Fire Effects Model Research Articles

Developing customized fuel models for shrub and bracken communities in Galicia (NW Spain)

Geospatial fire behaviour and fire hazard simulators, fire effects models and smoke emission software commonly use standard fuel models in order to simplify data collection and the inclusion of complex fuel scenarios. These fuel models are often mapped using remotely sensed data. However, given the great complexity of fuelbeds, with properties that vary widely in both time and space, the use of these standard fuel models can greatly limit accurate fuel mapping. This affects fuel hazard assessment, fuel reduction treatment plans, fire management decision-making and evaluation of the environmental impact of wildfire. In this study, we developed unique customized fire behaviour fuel models for shrub and bracken communities, by using k-medoids clustering analysis based on both fuel structural characteristics and potential fire behaviour. We used an original database of 722 destructive sample plots in nine different shrub and bracken communities covering the entire distribution area in Galicia (NW Spain), one of the regions in Europe most affected by forest fires. Measurements of cover, height and fuel fractions loads differentiated by size and vegetative state (live or dead) were used to estimate the potential rate of fire spread with five different models including fireline intensity, heat per unit area and the flame length for each sampling site and considering extreme environmental conditions. The optimal number of clusters was established by combining practical knowledge about the shrubland communities under study and their associated fire behaviour, with maximization of the mean value of the silhouette variable and minimization of the within-cluster sum of squares. The structural characteristics of the medoids derived from the analysis were associated with each of the proposed customized fuel models. Finally, a simple dichotomous classification based only on shrub height was developed to enable construction of spatially explicit fuel model maps based on remotely sensed data. Thus, the methodology applied allows generation of a more realistic representation of fuel distribution in the landscape, based on fuel structure measurements of natural regional ecosystems rather than on the use of standard models. We believe that the proposed methodology is generally applicable to communities composed of other shrub and fern species in different biogeographical regions.

Količine goriva u šumskoj prostirci sastojina hrasta crnike (Quercus ilex L.) i hrasta medunca (Quercus pubescens Willd.)

The application of newer remote sensing methods, such as aerial and terrestrial lidar scanning and the use of "Structure-from-motion" (SfM) photogrammetry, complemented field data collection and enabled 3D mapping of forest fuel layers, greatly simplifying and improving their characterization. However, these methods are not suitable for quantifying forest floor characteristics. For this purpose, it is still necessary to collect data using classical field methods, determining the presence of subhorizons and their depth, while the characteristics of the forest floor: bulk density, load, carbon concentration and carbon stock are determined in the laboratory. Therefore, it is still common practice to create regression equations that allow operatives to determine the amount of available forest floor fuel and the carbon stock it contains based on the depth of the forest floor, which is an easily measurable variable, or to determine forest floor loading by subhorizon and overall. Forest floor information is used in models for predicting forest fire behavior and spread, in fire effects models, in planning and monitoring mechanical fuel reduction, in quantifying fuel consumption and smoke emissions, in quantifying carbon stocks, in describing habitat and its productivity, and in planning for preparedness. As stands of holm oak (Quercus ilex L.) and pubescent oak (Quercus pubescens Willd.) are located in the Mediterranean part of Croatia, where the risk of forest fires is the highest, and the previously published data on the forest floor are not suitable for the models, the main objectives of the research were to determine the depth, bulk density and load of individual subhorizons of the forest floor and to create regression equations that allow estimating the amount of available fuel in the forest floor based on its depth. Sampling of forest floor in holm oak stands (60, 90 and 100 years old) was conducted on the islands of Lastovo and Mljet, while sampling in pubescent oak stands (53 and 90 years old) was conducted in Dugopolje and Biograd na Moru. The amount of forest floor and OC stock in old stands of holm oak and pubescent oak is twice as small as in old stands of Aleppo pine and Dalmatian black pine. This suggests that pine stands have a significantly greater amount of potentially available fuel in the forest floor and are therefore potentially more at risk from fire, but also that they store twice as much carbon in the forest floor. In this study, for the first time in Croatia, bulk densities of individual subhorizons of the forest floor were determined for holm oak and pubescent oak and regression equations were created that allow estimating the amount of available fuel in the forest floor based on its depth per subhorizon and overall. The results of this research have practical value in simpler quantification of forest floor fuel loading, which is important for operational use of models to predict forest fire behavior and spread, but they can also be used in other models mentioned earlier.

