Background: Wildfire simultaneity affects the availability and distribution of resources for fire management: multiple small fires require more resources to fight than one large fire does. Aims: The aim of this study was to project the effects of climate change on simultaneous large wildfires in the Western USA, regionalised by administrative divisions used for wildfire management.
Background Wildland fire in arid and semi-arid (dryland) regions can intensify when climatic, biophysical, and land-use factors increase fuel load and continuity.
Wildland fire is an important natural disturbance in many vegetated areas of the world. However, fire management actions are critical not only to prevent and suppress unwanted fires, but also mitigate and recover from the negative impacts of fire on people and communities. Advancements in wildland fire science can help inform these necessary actions in wildland fire management.
Over the past four decades, annual area burned has increased significantly in California and across the western USA. This trend reflects a confluence of intersecting factors that affect wildfire regimes. It is correlated with increasing temperatures and atmospheric vapour pressure deficit.
Background: Fire research and management applications, such as fire behaviour analysis and emissions modelling, require consistent, highly resolved spatiotemporal information on wildfire growth progression.
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).
Background Maximizing the effectiveness of fuel treatments at landscape scales is a key research and management need given the inability to treat all areas at risk from wildfire. We synthesized information from case studies that documented the influence of fuel treatments on wildfire events.
Background: Wildland–urban interface (WUI) maps identify areas with wildfire risk, but they are often outdated owing to the lack of building data. Convolutional neural networks (CNNs) can extract building locations from remote sensing data, but their accuracy in WUI areas is unknown.