Models of fire behavior and effects do not always make accurate predictions, and there is not enough systematically gathered data to validate them. To help advance fire behavior and fire effects model development, the Joint Fire Science Program is helping fund the RxCADRE, which is made up of scientists from the U.S.
Mixed-conifer forests in the interior Pacific Northwest are subject to sporadic outbreaks of the western spruce budworm, the most destructive defoliator in western North America. Such outbreaks usually occur synchronously over broad regions and lead to widespread decreases in growth rates and low to moderate levels of mortality.
Fuel consumption predictions are necessary to accurately estimate or model fire effects, including pollutant emissions during wildland fires. Fuel and environmental measurements on a series of operational prescribed fires were used to develop empirical models for predicting fuel consumption in big sagebrush (Artemisia tridentata Nutt.) ecosystems.
The BehavePlus Fire Modeling System is among the most widely used systems for wildland fire prediction. It is designed for use in a range of tasks including wildfire behaviour prediction, prescribed fire planning, fire investigation, fuel hazard assessment, fire model understanding, communication and research.
We used a combination of field measurements and simulation modelling to quantify the effects of salvage logging, and a combination of salvage logging and pile-and-burn fuel surface fuel treatment (treatment combination), on fuel loadings, fire behaviour, fuel consumption and pollutant emissions at three points in time: post-windstorm (before salvage logging), post-salvage logging and post-surfa
Biomass burning by wildland fires has significant ecological, social and economic impacts. Satellite remote sensing provides direct measurements of radiative energy released by the fire (i.e. fire intensity) and surrogate measures of ecological change due to the fire (i.e. fire or burn severity).
Fuel treatments have been widely used as a tool to reduce catastrophic wildland fire risks in many forests around the world. However, it is a challenging task for forest managers to prioritise where, when and how to implement fuel treatments across a large forest landscape. In this study, an optimisation model was developed for long-term fuel management decisions at a landscape scale.
Wildland fire management has moved beyond a singular focus on suppression, calling for wildfire management for ecological benefit where no critical human assets are at risk. Processes causing direct effects and indirect, long-term ecosystem changes are complex and multidimensional.
We present a technique for modelling conditional burn probability patterns in two dimensions for large wildland fires. The intended use for the model is strategic program planning when information about future fire weather and event durations is unavailable and estimates of the average probabilistic shape and extent of large fires on a landscape are needed.
Declining forest health attributed to associations between extensive bark beetle-caused tree mortality, accumulations of hazardous fuels, wildfire, and climate change have catalyzed changes in forest health and wildfire protection policies of land management agencies.