Table 2.
Regional predictors of fire.
Region code | Region name | Monthly cor. | Interannual cor. | Spatial cor. | Burned area | BA trend | Anomaly cor. | Score | ||
---|---|---|---|---|---|---|---|---|---|---|
BA | LT | P | Vars | Variables | ||||||
NHAF | Northern Hemisphere Africa | 0.87 | 0.77 | 0.88 | 151.9 | − 1.60 | 0.53 | 88.1 | 3 | pr, cld, pop |
SHAF | Southern Hemisphere Africa | 0.91 | 0.32 | 0.92 | 180.2 | 0.37 | 0.36 | 73.5 | 3 | gppl1, ts, cld |
SA | South America | 0.92 | 0.89 | 0.85 | 30.7 | − 0.17 | 0.91 | 94.8 | 7 | gpp, gppm1s, pr, ts, cld, pop, rdtot |
SA | 0.91 | 0.81 | 0.79 | 30.3 | − 0.07 | 0.85 | 91.9 | 5 | gpp, gppm1s, pr, ts, cld | |
SEAS | South and Southeast Asia | 0.89 | 0.68 | 0.87 | 11.8 | − 0.19 | 0.77 | 87.7 | 8 | gpp, gppm1, pr, ts, cld, vp, pop, rdtot |
SEAS | 0.88 | 0.64 | 0.89 | 12.0 | − 0.22 | 0.73 | 86.0 | 6 | gpp, gppm1, pr, ts, cld, pop | |
TCAM | Temperate and Central America | 0.77 | 0.84 | 0.74 | 6.3 | − 0.01 | 0.84 | 90.7 | 5 | gpp, gppl1, pr, ts, cld |
TCAM | 0.77 | 0.78 | 0.73 | 6.0 | 0.01 | 0.79 | 89.1 | 4 | gpp, gppl1, pr, ts | |
BONA | Boreal North America | 0.83 | 0.56 | 0.67 | 3.3 | 0.00 | 0.57 | 81.2 | 6 | gpp, gppl1, pr, ts, cld, vp |
BONA | 0.83 | 0.55 | 0.56 | 3.8 | − 0.01 | 0.57 | 79.4 | 3 | gpp, gppl1, pr | |
AUS | Australia | 0.86 | 0.91 | 0.91 | 50.2 | 0.21 | 0.92 | 95.1 | 6 | gppm1s, gpp, gppl1, ts, cld, vp |
AUS | 0.86 | 0.90 | 0.88 | 50.5 | 0.03 | 0.90 | 94.6 | 3 | gpp, gppl1, cld | |
CEAS | Central Asia | 0.70 | 0.72 | 0.79 | 16.2 | − 0.18 | 0.55 | 85.5 | 4 | gppl1, pr, cld, vp |
BOAS | Boreal Asia | 0.65 | 0.59 | 0.82 | 9.5 | 0.01 | 0.62 | 82.2 | 4 | gppm1, pr, ts, vp |
EQAS | Equatorial Asia | 0.80 | 0.93 | 0.87 | 2.0 | 0.02 | 0.95 | 95.0 | 3 | pr, ts, cld |
EQAS | 0.76 | 0.93 | 0.78 | 1.9 | 0.00 | 0.94 | 93.7 | 1 | pr | |
EUME | Europe and Middle East | 0.83 | 0.34 | 0.67 | 2.6 | 0.00 | 0.35 | 70.7 | 2 | pr, cld |
Performance of the best and minimal models for each region with respect to each of the five performance measures described in Methods, along with the aggregate performance score. In some regions, the best model is the same as the minimal model. Also mentioned are the variables that form the inputs of the models. BA is Burned Area, and LT is long-term trend in spatially aggregated yearly timeseries. Variables are as follows: gppl1—cumulative GPP, gppm1—growing season GPP (northern hemisphere), gppm1s—growing season GPP (southern hemisphere), pr—precipitation, ts—temperature, cld—cloud cover, vp—vapour pressure, rdtot—total road network density, pop—population density. All models include vegetation type fractions, including cropland fraction. The model for NHAF uses yearly vegetation fractions, whereas rest of the models use a single snapshot.