Fuel treatment effectiveness in the context of landform, vegetation, and large, wind-driven wildfires
| Autor: | Nicholas A. Povak, Susan J. Prichard, Maureen C. Kennedy, David W. Peterson |
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| Rok vydání: | 2019 |
| Předmět: |
0106 biological sciences
Washington geography geography.geographical_feature_category Ecology Landform 010604 marine biology & hydrobiology Prescribed burn Linear model Climate change Context (language use) Vegetation Wind 010603 evolutionary biology 01 natural sciences Fires Wildfires Treatment Outcome Environmental science Physical geography Fuel treatment Spatial analysis Retrospective Studies |
| Zdroj: | Ecological applications : a publication of the Ecological Society of AmericaLiterature Cited. 30(5) |
| ISSN: | 1051-0761 |
| Popis: | Large wildfires (>50,000 ha) are becoming increasingly common in semiarid landscapes of the western United States. Although fuel reduction treatments are used to mitigate 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 combination of simultaneous autoregression (SAR) and random forest (RF) approaches to model drivers 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 nonlinearities 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 pronounced. 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. |
| Databáze: | OpenAIRE |
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