In the province of British Columbia, Canada, four of the most severe wildfire seasons of the last century occurred in the past 7 years: 2017, 2018, 2021, and 2023. To investigate trends in wildfire activity and fire-conducive climate, we conducted an analysis of mapped wildfire perimeters and annual climate data for the period of 1919–2021. Results show that after a century-long decline, fire activity increased from 2005 onwards, coinciding with a sharp reversal in the wetting trend of the 20th century. Even as precipitation levels remain high, moisture deficits have increased due to rapid warming and increased evaporative demand. Bottom-up factors further influence fire activity, as the legacy of past wildfires, insect outbreaks, and land-use practices continually influence fire regimes. The compound effects of climate-induced moisture changes and altered fuels now force British Columbians to confront the harsh reality of more frequent years of intense and prolonged wildfire activity.

We developed spatially explicit models of the daily probability of aircraft use in initial attack (IA) on a fire (hereafter the conditional models), and estimates of the unconditional probability of daily aircraft IA targets to support preparedness planning in the province of British Columbia, Canada, using a grid cell × day lasso-logistic framework. Novel aspects of our work include: (1) inclusion of an historical aircraft baseline covariate to account for missing or poorly estimated factors in our models; and of 2 day lead weather and Forest Fire Weather Index (FWI) covariates as proxies for fire potential trend, and (2) linking the conditional models of aircraft use in IA to models of daily fire occurrence to estimate the daily number of aircraft IA targets. The baseline risk of using an aircraft, population and road density were highly influential spatial covariates in both aircraft conditional models. The probability of sustained flaming, temperature, and FWI lead, and Sheltered Duff Moisture Code, temperature, and the Showalter Index were the three most influential meteorological variables in the conditional airtanker and helicopter IA models, respectively. We demonstrate the application of the models to portray the distribution of the expected number of daily aircraft IA targets.

Whereas many carbon cycle models track CO2 perturbations relative to a pre-industrial equilibrium, this paper uses absolute quantities to describe atmospheric CO2 sinks, source and flow rates. This method, when combined with the notion of source and sink resistance, and a finite biospheric reservoir, accurately describes 14C levels between 1820 and 2020 using only five external parameters. The inputs are:- global records of fossil-fuel emissions, records of CO2 mixing-ratio and listings of atmospheric atomic weapons tests. Over the same period 13C flows are also accurately described given a ð13C value for fossil fuel and a ð13C value for the initial background. This top-down approach differs from complex climate models since it circumvents the necessity to catalogue individual processes. The paper proceeds to use the method to examine the anthropogenic fossil-fuel emissions contributions during the period 1750 to 2020, deducing that around 24% remains in the atmosphere, while 76% has been absorbed in the land, terrestrial biosphere and surface ocean. During the same period 13% of the total CO2 atmospheric concentration is due to fossil fuels. However, regarding the increase, fossil fuels contributed to 38% of the rise during this period.

Fire regime zonation systems are critical tools for research and management activities. In this study, we develop a hierarchical framework that applies both qualitative and quantitative approaches to create a two-level fire regime zonation system for Canada. The finer scale level, Fire Regime Units (FRUs), was created through a stepwise synthesis of fire regime metrics based on 1970–2016 fire records, environmental attributes such as topographic features and vegetation, literature review, and expert advice. Each of these 60 FRUs exhibits an internal homogeneity in fire regime. As non-contiguous units can show similar patterns in fire-related measurements, we performed a clustering analysis on the FRUs to define 15 broad-scale Fire Regime Types (FRTs). Each type is characterized by a unique set of indices related to fire activity, seasonality, and ignition cause. This two-level fire regime zonation system has a large range of applications (e.g., modeling, gradient analysis) and is flexible enough to be updated with new data or when notable shifts in fire dynamics occur.

The Fire Weather Index (FWI), an indicator of fire potential, is calculated from weather measurements and thus expected to be responsive to climate change. The data were drawn from records of FWI within the years 1970 to 2018 and from 861 stations in British Columbia, Canada. Since high FWI increases fire risk and monthly and geographic variation in fire potential is known to exist, models of maximum FWI were fitted within month-region groups of stations. Separate for each station, parameters of the generalized extreme-value distribution with linear dependence on time in both location and scale parameters were fitted by the maximum likelihood method. To include spatial dependence, max-stable spatial processes with different distributional assumptions on the components of the spectral representation were fitted by the maximum composite likelihood method. Takeuchi's information criterion was used for model selection. Station p-values from the separate models identified tendencies for increasing or decreasing trends in location and scale parameters. May, July and August had the most stations with stronger increasing trends in location parameter of maximum FWI and this tended to occur in regions where maximum FWI was higher. In contrast, trends in the scale parameter of maximum FWI showed decrease in variability in some regions, particularly in August. Spatial modeling showed trends in some months and regions, not necessarily consistent with the separate modeling results, not unexpected since the two methods would pick up local effects and regional effects, respectively. The analyses demonstrated the usefulness of these extreme value methods for fire weather variables.

