Fire Spread Research Articles

North Atlantic and the Barents Sea variability contribute to the 2023 extreme fire season in Canada

In the late spring to summer season of 2023, Canada witnessed unprecedented wildfires, with an extensive burning area and smoke spreading as far as the East Coast of the United States and Europe. Here, using multisource data analysis and climate model simulations, we show that an abnormally warm North Atlantic, as well as an abnormally low Barents Sea ice concentration (SIC), are likely key climate drivers of this Canadian fire season, contributing to ~80% of the fire weather anomaly over Canada from June to August 2023. Specifically, the warm North Atlantic forms an anomalous regional zonal cell with ascending air over the Atlantic and descending air encircling Canada, creating hot and dry local conditions. Meanwhile, reduced Barents SIC leads to a high-pressure center and reinforces the dry northern winds in Canada through Rossby wave dynamics. These exacerbated dry and hot conditions create a favorable environment for the ignition and spread of fires, thus contributing to the prolonged and extreme fire season in Canada. These teleconnections can extend to decadal scales and have important implications for understanding and predicting decadal fire activity in Canada and the surrounding regions.

Integrating Multi-Source Remote Sensing Data for Forest Fire Risk Assessment

Forest fires are a frequent and destructive phenomenon in Southwestern China, posing significant threats to ecological systems and human lives and property. In response to the growing need for effective forest fire prevention, this study introduces an innovative method for predicting and assessing forest fire risk. By integrating multi-source data, including optical and microwave remote sensing, meteorological, topographic, and human activity data, the approach enhances the sensitivity of risk models to vegetation water content and other critical factors. The vegetation water content is derived from both Vegetation Optical Depth and optical remote sensing data, allowing for a more accurate assessment of changes in vegetation moisture that influence fire risk. A time series prediction model, incorporating attention mechanisms, is used to assess the probability of fire occurrence. Additionally, the method includes fire spread simulations based on Cellular Automaton and Monte Carlo approaches to evaluate potential burn areas. This combined approach can provide a comprehensive fire risk assessment using the probability of both fire occurrence and potential fire spread. Experimental results show that the integration of microwave data and attention mechanisms improves prediction accuracy by 2.8%. This method offers valuable insights for forest fire management, aiding in targeted prevention strategies and resource allocation.

Managing fires in a woody encroachment context: Fine fuel load does not change across fire seasons in a Guinean savanna (West Africa)

Fine surface fuels play a key role in driving fire spread, and therefore play an important role in wildfire management in savannas. In protected areas of the Guinean savannas (humid savannas of West Africa), despite prescribed early-dry season (EDS) or mid-dry season fire (MDS), woody encroachment is increasingly occurring. Recently, N'Dri et al. (2022) showed that late-dry season (LDS) fire applied at least three successive years alternating with a maximum of two successives EDS or MDS fires could reduce woody encroachment in protected areas of the regions. However, the relationship between time of burning (TOB) and fine fuel load (standing grass and fallen leaves of grasses and trees) remains uncertain. In a 10-year experimental field study (2013–2023), we monitored fine fuel load under annual fires set in three different TOB within the dry season: EDS, MDS and LDS. We also explored how fuel loads were influenced by fireline intensity and vice-versa, in addition to grass growth dynamic during one year after each fire. Results showed that grass grows faster in height after fires, reaching the highest peak (∼2 m) in November regardless of the TOB. Overall, the TOB within the dry season did not affect significantly the total fine fuel load even after 10 years (1.22 ± 0.12, 1 ± 0.09 and 0.96 ± 0.09 kg m−2 respectively for EDS, MDS and LDS fires). Nevertheless, in EDS plots, the greatest biomass of grass and litter was generally observed in alternate years. This is most likely due to a high decomposition rate and termite activity which played a rôle in maintaining basic levels. Overall, fireline intensity did not influence the subsequent fine fuel load recorded in November and fine fuel load recorded just before fires did not influence fireline intensity. Given the results of the current study, the best procedure in setting successive fires to reduce woody encroachment in Guinean savannas, remains that of N'Dri et al. (2022).

South America Is Drying Up

A new study shows that dry, warm, and flammable conditions have skyrocketed across the continent, favoring the spread of uncontrolled fire.

