Prescribed fire science: the case for a refined research agenda

Hiers, J. Kevin; O’Brien, Joseph J.; Varner, J. Morgan; Butler, Bret W.; Dickinson, Matthew B.; Furman, James H.; Gallagher, Michael R.; Godwin, Dave; Goodrick, Scott L.; Hood, Sharon M.; Hudak, Andrew T.; Kobziar, Leda N.; Linn, Rodman R.; Loudermilk, E. Louise; McCaffrey, Sarah; Robertson, Kevin; Rowell, Eric; Skowronski, Nicholas S.; Watts, Adam C.; Yedinak, Kara M.

doi:10.1186/s42408-020-0070-8

Cited by 122 publications

(78 citation statements)

References 94 publications

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“…Interest has been growing in the application of coupled fire-atmosphere modeling to study prescribed fires burning in marginal burn conditions [1,2]. Here, marginal conditions refer to those under which fire spread depends on the localized (in time or space) heterogeneities in fuels and wind fields.…”

Section: Introductionmentioning

confidence: 99%

“…Fire model applications have historically focused on fires that pose a significant risk to lives or infrastructure. However, more recent applications include modeling of fire behavior in the context of prescribed fires [1,2]. Prescribed fires target a narrow window of fire intensities, bounded by conditions where (1) fire can be sustained and will carry through the fuels [9], and (2) fire intensity is not sufficient to escape the confines of the managed area or cause unwanted ecological damage [10,11].…”

Section: Introductionmentioning

confidence: 99%

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Modeling Low Intensity Fires: Lessons Learned from 2012 RxCADRE

et al. 2021

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Coupled fire-atmosphere models are increasingly being used to study low-intensity fires, such as those that are used in prescribed fire applications. Thus, the need arises to evaluate these models for their ability to accurately represent fire spread in marginal burning conditions. In this study, wind and fuel data collected during the Prescribed Fire Combustion and Atmospheric Dynamics Research Experiments (RxCADRE) fire campaign were used to generate initial and boundary conditions for coupled fire-atmosphere simulations. We present a novel method to obtain fuels representation at the model grid scale using a combination of imagery, machine learning, and field sampling. Several methods to generate wind input conditions for the model from eight different anemometer measurements are explored. We find a strong sensitivity of fire outcomes to wind inputs. This result highlights the critical need to include variable wind fields as inputs in modeling marginal fire conditions. This work highlights the complexities of comparing physics-based model results against observations, which are more acute in marginal burning conditions, where stronger sensitivities to local variability in wind and fuels drive fire outcomes.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling Low Intensity Fires: Lessons Learned from 2012 RxCADRE

et al. 2021

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“…While wildfires may typically burn under higher intensities than prescribed fire, theoretically, if fire injuries to trees are similar, there should be no difference among fire types (i.e., wildfire, wildland fire use, or prescribed fires). Conversely, differences in the patchiness of surface fuel combustions between wildfires and prescribed fires (Blomdahl et al 2019) or between prescribed fires with differing ignition patterns (Hiers et al 2020) could translate to differences in model performance between wildfires and prescribed fires. Future research could explore these sources of spatial error and determine if regionally specific models are needed for some species.…”

Section: And Ganiomentioning

confidence: 99%

A large database supports the use of simple models of post-fire tree mortality for thick-barked conifers, with less support for other species

