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.

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.

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.

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. Study Implications: Regulations are one avenue for mitigating risk associated with the accidental ignition of fires by forestry operations. The Modifying Industrial Operations Protocol (MIOP) aims to be more flexible than its predecessor, so we investigate whether forestry-caused fires are tending to grow larger or smaller under MIOP compared to previous time periods. Quantile regression allows us to model individual quantiles of the distribution of incremental growth, the difference between a fire’s discovery and final sizes, while controlling for several confounders that influence growth. We find evidence of improvements to the right tail of this distribution, with fires growing less under MIOP.