Projected changes in forest fire season, the number of fires, and burnt area in Fennoscandia by 2100

Abstract. Forest fire dynamics are expected to alter due to climate change. Despite the projected increase in precipitation, rising temperatures will amplify forest fire risk from the present to the end of the century. Here, we analysed changes in fire season, the number of fires, and burnt area in Fennoscandia from 1951 to 2100. Regional simulations from the JSBACH–SPITFIRE ecosystem model (where SPITFIRE stands for SPread and InTensity of FIRE) were performed under two climate change forcing scenarios (Representative Concentration Pathway (RCP) 4.5 and RCP 8.5) and three global climate driver models (CanESM2, CNRM-CM5, and MIROC5) with a 0.5° resolution. Simulations were forced by downscaled and bias-corrected EURO-CORDEX data. Generally, as a consequence of the projected longer fire season and drier fuel, the probability of fires is projected to increase. However, changes in fire season, the number of fires, and burnt area are highly dependent on climate projections and location. The fire season is estimated to increase on average from 20 ± 7 to 52 ± 12 d, starting from 10 ± 9 to 23 ± 11 d earlier and ending from 10 ± 10 to 30 ± 16 d later, compared to the reference period (1981–2010), by the end of the century (2071–2100). The results for Finland indicate a change in the number of fires, ranging from −7 ± 4 % to 98 ± 56 %, and a change in burnt area, ranging from −19 ± 24 % to 87 ± 42 %. These findings contribute to a better understanding of potential changes in the future fire seasons of northern Europe.

Data-driven fire modeling: Learning first arrival times and model parameters with neural networks

Data-driven techniques are increasingly being applied to complement physics-based models in fire science. However, the lack of sufficiently large datasets continues to hinder the application of certain machine learning techniques. In this paper, we use simulated data to investigate the ability of neural networks to parameterize dynamics in fire science. In particular, we investigate neural networks that map five key parameters in fire spread to the first arrival time, and the corresponding inverse problem. By using simulated data, we are able to characterize the error, the required dataset size, and the convergence properties of these neural networks. For the inverse problem, we quantify the network’s sensitivity in estimating each of the key parameters. The findings demonstrate the potential of machine learning in fire science, highlight the challenges associated with limited dataset sizes, and quantify the sensitivity of neural networks to estimate key parameters governing fire spread dynamics.

Numerical simulation of fire spread in a large-scale open CLT compartment

Numerical simulation of fire spread in a large-scale open CLT compartment

Cooperative control of environmental extremes by artificial intelligent agents.

Humans have been able to tackle biosphere complexities by acting as ecosystem engineers, profoundly changing the flows of matter, energy and information. This includes major innovations that allowed to reduce and control the impact of extreme events. Modelling the evolution of such adaptive dynamics can be challenging, given the potentially large number of individual and environmental variables involved. This article shows how to address this problem by using fire as the source of extreme events. We implement a simulated environment where fire propagates on a spatial landscape, and a group of artificial agents learn how to harvest and exploit trees while avoiding the damaging effects of fire spreading. The agents need to solve a conflict to reach a group-level optimal state: while tree harvesting reduces the propagation of fires, it also reduces the availability of resources provided by trees. It is shown that the system displays two major evolutionary innovations that end up in an ecological engineering strategy that favours high biomass along with the suppression of large fires. The implications for potential artificial intelligence management of complex ecosystems are discussed.

Numerical Study on the Effects of Horizontal Barriers on Fire Behavior in Logistics Facility Racks

In this study, a numerical analysis was conducted to derive the optimal installation conditions for flame-spread barriers within racks, to reduce the damage caused by recurrent fire accidents in logistics facilities. The effectiveness of the fire extinguishing system according to domestic regulations was reviewed to ensure the feasibility of installation of the horizontal barrier. This review revealed that sprinkler systems were ineffective in delaying or preventing the initial spread of fire under certain fire conditions, confirming the need for horizontal barriers. Furthermore, although the installation of horizontal barriers was effective in delaying the initial fire spread, it led to an increase in fire size by blocking the extinguishing water and altering the fire spread path. It was confirmed that deriving the optimal installation conditions for barriers requires the consideration of the interactions between the in-rack sprinkler heads and the surrounding fire extinguishing systems.

