Fire Behavior Research Articles

Fuel traits are important determinants of fire behavior and regime in savannas and, thus, of how fire affects plant communities. However, whether these traits are correlated, predictable and how they are influenced by biotic and abiotic drivers remain to be rigorously evaluated. We hypothesized that, given their overall dependence on grass biomass, fuel traits were mutually correlated (via correlations to grass biomass), change predictably in space and time, and that they influence fire regimes. We sampled 31 plots distributed in five soil classes in a savanna‐dominated landscape in Brazil and measured the following surface fuel traits: fuel height, continuity, bulk density, bed flammability, composition, total load and grass load. We also obtained data on landscape predictors, such as soil clay content, fire history, climate, canopy cover, elevation , and on future (post‐sampling) fire incidence. We used Pearson correlation and principal component analyses to test for associations among fuel traits, and generalized linear model for assessing 1) landscape predictors effects on fuel traits; and 2) fuel trait effects on future fire incidence. We found two leading axes of fuel trait variability. The first axis was positively correlated with fuel height, continuity, total load, bed flammability, grass load and cover. In this axis, flammability increased with time since last fire and clay content and decreased with canopy cover and rainfall seasonality. The second axis was positively correlated with fuel bulk density, continuity, shrub and litter covers, and negatively with fuel bed flammability. In this axis, flammability decreased with canopy cover and clay content. Grass fuel load was the best predictor of future fire incidence. Our results suggest that fuel traits change predictably in space and time and explain variability in fire regimes in savannas. These findings contribute to a better understanding of fire regimes while providing important information for managers and decision makers.

Mid-term effects of silvicultural treatments on fuel dynamics and fire behavior in different developmental stages of Larix principis-rupprechtii

Impacts of commercial thinning on stand demography, fuel loads, microclimate and fire behaviour in Eucalyptus delegatensis forest in eastern Tasmania

The hidden variable: Impacts of human decision-making on prescribed fire outcomes.

Abstract This study examines the impact of a low-intensity forest understory fire on turbulent heat transfer using sonic anemometer measurements at lower (3 m), mid- (10 m), and upper (19 m) canopy from five in situ towers within the burn plot and one upwind control tower. The fire induced up to a 50-fold increase in kinematic vertical turbulent heat flux, primarily driven by increased temperature perturbations. The maximum flux occurred at midcanopy due to decreasing temperature and increasing vertical velocity perturbations from the lower to upper canopy. A double peak in turbulent heat flux during fire-front passage resulted from interactions between fire-induced turbulence and atmospheric flow, where a downdraft immediately following the fire front temporarily suppressed flux before the second peak emerged. The flux increase was largely due to intensified ejection events rather than a higher event frequency, indicating enhanced heat transfer efficiency. At mid- and upper canopy levels, sweep events increased at all but one tower, while at the lower canopy, inward interactions increased and outward interactions decreased across all towers. The arrival of a sea-breeze front, bringing cooler air and stronger winds, dampened fire effects. During the fire, spectral energy for temperature and heat flux intensified at higher frequencies, particularly in the lower canopy, flattening the spectral curve in the inertial subrange. Buoyancy-driven turbulence dominated heat transfer, although some mechanically generated turbulence was also present. These findings enhance the understanding of fire-induced turbulence and heat transfer, aiding the development of fire behavior and smoke dispersion models for improved fire management. Significance Statement This study investigates fire-induced turbulence and heat transfer during a low-intensity forest understory fire, using unprecedented sonic anemometer data from multiple in situ towers at lower, mid-, and upper canopy levels, along with an upwind control tower. The findings reveal up to a 50-fold increase in vertical turbulent heat flux, peaking at midcanopy, and highlight complex fire–atmosphere interactions. Key dynamics include a strong downdraft trailing the fire front, producing a double peak in heat flux, a substantial rise in ejection event contributions across the canopy, increased inward interactions in the lower canopy, and interactions with an incoming sea-breeze front. These insights enhance fire behavior and smoke dispersion models, informing more effective fire management strategies.

