A Model-Based Framework to Evaluate Alternative Wildfire Suppression Strategies

Abstract

Abstract:The complexity and demands of wildland firefighting in the western U.S. have increased over recent decades due to factors including the expansion of the wildland-urban interface, lengthening fire seasons associated with climate change, and changes in vegetation due to past fire suppression and timber harvest. In light of these changes, the use of more wildland fire on the landscape could reduce fuels and form barriers to the spread of future fires while performing forest restoration in some areas. However, the risks, costs and benefits of changing fire response strategy have not been quantified. Here, we identify gaps regarding the ability to simulate alternative wildfire suppression strategies, due to a number of factors including limited data collected on fireline construction, as well as synergies between firefighting resources and resource effectiveness. We present a fire management continuum: at one end lies full suppression of all fires under all circumstances, and at the opposite end lies no suppression of any fires regardless of location or time in season, with a wide array of managed fire options falling in between. Next, we demonstrate the proof-of-concept using a stochastic fire simulation model, FSim, to simulate two alternative fire suppression strategies close to opposite ends of this continuum for the Sierra National Forest of California: (1) business-as-usual, which equates to nearly full fire suppression; and (2) full suppression of human-caused fires and no suppression actions on lightning-caused fires. Results indicate that fire management strategy can substantially affect the number of large fires and landscape burn probabilities, both of which were shown to increase under the second scenario. However, temporal feedbacks are expected to play an important role: we show that increases in burned area substantially limit ignition potential and the extent of subsequent fires within the first five to ten years, especially under the second scenario. While subject to current data gaps and limitations in fire modeling, the methodology presented here can be used to simulate a number of alternative fire suppression strategies, including decisions to suppress or not suppress fires based on location, time of season or other factors. This method also provides basic inputs needed to estimate risks, costs and benefits of various alternative suppression strategies in future work. In future work, uncertainties resulting from current limitations in knowledge can be addressed using techniques such as scenario planning in order to provide land managers with a set of possible fire outcomes.

Modeling Fuel Treatment Leverage: Encounter Rates, Risk Reduction, and Suppression Cost Impacts

“…To parameterize and test this model, we selected a landscape encompassing the SNF, which is a relatively well-studied area, and where the authors have worked directly with local managers and staff in the past. Because of this, we were able to leverage a bevy of preexisting, vetted data and analytical products to streamline and enhance our modeling process [33,[54][55][56]. Reliance on local expertise and intensive model calibration lead to what we believe are credible results in terms of simulated fire activity and corresponding leverage metrics.…”

“…The joint design and creation of an optimal POD network through identification of potential control locations [88] and prioritizing treatments to create or enhance possible control locations [81] is an avenue ripe for future work. So too is leveraging recent advances in modeling alternative suppression response policies [56], and embedding them in a broader simulation exercise that evaluates the possibly synergistic effects of alternative fuel strategies and suppression responses. We hope that this study motivates other managers and researchers to explore opportunities to plan and implement fuel treatments that improve fire outcomes and that facilitate safe and effective suppression operations.…”

“…Over the time period from 1992-2013, the mean annual area burned by large fires (>100 ha) that ignited in the SNF was 12,472 ha, and the mean annual burn probability for the SNF was 0.0053; the median large-fire size for the fire occurrence area we modeled (see Section 2.5) was 309 ha [60]. More information on historical fire sizes and relation to contemporary burn probabilities and burn probability modeling is available in [54][55][56].…”

“…Model parameterization was facilitated by leveraging previous simulations performed on the same landscape [54][55][56]69]. Daily ignition probabilities were based on logistic regression of Energy Release Component for fuel model G values (ERC-G) in a gridded dataset for the pixel in which the Trimmer Remote Automated Weather Station is located [70].…”

“…Further, we design spatial treatment strategies as a function of treatment opportunities within predefined operational fire management units, with the aim of strengthening the nexus between fuel treatment and suppression response planning [53]. We present results from a case study landscape encompassing the Sierra National Forest (SNF) in California, USA, building from recent geographically relevant research on fuel treatment opportunities, fire simulation, risk assessment, and incident response planning [33,[54][55][56].…”