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Climate change is increasing the frequency of droughts and the risk of severe wildfires, which can interact with shrub encroachment and browsing by wild ungulates. Wild ungulate populations are expanding due, among other factors, to favorable habitat changes resulting from land abandonment or land-use changes. Understanding how ungulate browsing interacts with drought to affect woody plant mortality, plant flammability, and fire hazard is especially relevant in the context of climate change and increasing frequency of wildfires. The aim of this study is to explore the combined effects of cumulative drought, shrub encroachment, and ungulate browsing on the fire hazard of Mediterranean oak woodlands in Portugal. In a long-term (18 years) ungulate fencing exclusion experiment that simulated land abandonment and management neglect, we investigated the population dynamics of the native shrub Cistus ladanifer, which naturally dominates the understory of woodlands and is browsed by ungulates, comparing areas with (no fencing) and without (fencing) wild ungulate browsing. We also modeled fire behavior in browsed and unbrowsed plots considering drought and nondrought scenarios. Specifically, we estimated C. ladanifer population density, biomass, and fuel load characteristics, which were used to model fire behavior in drought and nondrought scenarios. Overall, drought increased the proportion of dead C. ladanifer shrub individuals, which was higher in the browsed plots. Drought decreased the ratio of live to dead shrub plant material, increased total fuel loading, shrub stand flammability, and the modeled fire parameters, that is, rate of surface fire spread, fireline intensity, and flame length. However, total fuel load and fire hazard were lower in browsed than unbrowsed plots, both in drought and nondrought scenarios. Browsing also decreased the population density of living shrubs, halting shrub encroachment. Our study provides long-term experimental evidence showing the role of wild ungulates in mitigating drought effects on fire hazard in shrub-encroached Mediterranean oak woodlands. Our results also emphasize that the long-term effects of land abandonment can interact with climate change drivers, affecting wildfire hazard. This is particularly relevant given the increasing incidence of land abandonment.

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Fuel-treatments targeting shrubs and fire-prone exotic annual grasses (EAGs) are increasingly used to mitigate increased wildfire risks in arid and semiarid environments, and understanding their response to natural factors is needed for effective landscape management. Using field-data collected over four years from fuel-break treatments in semiarid sagebrush-steppe, we asked 1) how the outcomes of EAG and sagebrush fuel treatments varied with site biophysical properties, climate, and weather, and 2) how predictions of fire behavior using the Fuel Characteristic Classification System fire model related to land-management objectives of maintaining fire behavior expected of low-load, dry-climate grasslands. Generalized linear mixed effect modeling with build-up model selection was used to determine best-fit models, and marginal effects plots to assess responses for each fuel type. EAG cover decreased as antecedent-fall precipitation increased and increased as antecedent-spring temperatures and surface soil clay contents increased. Herbicides targeting EAGs were less effective where pre-treatment EAG cover was >40 % and antecedent spring temperatures were >9.5 °C. Sagebrush cover was inversely related to soil clay content, especially where clay contents were >17 %. Predicted fire behavior exceeded management objectives under 1) average fire weather conditions when EAG or sagebrush cover was >50 % or >26 %, respectively, or 2) extreme fire weather conditions when EAG or sagebrush cover was >10 % or >8 %, respectively. Consideration of the strong effects of natural variability in site properties and antecedent weather can help in justifying, planning and implementing fuel-treatments.

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Wildfires have been systematically studied from the early 1950s, with significant progress in the applied computational methodologies during the 21st century. However, modern methods are barely adopted by administrative authorities, globally, especially those considering probabilistic models concerning human-caused fires. An exhaustive review on wildfire danger studies has not yet been performed. Therefore, the present review aims at collecting and analyzing integrated modeling approaches in estimating forest fire danger, examining the driving factors, and evaluating their influence on fire occurrence. The main objective is to propose the top performing methods and the most important risk factors for the development of an Integrated Wildfire Danger Risk System (IWDRS). Studies were classified based on the applied technique, i.e., geographic information systems, remote sensing, statistics, machine learning, simulation modeling and miscellaneous techniques. The conclusions of each study concerning the relative importance of model input variables are also reported. Online search engines such as 'Scopus', 'Google Scholar', 'WorldWideScience', 'ScienceDirect' and 'ResearchGate' were used in relevant literature searches published in scientific journals, manuals and technical documentation. A total of 230 studies were gathered with a selected subset being evaluated in a meta-analysis process. Machine learning techniques outperform average classic statistics, although their predictability relies heavily on the quantity and the quality of the input data. Geographic information systems and remote sensing are considered valuable yet supplementary tools. Modeling techniques apply best to fire behavior prediction, while other techniques referenced in the current review are potentially useful but further investigation is needed. In conclusion, wildfire danger is a function of seven thematic groups of variables: meteorology, vegetation, topography, hydrology, socio-economy, land use and climate. Ninety-five explanatory drivers are proposed.

