Wildfires Modeling: Recent Trends, Current Progress and Future Directions

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Atmospheric Techniques, Instruments, and Modeling".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 9627

Special Issue Editors

IMATH Laboratory, EA 2134, Toulon University, 83160 Toulon, France
Interests: wildfire modeling; theoretical turbulence; computational fluid dynamics; high-performance computing; data assimilation; deep learning
Special Issues, Collections and Topics in MDPI journals
School of Engineering and Information Technology, University of New South Wales Canberra, Canberra, ACT 2610, Australia
Interests: CFD; thermodynamic analysis; waste to energy; thermofluids; environmental sustainability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the last few decades, considerable efforts have been focused on developing and implementing relatively relevant statistical and physics-based models to account for the interactions between fire, vegetation, and the atmosphere. These models have led to a better understanding of the dynamics of fire spread in a landscape and are continuing to contribute to the state of art of wildfire science. However, despite considerable progress in modeling fire behavior, fire can still create unexpected scenarios for emergency services during real situations and result in significant injury, even fatalities, in addition to the numerous socioeconomic and the irreversible ecological impacts.

This Special Issue offers an opportunity for those involved in wildfire modeling to present their work in a dedicated volume. We therefore invite you to contribute articles to this Special Issue that highlight advances, new concepts, technical issues, and innovative research directions associated with wildfire modelling frameworks. Recently, using data assimilation and deep learning techniques to better predict wildfire behavior has aroused considerable interest. These emerging approaches coupled to standard models seem very promising. Contributions based on these different approaches and their coupling are highly appreciated. Any work on wildfire modeling that can provide new insights is welcome.

It is our hope that this Special Issue, dedicated to the latest developments in wildfire modelling, will help to promote discussion of numerous modeling issues and highlight synergies and connections across the various modeling platforms.

Dr. Sofiane Meradji
Dr. Maryam Ghodrat
Guest Editor

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Keywords

  • wildfire science
  • wildland fire
  • fire physics
  • heat and mass transfers
  • turbulent flows
  • combustion
  • computational fluid dynamics
  • artificial neural network
  • deep learning
  • data assimilation

Published Papers (7 papers)