Experimental and numerical study on the fire resistance behavior of steel truss girder structure in double deck suspension bridges

This study develops a combustible material pyrolysis model capable of numerically predicting and analyzing ultra-fast fire scenarios. The model was subsequently applied to investigate fire behavior in industrial complex facilities. Based on a propolis model and a User-Defined Function (UDF), the proposed approach simulated the mass loss of specimens due to pyrolysis and combustion, and the results were compared with experimental data. A strong correlation confirmed the reliability of the model. Using this validated framework, flame propagation patterns under various fire scenarios were analyzed, providing a quantitative characterization of the thermal behavior and propagation mechanisms of ultra-fast fires in industrial complexes.

Development of burn prescriptions using quantitative expert judgement.

Remote sensing of wildland fires has become an integral part of fire science. Airborne sensors provide high spatial resolution and can provide high temporal resolution, enabling fire behavior monitoring at fine scales. Fire agencies frequently use airborne long-wave infrared (LWIR) imagery for fire monitoring and to aid in operational decision-making. While tactical remote sensing systems may differ from scientific instruments, our objective is to illustrate that operational support data has the capacity to aid scientific fire behavior studies and to facilitate the data analysis. We present an image processing algorithm that automatically delineates active fire edges in tactical LWIR orthomosaics. Several thresholding and edge detection methodologies were investigated and combined into a new algorithm. Our proposed method was tested on tactical LWIR imagery acquired during several fires in California in 2020 and compared to manually annotated mosaics. Jaccard index values ranged from 0.725 to 0.928. The semi-automated algorithm successfully extracted active fire edges over a wide range of image complexity. These results contribute to the integration of infrared fire observations captured during firefighting operations into scientific studies of fire spread and support landscape-scale fire behavior modeling efforts.

Semi flexible polyurethane (PU) foam provides various applications in aerospace as well as daily life due to its vibration dampening, sound absorption, thermal insulation properties etc. One of the major disadvantages of PU foams is its poor fire-resistant characteristics. The present study aimed to improve the fire retardant behavior of PU foam by incorporation of fillers like melamine powder (M.P) and tricresyl phosphate (TCP) in PU matrix. A set of PU foams were prepared with M.P alone, with TCP alone and with a combination of both melamine powder and TCP. The effect of these fire retardants on mechanical, thermal and fire retardant properties of semi flexible PU foam were evaluated based on density, resilience ball rebound, compressive strength, tensile strength, thermal stability, thermal conductivity, limiting oxygen index (LOI) and fire test. In addition, Cell morphology of PU foam was examined using scanning electron Microscope (SEM). LOI was improved from 19% to 26% with the addition of fire retardants. Cushioning property was retained however density was increased with the addition of fire retardants. 40% melamine powder and 40% TCP with respect to premix have been chosen as the optimized composition based on the fire resistant and physical properties. Scale up studies of the developed PU foam was done up to 1 m × 1 m size foam pad. With respect to the lab level properties, LOI value of 26% was retained and resilience ball rebound value change was found to be minimal.

The availability of water is decreasing because of climate change, the use of environmentally friendly building materials is becoming more widespread, and the operation of intelligent infrastructure is growing more challenging. Every one of these issues is something that contemporary fire protection systems have never encountered before. To improve fire protection tactics that are both sustainable and intelligent, this study investigates three research pillars that are interconnected with one another: (i) the development of water-efficient suppression systems; (ii) the fire behavior of developing construction materials; and (iii) AI-driven safety and monitoring approaches. The results of experimental evaluations of high-pressure water mist and hybrid water–foam systems revealed water conservation of fifty to seventy percent in comparison to conventional sprinklers, with only small extensions in knockdown time. Compared to hybrid systems, mist systems demonstrated superior residual cooling, while hybrid systems achieved a balance between rapid flame suppression and long-term temperature drop. Cross-laminated timber (CLT), biocomposites, and lightweight cementitious panels were all subjected to fire testing, and the results showed that each of these materials reacted differently to heat and had a greater likelihood of having another fire. CLT was able to maintain heat conservation within the char layers, biocomposites exhibited surface delamination, and cementitious panels maintained their stability. The evidence presented here demonstrates that various materials require distinct suppression procedures. AI models that combine CNN-based detection, LSTM fire growth prediction, and reinforcement learning control were able to detect fires earlier, make accurate predictions, and reduce water use by 41% while speeding up suppression by 18%. It is clear from this that intelligent adaptive suppression has the potential to revolutionize the way we battle flames. Another finding from scenario modeling was that multi-agent techniques, such as mist cooling and dry chemicals, performed exceptionally well for hydrogen fire scenarios. These scenarios are extremely significant for the infrastructure of renewable energy sources. When it comes to the development of the next generation of fire prevention systems that are sustainable, intelligent, and resilient, these findings highlight the importance of real-scale validation, standardized testing, and interdisciplinary integration.

