Fuel Characteristic Classification System Research Articles

Vegetation, fuels, and fire-behavior responses to linear fuel-break treatments in and around burned sagebrush steppe: are we breaking the grass-fire cycle?

BackgroundLinear fuel breaks are being implemented to moderate fire behavior and improve wildfire containment in semiarid landscapes such as the sagebrush steppe of North America, where extensive losses in perennial vegetation and ecosystem functioning are resulting from invasion by exotic annual grasses (EAGs) that foster large and recurrent wildfires. However, fuel-break construction can also pose EAG invasion risks, which must be weighed against the intended fire-moderation benefits of the treatments. We investigated how shrub reductions (mowing, cutting), pre-emergent EAG-herbicides, and/or drill seedings of fire-resistant perennial bunchgrasses (PBGs) recently applied to create a large fuel-break system affected native and exotic plant abundances and their associated fuel loading and predicted fire behavior.ResultsIn heavily EAG-invaded areas, herbicides reduced EAG and total herbaceous cover without affecting PBGs for 2–3 years and reduced predicted fire behavior for 1 year (from the Fuel Characteristic Classification System). However, surviving post-herbicide EAG cover was still > 30%, which was sufficient fuel to exceed the conventional 1.2-m-flame length (FL) threshold for attempting wildfire suppression with hand tools. In less invaded shrubland, shrub reduction treatments largely reduced shrub cover and height by ~ half without increasing EAGs, but then redistributed the wood to ground level and increased total herbaceous cover. Herbicides and/or drill seeding after shrub reductions did not affect EAG cover, although drill seedings increased PBG cover and exotic forbs (e.g., Russian thistle). Fire behavior was predicted to be moderated in only one of the many yearly observations of the various shrub-reduction treatment combinations. Over all treatments and years, FLs were predicted to exceed 1.2 m in 13% of simulations under average (11 km h−1) or high (47 km h−1) wind speed conditions and exceed the 3.4-m threshold for uncontrollable fire in 11% of simulations under high-wind speeds only.ConclusionsPredicted fire-moderation benefits over the first 4 years of fuel break implementation were modest and variable, but, generally, increases in EAGs and their associated fire risks were not observed. Nonetheless, ancillary evidence from shrublands would suggest that treatment-induced shifts from shrub to herbaceous fuel dominance are expected to improve conditions for active fire suppression in ways not readily represented in available fire models.

Fuel treatments in shrublands experiencing pinyon and juniper expansion result in trade-offs between desired vegetation and increased fire behavior

BackgroundNative pinyon (Pinus spp.) and juniper (Juniperus spp.) trees are expanding into shrubland communities across the Western United States. These trees often outcompete with native sagebrush (Artemisia spp.) associated species, resulting in increased canopy fuels and reduced surface fuels. Woodland expansion often results in longer fire return intervals with potential for high severity crown fire. Fuel treatments are commonly used to prevent continued tree infilling and growth and reduce fire risk, increase ecological resilience, improve forage quality and quantity, and/or improve wildlife habitat. Treatments may present a trade-off; they restore shrub and herbaceous cover and decrease risk of canopy fire but may increase surface fuel load and surface fire potential. We measured the accumulation of surface and canopy fuels over 10 years from ten sites across the Intermountain West in the Sagebrush Steppe Treatment Evaluation Project woodland network (www.SageSTEP.org), which received prescribed fire or mechanical (cut and drop) tree reduction treatments. We used the field data and the Fuel Characteristic Classification System (FCCS) in the Fuel and Fire Tools (FFT) application to estimate surface and canopy fire behavior in treated and control plots in tree expansion phases I, II, and III.ResultsIncreased herbaceous surface fuel following prescribed fire treatments increased the modeled rate of surface fire spread (ROS) 21-fold and nearly tripled flame length (FL) by year ten post-treatment across all expansion phases. In mechanical treatments, modeled ROS increased 15-fold, FL increased 3.8-fold, and reaction intensity roughly doubled in year ten post-treatment compared to pretreatment and untreated controls. Treatment effects were most pronounced at 97th percentile windspeeds, with modeled ROS up to 82 m min−1 in mechanical and 106 m min−1 in prescribed fire treatments by 10 years post-treatment compared to 5 m min−1 in untreated controls. Crown fire transmissivity risk was eliminated by both fuel treatments.ConclusionsWhile prescribed fire and mechanical treatments in shrublands experiencing tree expansion restored understory vegetation and prevented continued juniper and pinyon infilling and growth, these fuel treatments also increased modeled surface fire behavior. Thus, management tradeoffs occur between desired future vegetation and wildfire risk after fuel treatments.

