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Understanding the potential effects of climate change on forest fire behavior and the resulting release of combustion products is critical for effective mitigation strategies in Greece. This study utilizes data from the MAGICC 2.4 (Model for the Assessment of Greenhouse Gas-Induced Climate Change) climate model and the SCENGEN 2.4 (SCENarioGENerator) database to assess these impacts. By manipulating various model parameters such as climate sensitivity, scenario, time period, and global climate models (GCMs) within the SCENGEN 2.4 database, we analyzed climatic trends affecting forest fire generation and evolution. The results reveal complex and nuanced findings, indicating a need for further investigation. Case studies are conducted using the FARSITE 4 (Fire Area Simulator) model, incorporating meteorological changes derived from climate trends. Simulations of two fires in East Attica, accounting for different fuel and meteorological conditions, demonstrate an increase in the rate of combustion product release. This underscores the influence of changing meteorological parameters on forest fire dynamics and highlights the importance of proactive measures to mitigate future risks. Our findings emphasize the urgency of addressing climate change impacts on wildfire behavior to safeguard environmental and public health in Greece. [ABSTRACT FROM AUTHOR]

Accurate estimation of crown fire potential (CFP) can improve guidance on crown fire control and management. However, robust simulations of crown fire behavior are still challenging, limiting the accuracy of regional-scale CFP mapping. This study aims to incorporate fire spread simulation and machine learning algorithms to improve CFP mapping at a regional scale. First, we built a crown fire dataset using the fire simulations from the FARSITE model, as well as multi-source data, including fuel, weather, and topography variables. Fuel model parameters were optimized with four metaheuristic algorithms for robust fire simulations. Then, the hybrid models of CFP estimation (TBA-ML) were established by coupling with the transfer AdaBoost (TrAdaBoost) algorithm and three machine learning (ML) algorithms, i.e., Bayesian Network (BN), Random Forest (RF), and Support Vector Machine (SVM), to estimate CFP for crown fire danger assessment spatially. Results showed that the TBA-BN model performed best in estimating CFP with higher accuracy (AUC>0.9 and F1 score > 0.8) than the RF- and SVM-based CFP models. The variable importance and causal analysis showed that fuel and topography variables have major contributions to crown fire occurrence. Finally, we mapped monthly average passive and active CFP at regional scales and qualitatively demonstrated that our CFP time-series products successfully captured the dynamic change of crown fire danger. The above results suggest the potential of integrating fire spread simulation and machine learning algorithms to accurately estimate CFP to improve crown fire management.

The 2022 summer fire in the Bohemian Switzerland National Park (BSNP) is the largest in the 30-year recorded history of the Czech Republic, with an affected area of over 1000 ha. The FlamMap fire modeling system was used to investigate the fire behavior in the BSNP and to evaluate scenarios under a range of fuel types, fuel moistures, and weather conditions. The model was used to simulate fire conditions, propagation, and extent. We focused on matching the observed fire perimeter and fire behavior characteristics. The fire occurred in a region of the BSNP heavily affected by Spruce bark beetle (Ips typographus L.) infestation; hence, most of the burned area encompassed dead spruce forest (Picea abies Karst.). The best FlamMap simulations of the observed fire behavior and progression were compared with several created scenarios exhibiting various input conditions. These scenarios included a fire in a healthy spruce forest, clearcuts, or different meteorological conditions. We could calibrate and use FlamMap to recreate the 2022 summer wildfire in the BSNP under the observed conditions. It was found that the fire would have likely spread to the observed final perimeter even if standing dead trees had been removed, albeit at a lower fire intensity and with a considerably shorter duration. Alternatively, if healthy standing vegetation with a closed canopy had been present, the wildfire perimeter would have reached approximately half the observed value. Similar results were obtained for both the non-native spruce forest and deciduous forest, which is a native alternative. [ABSTRACT FROM AUTHOR]

Each year, wildfires ravage the western U.S. and change the lives of millions of inhabitants. Situated in southern California, coastal Santa Barbara has witnessed devastating wildfires in the past decade, with nearly all ignitions started by humans. Therefore, estimating the risk imposed by unplanned ignitions in this fire-prone region will further increase resilience toward wildfires. Currently, a fire-risk map does not exist in this region. The main objective of this study is to provide a spatial analysis of regions at high risk of fast wildfire spread, particularly in the first two hours, considering varying scenarios of ignition locations and atmospheric conditions. To achieve this goal, multiple wildfire simulations were conducted using the FARSITE fire spread model with three ignition modeling methods and three wind scenarios. The first ignition method considers ignitions randomly distributed in 500 m buffers around previously observed ignition sites. Since these ignitions are mainly clustered around roads and trails, the second method considers a 50 m buffer around this built infrastructure, with ignition points randomly sampled from within this buffer. The third method assumes a Euclidean distance decay of ignition probability around roads and trails up to 1000 m, where the probability of selection linearly decreases further from the transportation paths. The ignition modeling methods were then employed in wildfire simulations with varying wind scenarios representing the climatological wind pattern and strong, downslope wind events. A large number of modeled ignitions were located near the major-exit highway running north–south (HWY 154), resulting in more simulated wildfires burning in that region. This could impact evacuation route planning and resource allocation under climatological wind conditions. The simulated fire areas were smaller, and the wildfires did not spread far from the ignition locations. In contrast, wildfires ignited during strong, northerly winds quickly spread into the wildland–urban interface (WUI) toward suburban and urban areas.

