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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]

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]

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. [ABSTRACT FROM AUTHOR]

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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]

Wildfires are a perennial problem in the Northwest of USA. Wallowa Whitman National Forest fires are quite frequent as a result forest completes between 10,000 and 15,000 acres of burning in a year. According to research, it was reported that the study area experienced fires from 1970 to 2006 with total burning of 149 fires and total burnt area of 7,583 hectares per year which observed to be between June and October. In Wallowa County, over 80% of fire starts are caused from lightning and 20% are attributed to humancaused fire starts. A GIS proved to be used effectively to map forest fire risk zone with the aid of LANDSAT satellite images and high resolution Google Earth images. FARSITE was used to predict fire intensities across a landscape. Results indicated that using GIS and FARSITE, one can generate a reliable management strategy, hence, minimising both life and property loss. [ABSTRACT FROM AUTHOR]

Abstract: Fuel load is often used to prioritize stands for fuel reduction treatments. However, wildfire size and intensity are not only related to fuel loads but also to a wide range of other spatially related factors such as topography, weather and human activity. In prioritizing fuel reduction treatments, we propose using burn probability to account for the effects of spatially related factors that can affect wildfire size and intensity. Our burn probability incorporated fuel load, ignition probability, and spread probability (spatial controls to wildfire) at a particular location across a landscape. Our goal was to assess differences in reducing wildfire size and intensity using fuel-load and burn-probability based treatment prioritization approaches. Our study was conducted in a boreal forest in northeastern China. We derived a fuel load map from a stand map and a burn probability map based on historical fire records and potential wildfire spread pattern. The burn probability map was validated using historical records of burned patches. We then simulated 100 ignitions and six fuel reduction treatments to compare fire size and intensity under two approaches of fuel treatment prioritization. We calibrated and validated simulated wildfires against historical wildfire data. Our results showed that fuel reduction treatments based on burn probability were more effective at reducing simulated wildfire size, mean and maximum rate of spread, and mean fire intensity, but less effective at reducing maximum fire intensity across the burned landscape than treatments based on fuel load. Thus, contributions from both fuels and spatially related factors should be considered for each fuel reduction treatment. [Copyright &y& Elsevier]

Abstract: During the late 19th and early 20th centuries, a combination of weather, land use, vegetation, and complacency about forest fires amongst the public caused a series of extreme fires that resulted in the death of over 2500 people in the Great Lakes Region of the United States. Eyewitness accounts of these fires were generally stories of individual human tragedies with fire behavior often described using anthropomorphic or even religious prose. However, the scenes described by the survivors are now recognized as cases of extreme fire behavior. In this paper we describe development of an animated visual representation of the Hinckley Fire in east-central Minnesota that devastated the town of Hinckley and surrounding communities on September 1, 1894. This was accomplished using modern science and technology coupled with eyewitness accounts, environmental data, and other information, all of which are easily available to educators dealing with forest fires. Data layers developed by the Minnesota Department of Natural Resources allowed reconstruction of the original forest cover types. The local fire museum’s preservation of historical landuse information allowed reconstruction of the built environment to accompany the eyewitness accounts. ArcGIS was used to develop geographic gridded data layers of these known historic parameters and provide input for the fire simulation program FARSITE. ArcGIS was also used to develop geometries for Visual Nature Studio to render 3D visualization of the historic built and forested environments. ArcGIS was also used to transfer fire perimeters from FARSITE to Visual Nature Studio. ParticleIllusion 3 was used to apply certain visual effects associated with extreme fire behavior consistent with both FARSITE predictions and eyewitness accounts. Both the fire simulation and visualization were able to capture many aspects of the eyewitness accounts as a single coherent fire event. [Copyright &y& Elsevier]

Mathematical modelling of forest fire propagation in time plays a key role in existing fire propagation predicting systems. Such systems are capable of simulating the growth of fire front in time and space and predicting spatial and temporal fire behaviour. In our previous papers, we studied mathematical foundations of the elliptical fire propagation model implemented in several advanced simulation systems. The model is based on Huygens' principle applied on fire propagation assuming locally elliptical fire spread. However, in the literature various other mathematical representations of local fire propagation have been reported, such as double ellipse, lemniskata, oval shape, tear shape, and others. Such types of local fire propagation have been experimentally observed in certain conditions during laboratory and field fires. In this paper, we demonstrate several simple examples of fire propagation corresponding both to the elliptical and non-elliptical local fire propagation in homogeneous conditions. [ABSTRACT FROM AUTHOR]

