Assessing the effectiveness and measuring the performance of fuel treatments and other wildfire risk mitigation efforts are challenging endeavors. Perhaps the most complicated is quantifying avoided impacts. In this study, we show how probabilistic counterfactual analysis can help with performance evaluation.
We integrated a mechanistic wildfire simulation system with an agent-based landscape change model to investigate the feedbacks among climate change, population growth, development, landowner decision-making, vegetative succession, and wildfire.
Heterogeneity in surface fuels produced by overstory trees and understory vegetation is a major driver of fire behavior and ecosystem dynamics.
Background. Prescribed fire is a land management tool used extensively across the United States. Owing to health and safety risks, smoke emitted by burns requires appropriate manage- ment. Smoke modelling tools are often used to mitigate air pollution impacts. However, direct comparisons of tools’ predictions are lacking. Aims.
Background: Live fuel moisture content (LFMC) is a key environmental indicator used to monitor for high wildfire risk conditions. Many statistical models have been proposed to predict LFMC from remotely sensed data; however, almost all these estimate current LFMC (nowcasting models).
Wildfire occurrence varies among regions and through time due to the long-term impacts of climate on fuel structure and short-term impacts on fuel flammability.
Soil moisture conditions are represented in fire danger rating systems mainly through simple drought indices based on meteorological variables, even though better sources of soil moisture information are increasingly available.
Smoke from wildfires has become a growing public health issue around the world but especially in western North America and California. At the same time, managers and scientists recommend thinning and intentional use of wildland fires to restore forest health and reduce smoke from poorly controlled wildfires.
Human use and management of fire in landscapes have a long history and vary globally in purpose and impact. Existing local research on how people use and manage fire is fragmented across multiple disciplines and is diverse in methods of data collection and analysis.
The methodology used by the First Street Foundation Wildfire Model (FSF-WFM) to compute estimates of the 30-year, climate-adjusted aggregate wildfire hazard for the contiguous United States at 30 m horizontal resolution is presented.