Background
Debris-flow runout modeling is a valuable component of the prefire assessment of post-fire hazards. The application and benefits of runout modeling are limited by uncertainty in debris-flow volume as well as model parameters related to flow mobility.
Aims
During periods of heightened wildland fire activity in the United States, multiagency coordinating groups must prioritize among multiple on-going fires to allocate scarce suppression resources.
Widespread impacts of landscape fire on ecosystems, societies, and the climate system itself have heightened the need to understand the potential future trajectory of fire under continued climate change. However, the complexity of fire makes climate change impact assessment challenging.
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.
Background
Prevailing American wildland fire modelling systems fail to predict fire growth in urban areas due to the absence of burnable urban fuels.
Aims
This research aims to identify fuel models that optimise fire spread in urban areas relative to a hypothetical fire spread model derived from observations of recent urban fires.
Increasing wildfires are causing global concerns about ecosystem functioning and services. Although some wildfires are caused by natural ignitions, it is also important to understand how human ignitions and human-related factors can contribute to wildfires.
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.