Susan J. Prichard

An overview of the Fuel Characteristic Classification System — Quantifying, classifying, and creating fuelbeds for resource planningThis article is one of a selection of papers published in the Special Forum on the Fuel Characteristic Classification System.

We present an overview of the Fuel Characteristic Classification System (FCCS), a tool that enables land managers, regulators, and scientists to create and catalogue fuelbeds and to classify those fuelbeds for their capacity to support fire and consume fuels. The fuelbed characteristics and fire classification from this tool will provide inputs for current and future sophisticated models for the quantification of fire behavior, fire effects, and carbon accounting and enable assessment of fuel treatment effectiveness. The system was designed from requirements provided by land managers, scientists, and policy makers gathered through six regional workshops. The FCCS contains a set of fuelbeds representing the United States, which were compiled from scientific literature, fuels photo series, fuels data sets, and expert opinion. The system enables modification and enhancement of these fuelbeds to represent a particular scale of interest. The FCCS then reports assigned and calculated fuel characteristics for ea...

Forest fire and climate change in western North America: insights from sediment charcoal records

Millennial-scale records of forest fire provide important baseline information for ecosystem management, especially in regions with too few recent fires to describe the historical range of variability. Charcoal records from lake sediments and soil profiles are well suited for reconstructing the incidence of past fire and its relationship to changing climate and vegetation. We highlight several records from western North America and their relevance in reconstructing historical forest dynamics, fire-climate relationships, and feedbacks between vegetation and fire under climate change. Climatic effects on fire regimes are evident in many regions, but comparisons of paleo-fire records sometimes show a lack of synchrony, indicating that local factors substantially affect fire occurrence, even over long periods. Furthermore, the specific impacts of vegetation change on fire regimes differ among regions with different vegetation histories. By documenting the effects on fire patterns of major changes in climate and vegetation, paleo-fire records can be used to test the mechanistic models required for the prediction of future variations in fire.

Fuel treatments and landform modify landscape patterns of burn severity in an extreme fire event

Under a rapidly warming climate, a critical management issue in semiarid forests of western North America is how to increase forest resilience to wildfire. We evaluated relationships between fuel reduction treatments and burn severity in the 2006 Tripod Complex fires, which burned over 70,000 ha of mixed-conifer forests in the North Cascades range of Washington State and involved 387 past harvest and fuel treatment units. A secondary objective was to investigate other drivers of burn severity including landform, weather, vegetation characteristics, and a recent mountain pine beetle outbreak. We used sequential autoregression (SAR) to evaluate drivers of burn severity, represented by the relative differenced Normalized Burn Ratio index, in two study areas that are centered on early progressions of the wildfire complex. Significant predictor variables include treatment type, landform (elevation), fire weather (minimum relative humidity and maximum temperature), and vegetation characteristics, including canopy closure, cover type, and mountain pine beetle attack. Recent mountain pine beetle damage was a statistically significant predictor variable with red and mixed classes of beetle attack associated with higher burn severity. Treatment age and size were only weakly correlated with burn severity and may be partly explained by the lack of treatments older than 30 years and the low rates of fuel succession in these semiarid forests. Even during extreme weather, fuel conditions and landform strongly influenced patterns of burn severity. Fuel treatments that included recent prescribed burning of surface fuels were particularly effective at mitigating burn severity. Although surface and canopy fuel treatments are unlikely to substantially reduce the area burned in regional fire years, recent research, including this study, suggests that they can be an effective management strategy for increasing forest landscape resilience to wildfires.

Quantifying physical characteristics of wildland fuels using the fuel characteristic classification system.

Wildland fuel characteristics are used in many applications of operational fire predictions and to understand fire effects and behaviour. Even so, there is a shortage of information on basic fuel properties and the physical characteristics of wildland fuels. The Fuel Characteristic Classification System (FCCS) builds and catalogues fuelbed descriptions based on realistic physical properties derived from direct or indirect observation, inventories, expert knowledge, inference, or simulated fuel characteristics. The FCCS summarizes and calculates wildland fuel characteristics, including fuel depth, loading, and surface area. Users may modify fuelbeds and thereby capture changing fuel conditions over time and (or) under different management prescriptions. Fuel loadings from four sample fuelbed pairs (i.e., pre- and post-prescribed fire) were calculated and compared by using FCCS to demonstrate the versatility of the system and how individual fuel components, such as shrubs, nonwoody fuels, woody fuels, and l...

