An increase in the occurrence of large, high severity wildfires in the western Pacific Northwest (PNW), USA, has created an urgent need for science to better inform forest management and policy decisions to maintain source water quality in the region.
Mature and old-growth forests (MOG) provide essential ecosystem services, yet they face increasing threats. Currently, high-intensity, high-severity wildfires are the main driver for loss of MOG on federally managed forests across the United States. Quantifying MOG forests with greatest exposure to stand-replacing wildfires provides essential information for land managers.
Accurate prediction of forest fire spread is a critical management and scientific challenge as the world adapts to rapidly changing fire regimes. We reconstructed 5,400 daily burned area progression maps for 196 U.S. Northern Rocky Mountain wildfires (2012–2021) and used machine learning to estimate daily fire growth given local weather, hydroclimate, fuels and topography.
Background
Post-wildfire debris flows (PFDFs) frequently threaten life, property and infrastructure in California. To date, there is no comprehensive assessment of their spatial distribution, seasonality, atmospheric drivers and interannual variability across the state.
Aims
This study investigates the key drivers influencing prescribed fire effects across 16 sites in northern and central California, with particular emphasis on how operational decisions by fire practitioners shape burn outcomes.
Concepts and models of fire refugia are increasingly important components of forest management and adaptation discussions in the context of wildland fire, forest and habitat conservation, and global change.
Managers grappling with questions about dead wood in productive, often high-volume, westside Pacific Northwest forests seek to balance wildlife habitat quality and fire hazard reduction, especially following wildfires which can both consume and generate exceptional quantities of dead wood.
Background
Phenological correction of pre- and post-fire imagery is used to improve remotely sensed burn severity evaluations. Unburned offset values standardize greenness between image pairs; however, efficacy across diverse scenarios remains underexplored.
Aims