Weather, Risk, and Resource Orders on Large Wildland Fires in the Western US

Abstract

Research Highlights: Our results suggest that weather is a primary driver of resource orders over the course of extended attack efforts on large fires. Incident Management Teams (IMTs) synthesize information about weather, fuels, and order resources based on expected fire growth rather than simply reacting to observed fire growth. Background and Objectives: Weather conditions are a well-known determinant of fire behavior and are likely to become more erratic under climate change. Yet, there is little empirical evidence demonstrating how IMTs respond to observed or expected weather conditions. An understanding of weather-driven resource ordering patterns may aid in resource prepositioning as well as forecasting suppression costs. Our primary objective is to understand how changing weather conditions influence resource ordering patterns. Our secondary objective is to test how an additional risk factor, evacuation, as well as a constructed risk metric combining fire growth and evacuation, influences resource ordering. Materials and Methods: We compile a novel dataset on over 1100 wildfires in the western US from 2007–2013, integrating data on resource requests, detailed weather conditions, fuel and landscape characteristics, values at risk, fire behavior, and IMT expectations about future fire behavior and values at risk. We develop a two-step regression framework to investigate the extent to which IMTs respond to realized or expected weather-driven fire behavior and risks. Results: We find that IMTs’ expectations about future fire growth are influenced by observed weather and that these expectations influence resource ordering patterns. IMTs order nearly twice as many resources when weather conditions are expected to drive growth events in the near future. However, we find little evidence that our other risk metrics influence resource ordering behavior (all else being equal). Conclusion: Our analysis shows that incident management teams are generally forward-looking and respond to expected rather than recently observed weather-driven fire behavior. These results may have important implications for forecasting resource needs and costs in a changing climate.

A Geospatial Framework to Assess Fireline Effectiveness for Large Wildfires in the Western USA

“…Given that, on average, only 50% of fireline engages with fire, future research should focus on understanding the motivations for indirect fireline construction and how it influences the decision space for other containment measures. An empirical statistical approach, similar to what others have done for suppression resource use and expenditures [46][47][48][49][50][51], may be a useful direction to help refine what levels of Er are appropriate for different social and ecological settings.…”

A quantitative wildfire risk assessment using a modular approach of geostatistical clustering and regionally distinct valuations of assets—A case study in Oregon

“…Meteorological data has a large effect on the results and accuracy of fire models [ 28 , 29 ]. Our model utilized worst-case meteorological data instead of long-term average conditions to parameterize the fire model runs.…”

All-hazards dataset mined from the US National Incident Management System 1999–2020

“…Annually archived ICS-209 data have been used in research 19 – 27 relating wildfire activity to weather, fuel treatment activities, firefighting response, and a range of socioeconomic impacts. However useful, the data were not originally collected with research applications in mind, and the raw data must be carefully prepared for scientific analyses.…”