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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. Key words: fire management, forest fire occurrence, person-caused, prediction interval, wildfire occurrence. Nous traitons du developpement et de la mise en oeuvre d'un systeme operationnel de prevision de l'occurrence des feux de foret (POF) d'origine humaine dans la province d'Ontario, au Canada. Une suite de modeles d'apprentissage statistique supervise a ete developpee en utilisant plus de 50 annees de donnees a haute resolution couvrant une aire d'etude de 73,8 millions d'hectares repartis dans les regions de gestion du feu du nord-ouest et du nord-est. Une approche de modelisation stratifiee tient compte a l'echelle regionale de differents niveaux de reference saisonniers et pour un ensemble de communautes de l'extreme nord. Des techniques de modelisation et d'echantillonnage dependantes de la reaction utilisant des modeles logistiques additifs generalises ont servi a elaborer un systeme de POF spatio-temporel a fine echelle avec des modeles qui incluent des relations non lineaires avec des variables predictives cles. Ces variables predictives incluent des tendances temporelles intra-annuelles, des tendances spatiales, des variables ecologiques, des mesures de teneur en humidite des combustibles, des caracteristiques de l'utilisation des terres par l'homme et une nouvelle mesure de l'activite humaine. Le systeme produit des cartes a fine echelle spatialement explicites qui illustrent la probabilite quotidienne de la POF d'origine humaine sur la base des conditions observees localement avec une prevision ponctuelle et un intervalle du nombre attendu de feux dans chaque region. Une approche fondee sur la simulation pour generer les intervalles de prevision est decrite. Les previsions quotidiennes ont ete mises a la disposition des responsables de la gestion du feu par l'entremise d'un tableau de bord personnalise et integrees dans la planification regionale quotidienne en support aux besoins de preparation pour la detection et la suppression des feux. [Traduit par la Redaction] Mots-cles: gestion du feu, occurrence des feux de foret, d'origine humaine, intervalle de prevision, occurrence des feux de vegetation., 1. Introduction 1.1. The need for and importance of fire occurrence prediction (FOP) systems Although the province of Ontario's forest fire management organization dates back as far as 1878 with [...]

Communities interspersed throughout the Canadian wildland are threatened by fires that have become bigger and more frequent in some parts of the country in recent decades. Identifying the fireshed (source area) and pathways from which wildland fire may ignite and spread from the landscape to a community is crucial for risk‐reduction strategy and planning. We used outputs from a fire simulation model, including fire polygons and rate of spread, to map firesheds, fire pathways and corridors and spread distances for 1980 communities in the forested areas of Canada. We found fireshed sizes are larger in the north, where the mean distances between ecumene and fireshed perimeters were greater than 10 km. The Rayleigh Z test indicated that simulated fires around a large proportion of communities show significant directional trends, and these trends are stronger in the Boreal Plains and Shields than in the Rocky Mountain area. The average distance from which fire, when spreading at the maximum simulated rate, could reach the community perimeter was approximately 5, 12 and 18 km in 1, 2 and 3 days, respectively. The average daily spread distances increased latitudinally, from south to north. Spread distances were the shortest in the Pacific Maritime, Atlantic Maritime and Boreal Plains Ecozones, implying lower rates of spread compared to the rest of the country. The fire corridors generated from random ignitions and from ignitions predicted from local fire history differ, indicating that factors other than fuel (e.g. fire weather, ignition pattern) play a significant role in determining the direction that fires burn into a community. [ABSTRACT FROM AUTHOR]