Prescribed Fire Causes Wounding and Minor Tree Quality Degradation in Oak Forests

Despite the adaptation of many oak (Quercus) species to repeated surface fire, many public land managers in eastern North America resist using prescribed fire as a regeneration tool because of fire’s perceived negative impacts on timber values through the wounding of overstory trees. We retrospectively quantified fire-associated wounds in 139 oak-dominated stands across four national forests, each with a history of zero to six prescribed fires within the last 30 years. For trees > 25.4 cm dbh (n = 8093), fire-associated wounds within the first 3.67 m of height were categorized by type, measured for defect size and graded both accounting for and then ignoring the fire-associated wounds. Most fire-associated wounds (n = 3403) were catfaces (32.5%), seams (30.5%) or bark slough (30.1%), although catfaces had 2.1–6.4 times the average volume loss of any other wound type (9.90 ± 0.72 bd ft). Among the 2160 wounded trees sampled, 741 had multiple (≥2) wounds. Although 29.1% of all trees had at least one wound associated with prescribed fire, only 7.0% of those trees exhibited a reduction in tree grade. The likelihood of wounding was greater in stands receiving more prescribed burns, but unaffected by tree diameter for either thin- or thick-barked species. Considering both the likelihoods of wounding and grade reduction, white oak (Q. alba), chestnut oak (Q. montana), hickory (Carya sp.), shortleaf pine (Pinus echinata) and yellow-poplar (Liriodendron tulipifera) trees were more resistant to prescribed fire damage than other species. While our findings cannot be related directly to individual fire parameters, such as fireline intensity or fire duration, these results do provide estimates of the cumulative effects of multiple management-based prescribed fires that can be incorporated into fire effects models.

Mapping organic layer thickness and fuel load of the boreal forest in Alberta, Canada

Forest organic layers are important soil carbon pools that can, in the absence of disturbance, accumulate to great depths, especially in lowland areas. Across the Canadian boreal forest, fire is the primary disturbance agent, often limiting organic layer accumulation through the direct consumption of these fuels. Organic layer thickness (OLT) and fuel load (OLFL) are common physical attributes used to characterize these layers, especially for wildland fire science. Understanding the drivers and spatial distribution of these attributes is important to improve predictions of fire behaviour, emissions and effects models. We developed maps of OLT and OLFL using machine learning approaches (weighted K-nearest neighbour and random forests) for the forested region of the province of Alberta, Canada (538, 058 km2). The random forests approach was found to be the best approach to model the spatial distribution of these forest floor attributes. A databased of 3, 237 OLT and 594 OLFL plots were used to train the models. The error in our final model, particularly for OLT (5 cm), was relatively close to the variability we would expect to find naturally (3 cm). The dominant tree species was the most important covariate in the models. Age, solar radiation, spatial location, climate variables and surficial geology were also important drivers, although their level of importance varied between tree species and depended on the modelling method that was used.

Dead and down woody debris fuel loads in Canadian forests

In Canada, fire behaviour is modelled based on a fuel classification system of 16 fuel types. Average fuel loads are used to represent a wide range of variability within each fuel type, which can lead to inaccurate predictions of fire behaviour. Dead and down woody debris (DWD) is a major component of surface fuels affecting surface fuel consumption, potential crown fire initiation, and resulting crown fuel consumption and overall head fire intensity. This study compiled a national database of DWD fuel loads and analysed it for predictive driving variables. The database included DWD fuel loads for all dominant Canadian forest types at three size classes: fine (<1 cm), medium (1–7 cm) and coarse (>7 cm). Predictive models for DWD fuel load by size classes individually and collectively for various forest types and ecozones were analysed. Bioclimatic regime, age, spatial position, drainage, and structural components including diameter at breast height and stem density were significant variables. This study provides tools to improve our understanding of the spatial distribution of DWD across Canada, which will enhance our ability to represent its contribution within fire behaviour and fire effects models.

