Large airtanker use is widespread in wildfire suppression in the United States. The current approach to nationally dispatching the fleet of federal contract airtankers relies on filling requests for airtankers to achieve suppression objectives identified by fire managers at the incident level. In general, demand is met if resources are available, and the dispatch model assumes that this use is both necessary and effective. However, proof of effectiveness under specific conditions of use in complex environments has not been empirically established. We geospatially intersected historical drop data from the federal contract large airtanker fleet with operational and environmental factors to provide a post hoc assessment of conditions of use for the 2010–12 fire seasons in the conterminous United States. Our findings confirm previous results demonstrating extensive use in extended attack. Additionally, we show that use is generally within guidelines for operational application (aircraft speed and height above ground level) and often outside of environmental guidelines suggestive of conditions conducive for most effective use, including drop timing with respect to response phase (initial attack v. extended attack), terrain, fuels and time of day. Finally, our results suggest that proximity to human populations plays a role in whether airtankers are dispatched, suggesting that prioritisation of community protection is an important consideration. This work advances efforts to understand the economic effectiveness of aviation use in federal fire suppression.

The Forest Service, U.S. Department of Agriculture, defines success in the wildland fire response environment as “safely achieving reasonable objectives with the least firefighter exposure necessary while enhancing stakeholder support for our management efforts”. However, persistent information and knowledge gaps challenge the agency’s ability to measure success in coming fire seasons. In this paper, we outline a roadmap to help fill these gaps, describing progress towards developing meaningful fire response key performance indicators (KPIs). We focus on characterizing suppression resource use and effectiveness as requisite initial steps towards reducing unnecessary exposure. Our intentions are to articulate the rationale for embracing KPIs for fire response operations, briefly review best practices as they relate to organizational performance measurement, and describe recent and emerging analysis techniques designed to ultimately improve responder exposure assessment. Specifically, we review tangible research products that could be operationalized as KPIs in the near future, and illustrate their calculation and interpretation for a set of large fires that occurred in the U.S. in 2017. To conclude, we offer thoughts on productive pathways forward with performance measurement.

Wildland fire occurrence is highly variable in time and space, and in the United States where total area burned can vary substantially, acquiring resources (firefighters, engines, aircraft, etc.) to respond to fire demand is an important consideration. To determine the composition and scale of this set of suppression resources managers may utilize data produced by past supply and demand information. The key challenge with this approach is that there is currently no clear system of record to track suppression resource supply and demand, and there are potential pitfalls within existing systems that may provide misleading information regarding the true levels of resource scarcity. In this manuscript, we investigate the issue of resource scarcity by examining two key resources that operations personnel have identified as both high value and scarce: type 1 firefighting crews and large airtankers. We examine data from the Resource Ordering and Status System and analyze the level of resource scarcity indicated by these data over the 2014–2018 fire seasons. We focus on data metrics with potential utility for managers responsible for annual national-level decisions regarding crew and airtanker acquisition; some of these metrics are already used to inform such decisions. We examine the limitations of each metric and suggest new metrics that could more accurately reflect true resource use and scarcity. Such metrics could lead to a substantially improved decision-making process.

Requirements for describing coniferous forests are changing in response to wildfire concerns, bio-energy needs, and climate change interests. At the same time, technology advancements are transforming how forest properties can be measured. Terrestrial Laser Scanning (TLS) is yielding promising results for measuring tree biomass parameters that, historically, have required costly destructive sampling and resulted in small sample sizes. Here we investigate whether TLS intensity data can be used to distinguish foliage and small branches (≤0.635 cm diameter; coincident with the one-hour timelag fuel size class) from larger branchwood (>0.635 cm) in Douglas-fir (Pseudotsuga menziesii) branch specimens. We also consider the use of laser density for predicting biomass by size class. Measurements are addressed across multiple ranges and scan angles. Results show TLS capable of distinguishing fine fuels from branches at a threshold of one standard deviation above mean intensity. Additionally, the relationship between return density and biomass is linear by fuel type for fine fuels (r² = 0.898; SE 22.7%) and branchwood (r² = 0.937; SE 28.9%), as well as for total mass (r² = 0.940; SE 25.5%). Intensity decays predictably as scan distances increase; however, the range-intensity relationship is best described by an exponential model rather than 1/d². Scan angle appears to have no systematic effect on fine fuel discrimination, while some differences are observed in density-mass relationships with changing angles due to shadowing.

A severe outbreak of wildfire across the US Pacific Coast during August 2020 led to persistent fire activity through the end of summer. In late September, Fire Weather Outlooks predicted higher than usual fire activity into the winter in parts of California, with concomitant elevated fire danger in the Southeastern US. To help inform the regional and national allocation of firefighting personnel and equipment, we developed visualizations of resource use during recent late season, high-demand analogs. Our visualizations provided an overview of the crew, engine, dozer, aerial resource, and incident management team usage by geographic area. While these visualizations afforded information that managers needed to support their decisions regarding resource allocation, they also revealed a potentially significant gap between resource demand and late-season availability that is only likely to increase over time due to lengthening fire seasons. This gap highlights the need for the increased assessment of suppression resource acquisition and allocation systems that, to date, have been poorly studied.

Across the globe, aircraft that apply water and suppressants during active wildfires play key roles in wildfire suppression, and these suppression resources can be highly effective. In the United States, US Department of Agriculture Forest Service (USFS) aircraft account for a substantial portion of firefighting expense and higher fatality rates compared to ground resources. Existing risk management practices that are fundamental to aviation safety (e.g., routinely asking, “Is this flight necessary?”) may not be appropriately scaled from a risk management perspective to ensure that the tactical use of aircraft is in clear alignment with a wildfire’s incident strategy and with broader agency and interagency fire management goals and objectives. To improve strategic risk management of aviation assets in wildfire suppression, we present a framework demonstrating a risk-informed strategic aviation decision support system, the Aviation Use Summary (AUS). This tool utilizes aircraft event tracking data, existing geospatial datasets, and emerging analytics to summarize incident-scale aircraft use and guide decision makers through a strategic risk management process. This information has the potential to enrich the decision space of the decision maker and supports programmatic transparency, enhanced learning, and a broader level of accountability.