A Comparison of Multitemporal Airborne Laser Scanning Data and the Fuel Characteristics Classification System for Estimating Fuel Load and Consumption

AbstractCharacterizing pre‐fire fuel load and fuel consumption are critical for assessing fire behavior, fire effects, and smoke emissions. Two approaches for quantifying fuel load are airborne laser scanning (ALS) and the Fuel Characteristic Classification System (FCCS). The implementation of multitemporal ALS (i.e., the use of two or more ALS datasets across time at a given location) in conjunction with empirical models trained with field data can be used to measure fuel and estimate fuel consumption from a fire. FCCS, adapted for use in LANDFIRE (LF), provides 30 m resolution estimates of fuel load across the contiguous United States and can be used to estimate fuel consumption through software programs such as Fuel and Fire Tools (FFT). This study compares the two approaches for two wildfires in the northwestern United States having predominantly sagebrush steppe and ponderosa pine savanna ecosystems. The results showed that the LF FCCS approach yielded higher pre‐fire fuel loads and fuel consumption than the ALS approach and that the coarser scale LF FCCS data did not capture as much heterogeneity as the ALS data. At Tepee, 50.0% of the difference in fuel load and 87.3% of the difference in fuel consumption were associated with distinguishing sparse trees from rangeland. At Keithly, this only accounted for 8.2% and 8.6% of the differences, demonstrating the significance of capturing heterogeneity in rangeland vegetation structure and fire effects. Our results suggest future opportunities to use ALS data to better parametrize fine‐scale fuel load variability that LF FCCS does not capture.

Fuel dynamics and its implication to fire behavior in loblolly pine-dominated stands after southern pine beetle outbreak

Southern pine beetle (SPB) (Dendroctonus frontalis Zimm) outbreaks and wildfire occurrence in recent decades has triggered growing concerns regarding their possible interactions. However, few studies have quantified the fuel characteristics of SPB-killed stands, especially the dynamics of the resulting fuel complex. More importantly, how changes in the fuel complex will affect fire behavior remains unclear. Using field measurements in combination with fire modeling systems, we studied fuel dynamics and its implication to potential fire behaviors by sampling non-outbreak, early post-outbreak (2–3 years), and late post-outbreak (7–8 years) loblolly pine-dominated stands in Georgia and South Carolina, USA. We found that the loadings of 1-hr, 10-hr, 100-hr, 1000-hr fuels, and litter depth were significantly greater in post-outbreak stands than in non-outbreak stands. Stand structure was altered in post-outbreak stands, which contained fewer live pines and more hardwoods. We used the Fuel Characteristic Classification System (FCCS) combined with actual data to construct representative fuel beds from the field data. These customized fuel beds were then used to model surface fire behavior that could be altered by the changes in fuel loading and stand structure resulting from SPB outbreaks. Both FCCS and BehavePlus fire modeling systems predicted a faster rate of spread and longer flame lengths in post-outbreak stands than in non-outbreak stands. Late post-outbreak stands under extremely dry conditions were predicted to have the most extreme fire behavior. Our results fill a critical knowledge gap, which will help forest managers make informed decisions on the management of loblolly pine-dominated stands after the southern pine beetle outbreak.

