Fire Behavior Prediction System Research Articles

A hierarchical, multi‐sensor framework for peatland sub‐class and vegetation mapping throughout the Canadian boreal forest

AbstractPeatlands in the Canadian boreal forest are being negatively impacted by anthropogenic climate change, the effects of which are expected to worsen. Peatland types and sub‐classes vary in their ecohydrological characteristics and are expected to have different responses to climate change. Large‐scale modelling frameworks such as the Canadian Model for Peatlands, the Canadian Fire Behaviour Prediction System and the Canadian Land Data Assimilation System require peatland maps including information on sub‐types and vegetation as critical inputs. Additionally, peatland class and vegetation height are critical variables for wildlife habitat management and are related to the carbon cycle and wildfire fuel loading. This research aimed to create a map of peatland sub‐classes (bog, poor fen, rich fen permafrost peat complex) for the Canadian boreal forest and create an inventory of peatland vegetation height characteristics using ICESat‐2. A three‐stage hierarchical classification framework was developed to map peatland sub‐classes within the Canadian boreal forest circa 2020. Training and validation data consisted of peatland locations derived from various sources (field data, aerial photo interpretation, measurements documented in literature). A combination of multispectral data, L‐band SAR backscatter and C‐Band interferometric SAR coherence, forest structure and ancillary variables was used as model predictors. Ancillary data were used to mask agricultural areas and urban regions and account for regions that may exhibit permafrost. In the first stage of the classification, wetlands, uplands and water were classified with 86.5% accuracy. In the second stage, within the wetland areas only, peatland and mineral wetlands were differentiated with 93.3% accuracy. In the third stage, constrained to only the peatland areas, bogs, rich fens, poor fens and permafrost peat complexes were classified with 71.5% accuracy. Then, ICESat‐2 ATL08 spaceborne lidar data were used to describe regional variations in peatland vegetation height characteristics and regional and class‐wise variations based on a boreal forest wide sample. This research introduced a comprehensive large‐scale peatland sub‐class mapping framework for the Canadian boreal forest, presenting the first moderate resolution map of its kind.

Fuel types misrepresent forest structure and composition in interior British Columbia: a way forward

BackgroundA clear understanding of the connectivity, structure, and composition of wildland fuels is essential for effective wildfire management. However, fuel typing and mapping are challenging owing to a broad diversity of fuel conditions and their spatial and temporal heterogeneity. In Canada, fuel types and potential fire behavior are characterized using the Fire Behavior Prediction (FBP) System, which uses an association approach to categorize vegetation into 16 fuel types based on stand structure and composition. In British Columbia (BC), provincial and national FBP System fuel type maps are derived from remotely sensed forest inventory data and are widely used for wildfire operations, fuel management, and scientific research. Despite their widespread usage, the accuracy and applicability of these fuel type maps have not been formally assessed. To address this knowledge gap, we quantified the agreement between on-site assessments and provincial and national fuel type maps in interior BC.ResultsWe consistently found poor correspondence between field assessment data and both provincial and national fuel types. Mismatches were particularly frequent for (i) dry interior ecosystems, (ii) mixedwood and deciduous fuel types, and (iii) post-harvesting conditions. For 58% of field plots, there was no suitable match to the extant fuel structure and composition. Mismatches were driven by the accuracy and availability of forest inventory data and low applicability of the Canadian FBP System to interior BC fuels.ConclusionsThe fuel typing mismatches we identified can limit scientific research, but also challenge wildfire operations and fuel management decisions. Improving fuel typing accuracy will require a significant effort in fuel inventory data and system upgrades to adequately represent the diversity of extant fuels. To more effectively link conditions to expected fire behavior outcomes, we recommend a fuel classification approach and emphasis on observed fuels and measured fire behavior data for the systems we seek to represent.

Improved logistic models of crown fire probability in Canadian conifer forests

Background Crown fires are an ecologically necessary but hazardous process in conifer forests. Prediction of their behaviour in Canada has largely depended on the Canadian Forest Fire Behaviour Prediction System, in which fire weather indices drive primarily fixed fuel type models. The Crown Fire Initiation and Spread (CFIS) system presents a more flexible approach to predicting crown fire occurrence than fixed fuel type models. Aims Using a multi-decadal database of experimental fires carried out in conifer plots (1960–2019, n = 113), our aim was to develop updated models based on the CFIS system approach, fitting crown fire occurrence models to fire environment variables using logistic regression. Methods We tested alternative fuel moisture estimates and compared various model forms using repeated cross-validation. In two-storeyed stands, crown fire occurrence was defined as the involvement of lower canopy stratum fuels. Key results Final models based on wind speed, fuel strata gap, litter moisture and surface fuel consumption predicted crowning events correctly in up to 92% of cases in training data (89% in cross-validation). Conclusions and implications These new models offer improved accuracy and flexibility that will help users assess how competing environmental factors interact under different fuel treatments and wildfire scenarios.