A series of urban wildfires that engulfed the Mediterranean in recent decades raised the problem of introducing urban vegetation into fire management. While the Wildland-Urban Interface (WUI) concept includes urban areas in the fire risk analysis exists today, the common approach used in WUI for cities as fire-resistant territories proved to be a failure in practice since wildfire not only penetrates the urban areas but also crosses them by jumping from one green patch to another. Understanding urban areas as flammable demands evaluating the ability to predict the wildfire spread in the city. Among the existing risk assessment approaches only fire behaviour modelling describes the physical processes that occur in an urban wildfire case: the rapid fire spread in the city by ember attacks can be simulated by the spotting analysis. Implementation of the mentioned method requires information about the fuel-related areas within the urban landscape. The suggested method for identifying urban fire-supporting areas based on the vegetation proportion threshold allows a highly accurate prediction of fire spread distance for a certain time step. The proposed approach serves the fire behaviour simulation in large (10-20 ha) urban areas, which previously was challenging due to the high computational load. The outcomes of the presented study can support fire management in an urban landscape both by producing fire risk maps and assessing the spatial distribution of fire-supporting patches for better fuel treatment planning. [ABSTRACT FROM AUTHOR]

Understanding the potential effects of climate change on forest fire behavior and the resulting release of combustion products is critical for effective mitigation strategies in Greece. This study utilizes data from the MAGICC 2.4 (Model for the Assessment of Greenhouse Gas-Induced Climate Change) climate model and the SCENGEN 2.4 (SCENarioGENerator) database to assess these impacts. By manipulating various model parameters such as climate sensitivity, scenario, time period, and global climate models (GCMs) within the SCENGEN 2.4 database, we analyzed climatic trends affecting forest fire generation and evolution. The results reveal complex and nuanced findings, indicating a need for further investigation. Case studies are conducted using the FARSITE 4 (Fire Area Simulator) model, incorporating meteorological changes derived from climate trends. Simulations of two fires in East Attica, accounting for different fuel and meteorological conditions, demonstrate an increase in the rate of combustion product release. This underscores the influence of changing meteorological parameters on forest fire dynamics and highlights the importance of proactive measures to mitigate future risks. Our findings emphasize the urgency of addressing climate change impacts on wildfire behavior to safeguard environmental and public health in Greece.

The present study investigates the effect of climatic parameters, such as air relative humidity and wind speed, on fire spread propagation indexes in the Malekroud Forest, Iran using the FARSITE simulator based on Rothermel's original fire spread equation. Standard fuel models are used to calibrate the vegetation cover. Sorensen (SC) and kappa ( κ ) coefficients, as well as the Overestimation Index (OI), are used to estimate the simulation's accuracy. The results confirm that using both ambient condition data and appropriate fuel models is crucial to reaching reasonable results in fire propagation simulations. The values of the Rate of Fire Spread (ROS), Flame Length (FML), and Fire Line Intensity (FLI) are reported for each particular scenario. The simulation results show that the Sorensen and Kappa coefficient for situations most similar to the real fire reached 0.82 and 0.80, respectively. The investigated fire's severity is categorized as low-condition fire behavior. The simulation shows that fire propagation falls harshly in the case of air relative humidity by more than 72%, and we will not witness natural fire propagation on a large scale. [ABSTRACT FROM AUTHOR]

