The FireFlux II experiment: a model-guided field experiment to improve understanding of fire–atmosphere interactions and fire spread

Clements, Craig B.; Kochanski, Adam K.; Seto, Daisuke; Davis, Braniff; Camacho, C. P.; Lareau, Neil P.; Contezac, Jonathan; Restaino, Joseph C.; Heilman, Warren E.; Krueger, Steven K.; Butler, Bret W.; Ottmar, Roger D.; Vihnanek, Robert E.; Flynn, James; Filippi, Jean-Baptiste; Barboni, Toussaint; Hall, Dianne; Mandel, Jan; Jenkins, Mary Ann; O’Brien, Joseph J.; Hornsby, Ben; Teske, Casey

doi:10.1071/wf18089

Cited by 50 publications

(50 citation statements)

References 26 publications

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“…However, not only carbon is interesting for forest modelers, and other regional issues such as fire spread are also important drivers for modelling. In this sense, Cardil et al (2019b) have developed models to estimate the effects of fire suppression in Quebec (eastern Canada), Clements et al (2019) have studied fire spread rates in Texas (SW USA), Ruffaut and Mouillot (2017) in SE France and Molina et al (2019) in southern Spain. Another issue that has attracted the attention of forest modelers working at regional to continental scales has been regeneration processes.…”

Section: Bringing Down the Barriers Between Scales: Still A Challenge?mentioning

confidence: 99%

Modelling Forest Ecosystems: a crossroad between scales, techniques and applications

Blanco

Améztegui

Rodríguez

2020

Ecological Modelling

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into different modelling approaches to try to understand forest systems, from continental, regional or landscape-scales to individual-base models. Similarly, forest models have been developed at all kind of complexity levels, from very complex, integrative models to simpler models focused on one specific ecophysiological process. Thus, continuous efforts by the forest modelling community have provided a wealth of research and expertise, but at the same time have generated a dispersion and lack of linkages between different modelling approaches and applications.Forest science is integrative by nature: understanding climate, soil and water processes is as important as understanding plant eco-physiology to understand how forests work. Therefore, forest science has evolved during the last centuries to understand how trees and forests grow, and which patterns and processes rule their development through time and space. Following the common scientific method, empirical reductionist approaches tried to isolate and study one ecological factor or variable at a time (Popper 1968), trying to reduce the complexity inherent to ecological processes. On the contrary, theoretical ecological models explore the ecological interactions among the components that exist in a forest, assembling the pieces of evidence generated through traditional field and lab work (Kimmins et al. 2010). In this special issue, we want to show some of the latest developments in forest modelling, understanding forests like working systems in which human intervention has played and is still playing a historical role, and will likely do so in the future.Following the sharp increase in data availability, modelling is now being applied in forest ecosystems at multiple scales, from continental and regional studies on forests and species distribution to leaf scales modelling plague and pathogen infestation in individual branches. Similarly, models are being applied to understand short-time processes such as nutrient transport in cells, to extremely long scales such as genetic drift in tree populations. From the application point of view, models are also being applied from purely ecological issues, such as coexistence and resource competition among tree species, to multidisciplinary socio-economic-ecological issues, such as design of tourist routes inside natural reserves, estimating timber flow to sawmills under climate change scenarios, or engineering concerns such as designing forest harvesting machinery with lower ecological footprint (in energy, rut formation, noise, etc.).When organizing this special issue, we (the guest editors) decided to carry out a quick overview of current trends in forest modelling. To do so, we used Clarivate's Web of Science to carry out a search in the top five journals with "Forestry" as their unique category

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Section: Bringing Down the Barriers Between Scales: Still A Challenge?mentioning

confidence: 99%

Modelling Forest Ecosystems: a crossroad between scales, techniques and applications

Blanco

Améztegui

Rodríguez

2020

Ecological Modelling

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“…In response to Cruz et al (2020b) (hereafter: CSG20), we would like to clarify that we are committed to further research into the mechanisms that drive fire spread in grassland fuels, and are open to making the most of synergies that exist between various programs of research. As demonstrated by Filippi et al (2013), Kochanski et al (2013), Clements et al (2019), etc., there is much to be gained by combining detailed experimental data with rigorous computational modelling.…”

mentioning

confidence: 92%

A response to comments of Cruz et al. on: ‘Simulation study of grass fire using a physics-based model: striving towards numerical rigour and the effect of grass height on the rate of spread'

Sutherland

Sharples

Mell

et al. 2021

Int. J. Wildland Fire

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“…After all, a common fire mitigation measure is to mow or cut grass to reduce the spread and intensity potential of a wildfire (e.g. NSW Rural Fire Service (RFS) 2010; Clements et al 2019;Country Fire Authority (CFA) 2019). As they stand, the MSM18 results would necessitate a rethink of how grass fire mitigation measures should be conducted; instead of decreasing grass fuel height as a fuel hazard reduction treatment, grasses should be left in their natural state and their growth even encouraged.…”

Section: Introductionmentioning

confidence: 99%

The effect of fuel bed height in grass fire spread: addressing the findings and recommendations of Moinuddin et al. (2018)

Cruz

Sullivan

Gould

2021

Int. J. Wildland Fire

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A recent numerical simulation study by Moinuddin et al. (2018) determined that over a specific range of Froude numbers defined by them as 'plume mode', grass fuel height has a strong inverse effect on the rate of fire spread in grasslands. They then suggested that a relationship for effect of fuel height derived from their simulation results could be used to support fire management decision-making. The present analysis used fire spread measurement data from two outdoor experimental burning studies in grass fuels where an explicit control of fuel height was imposed to verify the realism of their results. It was found that a reduction in grass height, with or without removal of the cut fuel and regardless of the Froude number, led to a significant reduction in rate of fire spread, a result opposite to the simulations obtained by Moinuddin et al. (2018).

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The FireFlux II experiment: a model-guided field experiment to improve understanding of fire–atmosphere interactions and fire spread

Cited by 50 publications

References 26 publications

Modelling Forest Ecosystems: a crossroad between scales, techniques and applications

Modelling Forest Ecosystems: a crossroad between scales, techniques and applications

A response to comments of Cruz et al. on: ‘Simulation study of grass fire using a physics-based model: striving towards numerical rigour and the effect of grass height on the rate of spread'

The effect of fuel bed height in grass fire spread: addressing the findings and recommendations of Moinuddin et al. (2018)

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