Cell2Fire: A Cell-Based Forest Fire Growth Model to Support Strategic Landscape Management Planning

Cell2Fire is a new cell-based wildland fire growth simulator designed to integrate data-driven landscape management planning models. The fire environment is modeled by partitioning the landscape into cells characterized by fuel, weather, moisture content, and topographic attributes. The model can use existing fire spread models such as the Canadian Forest Fire Behavior Prediction System to model fire growth. Cell2Fire is structured to facilitate its use for predicting the growth of individual fires or by embedding it in landscape management simulation models. Decision-making models such as fuel treatment/harvesting plans can be easily integrated and evaluated. It incorporates a series of out-of-the-box planning heuristics that provide benchmarks for comparison. We illustrate their use by applying and evaluating a series of harvesting plans for forest landscapes in Canada. We validated Cell2Fire by using it to predict the growth of both real and hypothetical fires, comparing our predictions with the fire scars produced by a validated fire growth simulator (Prometheus). Cell2Fire is implemented as an open-source project that exploits parallelism to efficiently support the modeling of fire growth across large spatial and temporal scales. Our experiments indicate that Cell2Fire is able to efficiently simulate wildfires (up to 30x faster) under different conditions with similar accuracy as state-of-the-art simulators (above 90% of accuracy). We demonstrate its effectiveness as part of a harvest planning optimization framework, identifying relevant metrics to capture and actions to mitigate the impact of wildfire uncertainty.

Georeferencing Oblique Aerial Wildfire Photographs: An Untapped Source of Fire Behaviour Data

In this study, we investigate a novel application of the photogrammetric monoplotting technique for assessing wildfires. We demonstrate the use of the software program WSL Monoplotting Tool (MPT) to georeference operational oblique aerial wildfire photographs taken during airtanker response in the early stages of fire growth. We located the position of the fire front in georeferenced pairs of photos from five fires taken 31–118 min apart, and calculated the head fire spread distance and head fire rate of spread (HROS). Our example photos were taken 0.7 to 4.7 km from fire fronts, with camera angles of incidence from −19° to −50° to image centre. Using high quality images with detailed landscape features, it is possible to identify fire front positions with high precision; in our example data, the mean 3D error was 0.533 m and the maximum 3D error for individual fire runs was less than 3 m. This resulted in a maximum HROS error due to monoplotting of only ~0.5%. We then compared HROS estimates with predictions from the Canadian Fire Behavior Prediction System, with differences mainly attributed to model error or uncertainty in weather and fuel inputs. This method can be used to obtain observations to validate fire spread models or create new empirical relationships where databases of such wildfire photos exist. Our initial work suggests that monophotogrammetry can provide reproducible estimates of fire front position, spread distance and rate of spread with high accuracy, and could potentially be used to characterize other fire features such as flame and smoke plume dimensions and spotting.

Multispectral LiDAR-Based Estimation of Surface Fuel Load in a Dense Coniferous Forest

Surface fuel load (SFL) constitutes one of the most significant fuel components and is used as an input variable in most fire behavior prediction systems. The aim of the present study was to investigate the potential of discrete-return multispectral Light Detection and Ranging (LiDAR) data to reliably predict SFL in a coniferous forest characterized by dense overstory and complex terrain. In particular, a linear regression analysis workflow was employed with the separate and combined use of LiDAR-derived structural and pulse intensity information for the load estimation of the total surface fuels and individual surface fuel types. Following a leave-one-out cross-validation (LOOCV) approach, the models developed from the different sets of predictor variables were compared in terms of their estimation accuracy. LOOCV indicated that the predictive models produced by the combined use of structural and intensity metrics significantly outperformed the models constructed with the individual sets of metrics, exhibiting an explained variance (R2) between 0.59 and 0.71 (relative Root Mean Square Error (RMSE) 19.3–37.6%). Overall, the results of this research showcase that both structural and intensity variables provided by multispectral LiDAR data are significant for surface fuel load estimation and can successfully contribute to effective pre-fire management, including fire risk assessment and behavior prediction in case of a fire event.

