Airtankers are often used on initial attack (IA) to suppress unwanted wildland fires quickly and contain them before they grow large. Skimmer airtankers are commonly used in the province of Ontario owing to its abundance of waterbodies. We examined the influence of airtanker use on IA success on fires actioned by air attack in Ontario using historical fire records and developed three statistical models to estimate the probability of IA success using information available at three different times during the fire response process. These models include information available to the fire management agency at the time the fire was reported, when IA began and during the IA suppression operations. Our findings indicate that the situational information about a fire obtained during IA provides better estimates of the probability of IA success, as demonstrated by increases in the predictive accuracy and area under the receiver operating characteristic curve compared with a model that is based only on information available at the time a fire is reported. Our results can inform pre-suppression planning and suppression resource allocation decision-making, particularly on days during which many new fires are expected to be reported.

This study describes the development of a prototype bi-spectral microbolometer sensor system designed explicitly for radiometric measurement and characterization of wildfire mid- and long-wave infrared radiances. The system is tested experimentally over moderate-scale experimental burns coincident with FLIR reference imagery. Statistical comparison of the fire radiative power (FRP; W) retrievals suggest that this novel system is highly reliable for use in collecting radiometric measurements of biomass burning. As such, this study provides clear experimental evidence that mid-wave infrared microbolometers are capable of collecting FRP measurements. Furthermore, given the low resource nature of this detector type, it presents a suitable option for monitoring wildfire behaviour from low resource platforms such as unmanned aerial vehicles (UAVs) or nanosats.

Wildland fire management decision-makers need to quickly understand large amounts of quantitative information under stressful conditions. Categorization and visualization “schemes” have long been used to help, but how they are done affects the speed and accuracy of interpretation. Using traditional fire management schemes can unduly restrict the design of new products. Our design process for Ontario’s fine-scale, spatially explicit, daily fire occurrence prediction (FOP) models led us to develop guidance for designing new schemes. We show selected historical fire management schemes and describe our method. It includes specifying goals and requirements, exploring design options and making trade-offs. The design options include gradient continuity, hue selection, range completeness and scale linearity. We apply our method to a case study on designing the scheme for Ontario’s FOP models. We arrived at a smooth, nonlinear scale that accommodates data spanning many orders of magnitude. The colouring draws attention according to levels of concern, reveals meaningful spatial patterns and accommodates some colour vision deficiencies. Our method seems simple now but reconciles complex considerations and is useful for mapping many other datasets. Our method improved the clarity and ease of interpretation of several information products used by fire management decision-makers.

Airtankers are commonly used for initial attack (IA) to reduce the likelihood of wildland fires escaping containment efforts. We examined IA airtanker dispatch decisions for forest fires in Ontario, Canada, through an analysis of historical fire records from 2001 to 2019. A hurdle modelling approach that predicts the probability of airtanker(s) being dispatched and then the number of airtankers sent was used. Two different hurdle models were considered depending on the timing of the information available to the decision maker: a “fire report model” based upon the information available when a fire is first reported, and an “initial attack model” using information available at the time IA action began. Both models indicated that the most influential covariates for airtanker dispatch are the fire weather index, fuel volatility, observed fire rate of spread, fire size at IA, and cause of ignition. When evaluating the predictive ability of the models on a validation data set, the “initial attack model” performed better than the “fire report model”. Our models generally perform well when predicting none, one, or two airtankers on IA, but they generally underpredict when more than two airtankers are dispatched, which suggests risk-averse decision-making in fire management resource dispatching.

Background: There has been little quantification of the extent and duration of micrometeorological changes within a forest after airtanker drops of water-based suppressant. It has been speculated that a period of prolonged relative humidity – referred to as a ‘relative humidity (RH) bubble’ – temporarily exists in the canopy understorey post-drop. Aims: We quantify the RH bubble from the drops of five airtankers commonly used by wildland fire management organisations in Canada. Methods: We measured airtankers dropping water, foam concentrates, and gel enhancers in a mature jack pine stand. We examined the duration of change in RH and temperature using Generalised Additive Models, and the consequence of these changes on fine fuel moisture. Key results: The average maximum RH increased and temperature decreased, indicating that the effects of the ‘RH bubble’ in-stand lasted from 25 to 76 min, depending upon the airtanker type and load configuration. Conclusion: Airtanker drops cause an in-stand increase in RH and decrease in temperature, but this ambient change has a limited effect on potential fire behaviour. Implications: The direct effect of water wetting the fuel is the most impactful effect of an airtanker drop. The ‘RH bubble’ created, though observable, does not change fine fuel moisture enough to impact fire behaviour.