Pile burning is increasingly used in many forest and woodland ecosystems to reduce hazardous fuel loads following fuel hazard reduction or forest restoration efforts. Pile burning is often linked to thinning practices where residual fuel is piled and subsequently burned; the burning is typically done in winter months when conditions reduce the risk of unwanted fire behavior such as escapes. A key aspect of pile burning is estimating the amount of pile biomass and the amount of fuel consumed during burning as these two variables are critical for estimating treatment efficacy and smoke emissions. Methods to estimate pile masses have been studied and developed previously, however, they are time consuming and require extensive user training. Terrestrial laser scanning (TLS) is a remote sensing tool that has been successfully used on broadcast burning for fuel characterization and has the potential to estimate pile masses at prescribed burning sites. TLS reduces measurement error, requires less extensive user training, and eliminates observer bias in measurements. A total of 16 pile masses were measured across Colorado, United States, using a previously developed pile measurement methodology, using TLS, and by taking apart the pile and weighing the contents of the pile, to determine if TLS would be an adequate method for predicting pile masses. Individually, TLS did not do a good job predicting pile masses, however, when comparing across all 15 piles, using three TLS scans of a pile to estimate pile mass had the lowest median percent error across all piles.

Forest and grassland fire behavior prediction is increasingly critical under climate change, as rising fire frequency and intensity threaten ecosystems and human societies worldwide. This paper reviews the status and future development trends of wildfire behavior modeling and prediction technologies. It provides a comprehensive overview of the evolution of models from empirical to physical and then to data-driven approaches, emphasizing the integration of multidisciplinary techniques such as machine learning and deep learning. While conventional physical models offer mechanistic insights, recent advancements in data-driven models have enabled the analysis of big data to uncover intricate nonlinear relationships. We underscore the necessity of integrating multiple models via complementary, weighted fusion and hybrid methods to bolster robustness across diverse situations. Ultimately, we advocate for the creation of intelligent forecast systems that leverage data from space, air and ground sources to provide multifaceted fire behavior predictions in regions and globally. Such systems would more effectively transform fire management from a reactive approach to a proactive strategy, thereby safeguarding global forest carbon sinks and promoting sustainable development in the years to come. By offering forward-looking insights and highlighting the importance of multidisciplinary approaches, this review serves as a valuable resource for researchers, practitioners, and policymakers, supporting informed decision-making and fostering interdisciplinary collaboration.

Fire is a key ecological process in tropical savannas, yet species-specific contributions to fuel flammability remain poorly understood in West Africa. Here, we present the first experimental assessment of flammability traits in the Lamto humid savanna (Côte d’Ivoire), focusing on five dominant perennial grasses, litter, and realistic mixtures. We quantified four plant flammability traits (ignitibility, combustibility, sustainability, consumability) and six fire behavior parameters (flame height, fuel consumption, and maximum temperatures at three heights). Our results show that ignitibility varied strongly among species, with Hyparrhenia diplandra and Loudetia simplex igniting more rapidly than others. In contrast, combustibility, sustainability, and consumability were relatively consistent across fuel types. Fire behaviour also varied: complete combustion occurred in some mixtures, whereas Andropogon schirensis and litter-containing mixtures left significant unburned material, likely due to lower fuel porosity. Importantly, mixture flammability was non-additive: mixtures did not reflect the sum of their components but instead approximated the average flammability of constituent species. Principal Component Analysis and hierarchical clustering identified three functional flammability groups: (i) highly flammable fuels (Imperata cylindrica, L. simplex, litter), (ii) moderately flammable fuels (A. schirensis, H. diplandra, mixture 3), and (iii) poorly flammable fuels (A. canaliculatus and mixtures 1, 2, 4). These findings highlight the non-additive and species-specific nature of savanna fuel flammability, with direct implications for fire intensity, severity, and management. This trait-based approach provides a foundation for predicting fire behavior in West African savannas and for integrating species-level flammability into conservation-oriented fire management.