LiDAR-Based Estimates of Canopy Base Height for a Dense Uneven-Aged Structured Forest

Accurate canopy base height (CBH) information is essential for forest and fire managers since it constitutes a key indicator of seedling growth, wood quality and forest health as well as a necessary input in fire behavior prediction systems such as FARSITE, FlamMap and BEHAVE. The present study focused on the potential of airborne LiDAR data analysis to estimate plot-level CBH in a dense uneven-aged structured forest on complex terrain. A comparative study of two widely employed methods was performed, namely the voxel-based approach and regression analysis, which revealed a clear outperformance of the latter. More specifically, the voxel-based CBH estimates were found to lack correlation with the reference data ( R 2 = 0.15 , r R M S E = 42.36 % ) while most CBH values were overestimated resulting in an r b i a s of − 17.52 % . On the contrary, cross-validation of the developed regression model showcased an R 2 , r R M S E and r b i a s of 0 . 61 , 18.19 % and − 0.09 % respectively. Overall analysis of the results proved the voxel-based approach incapable of accurately estimating plot-level CBH due to vegetation and topographic heterogeneity of the forest environment, which however didn’t affect the regression analysis performance.

Прогнозирование поведения пожаров растительности

The necessity for predicting the behavior of vegetation fires, including forest fires, is keenly felt in a time of severe droughts, which periodically recur in this or that area, and their precise prediction is still hampered. It is unfeasible to maintain sufficient forces and means in each region for suppressing all emerging fires. Merely the increase of technical power won’t solve the problem, as evidenced by the experience of developed countries, where much attention, along with fire danger rating, has long been given to the development of a fire behavior prediction system. Such system in Russia isn’t available yet, and the use of international practices seems to be impossible, since it is complicated by several factors and, above all, different historically developed approaches to the pyrological classification of vegetation and its inventory. Currently, there are all opportunities for creating the Russian system for vegetation fire behavior prediction (including forest fires): fundamental pyrological developments based on the research results of the nature of fires; a fire monitoring system has been created and is being developed; and fire danger (both natural and due to the weather conditions) rating is being improved. The article presents a principle diagram of the vegetation fire behavior prediction and considers its main components. A practical model was chosen for prediction the burning spread rate. The necessary data base for the model is available in the GIS system. Software for creation vegetation fuel (VF) maps and prediction the behavior of surface forest fires, which are up to 97 % of all occurring fires has been developed, retrospectively verified and registered. Examples of the VF maps for the Chunskoye Forest District (Krasnoyarsk Krai) for different periods of the fire season are given. They are created based on the use of forest management information and a type identifier of primary fire carriers (i.e. the first VF group), which is directly shown in the maps. Information on the other groups of VF supporting, delaying burning or not participating in the process of burning spread, is attached to the map in the form of a pyrological description. A list of the data included in the pyrological description is given, as well as the reasons, which hold back on practical application of pyrological developments available in Russia for predicting the behavior of vegetation fires into the forest fire protection service. For citation: Volokitina A.V., Sofronova T.M., Korets M.A. Vegetation Fire Behavior Prediction. Lesnoy Zhurnal [Russian Forestry Journal], 2020, no. 1, pp. 9–25. DOI: 10.37482/0536-1036-2020-1-9-25

Moisture Sorption Models for Fuel Beds of Standing Dead Grass in Alaska

Sorption models were developed to predict the moisture content in fuelbeds of standing dead grass from ambient weather measurements. Intuition suggests that the response time of standing dead grass to diurnal changes in weather is negligible and that moisture content tracks the equilibrium moisture content under most field conditions. This assumption suggests that moisture content could be modelled by empirically fitting coefficients to equations of equilibrium moisture content using field measurements. Here, six equations commonly used in wildland fire management and other industries were fit using 293 measurements of weather and moisture content in standing dead grass from Alaska, U.S.A. Predictors were air temperature and either relative humidity or dewpoint depression. Mean absolute errors of the best three models were approximately 1.16% of moisture content. The models predicted well the moisture content of an independently collected dataset from Canada but less so a set from Australia. The models may be used in wildland fire danger rating and fire behavior prediction systems.