Background: Accurate simulation of wildfires can benefit pre-ignition mitigation and preparedness, and post-ignition emergency response management. Aims: We evaluated the performance of Weather Research and Forecast-Fire (WRF-Fire), a coupled fire-atmosphere wildland fire simulation platform, in simulating a large historic fire (2018 Camp Fire). Methods: A baseline model based on a setup typically used for WRF-Fire operational applications is utilised to simulate Camp Fire. Simulation results are compared to high-temporal-resolution fire perimeters derived from NEXRAD observations. The sensitivity of the model to a series of modelling parameters and assumptions governing the simulated wind field are then investigated. Results of WRF-Fire for Camp Fire are compared to FARSITE. Key results: Baseline case shows non-negligible discrepancies between the simulated fire and the observations on rate of spread (ROS) and spread direction. Sensitivity analysis results show that refining the atmospheric grid of Camp Fire's complex terrain improves fire prediction capabilities. Conclusions: Sensitivity studies show the importance of refined atmosphere modelling for wildland fire simulation using WRF-Fire in complex terrains. Compared to FARSITE, WRF-Fire agrees better with the observations in terms of fire propagation rate and direction. Implications: The findings suggest the need for further investigation of other possible sources of wildfire modelling uncertainties and errors. Through sensitivity analyses and by comparing with real-world fire perimeters for the 2018 Camp Fire, the performance and challenges of wildland fire simulation with the WRF-Fire simulation platform are investigated. [ABSTRACT FROM AUTHOR]

This paper introduces a novel data-driven approach to enhance the prediction accuracy of regional-scale wildfire spread simulators using geostationary satellite wildfire detection data. First, we propose a new algorithm that maximizes accuracy by innovatively utilizing the conventional uniform Rate of Spread (ROS) adjustment factor. The ROS adjustment factor is estimated by a genetic algorithm based on near-real-time observations from geostationary satellites and the FARSITE wildfire simulator. The accuracy of wildfire spread prediction is improved by applying the estimated ROS adjustment factor within FARSITE. In addition, this paper proposes a Directional ROS adjustment factor approach, which applies different ROS adjustment factors to a given fuel model depending on their location, thereby allowing for a more accurate representation of a wildfire's perimeter in FARSITE-based simulations. The proposed methodology is demonstrated through the 2020 Creek Fire in California, USA. The results demonstrate that the proposed algorithm significantly enhances accuracy. Moreover, incorporating the directional ROS adjustment factor leads to more precise predictions than the conventional uniform ROS adjustment factors. The proposed methodology offers significant advantages in developing targeted wildfire response strategies, particularly under conditions where external environmental conditions make high-resolution data collection challenging. This approach enhances the accuracy and reliability of response planning, enabling more effective allocation of resources and potentially reducing the overall impact of wildfires., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

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The present study investigates the effect of climatic parameters, such as air relative humidity and wind speed, on fire spread propagation indexes in the Malekroud Forest, Iran using the FARSITE simulator based on Rothermel’s original fire spread equation. Standard fuel models are used to calibrate the vegetation cover. Sorensen (SC) and kappa (κ) coefficients, as well as the Overestimation Index (OI), are used to estimate the simulation’s accuracy. The results confirm that using both ambient condition data and appropriate fuel models is crucial to reaching reasonable results in fire propagation simulations. The values of the Rate of Fire Spread (ROS), Flame Length (FML), and Fire Line Intensity (FLI) are reported for each particular scenario. The simulation results show that the Sorensen and Kappa coefficient for situations most similar to the real fire reached 0.82 and 0.80, respectively. The investigated fire’s severity is categorized as low-condition fire behavior. The simulation shows that fire propagation falls harshly in the case of air relative humidity by more than 72%, and we will not witness natural fire propagation on a large scale.

The Tuscan Region (Central Italy) spends about 12 million euros every year in the prevention and suppression of forest fires. In this context, this study aims to analyse the economic and environmental benefits derived from fire suppression activities. Starting from a case study of a real fire event in Tuscany, we simulated three hypothetical scenarios (with different fire durations) without fire extinction activities planned by using the open source software FARSITE. Benefits derived from fire extinction activities can be quantified as the avoided damage, which has been calculated through the estimation of the total economic value of forests not destroyed by fire thanks to the extinction action. The avoided damage is represented by the difference between values of forest areas burned by the real fire event and those burned by simulated fire. By providing an economic estimation of avoided damages, our results confirm that forest fire services and forest management have a high impact on both the economy and the environment.