Enhanced prediction of extreme fire weather conditions in spring using the Hot-Dry-Windy Index in Alberta, Canada

Background Fire weather indices forecast fire behaviour and provide valuable information for wildland fire prevention, preparedness, and suppression. However, these indices do not directly account for atmospheric conditions aloft. The province of Alberta, Canada has experienced extreme fire weather conditions during spring for decades, leading to the continued occurrence of disastrous wildland fires. Aims We examined the Hot-Dry-Windy Index (HDWI) and spread days over the first 4 days of 80 large wildland fires that started in May 1990–2019 in Alberta. Methods HDWI values were calculated using ERA5 reanalysis data from the 1000, 975 and 950 hPa levels. Differences between HDWI distributions on spread days and non-spread days were examined using permutation tests. Initial Spread Index was also examined as it is considered an important Fire Weather Index System value for wildland fire spread during spring in Alberta. Key results Higher median HDWI values were observed on spread days than non-spread days, where median Initial Spread Index values showed little to no difference. Conclusions This analysis suggests that HDWI can contribute to the prediction of significant spring wildland fire spread in Alberta. Implications Forecasted HDWI and HDWI climatologies may provide additional decision support for wildland fire management agencies.

Simulating Daily Large Fire Spread Events in the Northern Front Range, Colorado, USA

Extreme spread events (ESEs), often characterized by high intensity and rapid rates of spread, can overwhelm fire suppression and emergency response capacity, threaten responder and public safety, damage landscapes and communities, and result in high socioeconomic costs and losses. Advances in remote sensing and geospatial analysis provide an improved understanding of observed ESEs and their contributing factors; however, there is a need to improve anticipatory and predictive capabilities to better prepare, mitigate, and respond. Here, leveraging individual-fire day-of-arrival raster outputs from the FSim fire modeling system, we prototype and evaluate methods for the simulation and categorization of ESEs. We describe the analysis of simulation outputs on a case study landscape in Colorado, USA, summarize daily spread event characteristics, threshold and probabilistically benchmark ESEs, spatially depict ESE potential, and describe limitations, extensions, and potential applications of this work. Simulation results generally showed strong alignment with historical patterns of daily growth and the proportion of cumulative area burned in the western US and identified hotspots of high ESE potential. Continued analysis and simulation of ESEs will likely expand the horizon of uses and grow in salience as ESEs become more common.

Defining a pattern in the loss of integrity by ribbed plates under fire conditions

This paper reports a study aimed at assessing fire resistance of reinforced concrete ribbed slabs at the onset of integrity loss limit state. EN 1992-1-2 lacks calculation methodology for determining the limit of fire resistance of reinforced concrete slabs when the limit state of integrity loss occurs. Scientific works are focused on two limit states of fire resistance: load-bearing capacity and heat-insulating capacity. Experimental tests are criticized because of difficulties in registering signs of the onset of the limit state of loss of integrity, in particular due to the need to control the unheated surface of the ribbed plate during a fire under the action of mechanical load. Therefore, there is no calculation methodology for assessing the fire resistance of reinforced concrete ribbed slabs upon the onset of the limit state of loss of integrity. At the same time, to ensure the safe evacuation of people in the event of a fire, to prevent the spread of fire, as well as to carry out the effective work of rescuers, it is necessary to use building structures with guaranteed fire resistance classes. The paper reports the results of solving thermal engineering and static problems, which relate to the temperature distribution and stress-strain state of the investigated ribbed plate. Conducting research into the fire resistance of reinforced concrete ribbed slabs, taking into account the onset of the limit state of loss of integrity, made it possible to establish the dependence of the fire resistance limit of these structures on the loss of integrity on the level of applied mechanical load. The resulting dependence plot makes it possible to evaluate reinforced concrete ribbed slabs according to the criterion of the onset of the limit state of loss of integrity, which provides an opportunity to determine fire resistance more objectively

Experimental Investigation of the Combustion Characteristics and Thermal Hazards of Methylsilyl-Modified Silica Aerogels