Pre-fire and post-fire surface fuel and cover measurements collected in the south-eastern United States for model evaluation and development – RxCADRE 2008, 2011 and 2012

A lack of independent, quality-assured data prevents scientists from effectively evaluating predictions and uncertainties in fire models used by land managers. This paper presents a summary of pre-fire and post-fire fuel, fuel moisture and surface cover fraction data that can be used for fire model evaluation and development. The data were collected in the south-eastern United States on 14 forest and 14 non-forest sample units associated with 6 small replicate and 10 large operational prescribed fires conducted during 2008, 2011, and 2012 as part of the Prescribed Fire Combustion and Atmospheric Dynamics Research Experiment (RxCADRE). Fuel loading and fuel consumption averaged 6.8 and 4.1 Mg ha–1 respectively in the forest units and 3.0 and 2.2 Mg ha–1 in the non-forest units. Post-fire white ash cover ranged from 1 to 28%. Data were used to evaluate two fuel consumption models, CONSUME and FOFEM, and to develop regression equations for predicting fuel consumption from ash cover. CONSUME and FOFEM produced similar predictions of total fuel consumption and were comparable with measured values. Simple linear models to predict pre-fire fuel loading and fuel consumption from post-fire white ash cover explained 46 and 59% of variation respectively.

Fuel treatment effects on tree mortality following wildfire in dry mixed conifer forests, Washington State, USA

Fuel reduction treatments are increasingly used to mitigate future wildfire severity in dry forests, but few opportunities exist to assess their effectiveness. We evaluated the influence of fuel treatment, tree size and species on tree mortality following a large wildfire event in recent thin-only, thin and prescribed burn (thin-Rx) units. Of the trees that died within the first 3 years, most died in the first year regardless of treatment. First-year mortality was much higher in control and thin-only units (65 and 52%) than in thin-Rx units (37%). Cumulative third-year mortality followed a similar trend (78 and 64% in control and thin-only units) v. 43% in thin-Rx units. Percentage crown scorch is a strong predictor of mortality and is highly dependent on fuel treatment. Across all treatments, Pinus ponderosa had a lower probability of post-fire mortality than did Pseudotsuga menziesii. Finally, the probability of beetle attack on surviving trees was highest in large-diameter trees within thin-only treatments and lowest within thin-Rx treatments. This study contributes further evidence supporting the effectiveness of thinning and prescribed burning on mitigating post-fire tree mortality. We also present evidence that a combination of thinning and prescribed burning is associated with lower incidence of post-fire bark beetle attack.

Modeling Regional-Scale Wildland Fire Emissions with the Wildland Fire Emissions Information System*

AbstractAs carbon modeling tools become more comprehensive, spatial data are needed to improve quantitative maps of carbon emissions from fire. The Wildland Fire Emissions Information System (WFEIS) provides mapped estimates of carbon emissions from historical forest fires in the United States through a web browser. WFEIS improves access to data and provides a consistent approach to estimating emissions at landscape, regional, and continental scales. The system taps into data and tools developed by the U.S. Forest Service to describe fuels, fuel loadings, and fuel consumption and merges information from the U.S. Geological Survey (USGS) and National Aeronautics and Space Administration on fire location and timing. Currently, WFEIS provides web access to Moderate Resolution Imaging Spectroradiometer (MODIS) burned area for North America and U.S. fire-perimeter maps from the Monitoring Trends in Burn Severity products from the USGS, overlays them on 1-km fuel maps for the United States, and calculates fuel ...