Abstract - With modern experimental techniques, fire safety research is becoming increasingly interested in determining the flammability and combustion characteristics of vital industrial materials. One such technique is the cone calorimeter, which is one of the most commonly used devices when it comes to quantitative evaluation of fire behaviour by providing accurate measurements of the radiant heat exposure conditions it applies. This review compiles results from several studies on combustion characteristics of transformer mineral oil, an important dielectric fluid used in electrical equipment, since it may pose a fire hazard in case of faults or overheating. Important fire parameters such as heat release rate, mass loss rate, peak heat release rate, and total heat release have been analysed, as these are important for interpreting fire development, spreading probability, and energy output. By comparing results from different studies, this paper indicates the common trends and the variations in experimental outcomes regarding the effect of oil composition and testing conditions on fire behaviour. The discussion will also draw attention to the importance of these combustion properties in risk assessment, material safety evaluation, and designing safer alternatives or prevention measures within industrial applications. Key Words: Cone calorimeter, mineral transformer oil, radiant heat, dielectric, thermal characteristics

Abstract Background Timely information on wildfire burn severity is critical to assess and mitigate potential post-fire impacts on soils, vegetation, and hillslope stability. Tracking individual fire spread and intensity using satellite active fire data provides a pathway to near real-time (NRT) information. Here, we generated a large database (n = 2177) of wildfire events in the western United States (U.S.) between 2012 and 2021 using active fire detections from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on the Suomi National Polar-orbiting Partnership (SNPP) satellite and the Fire Events Data Suite (FEDS) algorithm to track large fire growth every 12 h. We integrated fire tracking data with final fire perimeters and burn severity data from the Monitoring Trends in Burn Severity (MTBS) program to evaluate the relationship between burn severity and fire behavior metrics derived from the fire tracking approach, including the rate of fire spread and average fire radiative power (FRP) of fire detections for each 12-h growth increment. Results When stratified by vegetation type, FRP and rate of spread metrics were positively correlated with classified burn severity for each 12-h growth increment, highlighting the potential to rapidly identify areas of high and low severity burning. In forests, integrated measures of FRP over the fire lifetime captured persistent flaming and smoldering that compensated for initial differences between AM (01:30) and PM (13:30) fire detections. Predictive modeling of these relationships based on multiple fire behavior indicators and vegetation type from the LANDFIRE program yielded an accuracy of 78% for the separation of unburned/low and moderate/high burn severity classes. Conclusions These results demonstrate the ability to capture within-fire differences in burn severity using NRT indicators from fire tracking to assist with emergency management and disaster preparedness for post-fire hazards, such as landslides, debris flows, or changes in stream flow and water quality. As VIIRS data are available within minutes of each satellite overpass in the U.S., rapid estimates of burn severity based on fire tracking can be made days or weeks before a large wildfire is fully contained.

