Current methods of predicting fire spread in Canadian forests are suited to large wildfires that spread through natural forests. Recently, the use of mechanical and thinning treatments of forests in the wildland-urban interface of Canada has increased. To assist in community wildfire protection planning in forests not covered by existing operational fire spread models, we use FIRETEC to simulate fire spread in lowland black spruce fuel structures, the most common tree stand in Canada. The simulated treatments included the mechanical mulching of strips, and larger, irregularly shaped areas. In all cases, the removal of fuel by mulch strips broke up the fuels, but also caused wind speed increases, so little decrease in fire spread rate was modelled. For large irregular clearings, the fire spread slowly through the mulched wood chips, and large decreases in fire spread and intensity were simulated. Furthermore, some treatments in the black spruce forest were found to be effective in decreasing the distance and/or density of firebrands. The simulations conducted can be used alongside experimental fires and documented wildfires to examine the effectiveness of differing fuel treatment options to alter multiple components of fire behavior.

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 Niño 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.

Global climate change is leading to increased wildfire activity in many parts of the world, and with increasing development, a heightened threat to communities in the wildland urban interface. Evaluating the potential for fire to affect communities and critical infrastructure is essential for effective response decision-making and resource prioritization, including evacuation planning, with changing weather conditions during the fire season. Using a receptor–pathway–source assessment framework, we estimate the potential source area from which a wildfire could spread to a community in British Columbia by projecting fire growth outward from the community’s perimeter. The outer perimeter of the source area is effectively an evacuation trigger line for the forecast period. The novel aspects of our method are inverting fire growth in both space and time by reversing the wind direction, the time course of hourly weather, and slope and aspect inputs to a time-evolving fire growth simulation model Prometheus. We also ran a forward simulation from the perimeter of a large fire that was threatening the community to the community edge and back. In addition, we conducted a series of experiments to examine the influence of varying environmental conditions and ignition patterns on the invertibility of fire growth simulations. These cases demonstrate that time-evolving fire growth simulations can be inverted for practical purposes, although caution is needed when interpreting results in areas with extensive non-fuel cover or complex community perimeters. The advantages of this method over conventional simulation from a fire source are that it can be used for pre-attack planning before fire arrival, and following fire arrival, it does not require having an up-to-the-minute map of the fire location. The advantage over the use of minimum travel time methods for inverse modeling is that it allows for changing weather during the forecast period. This procedure provides a practical tool to inform real-time wildfire response decisions around communities, including resource allocation and evacuation planning, that could be implemented with several time-evolving fire growth models.

The Global Forest Fire Emissions Prediction System (GFFEPS) is a model that estimates biomass burning in 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 forest fire emissions models, GFFEPS provides dynamic estimates of fuel consumption and fire behaviour based on the Canadian Forest Fire Danger Rating System. Combining forecasts of daily fire weather and hourly meteorological conditions with a global land classification, GFFEPS produces fuel consumption and emission predictions in 3-hour time steps (in contrast to non-dynamic models that use fixed consumption rates and require collection of burned area to make post-burn estimates of emissions). GFFEPS has been designed for use in near-real-time forecasting applications as well as historical simulations for which data are available. A study was conducted running GFFEPS through a six-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 distinguished grass-dominated regions from forested, while also showed high variability in regions affected by El Niño and deforestation. GFFEPS carbon emissions and burned area were then compared to other global wildfire emissions models, including GFAS, GFED4.1s and FINN1.5/2.5. GFFEPS estimated values lower than GFAS/GFED (80 %/74 %), and estimated values similar to FINN1.5 (97 %). This was largely due to the impact of fuel moisture on consumption rates as captured by the dynamic weather modelling. An effort is underway to validate the model, with further developments and improvements expected.

A field project was conducted to observe and measure smoke plumes from wildland fires in Alberta. This study used handheld inclinometer measurements and photos taken at lookout towers in the province. Observations of 222 plumes were collected from 21 lookout towers over a 6-year period from 2010 to 2015. Observers reported the equilibrium and maximum plume heights based on the plumes' final levelling heights and the maximum lofting heights, respectively. Observations were tabulated at the end of each year and matched to reported fires. Fire sizes at assessment times and forest fuel types were reported by the province. Fire weather conditions were obtained from the Canadian Wildland Fire Information System (CWFIS). Assessed fire sizes were adjusted to the appropriate size at plume observation time using elliptical fire-growth projections. Though a logical method to collect plume observations in principle, many unanticipated issues were uncovered as the project developed. Instrument limitations and environmental conditions presented challenges to the investigators, whereas human error and the subjectivity of observations affected data quality. Despite these problems, the data set showed that responses to fire behaviour conditions were consistent with the physical processes leading to plume rise. The Alberta smoke plume observation study data can be found on the Canadian Wildland Fire Information System datamart (Natural Resources Canada, 2018) at http://cwfis.cfs.nrcan.gc.ca/datamart.

A field project was conducted to observe and measure smoke plumes from wildland fires in Alberta. This study used hand-held inclinometer measurements and photos taken at lookout towers in the province. Observations of 222 plumes were collected from 21 lookout towers over a 6-year period from 2010 to 2015. Observers reported the equilibrium and maximum plume heights based on the plume's final levelling height and the maximum lofting height, respectively. Observations were tabulated at the end of each year and matched to reported fires. Fire sizes at assessment times and forest fuel types were reported by the province. Fire weather conditions were obtained from the Canadian Wildland Fire Information System (CWFIS). Assessed fire sizes were adjusted to the appropriate size at plume observation time using elliptical fire-growth projections. Though a logical method to collect plume observations in principle, many unanticipated issues were uncovered as the project developed. Instrument limitations and environmental conditions presented challenges to the investigators whereas human error and the subjectivity of observations affected data quality. Despite these problems, the data set showed that responses to fire behaviour conditions were consistent with the physical processes leading to plume rise. The Alberta Smoke Plume Observation Study data can be found on the BlueSky Canada data page (http://firesmoke.ca/data/) and the Canadian Wildland Fire Information System datamart (http://cwfis.cfs.nrcan.gc.ca/datamart).