Kerry Anderson

Estimating direct carbon emissions from Canadian wildland fires 1

In support of Canadas National Forest Carbon Monitoring, Accounting and Reporting System, a project was initiated to develop and test procedures for estimating direct carbon emissions from fires. The Canadian Wildland Fire Information System (CWFIS) provides the infrastructure for these procedures. Area burned and daily fire spread estimates are derived from satellite products. Spatially and temporally explicit indices of burning conditions for each fire are calculated by CWFIS using fire weather data. The Carbon Budget Model of the Canadian Forest Sector (CBM- CFS3) provides detailed forest type and leading species information, as well as pre-fire fuel load data. The Boreal Fire Effects Model calculates fuel consumption for different live biomass and dead organic matter pools in each burned cell according to fuel type, fuel load, burning conditions, and resulting fire behaviour. Carbon emissions are calculated from fuel consumption. CWFIS summarises the data in the form of disturbance matrices and provides spatially explicit estimates of area burned for national reporting. CBM-CFS3 integrates, at the national scale, these fire data with data on forest management and other disturbances. The methodology for estimating fire emissions was tested using a large-fire pilot study. A framework to implement the procedures at the national scale is described.

Fire-growth modelling using meteorological data with random and systematic perturbations

The focus of this investigation is to quantify the effects of perturbations in the meteorological data used in a fire-growth model. Observed variations of temperature, humidity, wind speed, and wind direction are applied as perturbations to hourly values within a simulated weather forecast to produce several forecasts. In turn, these are used by a deterministic eight-point fire-growth model to produce an ensemble of possible final fire perimeters. Two studies were conducted to assess the value of applying perturbations. In the first study, fire growth using detailed, one-minute data was compared to growth based on the more commonly used hourly data. Results showed that the detailed weather produced fire growth larger and wider than the hourly based data. By applying perturbations, variations in the flank and back-fire spread were captured by the random-perturbation model while the forward spread fell within the 20 to 30% probability prediction. A sensitivity analysis based on the observed variations showed that wind speed accounted for a 44% difference in area burned, while temperature accounted for only a 16% difference. In the second study, case studies were conducted on four observed forest fires in Wood Buffalo National Park. Results showed that daily fire-growth predictions using simulated weather forecasts over-predicted fire growth using actual hourly weather observations by 27%. Systematic-perturbation models best compensated for this with most fire growth falling within the predicted range of the models (52 out of 63 days).

An evaluation of spatial and temporal patterns of lightning- and human-caused forest fires in Alberta, Canada, 1980-2007

WeusedtheK-functionandkernelestimationmethodstoevaluatethespatialandtemporalpatternsofignition locations of lightning- and human-caused forest fires in Alberta, Canada. Although both of these fire types have spatial patterns of cluster distribution, quantitative measures for evaluating the patterns in the province are lacking. Our results revealedannualdifferencesinthespatialpatternsbetweenthetwofiretypes,wherebyfirescausedbyhumanstendedtobe more clustered and had more complex spatial patterns than those caused by lightning. Spatial interactions of cluster and inhibition existed between the two fire types. Human-caused fires in the period 2003-07 were highly concentrated in the southern parts of the province, indicating the existence of an interaction between space and time. Kernel analysis confirmed the observation that in northern Alberta, lightning-caused fires were more likely to occur than human-caused fires;theoppositewastrueinsouthernAlberta.Thisstudyprovidedusefulspatialinformationthatisnotobviousorcannot be inferred from visual examination of raw data. Such quantitative knowledge could lead to the development of fire- response and fire-suppression strategies appropriate to specific regions within the province.

The FireWork air quality forecast system with near-real-time biomass burning emissions: Recent developments and evaluation of performance for the 2015 North American wildfire season

ABSTRACT Environment and Climate Change Canada’s FireWork air quality (AQ) forecast system for North America with near-real-time biomass burning emissions has been running experimentally during the Canadian wildfire season since 2013. The system runs twice per day with model initializations at 00 UTC and 12 UTC, and produces numerical AQ forecast guidance with 48-hr lead time. In this work we describe the FireWork system, which incorporates near-real-time biomass burning emissions based on the Canadian Wildland Fire Information System (CWFIS) as an input to the operational Regional Air Quality Deterministic Prediction System (RAQDPS). To demonstrate the capability of the system we analyzed two forecast periods in 2015 (June 2–July 15, and August 15–31) when fire activity was high, and observed fire-smoke-impacted areas in western Canada and the western United States. Modeled PM2.5 surface concentrations were compared with surface measurements and benchmarked with results from the operational RAQDPS, which did not consider near-real-time biomass burning emissions. Model performance statistics showed that FireWork outperformed RAQDPS with improvements in forecast hourly PM2.5 across the region; the results were especially significant for stations near the path of fire plume trajectories. Although the hourly PM2.5 concentrations predicted by FireWork still displayed bias for areas with active fires for these two periods (mean bias [MB] of –7.3 µg m−3 and 3.1 µg m−3), it showed better forecast skill than the RAQDPS (MB of –11.7 µg m−3 and –5.8 µg m−3) and demonstrated a greater ability to capture temporal variability of episodic PM2.5 events (correlation coefficient values of 0.50 and 0.69 for FireWork compared to 0.03 and 0.11 for RAQDPS). A categorical forecast comparison based on an hourly PM2.5 threshold of 30 µg m−3 also showed improved scores for probability of detection (POD), critical success index (CSI), and false alarm rate (FAR). Implications: Smoke from wildfires can have a large impact on regional air quality (AQ) and can expose populations to elevated pollution levels. Environment and Climate Change Canada has been producing operational air quality forecasts for all of Canada since 2009 and is now working to include near-real-time wildfire emissions (NRTWE) in its operational AQ forecasting system. An experimental forecast system named FireWork, which includes NRTWE, has been undergoing testing and evaluation since 2013. A performance analysis of FireWork forecasts for the 2015 wildfire season shows that FireWork provides significant improvements to surface PM2.5 forecasts and valuable guidance to regional forecasters and first responders.