Governmental legislation, regulations, and policies are used to prevent and mitigate the negative impact of human-caused wildland fires. In Ontario, Canada, the Modifying Industrial Operations Protocol (MIOP) aims to manage and limit the risk associated with fires ignited because of industrial forestry operations while maintaining flexibility in terms of daily restrictions. The MIOP was enacted in Ontario in 2008, when it replaced the Woods Modifications Guidelines, which had been in effect since 1989. We use quantile regression to quantify how the distribution of incremental growth has changed when contrasting three prevention time periods (MIOP, Woods Guidelines, Pre-Woods) while controlling for several possible confounding variables that drive fire growth. We analyze data of industrial forestry-caused wildland fires ignited on Crown forest land in Ontario from 1976 to 2019. This type of retrospective analysis is important for monitoring the performance of Ontario's prevention and mitigation efforts and providing insight for the future, especially in a changing environment. Our findings provide evidence of MIOP succeeding at its goal of mitigating the negative impact of ignited industrial forestry fires when compared against previous regulations. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Wildland fire prevention and mitigation is of mutual interest to both government and the forest industry. In 1989, the Ontario Ministry of Natural Resources and Forestry introduced the Woods Modification Guidelines that provided rules on how forestry operations should be modified based on local fire danger conditions. Those guidelines were replaced by the Modifying Industrial Operations Protocol (MIOP) in 2008. One objective of MIOP is to allow forestry operations to be done safely for as long as possible as the fire danger increases. We investigate the impacts of these sets of regulations on the frequency of industrial forestry-caused (IDF) wildland fires in the province of Ontario, Canada. Data from 1976 to 2019 are analyzed. A case-crossover study finds no evidence to suggest that MIOP's greater flexibility in operating hours has increased the probability of IDF fire occurrences. This result indicates that MIOP's regulations have had the desired effect of allowing longer working hours on days with heightened fire risk without adding to the seasonal wildland fire load. [ABSTRACT FROM AUTHOR]

Evidence‐based studies in the environmental sciences frequently rely on the presence of spatially dense climatological data. However, such data are often available only at a fixed set of locations that may be regularly or irregularly arranged across a region. Spatial interpolation enables the approximation of variables of interest at locations between those sites. When conducting interpolation in collaboration with an end user or in interdisciplinary research, mutual knowledge exchange allows for greater insight on what is required of an interpolation method since each may have different pros and cons. We outline and discuss several key considerations one should make in an interpolation study, such as the purpose of the variable and the goals of the end user, including how the variable is used to inform decisions. This process is then illustrated via case study within a wildland fire weather context. For the province of Ontario, Canada, we contrast several methods for interpolating the Fire Weather Index (FWI), comparing them quantitatively via metrics and qualitatively using a proposed categorical gradients visualization scheme. Conditional simulations and a spatial ensemble are also investigated. This work is in collaboration with the Ontario Ministry of Natural Resources and Forestry. [ABSTRACT FROM AUTHOR]

Wildfires are worsening in Canada and globally, partly due to climate change. The government of Canada is designing and building WildFireSat, the world's first purpose-built operational satellite system for wildfire monitoring. It will provide new fire intelligence to support decision-making. It takes time for fire management agencies to use new information: to understand it and its implications, change processes, develop training, and modify computer systems. Preparing for the system's prelaunch will allow agencies to benefit more rapidly from the new information. We present (1) an assessment of the readiness of 12 Canadian fire management agencies to integrate WildFireSat information and (2) guidance for reducing readiness gaps. We used survey and other data to score readiness indicators for three readiness components: understanding, organization, and information technology. We weighted the influence of each indicator score on each component. We modelled scoring and weighting uncertainties and used Monte Carlo simulation to generate distributions of aggregated agency readiness. The results indicated that most agencies have a moderate level of readiness while others have a higher level of readiness. Cluster analysis was used to group agencies by similarity in multiple dimensions. Strategies for increasing readiness are highlighted. This identifies opportunities for agencies and the WildFireSat team to collaborate on enhancing readiness for the forthcoming WildFireSat data products. [ABSTRACT FROM AUTHOR]

