Abstract. Occurrences of devastating wildfires have been increasing
in the United States for the past decades. While some environmental
controls, including weather, climate, and fuels, are known to play important
roles in controlling wildfires, the interrelationships between these factors
and wildfires are highly complex and may not be well represented by
traditional parametric regressions. Here we develop a model consisting of
multiple machine learning algorithms to predict 0.5∘×0.5∘ gridded
monthly wildfire burned area over the south central United States during
2002–2015 and then use this model to identify the relative importance of the
environmental drivers on the burned area for both the winter–spring and
summer fire seasons of that region. The developed model alleviates the issue
of unevenly distributed burned-area data, predicts burned grids with area
under the curve (AUC) of 0.82 and 0.83 for the two seasons, and achieves
temporal correlations larger than 0.5 for more than 70 % of the grids and
spatial correlations larger than 0.5 (p<0.01) for more than 60 %
of the months. For the total burned area over the study domain, the model
can explain 50 % and 79 % of the observed interannual variability for
the winter–spring and summer fire season, respectively. Variable importance
measures indicate that relative humidity (RH) anomalies and preceding
months' drought severity are the two most important predictor variables
controlling the spatial and temporal variation in gridded burned area for
both fire seasons. The model represents the effect of climate variability by
climate-anomaly variables, and these variables are found to contribute the
most to the magnitude of the total burned area across the whole domain for
both fire seasons. In addition, antecedent fuel amounts and conditions are
found to outweigh the weather effects on the amount of total burned area in
the winter–spring fire season, while fire weather is more important for the
summer fire season likely due to relatively sufficient vegetation in this
season.
Spatial and Temporal Variation in Physicochemical Properties of Dairy Lagoons in South-Central Idaho
