The variation in extent and severity of wildfire in boreal forest regions around the globe are the result of many various underlying and interacting variables. The main objective of this research was to identify these variables, and use them to create a GIS-based MCE model to predict areas of high risk to wildfire for Wood Buffalo National Park. This study found that wildfire is very sensitive to climatic and fuel characteristic inputs and thus had the largest influence on wildfire prediction. Specific to WBNP, we found that the southern, low-lying regions were most at risk for wildfire, as demonstrated within our map outputs.
The model in this study was able to predict, with a certain degree of accuracy, areas of high wildfire risk for the 2016 wildfire season. The results found in this study can be used to better plan for wildfire events in WBNP, so that socioeconomic and ecological damages can be reduced. Moreover, this study also displays the usefulness of a GIS-based model as a predictive tool for areas of high wildfire risk, and how it can be useful to protect other ecologcial and socioeconomic sensitive areas from wildfire. Future applications could involve determining the best site for fire suppression resources such as landing areas, supply depots, and other resources for better wildfire response times within the park.