Energy exchange in the fire environment is highly influenced by the heterogeneous and dynamic coupling between the atmosphere, fire, and fuels. While it has long been known that radiation and convection are the dominant heat transfer mechanisms in wildfire spread, much is unknown about their relative contribution. This is largely due to significant challenges observing these processes in the extreme wildfire environment. We describe a low cost, easy to deploy, simple heat transfer sensor and associated model, which was developed to measure heat transfer in wildfire. The sensor consists of stainless steel thermocouple probes differing in emissivity and similar in geometry and size to fine fuel elements responsible for carrying fire. Field and laboratory tests indicate that stainless steel probes with 3.2 mm and 1.6 mm diameters alone are not able to resolve the highly-dynamic heat transfer ahead of the fire. However, coupling the probes with a fine-wire thermocouple significantly improves its sensitivity. Results presented here indicate that the sensor and model are capable of measuring physically realistic convective and radiative heat transfer in high-intensity crown fire and simple laboratory scenarios when compared to observations in literature. Some limitations are identified for future investigation and additional testing and validation are required.

In Canada, early fire detection is an important component of wildfire management, and it utilizes a combined effort approach including public reports, aviation patrols, and satellite observations. The role of ground-based continuous smoke sensors has not been formally assessed in Canadian wildfire management detection systems. Dense networks of ground-based, internet-enabled continuous smoke sensors were deployed at three locations across southern Canada during 2023 and 2024, in concert with planned prescribed fire in grass fuels as well as incidental wildfire ignitions. Smoke sensor detection of fires was compared to polar orbiting and geostationary fire detection. Large fire events (50-600 ha) with a ground smoke detector distance of 1-2 km were observed on most occasions (n = 7), but the detection rate dropped to 30% for fires 1 ha or smaller. Follow-up smoke monitoring after the initial detection offered valuable information on smoke production and dispersion across multiple sensors. This typically nighttime smoldering smoke production fell below the threshold for geostationary satellite fire observation and is otherwise only captured sparingly by polar orbiting satellites. Thus, ground-based smoke detection systems likely fit an important niche for monitoring low-energy (i.e., smoldering) smoke events from fully contained fires or to monitor fires considered recently extinguished.

Among the many natural disturbances that affect Canada's boreal and temperate forest biomes, wildfire has the greatest impact on forest productivity, landscape structure, timber supply and greenhouse gas emissions. Fire severity represents the fate of biomass, by partitioning survival, consumption, and mortality without consumption. Currently, the impact of fire on carbon stocks in Canada's forest carbon accounting is limited to a single parametrization of fire severity that assumes only high severity fires, despite evidence of widespread mixed-severity fires. This paper describes a sub-model, termed FireDMs (Fire Disturbance Matrix: severity), of the National Forest Carbon Monitoring Accounting and Reporting System for Canada (NFCMARS). In this sub-model, field measurements of biomass consumption are related to satellite-derived burn severity maps and are interpreted from a fire physics and ecology perspective to derive estimates of the forest greenhouse gas emissions in the immediate aftermath of fires. The sub-model also quantifies fire-killed but uncombusted biomass as a set of distinct pools. Model outputs indicate total direct carbon emissions range from a 11 t C ha-1 in Boreal Shield West forests of Saskatchewan following low severity fire to over 60 t C ha-1 in Pacific Maritime forests of British Columbia under high severity fire. The existing approach to emissions in NFCMARS yields regional CO2-equivalent emissions that are typically 10 % to 25 % higher than this new method, owing to lower overall canopy consumption with mixed-severity fires, which is only partially offset by increased estimates of forest floor consumption in this new approach. Comparisons against directly observed fire plume emissions ratios as well as against annualized carbon emissions for Canada's 2023 fire season show good model agreement with observations.

