Wildland firefighter entrapment avoidance: Developing evacuation trigger points utilizing the Wildland Urban Interface Evacuation (WUIVAC) fire spread model.

摘要

Wildland firefighters are often called on to make tactical decisions under stressful conditions in order to suppress a fire. They frequently make these decisions based on their gained intuition over time, and also by considering previous specific fire experiences. This assists them in anticipating future fire behavior and developing tactics designed to suppress the fire while avoiding entrapment. These decisions can be hindered by human factors such as insufficient knowledge of surroundings and conditions, inexperience, overextension of resources, or loss of situational awareness. One potential tool for assisting fire managers in situations where human factors can hinder decision-making is the Wildland Urban Interface Evacuation (WUIVAC) model, which models minimum fire travel times to create geographic triggers for evacuation recommendations. Using a range of expected weather conditions and resource configurations, we generated a range of expected trigger buffer outcomes. Our objective was to use these outcomes to illustrate: (a) what spatial uncertainty is inherent in the geographic triggers produced by the range in expected conditions that contribute to fire behavior, and (b) after taking into account uncertainty, whether triggers are likely to be useful for rapid tactical decision-making.;Utilizing 80 different tactical, weather, and fuel condition inputs, we demonstrated the use of WUIVAC for setting trigger points intended for use in planned firefighting operations to ensure entrapment avoidance. These triggers were used to determine when firefighting resources should disengage the fire and evacuate to a safety zone, shelter in place, turn down an assignment, or reengage and change tactics altogether based on predicted conditions. Using the 2007 Zaca Fire in the Los Padres National Forest, California as a case study, we show that WUIVAC can provide analytically driven physically-based trigger points, and when coupled with intuitive decisions, it can assist in setting triggers for entrapment avoidance and ultimately contribute to firefighter safety.