Fire Behavior Simulation from Global Fuel and Climatic Information

Large-scale fire danger assessment has become increasingly relevant in the past few years, and is usually based on weather information. Still, fuel characteristics also play an important role in fire behavior. This study presents a fire behavior simulation based on a global fuelbed dataset and climatic and topographic information. The simulation was executed using the Fuel Characteristic Classification System (FCCS). The climatic information covered the period 1980–2010, and daily weather parameters were used to calculate the mean monthly fuel moisture content (FMC) and wind speed for the early afternoon period. Also, as the most severe fires occur with extreme environmental conditions, a worst-case scenario was created from the 30 days of each month with the lowest FMC values for the 1980–2010 period. The FMC and wind speed information was grouped into classes, and FCCS was used to simulate the reaction intensity, rate of spread and flame length of the fuelbeds for the average and worst-case monthly conditions. Outputs of the simulations were mapped at global scale, showing the variations in surface fire behavior throughout the year, both due to climatic conditions and fuel characteristics. The surface fire behavior parameters identified the fuels and environmental conditions that produced more severe fire events, as well as those regions where high fire danger only occurs in extreme climatic conditions. The most severe fire events were found in grasslands and shrublands in tropical dry biomes, and corresponding with the worst-case scenario environmental conditions. Also, the results showed the importance of including detailed fuel information into fire danger assessment systems, as the same weather and topographic conditions may have different danger rates, depending on fuel characteristics.

Generation of a global fuel data set using the Fuel Characteristic Classification System

Abstract. This study presents the methods for the generation of the first global fuel data set, containing all the parameters required to be input in the Fuel Characteristic Classification System (FCCS). The data set was developed from different spatial variables, both based on satellite Earth observation products and fuel databases, and is comprised by a global fuelbed map and a database that includes the parameters of each fuelbed that affect fire behavior and effects. A total of 274 fuelbeds were created and parameterized, and can be input into FCCS to obtain fire potentials, surface fire behavior and carbon biomass for each fuelbed. We present a first assessment of the fuel data set by comparing the carbon biomass obtained from our FCCS fuelbeds with the average biome values of four other regional or global biomass products. The results showed a good agreement both in terms of geographical distribution and biomass loads when compared to other biomass data, with the best results found for tropical and boreal forests (Spearman's coefficient of 0.79 and 0.77). This global fuel data set may be used for a varied range of applications, including fire danger assessment, fire behavior estimations, fuel consumption calculations and emissions inventories.

Effects of dormant and growing season burning on surface fuels and potential fire behavior in northern Florida longleaf pine (Pinus palustris) flatwoods

Prescribed fire is widely used to manage fuels in high-frequency, low-severity fire regimes including pine flatwoods of the southeastern USA where prescribed burning during the growing season (the frost-free period during the calendar year) has become more common in recent decades. Growing season prescribed fires address ecological management objectives that focus on increasing herb cover and decreasing shrub cover. The shift from shrub to herb dominance due to burning in the growing season corresponds to a change in surface fuels that could affect fire behavior, yet little has been done to assess the potential effects. We examined the effects of season-of-burn on shrub and herbaceous fuel layers and predicted fire behavior at replicate plots on frequently burned mesic pine flatwoods for two season-of-burn treatments (growing and dormant season prescribed fires) in two geographic regions in northern Florida. The Fuel Characteristic Classification System was used to construct a representative fuelbed for each plot at each sampling time to predict fire behavior. Predicted fire behavior was tested for correlation with measured surface fuel properties to determine if there was an effect from differences in fuels characteristics across treatments. In addition, fire temperature was measured in situ as a proxy for fire intensity and tested for treatment effects on the re-growth of live surface fuels. Compared to single dormant season burns, our single growing season burns caused no changes to live understory fuels and had no detectable effect on fire behavior, although predicted rate of spread and flame length were significantly reduced after all prescribed burns. Shrub cover and predicted fire behavior were, however, significantly different between geographic regions, and shrub height was significantly affected by fire temperature. Predicted fire behavior was strongly correlated with measures of the litter and herb strata. Results from this study suggest that land managers should not initially expect large changes in understory fuel properties or potential fire behavior from a shift to burning during the growing season and show that geographic location and fire intensity had significant effects on live fuels and potential fire behavior.