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Natural gas jet fire induced by igniting blowouts has the potential to cause critical structure damage and great casualties of offshore platforms. Real-time natural gas jet fire plume prediction is essential to support the emergency planning to mitigate subsequent damage consequence and ocean pollution. Deep learning based on a large amount of Computational fluid dynamics (CFD) simulations has recently been applied to real-time fire modeling. However, existing approaches based on point-estimation theory are 'over-confident' when prediction deficiency exists, which reduce robustness and accuracy for emergency planning support. This study proposes probabilistic deep learning approach for real-time natural gas jet fire consequence modeling by integrating variational Bayesian inference with deep learning. Numerical model of natural gas jet fire from offshore platform is built and the natural gas jet fire scenarios are simulated to construct the benchmark dataset. Sensitivity analysis of pre-defined parameters such as MC (Monte Carlo) sampling number m and dropout probability p is conducted to determine the trade-off between model's accuracy and efficiency. The results demonstrated our model exhibits competitive accuracy with R2 = 0.965 and real-time capacity with an inference time of 12 ms. In addition, the predicted spatial uncertainty corresponding to spatial jet fire flame plume provides more comprehensive and reliable support for the following mitigation decision-makings compared to the state-of-the-art point-estimation based deep learning model. This study provides a robust alternative for constructing a digital twin of fire and explosion associated emergency management on offshore platforms.

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We implemented a fire modeling approach to evaluate the effectiveness of silvicultural treatments in reducing potential losses to the Hyrcanian temperate forests of northern Iran, in the Siahkal National Forest (57,110 ha). We compared the effectiveness of selection cutting, low thinning, crown thinning, and clear-cutting treatments implemented during the last ten years (n = 241, 9500-ha) on simulated stand-scale and landscape-scale fire behavior. First, we built a set of fuel models for the different treatment prescriptions. We then modeled 10,000 fires at the 30-m resolution, assuming low, moderate, high, very high, and extreme weather scenarios and human-caused ignition patterns. Finally, we implemented a One-way ANOVA test to analyze stand-level and landscape-scale modeling output differences between treated and untreated conditions. The results showed a significant reduction of stand-level fire hazard, where the average conditional flame length and crown fire probability was reduced by about 12 and 22%, respectively. The conifer plantation patches presented the most significant reduction in the crown fire probability (>35%). On the other hand, we found a minor increase in the overall burn probability and fire size at the landscape scale. Stochastic fire modeling captured the complex interactions among terrain, vegetation, ignition locations, and weather conditions in the study area. Our findings highlight fuel treatment efficacy for moderating potential fire risk and restoring fuel profiles in fire-sensitive temperate forests of northern Iran, where the growing persistent droughts and fuel buildup can lead to extreme fires in the near future.

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Wildfires are a global crisis, but current fire models fail to capture vegetation response to changing climate. With drought and elevated temperature increasing the importance of vegetation dynamics to fire behavior, and the advent of next generation models capable of capturing increasingly complex physical processes, we provide a renewed focus on representation of woody vegetation in fire models. Currently, the most advanced representations of fire behavior and biophysical fire effects are found in distinct classes of fine-scale models and do not capture variation in live fuel (i.e. living plant) properties. We demonstrate that plant water and carbon dynamics, which influence combustion and heat transfer into the plant and often dictate plant survival, provide the mechanistic linkage between fire behavior and effects. Our conceptual framework linking remotely sensed estimates of plant water and carbon to fine-scale models of fire behavior and effects could be a critical first step toward improving the fidelity of the coarse scale models that are now relied upon for global fire forecasting. This process-based approach will be essential to capturing the influence of physiological responses to drought and warming on live fuel conditions, strengthening the science needed to guide fire managers in an uncertain future.

