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Browsing by Author "Linn, Rodman, committee member"

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    Advancing prescribed fire science through numerical simulation and improved reporting practices
    (Colorado State University. Libraries, 2022) Bonner, Sophie R., author; Hoffman, Chad, advisor; Linn, Rodman, committee member; Tinkham, Wade, committee member; Rocca, Monique, committee member
    Planning a prescribed burn that is safe and effective relies on land managers understanding how a complex suite of interactions between the burning environment (e.g., fuels, fire weather, and topography) and ignition factors influence fire behavior and effects. As the field of prescribed fire science has grown, more questions have arisen regarding how the spatial structure of forests and the ignition pattern affect the ecological outcomes of these burns. Advancing our understanding of these factors is crucial to provide managers with quality, evidence-based science that can inform prescribed fire planning. In this two-part thesis, my objectives were: i) to evaluate reporting quality in recent prescribed fire literature and suggest minimum reporting standards for future prescribed fire experiments, and ii) to explore the potential effects of complex forest fuel structures and ignition patterns on fire behavior and the resultant ecological effects during prescribed burns. In Chapter 1, I present results from a literature review of reporting standards from over 200 prescribed fire experiments conducted from 2016 to 2020. My results suggest substantial shortcomings in the reporting of critical data that limit the utility of prescribed fire research. Specifically, I found that specific information on burning conditions such as fuel moisture (22%), quantitative fuel loads (36%), fire weather (53%), and fire behavior (30%) were often not reported by the authors. Further, I found that only 54% of the studies provided descriptions of the ignition characteristics. Given these common deficiencies, suggested minimum reporting standards are proposed for future prescribed fire experiments which can be used to increase the quality, applicability, and reproducibility of prescribed fire science, facilitate future research syntheses, and foster actionable science. In Chapter 2, I evaluate how forest structural complexity and ignition pattern impact crown damage during simulated prescribed fires in longleaf pine (Pinus palustris) dominated forests of the southeastern United States. My results show that - regardless of forest structure – using a strip-head ignition pattern consistently produced more crown damage than spot-head or alternative spot-head ignition patterns. In terms of forest structure, I found forests with greater structural complexity resulted in more crown damage than less complex forests. More specifically, I observed forests with more aggregated horizontal spatial patterns, greater vertical complexity, and moderate to high amounts of canopy cover to produce more severe fire behavior than regularly spaced, single-story forests with sparse canopy cover. These findings suggest that managers need to consider a forest's structure and their choice of ignition pattern when planning prescribed burns to ensure they meet ecological objectives.
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    Comparing crown fire predictions in ponderosa pine stands among four fire behavior models
    (Colorado State University. Libraries, 2024) Ney, Jacob, author; Hoffman, Chad, advisor; Linn, Rodman, committee member; Fischer, Emily, committee member
    Fire and land managers commonly use fire behavior modeling systems to support their planning and decision-making process. Fire modeling systems have been increasingly used across the western United States to plan fuel treatments that reduce hazard fuels, especially as a drier climate has resulted in more frequent high severity wildfire. Given differences in model types, approaches, assumptions, and sensitivity to various input parameters, modeling systems can produce different predictions and lead to different management decisions. Variability arising from model selection results in increased uncertainty within the decision-making framework. Multi-model comparisons help identify areas of model agreement and disagreement, reduce uncertainty associated with management decisions, and identify directions for future experimentation. Here, I compare predictions of fire type and crown fire rate of spread (ROS) among four modeling systems that represent a range of model types and complexities—Wildland-urban interface Fire Dynamics Simulator (WFDS), QUIC-Fire, a Rothermel-based modeling framework, and Crown Fire Initiation and Spread (CFIS). Comparisons (n = 297) were made based on a range of forest structure and environmental conditions representative of treated and untreated ponderosa pine forest stands in the southern Rocky Mountains. All four models predicted crown fire occurrence for 71% of simulations in total. WFDS, QUIC-Fire, and CFIS agreed on fire type more than 65% of the time. Rothermel predicted crown fire for 41% of simulations with ROS predictions 45% lower than the other models. Models tended to agree on crown fire occurrence in scenarios with a low canopy base height and greater surface and canopy fuel loading, indicating lower uncertainty in predicted fire behavior among models when fuel hazard is greatest. Differences among model predictions were more evident in scenarios with greater canopy base heights, moderate surface and canopy fuel levels, and at lower windspeeds. These results suggest that uncertainty introduced by model selection is likely greatest for designing and evaluation of fuel treatments, and that further research on fire behavior in treated forests stands is needed.