Modelling and mapping dynamic variability in large fire probability in the lower Sonoran Desert of south-western Arizona

Gray, Miranda E.; Dickson, Brett G.; Zachmann, Luke J.

doi:10.1071/wf13115

Cited by 20 publications

(28 citation statements)

References 31 publications

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“…It has been shown that lower elevation and flatter areas correlate positively with fire occurrence [62,63]. This is the case in many regions of China [30], as the majority of people reside at lower elevations in the Chinese boreal forest, and the same findings have been reported elsewhere [64][65][66]. Elevation likely influences fire frequency through surfacemoisture [67], species composition [68], and fuel moisture [69], which have been shown to increase with elevation.…”

Section: Discussionsupporting

confidence: 53%

Spatial Modelling of Fire Drivers in Urban-Forest Ecosystems in China

Guo

Tigabu

et al. 2017

Forests

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Fires in urban-forest ecosystems (UFEs) are frequent with complex causes, posing a serious hazard to human lives and infrastructure. Thus, quantifying wildfire risks in UFEs and their spatial pattern is quintessential to develop appropriate fire management strategies. The aim of this study was to explore spatial (geographically weighted logistic regression, GWLR) versus non-spatial (logistic regression, LR) modelling approaches to determine the relationship between forest fire occurrence and driving factors in Yichun, a typical urban-forest ecosystem in China. As drivers of fire, 13 factors related to topographic, vegetation, infrastructure, meteorological and socio-economy were considered and regressed against fire occurrence data from 1980 to 2010. Results demonstrate the superiority of GWLR models over LR in terms of prediction accuracy, goodness of fit and model residuals. The GWLR model further captured the spatial variability of driving factors over a broad study area, and the fire likelihood maps identified areas with different zones of fire risk in the study area.In conclusion, the study demonstrates quantitatively and spatially the importance of accounting for local variation in drivers of fires, thereby improving fire management and prevention strategies. The findings also contribute to the emerged field of fire management and fire risk assessment in UFEs.

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Section: Discussionsupporting

confidence: 53%

Spatial Modelling of Fire Drivers in Urban-Forest Ecosystems in China

Guo

Tigabu

et al. 2017

Forests

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“…Although our study occurred within the Mojave Desert, it is likely the burn severity pathway leading to long-term dominance of burned areas by invasive grasses occurs in other deserts in North America. Invasive plants have been widely implicated in the increased number and/or size of fires in the Great Basin and Sonoran Desert (Brooks & Pyke 2001;Balch et al 2013;McDonald & McPherson 2013;Gray, Dickson & Zachmann 2014), and the grass/fire cycle has been the main focus as the process responsible for the formation of alternative states. Prior to this study, it might have been thought that burned areas in the North American deserts that had longer fire return intervals were less likely to become dominated by invasive grass, but this may often not be the case.…”

Section: Discussionmentioning

confidence: 99%

Alternative pathways to landscape transformation: invasive grasses, burn severity and fire frequency in arid ecosystems

Klinger

Brooks

2017

Journal of Ecology

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Summary Arid ecosystems are often vulnerable to transformation to invasive‐dominated states following fire, but data on persistence of these states are sparse. The grass/fire cycle is a feedback process between invasive annual grasses and fire frequency that often leads to the formation of alternative vegetation states dominated by the invasive grasses. However, other components of fire regimes, such as burn severity, also have the potential to produce long‐term vegetation transformations. Our goal was to evaluate the influence of both fire frequency and burn severity on the transformation of woody‐dominated communities to communities dominated by invasive grasses in major elevation zones of the Mojave Desert of western North America. We used a chronosequence design to collect data on herbaceous and woody cover at 229 unburned reference plots and 578 plots that burned between 1972 and 2010. We stratified the plots by elevation zone (low, mid, high), fire frequency (1–3 times) and years post‐fire (YPF; 1–5, 6–10, 11–20 and 21–40 YPF). Burn severity for each plot was estimated by the difference normalized burn ratio. We identified two broad post‐fire successional pathways. One was an outcome of fire frequency, resulting in a strong potential transformation via the grass/fire cycle. The second pathway was driven by burn severity, the critical aspect being that long‐term transformation of a community could occur from just one fire in areas that burned at high or sometimes moderate severity. Dominance by invasive grasses was most likely to occur in low‐and high‐elevation communities; cover of native herbaceous species was often greater than that of invasive grasses in the mid‐elevation zone. Synthesis. Invasive grasses can dominate a site that burned only one time in many decades at high severity, or a site that burned at low severity but multiple times in the same time period. However, high burn severity may predispose areas to more frequent fire because they have relatively high cover of invasive annual grass, suggesting burn severity and fire frequency have both independent and synergistic effects. Resilience in vegetation structure following fire in many arid communities may be limited to a narrow window of low burn severity in areas that have not burned in many decades.