A large database supports the use of simple models of post-fire tree mortality for thick-barked conifers, with less support for other species

BackgroundPredictive models of post-fire tree and stem mortality are vital for management planning and understanding fire effects. Post-fire tree and stem mortality have been traditionally modeled as a simple empirical function of tree defenses (e.g., bark thickness) and fire injury (e.g., crown scorch). We used the Fire and Tree Mortality database (FTM)—which includes observations of tree mortality in obligate seeders and stem mortality in basal resprouting species from across the USA—to evaluate the accuracy of post-fire mortality models used in the First Order Fire Effects Model (FOFEM) software system. The basic model in FOFEM, the Ryan and Amman (R-A) model, uses bark thickness and percentage of crown volume scorched to predict post-fire mortality and can be applied to any species for which bark thickness can be calculated (184 species-level coefficients are included in the program). FOFEM (v6.7) also includes 38 species-specific tree mortality models (26 for gymnosperms, 12 for angiosperms), with unique predictors and coefficients. We assessed accuracy of the R-A model for 44 tree species and accuracy of 24 species-specific models for 13 species, using data from 93 438 tree-level observations and 351 fires that occurred from 1981 to 2016.ResultsFor each model, we calculated performance statistics and provided an assessment of the representativeness of the evaluation data. We identified probability thresholds for which the model performed best, and the best thresholds with either ≥80% sensitivity or specificity. Of the 68 models evaluated, 43 had Area Under the Receiver Operating Characteristic Curve (AUC) values ≥0.80, indicating excellent performance, and 14 had AUCs <0.7, indicating poor performance. The R-A model often over-predicted mortality for angiosperms; 5 of 11 angiosperms had AUCs <0.7. For conifers, R-A over-predicted mortality for thin-barked species and for small diameter trees. The species-specific models had significantly higher AUCs than the R-A models for 10 of the 22 models, and five additional species-specific models had more balanced errors than R-A models, even though their AUCs were not significantly different or were significantly lower.ConclusionsApproximately 75% of models tested had acceptable, excellent, or outstanding predictive ability. The models that performed poorly were primarily models predicting stem mortality of angiosperms or tree mortality of thin-barked conifers. This suggests that different approaches—such as different model forms, better estimates of bark thickness, and additional predictors—may be warranted for these taxa. Future data collection and research should target the geographical and taxonomic data gaps and poorly performing models identified in this study. Our evaluation of post-fire tree mortality models is the most comprehensive effort to date and allows users to have a clear understanding of the expected accuracy in predicting tree death from fire for 44 species.

Quantifying how sources of uncertainty in combustible biomass propagate to prediction of wildland fire emissions

Smoke emissions from wildland fires contribute to concentrations of atmospheric particulate matter and greenhouse gases, influencing public health and climate. Prediction of emissions is critical for smoke management to mitigate the effects on visibility and air quality. Models that predict emissions require estimates of the amount of combustible biomass. When measurements are unavailable, fuel maps may be used to define the inputs for models. Mapped products are based on averages that poorly represent the inherent variability of wildland fuels, but that variability is an important source of uncertainty in predicting emissions. We evaluated the sensitivity of emissions estimates to wildland fuel biomass variability using two models commonly used to predict emissions: Consume and the First Order Fire Effects Model (FOFEM). Flaming emissions were consistently most sensitive to litter loading (Sobol index 0.426–0.742). Smouldering emissions were most often sensitive to duff loading (Sobol 0.655–0.704) under the extreme environmental scenario. Under the moderate environmental scenario, FOFEM-predicted smouldering emissions were similarly sensitive to sound and rotten coarse woody debris (CWD) and duff fuel components (Sobol 0.193–0.376). High variability in loading propagated to wide prediction intervals for emissions. Direct measurements of litter, duff and coarse wood should be prioritised to reduce overall uncertainty.