امروزه پدیده آتش­سوزی در عرصه­های جنگلی به­عنوان یکی از بلایای طبیعی بخش وسیعی از جنگل­های جهان را مورد تهدید قرار داده است. با توجه به اثرات مخرب آتش­سوزی بر جنگل، انجام تحقیقاتی که با استفاده از روش­های کارآمد بتوانند گسترش آتش­سوزی را پیش­بینی کنند، بسیار ارزشمند است. بر همین مبنا شبیه­سازی آتش با استفاده از مدل FARSITE در جنگل­های استان ایلام در سال 1395 به انجام رسید. FARSITEمدل بررسی رفتار و گسترش آتش به­شمار می­رود که از عوامل متعددی ازجمله شیب، جهت، ارتفاع، ماده سوختی و اطلاعات هواشناسی بهره می­برد. در این پژوهش نیز از عوامل ذکر شده استفاده گردید. مدل ماده سوختنی به­عنوان یکی از ارکان اصلی در شبیه­سازی با توجه به شرایط پوشش گیاهی منطقه تعیین گردید. تغییرات محلی سرعت و جهت باد که در نتیجه شرایط توپوگرافی منطقه حادث می­شود، شبیه‌سازی و در FARSITE مورد استفاده قرار گرفت. نتایج نشان می‌دهد در چهار منطقه که آتش‌سوزی رخ داده آتش رفتار مختلفی داشته است که آن هم به علت شرایط طبیعی متفاوت در این مناطق می­باشد. پهنه­بندی خطر آتش­سوزی در استان ایلام نشان می­دهد که 5/1 درصد از منطقه دارای ریسک بسیار زیاد می­باشند، پس استفاده اجرایی از این مدل راهنمای مفیدی برای مدیریت آتش­سوزی جنگل می­باشد و براساس آن می­توان برنامه مدیریت بحران آتش­سوزی در سه فاز عملیات مقدماتی یا اقدامات قبل وقوع (طرح پیش­گیری)، اقدامات حین وقوع (طرح مقابله) و عملیات پس از وقوع (طرح بازیابی) تدوین شود.

Los incendios forestales provocan pérdidas materiales cuantiosas para la gente que vive en el sector afectado e inseguridad para la población. En este proyecto se propuso simular la propagación del fuego en el incendio forestal ocurrido el día 14 de septiembre de 2015 en el Volcán Ilaló - Sector La Toglla, Quito - Ecuador, utilizando los programas QGIS y FARSITE, además de estudiar soluciones alternativas para contener la propagación del fuego. Utilizando información topográfica y meteorológica, se construyó en FARSITE una simulación de la propagación del fuego y las repercusiones que se vieron reflejadas en varias hectáreas de naturaleza quemadas y la producción de gases de combustión. Los resultados del estudio de FARSITE pudieron ser validados con la información oficial de los contaminantes expulsados a la atmósfera. Finalmente, los métodos de contención desarrollados mostraron una disminución del área afectada y toneladas de contaminantes expulsados al ambiente, en comparación con las condiciones reales en el Volcán Ilaló correspondientes al incendio estudiado. Esta herramienta puede guiar los esfuerzos de diseño de cortafuegos y reforestación en zonas erosionadas, como el Ilaló.

Each year, wildfires ravage the western U.S. and change the lives of millions of inhabitants. Situated in southern California, coastal Santa Barbara has witnessed devastating wildfires in the past decade, with nearly all ignitions started by humans. Therefore, estimating the risk imposed by unplanned ignitions in this fire-prone region will further increase resilience toward wildfires. Currently, a fire-risk map does not exist in this region. The main objective of this study is to provide a spatial analysis of regions at high risk of fast wildfire spread, particularly in the first two hours, considering varying scenarios of ignition locations and atmospheric conditions. To achieve this goal, multiple wildfire simulations were conducted using the FARSITE fire spread model with three ignition modeling methods and three wind scenarios. The first ignition method considers ignitions randomly distributed in 500 m buffers around previously observed ignition sites. Since these ignitions are mainly clustered around roads and trails, the second method considers a 50 m buffer around this built infrastructure, with ignition points randomly sampled from within this buffer. The third method assumes a Euclidean distance decay of ignition probability around roads and trails up to 1000 m, where the probability of selection linearly decreases further from the transportation paths. The ignition modeling methods were then employed in wildfire simulations with varying wind scenarios representing the climatological wind pattern and strong, downslope wind events. A large number of modeled ignitions were located near the major-exit highway running north–south (HWY 154), resulting in more simulated wildfires burning in that region. This could impact evacuation route planning and resource allocation under climatological wind conditions. The simulated fire areas were smaller, and the wildfires did not spread far from the ignition locations. In contrast, wildfires ignited during strong, northerly winds quickly spread into the wildland–urban interface (WUI) toward suburban and urban areas.

