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Fuel Treatment Effectiveness in the Context of Landform, Vegetation, and Large, Wind-Driven Wildfires

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Fuel Treatment Effectiveness in the Context of Landform, Vegetation, and Large, Wind-Driven Wildfires Ecological Applications, 30(5), 2020, e02104 Published 2020. This article is a U.S. Government work and is in the public domain in the USA. Fuel treatment effectiveness in the context of landform, vegetation, and large, wind-driven wildfires 1,5 2,3 4 2 SUSAN J. P RICHARD , NICHOLAS A. POVAK , MAUREEN C. KENNEDY , AND DAVID W. P ETERSON 1School of Environmental and Forest Sciences, University of Washington, Box 352100, Seattle, Washington 98195-2100 USA 2USDA Forest Service, Pacific Northwest Research Station, Wenatchee Forestry Sciences Lab, Wenatchee, Washington 98801 USA 3Oak Ridge Institute for Science and Education (ORISE), Oak Ridge, Tennessee 37830 USA 4Sciences and Mathematics, Division of the School of Interdisciplinary Arts and Sciences, University of Washington – Tacoma, Tacoma, Washington 98801 USA Citation: Prichard, Susan J., Nicholas A. Povak, >Maureen C. Kennedy, and David W. Peterson. 2020. Fuel treatment effectiveness in the context of landform, vegetation, and large, wind-driven wildfires. Eco- logical Applications 30(5):e02104. 10.1002/eap.2104 Abstract. Large wildfires (>50,000 ha) are becoming increasingly common in semiarid landscapes of the western United States. Although fuel reduction treatments are used to miti- gate potential wildfire effects, they can be overwhelmed in wind-driven wildfire events with extreme fire behavior. We evaluated drivers of fire severity and fuel treatment effectiveness in the 2014 Carlton Complex, a record-setting complex of wildfires in north-central Washington State. Across varied topography, vegetation, and distinct fire progressions, we used a combina- tion of simultaneous autoregression (SAR) and random forest (RF) approaches to model dri- vers of fire severity and evaluated how fuel treatments mitigated fire severity. Predictor variables included fuel treatment type, time since treatment, topographic indices, vegetation and fuels, and weather summarized by progression interval. We found that the two spatial regression methods are generally complementary and are instructive as a combined approach for landscape analyses of fire severity. Simultaneous autoregression improves upon traditional linear models by incorporating information about neighboring pixel burn severity, which avoids type I errors in coefficient estimates and incorrect inferences. Random forest modeling provides a flexible modeling environment capable of capturing complex interactions and non- linearities while still accounting for spatial autocorrelation through the use of spatially explicit predictor variables. All treatment areas burned with higher proportions of moderate and high- severity fire during early fire progressions, but thin and underburn, underburn only, and past wildfires were more effective than thin-only and thin and pile burn treatments. Treatment units had much greater percentages of unburned and low severity area in later progressions that burned under milder fire weather conditions, and differences between treatments were less pro- nounced. Our results provide evidence that strategic placement of fuels reduction treatments can effectively reduce localized fire spread and severity even under severe fire weather. During wind-driven fire spread progressions, fuel treatments that were located on leeward slopes tended to have lower fire severity than treatments located on windward slopes. As fire and fuels managers evaluate options for increasing landscape resilience to future climate change and wildfires, strategic placement of fuel treatments may be guided by retrospective studies of past large wildfire events. Key words: climate change; fire severity; fuel treatments; landscape restoration; prescribed burning; ran- dom forest; simultaneous autoregression; Washington; wildfires. Abatzoglou and Williams 2016). In forested areas that INTRODUCTION burn mostly as stand-replacing wildfires, an additional With a warming climate combined with a legacy of fire concern is that large (>50,000 ha), high-severity fires can exclusion, large wind-driven wildfire events are becom- accelerate vegetation responses to climate change (Stav- ing more common in semiarid landscapes of the western ros et al. 2014, Coop et al. 2016, Crausbey et al. 2017, United States (Miller et al. 2012, Stephens et al. 2014, Tepley et al. 2018). Landscape restoration treatments are often used to mitigate the impacts of future wildfires by implementing treatments focused on reducing surface Manuscript received 9 August 2019; revised 3 December and canopy fuel loads through mechanical removal of 2019; accepted 6 January 2020; final version received 19 Febru- ary 2020. Corresponding