, Arctic tundra is an unusual
environment that is rarely moisture-limited and are not highly
flammable, largely due to widespread underlying permafrost (Bliss et
al., 1973; Wielgolaski and Goodall, 1997). Evidences from both modeling
and statistical analyses in this study highlight the importance of warm
and dry weather conditions in driving fire occurrence in Alaskan tundra,
with near-surface air temperature and RH significantly related to fires.
Higher temperature and lower moisture conditions have the potential to
increase the flammability of the environment in general. In addition to
the impacts of air temperature and RH on fuel flammability, they might
also reflect the high likelihood of convective potential, which in turn
leads to atmospheric instability and ultimately lightning occurrence.
Moreover, despite the minimal elevation variations in the tundra,
topographic features such as elevation could indirectly affect fire
activity through their impacts on lightning potential, temperature and
moisture availability (Dissing and Verbyla, 2003; Podur et al., 2003).
Our results also demonstrate the suitability of fuel moisture codes from
the CFFWIS for monitoring tundra fire potential. Primarily composed of
herbaceous and dwarf shrub species, the dominant fuels in the tundra are
considered fine surface fuels as defined in the CFFWIS (Innes, 2013). As
the most influential indicator among all fire weather indices, DC is a
slow-reacting code that tracks deeper drying of fuels that responds to
changes in deep moisture levels in the tundra (Lawson and Armitage,
2008). Its significance in the logistic regression highlights that
long-term dry conditions of tundra fuels that accumulate for days
contribute more to burnings than the short-term changes. It is also
worth mentioning that that FFMC is a highly predictive variable, since
it is originally designed to describe the fine surface fuels in boreal
forests (Lawson and Armitage, 2008). With larger FFMC indicating higher
fuel flammability, FFMC of the “Fire” events can generally reach
higher than 70 for the tundra, representing dry fuels for fire
occurrence. Although the CFFWIS was originally developed for boreal
forests and its ability to forecast tundra conditions was most generally
assumed rather than tested, our study shows that both FFMC and DC
provide a reasonable approximation of fuel moisture changes that can
more readily support burning. Given the impacts of fire weather on fire
potential, the future increase of FWI in the tundra (French et al.,
2015) will absobutely contribute to higher fire risks in this region.
More importantly, our empirical-dynamic framework involving NWPs like
WRF and statistical models has demonstrated its strong capability and
effectiveness for contemporary fire modeling in data-scarce regions like
the tundra. The modeling experiments with both the “Current-day model”
and the “Previous-day model” further indicate that using data
simulated from one day earlier can achieve reasonable accuracy in
forecasting fire occurrence. The critical role of CG lightning
probability also suggests that current fire management efforts are
inadequate without incorporating CG lightning probability for fire
danger monitoring and modeling in the tundra, where fires are primarily
ignited by lightning. With the future increases of lightning in the HNL
(Chen et al., 2021), Arctic tundra will experience higher fire
occurrence in the future under the rapid climate warming. By monitoring
lightning potential and fire weather, it is promising that fire
occurrence can be predicted with high accuracy in remote regions at 5km
resolution.
Though existing efforts have been made to incorporate lightning
characteristics for fire modeling by matching lightning strikes detected
by ground-based networks and fires (Peterson et al., 2010; Wotton and
Martell, 2005), we recommend using simulated CG lightning probability
for fire management efforts for several reasons. The ground-based
lightning detection networks typically have a location accuracy of 1
~ 5km and a detection efficiency of about 70%
~ 90% (Biagi et al., 2007; Dissing and Verbyla, 2003;
Koshak et al., 2015; Nag et al., 2014). This suggests the potential
missing of lightning strikes by the detection systems and the inaccuracy
of the triangulated lightning locations. Therefore, the commonly used
method of matching lightning and fire locations can largely miss the
actual lightning strikes that ignite the fires, further introducing
errors and uncertainties in the modeling and analysis efforts. The
modeling results could be affected by the choices of matching methods as
well (Moris et al., 2020). Finally, since no simulation of lightning
characteristics has been developed based on existing NWPs so far, this
limits the potential of integrating NWPs for fire ignition modeling and
forecasting.
5 Conclusions
This study explores the key drivers of wildfire occurrences in Arctic
tundra of Alaska by modeling the impacts of environmental factors on
fire probability from 2001 to 2019. Among all factors, CG lightning
probability is found to be the most important driver of tundra fire
occurrences in Alaska, with a significant positive relationship between
lightning and fire probabilities. Warmer and drier weather conditions
also support burnings in the tundra. Air temperature, fuel moisture
codes show significant positive relationships with fire occurrences,
while RH is negatively related. Moreover, the empirical-dynamical
modeling method in this study has demonstrated a strong capability in
predicting fire occurrence probability, using the WRF-simulated fire
weather variables on both fire ocurrence day and one day before. Our
findings highlight the necessity of incorporating CG lightning modeling
and the benefits of WRF simulation for wildfire monitoring efforts in
data-scarce regions like tundra.
Availability Statement
Data and software to support this manuscript are publicly and freely
available online from their websites. CAVM was obtained from Alaska
Geobotany Center, University of Alaska, Fairbanks
(https://www.geobotany.uaf.edu/cavm/).
MODIS fire product MCD14ML was obtained from NASA’s Fire Information
for Resource Management System
(https://firms.modaps.eosdis.nasa.gov/).
Fuel component maps were accessed from the Oak Ridge National
Laboratory Distributed Active Archive Center (ORNL DAAC;https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1761).
MODIS surface reflectance data MOD09A1 was downloaded from NASA’s Land
Processes Distributed Active Archive Center (LP DAAC;https://e4ftl01.cr.usgs.gov/MOLT/MOD09A1.006/).
IfSAR DEM product was downloaded from the Alaska Elevation Portal
(https://elevation.alaska.gov)
hosted by Alaska Division of Geological and Geophysical Surveys. NCEP
FNL data were obtained from the Research Data Archive
(https://rda.ucar.edu/datasets/ds083.2/)
managemend by the National Center for Atmospheric Research (NCAR).
The Advanced Research WRF Model Version 4.0 used for simulation
lightning and near-surface weather is available via the Mesoscale and
Microscale Meteorology Laboratory of NCAR
(https://www2.mmm.ucar.edu/wrf/users/).
Acknowledgments
This study was supported by the NASA Terrestrial Ecology program grant
NNX15AT79A.
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