Phenomena in ecosystems such as forest fires, oil spills, tornados, etc., are complex processes both in time and space. Understanding their dynamics in order to predict their future states is a challenging procedure involving the propagation of several events concerning, e.g., the transfer of energy or material. A common solution used to study such phenomena is the utilization of discrete event models and simulators. Among the phenomena studied by researchers so far, of particular interest and importance, is the forecasting of forest fire propagation since life on earth is greatly dependent on healthy forests. This paper is focused on the investigation of existing simulator models applicable in forecasting forest fire propagation. Twenty-three simulators were found in the literature and are presented here. The comparison discussion concludes with the FARSITE simulator model which is the one that stands out from the rest, and for this reason, it is investigated in depth and evaluated in a test environment. [ABSTRACT FROM PUBLISHER]

Abstract: Fire simulation studies that use models such as FARSITE often assume that ignition locations are distributed randomly, because spatially explicit information about actual ignition locations are difficult to obtain. However, many studies show that the spatial distribution of ignition locations, whether human-caused or natural, is non-random. Thus, predictions from fire simulations based on random ignitions may be unrealistic. However, the extent to which the assumption of ignition location affects the predictions of fire simulation models has never been systematically explored. Our goal was to assess the difference in fire simulations that are based on random versus non-random ignition location patterns. We conducted four sets of 6000 FARSITE simulations for the Santa Monica Mountains in California to quantify the influence of random and non-random ignition locations and normal and extreme weather conditions on fire size distributions and spatial patterns of burn probability. Under extreme weather conditions, fires were significantly larger for non-random ignitions compared to random ignitions (mean area of 344.5 ha and 230.1 ha, respectively), but burn probability maps were highly correlated (r = 0.83). Under normal weather, random ignitions produced significantly larger fires than non-random ignitions (17.5 ha and 13.3 ha, respectively), and the spatial correlations between burn probability maps were not high (r = 0.54), though the difference in the average burn probability was small. The results of the study suggest that the location of ignitions used in fire simulation models may substantially influence the spatial predictions of fire spread patterns. However, the spatial bias introduced by using a random ignition location model may be minimized if the fire simulations are conducted under extreme weather conditions when fire spread is greatest. [Copyright &y& Elsevier]

Abstract: This paper describes a wildfire forecasting application based on a 3D virtual environment and a fire simulation engine. A novel open-source framework is presented for the development of 3D graphics applications over large geographic areas, offering high performance 3D visualization and powerful interaction tools for the Geographic Information Systems (GIS) community. The application includes a remote module that allows simultaneous connections of several users for monitoring a real wildfire event. The system is able to make a realistic composition of what is really happening in the area of the wildfire with dynamic 3D objects and location of human and material resources in real time, providing a new perspective to analyze the wildfire information. The user is enabled to simulate and visualize the propagation of a fire on the terrain integrating at the same time spatial information on topography and vegetation types with weather and wind data. The application communicates with a remote web service that is in charge of the simulation task. The user may specify several parameters through a friendly interface before the application sends the information to the remote server responsible of carrying out the wildfire forecasting using the FARSITE simulation model. During the process, the server connects to different external resources to obtain up-to-date meteorological data. The client application implements a realistic 3D visualization of the fire evolution on the landscape. A Level Of Detail (LOD) strategy contributes to improve the performance of the visualization system. [Copyright &y& Elsevier]

Abstract: The rapid growth of housing in and near the wildland–urban interface (WUI) increases wildfire risk to lives and structures. To reduce fire risk, it is necessary to identify WUI housing areas that are more susceptible to wildfire. This is challenging, because wildfire patterns depend on fire behavior and spread, which in turn depend on ignition locations, weather conditions, the spatial arrangement of fuels, and topography. The goal of our study was to assess wildfire risk to a 60,000ha WUI area in northwestern Wisconsin while accounting for all of these factors. We conducted 6000 simulations with two dynamic fire models: Fire Area Simulator (FARSITE) and Minimum Travel Time (MTT) in order to map the spatial pattern of burn probabilities. Simulations were run under normal and extreme weather conditions to assess the effect of weather on fire spread, burn probability, and risk to structures. The resulting burn probability maps were intersected with maps of structure locations and land cover types. The simulations revealed clear hotspots of wildfire activity and a large range of wildfire risk to structures in the study area. As expected, the extreme weather conditions yielded higher burn probabilities over the entire landscape, as well as to different land cover classes and individual structures. Moreover, the spatial pattern of risk was significantly different between extreme and normal weather conditions. The results highlight the fact that extreme weather conditions not only produce higher fire risk than normal weather conditions, but also change the fine-scale locations of high risk areas in the landscape, which is of great importance for fire management in WUI areas. In addition, the choice of weather data may limit the potential for comparisons of risk maps for different areas and for extrapolating risk maps to future scenarios where weather conditions are unknown. Our approach to modeling wildfire risk to structures can aid fire risk reduction management activities by identifying areas with elevated wildfire risk and those most vulnerable under extreme weather conditions. [Copyright &y& Elsevier]