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Research

18 pages, 2564 KiB  
Article
Incorporating Stochastic Wind Vectors in Wildfire Spread Prediction
by Sahar Masoudian, Jason Sharples, Zlatko Jovanoski, Isaac Towers and Simon Watt
Atmosphere 2023, 14(11), 1609; https://doi.org/10.3390/atmos14111609 - 27 Oct 2023
Viewed by 724
Abstract
The stochastic nature of environmental factors that govern the behavior of fire, such as wind and fuel, exposes wildfire modeling to a degree of uncertainty. In order to produce more realistic wildfire predictions, it is, therefore, necessary to incorporate these uncertainties within wildfire [...] Read more.
The stochastic nature of environmental factors that govern the behavior of fire, such as wind and fuel, exposes wildfire modeling to a degree of uncertainty. In order to produce more realistic wildfire predictions, it is, therefore, necessary to incorporate these uncertainties within wildfire models in a way that reflects the influence of environmental stochasticity on wildfire propagation. Otherwise, the risks of the potential danger of a given wildfire may be under-represented. Specifically, environmental stochasticity in the form of wind variability results in considerable uncertainty in the output of fire spread models. Here, we consider two stochastic wind models and their implementation in the spark fire simulator framework to capture the environmental uncertainty related to wind variability. The results are compared with the output from purely deterministic wildfire spread models and are discussed in the context of the potential ramifications for wildfire risk management. Full article
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18 pages, 9887 KiB  
Article
Investigating the Effect of Fuel Moisture and Atmospheric Instability on PyroCb Occurrence over Southeast Australia
by Wenyuan Ma, Caleb S. Wilson, Jason J. Sharples and Zlatko Jovanoski
Atmosphere 2023, 14(7), 1087; https://doi.org/10.3390/atmos14071087 - 28 Jun 2023
Viewed by 858
Abstract
The incidence of pyro-cumulonimbus (pyroCb) caused by extreme wildfires has increased markedly in Australia over the last several decades. This increase can be associated with a dangerous escalation of wildfire risk and severe stratospheric pollution events. Atmospheric and fuel conditions are important influences [...] Read more.
The incidence of pyro-cumulonimbus (pyroCb) caused by extreme wildfires has increased markedly in Australia over the last several decades. This increase can be associated with a dangerous escalation of wildfire risk and severe stratospheric pollution events. Atmospheric and fuel conditions are important influences on pyroCb occurrence, but the exact causal relationships are still not well understood. We used the Continuous Haines Index (C-Haines) to represent atmospheric instability and the Fuel Moisture Index (FMI) to represent fuel moisture to provide better insight into the effects of atmospheric and fuel conditions on pyroCb occurrence over southeast Australia. C-Haines and FMI were related to the probability of pyroCb occurrence by employing a logistic regression on data gathered between 1980 and 2020. Emphasis is placed on investigating the independent effects and combined effects of FMI and C-Haines, as well as assessing their potential to predict whether a pyroCb develops over a fire. The main findings of this study are: (1) high C-Haines and low FMI values are representative of favorable conditions for pyroCb occurrence, but C-Haines can offset the effect of FMI—the addition of C-Haines to the logistic model muted the significance of FMI; (2) among the components of C-Haines, air temperature lapse rate (CA) is a better predictor of pyroCb occurrence than the dryness component (CB); (3) there are important regional differences in the effect of C-Haines and FMI on pyroCb occurrence, as they have better predictive potential in New South Wales than in Victoria. Full article
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18 pages, 7785 KiB  
Article
Comparison of Meteorological Drivers of Two Large Coastal Slope-Land Wildfire Events in Croatia and South-East Australia
by Ivana Čavlina Tomašević, Višnjica Vučetić, Kevin K. W. Cheung, Paul Fox-Hughes, Paul J. Beggs, Maja Telišman Prtenjak and Barbara Malečić
Atmosphere 2023, 14(7), 1076; https://doi.org/10.3390/atmos14071076 - 26 Jun 2023
Viewed by 1024
Abstract
Understanding the relationship between fire behavior and the driving weather conditions is critical for fire management and long-term fire risk assessment. In this study, we focus on two wildfire events: the Split wildfire in Croatia and the Forcett–Dunalley wildfire in Tasmania, Australia. The [...] Read more.
Understanding the relationship between fire behavior and the driving weather conditions is critical for fire management and long-term fire risk assessment. In this study, we focus on two wildfire events: the Split wildfire in Croatia and the Forcett–Dunalley wildfire in Tasmania, Australia. The antecedent weather in both events included extremely dry conditions and higher-than-average air temperatures in the months prior to the events. The synoptic patterns in both events consisted of a large surface pressure gradient, which generated strong wind, driving the fire’s spread. The Weather Research and Forecasting (WRF) model was utilized to simulate fire weather conditions during the development of the two events. In the innermost domain of WRF, resolution is 500 m with explicit moisture calculation only, and there are 66 vertical levels, with about 20 of them to resolve the boundary layer. The WRF simulations are well verified by station observations, including upper-level wind speeds. The convergence line pattern in the Tasmanian event, which was conducive to intense plume development, has been well simulated. Only a slight discrepancy was identified in the simulation of the coastal change in wind direction in the Croatian event. It is identified that in the Split case, bura wind was highly coupled with an upper-level trough, which induced subsidence of the upper-level dry and cold air to the surface, causing rapid drying of the fuel. During the Forcett–Dunalley fire, the atmosphere was unstable, which enabled deep pyrocumulonimbus development. In general, the development from ignition to the timing of the most extreme fire intensity in both events was largely determined by the evolution of the surface to upper-level meteorological drivers. While these extreme meteorological conditions would impact fire-fighting strategies such as aircraft operations, a model-based estimate of the high-risk areas is critical. Our findings would also benefit an estimate of the climatology of fire events with similar behavior and thus a long-term fire risk assessment. Full article
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17 pages, 3969 KiB  
Article
Visualization of Prediction Methods for Wildfire Modeling Using CiteSpace: A Bibliometric Analysis
by Mengya Pan and Shuo Zhang
Atmosphere 2023, 14(6), 1009; https://doi.org/10.3390/atmos14061009 - 11 Jun 2023
Viewed by 1343
Abstract
