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.

Wildland firefighters in the United States are exposed to a variety of hazards while performing their jobs. Although vehicle accidents and aircraft mishaps claim the most lives, situations where firefighters are caught in a life-threatening, fire behaviour-related event (i.e. an entrapment) constitute a considerable danger because each instance can affect many individuals. In an attempt to advance our understanding of the causes of firefighter entrapments, a review of the pertinent literature and a synthesis of existing data were undertaken. Examination of the historical literature indicated that entrapment potential peaks when fire behaviour rapidly deviates from an assumed trajectory, becomes extreme and compromises the use of escape routes, safety zones or both. Additionally, despite the numerous safety guidelines that have been developed as a result of analysing past entrapments, we found issues with the way factual information from these incidents is reported, recorded and stored that make quantitative investigations difficult. To address this, a fire entrapment database was assembled that revealed when details about the location and time of entrapments are included in analyses, it becomes possible to ascertain trends in space and time and assess the relative influence of various environmental variables on the likelihood of an entrapment. Several research needs were also identified, which highlight the necessity for improvements in both fundamental knowledge and the tools used to disseminate that knowledge.

Wildland firefighters in the US are mandated to identify areas that provide adequate separation between themselves and the flames (i.e. safety zones) to reduce the risk of burn injury. This study presents empirical models that estimate the distance from flames that would result in a low probability (1 or 5%) of either fatal or non-fatal injuries. The significant variables for the fatal injury model were fire shelter use, slope steepness and flame height. The separation distances needed to ensure no more than a 1 or 5% probability of fatal injury, without the use of a fire shelter, for slopes less than 25% were 20 to 50 m for flame heights less than 10 m, and 1 to 4 times the flame height for flames taller than 10 m. The non-fatal injury model significant variables were fire shelter use, vehicle use and fuel type. At the 1 and 5% probability thresholds for a non-fatal injury, without the use of a fire shelter, the separation distances were 1 to 2, 6 to 7, and 12 to 16 times greater than the current safety zone guideline (i.e. 4 times the flame height) for timber, brush and grass fuel types respectively.

An open source computational fluid dynamics (CFD) solver has been incorporated into the WindNinja modeling framework. WindNinja is widely used by wildland fire managers, as well as researchers and practitioners in other fields, such as wind energy, wind erosion, and search and rescue. Here, we describe the CFD solver and evaluate its performance against the WindNinja conservation of mass (COM) solver, and previously published large-eddy simulations (LES), for three field campaigns with varying terrain complexity: Askervein Hill, Bolund Hill, and Big Southern Butte. We also compare the effects of two model settings in the CFD solver, namely the discretization scheme used for the advection term of the momentum equation and the turbulence model, and provide guidance on model sensitivity to these settings. Additionally, we investigate the computational mesh and difficulties regarding terrain representation. Two important findings from this work are: (1) CFD solver predictions are significantly better than COM solver predictions at windward and lee side observation locations, but no difference was found in predicted speed-up at ridgetop locations between the two solvers, and (2) the choice of discretization scheme for advection has a significantly larger effect on the simulated winds than the choice of turbulence model.

For wildland firefighters, the ability to efficiently evacuate the fireline is limited by terrain, vegetation, and fire conditions. The impacts of terrain and vegetation on evacuation time to a safety zone may not be apparent when considering potential control locations either at the time of a wildfire or during pre-suppression planning. To address the need for a spatially-explicit measure of egress capacity, this paper introduces the Escape Route Index (ERI). Ranging from 0 to 1, ERI is a normalized ratio of the distance traveled within a time frame, accounting for impedance by slope and vegetation, to the optimal distance traveled in the absence of these impediments. An ERI approaching 1 indicates that terrain and vegetation conditions should have little impact on firefighter mobility while an ERI approaching 0 is representative of limited cross-country travel mobility. The directional nature of evacuation allows for the computation of four ERI metrics: (1) ERI_mean (average ERI in all travel directions); (2) ERI_min (ERI in direction of lowest egress); (3) ERI_max (ERI in direction of highest egress); and (4) ERI_azimuth (azimuth of ERI_max direction). We demonstrate the implementation of ERI for three different evacuation time frames (10, 20, and 30 min) on the Angeles National Forest in California, USA. A previously published, crowd-sourced relationship between slope and travel rate was used to account for terrain, while vegetation was accounted for by using land cover to adjust travel rates based on factors from the Wildland Fire Decision Support System (WFDSS). Land cover was found to have a stronger impact on ERI values than slope. We also modeled ERI values for several recent wildland firefighter entrapments to assess the degree to which landscape conditions may have contributed to these events, finding that ERI values were generally low from the crews’ evacuation starting points. We conclude that mapping ERI prior to engaging a fire could help inform overall firefighter risk for a given location and aid in identifying locations with greater egress capacity in which to focus wildland fire suppression, thus potentially reducing risk of entrapment. Continued improvements in accuracy of vegetation density mapping and increased availability of light detection and ranging (lidar) will greatly benefit future implementations of ERI.

Wildland fire managers in the United States currently utilize the gridded forecasts from the National Digital Forecast Database (NDFD) to make fire behavior predictions across complex landscapes during large wildfires. However, little is known about the NDFDs performance in remote locations with complex topography for weather variables important for fire behavior prediction, including air temperature, relative humidity, and wind speed. In this study NDFD forecasts for calendar year 2015 were evaluated in fire-prone locations across the conterminous United States during periods with the potential for active fire spread using the model performance statistics of root-mean-square error (RMSE), mean fractional bias (MFB), and mean bias error (MBE). Results indicated that NDFD forecasts of air temperature and relative humidity performed well with RMSEs of about 2°C and 10%–11%, respectively. However, wind speed was increasingly underpredicted when observed wind speeds exceeded about 4 m s−1, with MFB and MBE values of approximately −15% and −0.5 m s−1, respectively. The importance of accurate wind speed forecasts in terms of fire behavior prediction was confirmed, and the forecast accuracies needed to achieve “good” surface head fire rate-of-spread predictions were estimated as ±20%–30% of the observed wind speed. Weather station location, the specific forecast office, and terrain complexity had the largest impacts on wind speed forecast error, although the relatively low variance explained by the model (~37%) suggests that other variables are likely to be important. Based on these results it is suggested that wildland fire managers should use caution when utilizing the NDFD wind speed forecasts if high wind speed events are anticipated.