Abstract: A key challenge in modern wildfire mitigation and forest management is accurate mapping of forest fuels in order to determine spatial fire hazard, plan mitigation efforts, and manage active fires. This study quantified forest fuels of the montane zone of Boulder County, CO, USA in an effort to aid wildfire mitigation planning and provide a metric by which LANDFIRE national fuel maps may be compared. Using data from 196 randomly stratified field plots, pre-existing vegetation maps, and derived variables, predictive classification and regression tree models were created for four fuel parameters necessary for spatial fire simulation with FARSITE (surface fuel model, canopy bulk density, canopy base height, and stand height). These predictive models accounted for 56–62% of the variability in forest fuels and produced fuel maps that predicted 91.4% and 88.2% of the burned area of two historic fires simulated in the FARSITE model. Simulations of areas burned based on LANDFIRE national fuel maps were less accurate, burning 77.7% and 40.3% of the historic fire areas. Our results indicate that fuel mapping efforts that utilize local area information and biotic as well as abiotic predictors will more accurately simulate fire spread rates and reflect the inherent variability of forested environments than do current LANDFIRE data products. [Copyright &y& Elsevier]

The increasing wildfire occurrence due to global climate changes urged the improvement of present wildfire growth prediction and evaluation methods. This study aimed to propose novel solutions to their two primary limitations, including the lack of robust fuel classification method and the low spatial resolution of wildfire growth accuracy assessment while ensuring wide applicability using open data satellite missions and software. The first objective was to create a robust two-step fuel model classification method consisted of the supervised machine learning classification of generalized land cover classes in the 1st level and their individual unsupervised classification to vegetation subtypes in the 2nd level. The second objective was creating a wildfire prediction accuracy assessment method using MODIS 250 m images, which overcome the limitations of low spatial resolution while preserving sub-daily temporal resolution. The wildfire on the Korčula island in Croatia was analyzed in the study, being specific for its long duration from 18 to 24 July 2015. The wildfire ignition occurred in the isolated area, which prolonged the response time from emergency agencies. Random Forest (RF) with input Landsat 8 spectral bands and indices resulted in the highest classification accuracy in the 1st classification level with an overall agreement of 83.6%. The vegetation subclasses from the 2nd classification level were matched to the 13 standard fuel models for the input in FARSITE software. The predicted wildfire evaluation showed the highest mean accuracy of 0.906 for the first two days, which decreased to 0.722 in the latter stages of the active wildfire caused by overprediction. The proposed two-step fuel model classification presented a cost-efficient solution to the fuel map creation in any part of the world, with a disadvantage of no in-situ ground truth identification and accuracy assessment for 2nd classification level. The evaluation of wildfire growth prediction with 250 m images enabled high spatial and temporal resolution of the assessment, while its limitations of wildfire overprediction and the negative effects of wildfire smoke in MODIS images should be addressed in future research. [Display omitted] • Wildfire growth was predicted and evaluated using Landsat and MODIS images. • The fuel model was classified using a robust two-step classification method. • Wildfire perimeters were evaluated using 250 m daily NDVI time series. • FARSITE prediction mean accuracy was 0.906 in the first two days of active wildfire. • Proposed cost-efficient methods support the decision-making in web services. [ABSTRACT FROM AUTHOR]

Conservation lands face mounting threats of ecosystem transformation and loss of biodiversity from the invasion of fire-prone grasses. Managers must make difficult decisions to find efficient ways to expend limited resources across large and complex landscapes amidst substantial uncertainty regarding effective treatment strategies, climates, and invader-induced novel processes. Coupled empirical and process-based models can simulate the effects of management activities, quantify potential management costs and ecological impacts, while considering uncertainties associated with climate, spread rates, and wildfires lacking historical precedent. We developed a state-and-transition simulation model coupled with a fire behavior model to study impacts to native biodiversity and fire regimes in a national park invaded by a perennial grass. We evaluated resources required to meet management objectives, and efficient and effective spatial allocation of those resources. Management strategies and ecological scenarios strongly influenced the ability to minimize potential invasion impacts. Adding aerial precision spot spraying, which can target low cover levels in remote regions, may be enough to conserve the desert ecosystem from small scale transformation through invasive competition and from broad functional transformations through invasive-induced fire regime changes. Spot spraying may also be beneficial if wetter monsoonal conditions create faster invader growth rates when likelihoods of achieving management goals decreases even with unlimited resources. Given current park budgets with the addition of spot spraying, management goals may be achievable regardless of spatial prioritization. Our techniques could be applied to other situations to evaluate conservation goal feasibility and determine actions that would be most efficient in meeting those goals. [ABSTRACT FROM AUTHOR]