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This work aims at revealing and optimizing the mechanism, to promote the design of phosphorus-based flame retardants (PFRs) for controlling the spread of fire risk caused by the continuous spread of polymers. Herein, we synthesized about 10 nm TiO2 grown in situ on the surface of BP through a simple hydrothermal procedure to introduce it into epoxy (EP/BP-TiO2). In the first place, EP/BP-TiO22.0 nanocomposite achieves a reduction of 58.96% and 50.35% in PHRR and THR, respectively. Secondly, the pyrolysis of BP from Pn to P4, P3 and P2 is revealed. As a guide, P4 is established as a characteristic product of the analytical model for evaluating the effects in the gas phase for BP-based hybrids. Finally, this work clarifies the enhancement path for BP-TiO2 is optimized for the capturing of OH· and H· radicals by P4(POx). Crucially, this study reveals and controls the mechanism of the BP-based hybrids at the molecular level, which is expected to provide a promising analytical model for broad market PFRs design to address the risks and challenges of casualties and ecology caused by composites fire.

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Research was conducted to examine the use of Support Vector Regression (SVR) to build a model to forecast the potential occurrence of flashover in a single-floor, multi-room compartment fire. Synthetic temperature data for heat detectors in different rooms were generated, 1000 simulation cases are considered, and a total of 8 million data points are utilized for model development. An operating temperature limitation is placed on heat detectors where they fail at a fixed exposure temperature of 150 °C and no longer provide data to more closely follow actual performance. The forecast model P-Flash (Prediction model for Flashover occurrence) is developed to use an array of heat detector temperature data, including in adjacent spaces, to recover temperature data from the room of fire origin and predict potential for flashover. Two special treatments, sequence segmentation and learning from fitting, are proposed to overcome the temperature limitation of heat detectors in real-life fire scenarios and to enhance prediction capabilities to determine if the flashover condition is met even with situations where there is no temperature data from all detectors. Experimental evaluation shows that P-Flash offers reliable prediction. The model performance is approximately 83 % and 81 %, respectively, for current and future flashover occurrence, considering heat detector failure at 150 °C. Results demonstrate that P-Flash, a new data-driven model, has potential to provide fire fighters real-time, trustworthy, and actionable information to enhance situational awareness, operational effectiveness, and safety for firefighting.

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Tree spatial patterns in dry coniferous forests of the western United States, and analogous ecosystems globally, were historically aggregated, comprising a mixture of single trees and groups of trees. Modern forests, in contrast, are generally more homogeneous and overstocked than their historical counterparts. As these modern forests lack regular fire, pattern formation and maintenance is generally attributed to fire. Accordingly, fires in modern forests may not yield historically analogous patterns. However, direct observations on how selective tree mortality among pre-existing forest structure shapes tree spatial patterns is limited. In this study, we (a) simulated fires in historical and contemporary counterpart plots in a Sierra Nevadan mixed-conifer forest, (b) estimated tree mortality, and (c) examined tree spatial patterns of live trees before and after fire, and of fire-killed trees. Tree mortality in the historical period was clustered and density-dependent, because trees were aggregated and segregated by tree size before fire. Thus, fires maintained an aggregated distribution of tree groups. Tree mortality in the contemporary period was widespread, except for dispersed large trees, because most trees were a part of large, interconnected tree groups. Thus, postfire tree patterns were more uniform and devoid of moderately sized tree groups. Postfire tree patterns in the historical period, unlike the contemporary period, were within the historical range of variability identified for the western United States. This divergence suggests that decades of forest dynamics without significant disturbances have altered the historical means of pyric pattern formation. Our results suggest that ecological silvicultural treatments, such as forest restoration thinnings, which emulate qualities of historical forests may facilitate the reintroduction of fire as a means to reinforce forest structural heterogeneity.

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In Southern California, the Santa Ana winds are famous for their role in spreading large wildfires during the fall/winter season. Combined with Southern California's complex topography, Santa Anas create challenges for modeling wind-fire relationships in this region. Here, we assess heterogeneity of winds during Santa Ana and non-Santa Ana days, on days with and without large-fire ignitions, across a modern high-density observational network of 30 meteorological stations. Wind speeds on Santa Ana days with a large fire ignition (mean windspeed = 5.19 m/s) are significantly higher than on Santa Ana days without large fire ignitions (3.96 m/s), while on non-Santa Ana days winds are generally weaker, during both fire (2.30 m/s) and non-fire (2.38 m/s) days. Hierarchical clustering of meteorological stations during both Santa Ana and non-Santa Ana days reveals groups of stations with consistently similar wind speed and directions. All stations clearly exhibit high wind speeds on Santa Ana days, and most record contrasting wind characteristics during Santa Ana versus non-Santa Ana ignitions. Additionally, our analysis revealed that key geographic siting traits are not represented in the network, including few stations with northwest aspect and upper slope in the southern mountains.