Wildfire management entities (FME) across the globe operate under diverse situations, all while facing escalating challenges from climate change. Earth observation (EO) satellites offer unique value for active fire monitoring, however, the extent to which EO-derived active fire data (EO-AFD) is adopted remains unclear. To better understand adoption by FMEs, we analyzed responses from a global survey with representation from 56 countries, conducted under the Committee on Earth Observation Satellites Working Group on Disasters, Wildfire Pilot. We applied a mixed-method approach using hierarchical agglomerative clustering and inductive reasoning to identify common FME functional ‘archetypes’ and provide a notional archetype development pathway relative to operational dependence on EO-AFD. Six end-user archetypes were identified: Curious, Nascent, Pragmatic Generalist, Specialist, and Leader. While 75% of all end-users had at least some degree of trust in EO-AFD and trust was generally high across all archetypes, integration levels varied, often constrained by technical, cost, resource, and expertise related barriers. The spatial distribution of archetypes and the barriers to EO-AFD use varied by region. Considered together, these may indicate where barriers play a stronger role in shaping archetypes. The archetypes can serve as a baseline for developing EO-AFD adoption engagements.

Increasingly severe wildfires and droughts are reducing black spruce recruitment and favouring early successional species like jack pine and trembling aspen in Canada’s western boreal forests. Adjacent peatlands may mitigate these changes, depending on topographic position and soil texture, which influence groundwater connectivity. We examined tree regeneration in 58 post-fire upland forest stands (5–20 years old) across various local (adjacent peatland) and regional (relative to a regional low) topographic positions, under different post-fire drought conditions (i.e., post-fire climate moisture deficit). We hypothesized that regenerating forests at lower topographic positions, supported by primarily groundwater-fed (largely rich fen) peatlands, would be relatively buffered against post-fire drought as primarily precipitation-fed (bog and poor fen) peatlands at higher positions are more drought-sensitive. Regenerating black spruce proportions were negatively correlated with post-fire drought at regional high topographic positions, across soil textures. Post-fire stem density, tree volume, and proportions of jack pine and aspen were not correlated with post-fire drought. This study highlights that areas of Alberta’s boreal forest with large-scale hydrological connectivity may act as drought refugia for post-fire black spruce, while jack pine, and aspen are likely to remain resilient across a range of physical settings.

Climate change has exacerbated fire activity in the boreal region. Consequently, smoldering boreal peatland fires are an increasingly important source of light-absorbing atmospheric organic carbon ("brown carbon"; BrC). To date, however, BrC from this source remains largely unstudied, which limits our ability to predict its climate impact. Here, we use size-exclusion chromatography coupled with diode array UV-vis detection to examine the molecular-size-dependent light absorption properties of fresh and photoaged aqueous BrC extracts collected during laboratory combustion of boreal peat and live spruce foliage. The atmospheric stability of BrC extracts varies with chromophore molecular size and fuel type: in particular, the high-molecular-weight fractions of both peat- and spruce-BrC are more resistant to photobleaching than their corresponding low-molecular-weight fractions, and total light absorption by peat-BrC persists over longer illumination timescales than that of spruce-BrC. Importantly, the BrC molecular size distribution itself varies with fuel properties (e.g., moisture content) and to an even greater extent with fuel type. Overall, our findings suggest that the accurate estimation of BrC radiative forcing, and the overall climate impact of wildfires, will require atmospheric models to consider the impact of regional diversity in vegetation/fuel types.

This study applied the Canadian Model for Peatlands (CaMP) to 63.9 million hectares of peatlands within boreal and temperate ecozones of Canada to assess the trends in atmospheric carbon (C) emissions and removals and C sequestration over 30 years (1990–2019). The CaMP modelled net ecosystem productivity (NEP) for peatlands within the study area indicated a net C sink at an annual mean rate of 30.9 Mt C y−1 (48.4 g C m−2 y−1). Net Biome Productivity (NBP), which accounts for losses of carbon due to wildfire, reduced the C sink to 19.0 Mt C y−1 (29.8 g C m−2 y−1). On an area-weighted basis, the Hudson Plains and the Boreal Plains had the highest NBP (34.9 and 34.0 g C m−2 y−1, respectively) and the Atlantic Maritime and Boreal Shield West had the lowest (25.3 and 24.6 g C m−2 y−1 respectively), with the Boreal Shield East having intermediate NBP (27.5 g C m−2 y−1). NBP was highest in peatlands with forest cover, rising with increasing nutrient status (bog < poor fen < rich fen). These modelled values compare well with long-term carbon accumulation rates found in the literature for Canadian peatlands ranging from 6 to 70 g C m−2 y−1. While most years peatlands were a net sink of C, years with extensive fires resulted in peatlands being a small net source of C. The study highlighted that forested peatlands were important in driving the C sequestration sink but were also sensitive to climate warming due to high rates of soil CO2 emission and large wildfire C emissions. This highlights an important, yet vulnerable role these forested peatlands play in Canada's national greenhouse gas accounting. While this research is the first to produce estimates of C sequestration and greenhouse gas emission and removal rates across such a large area of Canada, further research is required across peatland types and ecozones to improve parameterization, validation, and process representations. Our results stress the importance of ecozone-specific analyses and accounting for infrequent large fire years and fire risk in land management policy and carbon accounting.