Potential climate change impacts on fire intensity and key wildfire suppression thresholds in Canada

Much research has been carried out on the potential impacts of climate change on forest fire activity in the boreal forest. Indeed, there is a general consensus that, while change will vary regionally across the vast extent of the boreal, in general the fire environment will become more conducive to fire. Land management agencies must consider ways to adapt to these new conditions. This paper examines the impact of that changed fire environment on overall wildfire suppression capability. We use multiple General Circulation Models and carbon emission pathways to generate future fire environment scenarios for Canada’s forested region. We then use these scenarios with the Canadian Forest Fire Behaviour Prediction System and spatial coverages of the current forest fuel composition across the landscape to examine potential variation in key fire behaviour outputs that influence whether fire management resources can effectively suppress fire. Specifically, we evaluate how the potential for crown fire occurrence and active growth of fires changes with the changing climate. We also examine future fire behaviour through the lens of operational fire intensity thresholds used to guide decisions about resources effectiveness. Results indicate that the proportion of days in fire seasons with the potential for unmanageable fire will increase across Canada’s forest, more than doubling in some regions in northern and eastern boreal forest.

Cffdrs: an R package for the Canadian Forest Fire Danger Rating System

IntroductionThe Canadian Forest Fire Danger Rating System (CFFDRS) is a globally known wildland fire risk assessment system, and two major components, the fire weather index system and the fire behavior prediction system, have been extensively used both nationally and internationally to aid operational wildland fire decision making.MethodsIn this paper, we present an overview of an R package cffdrs, which is developed to calculate components of the CFFDRS, and highlight some of its functionality. In particular, we demonstrate how these functions could be used for large data analysis.Results and DiscussionWith this cffdrs package, we provide a portal for not only a collection of R functions dealing with all available components in CFFDRS but also a platform for various additional developments that are useful for the understanding of fire occurrence and behavior. This is the first time that all relevant CFFDRS methods are incorporated into the same platform, which can be accessed by both the management and research communities.

Burning Potential of Fire Refuges in the Boreal Mixedwood Forest

In boreal ecosystems, wildfire severity (i.e., the extent of fire-related tree mortality) is affected by environmental conditions and fire intensity. A burned area usually includes tree patches that partially or entirely escaped fire. There are two types of post-fire residual patches: (1) patches that only escaped the last fire; and (2) patches with lower fire susceptibility, also called fire refuges, that escaped several consecutive fires, likely due to particular site characteristics. The main objective of this study was to test if particular environmental conditions and stand characteristics could explain the presence of fire refuges in the mixedwood boreal forest. The FlamMap3 fire behavior model running at the landscape scale was used on the present-day Lake Duparquet forest mosaic and on four other experimental scenarios. FlamMap3 was first calibrated using BehavePlus and realistic rates of fire spread obtained from the Canadian Fire Behavior Prediction system. The results, based on thousands of runs, exclude the effects of firebreaks, topography, fuel type, and microtopography to explain the presence of fire refuges, but rather highlight the important role of moisture conditions in the fuel beds. Moist conditions are likely attributed to former small depressions having been filled with organic matter rather than present-day variations in ground surface topography.

Estimating the heat transfer to an organic soil surface during crown fire

The Peatland Smouldering and Ignition (PSI) model was developed to quantify the heat transfer from a wildfire to an organic soil or moss surface in a Sphagnum–black spruce peatland. The Canadian Fire Behaviour Prediction system was used as a basis for the relationship between wind speed and rate of spread. Convection, conduction, and radiation processes were modelled before and during the arrival of the flaming front. The net heat flux to the soil from fire varied between 1.1 and 8.6 MJ m–2, with moderate-intensity fires transferring more energy to the surface compared with higher-intensity fires under higher winds. Radiative heat transfer to the soil surface both before the fire’s arrival and within the flaming front were the primary mechanisms of energy gain to the peatland surface. The role of convective and conductive cooling was no greater than 30% of gross energy gain. Peatland surface ignition in hummock and hollow microforms was modelled under normal and drought conditions. Hollow microforms dried out significantly during the course of a summer and increased their ignition vulnerability. Small-scale changes in slope and aspect of the peatland surface increased the amount of heat transferred by radiation by up to 30%, allowing some areas of higher soil moisture content to ignite. While no direct model validation is available, model outputs showing the preferential combustion of lichen and feathermoss, and the lack of ignition in Sphagnum in all but the most severe drought generally mimic observed ignitions patterns. The modelled peak of net energy input to the surface occurred at moderate wind speeds, suggesting that high-intensity fires do not necessarily lead to greater energy transfer and risk of smouldering combustion.

