Global and regional analysis of climate and human drivers of wildfire

Aldersley, Andrew; Murray, S.; Cornell, Sarah

doi:10.1016/j.scitotenv.2011.05.032

Cited by 254 publications

(189 citation statements)

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“…The availability of remotely sensed data in a number of active fires (Giglio et al, 2006;Bartlein et al, 2008) and burnt areas during the past decade makes it possible now to analyse the controls on wildfire on a global scale Aldersley et al, 2011;Daniau et al, 2012;Moritz et al, 2012;Knorr et al, 2014). Each of these studies considered different sets of potential controls and used different methods.…”

Section: Introductionmentioning

confidence: 99%

“…Different subsets of variables were selected by individual GAMs, but the availability of fuel (quantified by net primary production, NPP) was the strongest single predictor in all cases. Aldersley et al (2011) used a regression-tree and random-forest approach to examine the influence of climate, vegetation and human impact on burnt area. Climate and climate-determined vegetation properties were the most important controls.…”

Section: Introductionmentioning

confidence: 99%

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Causal relationships versus emergent patterns in the global controls of fire frequency

et al. 2014

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Abstract. Global controls on month-by-month fractional burnt area (2000)(2001)(2002)(2003)(2004)(2005) were investigated by fitting a generalised linear model (GLM) to Global Fire Emissions Database (GFED) data, with 11 predictor variables representing vegetation, climate, land use and potential ignition sources. Burnt area is shown to increase with annual net primary production (NPP), number of dry days, maximum temperature, grazing-land area, grass/shrub cover and diurnal temperature range, and to decrease with soil moisture, cropland area and population density. Lightning showed an apparent (weak) negative influence, but this disappeared when pure seasonal-cycle effects were taken into account. The model predicts observed geographic and seasonal patterns, as well as the emergent relationships seen when burnt area is plotted against each variable separately. Unimodal relationships with mean annual temperature and precipitation, population density and gross domestic product (GDP) are reproduced too, and are thus shown to be secondary consequences of correlations between different controls (e.g. high NPP with high precipitation; low NPP with low population density and GDP). These findings have major implications for the design of global fire models, as several assumptions in current models -most notably, the widely assumed dependence of fire frequency on ignition rates -are evidently incorrect.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Causal relationships versus emergent patterns in the global controls of fire frequency

et al. 2014

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“…Regional scale analyses that span substantial gradients of bioclimatic conditions while still permitting a mechanistic understanding of how human factors affect fire occurrence, offer a connection between coarse-scale global scale studies and fine-scale studies limited to a single biome type or single socioeconomic context. Coarse-scale studies are dependent on aggregated socioeconomic data across large heterogeneous areas, such as entire countries (e.g., Chuvieco et al 2008, Aldersley et al 2011) that obfuscate the processes by which human factors affect fire activity in different biophysical settings. In contrast, most fine-scale studies, by being limited to a small range of fuel types and socioeconomic conditions (e.g., Kennedy and McKenzie 2010), have unknown applicability across a broader range of biome types or in different socioeconomic contexts.…”

Section: Introductionmentioning

confidence: 99%

Habitat distribution modeling reveals vegetation flammability and land use as drivers of wildfire in SW Patagonia

et al. 2013

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Abstract. Despite important recent advances in modeling current and future global fire activity in relation to biophysical predictors there remain important uncertainties about finer-scale spatial heterogeneity of fire and especially about human influences which are typically assessed at coarse-spatial resolutions. The purpose of the current study is to quantify the influence of biophysical and anthropogenic variables on the spatial distribution of wildfire activity between 1984 and 2010 over an extensive southern Patagonian-Andean region from ca. 438 to 538 S extending from coastal rainforests to xeric woodland and steppe.We used satellite imagery to map all detectable fires .5 ha from 1984 to 2010 in four study areas (each of 13,100 to 36,635 km 2 ) and field checked 65 of these burns for accuracy of burned vegetation class and fire perimeters. Then, we used the MaxEnt modeling technique to assess the relationships of wildfire distributions to biophysical and human environmental variables in each of the four regions. The 232 fires .5 ha mapped in the four study areas accounted for an area of 1,314 km 2 indicating that at least 1.8% of the total area burned between 1984 and 2010. In general, areas with intermediate productivity levels (e.g., shrublands) have higher fire probability compared with areas of low and high productivity levels, such as steppe and wet forests, respectively. There is a marked contrast in the flammability of broad vegetation classes in determining fire activity at a regional scale, as well as a strong spatial relationship of wildfires to anthropogenic variables. The juxtaposition of fire-resistant tall forests with fire-prone shrublands and woodlands creates the potential for positive feedbacks from human-set fires to gradually increase the flammability of extensive landscapes through repeated burning. Distance to roads and settlements were also strong predictors, suggesting that fire in all regions is ignition-limited. However, these anthropogenic predictors influenced probability of fire differently among study regions depending on their main land-use practices and their past and present socioeconomic contexts.

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“…Wildfire burn area is closely associated with a moist enough climate to produce vegetative fuel and temporary high temperature and dry conditions [14,20,21]. It can be modulated by vegetation [22][23][24], land use [25], and suppression [26][27][28] after being ignited by either lightning strikes [29] or people [30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Differences in Human versus Lightning Fires between Urban and Rural Areas of the Boreal Forest in Interior Alaska

Calef

Varvak

McGuire

2017

Forests

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Abstract:In western North America, the carbon-rich boreal forest is experiencing warmer temperatures, drier conditions and larger and more frequent wildfires. However, the fire regime is also affected by direct human activities through suppression, ignition, and land use changes. Models are important predictive tools for understanding future conditions but they are based on regional generalizations of wildfire behavior and weather that do not adequately account for the complexity of human-fire interactions. To achieve a better understanding of the intensity of human influence on fires in this sparsely populated area and to quantify differences between human and lightning fires, we analyzed fires by both ignition types in regard to human proximity in urban (the Fairbanks subregion) and rural areas of interior Alaska using spatial (Geographic Information Systems) and quantitative analysis methods. We found substantial differences in drivers of wildfire: while increases in fire ignitions and area burned were caused by lightning in rural interior Alaska, in the Fairbanks subregion these increases were due to human fires, especially in the wildland urban interface. Lightning fires are starting earlier and fires are burning longer, which is much more pronounced in the Fairbanks subregion than in rural areas. Human fires differed from lightning fires in several ways: they started closer to settlements and highways, burned for a shorter duration, were concentrated in the Fairbanks subregion, and often occurred outside the brief seasonal window for lightning fires. This study provides important insights that improve our understanding of the direct human influence on recently observed changes in wildfire regime with implications for both fire modeling and fire management.

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Global and regional analysis of climate and human drivers of wildfire

Cited by 254 publications

References 50 publications

Causal relationships versus emergent patterns in the global controls of fire frequency

Causal relationships versus emergent patterns in the global controls of fire frequency

Habitat distribution modeling reveals vegetation flammability and land use as drivers of wildfire in SW Patagonia

Differences in Human versus Lightning Fires between Urban and Rural Areas of the Boreal Forest in Interior Alaska

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