Aspen (Populus tremuloides) forests are generally thought to impede fire spread, yet the extent of this effect is not well quantified in relation to other vegetation types.
Background. Fuel treatments are increasingly used to mitigate wildfire risks.
Aims. Proposing a novel, scalable and transferable methodology, this study investigates which treatment is (more) effective at a regional scale.
Dangerous fire weather is increasing under climate change, but there is limited knowledge of how this will affect fire intensity, a critical determinant of the socioecological effects of wildfire.
The frequency, severity, and scale of extreme wildfire events is increasing globally, with certain regions such as the western United States disproportionately impacted. As attention shifts toward understanding how to adapt to and recover from extreme wildfire, there is a need to prioritize where additional research and evidence are needed to inform decision-making.
Wildfire’s destruction of homes is an increasingly serious global problem. Research indicates that characterizing home hardening and defensible space at the individual structure level may reduce loss through enriched understanding of structure susceptibility in the built environment. However, improved data and methods are required to accurately characterize these features at scale.
Existing research indicates that NGOs can serve important roles during recovery from wildfires and other hazard events. Yet less work explores the specific, place-based conditions that influence NGO participation in the recovery process, or the specific tactics they might use when facilitating the transfer of knowledge and resources that meet emergent recovery needs.
Flood events in post-fire environments produce cascading social and ecological consequences that are challenging to anticipate, mitigate, and manage. Engaging private property owners in mitigation is complex, and the drivers that motivate action or inaction are not yet well defined.
Warming temperatures and increasingly variable precipitation patterns are reducing winter snowpack and critical late-season streamflows. Here, we used two models (LANDIS-II and DHSVM) in linked simulations to evaluate the effects of wildfire and forest management scenarios on future snowpack and streamflow dynamics.