The thermal safety of hydrophobic silica aerogels (SAs) is essential to thermal insulation applications. Herein, trimethylchlorosilane (TMCS), dimethyldichlorosilane (DMDCS), and methyltrichlorosilane (MTCS) were employed as surface modifiers to prepare three different methylsilyl-modified SAs (i.e., TSA, DSA, and MSA) and their combustion characteristics and thermal hazards were experimentally studied in detail. The cone calorimeter test found that the three SAs have similar combustion processes and the variations in ignition time and fire spread rate with the heat flux obey simple logarithmic and linear relationships, respectively. It further found that TSA has the most methylsilyl groups on silica skeletons and thus has the largest heat release, followed by DSA and MSA in turn, implying that TSA has the greatest fire hazard among the three SAs. These results further demonstrate that the type and quantity of methylsilyl groups on the skeletons of SAs significantly affect the thermal hazard of methylsilyl-modified SAs. In addition, the combustion mechanism of the methylsilyl-modified SAs is discussed. In total, this work experimentally studies the combustion characteristics of methylsilyl-modified SAs and compares their thermal hazards, clarifying the potential fire risk of methylsilyl-modified SAs in practical thermal insulation applications.

Influence of Wind Direction on Fire Spread on High-Rise Building Facades

In order to study the influence of wind direction on fire spread on building facades, a three-dimensional model of a high-rise building was built with PyroSim software. Numerical simulations were conducted with five wind directions (0°, 45°, 90°, 135° and 180°) and a reference wind speed of 3 m/s. The results show that the fire spread on the building facade was most significant on the leeward side, followed by the upwind condition. The horizontal spread of fire was promoted by crosswinds at 45°, 90° and 135°, while its vertical spread was inhibited. Among these, the inhibitory effect of the 45° crosswind was the greatest. The fire spread area under the leeward condition was the greatest at approximately 341.3 m2, accounting for 17.1% of the facade, and the vertical spread velocity under the condition of no wind was the fastest at 0.075 m/s. In this study, the motion characteristics of facade fire spread under different wind directions were determined, providing supporting evidence to enhance fire safety prevention and control measures in high-rise buildings.

Numerical simulation and risk assessment of toluene tank leakage in petrochemical industries, China.

Large-capacity toluene storage tank leakage accidents can lead to severe casualties and environmental damage. In this study, numerical simulation of the toluene leakage accidents is performed using PHAST and ALOHA softwares across 16 scenarios. The present research focuses on the dispersion of toluene lethal and toxicity clouds, as well as the associated risks from vapor cloud explosions and pool fires resulting from the large-capacity toluene storage tank leakage. Meanwhile, the effects of influential factors such as leakage height, wind speed, ambient temperature, and atmospheric stability on toluene leakage accidents are also discussed, and then the worst-case scenario of the toluene leakage accident is obtained. The effects of wind speed, ambient temperature, leakage height, and atmospheric stability on outcomes such as toxic clouds, vapor dispersion, explosions, and pool fires are also analyzed. The result reveals that toxic cloud dispersion increases with ambient temperature, while the impact of vapor cloud explosions and pool fires decreases with the increase of leak heights. Wind speed significantly affects pool fire spread. The influential factors related to maximizing hazard range including low leak heights, low wind speeds, high temperatures, and stable conditions are obtained. Risk analysis indicates that vapor cloud explosions significantly impact outdoor potential loss of life (PLLoutdoor) compared with other incidents. Scenario 4 within a directional range from 33.8 to 56.3° shows the highest incident frequencies, highlighting its importance for risk monitoring. These findings are crucial for enhancing emergency response strategies and safety protocols in industrial safety management.

A Case Study on the Integration of Remote Sensing for Predicting Complicated Forest Fire Spread