Integrating fire effects on vegetation carbon cycling within an ecohydrologic model

Wildfire affects landscape ecohydrologic processes through feedbacks between fire effects, vegetation growth and water availability. Despite the links between these processes, fire is rarely incorporated dynamically into ecohydrologic models, which couple vegetation growth with water and nutrient fluxes. This omission has the potential to produce inaccurate estimates of long-term changes to carbon and water cycling in response to climate change and management. In this study, we describe a fire-effects model that is coupled to a distributed ecohydrologic model, RHESSys, and a fire-spread model, WMFire. The fire-effects model has intermediate structural complexity so as to be commensurate with the ecohydrologic model. The fire-effects model includes processes for litter and coarse woody debris consumption, processes for fire-associated vegetation mortality and consumption, and takes into account canopy structure (i.e. ladder fuels) for propagation of fire effects into a forest canopy. We evaluated the model in four Western U.S. sites representing different vegetation, climate, and fire regimes. The fire-effects model was able to replicate patterns of expected fire effects across different ecosystems and stand ages without being tuned to produce them; an emergent property of the model. Fire effects of shrubland and understory vegetation varied with surface fire intensity, by design, and fire effects in forest canopies were sensitive to parameters associated with the buildup of litter and understory ladder fuels. These findings demonstrate that the fire-effects model provides an effective tool for evaluating the post-fire changes to physical and ecological processes. Future work will project future fire regimes and improve understanding of watershed dynamics under climate change and land management via the simulation of the fire-effects model with fire spread and ecohydrology.

Resilience of Oregon white oak to reintroduction of fire

BackgroundPacific Northwest USA oak woodlands and savannas are fire-resilient communities dependent on frequent, low-severity fire to maintain their structure and understory species diversity, and to prevent encroachment by fire-sensitive competitors. The re-introduction of fire into degraded ecosystems is viewed as essential to their restoration, yet can be fraught with unintended negative consequences. We examined the response of mature Oregon white oak (Quercus garryana Douglas ex Hook.; Garry oak) to “first entry” woodland restoration burns following long fire-free periods.ResultsThirteen to twenty-five months post burn, topkill of oaks was minimal (3%) and mortality was rare in three prescribed burns, despite high levels (mean = 92%) of crown scorching, and irrespective of proportional duff consumption around oak bases (mean = 21%). Percentage of crown scorch volume was the strongest predictor of oak crown dieback, but response was highly variable, especially when canopy scorch was ≥80%. Comparison of our results with FOFEM (First Order Fire Effects Model), a common fire effects model, revealed high model inaccuracy, likely due to lack of a species-specific equation for prediction of Oregon white oak mortality.ConclusionsThe results of this study indicate that Oregon white oak is highly resistant to mortality in restoration burns, even following long fire-free intervals. Prescribed fire is not contraindicated in areas with extant mature oaks, and may promote oak regeneration via basal sprouting.

Short-term responses of boreal carbon stocks to climate change: A simulation study of black spruce forests

Boreal forest ecosystems play a significant role in the global C cycle. Over the current century, these forests are expected to undergo one of the largest increases in temperatures. Climate change is expected to affect boreal C storage through changes in fire regimes, tree growth and decomposition rates. We developed a diameter-size structured model, coupled with a boreal soil C dynamics model and a fire effects model to investigate the short-term responses of boreal carbon storage to climate change. We applied this model to 1.0 ha patches of monospecific black spruce stands under four climatic periods: 1980–2010, 2010–2040, 2040–2070 and 2070–2100. Forest inventory and historical fire intensity records representative of our study area were used to calibrate the model. Historical and future fire return interval (FRI) maps and projected weather data estimated by CanESM2 RCP8.5 climate scenario were used to drive historical and future disturbance frequency, forest growth, decomposition rates, and C dynamics. Black spruce trees showed amplified decreases in growth and temperature-sensitive decomposition rates of soil C pools were enhanced as projected air temperature increased. This study indicates that the short-term effects of climate change on black spruce forests productivity and C storage are substantially negative. In our simulation experiments, ecosystem C storage was reduced by 10% by the end of 2100. We predicted a marked and widespread change from C sink to C source, which has already started and will persist at least until 2100. The results of this study indicate that black spruce forest of northern Québec could be losing their capacity to sequester and store organic C over the next coming decades due to climate change.