Forest fires represent threat mostly in countries situated in temperate climate zone, however, because of the changing climate, longer periods of high temperatures and drought, we can expect that their number and extent will increase in Slovakia, too. There are several factors influencing the initiation and further propagation of forest fire. Among the most important factors belong the volume and type of fuel available, topography, meteorological conditions and the time of fire. Those influence mostly the fire propagation, i.e. fire behaviour. In the paper, we introduce the results of a study focusing the fire behaviour in changing wind speed conditions. To model the fire behaviour, the FARSITE software was applied. In the modelling, we applied the data on real forest fire that occurred in the Slovensky Raj National Park, Koc locality in 2007. We modelled the fire behaviour for 24 h, using the specific wind speeds (0 km/h, 34 km/h, 68 km/h). According to the modelling results, we analysed the trend of fire propagation for each wind speed and provided the correlation analysis between the fire site extent (fire area and fire perimeter) and wind speed in Statistica 12 software. The fire propagation in time trend analysis results showed, that during the night, the extent of fire site was not increasing, probably because of the lower night temperatures and increasing moisture of the vegetation, over the moisture of extinction critical point, but not because of wind. The process of fire propagation started in the morning, after sun rise, again. The correlation analysis results showed very strong dependency between the fire site extent and wind speed. The results of this study have its immediate utilisation in the fire-fighters practice as for intervention commanders’ decision support as for fire-fighters education and training.

Abstract Invasive species can alter ecosystem properties and cause state shifts in landscapes. Resource managers charged with maintaining landscapes require tools to understand implications of alternative actions (or inactions) on landscape structure and function. Simulation models can serve as a virtual laboratory to explore these alternatives and their potential impacts on a landscape. To be useful, however, managers need to participate in model development to ensure that model structure can evaluate the response of key resources to plausible actions. Here, we detail development of a state‐and‐transition simulation model (STSM) to evaluate buffelgrass (Cenchrus ciliaris L. syn Pennisetum ciliare (L.) Link) in Saguaro National Park (SNP), Arizona, USA, through collaboration between managers and researchers. We integrate expert knowledge and research to create and parameterize a stochastic, spatially explicit STSM to evaluate specific management objectives. We also develop a dynamic link between the STSM and a fire behavior model to allow exploration of potential novel processes introduced to the ecosystem by buffelgrass invasion. Our projections show that buffelgrass can be expected to increase on the landscape and that the integration of fire into the model accelerates the projected rate of invasion and increases degradation of resources of management concern. We highlight the benefit of engaging end users in the modeling process so that the model is targeted to evaluate management objectives, in this case retention of saguaro cacti (Carnegiea gigantea (Engelm.) Britton & Rose) on the landscape. Being able to integrate an external model that can help address the unique characteristics of a problem such as the introduction of fire into the SNP desert ecosystem increases the ability of simulations to address complex ecological and management questions.

Fire propagation is inevitably affected by fuel-model parameters during wildfire simulations and the uncertainty of the fuel-model parameters makes forecasting accurate fire behaviour very difficult. In this study, three different methods (Morris screening, first-order analysis and the Monte Carlo method) were used to analyse the uncertainty of fuel-model parameters with FARSITE model. The results of the uncertainty analysis showed that only a few fuel-model parameters markedly influenced the uncertainty of the model outputs, and many of the fuel-model parameters had little or no effect. The fire-spread rate is the driving force behind the uncertainty of other fire behaviours. Thus, the highly uncertain fuel-model parameters associated with spread rate should be used cautiously in wildfire simulations. Monte Carlo results indicated that the relationship between model input and output was non-linear and neglecting fuel-model parameter uncertainty of the model would magnify fire behaviours. Additionally, fuel-model parameters have high input uncertainty. Therefore, fuel-model parameters must be calibrated against actual fires. The highly uncertain fuel-model parameters with high spatial-temporal variability consisted of fuel-bed depth, live-shrub loading and 1-h time-lag loading are preferentially chosen as parameters to calibrate several wildfires. Uncertainty analysis in this study shows that neglecting fuel-model parameter uncertainty of the model would magnify fire behaviours. This emphasises the importance of considering fuel-model parameter uncertainty when simulating wildfire behaviours. The highly uncertain fuel-model parameters with highly temporal and spatial variability are preferentially chosen as parameters to adjust the calibration against several actual fires and to effectively improve the fire-prediction capabilities of fuel models. Moreover, the results of fuel-model parameter uncertainty analysis advance the fuel-model classification in boreal forests. [ABSTRACT FROM AUTHOR]

Extreme, downslope mountain winds often generate dangerous wildfire conditions. We used the wildfire spread model Fire Area Simulator (FARSITE) to simulate two wildfires influenced by strong wind events in Santa Barbara, CA. High spatial-resolution imagery for fuel maps and hourly wind downscaled to 100 m were used as model inputs, and sensitivity tests were performed to evaluate the effects of ignition timing and location on fire spread. Additionally, burn area rasters from FARSITE simulations were compared to minimum travel time rasters from FlamMap simulations, a wildfire model similar to FARSITE that holds environmental variables constant. Utilization of two case studies during strong winds revealed that FARSITE was able to successfully reconstruct the spread rate and size of wildfires when spotting was minimal. However, in situations when spotting was an important factor in rapid downslope wildfire spread, both FARSITE and FlamMap were unable to simulate realistic fire perimeters. We show that this is due to inherent limitations in the models themselves, related to the slope-orientation relative to the simulated fire spread, and the dependence of ember launch and land locations. This finding has widespread implications, given the role of spotting in fire progression during extreme wind events.