Editor: David S. Schimel. live and dead trees and by using prescribed fire to mimic 5 E-mail: [email protected] the influence of wildfires under controlled weather Article e02104; page 1 Ecological Applications Article e02104; page 2 SUSAN J. PRICHARD ET AL. Vol. 30, No. 5 conditions (Agee and Skinner 2005, Stephens et al. led to high-severity fire regardless of vegetation type and 2014). Planning and implementing landscape restoration pre-burn surface fuel loads (Lydersen et al. 2014). Dur- treatments requires considerable investment and, over ing the 2011 Wallow Fire in Arizona, severity of wild- the past few decades, the footprint of treated areas has land–urban interface fuel treatments was dependent on been dwarfed by wildfire area burned in western North neighboring forest conditions and resultant fire severity America (Barnett et al. 2016, Schoennagel et al. 2017). delivered to fuels treatments (Johnson and Kennedy Research is needed to characterize the ability of different 2019). treatments to reduce wildfire spread and severity, the Results from these studies suggest that, while fuel weather and biophysical conditions under which they reduction treatments are an effective tool for mitigating are expected to be most effective, and the longevity of future fire severity, their ultimate effectiveness is context treatment effectiveness. Such information is essential for specific and related to treatment type, the surrounding prioritizing landscapes to maximize the ecological and forest mosaic, their biophysical setting, and fire weather. economic benefits of treatment. Such complexity creates difficulties in predicting future Over the past decade, numerous post-fire studies have treatment success and landscape prioritizations for iden- evaluated the effectiveness of fuel reduction treatments tifying locations of future treatments on the landscape. at mitigating fire behavior and post-fire tree mortality To this end, plot-level evaluations provide a standard- and other fire effects in fire-prone forests (Stephens et al. ized method for evaluating treatment effectiveness while 2012, Hessburg et al. 2015, Prichard et al. 2017). A com- attempting to control for variability in fire behavior mon finding is that past fires generally act as a short- related to fire weather, topography, and biophysical envi- term barrier to fire spread (Teske et al. 2012, Parks et al. ronments (e.g., Prichard et al. 2010, Lydersen et al. 2015, Stevens-Rumann et al. 2016) but that duration of 2014). However, field data are limited in their inference treatment effectiveness depends on site productivity and space given their low sample size. As a complement to the rate of accumulation of flammable live and dead sur- field studies, retrospective landscape analyses of remo- face fuels. Once post-fire live and dead fuels reach levels tely sensed fire severity evaluate data on fire extent and that support fire spread, treatments still can mitigate the severity across large contiguous landscapes. Such data severity of subsequent wildfires for up to 20 yr (Prichard are capable of quantifying both the average effect of a et al. 2017). given treatment over the course of a fire or multiple fires, However, both treatment effectiveness and longevity as well as the variability in treatment effectiveness over a can vary by treatment type. Mechanical thinning of for- range of biophysical settings and fire progressions. These ests generally reduces the potential for crown fire, but analyses develop statistical relationships between fire where surface fuels support intense surface fire, post-fire severity and a variety of bottom-up variables (i.e., topo- tree mortality can be comparable to untreated forests graphic position, vegetation patterns, and fuel treat- (Safford et al. 2009, Prichard et al. 2010, Fule et al. ments) and top-down variables (i.e., fire weather and 2012). Pile burning is commonly used to reduce post- climate) to interpret main drivers of observed severity harvest surface fuels, and studies have reported a reduc- patterns and help predict future behavior. tion in subsequent wildfire severity following this treat- Empirical models have the dual goal of describing the ment relative to unmanaged forests (Strom and Fule complex relationships between fire severity and its dri- 2007, Safford et al. 2009). Chipping and mastication are vers as well as controlling for spatial autocorrelation alternative treatments to fire, but results from field (SA) that is inherent to all contagious processes. Spatial experiments suggest that these treatments can promote autocorrelation present in regression model residuals long-duration smoldering fires with substantial soil heat- violates the assumption that they are independent and ing, which can lead to high tree mortality and impact identically distributed and has been shown to result in understory vegetation recovery (see Kreye et
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