Forest fires can occur suddenly and have significant environmental, economic, and social consequences. The timely and accurate evaluation and prediction of their progression, particularly the spread speed in difficult-to-access areas, are essential for emergency management departments to proactively implement prevention strategies and extinguish fires using scientific methods. This paper provides an analysis of models for predicting forest fire spread in China and globally. Incorporating remote sensing (RS) technology and forest fire science as the theoretical foundation, and utilizing the Wang Zhengfei forest fire spread model (1983), which is noted for its broad adaptability in China as the technical framework, this study constructs a forest fire spread model based on remote sensing interpretation. The model improves the existing model by adding elevation an factor and optimizes the method for acquiring certain parameters. By considering regional landforms (ridge lines, valley lines, and slopes) and vegetation coverage, this paper establishes three-dimensional visual interpretation markers for identifying hotspots; the orientation of the hotspots can be identified to simulate the spread of the fire uphill, downhill, in the direction of the wind, left-level slope, and right-level slope. Then, the data of Sentinel-2 and DEM were used to invert the fuel humidity and slope of pixels in the fire line areas. The statistical inversion data from pixels, which replaced fixed-point values in traditional models, were utilized for predicting forest fire spread speed. In this paper, the model was applied to the case of a forest fire in Mianning County, Sichuan Province, China, and verified using high-time-resolution Himawari-8 data, Gaofen-4 data, and historical data. The results demonstrate that the direction and maximum speed of fire spread for the fire lines in Baifen Mountai, Jiaguer Villageand, Muchanggou, Xujiabaozi, and Zhaizigou are uphill, 16.5 m/min; wind direction, 17.32 m/min; wind direction, 1.59 m/min; and wind direction, 5.67 m/min. The differences are mainly due to the locations of the fire lines, moisture content of combustibles, and maximum slopes being different. Across the entire fire line area, the average rate of increase in the area of open flames within one hour was 3.257 hm2/10 min (square hectares per 10 min), closely matching the average increase rate (3.297 hm2/10 min) monitored by the Himawari-8 satellite in 10 min intervals. In contrast, conventional fixed-point fire spread models predicted an average rate of increase of 3.5637 hm2/10 min, which shows a larger discrepancy compared to the Himawari-8 satellite monitoring results. Moreover, when compared to the fire spot monitoring results from the Gaofen-4 satellite taken 54 min after the initial location of the fire line, the predictions from the RS-enabled fire spread model, which integrates remote sensing interpretations, closely matched the actual observed fire boundaries. Although the predictions from the RS-enabled fire spread model and the traditional model both align with historical data in terms of the overall fire development trends, the RS-enabled model exhibits higher reliability and can provide more accurate information for forest fire emergency departments, enabling effective pre-emptive measures and scientific firefighting strategies.

Study on the Driving Factors of the Spatiotemporal Pattern in Forest Lightning Fires and 3D Fire Simulation Based on Cellular Automata

Lightning-induced forest fires frequently inflict substantial damage on forest ecosystems, with the Daxing’anling region in northern China recognized as a high-incidence region for such phenomena. To elucidate the occurrence patterns of forest fires caused by lightning and to prevent such fires, this study employs a multifaceted approach, including statistical analysis, kernel density estimation, and spatial autocorrelation analysis, to conduct a comprehensive examination of the spatiotemporal distribution patterns of lightning-induced forest fires in the Greater Khingan Mountains region from 2016–2020. Additionally, the geographical detector method is utilized to assess the explanatory power of three main factors: climate, topography, and fuel characteristics associated with these fires, encompassing both univariate and interaction detections. Furthermore, a mixed-methods approach is adopted, integrating the Zhengfei Wang model with a three-dimensional cellular automaton to simulate the spread of lightning-induced forest fire events, which is further validated through rigorous quantitative verification. The principal findings are as follows: (1) Spatiotemporal Distribution of Lightning-Induced Forest Fires: Interannual variability reveals pronounced fluctuations in the incidence of lightning-induced forest fires. The monthly concentration of incidents is most significant in May, July, and August, demonstrating an upward trajectory. In terms of temporal distribution, fire occurrences are predominantly concentrated between 1:00 PM and 5:00 PM, conforming to a normal distribution pattern. Spatially, higher incidences of fires are observed in the western and northwestern regions, while the eastern and southeastern areas exhibit reduced rates. At the township level, significant spatial autocorrelation indicates that Xing’an Town represents a prominent hotspot (p = 0.001), whereas Oupu Town is identified as a significant cold spot (p = 0.05). (2) Determinants of the Spatiotemporal Distribution of Lightning-Induced Forest Fires: The spatiotemporal distribution of lightning-induced forest fires is influenced by a multitude of factors. Univariate analysis reveals that the explanatory power of these factors varies significantly, with climatic factors exerting the most substantial influence, followed by topographic and fuel characteristics. Interaction factor analysis indicates that the interactive effects of climatic variables are notably more pronounced than those of fuel and topographical factors. (3) Three-Dimensional Cellular Automaton Fire Simulation Based on the Zhengfei Wang Model: This investigation integrates the fire spread principles from the Zhengfei Wang model into a three-dimensional cellular automaton framework to simulate the dynamic behavior of lightning-induced forest fires. Through quantitative validation against empirical fire events, the model demonstrates an accuracy rate of 83.54% in forecasting the affected fire zones.

