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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]

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 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. [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]

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]

Fire-evolved forests that historically had high fire return intervals (<100 years) in the western United States are currently overstocked with fuels due to a century or more of fire suppression and anthropogenic modification. Additionally, some western rangelands have changed composition from fire maintained grasslands to grazed shrublands. Land managers are beginning to reintroduce fire to these ecosystems as a functional component. Estimating fire behavior through the use of computer simulations is one tool to assist in planning management-ignited fire. We evaluated the sensitivity of the fire model FARSITE to the level of detail in the fuels data, both spatially and quantitatively, to better understand requirements for mapping fuels to produce accurate fire simulations. Simulated fires generated using site specific fuel models mapped at 30 m and degraded to 210 m were compared to fires simulated using standard generic Northern Forest Fire Laboratory (NFFL) fuel types. Eight classes of surface fuels were mapped by classification of satellite imagery with an overall accuracy of 0.78. A percent tree canopy cover map was created from digital orthophotos using a linear regression model with an Radj2=0.93 of field sampled percent canopy cover data to a tree canopy shadow model. The dominant site specific fuel model (63% cover) was found to agree with the most suitable NFFL fuel model. Site specific fuel models mapped at fine resolution were found to produce statistically smaller fire areas than those produced with generic fuel models mapped at a fine scale and site specific fuels mapped at a coarse scale. In the worst case scenario (low fuel moistures and high wind speeds) the average fire size was about 20% larger with the fuel map using NFFL fuel models than with the fine scale map using site specific fuel models. [Copyright &y& Elsevier]