Fuel dynamics after reintroduced fire in an old-growth Sierra Nevada mixed-conifer forest

BackgroundSurface fuel loadings are some of the most important factors contributing to fire intensity and fire spread. In old-growth forests where fire has been long excluded, surface fuel loadings can be high and can include woody debris ≥100 cm in diameter. We assessed surface fuel loadings in a long-unburned old-growth mixed-conifer forest in Yosemite National Park, California, USA, and assessed fuel consumption from a management-ignited fire set to control the progression of the 2013 Rim Fire. Specifically, we characterized the distribution and heterogeneity of pre-fire fuel loadings, both along transects and contained in duff mounds around large trees. We compared surface fuel consumption to that predicted by the standard First Order Fire Effects Model (FOFEM) based on pre-fire fuel loadings and fuel moistures. We also assessed the relationship between tree basal area—calculated for two different spatial neighborhood scales—and pre-fire fuel loadings.ResultsPre-fire total surface fuel loading averaged 192 Mg ha−1 and was reduced by 79% by the fire to 41 Mg ha−1 immediately after fire. Most fuel components were reduced by 87% to 90% by the fire, with the exception of coarse woody debris (CWD), which was reduced by 60%. Litter depth in duff mounds were within 1 SD of plot means, but duff biomass for the largest trees (>150 cm diameter at breast height [DBH]) exceeded plot background levels. Overstory basal area generally had significant positive relationships with pre-fire fuel loadings of litter, duff, 1-hour, and 10-hour fuels, but the strength of the relationships differed between overstory components (live, dead, all [live and dead], species), and negative relationships were observed between live Pinus lambertiana Douglas basal area and CWD. FOFEM over-predicted rotten CWD consumption and under-predicted duff consumption.ConclusionsSurface fuel loadings were characterized by heterogeneity and the presence of large pieces. This heterogeneity likely contributed to differential fire behavior at small scales and heterogeneity in the post-fire environment. The reductions in fuel loadings at our research site were in line with ecological restoration objectives; thus, ecologically restorative burning during fire suppression is possible.

Seed Germination in Cistus ladanifer: Heat Shock, Physical Dormancy, Soil Temperatures and Significance to Natural Regeneration.

Seeds of Cistus ladanifer experience bursts of germination following fires. The effects of heat shock from 10 °C to 150 °C on seed germination were investigated by final germination plus the number of days required for germination to start and finish, and symmetry of cumulative germination. The occurrence of physical dormancy in C. ladanifer seeds was investigated by a variety of methods, including imbibition, scanning electron microscopy (SEM) and light microscopy, and use of dyes. The significance of responses of C. ladanifer seeds to fires was investigated essentially by abstracting existing literature and by using fire effects models and simulations. Parameters of germination were variously affected by heat treatments—positively in the range 80–100 °C, negatively above 130 °C. Non-dormancy was consistently found in about 30% of seeds but no evidence was obtained to support the existence of physical dormancy in the dormant fraction of C. ladanifer seeds. Two complementary processes seem to be in place in seeds response to fire. A direct fire-driven increase in germination of virtually all seeds in response to the appropriate heat load produced by fire or, in the absence of such heat loads, the germination of the non-dormant fraction provided that above-ground vegetation burns.

