This study presents a model developed using a risk-based framework that is calibrated by experts, and provides a spatially explicit measure of need for aerial detection daily in Ontario, Canada. This framework accounts for potential fire occurrence, behaviour and impact as well as the likelihood of detection by the public. A three-step assessment process of risk, opportunity and tolerance is employed, and the results represent the risk of not searching a specified area for the detection of wildland fires. Subjective assessment of the relative importance of these factors was elicited from Ontario Ministry of Natural Resources and Forestry experts to develop an index that captures their behaviour when they plan aerial detection patrol routes. The model is implemented to automatically produce a province-wide, fine-scale risk index map each day. A retrospective analysis found a statistically significant association between points that aerial detection patrols passed over and their aerial detection demand index values: detection patrols were more likely to pass over areas where the index was higher.

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.