Fuel Characteristic Classification System version 3.0: technical documentation

The Fuel Characteristic Classification System (FCCS) is a software module that records wildland fuel characteristics and calculates potential fire behavior and hazard potentials based on input environmental variables. The FCCS 3.0 is housed within the Integrated Fuels Treatment Decision Support System (Joint Fire Science Program 2012). It can also be run from command line as a stand-alone calculator. The flexible design of FCCS allows users to represent the structural complexity and diversity of fuels created through natural processes (e.g., forest succession and disturbance) and management activities (e.g., forest harvesting and fuels reduction). Each fuelbed is organized into six strata, including canopy, shrubs, herbaceous vegetation, woody fuels, litter-lichen-moss, and ground fuels. Strata are further divided into categories and subcategories. Fuelbeds representing common fuel types throughout much of North America are available in the FCCS reference library. Users may select an FCCS fuelbed to represent their specific project or customize a fuelbed to reflect actual site conditions. The FCCS reports the following results: (1) fuel characteristics by fuelbed, stratum, category, and subcategory; (2) surface fire behavior (i.e., reaction intensity, rate of spread, and flame length); and (3) FCCS fire potential ratings of surface fire behavior, crown fire behavior, and available fuels. With its large fuels data set and ability to represent a wide variety of fuel conditions, the FCCS has numerous applications, from small-scale fuel reduction projects to large-scale emissions and carbon assessments. This report provides technical documentation of the required inputs and computations in the FCCS.

Development and mapping of fuel characteristics and associated fire potentials for South America

The characteristics and spatial distribution of fuels are critical for assessing fire hazard, fuel consumption, greenhouse gas emissions and other fire effects. However, fuel maps are difficult to generate and update, because many regions of the world lack fuel descriptions or adequate mapped vegetation attributes to assign these fuelbeds spatially across the landscape. This paper presents a process to generate fuel maps for large areas using remotely sensed information and ancillary fuel characteristic data. The Fuel Characteristic Classification System was used to build fuelbeds for South America and predict potential fire hazard using a set of default environmental variables. A land-cover map was combined with a biome map to define 98 fuelbeds, and their parameters were assigned based on information from global datasets and existing Fuel Characteristic Classification System fuelbeds or photo series. The indices of potential surface fire behaviour ranged from 1.32 to 9, whereas indices of potential crown fire and available fuel for combustion had low to medium values (0–6). This paper presents a geospatial fuels map for South America. This map could be used to assess fire hazard, predict fire behaviour under defined environmental conditions or calculate fuel consumption and greenhouse gas emissions. It could also be easily updated as new remotely sensed information on vegetation becomes available.

Choose your neighborhood wisely: implications of subsampling and autocorrelation structure in simultaneous autoregression models for landscape ecology

ContextLarge datasets that exhibit residual spatial autocorrelation are common in landscape ecology, introducing issues with model inference. Computationally intensive statistical techniques such as simultaneous autoregression (SAR) are used to provide credible inference, yet landscape studies make choices about autocorrelation structure and data reduction techniques without adequate understanding of the consequences for model estimation and inference.ObjectivesOur goal is to understand the effects of misspecification of neighborhood size, subsampling, and data partitioning on SAR estimation and inference.MethodsWe use remotely sensed burn severity for a large wildfire in north-central Washington State as a case study. First we estimate SAR for remotely sensed burn severity data at multiple subsampling intensities, data partitions, and neighborhood distances. Second, we simulate landscape burn severity data with SAR errors and calculate type I error rates for SAR estimated at the simulation neighborhood distance, and at misspecified neighborhood distances.ResultsSubsampling and misspecification of the neighborhood result in spurious inference and modified coefficient estimates. Type I error rates are close to the specified α-level when the model is estimated at both the simulation neighborhood and the distance that minimizes AIC.ConclusionsBy evaluating the effectiveness of pre-burn fuel reduction treatments on subsequent wildfire burn severity, we demonstrate that misspecification of the neighborhood distance and subsampling the data compromises inference and estimation. Using AIC to choose the neighborhood distance provides type I error rates near the stated α-level in simulated data.