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.

Evaluating the performance and mapping of three fuel classification systems using Forest Inventory and Analysis surface fuel measurements

Fuel Loading Models (FLMs) and Fuel Characteristic Classification System (FCCSs) fuelbeds are used throughout wildland fire science and management to simplify fuel inputs into fire behavior and effects models, but they have yet to be thoroughly evaluated with field data. In this study, we used a large dataset of Forest Inventory and Analysis (FIA) surface fuel estimates (n=13,138) to create a new fuel classification called Fuel Type Groups (FTGs) from FIA forest type groups, and then keyed an FLM, FCCS, and FTG class to each FIA plot based on fuel loadings and stand conditions. We then compared FIA sampled loadings to the keyed class loading values for four surface fuel components (duff, litter, fine woody debris, coarse woody debris) and to mapped FLM, FCCS, and FTG class loading values from spatial fuel products. We found poor performances (R2<0.30) for most fuel component loadings in all three classifications that, in turn, contributed to poor mapping accuracies. The main reason for the poor performances is the high variability of the four fuel component loadings within classification categories and the inherent scale of this variability does not seem to match the FIA measurement scale or LANDFIRE mapping scale.

Modeling of Emissions from Open Biomass Burning in Asia Using the BlueSky Framework

Open biomass burning (excluding biofuels) is an important contributor to air pollution in the Asian region. Estimation of emissions from fires, however, has been problematic, primarily because of uncertainty in the size and location of sources and in their temporal and spatial variability. Hence, more comprehensive tools to estimate wildfire emissions and that can characterize their temporal and spatial variability are needed. Furthermore, an emission processing system that can generate speciated, gridded, and temporally allocated emissions is needed to support air-quality modeling studies over Asia. For these reasons, a biomass-burning emissions modeling system based on satellite imagery was developed to better account for the spatial and temporal distributions of emissions. The BlueSky Framework, which was developed by the USDA Forest Service and US EPA, was used to develop the Asian biomass-burning emissions modeling system. The sub-models used for this study were the Fuel Characteristic Classification System (FCCS), CONSUME, and the Emissions Production Model (EPM). Our domain covers not only Asia but also Siberia and part of central Asia to assess the large boreal fires in the region. The MODIS fire products and vegetation map were used in this study. Using the developed modeling system, biomass-burning emissions were estimated during April and July 2008, and the results were compared with previous studies. Our results show good to fair agreement with those of GFEDv3 for most regions, ranging from 9.7% in East Asia to 52% in Siberia. The SMOKE modeling system was combined with this system to generate three-dimensional model-ready emissions employing the fire-plume rise algorithm. This study suggests a practicable and maintainable methodology for supporting Asian air-quality modeling studies and to help understand the impact of air-pollutant emissions on Asian air quality.

Developing custom fire behavior fuel models from ecologically complex fuel structures for upper Atlantic Coastal Plain forests

