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Containment lines, PODs and suppression success: A case study of the 2021 Schneider Springs Fire

Jesse Young , Christopher O'Connor , Alexander Arkowitz , Elijah Orland , Scott Ritter , David Calkin, rahul wadhwani , C. Cansler

Abstract

Background Effective wildfire suppression is shaped by a complex interplay of environmental conditions, resource allocation, and fire management strategies. Aims Through our examination of the 2021 Schnieder Spring Fire, which occurred in the Eastern Cascades of Washington State, USA, we emphasize the critical role of variable selection, representative sampling, and suppression-specific factors when analyzing wildfire containment. Methods Using descriptive, predictive and causal models, we examined the influence of weather conditions, terrain features, personnel availability, tree canopy cover and fire containment lines (e.g. bulldozer and hand line). Key results High vapor pressure deficit and strong winds were consistently associated with declining containment success. In contrast, terrain features such as valleys and ridges facilitated suppression operations, while steep slopes posed significant challenges. Personnel availability improved containment outcomes, though diminishing returns were observed in descriptive and predictive models. Breaks in tree canopy cover enhanced suppression effectiveness with declining utility during windy conditions. Containment lines played a pivotal role, whereas the effectiveness of pre-identified containment features was more context-dependent, likely influenced by broader strategic fire management decisions. Conclusions Wildfire containment is influenced by multiple interacting variables, and the effectiveness of suppression strategies is context dependent. Causal inference models provide valuable insight by isolating the total effect of individual variables. Implications. Findings underscore the need for adaptive fire management strategies that incorporate context-specific information. Future research should integrate fine-scale weather metrics and additional fire behavior drivers to improve model accuracy and guide effective decision making under dynamic operational conditions.

WF25124  Accepted 21 September 2025

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