Predicting large wildfires across western North America by modeling seasonal variation in soil water balance

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This study analyzed seasonal variation in the relative availability of soil water for the years 2001, 2004, and 2007, representing respectively, low, moderate, and high rankings of areas burned. For these selected years, the model predicted where forest fires >1 km occurred and did not occur at ~100,000 randomly located pixels with an average accuracy of 69%. The model identified four seasonal combinations, most of which included exhaustion of available water storage capacity during the summer as critical; two combinations involving antecedent conditions the previous spring or fall accounted for 86% of the predicted fires. The approach introduced in this paper can help identify forested areas where management efforts to reduce fire hazards might prove most beneficial.

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