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.

The 2023 wildfire season in Canada was unprecedented in its scale and intensity, spanning from mid-April to late October and across much of the forested regions of Canada. Here, we summarize the main causes and impacts of this exceptional season. The record-breaking total area burned (similar to 15 Mha) can be attributed to several environmental factors that converged early in the season: early snowmelt, multiannual drought conditions in western Canada, and the rapid transition to drought in eastern Canada. Anthropogenic climate change enabled sustained extreme fire weather conditions, as the mean May-October temperature over Canada in 2023 was 2.2 degrees C warmer than the 1991-2020 average. The impacts were profound with more than 200 communities evacuated, millions exposed to hazardous air quality from smoke, and unmatched demands on fire-fighting resources. The 2023 wildfire season in Canada not only set new records, but highlights the increasing challenges posed by wildfires in Canada.

Recently burned boreal forests have lower aboveground fuel loads, generating a negative feedback to subsequent wildfires. Despite this feedback, short-interval reburns (≤20 years between fires) are possible under extreme weather conditions. Reburns have consequences for ecosystem recovery, leading to enduring vegetation change. In this study, we characterize the strength of the fire-fuel feedback in recently burned Canadian boreal forests and the weather conditions that overwhelm resistance to fire spread in recently burned areas. We used a dataset of daily fire spread for thousands of large boreal fires, interpolated from remotely sensed thermal anomalies to which we associated local weather from ERA5-Land for each day of a fire's duration. We classified days with >3 ha of fire growth as spread days and defined burned pixels overlapping a fire perimeter ≤20 years old as short-interval reburns. Results of a logistic regression showed that the odds of fire spread in recently burned areas were ~50% lower than in long-interval fires; however, all Canadian boreal ecozones experienced short-interval reburning (1981-2021), with over 100,000 ha reburning annually. As fire weather conditions intensify, the resistance to fire spread declines, allowing fire to spread in recently burned areas. The weather associated with short-interval fire spread days was more extreme than the conditions during long-interval spread, but overall differences were modest (e.g. relative humidity 2.6% lower). The frequency of fire weather conducive to short-interval fire spread has significantly increased in the western boreal forest due to climate warming and drying (1981-2021). Our results suggest an ongoing degradation of fire-fuel feedbacks, which is likely to continue with climatic warming and drying.

The Global Forest Fire Emissions Prediction System (GFFEPS) is a model that estimates biomass burning in near-real time for global air quality forecasting. The model uses a bottom-up approach, based on remotely sensed hotspot locations, and global databases linking burned area per hotspot to ecosystem-type classification at a 1 km resolution. Unlike other global fire emissions models, GFFEPS provides dynamic estimates of fuel consumption, fire behaviour and fire growth based on the Canadian Forest Fire Danger Rating System, plant phenology as calculated from daily global weather and burned-area estimates using near-real-time Visible Infrared Imaging Radiometer Suite (VIIRS) satellite-detected hotspots and historical burned-area statistics. Combining forecasts of daily fire weather and hourly meteorological conditions with a global land classification, GFFEPS produces fuel consumption and emission predictions in 3 h time steps (in contrast to non-dynamic models that use fixed consumption rates and require a collection of burned area to make post-burn estimates of emissions). GFFEPS has been designed for use in operational forecasting applications as well as historical simulations for which data are available. A study was conducted showing GFFEPS predictions through a 6-year period (2015-2020). Regional annual total smoke emissions, burned area and total fuel consumption per unit area as predicted by GFFEPS were generated to assess model performance over multiple years and regions. The model's fuel consumption per unit area results clearly distinguished regions dominated by grassland (Africa) from those dominated by forests (boreal regions) and showed high variability in regions affected by El Ni & ntilde;o and deforestation. GFFEPS carbon emissions and burned area were then compared to other global wildfire emissions models, including the Global Fire Assimilation System (GFAS), the Global Fire Emissions Database (GFED4.1s) and the Fire INventory from NCAR (FINN 1.5 and 2.5). GFFEPS estimated values lower than GFAS and GFED (80 % and 74 %) and had values similar to FINN 1.5 (97 %). This was largely due to the impact of fuel moisture on consumption rates as captured by the dynamic weather modelling. Model evaluation efforts to date are described - an ongoing effort is underway to further validate the model, with further developments and improvements expected in the future.