Pyrolysis models for pressure treated wood and wood–plastic composite

AbstractPressure treated wood (PTW) and wood–plastic composites such as Trex® are popular materials for the construction of decks and other auxiliary structures, which are known to significantly contribute to spread of wildland fires into communities. In this work, representative samples of these materials were studied to determine their pyrolysis and combustion properties to enable simulation of fire growth on the surface of these building products. The pyrolysis property development process followed a well‐established hierarchical approach where thermogravimetric analysis, differential scanning calorimetry, and microscale combustion calorimetry were used to parametrize kinetics and thermodynamics of the thermal decomposition and combustion, while controlled atmosphere pyrolysis and cone calorimetry tests performed on coupon‐sized samples were used to parameterize thermal transport properties and validate performance of the fully parametrized pyrolysis models. PTW decomposition was captured using four sequential reactions with one additional reaction used to model vaporization of water. Trex® board was found to consist of two distinct layers: a thin outer layer and an internal core. The pyrolysis model for this material was constructed using some known properties of high‐density polyethylene (PE) and the properties of PTW determined in this work. The outer layer was defined in the model to consist of PE and an inert additive, while the core was defined as a blend of PE and wood particles, which kinetics and thermodynamics of the thermal decomposition and combustion were successfully captured using the model developed for PTW.

Assessing the Cascading Post-Earthquake Fire-Risk Scenario in Urban Centres

The frequency of urban fires has grown in recent years everywhere, especially in historic districts, including in Portugal, due to the existence of sensitive igniting materials, the proximity of buildings, the complex urban layout, and the presence of many people. The current study proposes a technique, applied in the Baixa Pombalina (downtown) area in Lisbon, to undertake an appropriate evaluation of the post-earthquake fire cascading effect, which may cause major damage. The earthquake vulnerability and damage scenario were carried out using the Risk-UE method. An empirical fire ignition model was then applied to determine the quantity and location of fire ignitions for different return periods. Furthermore, the simple fire spread Hamada’s model was applied to both the equally spaced grid buildings, as in the original Hamada procedure, and the current study area layout for different time thresholds. Finally, the risk assessment for both models was carried out, allowing for the estimation of earthquake and fire losses, respectively. The results demonstrated that the models are comparable, showing that the Hamada model might be a useful tool for large-scale evaluations aimed at disaster-risk reduction and management since it gives useful information for managing and reducing natural and anthropogenic hazards.

Integrating an urban fire model into an operational wildland fire model to simulate one dimensional wildland–urban interface fires: a parametric study

Background Wildland fires that occur near communities, in the wildland–urban interface (WUI), can inflict significant damage to urban structures. Although computational models are vital in wildfires, they often focus solely on wildland landscapes. Aim We conducted a computational study to investigate WUI fire spread, encompassing both urban and wildland landscapes. Methods We developed a 1D landscape-scale semi-physical model by integrating a semi-physical urban fire spread model into an Eulerian level set model of wildfire. The model includes ignition and spread through radiation, direct flame contact and ember deposition. Key results Through a parametric study, we compare the relative change of spread rate from various structural properties and landscape layouts represented by model parameters, highlighting the significant impact of fire-resistant structure materials over surface treatments. Layout configurations play a pivotal role in fire spread, with isolated islands of combustibles effective in reducing spread rate, aligning with existing mitigation strategies. Conclusion Despite using a 1D domain and limitations on spatial and temporal variability, our model provides insights into underlying phenomena observed in WUI fires and their mitigation. It offers early-stage development of strategies for managing structure materials and landscape layouts. Implications Our model and findings provide insights into WUI fire dynamics, paving the way for advanced mitigation strategies.