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The joint and relative effects of future land-use and climate change on fire occurrence in the Amazon, as well its seasonal variation, are still poorly understood, despite its recognized importance. Using the maximum entropy method (MaxEnt), we combined regional land-use projections and climatic data from the CMIP5 multimodel ensemble to investigate the monthly probability of fire occurrence in the mid (2041-2070) and late (2071-2100) 21st century in the Brazilian Amazon. We found striking spatial variation in the fire relative probability (FRP) change along the months, with October showing the highest overall change. Considering climate only, the area with FRP ≥ 0.3 (a threshold chosen based on the literature) in October increases 6.9% by 2071-2100 compared to the baseline period under the representative concentration pathway (RCP) 4.5 and 27.7% under the RCP 8.5. The best-case land-use scenario ("Sustainability") alone causes a 10.6% increase in the area with FRP ≥ 0.3, while the worse-case land-use scenario ("Fragmentation") causes a 73.2% increase. The optimistic climate-land-use projection (Sustainability and RCP 4.5) causes a 21.3% increase in the area with FRP ≥ 0.3 in October by 2071-2100 compared to the baseline period. In contrast, the most pessimistic climate-land-use projection (Fragmentation and RCP 8.5) causes a widespread increase in FRP (113.5% increase in the area with FRP ≥ 0.3), and prolongs the fire season, displacing its peak. Combining the Sustainability land-use and RCP 8.5 scenarios causes a 39.1% increase in the area with FRP ≥ 0.3. We conclude that avoiding the regress on land-use governance in the Brazilian Amazon (i.e., decrease in the extension and level of conservation of the protected areas, reduced environmental laws enforcement, extensive road paving, and increased deforestation) would substantially mitigate the effects of climate change on fire probability, even under the most pessimistic RCP 8.5 scenario.

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Large wildfires can cover millions of hectares of forest every year worldwide, causing losses in ecosystems and assets. Fire simulation and modeling provides an analytical scheme to characterize and predict fire behavior and spread in several and complex environments. Spatial dynamics of large wildfires can be analyzed using satellite active fire data, a cost-effective way to acquire information systematically worldwide. The simulated growth of three large wildland fires from the USA, Chile and Spain with different fire spread pattern, duration and size has been compared to satellite active fire data. Additionally, a new approach to reinitialize fire simulations in near real-time and predict a more accurate fire spread is shown in this work. Discrepancies between the simulated fire growth and satellite active data were measured spatially and temporally in the three fires, increasing along the fire duration. The reinitialization approach meaningfully improved the accuracy of fire simulations in all case studies. Satellite active fire data showed a high potential to be used in real fire incidents, improving fire monitoring and simulation and, therefore, supporting the decision-making process of the fire analyst. The reinitialization approach could be applied by using the current satellite active fire data such as MODIS or VIIRS as well as Unmanned Aerial Vehicles or GPS locations from suppression resources.

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Southern European countries rely largely on fire suppression and ignition prevention to manage a growing wildfire problem. We explored a more wholistic, long-term approach based on priority maps for the implementation of diverse management options aimed at creating fire resilient landscapes, restoring cultural fire regimes, facilitating safe and efficient fire response, and creating fire-adapted communities. To illustrate this new comprehensive strategy for fire-prone Mediterranean areas, we developed and implemented the framework in Catalonia (northeastern Spain). We first used advanced simulation modeling methods to assess various wildfire exposure metrics across spatially changing fire-regime conditions, and these outputs were then combined with land use maps and historical fire occurrence data to prioritize different fuel and fire management options at the municipality level. Priority sites for fuel management programs concentrated in the central and northeastern high-hazard forestlands. The suitable areas for reintroducing fires in natural ecosystems located in scattered municipalities with ample lightning ignitions and minimal human presence. Priority areas for ignition prevention programs were mapped to populated coastal municipalities and main transportation corridors. Landscapes where fire suppression is the principal long-term strategy concentrated in agricultural plains with a high density of ignitions. Localized programs to build defensible space and improve self-protection on communities could be emphasized in the coastal wildland-urban interface and inner intermix areas from Barcelona and Gerona. We discuss how the results of this study can facilitate collaborative landscape planning and identify the constraints that prevent a longer term and more effective solution to better coexist with fire in southern European regions.

