Title: Comparing fuel reduction treatments for reducing wildfire size and intensity in a boreal forest landscape of northeastern China
Abstract: Fuel load is often used to prioritize stands for fuel reduction treatments. However, wildfire size and intensity are not only related to fuel loads but also to a wide range of other spatially related factors such as topography, weather and human activity. In prioritizing fuel reduction treatments, we propose using burn probability to account for the effects of spatially related factors that can affect wildfire size and intensity. Our burn probability incorporated fuel load, ignition probability, and spread probability (spatial controls to wildfire) at a particular location across a landscape. Our goal was to assess differences in reducing wildfire size and intensity using fuel-load and burn-probability based treatment prioritization approaches. Our study was conducted in a boreal forest in northeastern China. We derived a fuel load map from a stand map and a burn probability map based on historical fire records and potential wildfire spread pattern. The burn probability map was validated using historical records of burned patches. We then simulated 100 ignitions and six fuel reduction treatments to compare fire size and intensity under two approaches of fuel treatment prioritization. We calibrated and validated simulated wildfires against historical wildfire data. Our results showed that fuel reduction treatments based on burn probability were more effective at reducing simulated wildfire size, mean and maximum rate of spread, and mean fire intensity, but less effective at reducing maximum fire intensity across the burned landscape than treatments based on fuel load. Thus, contributions from both fuels and spatially related factors should be considered for each fuel reduction treatment.
Publication Year: 2013
Publication Date: 2013-06-01
Language: en
Type: article
Indexed In: ['crossref', 'pubmed']
Access and Citation
Cited By Count: 27
AI Researcher Chatbot
Get quick answers to your questions about the article from our AI researcher chatbot