Currently geospatial fire behavior analyses are performed with an array of fire behavior modeling systems such as FARSITE, FlamMap, and the Large Fire Simulation System. These systems currently require standard or customized surface fire behavior fuel models as inputs that are often assigned through remote sensing information. The ability to handle hundreds or thousands of measured surface fuelbeds representing the fine scale variation in fire behavior on the landscape is constrained in terms of creating compatible custom fire behavior fuel models. In this study, we demonstrate an objective method for taking ecologically complex fuelbeds from inventory observations and converting those into a set of custom fuel models that can be mapped to the original landscape. We use an original set of 629 fuel inventory plots measured on an 80,000ha contiguous landscape in the upper Atlantic Coastal Plain of the southeastern United States. From models linking stand conditions to component fuel loads, we impute fuelbeds for over 6000 stands. These imputed fuelbeds were then converted to fire behavior parameters under extreme fuel moisture and wind conditions (97th percentile) using the fuel characteristic classification system (FCCS) to estimate surface fire rate of spread, surface fire flame length, shrub layer reaction intensity (heat load), non-woody layer reaction intensity, woody layer reaction intensity, and litter–lichen–moss layer reaction intensity. We performed hierarchical cluster analysis of the stands based on the values of the fire behavior parameters. The resulting 7 clusters were the basis for the development of 7 custom fire behavior fuel models from the cluster centroids that were calibrated against the FCCS point data for wind and fuel moisture. The latter process resulted in calibration against flame length as it was difficult to obtain a simultaneous calibration against both rate of spread and flame length. The clusters based on FCCS fire behavior parameters represent reasonably identifiable stand conditions, being: (1) pine dominated stands with more litter and down woody debris components than other stands, (2) hardwood and pine stands with no shrubs, (3) hardwood dominated stands with low shrub and high non-woody biomass and high down woody debris, (4) stands with high grass and forb (i.e., non-woody) biomass as well as substantial shrub biomass, (5) stands with both high shrub and litter biomass, (6) pine-mixed hardwood stands with moderate litter biomass and low shrub biomass, and (7) baldcypress-tupelo stands. Models representing these stand clusters generated flame lengths from 0.6 to 2.3m using a 30kmh−1 wind speed and fireline intensities of 100–1500kWm−1 that are typical within the range of experience on this landscape. The fuel models ranked 1<2<7<5<4<3<6 in terms of both flame length and fireline intensity. The method allows for ecologically complex data to be utilized in order to create a landscape representative of measured fuel conditions and to create models that interface with geospatial fire models.

Evaluating Potential Changes in Fire Risk fromEucalyptusPlantings in the Southern United States

Renewed interest in short-rotation woody crops for bioenergy and bioproducts has prompted a reevaluation of theEucalyptusspecies for the southern United States. One question that arises about the potential effects of introducing a nonnative species is what effect will there be on fire behavior. Our approximate answer based on modeling fire behavior using the Fuel Characteristic Classification System is that surface fire behavior in young stands differs little from surface fires common to pine plantations in the southern Coastal Plain. By the age of 9, the absence of a shrub layer, along with an increased height to live crown, reduced initiation potential despite increased bark shedding. When a shrub layer was introduced in the model, the initiation potential became equivalent to commonPinusfuelbeds. If a crown is ignited, however, the potentials for transmissivity and spread are very high, and the potential for crown fire behavior is more severe. Our modeling effort suggests that fire behavior at the stand level differs little from current conditions and points to the importance of avoiding the development of a shrub layer. Stands managed on short rotation (less than 10 years) will likely be harvested before bark shedding presents a significant spotting problem.

Using fine-scale fuel measurements to assess wildland fuels, potential fire behavior and hazard mitigation treatments in the southeastern USA

The inherent spatial and temporal heterogeneity of fuelbeds in forests of the southeastern United States may require fine scale fuel measurements for providing reliable fire hazard and fuel treatment effectiveness estimates. In a series of five papers, an intensive, fine scale fuel inventory from the Savanna River Site in the southeastern United States is used for building fuelbeds and mapping fire behavior potential, evaluating fuel treatment options for effectiveness, and providing a comparative analysis of landscape modeled fire behavior using three different data sources including the Fuel Characteristic Classification System, LANDFIRE, and the Southern Wildfire Risk Assessment. The research demonstrates that fine scale fuel measurements associated with fuel inventories repeated over time can be used to assess broad scale wildland fire potential and hazard mitigation treatment effectiveness in the southeastern USA and similar fire prone regions. Additional investigations will be needed to modify and improve these processes and capture the true potential of these fine scale data sets for fire and fuel management planning.