Predicting fire spread and behavior correctly is crucial to minimize the dramatic consequences of wildfires. However, our capability of accurately predicting fire spread is still very limited, undermining the utility of such simulations to support decision-making. Improving fire spread predictions for fire management purposes, by using higher quality input data or enhanced models, can be expensive, unfeasible or even impossible. Fire managers would benefit from fast and inexpensive ways of improving their decision-making. In the present work, we focus on i) understanding if fire spread predictions can be improved through model parameter calibration based on information collected from a set of large historical wildfires in Portugal; and ii) understanding to what extent decreasing parametric uncertainty can counterbalance the impact of input data uncertainty. Our results obtained with the Fire Area Simulator (FARSITE) modeling system show that fire spread predictions can be continuously improved by ‘learning’ from past wildfires. The uncertainty contained in the major input variables (wind speed and direction, ignition location and fuel models) can be ‘swept under the rug’ through the use of more appropriate parameter sets. The proposed framework has a large potential to improve future fire spread predictions, increasing their reliability and usefulness to support fire management and decision making processes, thus potentially reducing the negative impacts of wildfires. [ABSTRACT FROM AUTHOR]

Forest fire is one of the major hazards causing destruction to biodiversity, environment and humans in the Taradevi forest of Himachal Pradesh (India). The study was carried out for forest fire spread analysis and loss assessment using simulation modeling techniques. Knowledge of the factors which includes forest types, canopy cover, meteorological status, topographic feature accelerating forest fire were taken into considerations. The parameters derived from remote sensing data and Geographical Information System (GIS) were used to generate input files for forest fire simulation modeling using FARISTE. Finally, a fire spread maps and fire areas were predicted in this simulation, where relative importance is given to each theme built based on GIS and climate parameters. The study aims to develop a simulation method to find out fire spread and direction according to the climate data like wind direction, wind speed, rainfall, forest fuel type & density, canopy cover and other maps required for it. Findings of the research are helpful in development of forest fire management. Fast and appropriate direction will be used by the management to stop the spread of fire effectively. It helps to provides effective means for protecting forests from fires as well as to formulate appropriate methods to control and manage forest fire damages and its spread. [ABSTRACT FROM AUTHOR]

This paper shows the extension of the wildfire simulation tool FARSITE to allow for data assimilation capabilities on both fire perimeters and fuel adjustment factors to improve the accuracy of wildfire spread predictions. While fire perimeters characterize the overall burn scar of a wildfire, fuel adjustment factors are fuel model specific calibration numbers that adjust the rate of spread for each fuel type independently. Data assimilation updates of both fire perimeters and fuel adjustment factors are calculated from an Ensemble Kalman Filter (EnKF) that exploits the uncertainty information on the simulated fire perimeter, fuel adjustment factors and a measured fire perimeter. The effectiveness of the proposed data assimilation is illustrated on a wildfire simulation representing the 2014 Cocos fire, tracking time varying fuel adjustment factors based on noisy and limited spatial resolution observations of the fire perimeter. [ABSTRACT FROM AUTHOR]

The recognized shortcomings of fire spread modeling systems and the widespread use of their outcomes in fire management decisions render the evaluation of fire simulation results crucial for model calibration and improvement. Additionally, new methods are essential to avoid model misapplication in fuel and fire management decision processes. This study proposes an exploratory evaluation framework of fire growth simulations using satellite active fire data. It uses nine very large fires that occurred in Portugal between 2003 and 2012. Their fire growth was simulated using the Fire Area Simulator (FARSITE) and compared with active fire data from the MODerate-resolution Imaging Spectroradiometer (MODIS). The evaluation is based on two spatial measures that quantify the absolute ( SpD ) and relative ( NRSpD ) spatial discrepancies between fire growth simulations and satellite active fires. Both measures account for the uncertain location of the fire front(s) inside the active fire footprint. Results highlight the contribution of the spatial discrepancy measures to locate areas of low/high spatio-temporal agreement between simulated fire growth and MODIS active fires, thereby aiding the assessment of potential sources of simulation error. Results also show that despite the coarse spatial resolution of MODIS active fires, these data are able to capture the spatial dynamics of fire growth. Limitations on the spatial measures were identified, particularly the lack of independence of evaluations using the NRSpD . In spite of being exploratory, this study represents a novel contribution to the evaluation of fire growth simulations because: 1) it uses satellite active fire data and is independent of the collection of reference burnt area perimeters; 2) it proposes two simple quantitative spatial discrepancy measures; and 3) it can be applied to a large number of wildfires, regardless of their geographical location. This innovative approach represents a potential cost effective evaluation scheme, that can be used systematically whenever evaluation of a large number of large wildfires is required and reference burned area perimeters are not available. [ABSTRACT FROM AUTHOR]