The effect of area-of-edge influence (AEI) on fire size and movement was simulated by considering the distribution of single and multiple edges in the Chequamegon-Nicolet National Forest in Northern Wisconsin, USA. Six hypothetical landscapes with different delineations of AEIs were created for simulating fire spread using FARSITE to evaluate the influence of edges on the rate and direction of fire spread. The burned area differed significantly among the six landscapes. In the three scenarios with buffered edges, the burned area increased by 35% with high loading fuel in AEIs, while it decreased by 21 and 46% with medium and low fuel loading in the AEIs, respectively, as compared to the no edge scenario. In two scenarios we delineated the area-of-multiple-edge influence (AMEI) and placed more than one high loading fuel within it. This increased the burned area by 5% from the high buffered edge scenario and by 40% from the control. When the depth-of-edge influence (DEI) was doubled to 60 m using AMEI with high fuels, the burned area increased by 20% from the high buffered edge scenario and by 60% from the control. We found that low and medium fuel loading slowed the fire spread and over time, caused the fire front to change direction. In high fuel loading scenarios, AEIs acted as corridors facilitating the fire spread by providing a contiguous patch of fuel which allowed fires to increase in size and pulled the fire front in the same direction. [ABSTRACT FROM AUTHOR]

Abstract: Each year, wildland fires burn millions of hectares of forest worldwide. Fire managers need to provide effective methods for mapping fire fuels accurately. Fuel distribution is very important for predicting fire behavior. The overall aim of this project is to model fire behavior using FARSITE (Fire Area Simulator) and investigate differences in modeling outputs using fuel model maps, which differ in accuracy, in east Texas. This simulator model requires as input spatial data themes such as elevation, slope, aspect, surface fuel model, and canopy cover along with separate weather and wind data. Seven fuel models, including grass, brush, and timber models, are identified in the study area. To perform modeling sensitivity analysis, two different fuel model maps were used, one obtained by classifying a QuickBird image and the other obtained by classifying a LIDAR (LIght Detection and Ranging) and QuickBird fused data set. Our previous investigations showed that LIDAR improves the accuracy of fuel mapping by at least 13%. According to our new results, LIDAR-derived variables also provides more detailed information about characteristics of fire. This study will show the importance of using accurate maps of fuel models derived using new LIDAR remote sensing techniques. [Copyright &y& Elsevier]

Abstract: Wildfire behavior can be modified by altering the quantity, structure, and arrangement of fuel (flammable vegetation) by silvicultural treatments such as forest thinning and prescribed burning. The type and arrangement (including landscape location) of treated areas have been demonstrated to influence wildfire behavior. This study analyzes the response of several key fire behavior variables to variation in the type, amount, and spatial arrangement of fuel treatments for simulated wildfires in mixed-conifer forests of the southern Cascades in the Goosenest Adaptive Management Area (GAMA). NEXUS and BehavePlus were used to simulate pre- and post-treatment stand-level fire behavior. Fire area simulator (FARSITE) was used to simulate landscape-level wildfire behavior in both untreated and treated forest landscapes. In the forest landscape, treatment areas were placed in the landscape according to two strategically designed arrangements and one random treatment arrangement. Treatments included thinning by prescribed burning (burn-only), mechanical thinning (mechanical-only), mechanical thinning followed by burning (mechanical-burn), and no treatment (control). At the stand level, the mechanical-burn treatment most effectively reduced both surface fire (e.g., decreased flame length) and crown fire behavior (e.g., torching index). At the landscape level, treatment type, amount, and arrangement had important effects on both fire spread and fire intensity. In this landscape the most effective treatment arrangement was Finney''s optimal SPLATs design. This study shows that there is potential to efficiently reduce high-intensity fire behavior while treating less area by relying on strategically placed treatments. [Copyright &y& Elsevier]