Fuel treatment effectiveness in forests of the upper Atlantic Coastal Plain – An evaluation at two spatial scales

Fuel treatment effectiveness in Southern forests has been demonstrated using fire behavior modeling and observations of reduced wildfire area and tree damage. However, assessments of treatment effectiveness may be improved with a more rigorous accounting of the fuel characteristics. We present two case studies to introduce a relatively new approach to characterizing fuels and predicting potential fire behavior, fuel consumption, and emissions in Southern forests using the Fuel Characteristic Classification System (FCCS) and Consume. The first case study provides fine-scale (<100ha) examples of fuel treatments (prescribed fire, mechanical thinning, mastication, and herbicide treatment) and their potential effect on predicted fire behavior and effects on measured treatment units. The second case study evaluates potential fire behavior across a managed forest landscape (74,000ha) in the upper Atlantic Coastal Plain, South Carolina.Results from the fine-scale assessment indicate that fuel treatments reduce reaction intensity, rate of spread, and flamelength by up to 58%, 57%, and 63%, respectively. Fuel loading of strata that control surface fire behavior (i.e., shrubs, grasses, fine woody fuels, and litter) range from 32.0Mgha−1 in the thinned, untreated unit to 8.5Mgha−1 in the unthinned unit treated with herbicides and prescribed fire. Based on model predictions, up to 80% less fuel would be consumed with concomitant reduction in emissions during a wildfire occurring in the treated units compared to the untreated unit.Assessments of potential fire behavior across the study area indicate that overall hazard is low to moderate for this forested landscape. However, localized areas of high surface fire and crown fire potential were identified. Plot- and stand-based modeling both suggest that the potential for high to extreme fire behavior exists for this landscape. Combined, the two case studies highlight the ability of the FCCS to represent measured fuel characteristics and predict differences in potential fire behavior resulting from fuel treatments. Even small differences in fuel characteristics resulting from fuel treatments or site variation could be detected, allowing the effects of both ecological processes and management actions to be quantified.

Evaluating fuel complexes for fire hazard mitigation planning in the southeastern United States

Fire hazard mitigation planning requires an accurate accounting of fuel complexes to predict potential fire behavior and effects of treatment alternatives. In the southeastern United States, rapid vegetation growth coupled with complex land use history and forest management options requires a dynamic approach to fuel characterization. In this study we assessed potential surface fire behavior with the Fuel Characteristic Classification System (FCCS), a tool which uses inventoried fuelbed inputs to predict fire behavior.Using inventory data from 629 plots established in the upper Atlantic Coastal Plain, South Carolina, we constructed FCCS fuelbeds representing median fuel characteristics by major forest type and age class. With a dry fuel moisture scenario and 6.4kmh−1 midflame wind speed, the FCCS predicted moderate to high potential fire hazard for the majority of the fuelbeds under study. To explore fire hazard under potential future fuel conditions, we developed fuelbeds representing the range of quantitative inventory data for fuelbed components that drive surface fire behavior algorithms and adjusted shrub species composition to represent 30% and 60% relative cover of highly flammable shrub species.Results indicate that the primary drivers of surface fire behavior vary by forest type, age and surface fire behavior rating. Litter tends to be a primary or secondary driver in most forest types. In comparison to other surface fire contributors, reducing shrub loading results in reduced flame lengths most consistently across forest types. FCCS fuelbeds and the results from this project can be used for fire hazard mitigation planning throughout the southern Atlantic Coastal Plain where similar forest types occur. The approach of building simulated fuelbeds across the range of available surface fuel data produces sets of incrementally different fuel characteristics that can be applied to any dynamic forest types in which surface fuel conditions change rapidly.