Landscape composition influences local pattern of fire size in the eastern Canadian boreal forest: role of weather and landscape mosaic on fire size distribution in mixedwood boreal forest using the Prescribed Fire Analysis System

Wildfire simulations were carried out using the Prescribed Fire Analysis System (PFAS) to study the effect of landscape composition on fire sizes in eastern Canadian boreal forests. We used the Lake Duparquet forest as reference, plus 13 forest mosaic scenarios whose compositions reflected lengths of fire cycle. Three fire weather risks based on duff moisture were used. We performed 100 simulations per risk and mosaic, with topography and hydrology set constant for the reference. Results showed that both weather and landscape composition significantly influenced fire sizes. Weather related to fire propagation explained almost 79% of the variance, while landscape composition and weather conditions for ignition explained ∼14 and 2% respectively. In terms of landscape, burned area increased with increasing presence of shade-tolerant species, which are related to long fire cycles. Comparisons among the distributions of cumulated area burned from scenarios plus those from the Societe de Protection des Forets contre le Feu database archives showed that PFAS simulated realistic fire sizes using the 80–100% class of probable fire extent. Future analyses would best be performed on a larger region as the limited size of the study area could not capture fires larger than 11 000 ha, which represent 3% of fires but 65% of the total area burned at the provincial scale.

Modeling of Fire Occurrence in the Boreal Forest Region of Canada

Fire is a significant component of most boreal forest ecosystems. It is important to understand its occurrence and spread to assess the potential impact of global climate change on boreal forest ecosystems. This chapter presents an overview of our understanding of the processes and models that have been developed and used to predict both people-caused and lightning-caused fire occurrences in the boreal forest. We draw heavily on our experience with fire occurrence in the boreal forest region of Canada, but some of our observations may be applicable to other parts of the circumpolar boreal forest as well as other biomes.

A climatologically based long-range fire growth model

A long-range fire growth model was developed to predict the potential size or probable extent of a wildfire if it was allowed to grow unimpeded for the course of the fire season. The model combines the probabilities of fire spread and of survival to produce a probable fire extent map. Probability of spread is determined from exponential distributions of potential rates of spread for various fuel types and eight compass directions, based on 30 years of fire weather data and elliptical fire growth. Probability of survival is determined from the probable evolution of the duff moisture code over the fire season predicted with Markov chains and a duff moisture code of extinction. A comparative study was conducted within Wood Buffalo National Park. The long-term model was compared with a distribution of fire perimeters predicted by repeated simulations using an hourly based, deterministic fire growth model. The study was conducted in stages, starting with a homogeneous fuel type and weather from one station. Later, fuels and weather were introduced to determine their effects on the model. The study showed a close agreement between the long-range model and the deterministic model, supporting the probabilistic approach used by the long-range model.

Predicting sustained smouldering combustion in trembling aspen duff in Elk Island National Park, Canada

Fire is one of the key disturbances affecting trembling aspen (Populus tremuloides Michx.) forest ecosystems within western Canadian wildlands, including Elk Island National Park in central Alberta, Canada. Although prescribed fire is a tool available to modify aspen forests, a clear understanding of its potential impact is necessary to successfully manage this disturbance. Undesirable social and ecological consequences of severe, deep-burning ground fires include smoke generation and impaired vegetation regrowth. Data on the duff moisture conditions under which ground or subsurface fires may ignite and spread in aspen forest duff layers are presented, as well as experimental test fire results. Different topographic positions, plant communities and seasonality were factored into the research design. The Duff Moisture Code (DMC) and Drought Code (DC) components of the Canadian Forest Fire Weather Index System were calculated and factors including duff moisture content, bulk density and inorganic content measured before ignition of experimental test fires. Probability of sustained smouldering combustion models were developed for the duff layer in the aspen forest fuel type in Elk Island National Park, with values of 27 for DMC and 300 for DC at the 50% probability level.

Integrating forest fuels and land cover data for improved estimation of fuel consumption and carbon emissions from boreal fires

Estimating carbon emissions from wildland fires is complicated by the large variation in both forest fuels and burning conditions across Canada’s boreal forest. The potential for using spatial fuel maps to improve wildland fire carbon emission estimates in Canada’s National Forest Carbon Monitoring, Accounting and Reporting System (NFCMARS) was evaluated for select wildfires (representing a transect across western Canada) occurring in 2003 and 2004 at four study areas in western Canada. Area-normalised emission rates and total emissions differed by fuels data source, mainly as a function of the treatment of open fuels in the higher resolution spatial fuel models. The use of spatial data to refine the selection of stand types that probably burned and the use of fire weather conditions specific to the fire increased the precision of total carbon emission estimates, relative to computational procedures used by Canada’s NFCMARS. Estimates of total emissions from the NFCMARS were consistent with the regional and national data sources following the spatial approach, suggesting the two approaches had equivalent accuracies. Though it cannot be said with certainty that the inclusion of this detailed information improved accuracy, the spatial approach offers the promise or potential for more accurate results, pending more consistent fuel maps, especially at finer scales.

Correction to cffdrs: an R package for the Canadian Forest Fire Danger Rating System

The original publication [1] has an error in the citation of figure 1. Below you will find the correct version.