et al. 2020

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Background Predictive models of post-fire tree and stem mortality are vital for management planning and understanding fire effects. Post-fire tree and stem mortality have been traditionally modeled as a simple empirical function of tree defenses (e.g., bark thickness) and fire injury (e.g., crown scorch). We used the Fire and Tree Mortality database (FTM)—which includes observations of tree mortality in obligate seeders and stem mortality in basal resprouting species from across the USA—to evaluate the accuracy of post-fire mortality models used in the First Order Fire Effects Model (FOFEM) software system. The basic model in FOFEM, the Ryan and Amman (R-A) model, uses bark thickness and percentage of crown volume scorched to predict post-fire mortality and can be applied to any species for which bark thickness can be calculated (184 species-level coefficients are included in the program). FOFEM (v6.7) also includes 38 species-specific tree mortality models (26 for gymnosperms, 12 for angiosperms), with unique predictors and coefficients. We assessed accuracy of the R-A model for 44 tree species and accuracy of 24 species-specific models for 13 species, using data from 93 438 tree-level observations and 351 fires that occurred from 1981 to 2016. Results For each model, we calculated performance statistics and provided an assessment of the representativeness of the evaluation data. We identified probability thresholds for which the model performed best, and the best thresholds with either ≥80% sensitivity or specificity. Of the 68 models evaluated, 43 had Area Under the Receiver Operating Characteristic Curve (AUC) values ≥0.80, indicating excellent performance, and 14 had AUCs <0.7, indicating poor performance. The R-A model often over-predicted mortality for angiosperms; 5 of 11 angiosperms had AUCs <0.7. For conifers, R-A over-predicted mortality for thin-barked species and for small diameter trees. The species-specific models had significantly higher AUCs than the R-A models for 10 of the 22 models, and five additional species-specific models had more balanced errors than R-A models, even though their AUCs were not significantly different or were significantly lower. Conclusions Approximately 75% of models tested had acceptable, excellent, or outstanding predictive ability. The models that performed poorly were primarily models predicting stem mortality of angiosperms or tree mortality of thin-barked conifers. This suggests that different approaches—such as different model forms, better estimates of bark thickness, and additional predictors—may be warranted for these taxa. Future data collection and research should target the geographical and taxonomic data gaps and poorly performing models identified in this study. Our evaluation of post-fire tree mortality models is the most comprehensive effort to date and allows users to have a clear understanding of the expected accuracy in predicting tree death from fire for 44 species.

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“…This led the authors to question N limitation in these soils and also consider the impact of anthropogenic N sources that might enhance soil N. When soil results are presented as a result of anthropogenic natural resource management or natural disturbance, one should consider many factors, including but not limited to (1) the soil property in question, (2) soil type, (3) time of sampling (since treatment implementation), (4) treatment intensity, (5) method of soil analysis, (6) sampling depth, (7) primary soil forming factors, (8) spatial variability of sampling, and (9) overall management objectives. Caution should be taken when comparing wildfire and prescribed fire soil effects because fire intensity and severity may vary greatly between these fire types in a given ecosystem [30].…”

Section: Temporal and Spatial Variability Of Soil Properties And Fire Effectsmentioning

confidence: 99%

Long-Term Effects of Repeated Prescribed Fire and Fire Surrogate Treatments on Forest Soil Chemistry in the Southern Appalachian Mountains (USA)

Dukes

Coates

Hagan

et al. 2020

Fire

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From 2001–2018, a series of fuel reduction and ecosystem restoration treatments were implemented in the southern Appalachian Mountains near Asheville, North Carolina, USA. Treatments consisted of prescribed fire (four burns), mechanical cutting of understory shrubs and mid-story trees (two cuttings), and a combination of both cutting and prescribed fire (two cuts + four burns). Soils were sampled in 2018 to determine potential treatment impacts for O horizon and mineral soil (0–10 cm depth) carbon (C) and nitrogen (N) and mineral soil calcium (Ca), magnesium (Mg), phosphorus (P), potassium (K), and pH. Results suggested that mean changes in O horizon C and N and mineral soil C, N, C:N, Ca, and P from 2001–2018 differed between the treatments, but only mineral soil C, N, C:N, and Ca displayed differences between at least one fuel reduction treatment and the untreated control. One soils-related restoration objective was mineral soil N reduction and the cut + burn treatment best achieved this result. Increased organic matter recalcitrance was another priority, but this was not obtained with any treatment. When paired with previously reported fuels and vegetation results from this site, it appeared that continued use of the cut + burn treatment may best achieve long-term management objectives for this site and other locations being managed for similar long-term restoration and fuels management objectives.

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Prescribed fire science: the case for a refined research agenda

Cited by 122 publications

References 94 publications

Modeling Low Intensity Fires: Lessons Learned from 2012 RxCADRE

Modeling Low Intensity Fires: Lessons Learned from 2012 RxCADRE

A large database supports the use of simple models of post-fire tree mortality for thick-barked conifers, with less support for other species

Long-Term Effects of Repeated Prescribed Fire and Fire Surrogate Treatments on Forest Soil Chemistry in the Southern Appalachian Mountains (USA)

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