It is unclear to what extent Native Americans in the pre-European forests of northeast North America used fire to manipulate their landscape. Conflicting historical and archaeological evidence has led authors to differing conclusions regarding the importance of fire. Ecological models provide a way to test different scenarios of historical landscape change. We applied FARSITE, a spatially explicit fire model, and linked tree mortality and successional models, to predict the landscape structure of the Harlem Plains in pre-European times under different scenarios of Native American fire use. We found that annual burning sufficed to convert the landscape to a fire-maintained grassland ecosystem, burning less often would have produced a mosaic of forest and grasslands, and even less frequent burning (on the order of once every 20 years) would not have had significant landscape level effects. These results suggest that if the Harlem Plains had been grasslands in the 16th century, they must have been intentionally created through Native American use of fire. [Copyright &y& Elsevier]

The number of large fires increased in the 1970s in the Valencia region (eastern Spain), as in most northern Mediterranean countries, owing to the fuel accumulation that affected large areas as a consequence of an intensive land abandonment. The Ayora site (Valencia province) was affected by a large fire in July 1979. We parameterised the fire growth model FARSITE for the 1979 fire conditions using remote sensing-derived fuel cartography. We simulated different fuel scenarios to study the interactions between fuel spatial distribution and fire characteristics (area burned, rate of spread and fireline intensity). We then tested the effectiveness of several firebreak networks on fire spread control. Simulations showed that fire propagation and behaviour were greatly influenced by fuel spatial distribution. The fragmentation of large dense shrubland areas through the introduction of wooded patches strongly reduced fire size, generally slowing fire and limiting fireline intensity. Both the introduction of forest corridors connecting woodlands and the promotion of complex shapes for wooded patches decreased the area burned. Firebreak networks were always very effective in reducing fire size and their effect was enhanced in appropriate fuel-altered scenarios. Most firebreak alternatives, however, did not reduce either rate of fire spread or fireline intensity. [ABSTRACT FROM AUTHOR]

Research has shown that remote sensing techniques can be used for assessing live fuel moisture content (LFMC) from space. The need for dynamic monitoring of the fire risk environment favors the use of fast, site-specific, empirical models for assessing local vegetation moisture status, albeit with some uncertainties. These uncertainties may affect the accuracy of decisions made by fire managers using remote sensing derived LFMC. Consequently, the analysis of these LFMC retrieval uncertainties and their impact on applications, such as fire spread prediction, is needed to ensure the informed use of remote sensing derived LFMC measurements by fire managers. The Okefenokee National Wildlife Refuge, one of the most fire-prone regions in the southeastern United States was chosen as our study area. Our study estimates the uncertainties associated with empirical site specific retrievals using NDWI (Normalized Difference Water Index; (R 0. 86 − R 1. 24) / (R 0. 86 + R 1. 24)) and NDII (Normalized Difference Infrared Index; (R 0. 86 − R 1. 64) / (R 0. 86 + R 1. 64)) that are simulated by coupled leaf and canopy radiative transfer models. In order to support the findings from those simulations, a second approach estimates uncertainties using actual MODIS derived indices over Georgia Forestry Commission stations that provide NFDRS model estimates of LFMC. Finally, we used the FARSITE surface fire behavior model to examine the sensitivity of fire spread rates to live fuel moisture content for the NFDRS high pocosin and southern rough fuel models found in Okefenokee. This allowed us to evaluate the effectiveness of satellite based LFMC estimations for use in fire behavior predictions. Sensitivity to LFMC (measured as percentage of moisture weight per unit dry weight of fuel) was analyzed in terms of no-wind no-slope spread rates as well as normalized spread rates. Normalized spread rates, defined as the ratio of spread rate at a particular LFMC to the spread rate at LFMC of 125 under similar conditions, were used in order to make the results adaptable to any wind-slope conditions. Our results show that NDWI has a stronger linear relationship to LFMC than NDII, and can consequently estimate LFMC with lesser uncertainty. Uncertainty analysis shows that 66% of NDWI based LFMC retrievals over non-sparsely vegetated regions are expected to have errors less than 32, while 90% of retrievals should be within an error margin of 56. In pocosin fuel models, under low LFMC conditions (<100), retrieval errors could lead to normalized spread rate errors of 6. 5 which may be equivalent to an error of 47 m/h in no-wind no-slope conditions. For southern rough fuel models, when LFMC <175, LFMC retrieval errors could amount to normalized spread rate errors of 0. 6 or an equivalent error of 9. 3 m/h in no-wind no-slope conditions. These spread rate error estimates represent approximately the upper bound of errors resulting from uncertainties in empirical retrievals of LFMC over forested regions. [Copyright &y& Elsevier]

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