We developed three models of daily human- and lightning-caused fire occurrence to support fire management preparedness and detection planning in the province of British Columbia, Canada, using a lasso-logistic framework. Novel aspects of our work involve (1) using an ensemble of models that were created using 500 datasets balanced (through response-selective sampling) to have equal numbers of fire and non-fire observations; (2) the use of a new ranking algorithm to address the difficulty in interpreting variable importance in models with a large number of covariates. We also introduce the use of cause-specific average spatial daily fire occurrence, termed baseline risk, as a covariate for missing or poorly estimated factors that influence human and lightning fire occurrence. All three models have strong predictive ability, with areas under the Receiver Operator Characteristic curve exceeding 0.9.

The broad consensus indicates that climate change will cause larger and more frequent fires, resulting in a growing annual area burned (AAB) in much of Canada. Our ability to predict future changes in fire size (FS) and AAB is limited due to the uncertainty embedded in climate change models and our inability to quantify the complex interactions between the changing environment and fire activity. In this study, we introduce a new method to predict future FS and AAB across Canada over the 21st century based on fire-conducive weather and how it translates to on-the-ground fire spread (i.e. spread days). We found that the potential for an extreme fire year (99th percentile of AAB) could quadruple by the end of the century across Canada, and ≥ 10 times more common in the boreal biome. Specifically, FS and AAB may increase 20%–64% and 25%–93%, respectively, and the average fire year under the extreme climate scenario may burn ∼11 Mha, which is ∼4 Mha higher than the most extreme fire year in Canada's modern history (∼7 Mha). Our results demonstrate that by accounting for the strong nonlinear expansion of wildfires as a function of number of fire spread days, even conservative climate-change scenarios may yield significant increase in fire activity.

This study uses an interdisciplinary approach to investigate variability in fire weather, fire activity and fire management decision spaces in western Canada from three separate perspectives. We used time series analysis to identify periodic and quasi-periodic components of fire weather measures at second, hourly, daily, yearly, and multi-decadal resolution in 3 ecozones. Examples of relationships between scales of fire weather and fire activity were taken from the literature. Through interviews with and observation of Canadian wildland fire management agencies we identified 20 typical decision problems which we mapped to 16 spatio-temporally cohesive decision spaces extending from incident to national levels and immediate to multi-decadal time spans. To connect these domains, we propose that space time cascades of atmospheric kinetic energy are reflected in an inverse cascade of wildfire activity, and shape the spatio-temporal dimensions of decision spaces and the pace of fire management decisions.

Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems. Since then, the field has rapidly progressed congruently with the wide adoption of machine learning (ML) methods in the environmental sciences. Here, we present a scoping review of ML applications in wildfire science and management. Our overall objective is to improve awareness of ML methods among wildfire researchers and managers, as well as illustrate the diverse and challenging range of problems in wildfire science available to ML data scientists. To that end, we first present an overview of popular ML approaches used in wildfire science to date and then review the use of ML in wildfire science as broadly categorized into six problem domains, including (i) fuels characterization, fire detection, and mapping; (ii) fire weather and climate change; (iii) fire occurrence, susceptibility, and risk; (iv) fire behavior prediction; (v) fire effects; and (vi) fire management. Furthermore, we discuss the advantages and limitations of various ML approaches relating to data size, computational requirements, generalizability, and interpretability, as well as identify opportunities for future advances in the science and management of wildfires within a data science context. In total, to the end of 2019, we identified 300 relevant publications in which the most frequently used ML methods across problem domains included random forests, MaxEnt, artificial neural networks, decision trees, support vector machines, and genetic algorithms. As such, there exists opportunities to apply more current ML methods — including deep learning and agent-based learning — in the wildfire sciences, especially in instances involving very large multivariate datasets. We must recognize, however, that despite the ability of ML models to learn on their own, expertise in wildfire science is necessary to ensure realistic modelling of fire processes across multiple scales, while the complexity of some ML methods such as deep learning requires a dedicated and sophisticated knowledge of their application. Finally, we stress that the wildfire research and management communities play an active role in providing relevant, high-quality, and freely available wildfire data for use by practitioners of ML methods.

In the face of climate change, predicting and understanding future fire regimes across Canada is a high priority for wildland fire research and management. Due in large part to the difficulties in obtaining future daily fire weather projections, one of the major challenges in predicting future fire activity is to estimate how much of the change in weather potential could translate into on-the-ground fire spread. As a result, past studies have used monthly, annual, or multi-decadal weather projections to predict future fires, thereby sacrificing information relevant to day-to-day fire spread. Using climate projections from the fifth phase of the Coupled Model Intercomparison Project (CMIP5), historical weather observations, MODIS fire detection data, and the national fire database of Canada, this study investigated potential changes in the number of active burning days of wildfires by relating 'spread days' to patterns of daily fire-conducive weather. Results suggest that climate change over the next century may have significant impacts on fire spread days in almost all parts of Canada's forested landmass; the number of fire spread days could experience a 2-to-3-fold increase under a high CO2 forcing scenario in eastern Canada, and a greater than 50% increase in western Canada, where the fire potential is already high. The change in future fire spread is critical in understanding fire regime changes, but is also imminently relevant to fire management operations and in fire risk mitigation.