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We used spatial optimization to allocate and prioritize prescribed fire treatments in the fire-prone Bages County, central Catalonia (northeastern Spain). The goal of this study was to identify suitable strategic locations on forest lands for fuel treatments in order to: 1) disrupt major fire movements, 2) reduce ember emissions, and 3) reduce the likelihood of large fires burning into residential communities. We first modeled fire spread, hazard and exposure metrics under historical extreme fire weather conditions, including node influence grid for surface fire pathways, crown fraction burned and fire transmission to residential structures. Then, we performed an optimization analysis on individual planning areas to identify production possibility frontiers for addressing fire exposure and explore alternative prescribed fire treatment configurations. The results revealed strong trade-offs among different fire exposure metrics, showed treatment mosaics that optimize the allocation of prescribed fire, and identified specific opportunities to achieve multiple objectives. Our methods can contribute to improving the efficiency of prescribed fire treatment investments and wildfire management programs aimed at creating fire resilient ecosystems, facilitating safe and efficient fire suppression, and safeguarding rural communities from catastrophic wildfires. The analysis framework can be used to optimally allocate prescribed fire in other fire-prone areas within the Mediterranean region and elsewhere.

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This paper provides a report of the discussions held at the first workshop on Measurement and Computation of Fire Phenomena (MaCFP) on June 10-11 2017. The first MaCFP work-shop was both a technical meeting for the gas phase subgroup and a planning meeting for the condensed phase subgroup. The gas phase subgroup reported on a first suite of experimental- computational comparisons corresponding to an initial list of target experiments. The initial list of target experiments identifies a series of benchmark configurations with databases deemed suitable for validation of fire models based on a Computational Fluid Dynamics approach. The simulations presented at the first MaCFP workshop feature fine grid resolution at the millimeter- or centimeter- scale: these simulations allow an evaluation of the performance of fire models under high-resolution conditions in which the impact of numerical errors is reduced and many of the discrepancies between experimental data and computational results may be attributed to modeling errors. The experimental-computational comparisons are archived on the MaCFP repository [1]. Furthermore, the condensed phase subgroup presented a review of the main issues associated with measurements and modeling of pyrolysis phenomena. Overall, the first workshop provided an illustration of the potential of MaCFP in providing a response to the general need for greater levels of integration and coordination in fire research, and specifically to the particular needs of model validation.

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The strong El Niño Southern Oscillation (ENSO) event that occurred in 2015-2016 caused extreme drought in the northern Brazilian Amazon, especially in the state of Roraima, increasing fire occurrence. Here we map the extent of precipitation and fire anomalies and quantify the effects of climatic and anthropogenic drivers on fire occurrence during the 2015-2016 dry season (from December 2015 to March 2016) in the state of Roraima. To achieve these objectives we first estimated the spatial pattern of precipitation anomalies, based on long-term data from the TRMM (Tropical Rainfall Measuring Mission), and the fire anomaly, based on MODIS (Moderate Resolution Imaging Spectroradiometer) active fire detections during the referred period. Then, we integrated climatic and anthropogenic drivers in a Maximum Entropy (MaxEnt) model to quantify fire probability, assessing (1) the model accuracy during the 2015-2016 and the 2016-2017 dry seasons; (2) the relative importance of each predictor variable on the model predictive performance; and (3) the response curves, showing how each environmental variable affects the fire probability. Approximately 59% (132,900 km2 ) of the study area was exposed to precipitation anomalies ≤-1 standard deviation (SD) in January and ~48% (~106,800 km2 ) in March. About 38% (86,200 km2 ) of the study area experienced fire anomalies ≥1 SD in at least one month between December 2015 and March 2016. The distance to roads and the direct ENSO effect on fire occurrence were the two most influential variables on model predictive performance. Despite the improvement of governmental actions of fire prevention and firefighting in Roraima since the last intense ENSO event (1997-1998), we show that fire still gets out of control in the state during extreme drought events. Our results indicate that if no prevention actions are undertaken, future road network expansion and a climate-induced increase in water stress will amplify fire occurrence in the northern Amazon, even in its humid dense forests. As an additional outcome of our analysis, we conclude that the model and the data we used may help to guide on-the-ground fire-prevention actions and firefighting planning and therefore minimize fire-related ecosystems degradation, economic losses and carbon emissions in Roraima.