Multi-scale assessment of post-fire tree mortality models

Post-fire tree mortality models are vital tools used by forest land managers to predict fire effects, estimate delayed mortality and develop management prescriptions. We evaluated the performance of mortality models within the First Order Fire Effects Model (FOFEM) software, and compared their performance to locally-parameterised models based on five different forms. We evaluated all models at the individual tree and stand levels with a dataset comprising 34174 trees from a mixed-conifer forest in the Sierra Nevada, California that burned in the 2013 Rim Fire. We compared stand-level accuracy across a range of spatial scales, and we used point pattern analysis to test the accuracy with which mortality models predict post-fire tree spatial pattern. FOFEM under-predicted mortality for the three conifers, possibly because of the timing of the Rim Fire during a severe multi-year drought. Locally-parameterised models based on crown scorch were most accurate in predicting individual tree mortality, but tree diameter-based models were more accurate at the stand level for Abies concolor and large-diameter Pinus lambertiana, the most abundant trees in this forest. Stand-level accuracy was reduced by spatially correlated error at small spatial scales, but stabilised at scales ≥1ha. The predictive error of FOFEM generated inaccurate predictions of post-fire spatial pattern at small scales, and this error could be reduced by improving FOFEM model accuracy for small trees.

Advancing Fire Science with Large Forest Plots and a Long-Term Multidisciplinary Approach

Large, spatially explicit forest plots have the potential to address currently understudied aspects of fire ecology and management, including the validation of physics-based fire behavior models and next-generation fire effects models. Pre-fire forest structures, fire-mediated mortality, and post-fire forest development can be examined in a spatial context, and value can be added to current multidisciplinary approaches by adding a long-term perspective. Here we propose that the fire science community begin to build a collaborative network of fire-related large forest dynamics plots to examine explicit spatial patterns of surface fuels, tree mortality, and post-fire regeneration throughout ecosystems with frequent-fire forests.

Predicting Post-Fire Tree Mortality for 12 Western US Conifers Using the First Order Fire Effects Model (FOFEM)

Accurate prediction of fire-caused tree mortality is critical for making sound land management decisions such as developing burning prescriptions and post-fire management guidelines. To improve efforts to predict post-fire tree mortality, we developed 3-year post-fire mortality models for 12 Western conifer species—white fir (Abies concolor [Gord. & Glend.] Lindl. ex Hildebr.), red fir (Abies magnifica A. Murray bis), subalpine fir (Abies lasiocarpa [Hook.] Nutt.), incense cedar (Calocedrus decurrens [Torr.] Florin), western larch (Larix occidentalis Nutt.), lodgepole pine (Pinus contorta Douglas ex Loudon var. latifolia Engelm. ex S. Watson), whitebark pine (Pinus albicaulis Engelm.), ponderosa pines (Pinus ponderosa Lawson & C. Lawson var. scopulorum Engelm and var. ponderosa C. Lawson), Jeffrey pine (Pinus jeffreyi Balf.), sugar pine (Pinus lambertiana Douglas), Engelmann spruce (Picea engelmannii Parry ex Engelm.), and Douglas-fir (Pseudotsuga menziesii [Mirb.] Franco var. glauca [Beissn.] Franco)—by pooling data collected from multiple fire-injury studies. Two sets of models were created: one for use in pre-fire planning in which only crown injury and tree diameter (DBH) were potential variables, and a second, optimal model for use in post-fire planning that used all significant variables. Predictive accuracy of all models was compared to the accuracy of the general, non-species specific mortality model used in the First Order Fire Effects Model (FOFEM) prior to version 5.7. The new species-specific models improved prediction of fire-caused tree mortality by 0 % to 48 %. Model accuracy increased the most for red fir, incense cedar, western larch, and whitebark pine, and increased the least for Engelmann spruce. The models in the post-fire option provided higher accuracy compared to the pre-fire models, but also required additional inputs. These new models were added to FOFEM beginning with version 5.7, and the options in the FOFEM Mortality Module were expanded. We describe the new options in FOFEM and how to use the software to predict tree mortality for pre-fire and post-fire planning, as well as modeling limitations and assumptions. The additions to FOFEM offer improved accuracy in predicting post-fire tree mortality for 12 Western conifer species and allow direct inputs of fire injury to increase software applicability to prescribed fire and post-fire forest management.