Abstract Satellite data are effective for mapping wildfires, particularly in remote locations where monitoring is rare. Geolocated fire detections can be used for enhanced fire management and fire modelling through daily fire progression mapping. Here we present the Canadian Fire Spread Dataset (CFSDS), encompassing interpolated progressions for fires >1,000 ha in Canada from 2002–2021, representing the day-of-burning and 50 environmental covariates for every pixel. Day-of-burning was calculated by ordinary kriging of active fire detections from the Moderate Resolution Imaging Spectroradiometer and the Visible Infrared Imaging Radiometer Suite, enabling a substantial improvement in coverage and resolution over existing datasets. Day of burning at each pixel was used to identify environmental conditions of burning such as daily weather, derived weather metrics, topography, and forest fuels characteristics. This dataset can be used in a broad range of research and management applications, such as retrospective analysis of fire spread, as a benchmark dataset for validating statistical or machine-learning models, and for forecasting the effects of climate change on fire activity.

Many wildfire behavior modeling studies have focused on fires during extreme conditions, where the dominant processes are resolved and smaller-scale variations have less influence on fire behavior. As such, wildfire behavior models typically perform well for these cases. However, they can struggle in marginal conditions (e.g. low-intensity fire) as small-scale variations significantly influence fire physics at scales below grid resolution. In an effort to generalize wildfire behavior models and improve their overall performance, we have developed a new set of equations for wet and dry fuel to capture the finer-scale sub-grid variations in temperature and moisture. We explore the behavior of these equations in simple scenarios ranging from high- to low-intensity fire. Furthermore, we evaluate the performance against observations of surface fire. In all cases the proposed model performs well after peak temperature is reached; however, the rise of fuel temperature at the onset of combustion is faster than expected.

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.

Canada faces an unprecedented fire management challenge due to changing ecological and social conditions. The upcoming WildFireSat mission will provide innovative fire monitoring solutions to support operational wildfire management and response. As the world's first dedicated operational wildfire monitoring system, this mission will offer strategic fire intelligence through value-added products. In this paper, we preview the development of Tier 2 products for WildFireSat. Tier 2 will utilize data from other Earth observation satellites to create multi-source fire events and observed fire behaviour products. These products will support fire management needs and contribute to broader advancements in remote sensing, including multi-sensor fusion, fire event characterization, and satellite-informed fire behaviour.

In the boreal forests of North America, large wildfires often leave residual patches of unburned vegetation, termed fire refugia, which can affect post-fire ecosystem processes. Although topographic complexity is a major driver of fire refugia in mountainous terrain, refugia in boreal plains are more likely driven by a combination of other bottom-up controls on fuel configuration as well as top-down climate controls. In this study, we investigated the role of hydrological, ecological, and topographic heterogeneity, as well as climate moisture patterns, on the presence of fire refugia in forested upland and peatland ecosystems within Alberta's subhumid boreal forests over a 33-year (1985-2018) period. Generalized linear models were used to model the probability of refugia in forested stands as a function of bottom-up (vegetation, topography, site moisture, and ecosystem) and top-down (normal and annual climate moisture deficit) controls. We then developed predictive maps of refugia probability for a range of normal and interannual climate moisture deficit values. We found that forested fens had a probability of refugia that was 64% higher than upland forests, while forested bogs did not differ from forested uplands in refugia likelihood. Climate and physical setting presented the strongest controls on fire refugia in uplands and peatlands, respectively. Increasing amounts of adjacent bogs, but not fens, produced a sixfold increase in refugia probability in uplands, while increasing amounts of adjacent bogs and fens produced roughly two times the refugia probability in forested peatlands. In these upland forest stands, fire refugia probability was negatively related to the interaction between regional climate moisture deficits and interannual deviations from these norms, thus increasing the probability of fire refugia during more severe drought conditions in areas with less arid climates, while decreasing refugia probabilities in drier climates. However, in peatlands themselves, neither regional climate moisture conditions nor the interannual deviations affected refugia. Fire size had a negative effect on fire refugia in all upland-based models and a positive effect in all peatland-based models. Our results suggest that large areas of intact peatlands may be capable of promoting fire refugia and thereby slowing climate-driven, fire-mediated vegetation transitions in surrounding forest ecosystems.

