Wildland surface fire spread: Mechanism transformation and behavior transition

Liu, Naian

doi:10.1016/j.firesaf.2023.103974

Cited by 3 publications

(1 citation statement)

References 158 publications

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“…With in-depth study, laboratory burning experiments have gradually entered a bottleneck, and most studies are focused on the applicability of Rothermel and other classic fire behavior models to different regions and different mechanisms. As well as the established traditional physical and empirical models, more types of forest fire behavior models have been established, with progress made in their abilities [27]; flame geometry modeling has focused on the effect of the wind on flame characteristics [28]. Based on logistics, the established probabilistic models are based more on the occurrence of extreme fire behavior such as spotting fires and canopy fires [29].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Different Models to Simulate Forest Fire Spread: A Case Study

Ning,

Liu,

et al. 2024

Forests

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With the development of computer technology, forest fire spread simulation using computers has gradually developed. According to the existing research on forest fire spread, the models established in various countries have typical regional characteristics. A fire spread model established in a specific region is only suitable for the local area, and there is still a great deal of uncertainty as to whether or not the established model is suitable for fire spread simulation for the same fuel in other regions. Although many fire spread models have been established, the fuel characteristics applicable to each model, such as the fuel loading, fuel moisture content, combustibility, etc., are not similar. It is necessary to evaluate the applicability of different fuel characteristics to different fire spread models. We combined ground investigation, historical data collection, model improvements, and statistical analysis to establish a multi-model forest fire spread simulation method (FIRER) that shows the burning time, perimeter, burning area, overlap area, and spread rate of fire sites. This method is a large-scale, high-resolution fire growth model based on fire spread in eight directions on a regular 30 m grid. This method could use any one of four different physical models (McArthur, Rothermel, FBP, and Wang Zhengfei (China)) for fire behavior. This method has an option to represent fire breaks from roads, rivers, and fire suppression. We can evaluate which model is more suitable in a specific area. This method was tested on a single historical lightning fire in the Daxing’an Mountains. Different scenarios were tested and compared: using each of the four fire behavior models, with fire breaks on or off, and with a single or suspected double fire ignition location of the historical fire. The results show that the Rothermel model is the best model in the simulation of the Hanma lightning fire; the overlap area is 5694.4 hm2. Meanwhile, the real fire area in FIRER is 5800.9 hm2; both the Kappa and Sørensen values exceed 0.8, providing high accuracy in fire spread simulations. FIRER performs well in the automatic identification of fire break zones and multiple ignited points. Compared with FARSITE, FIRER performs well in predicting accuracy. Compared with BehavePlus, FIRER also has advantages in simulating large-scale fire spread. However, the complex data preparation stage of FIRER means that FIRER still has great room for improvement. This research provides a practical basis for the comparison of the practicability and applicability of various fire spread models and provides more effective practical tools and a scientific basis for decision-making and the management of fighting forest fires.

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Section: Introductionmentioning

confidence: 99%

Comparison of Different Models to Simulate Forest Fire Spread: A Case Study

Ning,

Liu,

et al. 2024

Forests

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Enhancing the Realism of Wildfire Simulation Using Composite Bézier Curves

González,

Carrillo,

Cortés

et al. 2024

Lecture Notes in Computer Science

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A high-fidelity ensemble simulation framework for interrogating wildland-fire behaviour and benchmarking machine learning models

Wang,

Ihme,

Gazen

et al. 2024

Int. J. Wildland Fire

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Background Wildfire research uses ensemble methods to analyse fire behaviours and assess uncertainties. Nonetheless, current research methods are either confined to simple models or complex simulations with limitations. Modern computing tools could allow for efficient, high-fidelity ensemble simulations. Aims This study proposes a high-fidelity ensemble wildfire simulation framework for studying wildfire behaviour, assessing fire risks, analysing uncertainties, and training machine learning (ML) models. Methods We present a simulation framework that integrates the Swirl-Fire large-eddy simulation tool for wildfire predictions with the Vizier optimisation platform for automated run-time management of ensemble simulations and large-scale batch processing. All simulations are executed on tensor-processing units to enhance computational efficiency. Key results A dataset of 117 simulations is created, each with 1.35 billion mesh points. The simulations are compared to existing experimental data and show good agreement in terms of fire rate of spread. Analysis is performed for fire acceleration, mean rate of spread, and fireline intensity. Conclusions Strong coupling between wind speed and slope is observed for fire-spread rate and intermittency. A critical Froude number that delineates fires from plume-dominated to wind-dominated is identified and confirmed with literature observations. Implications The ensemble simulation framework is efficient in facilitating large-scale parametric wildfire studies.

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Wildland surface fire spread: Mechanism transformation and behavior transition

Cited by 3 publications

References 158 publications

Comparison of Different Models to Simulate Forest Fire Spread: A Case Study

Comparison of Different Models to Simulate Forest Fire Spread: A Case Study

Enhancing the Realism of Wildfire Simulation Using Composite Bézier Curves

A high-fidelity ensemble simulation framework for interrogating wildland-fire behaviour and benchmarking machine learning models

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