Background: Among the most successful examples of Knowledge Exchange (KE) between researchers and practitioners in Canadian wildland fire management is the development and integration of the Canadian Forest Fire Danger Rating System (CFFDRS) into operational use. Aims: Our aim was to identify key factors for this success. Methods: Through a case study, we investigated historical KE of two CFFDRS components in Ontario, Canada. We held semi-structured interviews with principal Canadian Forest Service researchers and Ontario fire management practitioners active in development and implementation of CFFDRS from the late 1960s to 2010s. Key results: The importance of both formal and informal facilitators to support KE was emphasised. Conclusion: Participants were most likely to associate successful implementation with informal facilitators such as personal relationships, shared field-based experiences, and opportunities for dialogue between researchers and practitioners. Critical to success were the credibility and soft skills of the knowledge brokers, early engagement, and consideration of training needs for end users in the design of products. Implications: This identification of factors that facilitated or hindered the development and implementation of CFFDRS can enhance the impact of research that will help wildland fire management deal with its present and future challenges. We describe the barriers and facilitators for knowledge exchange for wildland fire management elicited from a case study of the Canadian Forest Fire Danger Rating System development and implementation in Ontario. These findings can help guide the development and implementation of future innovations for wildland fire management. [ABSTRACT FROM AUTHOR]

Background: Industrial forestry operations in Ontario, Canada, may be restricted to reduce the risk of wildland fires. This is currently done according to the Modifying Industrial Operations Protocol (MIOP), which was implemented in 2008 as a replacement for the Woods Modification Guidelines that had been in place since 1989. One of MIOP's objectives is to limit the negative impact or damage caused by fires ignited by industrial forestry operations. Aims: Treating the incremental growth between discovery and final sizes as a measure of suppression effectiveness, we aimed to characterise and contrast growth distributions for three successive time periods using data spanning 1976–2019 on Crown forest areas of Ontario. Methods: Stratifying by first responding group (Ontario Ministry vs forestry personnel), we tested for evidence of changes in the growth distribution using the Kruskal–Wallis and Mann–Whitney U tests. Key results: We found iterative improvements between successive time periods (Pre-Woods, then Woods Guidelines, then MIOP) in the growth distribution of fires first responded to by forestry personnel. Conclusions: MIOP appears to be successfully limiting the negative impact of industrial forestry fires while increasing operational flexibility relative to the Woods Modification Guidelines. Implications: MIOP has been implemented in a manner that still encourages safe operations while not contradicting this objective. Ontario's Modifying Industrial Operations Protocol aims to limit wildland fire risks associated with forestry operations. We empirically investigated how the distribution of incremental growth between discovery and final sizes of industrial forestry-caused fires have changed over time, finding evidence suggesting that fires tend to grow less under current regulations. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. A framework for an expert-calibrated, risk-based spatially explicit wildland fire aerial detection demand index (ADDI) is presented. The ADDI incorporates factors influencing aerial detection patrol routeing such as potential fire occurrence and behaviour, and impacts on resources and assets while accounting for fires that may be reported by the public. [ABSTRACT FROM AUTHOR]

A modelling framework to spatially score the impacts from wildland fire effects on specific resources and assets was developed for and applied to the province of Ontario, Canada. This impact model represents the potential 'loss', which can be used in the different decision-making methods common in fire response operations (e.g. risk assessment, decision analysis and expertise-based). Resources and assets considered include point features such as buildings, linear features such as transmission lines, and areal features such as forest management areas. Three categories of fire impacts were included: social, economic and emergency response. Category-specific scores were determined through expert elicitation and then adjusted to account for fire intensity. Expert elicitation was shown to compare favourably with other methods in terms of the complexity, time, set-up cost and operational use. When compared with historical fire data from Ontario, it was found that impact model scores were associated with the objective to suppress or monitor fires. The model framework provides a consistent pre-fire impact assessment to support individual fire response decisions. The impact assessment can also represent the total impact for areas of Ontario that do not have prescriptive response in a formal fire response plan. Planning for and responding to wildland fires require assessment of the potential impacts, which is generally difficult to do. An easily implemented expert elicitation approach for quantifying the potential impacts on resources and assets was developed, applied and validated. [ABSTRACT FROM AUTHOR]