From fire danger to fire risk: an integrative framework for near-term wildfire risk forecasting

Abstract

Wildfires are a common global disturbance. Many of these fires fill important ecosystem roles but others must be suppressed to prevent loss of life or property. Decision support tools can provide critical information to support effective wildfire management but the many components of these tools lack a supporting framework to integrate risk components to produce effective and useful wildfire risk forecasts. Here we present a framework to support wildfire preparedness and response decision making that incorporates both static and dynamic components of wildfire hazard to produce risk forecasting. Static model components provide a time-invariant ignition probability for both human and natural caused ignitions and are developed using topographic and fuel quantity metrics. Dynamic model components are evaluated using a combination of fire danger rating indices from the new US National Fire Danger Rating System Version 4.0 released in 2016 and the newly derived Severe Fire Danger Index. When combined together, we demonstrate the effectiveness of this model forecast system to predict the spatial and temporal locations of new wildfire ignitions across large areas using a blend of high, moderate and low resolution spatial terrain, fuels and weather forecast data. We demonstrate components of this framework across the United States and Northern South America specifically across Colombia, Ecuador and Peru. This work will improve our ability to leverage modern fire danger rating systems with over conventional wildfire risk assessments to provide better decision support products throughout the world and these products have the potential to reduce wildfire impacts to firefighters and communities.