Evaluating spatially-explicit burn probabilities for strategic fire management planning

Abstract

Spatially explicit information on the probability of burning is necessary for virtually all strategic fire and fuels management planning activities, including conducting wildland fire risk assessments, optimizing fuel treatments, and prevention planning. Predictive models providing a reliable estimate of the annual likelihood of fire at each point on the landscape have enormous potential to support strategic fire and fuels management planning decisions, especially when combined with information on the values at risk and the expected fire impacts. To this end, a spatially-explicit modelling technique, termed 'burn probability' (BP) modelling, has been developed to simulate fires as a function of the physical factors that drive their spread -fuels, weather, and topographyusing the most sophisticated landscape-scale fire spread algorithms available. Despite several applications of the BP technique, much remains to be learned about their predictive ability. To achieve this goal, we are conducting experiments to not only unearth new discoveries about the complexities of fireenvironment relationships, but also to test and compare the relevance and accuracy of modelling approaches.

Advancing effects analysis for integrated, large-scale wildfire risk assessment

“…Robust exposure analysis is made possible by advancements in fire simulation tools, such as development of the minimum travel time algorithm (Finney 2002) that allows for realistic modeling of fire behavior across real-world landscapes (Finney et al, in review). These tools output spatially explicit burn probability information, a crucial input to strategic fire and fuels management planning (Miller et al 2008). Thus, managers are able, for example, to project near-term fire behavior using real-time weather information to inform suppression decision making (Andrews et al 2007) or to examine how simulated wildfire class (flame length > 12 feet) was not predicted to occur in any FPU and is, therefore, excluded from the legend behavior changes in response to fuel management activities (e.g., Kim et al 2009).…”

Spatial bottom‐up controls on fire likelihood vary across western North America

“…For the three U.S. study areas, we used a customized version of the FlamMap model, called Randig (Finney 2006), and for WBNP we used the Burn-P3 model (Parisien et al 2005). Conceptually, Randig and Burn-P3 are very similar (Miller et al 2008) although some inputs and internal mechanisms may differ. Detailed descriptions of the modeling processes and inputs can be found in Parks et al (2011) and Parisien et al (2011).…”

“…A standard approach for comparing diverse ecosystems would enhance our understanding of bottomup factors on fire regimes. The increasing availability of fire and fire environment data, as well as recent advances in fire simulation modeling (Miller et al 2008), makes this comparison possible. Specifically, simulation models that produce spatially continuous estimates of fire likelihood (hereafter, burn probability [BP]) provide a platform for a systematic comparison of the influence of bottom-up controls (i.e., ignitions, fuels, and topography) among landscapes.…”

Fire Ignition Trends in Durango, México

“…Many studies have reported from simulations that the clustering varies with vegetation cover (Keane et al, 2002;Miller, et al, 2008). According to Cumming (2001), and Duncan and Schmalzer (2004), the extent and configuration of flammable vegetation and non-flammable landscape features clearly influence patterns of fire ignition.…”