Figure 2 . Variable importance rankings of (a) the “Current-day
model” and (b) the “Previous-day model”. Boxplots of CG lightning
probability for the “Fire” and “No fire” events in the three tundra
regions on (c) fire-occurrence days and (b) the previous days before
occurrence.
WRF-simulated near-surface meteorological variables and fuel moisture
codes, particularly air temperature, RH, and DC, were also found
important in modeling tundra fire occurrences, as indicated by MDA
values from the RF models (Figure 2 a-b). Specifically, higher air
temperature and drier fuels could contribute to increases in fire
occurrence probability, according to the significantly positive
relationships between temperature and DC with fire occurrence (p
< 0.05; Table 1). The mean air temperature was significantly
higher in most tundra regions when fires occurred, while RH was
significantly lower (Table S4). On fire-occurrence days, the air
temperature of the regions with fires can reach 24.8◦C and 23.5◦C in
Southwest Alaska and the North Slope on average, respectively. In
comparison, regions with no fires were much cooler, with 18.4◦C and
16.5◦C, respectively (Figure 3 a). As expected, drier conditions were
also likely to support fire occurrence. The RH values of “Fire” events
were about 9.6% lower than those of “No fire” events in these two
tundra regions on average (Figure 3 b). In addition, all fire weather
indices were significantly higher on fire days in North Slope and
Southwest Alaska. Though Alaskan tundra is not a moisture-limited
ecosystem, surface vegetation fuels can dry out rapidly to support
burnings, with FFMC reaching above 80 across the tundra regions on the
fire-occurrence days (Figure 3 c). Moreover, the significantly negative
relationships between NDII6 and fire occurrences in both
logistic regression models indicated that drier fuels support burnings
in the tundra (p < 0.05; Table 1). Mean values of the
vegetation indices related to fuel moisture state were slightly but
significantly lower for the “Fire” events (Figure S5; Table S4).
Table 1. Logistic regression results of the two models.