LANDFIRE – A national vegetation/fuels data base for use in fuels treatment, restoration, and suppression planning

LANDFIRE is the working name given to the Landscape Fire and Resource Management Planning Tools Project (http://www.landfire.gov). The project was initiated in response to mega-fires and the need for managers to have consistent, wall-to-wall (i.e., all wildlands regardless of agency/ownership), geospatial data, on vegetation, fuels, and terrain to support use of fire behavior and effects prediction systems in guiding policy and management decisions. Base layers were created in a 5-year program of research and development ending in 2009, with processes in place to periodically update fuel and vegetation layers in response to anthropogenic and natural disturbances. LANDFIRE has been institutionalized as the primary data source for modeling activities aimed at meeting the goals of the United States’ National Cohesive Wildland Fire Management Strategy, and the data are available on-line to any user for conducting landscape analyses. Data access and use are high and expected to grow with the increasing scope and complexity of wildland fire management, thus requiring continued LANDFIRE improvements and updates.

Development of Customized Fire Behavior Fuel Models for Boreal Forests of Northeastern China

Knowledge of forest fuels and their potential fire behavior across a landscape is essential in fire management. Four customized fire behavior fuel models that differed significantly in fuels characteristics and environmental conditions were identified using hierarchical cluster analysis based on fuels data collected across a boreal forest landscape in northeastern China. Fuel model I represented the dense and heavily branched Pinus pumila shrubland which has significant fine live woody fuels. These forests occur mainly at higher mountain elevations. Fuel model II is applicable to forests dominated by Betula platyphylla and Populus davidiana occurring in native forests on hill slopes or at low mountain elevations. This fuel model was differentiated from other fuel models by higher herbaceous cover and lower fine live woody loading. The primary coniferous forests dominated by Larix gmelini and Pinus sylvestris L. var. mongolica were classified as fuel model III and fuel model IV. Those fuel models differed from one another in average cover and height of understory shrub and herbaceous layers as well as in aspect. The potential fire behavior for each fuel model was simulated with the BehavePlus5.0 fire behavior prediction system. The simulation results indicated that the Pinus pumila shrubland fuels had the most severe fire behavior for the 97th percentile weather condition, and had the least severe fire behavior under 90th percentile weather condition. Fuel model II presented the least severe fire potential across weather conditions. Fuel model IV resulted in greater fire severity than Fuel model III across the two weather scenarios that were examined.

Emissions of air pollutants by Canadian wildfires from 2000 to 2004

A wildfire emission model, based on the Canadian Forest Fire Behaviour Prediction System and the Canadian weather forecast Global Environmental Multiscale model, was applied to forest fires that occurred in Canada between 2000 and 2004. Emissions of 21 chemical species and injection heights were calculated hourly for a regular 0.4° grid, with injection heights corresponding to the maximum altitude reached by a convective plume over a fire every hour. Wildfire emissions were compared with anthropogenic fossil fuel combustion sources at provincial, territorial and national levels. The 2002 fire season in central Quebec accounted for ~30, 60 and 80% of the annual primary greenhouse gases, carbon monoxide and black carbon emissions respectively for that province. In 2003, fires represented 60 and 20% of greenhouse gas emissions in Manitoba and British Columbia respectively. During the 2004 fire season in north-western Canada, when area burned was above average, fires were responsible for almost all greenhouse gas emissions occurring in the sparsely populated Yukon Territory and Northwest Territories. On average, between 2000 and 2004, fires contributed 10, 30 and 40% of Canadian annual greenhouse gases, CO and black carbon emissions respectively. This methodology for calculating wildland fire emissions is also applicable to other regions of the world.

Defining fire spread event days for fire-growth modelling

Forest fire managers have long understood that most of a fire’s growth typically occurs on a small number of days when burning conditions are conducive for spread. Fires either grow very slowly at low intensity or burn considerable area in a ‘run’. A simple classification of days into ‘spread events’ and ‘non-spread events’ can greatly improve estimates of area burned. Studies with fire-growth models suggest that the Canadian Forest Fire Behaviour Prediction System (FBP System) seems to predict growth well during high-intensity ‘spread events’ but tends to overpredict rate of spread for non-spread events. In this study, we provide an objective weather-based definition of ‘spread events’, making it possible to assess the probability of having a spread event on any particular day. We demonstrate the benefit of incorporating this ‘spread event’ day concept into a fire-growth model based on the Canadian FBP System.