Given that they can burn for weeks or months, wildfires in temperate and boreal forests may become immense (eg., 10(0)-10(4) km(2)). However, during the period within which a large fire is 'active', not all days experience weather that is conducive to fire spread; indeed most of the spread occurs on a small proportion (e. g., 1 - 15 days) of not necessarily consecutive days during the active period. This study examines and compares the Canada-wide patterns in fire-conducive weather ('potential' spread) and the spread that occurs on the ground ('realized' spread). Results show substantial variability in distributions of potential and realized spread days across Canada. Both potential and realized spread are higher in western than in eastern Canada; however, whereas potential spread generally decreases from south to north, there is no such pattern with realized spread. The realized-to-potential fire-spread ratio is considerably higher in northern Canada than in the south, indicating that proportionally more fire-conducive days translate into fire progression. An exploration of environmental correlates to spread show that there may be a few factors compensating for the lower potential spread in northern Canada: a greater proportion of coniferous (i.e., more flammable) vegetation, lesser human impacts (i.e., less fragmented landscapes), sufficient fire ignitions, and intense droughts. Because a linear relationship exists between the frequency distributions of potential spread days and realized spread days in a fire zone, it is possible to obtain one from the other using a simple conversion factor. Our methodology thus provides a means to estimate realized fire spread from weather-based data in regions where fire databases are poor, which may improve our ability to predict future fire activity.

This paper examines the risks associated with forest insect outbreaks in a changing climate from biological and forest management perspectives. Two important Canadian insects were considered: western spruce budworm (WSBW; Choristoneura occidentalis Freeman, Lepidoptera: Tortricidae), and spruce bark beetle (SBB; Dendroctonus rufipennis Kirby, Coleoptera: Curculionidae). This paper integrates projections of tree species suitability, pest outbreak risk, and bio-economic modelling.

Daily records of the location and timing of human- and lightning-caused fires in British Columbia from 1981 to 2000 were used to estimate the probability of fire occurrence within 950 20 × 20-km spatial units (~950 000 km2) using a binary logistic regression modelling framework. Explanatory variables included lightning strikes, forest cover, surface weather observations, atmospheric stability indices and fuel moisture codes of the Canadian Fire Weather Index System. Because the influence of the explanatory variables in the models varied from year to year, model coefficients were estimated for each year. The arithmetic mean of the model coefficients was used for making daily predictions in a future year. A confidence interval around the mean or a quantile was derived from the ensemble of 20 model predictions. A leave-1-year-out cross-validation procedure was used to assess model performance for random years. The daily number of lightning-caused fires was reasonably well predicted at the provincial level (R = 0.83) and slightly less well predicted for a smaller (75 000 km2) administrative region. The daily number of human-caused fires was less well predicted at both the provincial (R = 0.55) and the regional level. The ability to estimate confidence intervals from the ensemble of model predictions is an advantage of the year-specific approach.

Projections for forest disturbance and damage under a changing climate in British Columbia are summarized, with the objective of collating regionally specific expectations so that land managers can take pro-active steps to avoid or adapt to the changes expected. While some projections are based on extrapolations of recent multi-decadal trends, most are based on global climate models (GCMs) that must make assumptions about the range of CO2 emissions and the status of atmospheric greenhouse gas emissions over the next century. Regardless of emission scenarios, it is universally expected that British Columbia will experience warmer air temperatures, coupled with more precipitation in some regions and less in others. Forest fires are expected to be more frequent and more intense in the southern half of the province and in the Taiga Plains, but less important in other portions of the province. Forest insects and fungal pathogens are expected to more fully occupy the current range of their host tree species and expand ranges northward and to higher elevations along with their hosts. More frequent and detrimental pest outbreaks are expected in some regions when several years of favourable weather align. Wind damage, floods, and landslides can be expected to increase on terrain where they are already a risk factor. For many agents of tree mortality, expected changes in disturbance regime amount to an expansion or shifting of the seasonal window of activity, sometimes with different trends projected for different seasons and different regions of the province. The prediction of future forest disturbance regimes is in its infancy, requiring a much more concerted effort in compiling both empirical and simulated data, but managers may wish to adjust plans accordingly where there is largely a consensus among current and projected trends.

The potential for mountain pine beetle, Dendroctonus ponderosae Hopkins (Coleoptera: Curculionidae: Scolytinae), to expand its historical range in North America from west of the continental divide into the eastern boreal forest was assessed on the basis of analyses of the effects of climate and weather on brood development and survival, and key aspects of the interaction of mountain pine beetle with its hosts and associated organisms. Variation in climate suitability and high host susceptibility in the boreal forest create a finite risk of establishment and local persistence of low-level mountain pine beetle populations outside their historical range. Eventually, these populations could become widespread and cause epidemic infestations, creating an ecological pathway eastward through the boreal forest. Such infestations would reduce the commercial value of forests and impose an additional disturbance on native ecological systems.