Abstract
Larger, more frequent wildfires in arid and semi‐arid ecosystems have been associated with invasion by non‐native annual grasses, yet a complete understanding of fine fuel development and subsequent wildfire trends is lacking. We investigated the complex relationships among weather, fine fuels, and fire in the Great Basin, USA. We first modeled the annual and time‐lagged effects of precipitation and temperature on herbaceous vegetation cover and litter accumulation over a 26‐year period in the northern Great Basin. We then modeled how these fine fuels and weather patterns influence subsequent wildfires. We found that cheatgrass cover increased in years with higher precipitation and especially when one of the previous 3 years also was particularly wet. Cover of non‐native forbs and native herbs also increased in wet years, but only after several dry years. The area burned by wildfire in a given year was mostly associated with native herb and non‐native forb cover, whereas cheatgrass mainly influenced area burned in the form of litter derived from previous years’ growth. Consequently, multiyear weather patterns, including precipitation in the previous 1–3 years, was a strong predictor of wildfire in a given year because of the time needed to develop these fine fuel loads. The strong relationship between precipitation and wildfire allowed us to expand our inference to 10,162 wildfires across the entire Great Basin over a 35‐year period from 1980 to 2014. Our results suggest that the region's precipitation pattern of consecutive wet years followed by consecutive dry years results in a cycle of fuel accumulation followed by weather conditions that increase the probability of wildfire events in the year when the cycle transitions from wet to dry. These patterns varied regionally but were strong enough to allow us to model annual wildfire risk across the Great Basin based on precipitation alone.
Highlights
Wildfire frequencies have increased in many arid and semi-arid regions of the world, partly because of changes in climate, vegetation, and land use (Brooks et al, 2004; Krawchuk, Moritz, Parisien, Van Dorn, & Hayhoe, 2009; Dennison, Brewer, Arnold, & Moritz, 2014)
We present the results of Objective 1 in four parts: (1) weather predicts herbaceous vegetation and litter cover; (2) herbaceous vegetation predicts litter cover in subsequent years; (3) fine fuels predict wildfire characteristics; and (4) weather predicts wildfire characteristics
We found that plant cover, regardless of functional group, three years prior to a given year was a poor predictor of litter cover in that year, which suggests that most fine fuel litter persists for only 1–2 years in this landscape
Summary
Wildfire frequencies have increased in many arid and semi-arid regions of the world, partly because of changes in climate, vegetation, and land use (Brooks et al, 2004; Krawchuk, Moritz, Parisien, Van Dorn, & Hayhoe, 2009; Dennison, Brewer, Arnold, & Moritz, 2014). Our hypotheses were as follows: (1) higher than normal winter and spring precipitation increase herbaceous plant cover, cheatgrass, and other non-native annual species (Hsu, Powell, & Adler, 2012; Rao & Allen, 2010; Robinson et al, 2013); (2) higher annual cheatgrass cover is associated with more wildfires and larger area burned (D’Antonio & Vitousek, 1992); and (3) years with above- average antecedent precipitation, especially 1–2 years prior, result in more wildfires and more area burned (Balch et al, 2013; Billings, 1994; Knapp, 1998; Littell, McKenzie, Peterson, & Westerling, 2009) These hypotheses were relevant for each of our objectives, but we were only able to test the first two hypotheses in our focal study area in the northern Great Basin where long-term vegetation data were available. Continuous vegetation data are unavailable on an annual basis across the entire Great Basin at this time (but see Boyte & Wylie, 2016), and an additional goal of this study was to determine if interpolated, spatially continuous, monthly weather data could predict wildfire patterns and wildfire risk across this vast landscape in the absence of annual fuel load data
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