Improving the accuracy of fire behavior prediction requires better understanding of live fuel, the dominant component of tree crowns, which dictates the consumption and energy release of the crown fire flame-front. Live fuel flammability is not well represented by existing evaluation methods. High-flammability live fuel, e.g., in conifers, may maintain or increase the energy release of the advancing crown fire flame-front, while low-flammability live fuel, e.g., in boreal deciduous stands, may reduce or eventually suppress flame-front energy release. To better characterize these fuel–flame-front interactions, we propose a method for quantifying flammability as the fuel’s net effect on (contribution to) the frontal flame energy release, in which the frontal flame is simulated using a methane diffusion flame. The fuel’s energy release contribution to the methane flame was measured using oxygen consumption calorimetry as the difference in energy release between the methane flame interacting with live fuel and the methane flame alone. In-flame testing resulted in fuel ignition and consumption comparable to those in wildfires. The energy release contribution of live fuel was significantly lower than its energy content measured using standard methods, suggesting better sensitivity of the proposed metric to water content- and oxygen deficiency-associated energy release reductions within the combustion zone.

Wildland fires and anthropogenic disturbances can cause changes in vegetation species composition and structure in boreal peatlands. These could potentially alter regeneration trajectories following severe fire or through cumulative impacts of climate-mediated drying, fire, and/or anthropogenic disturbance. We used lidar-derived point cloud metrics, and site-specific locational attributes to assess trajectories of post-disturbance vegetation regeneration in boreal peatlands south of Fort McMurray, Alberta, Canada using a space-for-time-chronosequence. The objectives were to (a) develop methods to identify conifer trees vs. deciduous shrubs and trees using multi-spectral lidar data, (b) quantify the proportional coverage of shrubs and trees to determine environmental conditions driving shrub regeneration, and (c) determine the spatial variations in shrub and tree heights as an indicator of cumulative growth since the fire. The results show that the use of lidar-derived structural metrics predicted areas of deciduous shrub establishment (92% accuracy) and classification of deciduous and conifer trees (71% accuracy). Burned bogs and fens were more prone to shrub regeneration up to and including 38 years after the fire. The transition from deciduous to conifer trees occurred approximately 30 years post-fire. These results improve the understanding of environmental conditions that are sensitive to disturbance and impacts of disturbance on northern peatlands within a changing climate.

Firebrand travel and ignition of spot fires is a major concern in the Wildland-Urban Interface and in wildfire operations overall. Firebrands allow for the efficient breaching across fuel-free barriers such as roads, rivers and constructed fuel breaks. Existing observation-based knowledge on medium-distance firebrand travel is often based on single tree experiments that do not replicate the intensity and convective updraft of a continuous crown fire. Recent advances in acoustic analysis, specifically pattern detection, has enabled the quantification of the rate at which firebrands are observed in the audio recordings of in-fire cameras housed within fire-proof steel boxes that have been deployed on experimental fires. The audio pattern being detected is the sound created by a flying firebrand hitting the steel box of the camera. This technique allows for the number of firebrands per second to be quantified and can be related to the fire's location at that same time interval (using a detailed rate of spread reconstruction) in order to determine the firebrand travel distance. A proof of concept is given for an experimental crown fire that shows the viability of this technique. When related to the fire's location, key areas of medium-distance spotting are observed that correspond to regions of peak fire intensity. Trends on the number of firebrands landing per square metre as the fire approaches are readily quantified using low-cost instrumentation.