A new method has been developed to assess potential challenges by forest fire smoke on a cooling function of a decay heat removal system (DHRS) of a sodium-cooled fast reactor. Combinational numerical simulations of a forest fire propagation and a smoke transport were performed to evaluate a cumulative amount of smoke captured on air filters of the DHRS. The forest fire propagation simulations were performed using FARSITE code to evaluate a temporal increase of a forest fire spread area, a frontal fireline location, reaction intensity, and fireline intensity. Peripheral boundary of the forest fire spread area is shaped like an ellipse on the terrain, and the active forest fire area from which smoke is produced as a forest fire product is increased with forest fire spread. The smoke transport simulations were performed using ALOFT-FT code where a spatial distribution of smoke density, especially of particle matter (PM), is evaluated. The snapshot (i.e. at a certain time step) outputs by FARSITE on the reaction intensity and the fireline intensity were utilized as the input data for ALOFT-FT, while it was conservatively assumed that the smoke generated from the active forest fire area along the periphery boundary rises up from the frontal fireline location nearest to a nuclear power plant (NPP) and that prevailing wind transports all smoke to an NPP in the leeward side. The evaluated time-dependent changes of spatial PM density were utilized to calculate a cumulative amount of PM captured on the air filters of the DHRS. Sensitivity analysis was performed on prevailing wind speed to which both the fireline intensity and the smoke transport behavior are sensitive. The total amount of PM on the air filters was conservatively estimated around several hundred grams per m2 which is well below the utilization limit.

Computer modelling of dangerous phenomena related to fire in forest or in wildland-urban interface is an important tool to support emergency planning for such complicated events. In this paper, the use of FARSITE (Fire ARea SImulaTor), adapted for real conditions in Slovak Paradise National Park (Slovakia), and WFDS (Wildland-urban interface Fire Dynamics Simulator) for simulation of forest fires and fires in wildland-urban interface is demonstrated. Computer reconstruction of an especially tragic destructive forest fire happened in 2000 as well as simple examples of automobile fire in wildland-urban interface are illustrated.

This paper proposes a new method for rapid prediction of wildfire spread, which employs computational wildfire simulations by FARSITE and assimilates the simulation results with actual observation data by means of an ensemble Kalman filter. To expedite data assimilation, the wildfire perimeter is represented by a two-dimensional polyline simplification algorithm. In addition, to facilitate the data assimilation, a new process is developed to relate the prediction results with the actual observation data. The proposed method is tested and demonstrated by an example wildfire spread scenario generated based on actual climate, topography, and vegetation. The results confirm that the polyline simplification algorithm can drastically reduce the computational time required for data assimilation while maintaining the accuracy of predictions. The proposed method is expected to serve as a core algorithm for near-real-time prediction and data-driven updating of wildfire spread. • A new rapid prediction algorithm is developed for wildfire spread using EnKF and FARSITE. • The proposed algorithm simplifies the wildfire perimeter by applying polyline simplification. • To apply EnKF, a new method of reconstructing simplified perimeters is also proposed. • The algorithm significantly reduces computation time while maintaining prediction accuracy. • The proposed method is successfully demonstrated by wildfire spread scenarios. [ABSTRACT FROM AUTHOR]

Predicting wildfire spread is a challenging task fraught with uncertainties. ‘Perfect’ predictions are unfeasible since uncertainties will always be present. Improving fire spread predictions is important to reduce its negative environmental impacts. Here, we propose to understand, characterize, and quantify the impact of uncertainty in the accuracy of fire spread predictions for very large wildfires. We frame this work from the perspective of the major problems commonly faced by fire model users, namely the necessity of accounting for uncertainty in input data to produce reliable and useful fire spread predictions. Uncertainty in input variables was propagated throughout the modeling framework and its impact was evaluated by estimating the spatial discrepancy between simulated and satellite-observed fire progression data, for eight very large wildfires in Portugal. Results showed that uncertainties in wind speed and direction, fuel model assignment and typology, location and timing of ignitions, had a major impact on prediction accuracy. We argue that uncertainties in these variables should be integrated in future fire spread simulation approaches, and provide the necessary data for any fire model user to do so. [ABSTRACT FROM AUTHOR]