Extrapolating Forest Canopy Fuel Properties in the California Rim Fire by Combining Airborne LiDAR and Landsat OLI Data

Accurate, spatially explicit information about forest canopy fuel properties is essential for ecosystem management strategies for reducing the severity of forest fires. Airborne LiDAR technology has demonstrated its ability to accurately map canopy fuels. However, its geographical and temporal coverage is limited, thus making it difficult to characterize fuel properties over large regions before catastrophic events occur. This study presents a two-step methodology for integrating post-fire airborne LiDAR and pre-fire Landsat OLI (Operational Land Imager) data to estimate important pre-fire canopy fuel properties for crown fire spread, namely canopy fuel load (CFL), canopy cover (CC), and canopy bulk density (CBD). This study focused on a fire prone area affected by the large 2013 Rim fire in the Sierra Nevada Mountains, California, USA. First, LiDAR data was used to estimate CFL, CC, and CBD across an unburned 2 km buffer with similar structural characteristics to the burned area. Second, the LiDAR-based canopy fuel properties were extrapolated over the whole area using Landsat OLI data, which yielded an R2 of 0.8, 0.79, and 0.64 and RMSE of 3.76 Mg·ha−1, 0.09, and 0.02 kg·m−3 for CFL, CC, and CBD, respectively. The uncertainty of the estimates was estimated for each pixel using a bootstrapping approach, and the 95% confidence intervals are reported. The proposed methodology provides a detailed spatial estimation of forest canopy fuel properties along with their uncertainty that can be readily integrated into fire behavior and fire effects models. The methodology could be also integrated into the LANDFIRE program to improve the information on canopy fuels.

Influence of Fuel Load Dynamics on Carbon Emission by Wildfires in the Clay Belt Boreal Landscape

Old-growth forests play a decisive role in preserving biodiversity and ecological functions. In an environment frequently disturbed by fire, the importance of old-growth forests as both a carbon stock as well as a source of emissions when burnt is not fully understood. Here, we report on carbon accumulation with time since the last fire (TSF) in the dominant forest types of the Clay Belt region in eastern North America. To do so, we performed a fuel inventory (tree biomass, herbs and shrubs, dead woody debris, and duff loads) along four chronosequences. Carbon emissions by fire through successional stages were simulated using the Canadian Fire Effects Model. Our results show that fuel accumulates with TSF, especially in coniferous forests. Potential carbon emissions were on average 11.9 t·ha−1 and 29.5 t·ha−1 for old-growth and young forests, respectively. In conclusion, maintaining old-growth forests in the Clay Belt landscape not only ensures a sustainable management of the boreal forest, but it also optimizes the carbon storage.

Evaluation of FOFEM Fuel Loads and Consumption Estimates in Pine-Oak Forests and Woodlands of the Ouachita Mountains in Arkansas, USA

Accurate fuel load and consumption predictions are important to estimate fire effects and air pollutant emissions. The FOFEM (First Order Fire Effects Model) is a commonly used model developed in the western United States to estimate fire effects such as fuel consumption, soil heating, air pollutant emissions, and tree mortality. However, the accuracy of the model in the eastern United States has not been well tested. As a result, managers are turning to locally collected data sets from eastern forests to improve the accuracy of FOFEM and other models. FOFEM lacks local fuel load and consumption data for the Ouachita Mountains, an area with nearly 50,000 ha prescribe burned annually. In this study, we compared fuel loads and consumption using field-collected data with data predicted by FOFEM. We determined fuel loads before and after 15 prescribed fires by sampling live fuels, down woody debris, litter, and duff in three cover types (oak forest, pine-oak forest, and pine woodland) on the Ouachita National Forest in Arkansas. Default estimates of litter and duff fuel load in FOFEM were up to 346 and 1,307% greater than field estimates, respectively. FOFEM estimates of 10-hour, 1-hour, and litter fuel consumption were up to 182, 150, and 46% greater, respectively, than field-measured consumption. These overestimations of fuel load and consumption could result in overpredictions of air pollutant emissions and reduce the area of habitat restored and maintained by prescribed burning as fire managers seek to comply with air quality standards.