Surface fire spread potential in trembling aspen during summer in the Boreal Forest Region of Canada

In Canada, the importance of seasonality in forest fire danger rating associated with phenological changes in deciduous tree leaves and lesser ground vegetation has historically been taken into account by dividing the fire season into three distinct periods (i.e., spring, summer, and fall). During the mid-1980s, the developers of the Canadian Forest Fire Behavior Prediction (FBP) System did not envision that the M-2 Boreal Mixedwood – Green fuel type with 100% hardwood composition would eventually be explicitly interpreted by field users and other researchers to represent a trembling aspen (Populus tremuloides Michx.) fuel type in the summer following green-up or flushing of the overstory canopy and understory vegetation. Interest in what has become to be known as the D-2 FBP System fuel type to represent leafed-out trembling aspen stands during the summer fire season has steadily increased since. Formal recognition of such a fuel type may very well constitute an example of overextending the original basis and heuristics associated with the rate of fire spread model for the M-2 FBP System fuel type. Thus, the assumptions underlying a D-2 fuel type are explicitly restated here for the benefit of fire managers and researchers alike. Furthermore, an interim guideline is presented with respect to the threshold condition in fuel dryness necessary for surface fire spread in the D-2 fuel type to occur based on existing empirical observations garnered from experimental fires, prescribed burns and wildfires. This criterion was deduced from existing information and knowledge, and is expressed in terms of the Buildup Index (BUI) component of the Canadian Forest Fire Weather Index System. The rationale for the descriptive name assigned to the D-2 fuel type and the corresponding fuel strata characteristics are given. Improvements in the present basis of the D-2 fuel type could be realized from monitoring selected wildfires and operational prescribed fires and/or by carrying out an experimental burning study. Key words: Canadian Forest Fire Behavior Prediction System,Canadian Forest Fire Danger Rating System,Canadian Forest Fire Weather Index System,deciduous,fire behavior, fire danger, fire environment, fire hazard, fire potential, fire risk, forest flammability, fuel type, fuel moisture, green-up, hardwood, rate of fire spread.

The validity and utility of MODIS data for simple estimation of area burned and aerosols emitted by wildfire events

Wildfire emissions are challenging to measure and model, but simple and realistic estimates can benefit multiple disciplines. We evaluate the potential of MODIS (Moderate Resolution Imaging Spectroradiometer) data to address this objective. A total of 11 004 fire pixels detected over 92 days were clustered into 242 discrete fire events in a mountainous region of North America. Burned areas were estimated with spatial buffers around the MODIS detections, and all events were matched and compared with administrative fire records based on their location and duration. Linear regression between recorded and estimated burned areas showed excellent agreement (slope = 0.93 and R2 = 0.96). Aerosol emission rates were estimated for each MODIS detection using its fire radiative power measurement. Results were compared with estimates from the Canadian Fire Behaviour (CANFB) prediction system in Canada and the US Emissions Production Model (USEPM) for detections in the US. Median emission rates were similar for the MODIS and CANFB methods (600 and 579 g s–1 respectively) but not for the MODIS and USEPM methods (575 and 382 g s–1 respectively). The MODIS rates were much more variable in both comparisons. Linear regression on emission rates summed daily across the study area shows that the MODIS method is more consistent with CANFB (slope = 0.71, R2 = 0.71) than with USEPM (slope = 0.24, R2 = 0.68). We conclude that simple calculations based on remote sensing data can yield results that are comparable with those obtained with more complex methods.

Modelling emissions from Canadian wildfires: a case study of the 2002 Quebec fires

The present paper proposes an original approach to estimate gaseous and particulate emissions from boreal forest fires based on the Canadian Forest Fire Behaviour Prediction (FBP) System. The FBP System permits calculation of fuel consumption and rate of spread for individual fires on an hourly basis from meteorological conditions and fuel patterns. Weather data are obtained by running the Canadian weather forecast model GEM (Global Environmental Multiscale). Hourly emission point sources can then be generated from a given wildfire database. The smoke emission model was first applied to the boreal forest fires in Quebec in the summer of 2002. Geographical distribution and temporal variability of emission amounts, as well as injection heights, were assessed hourly. In July, ~150 wildfires released 39 Mt of CO2 equivalent of greenhouse gases and 470 kt of fine particulate matter to the atmosphere. They contributed 32 and 5% of Quebec’s and Canada’s annual greenhouse gas emissions, respectively. Black carbon was estimated to account for 4% of the total fine particulate matter. Wildfires were responsible for 51 and 90% of all Canada’s black carbon and particulate organic matter sources, respectively.