Background: An approach to predict fire growth in an operational setting, with the potential to be used as a decision-support tool for fire management, is described and evaluated. The operational use of fire behaviour models has mostly followed a deterministic approach, however, the uncertainty associated with model predictions needs to be quantified and included in wildfire planning and decision-making process during fire suppression activities. We use FARSITE to simulate the growth of a large wildfire. Probabilistic simulations of fire spread are performed, accounting for the uncertainty of some model inputs and parameters. Deterministic simulations were performed for comparison. We also assess the degree to which fire spread modelling and satellite active fire data can be combined, to forecast fire spread during large wildfires events. Results: Uncertainty was propagated through the FARSITE fire spread modelling system by randomly defining 100 different combinations of the independent input variables and parameters, and running the correspondent fire spread simulations in order to produce fire spread probability maps. Simulations were initialized with the reported ignition location and with satellite active fires. The probabilistic fire spread predictions show great potential to be used as a fire management tool in an operational setting, providing valuable information regarding the spatial-temporal distribution of burn probabilities. The advantage of probabilistic over deterministic simulations is clear when both are compared. Re-initializing simulations with satellite active fires did not improve simulations as expected. Conclusion: This information can be useful to anticipate the growth of wildfires through the landscape with an associated probability of occurrence. The additional information regarding when, where and with what probability the fire might be in the next few hours can ultimately help minimize the negative environmental, social and economic impacts of these fires. [ABSTRACT FROM AUTHOR]

This paper extends FARSITE (a software used for wildfire modeling and simulation) to incorporate data assimilation techniques based on noisy and limited spatial resolution observations of the fire perimeter to improve the accuracy of wildfire spread predictions. To include data assimilation in FARSITE, uncertainty on both the simulated wildfire perimeter and the measured wildfire perimeter is used to formulate optimal updates for the prediction of the spread of the wildfire. For data assimilation, Wildfire perimeter measurements with limited spatial resolution and a known uncertainty are used to to formulate an optimal adjustment in the fire perimeter prediction. The adjustment is calculated from the Kalman filter gain in an Ensemble Kalman filter that exploit the uncertainty information on both the simulated wildfire perimeter and the measured wildfire perimeter. The approach is illustated on a wildfire simulation representing the 2014 Cocos fire and presents comparison results for hourly data assimilation results. [ABSTRACT FROM AUTHOR]

Reduction of fire hazard is becoming increasingly important in managed landscapes globally. Fuels reduction prescribed burn treatments are the most common form of reducing fire hazard on landscapes around the world but often result in homogenized fuel age structures and habitats. Alternatively, the size of unplanned fires, and hence fire hazard, can be reduced by controlling the size and patterning of fuels treatments in a patch mosaic arrangement on landscapes. Patch mosaic burning is being implemented globally as a means to increase heterogeneity to mimic natural fire regime results. Funding for prescribed fire programs is often justified primarily on hazardous fuels reduction with secondary consideration given for ecological effectiveness, which can be increased by particular fire mosaic patterns in some systems. The question we address is: Which of two prescribed fire treatment regimes, fuels reduction or patch mosaic burning, reduces fire hazard most effectively? We address the question using computer simulation modeling on synthetic landscapes representing both fire regime treatments. Treatment scale was important. Among fuel reduction treatments, large blocks burned less area than small blocks. For the mosaic treatments, small blocks reduced fire size the most (out of all treatments) and had the least variance in area burned. It is possible to reduce fire hazard and to provide heterogeneous age fuels structure on the landscape, simultaneously benefiting humans and many native fire-dependent species requiring mosaic habitat patterns. [ABSTRACT FROM AUTHOR]

Lack of fire behavior studies and the immediate needs posed by the extent of the fire problem in forests of Iran require that extensive studies be conducted to develop models to predict fire behavior in the region. In this study, FARSITE Fire Area Simulator was applied to simulate spread and behavior of two real fires that had occurred in Northern Forests of Iran during 2010 summer and fall seasons in a spatially and temporally explicit manner taking into account the fuel, topography, and prevailing weather in the area. Spatial data themes of elevation, aspect, slope, canopy cover, and fuel model were prepared and formatted in GIS along with weather and wind files to run FARSITE fire behavior model. The effect of weather conditions on the accuracy of FARSITE simulations was evaluated in order to assess the capabilities of the simulator in accurately predicting the fire spread in the case study. The WindNinja model was used to derive local winds influenced by vegetation and topography. The simulations were validated with the real mapped fire scars by GPS mapping. Kappa Coefficient was used as measure of the accuracy of the simulation. The Kappa statistic was lower for spatially uniform wind data (0.5) as compared to spatially varying wind data (0.8) for the two studied events. The results confirm that the use of accurate wind field data is important in fire spread simulation, and can improve its accuracy and the predictive capabilities of the simulator. [ABSTRACT FROM AUTHOR]