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“…Landscape conductance in a circuit-theoretic model is a surrogate for the ease of movement through the modeled environment (McRae et al 2008), and we begin by parameterizing the conductance surface for fire spread. Logistic regression models are commonly used to derive continuous maps of the conditional probability that an ignition occurrence will become a large fire (e.g., Preisler et al 2011, Hawbaker et al 2013, Gray et al 2014. The probabilities reflect an isolated likelihood that an ignition will result in a fire of at least some observed size, and do not account for fire spread dynamics beyond that specific location and size threshold.…”

Section: Modeling Fire Connectivitymentioning

confidence: 99%

“…Conditions for large fire occurrence are rare in our study area and we were primarily concerned with a worst-case scenario of fire likelihood, which we represented with conditions of high fire hazard (Hardy 2005). To model fire likelihood under high fire hazard, we first used a spatial database of fire occurrence spanning 22 years, multiple phenometric and other landscape variables, and mixed-effects logistic regression to estimate the conditional probability of a large (!20 ha) fire (Gray et al 2014). We chose 20 ha as the large fire size threshold because fires of this size are a good indication that the annual fuel load is sufficient for further fire spread (W. Reaves, personal communication).…”

Section: Estimating Fire Likelihoodmentioning

confidence: 99%

“…Parameterization of circuittheoretic models is flexible enough that fuels can be represented in a way that is most appropriate to the focal ecosystem and management objective. In the hottest and driest desert shrubland of North America, the lower Sonoran Desert, heterogeneous annual fuel loads are amongst the most limiting factors for large fire occurrence, and need to be well represented in a model of fire likelihood (Gray et al 2014). Other factors known to influence large fires, such as fuel bed composition and fuel moisture, are less important for fire spread than the amount of accumulated fine fuels that fill the interspaces of otherwise sparse vegetation (Brooks and Pyke 2001).…”

Section: Introductionmentioning

confidence: 99%

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A new model of landscape‐scale fire connectivity applied to resource and fire management in the Sonoran Desert, USA

Gray

Dickson

2015

Ecological Applications

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Understanding where and when on the landscape fire is likely to burn (fire likelihood) and the predicted responses of valued resources (fire effects) will lead to more effective management of wildfire risk in multiple ecosystem types. Fire is a contagious and highly unpredictable process, and an analysis of fire connectivity that incorporates stochasticity may help predict fire likelihood across large extents. We developed a model of fire connectivity based on electrical circuit theory, which is a probabilistic approach to modeling ecological flows. We first parameterized our model to reflect the synergistic influences of fuels, landscape properties, and winds on fire spread in the lower Sonoran Desert of southwestern Arizona, and then defined this landscape as an interconnected network through which to model flow (i.e., fire spread). We interpreted the mapped outputs as fire likelihood and used historical burned area data to evaluate our results. Expected fire effects were characterized based on the degree to which future fire exposure might negatively impact native plant community recovery, taking into account the impact of repeated fire and major vegetation associations. We explored fire effects within habitat for the endangered Sonoran pronghorn antelope and designated wilderness. Model results indicated that fire likelihood was higher in lower elevations, and in areas with lower slopes and topographic roughness. Fire likelihood and effects were predicted to be high in 21% of the currently occupied range of the Sonoran pronghorn and 15% of the additional habitat considered suitable. Across 16 designated wilderness areas, highest predicted fire likelihood and effects fell within low elevation wilderness areas that overlapped large fire perimeters that occurred in 2005. As ongoing changes in climate and land cover are poised to alter the fire regime across extensive and ecologically important areas in the lower Sonoran Desert, an analysis of fire likelihood and effects can contribute new and important information to fire and fuels management. Our novel approach to modeling fire connectivity addresses challenges in quantifying and communicating wildfire risk and is applicable to other ecosystems and management issues globally.

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Modelling and mapping dynamic variability in large fire probability in the lower Sonoran Desert of south-western Arizona

Cited by 20 publications

References 31 publications

Spatial Modelling of Fire Drivers in Urban-Forest Ecosystems in China

Spatial Modelling of Fire Drivers in Urban-Forest Ecosystems in China

Alternative pathways to landscape transformation: invasive grasses, burn severity and fire frequency in arid ecosystems

A new model of landscape‐scale fire connectivity applied to resource and fire management in the Sonoran Desert, USA

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