A comparison of geospatially modeled fire behavior and fire management utility of three data sources in the southeastern United States

Landscape-scale fire behavior analyses are important to inform decisions on resource management projects that meet land management objectives and protect values from adverse consequences of fire. Deterministic and probabilistic geospatial fire behavior analyses are conducted with various modeling systems including FARSITE, FlamMap, FSPro, and Large Fire Simulation System. The fundamental fire intensity algorithms in these systems require surface fire behavior fuel models and canopy cover to model surface fire behavior. Canopy base height, stand height, and canopy bulk density are required in addition to surface fire behavior fuel models and canopy cover to model crown fire activity. Several surface fuel and canopy classification efforts have used various remote sensing and ecological relationships as core methods to develop the spatial layers. All of these methods depend upon consistent and temporally constant interpretations of crown attributes and their ecological conditions to estimate surface fuel conditions.This study evaluates modeled fire behavior for an 80,000ha tract of land in the Atlantic Coastal Plain of the southeastern US using three different data sources. The Fuel Characteristic Classification System (FCCS) was used to build fuelbeds from intensive field sampling of 629 plots. Custom fire behavior fuel models were derived from these fuelbeds. LANDFIRE developed surface fire behavior fuel models and canopy attributes for the US using satellite imagery informed by field data. The Southern Wildfire Risk Assessment (SWRA) developed surface fire behavior fuel models and canopy cover for the southeastern US using satellite imagery.Differences in modeled fire behavior, data development, and data utility are summarized to assist in determining which data source may be most applicable for various land management activities and required analyses. Characterizing fire behavior under different fuel relationships provides insights for natural ecological processes, management strategies for fire mitigation, and positive and negative features of different modeling systems. A comparison of flame length, rate of spread, crown fire activity, and burn probabilities modeled with FlamMap shows some similar patterns across the landscape from all three data sources, but there are potentially important differences. All data sources showed an expected range of fire behavior. Average flame lengths ranged between 1 and 1.4m. Rate of spread varied the greatest with a range of 2.4–5.7mmin−1. Passive crown fire was predicted for 5% of the study area using FCCS and LANDFIRE while passive crown fire was not predicted using SWRA data. No active crown fire was predicted regardless of the data source. Burn probability patterns across the landscape were similar but probability was highest using SWRA and lowest using FCCS.

Development of wildland fire particulate smoke marker to organic carbon emission ratios for the conterminous United States

Abstract Biomass burning is a significant source of aerosols that impact the global radiation budget, human health, and visibility. Molecular marker – chemical mass balance models are frequently employed to estimate the contribution of biomass burning smoke to pollution at receptor sites. These models require information about emissions of smoke tracer compounds to determine the smoke apportionment. This study provides new information about smoke marker emissions by combining laboratory measurements of the chemical composition of smoke with a fuelbed model to create spatially variable maps of smoke markers for the conterminous United States. The analysis incorporates novel measurements of anhydrosugars and water-soluble potassium collected from burning different wildland fuels during the Fire Lab at Missoula Experiment (FLAME). Several patterns emerged from the analyses of these measurements, particularly that different biomass tissue types (e.g., leaves, needles, branches and grasses) produced significantly different smoke marker to organic carbon emission ratios. Smoke marker to carbon emission ratios of biomass tissue types were coupled with the Fuel Characteristic Classification System fuelbed model and published combustion efficiencies to calculate 113 fuelbed smoke marker to carbon ratios. These fuelbed ratios were then mapped across the conterminous United States at 1 km resolution to understand the spatial variability of anhydrosugars and water-soluble potassium.

