We describe the development and implementation of an operational human-caused wildland fire occurrence prediction (FOP) system in the province of Ontario, Canada. A suite of supervised statistical learning models was developed using more than 50 years of high-resolution data over a 73.8 million ha study area, partitioned into Ontario’s Northwest and Northeast Fire Management Regions. A stratified modelling approach accounts for different seasonal baselines regionally and for a set of communities in the Far North. Response-dependent sampling and modelling techniques using logistic generalized additive models are used to develop a fine-scale, spatiotemporal FOP system with models that include nonlinear relationships with key predictors. These predictors include inter- and intra-annual temporal trends, spatial trends, ecological variables, fuel moisture measures, human land-use characteristics, and a novel measure of human activity. The system produces fine-scale, spatially explicit maps of daily probabilistic human-caused FOP based on locally observed conditions along with point and interval predictions for the expected number of fires in each region. A simulation-based approach for generating the prediction intervals is described. Daily predictions were made available to fire management practitioners through a custom dashboard and integrated into daily regional planning to support detection and fire suppression preparedness needs.

Weather forecasts are needed in fire management to support risk-based decision-making that considers both the probability of an outcome and its potential impact. These decisions are complicated by the large amount of uncertainty surrounding many aspects of the decision, such as weather forecasts. Wildland fires in Ontario, Canada can burn and actively spread for days, weeks, or even months, or be naturally limited or extinguished by rain. Conventional fire weather forecasts have typically been a single scenario for a period of one to five days. These forecasts have two limitations: they are not long enough to inform some fire management decisions, and they do not convey any uncertainty to inform risk-based decision-making. We present an overview of a method for the assembly and customization of forecasts that (1) combines short-, medium-, and long-term forecasts of different types, (2) calculates Fire Weather Indices and Fire Behaviour Predictions, including modelling seasonal weather station start-up and shutdown, (3) resolves differing spatial resolutions, and (4) communicates forecasts. It is used for burn probability modelling and other fire management applications.

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.

Background: Satellite-based remote sensing is important for managing wildfires. However, cloud cover could hinder its effectiveness. Aims: We examine potential cloud cover impacts on satellite-based wildfire detection and monitoring in Canada, analysing spatio-temporal patterns and relationships with local fire conditions. Methods: Geostationary Operational Environmental Satellite 16 (GOES-16) Clear Sky Mask (CSM) data at relevant satellite overpass times in Ontario and Alberta are used to characterise cloud cover at a variety of spatial and temporal scales. Two potential fire behaviour indicators are compared under cloudy and clear conditions. Key results: Cloud cover can vary by time of day, seasonally and by sub-region. In general, cloud cover was lowest overnight, during July–August and typically greatest at the overpass time of the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) instruments (13:30). Initial Spread Index (ISI) and Head Fire Intensity (HFI) distributions were similar under clear and cloudy conditions. Conclusions: Satellite-derived active fire products will experience omission error rates varying in space-time and this could occur under potentially dangerous fire behaviour conditions. Care may need to be taken when relying on these products for fire monitoring and detection. Implications: Wildfire-detection and monitoring strategies should leverage satellite, airborne and ground-based methods.