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The Electrosensory Lateral Line lobe (ELL) is the first central target where the electrosensory information encoded in the spatiotemporal pattern electroreceptor afferent discharges is processed. These afferents encode the minute amplitude changes of the basal electric field through both a change in latency and discharge rate. In the ELL the time and rate-coded input pattern of the sensory periphery goes through the granular cell layer before reaching the main efferent cells of the network: large fusiform (LF) and large ganglion (LG) cells. The evidence until now shows that granular cells are inhibitory. Given that large fusiform cells are excited by the sensory input, it remains a mystery how the afferent input produce excitation through a layer composed by only inhibitory cells. We addressed this problem by modeling how the known circuitry of the ELL could produce excitation in LF cells with only inhibitory granular cells. Alternatively we show that a network composed of a mix of excitatory and inhibitory granular cell not only performs better, as expected, carrying excitation to LF cells but it does so robustly and at higher sensitivity by enhancing the contrast of the electric image between the periphery and the ELLs output. We then show with refined histological methods that a subpopulation of the granular cells indeed are excitatory, providing the necessary input for this contrast enhancing mechanism.

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Novel fire regimes are an important cause and consequence of global environmental change that involve interactions among biotic, climatic, and human components of ecosystems. Plant flammability is key to these interactions, yet few studies directly measure flammability or consider how multiple species with different flammabilities interact to produce novel fire regimes. Deserts of the southwestern United States are an ideal system for exploring how novel fire regimes can emerge when fire-promoting species invade ecosystems comprised of species that did not evolve with fire. In these deserts, exotic annual grasses provide fuel continuity across landscapes that did not historically burn. These fires often ignite a keystone desert shrub, the fire-intolerant creosote bush, Larrea tridentata (DC.) Coville. Ignition of Larrea is likely catalyzed by fuels produced by native plants that grow beneath the shrubs. We hypothesize that invasive and native species exhibit distinct flammability characteristics that in combination determine spatial patterns of fire spread and intensity. We measured flammability metrics of Larrea, two invasive grasses, Schismus arabicus and Bromus madritensis, and two native plants, the sub-shrub Ambrosia dumosa and the annual herb Amsinckia menziesii. Results of laboratory experiments show that the grasses carry fire quickly (1.32 cm/s), but burn for short duration (0.5 min) at low temperatures. In contrast, native plants spread fire slowly (0.12 cm/s), but burn up to eight times longer (4 min) and produced hotter fires. Additional experiments on the ignition requirements of Larrea suggest that native plants burn with sufficient temperature and duration to ignite dead Larrea branches (time to ignition, 2 min; temperature at ignition 692°C). Once burning, these dead branches ignite living branches in the upper portions of the shrub. Our study provides support for a conceptual model in which exotic grasses are "spreaders" of fire and native plants growing beneath shrubs are "igniters" of dead Larrea branches. Once burning, flames produced by dead branches engulf the entire shrub, resulting in locally intense fires without historical precedent in this system. We suggest that fire models and conservation-focused management could be improved by incorporating the distinct flammability characteristics and spatial distributions of spreaders, igniters, and keystone shrubs.

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Comprehensive pyrolysis models that are integral to computational fire codes have improved significantly over the past decade as the demand for improved predictive capabilities has increased. High fidelity pyrolysis models may improve the design of engineered materials for better fire response, the design of the built environment, and may be used in forensic investigations of fire events. A major limitation to widespread use of comprehensive pyrolysis models is the large number of parameters required to fully define a material and the lack of effective methodologies for measurement of these parameters, especially for complex materials. The work presented here details a methodology used to characterize the pyrolysis of a low-pile carpet tile, an engineered composite material that is common in commercial and institutional occupancies. The studied material includes three distinct layers of varying composition and physical structure. The methodology utilized a comprehensive pyrolysis model (ThermaKin) to conduct inverse analyses on data collected through several experimental techniques. Each layer of the composite was individually parameterized to identify its contribution to the overall response of the composite. The set of properties measured to define the carpet composite were validated against mass loss rate curves collected at conditions outside the range of calibration conditions to demonstrate the predictive capabilities of the model. The mean error between the predicted curve and the mean experimental mass loss rate curve was calculated as approximately 20% on average for heat fluxes ranging from 30 to 70 kW·m-2, which is within the mean experimental uncertainty.