Bark Thickness Equations for Mixed-Conifer Forest Type in Klamath and Sierra Nevada Mountains of California

We studied bark thickness in the mixed-conifer forest type throughout California. Sampling included eight conifer species and covered latitude and elevation gradients. The thickness of tree bark at 1.37 m correlated with diameter at breast height (DBH) and varied among species. Trees exhibiting more rapid growth had slightly thinner bark for a given DBH. Variability in bark thickness obscured differences between sample locations. Model predictions for 50 cm DBH trees of each species indicated that bark thickness was ranked Calocedrus decurrens &gt; Pinus jeffreyi &gt; Pinus lambertiana &gt; Abies concolor &gt; Pseudotsuga menziesii &gt; Abies magnifica &gt; Pinus monticola &gt; Pinus contorta. We failed to find reasonable agreement between our bark thickness data and existing bark thickness regressions used in models predicting fire-induced mortality in the mixed-conifer forest type in California. The fire effects software systems generally underpredicted bark thickness for most species, which could lead to an overprediction in fire-caused tree mortality in California. A model for conifers in Oregon predicted that bark was 49% thinner in Abies concolor and 37% thicker in Pseudotsuga menziesii than our samples from across California, suggesting that more data are needed to validate and refine bark thickness equations within existing fire effects models.

A Case Study Comparison of LANDFIRE Fuel Loading and Emissions Generation on a Mixed Conifer Forest in Northern Idaho, USA

AbstractThe use of fire as a land management tool is well recognized for its ecological benefits in many natural systems. To continue to use fire while complying with air quality regulations, land managers are often tasked with modeling emissions from fire during the planning process. To populate such models, the Landscape Fire and Resource Management Planning Tools (LANDFIRE) program has developed raster layers representing vegetation and fuels throughout the United States; however, there are limited studies available comparing LANDFIRE spatially distributed fuel loading data with measured fuel loading data. This study helps address that knowledge gap by evaluating two LANDFIRE fuel loading raster options—Fuels Characteristic Classification System (LANDFIRE-FCCS) and Fuel Loading Model (LANDFIRE-FLM) layers—with measured fuel loadings for a 20 000 ha mixed conifer study area in northern Idaho, USA. Fuel loadings are compared, and then placed into two emissions models—the First Order Fire Effects Model (FOFEM) and Consume—for a subsequent comparison of consumption and emissions results. The LANDFIRE-FCCS layer showed 200%* higher duff loadings relative to measured loadings. These led to 23% higher total mean total fuel consumption and emissions when modeled in FOFEM. The LANDFIRE-FLM layer showed lower loadings for total surface fuels relative to measured data, especially in the case of coarse woody debris, which in turn led to 51% lower mean total consumption and emissions when modeled in FOFEM. When the comparison was repeated using Consume model outputs, LANDFIRE-FLM consumption was 59% lower relative to that on the measured plots, with 58% lower modeled emissions. Although both LANDFIRE and measured fuel loadings fell within the ranges observed by other researchers in US mixed conifer ecosystems, variation within the fuel loadings for all sources was high, and the differences in fuel loadings led to significant differences in consumption and emissions depending upon the data and model chosen. The results of this case study are consistent with those of other researchers, and indicate that supplementing LANDFIRE-represented data with locally measured data, especially for duff and coarse woody debris, will produce more accurate emissions results relative to using unaltered LANDFIRE-FCCS or LANDFIRE-FLM fuel loadings. Accurate emissions models will aid in representing emissions and complying with air quality regulations, thus ensuring the continued use of fire in wildland management.