fire behavior

Background

Background

Climate change contributes to the increased frequency and intensity of wildfires globally, with significant impacts on society and the environment. However, our understanding of the global distribution of extreme fires remains skewed, primarily influenced by media coverage and regionalised research efforts.

Accurate estimates of available live crown fuel loads are critical for understanding potential wildland fire behavior. Existing crown fire behavior models assume that available crown fuels are limited to all tree foliage and half of the fine branches less than 6 mm in diameter (1 h fuel). They also assume that this relationship is independent of the branchwood moisture content.

Fuel-treatments targeting shrubs and fire-prone exotic annual grasses (EAGs) are increasingly used to mitigate increased wildfire risks in arid and semiarid environments, and understanding their response to natural factors is needed for effective landscape management.

Fuel treatments are commonly applied to increase resilience to wildfire in dry and historically frequent-fire forests of western North America. The long-term effects of fuel treatments on forest structure, fuel profiles (amount and configuration of fuels), and potential wildfire behavior are not well known relative to short-term effects.

Background: Pre-fire fuels, topography, and weather influence wildfire behaviour and fire-driven ecosystem carbon loss. However, the pre-fire characteristics that contribute to fire behaviour and effects are often understudied for wildfires because measurements are difficult to obtain.

A deeper understanding of the influence of fine-scale fuel patterns on fire behavior is essential to the design of forest treatments that aim to reduce fire hazard, enhance structural complexity, and increase ecosystem function and resilience. Of particular relevance is the impact of horizontal and vertical forest structure on potential tree torching and large-tree mortality.

Background: Fire research and management applications, such as fire behaviour analysis and emissions modelling, require consistent, highly resolved spatiotemporal information on wildfire growth progression.