Wildfire is a growing concern worldwide with significant impacts on human lives and the environment. This study aimed to provide an overview of the current trends and research gaps in wildfire prediction by conducting a bibliometric analysis of papers in the Web of [...] Read more.
Wildfire is a growing concern worldwide with significant impacts on human lives and the environment. This study aimed to provide an overview of the current trends and research gaps in wildfire prediction by conducting a bibliometric analysis of papers in the Web of Science and Scopus databases. CiteSpace was employed to analyze the co-occurrence of keywords, identify clusters, and detect emerging trends. The results showed that the most frequently occurring keywords were “wildfire”, “prediction”, and “model” and the top three clusters were related to “air quality”, “history”, and “validation”. The analysis of emerging trends revealed a focus on vegetation, precipitation, land use, trends, and the random forest algorithm. The study contributes to a better understanding of the research trends and gaps in wildfire prediction and provides recommendations for future research, such as incorporating new data sources and using advanced techniques. Full article
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13 pages, 497 KiB  
Article
Applying Bayesian Models to Reduce Computational Requirements of Wildfire Sensitivity Analyses
by Ujjwal KC, Jagannath Aryal, K. Shuvo Bakar, James Hilton and Rajkumar Buyya
Atmosphere 2023, 14(3), 559; https://doi.org/10.3390/atmos14030559 - 15 Mar 2023
Viewed by 1466
Abstract
Scenario analysis and improved decision-making for wildfires often require a large number of simulations to be run on state-of-the-art modeling systems, which can be both computationally expensive and time-consuming. In this paper, we propose using a Bayesian model for estimating the impacts of [...] Read more.
Scenario analysis and improved decision-making for wildfires often require a large number of simulations to be run on state-of-the-art modeling systems, which can be both computationally expensive and time-consuming. In this paper, we propose using a Bayesian model for estimating the impacts of wildfires using observations and prior expert information. This approach allows us to benefit from rich datasets of observations and expert knowledge on fire impacts to investigate the influence of different priors to determine the best model. Additionally, we use the values predicted by the model to assess the sensitivity of each input factor, which can help identify conditions contributing to dangerous wildfires and enable fire scenario analysis in a timely manner. Our results demonstrate that using a Bayesian model can significantly reduce the resources and time required by current wildfire modeling systems by up to a factor of two while still providing a close approximation to true results. Full article
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21 pages, 3615 KiB  
Article
A Study of Two High Intensity Fires across Corsican Shrubland
by Jacky Fayad, Frédéric Morandini, Gilbert Accary, François-Joseph Chatelon, Clément Wandon, Antoine Burglin, Lucile Rossi, Thierry Marcelli, Dominique Cancellieri, Valérie Cancellieri, Dominique Morvan, Sofiane Meradji, Antoine Pieri, Gilles Planelles, René Costantini, Patrice Briot and Jean-Louis Rossi
Atmosphere 2023, 14(3), 473; https://doi.org/10.3390/atmos14030473 - 27 Feb 2023
Cited by 5 | Viewed by 1395
Abstract
This paper reports two experimental fires conducted at field-scale in Corsica, across a particular mountain shrubland. The orientation of the experimental plots was chosen in such a way that the wind was aligned along the main slope direction in order to obtain a [...] Read more.
This paper reports two experimental fires conducted at field-scale in Corsica, across a particular mountain shrubland. The orientation of the experimental plots was chosen in such a way that the wind was aligned along the main slope direction in order to obtain a high intensity fire. The first objective was to study the high intensity fire behavior by evaluating the propagation conditions related to its speed and intensity, as well as the geometry of the fire front and its impact on different targets. Therefore, an experimental protocol was designed to determine the properties of the fire spread using UAV cameras and its impact using heat flux gauges. Another objective was to study these experiments numerically using a fully physical fire model, namely FireStar3D. Numerical results concerning the fire dynamics, particularly the ROS, were also compared to other predictions of the FireStar2D model. The comparison with experimental measurements showed the robustness of the 3D approach with a maximum difference of 5.2% for the head fire ROS. The fire intensities obtained revealed that these experiments are representative of high intensity fires, which are very difficult to control in the case of real wildfires. Other parameters investigated numerically (flame geometry and heat fluxes) were also in fairly good agreement with the experimental measurements and confirm the capacity of FireStar3D to predict surface fires of high intensity. Full article
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9 pages, 3099 KiB  
Article
Crown Fire Modeling and Its Effect on Atmospheric Characteristics
by Egor Loboda, Denis Kasymov, Mikhail Agafontsev, Vladimir Reyno, Anastasiya Lutsenko, Asya Staroseltseva, Vladislav Perminov, Pavel Martynov, Yuliya Loboda and Konstantin Orlov
Atmosphere 2022, 13(12), 1982; https://doi.org/10.3390/atmos13121982 - 27 Nov 2022
Cited by 2 | Viewed by 1068
Abstract
The article is concerned with the experimental study of the crown fire effect on atmospheric transport processes: the formation of induced turbulence in the vicinity of the fire source and the transport of aerosol combustion products in the atmosphere surface layer at low [...] Read more.
The article is concerned with the experimental study of the crown fire effect on atmospheric transport processes: the formation of induced turbulence in the vicinity of the fire source and the transport of aerosol combustion products in the atmosphere surface layer at low altitudes. The studies were carried out in seminatural conditions on the reconstructed forest canopy. It was established that the structural characteristics of fluctuations of some atmosphere physical parameters in the case of a crown fire practically coincide with the obtained earlier values for a steppe fire. The highest concentration of aerosol combustion products was recorded at a height of 10–20 m from the ground surface. It was found that the largest number of aerosol particles formed during a crown fire had a particle diameter of 0.3 to 0.5 µm. As a result of experimental data extrapolation, it is concluded that an excess of aerosol concentration over the background value will be recorded at a distance of up to 2000 m for a given volume of burnt vegetation. It is of interest to further study these factors of the impact of wildfires on atmosphere under the conditions of a real large natural wildfire and determine the limiting distance of aerosol concentration excesses over background values. Full article
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