Seasonal fires in northern Thailand are a persistent environmental and public health concern, yet existing fire probability mapping approaches in Thailand rely heavily on subjective multi-criteria analysis (MCA) methods and temporally static data aggregation methods. To address these limitations, we present a flexible, replicable, and operationally viable seasonal fire probability mapping methodology using a Random Forest (RF) machine learning model in the Google Earth Engine (GEE) platform. We trained the model on historical fire occurrence and fire predictor layers from 2016–2023 and applied it to 2024 conditions to generate a probabilistic fire prediction. Our novel approach improves upon existing operational methods and scientific literature in several ways. It uses a more representative sample design which is agnostic to the burn history of fire presences and absences, pairs fire and fire predictor data from each year to account for interannual variation in conditions, empirically refines the most influential fire predictors from a comprehensive set of predictors, and provides a reproducible and accessible framework using GEE. Predictor variables include both socioeconomic and environmental drivers of fire, such as topography, fuels, potential fire behavior, forest type, vegetation characteristics, climate, water availability, crop type, recent burn history, and human influence and accessibility. The model achieves an Area Under the Curve (AUC) of 0.841 when applied to 2016–2023 data and 0.848 when applied to 2024 data, indicating strong discriminatory power despite the additional spatial and temporal variability introduced by our sample design. The highest fire probabilities emerge in forested and agricultural areas at mid elevations and near human settlements and roads, which aligns well with the known anthropogenic drivers of fire in Thailand. Distinct areas of model uncertainty are also apparent in cropland and forests which are only burned intermittently, highlighting the importance of accounting for localized burning cycles. Variable importance analysis using the Gini Impurity Index identifies both natural and anthropogenic predictors as key and nearly equally important predictors of fire, including certain forest and crop types, vegetation characteristics, topography, climate, human influence and accessibility, water availability, and recent burn history. Our findings demonstrate the heavy influence of data preprocessing and model design choices on model results. The model outputs are provided as interpretable probability maps and the methods can be adapted to future years or augmented with local datasets. Our methodology presents a scalable advancement in wildfire probability mapping with machine learning and open-source tools, particularly for data-constrained landscapes. It will support Thailand’s fire managers in proactive fire response and planning and also inform broader regional fire risk assessment efforts.

Forest fires significantly impact the global climate through carbon emissions, yet the multi-scale coupling mechanisms among meteorological factors, fire behavior, and emissions remain uncertain. Focusing on tropical Asia, this study integrated satellite-based fire behavior products, meteorological datasets, and emission factors, and employed machine learning together with structural equation modeling (SEM) to explore the mediating role of fire behavior in the meteorological regulation of carbon emissions. The results revealed significant differences among vegetation types in both carbon emission intensity and sensitivity to meteorological drivers. For example, average gas emissions (GEs) and particle emissions (PEs) in mixed forests (MF, 323.68 g/m2/year for GE and 0.73 g/m2/year for PE) were approximately 172% and 151% higher, respectively, than those in evergreen broadleaf forests (EBF, 118.92 g/m2/year for GE and 0.29 g/m2/year for PE), which exhibited the lowest emission intensity. Mixed forests and deciduous broadleaf forests exhibited stronger meteorological regulation effects, whereas evergreen broadleaf forests were comparatively stable. Temperature and vapor pressure deficit emerged as the core drivers of fire behavior and carbon emissions, exerting indirect control through fire behavior. Overall, the findings highlight fire behavior as a critical link between meteorological conditions and carbon emissions, with ecosystem-specific differences determining the responsiveness of carbon emissions to meteorological drivers. These insights provide theoretical support for improving the accuracy of wildfire emission simulations in climate models and for developing vegetation-specific fire management and climate adaptation strategies.

Analytical and numerical investigation of the fire behavior of glulam bolted joints incorporating a central core tube

ABSTRACT Due to its effective thermal insulation properties, extruded polystyrene (XPS) is extensively used in high-rise buildings. However, it can be easily ignited, which has resulted in numerous fire incidents. Pyrolysis, the initial stage preceding combustion, has kinetic parameters that are critical for predicting fire behavior. Therefore, micro-scale pyrolysis and bench-scale cone calorimeter experiments are conducted. XPS was analyzed by thermogravimetric experiments, and its kinetic parameters were estimated by multiple isoconversion methods. Then, the endothermic properties were obtained by the differential scanning calorimetry experiments, and the pyrolysis products were analyzed using the Fourier transform infrared spectrometer. Finally, the combustion characteristics were studied based on the cone calorimeter experiments. The results showed that the pyrolysis reaction of XPS could be complete within a short temperature range, meaning a fast reaction. The average activation energy of the single-step reaction was 183 kJ/mol. The only endothermic peak centered at about 700 K, which indicated the pyrolysis mechanism was depolymerization. The main functional groups indicated that many aromatic products were produced. XPS had high heat release along with significant smoke and gas emissions. It was determined to be a thermally thin specimen property, and multiple parameters were calculated, which reflected the fire risk of XPS, including theoretical critical heat flux, ignition temperature, fire performance and fire growth indexes.