Severe Fire Danger Index: A Forecastable Metric to Inform Firefighter and Community Wildfire Risk Management

Abstract

Despite major advances in numerical weather prediction, few resources exist to forecast wildland fire danger conditions to support operational fire management decisions and community early-warning systems. Here we present the development and evaluation of a spatial fire danger index that can be used to assess historical events, forecast extreme fire danger, and communicate those conditions to both firefighters and the public. It uses two United States National Fire Danger Rating System indices that are related to fire intensity and spread potential. These indices are normalized, combined, and categorized based on a 39-yr climatology (1979–2017) to produce a single, categorical metric called the Severe Fire Danger Index (SFDI) that has five classes; Low, Moderate, High, Very High, and Severe. We evaluate the SFDI against the number of newly reported wildfires and total area burned from agency fire reports (1992–2017) as well as daily remotely sensed numbers of active fire pixels and total daily fire radiative power for large fires (2003–2016) from the Moderate-Resolution Imaging Spectroradiometer (MODIS) across the conterminous United States. We show that the SFDI adequately captures geographic and seasonal variations of fire activity and intensity, where 58% of the eventual area burned reported by agency fire records, 75.2% of all MODIS active large fire pixels, and 81.2% of all fire radiative power occurred when the SFDI was either Very High or Severe (above the 90th percentile). We further show that SFDI is a strong predictor of firefighter fatalities, where 97 of 129 (75.2%) burnover deaths from 1979 to 2017 occurred when SFDI was either Very High or Severe. Finally, we present an operational system that uses short-term, numerical weather predictions to produce daily SFDI forecasts and show that 76.2% of all satellite active fire detections during the first 48 h following the ignition of nine high-profile case study fires in 2017 and 2018 occurred under Very High or Severe SFDI conditions. The case studies indicate that the extreme weather events that caused tremendous damage and loss of life could be mapped ahead of time, which would allow both wildland fire managers and vulnerable communities additional time to prepare for potentially dangerous conditions. Ultimately, this simple metric can provide critical decision support information to wildland firefighters and fire-prone communities and could form the basis of an early-warning system that can improve situational awareness and potentially save lives.

Variation in the Canadian Fire Weather Index Thresholds for Increasingly Larger Fires in Portugal

“…This indicates that, within a given landscape context, the response in large fire size is less than proportional to increasingly severe weather conditions reflected in the FWI. A limitation, as with any fire danger rating system, is that the FWI does not encapsulate all weather influences on fire growth, namely atmosphere structure and stability [25,63] and rapidly changing conditions or fine-scale variability resulting from thunderstorm outflows, frontal passages, or wind-terrain interactions [59]. However, results align with previous work revealing control of fire size by landscape structure [27,64].…”

“…The duration of active fire spread is even smaller, as fire duration in the database is the time elapsed between fire detection and fire extinction. Consequently, I reasonably assumed that fire weather on the day a fire starts determines whether it will grow to a size larger than the thresholds considered [59], which is further supported by the fact that in Portugal fires larger than 2500 ha typically (interquartile range) attain 1000 ha in 2 h to 10 h [25].…”

A Classification of US Wildland Firefighter Entrapments Based on Coincident Fuels, Weather, and Topography

“…Initially, a large set of environmental variables and transformations of those variables were included in the analysis. These encompassed the various climatic factors available from the Parameter-elevation Regressions on Independent Slopes Model (PRISM) [24], the topographic and fuel information from the Landscape Fire and Resource Management Tools (LANDFIRE) [25], Normalized Difference Vegetation Index (NDVI) data from the NOAA Center for Satellite Applications and Research [26], and historical fire danger information from Jolly et al [27]. Additional transformations of those variables were also initially included in the analysis, such as ratios of slope steepness and elevation within different sized buffers around the entrapment locations, relative greenness [28], and variables for assessing the shape of the landscape, such as the Topographic Position Index [29].…”

“…Until recently, spatially-explicit information on fire danger has not been widely available as most firefighters have relied on fire danger information available at specific weather stations, which are often summarized into Pocket Cards [83]. Fortunately, fire danger forecasts across CONUS are now available in a mobile-friendly format (see https://m.wfas.net) that can be displayed spatially for each of the fire danger indices separately or combined into a Severe Fire Danger Index [27].…”

Análisis comparativo del número e intervalos de clases de riesgo de incendios forestales

“…Así, se especifican, sin ningún soporte estadístico, tres, cuatro, cinco o más clases de riesgo de incendios forestales; por ejemplo: a) alto, medio y bajo (Atienza et al, 2012;Sevillano et al, 2015); b) muy alto, alto, moderado, bajo (Jaiswal et al, 2002); c) se añaden otras categorías extremas, como riesgo nulo y riesgo extremo (Julio, 1990), o muy bajo y muy alto (IDEAM, 2011). De igual manera, para la definición de mapas de índice de riesgo se pueden adoptar otros índices de determinación de riesgo: a) canadiense: bajo, moderado, alto, muy alto y extremo (Burriel et al, 2006); b) australiano: bajo, moderado, alto, muy alto y extremo (Dowdy et al, 2009); c) americano: bajo, moderado, alto, muy alto y severo (Jolly et al, 2019); brasileño: nulo, pequeño, medio, alto y muy alto (Ziccardi et al, 2020).…”