Verification of a Lagrangian particle model for short-range firebrand transport

Wadhwani, Rahul; Sutherland, Duncan; Ooi, Andrew; Moinuddin, Khalid; Thorpe, Graham

doi:10.1016/j.firesaf.2017.03.019

Cited by 24 publications

(23 citation statements)

References 26 publications

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“…The total number of spot fires was correlated with plume behaviour, fuel characteristics (hazard score, age), and wildfire behaviour (rate of spread, intensity, flame height). There have been several international studies focused on determining firebrand landing distributions based on small-scale laboratory experiments and/or mathematical modelling [19][20][21][22][23][24][25][26][27]. These have generally indicated right-skewed distributions of firebrands (e.g., exponential, Rayleigh, lognormal), but normal or bi-modal distributions have also been reported.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Variation in Distance, Number, and Distribution of Spotting in Southeast Australian Wildfires

Storey

Price

Bradstock

et al. 2020

Fire

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Spotting during wildfires can significantly influence the way wildfires spread and reduce the chances of successful containment by fire crews. However, there is little published empirical evidence of the phenomenon. In this study, we have analysed spotting patterns observed from 251 wildfires from a database of over 8000 aerial line scan images capturing active wildfire across mainland southeast Australia between 2002 and 2018. The images were used to measure spot fire numbers, number of “long-distance” spot fires (> 500 m), and maximum spotting distance. We describe three types of spotting distance distributions, compare patterns among different regions of southeast Australia, and associate these with broad measures of rainfall, elevation, and fuel type. We found a relatively high correlation between spotting distance and numbers; however, there were also several cases of wildfires with low spot fire numbers producing very long-distance spot fires. Most long-distance spotting was associated with a “multi-modal” distribution type, where high numbers of spot fires ignite close to the source fire and isolated or small clumps of spot fires ignite at longer distances. The multi-modal distribution suggests that current models of spotting distance, which typically follow an exponential-shaped distribution, could underestimate long-distance spotting. We also found considerable regional variation in spotting phenomena that may be associated with significant variation in rainfall, topographic ruggedness, and fuel descriptors. East Victoria was the most spot-fire-prone of the regions, particularly in terms of long-distance spotting.

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Section: Introductionmentioning

confidence: 99%

Analysis of Variation in Distance, Number, and Distribution of Spotting in Southeast Australian Wildfires

Storey

Price

Bradstock

et al. 2020

Fire

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“…In a recent study, Martin and Hillen (2016) also discuss the underlying physical processes for firebrands in detail and derive a landing distribution based on these physical processes. Besides these statistical approaches, a few numerical models based on large eddy simulation (LES) (Himoto and Tanaka, 2005;Pereira et al, 2015;Thurston et al, 2017;Tohidi and Kaye, 2017) or computational fluid dynamics (CFD) (Wadhwani et al, 2017), small world networks (Porterie et al, 2007) and cellular automata models (Perryman et al, 2013) also exist in the literature. Bhutia et al (2010) present one such study based on a coupled fire-atmosphere LES for predicting shortrange fire spotting.…”

Section: Introductionmentioning

confidence: 99%

RandomFront 2.3: a physical parameterisation of fire spotting for operational fire spread models – implementation in WRF-SFIRE and response analysis with LSFire+

Trucchia¹,

Egorova²,

Butenko³

et al. 2019

Geosci. Model Dev.

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Abstract. Fire spotting is often responsible for dangerous flare-ups in wildfires and causes secondary ignitions isolated from the primary fire zone, which lead to perilous situations. The main aim of the present research is to provide a versatile probabilistic model for fire spotting that is suitable for implementation as a post-processing scheme at each time step in any of the existing operational large-scale wildfire propagation models, without calling for any major changes in the original framework. In particular, a complete physical parameterisation of fire spotting is presented and the corresponding updated model RandomFront 2.3 is implemented in a coupled fire–atmosphere model: WRF-SFIRE. A test case is simulated and discussed. Moreover, the results from different simulations with a simple model based on the level set method, namely LSFire+, highlight the response of the parameterisation to varying fire intensities, wind conditions and different firebrand radii. The contribution of the firebrands to increasing the fire perimeter varies according to different concurrent conditions, and the simulations show results in agreement with the physical processes. Among the many rigorous approaches available in the literature to model firebrand transport and distribution, the approach presented here proves to be simple yet versatile for application to operational large-scale fire spread models.

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“…They showed that turbulence and wind speed had a significant effect in the distribution of the firebrands and noted the role of the well-known plume counterrotating vortex pair in the transport dynamics. Wadhwani et al (2017) modelled the trajectory of firebrands using the NIST Fire Dynamics Simulator (FDS) to compare with the experimental results. More recently Wadhwani et al (2019) have also modelled landing distributions for embers at the edge of a forest, providing detailed information of the flow structure and transition of the wind field.…”

Section: Introductionmentioning

confidence: 99%

Wind-terrain effects on firebrand dynamics

Hilton

Sharples

Garg

et al. 2019

El Sawah, S. (Ed.) MODSIM2019, 23rd International Congress on Modelling and Simulation.

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Despite its importance in bushfire propagation, firebrand transport and the spotting process are still poorly understood, and there is no definitive model that can adequately emulate the spotting process in general. The dynamics of firebrands are difficult to predict due to the complex flow structure resulting from the interaction of a buoyant plume with a boundary layer wind field. Understanding the nature of this flow structure, especially for complex terrain, is essential for determining the likely path of firebrands and subsequent distributions of new spot fires and risk levels on structures downwind from the fire.

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Verification of a Lagrangian particle model for short-range firebrand transport

Cited by 24 publications

References 26 publications

Analysis of Variation in Distance, Number, and Distribution of Spotting in Southeast Australian Wildfires

Analysis of Variation in Distance, Number, and Distribution of Spotting in Southeast Australian Wildfires

RandomFront 2.3: a physical parameterisation of fire spotting for operational fire spread models – implementation in WRF-SFIRE and response analysis with LSFire+

Wind-terrain effects on firebrand dynamics

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