Future changes in society and climate are expected to affect wildfire activity in the south-eastern United States. The objective of this research was to understand how changes in both climate and society may affect wildfire in the coming decades.We estimated a three-stage statistical model of wildfire area burned by ecoregion province for lightning and human causes (1992–2010) based on precipitation, temperature, potential evapotranspiration, forest land use, human population and personal income. Estimated parameters from the statistical models were used to project wildfire area burned from 2011 to 2060 under nine climate realisations, using a combination of three Intergovernmental Panel on Climate Change-based emissions scenarios (A1B, A2, B2) and three general circulation models. Monte Carlo simulation quantifies ranges in projected area burned by county by year, and in total for higher-level spatial aggregations. Projections indicated, overall in the Southeast, that median annual area burned by lightning-ignited wildfire increases by 34%, human-ignited wildfire decreases by 6%, and total wildfire increases by 4%by 2056–60 compared with 2016–20.Total wildfire changes varywidely by state (-47 to +30%) and ecoregion province (-73 to +79%). Our analyses could be used to generate projections of wildfire-generated air pollutant exposures, relevant to meeting the National Ambient Air Quality Standards.
Forest carbon (C) density varies tremendously across space due to the inherent heterogeneity of forest ecosystems. Variation of forest C density is especially pronounced in mountainous terrain, where environmental gradients are compressed and vary at multiple spatial scales. Additionally, the influence of environmental gradients may vary with forest age and developmental stage, an important consideration as forest landscapes often have a diversity of stand ages from past management and other disturbance agents. Quantifying forest C density and its underlying environmental determinants in mountain terrain has remained challenging because many available data sources lack the spatial grain and ecological resolution needed at both stand and landscape scales. The objective of this study was to determine if environmental factors influencing aboveground live carbon (ALC) density differed between young versus old forests. We integrated aerial light detection and ranging (lidar) data with 702 field plots to map forest ALC density at a grain of 25 m across the H.J. Andrews Experimental Forest, a 6369 ha watershed in the Cascade Mountains of Oregon, USA. We used linear regressions, random forest ensemble learning (RF) and sequential autoregressive modeling (SAR) to reveal how mapped forest ALC density was related to climate, topography, soils, and past disturbance history (timber harvesting and wildfires). ALC increased with stand age in young managed forests, with much greater variation of ALC in relation to years since wildfire in old unmanaged forests. Timber harvesting was the most important driver of ALC across the entire watershed, despite occurring on only 23% of the landscape. More variation in forest ALC density was explained in models of young managed forests than in models of old unmanaged forests. Besides stand age, ALC density in young managed forests was driven by factors influencing site productivity, whereas variation in ALC density in old unmanaged forests was also affected by finer scale topographic conditions associated with sheltered sites. Past wildfires only had a small influence on current ALC density, which may be a result of long times since fire and/or prevalence of non-stand replacing fire. Our results indicate that forest ALC density depends on a suite of multi-scale environmental drivers mediated by complex mountain topography, and that these relationships are dependent on stand age. The high and context-dependent spatial variability of forest ALC density has implications for quantifying forest carbon stores, establishing upper bounds of potential carbon sequestration, and scaling field data to landscape and regional scales. Keywords: Forest carbon; Lidar; Topography; Forest management; Wildfire; Landscape heterogeneity
Fire is a natural disturbance that is nearly ubiquitous in terrestrial ecosystems. The capacity to burn exists virtually wherever vegetation grows. In some forested landscapes, fi re is a principal driver of rapid ecosystem change, resetting succession ( McKenzie et al. 1996a ) and changing wildlife habitat (Cushman et al. 2011 ), hydrology ( Feikema et al. 2013 ), element cycles ( Smithwick 2011 ), and even landforms (Pierce et al. 2004 ). In boreal forests, for example, recurring wildfi res are the main cause of compositional and spatial patterns ( Wein and MacLean 1983 ), where a fi re-induced “shifting spatial mosaic” governs the heterogeneity in ecosystem patterns and processes on the landscape ( Goldammer and Furyaev 1996 ). In forest ecosystems where dominant species are long-lived, mature trees may provide a buffer against extreme weather such as drought or heat waves, but fi res and other disturbances such as insect outbreaks eliminate the buffering of the canopy, leaving a hotter and drier microclimate conducive to the establishment of new species. In a warming climate, fi re is expected to amplify and accelerate changes in forest composition, spatial pattern, and structure ( Littell et al. 2010 ; Loehman and Keane 2012 ; Raymond and McKenzie 2012 ; Cansler and McKenzie 2014 ). Anticipating these changes will be a key to successful forest management and conservation.
Wildfire is an ever present, natural process shaping landscapes. Having the ability to accurately measure and predict wildfire occurrence and impacts to ecosystem goods and services, both retrospectively and prospectively, is critical for adaptive management of landscapes. Landscape vulnerability is a concept widely utilized in the ecosystem management literature that has not been explicitly defined, particularly with regard to wildfire. Vulnerability more broadly is defined by three primary components: exposure to the stressor, sensitivity to a range of stressor variability, and resilience following exposure. In this synthesis, we define vulnerability in the context of wildfire. We first identify the components of a guiding framework for a vulnerability assessment with respect to wildfire. We then address retrospective assessments of wildfire vulnerability and the data that have been developed and utilized to complete these assessments. Finally, we review the modeling efforts that allow for predictive and probabilistic assessment of future vulnerability. Throughout the synthesis, we highlight gaps in the research, data availability, and models used to complete vulnerability assessments.
Very large fires (VLFs) have important implications for communities, ecosystems, air quality and fire suppression expenditures. VLFs over the contiguous US have been strongly linked with meteorological and climatological variability. Building on prior modelling of VLFs (>5000 ha), an ensemble of 17 global climate models were statistically downscaled over the US for climate experiments covering the historic and mid-21st-century periods to estimate potential changes in VLF occurrence arising from anthropogenic climate change. Increased VLF potential was projected across most historically fire-prone regions, with the largest absolute increase in the intermountain West and Northern California. Complementary to modelled increases in VLF potential were changes in the seasonality of atmospheric conditions conducive to VLFs, including an earlier onset across the southern US and more symmetric seasonal extension in the northern regions. These projections provide insights into regional and seasonal distribution of VLF potential under a changing climate, and serve as a basis for future strategic and tactical fire management options.