Predicting response of fuel load to future changes in climate and atmospheric composition in the Southern United States

The model projected ecosystem carbon dynamics were incorporated into the default (contemporary) fuel load map developed by FCCS (Fuel Characteristic Classification System) to estimate the dynamics of fuel load in the Southern United States in response to projected changes in climate and atmosphere (CO 2 and nitrogen deposition) from 2002 to 2050. The study results indicated that in 2002 the total fuel load of the Southern United States was about 1.15 P g (1 P = 10 15), which will decrease to 1.11 P g in 2050. The declination of fuel load is mainly due to the climate change, especially the reduced precipitation in 2050, while the effects of elevated CO 2 and nitrogen deposition will increase fuel load. Interactions among all factors will result in 1% reduction in the fuel load in 2050. In response to the spatial heterogeneity in environmental changes, the dynamics of fuel load from 2002 to 2050 vary strongly among the study states. The declined precipitation in the northern inland of the study region may lead to 20% fuel load reduction in Tennessee and Kentucky by the year of 2050, while the elevated precipitation and decreased daily mean temperature in the coastal states, especially in South Carolina, North Carolina, and Virginia, may result in fuel load accumulation. The temporal–spatial variation of the fuel load may be overestimated since the adjustments of forest management regime in response to climate change were not considered in current study.

Characterizing and classifying complex fuels — A new approach

In the United States, fire managers have relied on a small number of stylized fuel models for over 30 years (Anderson 1982)—standard descriptions of homogeneous surface fuel constituents associated with specific types of fire behaviour—as inputs to mathematical calculations of surface fire behaviour. Other systems for describing fuels and calculating fire behaviour have been developed for Canada (Forestry Canada Fire Danger Group 1992) and Australia (Cheney and Sullivan 1997). Scientists and managers are seeking more accurate predictions of fire behaviour and fire effects for operational use, planning, and simulations. While categorical descriptors of fuels have served managers reasonably well in the past, improved predictions might require more comprehensive descriptions of fuelbeds (soil organic matter and all live and dead vegetation) that discriminate among fuel components. Regional resource managers challenged scientists at the US Forest Service Pacific Wildland Fire Sciences Laboratory to develop a comprehensive library of measured fuelbeds that would meet their needs for quantifying fuels over a wide range of vegetative types, such as temperate rainforests, dry ponderosa pine (Pinus ponderosa Dougl. ex P. & C. Laws.) forests, juniper (Juniperus spp.) woodlands, and sagebrush (Artemisia spp.). In principle, realistic multistrata fuelbeds could better represent fuels than could stylized models, providing information about the biophysical environment that would be useful to assessment of fire and biological conditions. The Fuel Characteristic Classification System (FCCS) was developed in response to this challenge. A significant component of the FCCS is a large fuelbed library complied from federal agencies and the scientific literature. This data library assembled from ecosystems throughout the United States includes quantitative and qualitative variables for a wide range of structural components. The FCCS could potentially provide input for a variety of applications including fire behaviour, fire effects on ecological components (e.g., standing dead wood and large woody debris), smoke production (e.g., calculation of particulate emissions), and stand dynamics modeling (e.g., stem density and stem sizes). For example, the FCCS fuelbeds are already being used as inputs for calculating fuel consumption and large-scale emissions (Wiedinmyer et al. 2006). The articles in this feature describe and document the key components and applications of the FCCS. Because the FCCS is a substantial departure from traditional fuel inventory and modeling concepts, extensive input has been elicited from scientists and resource managers in North America during development and beta testing. In addition, the FCCS was formally reviewed by a panel of 12 fire scientists in November 2005. Scientists asked important questions about the validity and scientific foundation of the FCCS. For example, could measured fuelbeds better account for differences in fire behaviour and effects than could stylized fuel models? The following articles were revised in response to this extensive review prior to submission for publication and additional peer review.

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 each existing fuelbed stratum including the canopy, shrubs, nonwoody, woody, litter–lichen–moss, and duff. Finally, the system classifies each fuelbed by calculating fire potentials that provide an index of the intrinsic capacity of each fuelbed to support surface fire behavior, support crown fire, and provide fuels for flaming, smoldering, and residual consumption. The FCCS outputs are being used in a national wildland fire emissions inventory and in the development of fuelbed, fire hazard, and treatment effectiveness maps on several national forests. Although the FCCS was built for the United States, the conceptual framework is applicable worldwide.