Identifying Ignition Drivers of Lightning-Ignited Wildfires in Boreal Forests

Forest fires are the primary disturbance agent in global boreal forests, and they play a significant role in shaping their composition and structure. Boreal forests are also considered a carbon sink but rising temperatures in high-latitude regions are likely increasing wildfire activity, raising concerns that they may become net carbon emitters. Climate change has also increased the frequency and intensity of fire weather in high-latitude boreal forests and is expected to increase the frequency of lightning, a major source of ignition, which could potentially lead to a substantial increase in burned areas. Lightning-ignited wildfires (LIW) pose unique challenges due to their ability to (i) smoulder for long periods of time undetected, (ii) form fire clusters, and (iii) resist suppression efforts. Understanding drivers of ignition is critical for ignition prediction and for optimizing resource allocation for fire managers. Understanding the dynamics of LIWs is, however, challenging due to lack of spatially explicit data that would allow for pan-Boreal analyses of ignition drivers. Current LIW research is thus heavily concentrated in regions with detailed fire data (like North America). In a past study, we filled this data gap by introducing the Temporal Minimum Distance (TMin) method, a new approach to match lightning strikes to wildfires without ignition location data (Engle et al. 2024). The TMin method outperformed current methodologies like the Daily Minimum Distance and the Maximum Index A by identifying 74.71% of fires in boreal forests. Using this method, a comprehensive dataset - BoLtFire - was developed, encompassing 6,228 fires larger than 200 ha spanning across the entire boreal forest from 2012 to 2022. When benchmarked to agency reference datasets, BoLtFire performed reasonably well, with an overall commission error of 30.06% and omission error of 53.63%, but global extent. To model lighting ignition efficiency, the BoLtFire dataset was enhanced to include location data for over 6,000 lightning strikes that did not result in a fire. This expanded dataset also now integrates “ignition drivers,” identified through modelling over 80 different lightning characteristic, climatic, topographic, and fuel-related variables to identify the most influential factors in the ignition process. This enriched dataset provides valuable insights into why certain lightning events trigger wildfires, while others do not. It thus enables more accurate ignition prediction and improved wildfire management strategies. This expanded dataset provides new opportunities to model ignition and spread dynamics for wildfires in boreal forests, deepening our understanding of lightning-driven fire activity. By addressing key knowledge gaps and advancing methodological approaches, this research contributes to a more comprehensive framework for mitigating the growing risks of wildfires in boreal regions and their potential impacts on one of the most important land carbon sinks. References: Engle, B., Bratoev, I., Crowley, M. A., Zhu, Y., and Senf, C.: Distribution and Characteristics of Lightning-Ignited Wildfires in Boreal Forests – the BoLtFire database, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2024-465, in review, 2024.

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