ALBERT

Description: Wildfire behavior is a complex and stochastic phenomenon that can present unique tactical management challenges. This paper investigates a multistage stochastic mixed integer program with full recourse to model spatially explicit fire behavior and to select suppression locations for a wildland fire. Simplified suppression decisions take the form of “suppression nodes”, which are placed on a raster landscape for multiple decision stages. Weather scenarios are used to represent a distribution of probable changes in fire behavior in response to random weather changes, modeled using probabilistic weather trees. Multistage suppression decisions and fire behavior respond to these weather events and to each other. Nonanticipativity constraints ensure that suppression decisions account for uncertainty in weather forecasts. Test cases for this model provide examples of fire behavior interacting with suppression to achieve a minimum expected area impacted by fire and suppression.

Description: Wildfire suppression combines multiple objectives and dynamic fire behavior to form a complex problem for decision makers. This paper presents a mixed integer program designed to explore integrating spatial fire behavior and suppression placement decisions into a mathematical programming framework. Fire behavior and suppression placement decisions are modeled using nodes associated with cell centers from raster landscapes. The nodes at which suppression is located are determined by control variables. Response variables include fire spread paths, arrival times, and fireline intensities for each node. Both fire arrival times and fireline intensities are necessary to address ecological objectives and fire control. Test cases for this model provide examples of fire behavior interacting with suppression placement to achieve multiple objectives.

Description: Previously burned areas can influence the occurrence, extent, and severity of subsequent wildfires, which may influence expenditures on large fires. We develop a conceptual model of how interactions of fires with previously burned areas may influence fire management, fire behavior, expenditures, and test hypotheses using regression models of wildfire size and suppression expenditures. Using a sample of 722 large fires from the western United States, we observe whether a fire interacted with a previous fire, the percent area of fires burned by previous fires, and the percent perimeter overlap with previous fires. Fires that interact with previous fires are likely to be larger and have lower total expenditures on average. Conditional on a fire encountering a previous fire, a greater extent of interaction with previous fires is associated with reduced fire size but higher expenditures, although the expenditure effect is small and imprecisely estimated. Subsequent analysis suggests that fires that interact with previous fires may be systematically different from other fires along several dimensions. We do not find evidence that interactions with previous fires reduce suppression expenditures for subsequent fires. Results suggest that previous fires may allow suppression opportunities that otherwise might not exist, possibly reducing fire size but increasing total expenditures.

Description: Wildland firefighting requires managers to make decisions in complex decision environments that hold many uncertainties; these decisions need to be adapted dynamically over time as fire behavior evolves. Models used in firefighting decisions should also have the capability to adapt to changing conditions. In this paper, detailed line construction constraints are presented for use with a stochastic mixed integer fire growth and behavior program. These constraints allow suppression actions to interact dynamically with stochastic predicted fire behavior and account for many of the detailed line construction considerations. Such considerations include spatial restrictions for fire crew travel and operations. Crew safety is also addressed; crews must keep a variable safety buffer between themselves and the fire. Fireline quality issues are accounted for by comparing control line capacity with fireline intensity to determine when a fireline will hold. The model assumes crews may work at varying production rates throughout their shifts, providing flexibility to fit work assignments with the predicted fire behavior. Nonanticipativity is enforced to ensure solutions are feasible for all modeled weather scenarios. Test cases demonstrate the model’s utility and capability on a raster landscape.

Description: 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.

Description: 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.

Description: Wildland fire management agencies are responsible for assigning suppression resources to control fire spread and mitigate fire risks. This study implements a principle component analysis and an association rule analysis to study wildland fire response resource requests from 2016 to 2018 in the western US to identify daily resource ordering and assignment patterns for large fire incidents. Unsupervised learning can identify patterns in the assignment of individual resources or pairs of resources. Three national Geographic Area Coordination Centers (GACCs) are studied, including California (CA), Rocky Mountain (RMC), and Southwest (SWC) at both high and low suppression preparedness levels (PLs). Substantial differences are found in resource ordering and assignment between GACCs. For example, in comparison with RMC and SWC, CA generally orders and dispatches more resources to a fire per day; CA also likely orders and assigns multiple resource types in combination. Resources are more likely assigned to a fire at higher PLs in all GACCs. This study also suggests several future research directions including studying the causal relations behind different resource ordering and assignment patterns in different regions.

Description: 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.