scholarly journals Building Rothermel fire behaviour fuel models by genetic algorithm optimisation

2015 ◽  
Vol 24 (3) ◽  
pp. 317 ◽  
Author(s):  
Davide Ascoli ◽  
Giorgio Vacchiano ◽  
Renzo Motta ◽  
Giovanni Bovio
Keyword(s):  
Goodness Of Fit ◽  
Fire Spread ◽  
Validation Dataset ◽  
Model Parameters ◽  
Initial Population ◽  
Fire Behaviour ◽  
Continuous Space ◽  
Fuel Model ◽  
Spread Model ◽  
Fuel Models

A method to build and calibrate custom fuel models was developed by linking genetic algorithms (GA) to the Rothermel fire spread model. GA randomly generates solutions of fuel model parameters to form an initial population. Solutions are validated against observations of fire rate of spread via a goodness-of-fit metric. The population is selected for its best members, crossed over and mutated within a range of model parameter values, until a satisfactory fitness is reached. We showed that GA improved the performance of the Rothermel model in three published custom fuel models for litter, grass and shrub fuels (root mean square error decreased by 39, 19 and 26%). We applied GA to calibrate a mixed grass–shrub fuel model, using fuel and fire behaviour data from fire experiments in dry heathlands of Southern Europe. The new model had significantly lower prediction error against a validation dataset than either standard or custom fuel models built using average values of inventoried fuels, and predictions of the Fuel Characteristics Classification System. GA proved a useful tool to calibrate fuel models and improve Rothermel model predictions. GA allows exploration of a continuous space of fuel parameters, making fuel model calibration computational effective and easily reproducible, and does not require fuel sampling. We suggest GA as a viable method to calibrate custom fuel models in fire modelling systems based on the Rothermel model.

Sensitivity of a surface fire spread model and associated fire behaviour fuel models to changes in live fuel moisture

2007 ◽  
Vol 16 (4) ◽  
pp. 503 ◽  
Author(s):  
W. Matt Jolly
Keyword(s):  
Fire Spread ◽  
Fuel Moisture ◽  
Fire Behaviour ◽  
Surface Fire ◽  
Fuel Model ◽  
Spread Model ◽  
Fuel Models ◽  
Live Fuel Moisture ◽  
Behaviour Models ◽  
Fire Spread Model

Fire behaviour models are used to assess the potential characteristics of wildland fires such as rates of spread, fireline intensity and flame length. These calculations help support fire management strategies while keeping fireline personnel safe. Live fuel moisture is an important component of fire behaviour models but the sensitivity of existing models to live fuel moisture has not been thoroughly evaluated. The Rothermel surface fire spread model was used to estimate key surface fire behaviour values over a range of live fuel moistures for all 53 standard fuel models. Fire behaviour characteristics are shown to be highly sensitive to live fuel moisture but the response is fuel model dependent. In many cases, small changes in live fuel moisture elicit drastic changes in predicted fire behaviour. These large changes are a result of a combination of the model-calculated live fuel moisture of extinction, the effective wind speed limit and the dynamic load transfer function of some of the fuel models tested. Surface fire spread model sensitivity to live fuel moisture changes is discussed in the context of predicted fire fighter safety zone area because the area of a predicted safety zone may increase by an order of magnitude for a 10% decrease in live fuel moisture depending on the fuel model chosen.

Development of fuel models for fire behaviour prediction in maritime pine (Pinus pinaster Ait.) stands

2008 ◽  
Vol 17 (2) ◽  
pp. 194 ◽  
Author(s):  
Miguel G. Cruz ◽  
Paulo M. Fernandes
Keyword(s):  
Pinus Pinaster ◽  
Complex Structure ◽  
Fire Spread ◽  
Maritime Pine ◽  
Validation Dataset ◽  
Fire Behaviour ◽  
Functional Relationships ◽  
Rate Of Spread ◽  
Pinus Pinaster Ait ◽  
Fuel Models

A dataset of 42 experimental fires in maritime pine (Pinus pinaster Ait.) stands was used to develop fuel models to describe pine litter and understorey surface fuel complexes. A backtracking calibration procedure quantified the surface fuel bed characteristics that best explained the observed rate of fire spread. The study suggested the need for two distinct fuel models to adequately characterise the variability in fire behaviour in this fuel type. In these heterogeneous fuel beds the fuel models do not necessarily represent the inventoried average fuel conditions. Evaluation against the modelling data produced mean absolute errors of 0.8 and 0.6 m min–1 in rate of spread, respectively, for the litter and understorey fuel models, with little evidence of bias. The fuel models predicted the rate of spread of a validation dataset with comparable error. Comparison of the behaviour and evaluation statistics produced by the study fuel models with fuel models developed from inventoried fuel data alone revealed an improvement on model performance for the current study approach for the litter fuel model and comparable behaviour for the understorey one. We examined model behaviour through comparative analysis with models used operationally to predict fire spread in pine stands. Large departures from model behaviour essentially occur when the models are exercised outside the range of the model development dataset. The discrepancies in predicted fire behaviour were hypothesised to arise not from differences in fuel complex structure but from the selected functional relationships that determine the effect of wind and fuel moisture on rate of spread.

Analysis of the uncertainty of fuel model parameters in wildland fire modelling of a boreal forest in north-east China

2019 ◽  
Vol 28 (3) ◽  
pp. 205 ◽  
Author(s):  
Longyan Cai ◽  
Hong S. He ◽  
Yu Liang ◽  
Zhiwei Wu ◽  
Chao Huang
Keyword(s):  
Monte Carlo ◽  
Wildland Fire ◽  
Time Lag ◽  
Fire Spread ◽  
Model Parameters ◽  
Spread Rate ◽  
North East ◽  
Fire Modelling ◽  
Fuel Model ◽  
Model Parameter Uncertainty

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.

Fire behaviour in wheat crops – effect of fuel structure on rate of fire spread

2020 ◽  
Vol 29 (3) ◽  
pp. 258 ◽  
Author(s):  
Miguel G. Cruz ◽  
Richard J. Hurley ◽  
Rachel Bessell ◽  
Andrew L. Sullivan
Keyword(s):  
Experimental Study ◽  
Prediction Models ◽  
Fire Spread ◽  
Propagation Rate ◽  
Forward Rate ◽  
Fire Behaviour ◽  
Natural Grass ◽  
Spread Model ◽  
Crop Condition ◽  
Fire Spread Model

A field-based experimental study was conducted in 50×50m square plots to investigate the behaviour of free-spreading fires in wheat to quantify the effect of crop condition (i.e. harvested, unharvested and harvested and baled) on the propagation rate of fires and their associated flame characteristics, and to evaluate the adequacy of existing operational prediction models used in these fuel types. The dataset of 45 fires ranged from 2.4 to 10.2kmh−1 in their forward rate of fire spread and 3860 and 28000 kWm−1 in fireline intensity. Rate of fire spread and flame heights differed significantly between crop conditions, with the unharvested condition yielding the fastest spreading fires and tallest flames and the baled condition having the slowest moving fires and lowest flames. Rate of fire spread in the three crop conditions corresponded directly with the outputs from the models of Cheney et al. (1998) for grass fires: unharvested wheat → natural grass; harvested wheat (~0.3m tall stubble) → grazed or cut grass; and baled wheat (<0.1m tall stubble) → eaten-out grass. These models produced mean absolute percent errors between 21% and 25% with reduced bias, a result on par with the most accurate published fire spread model evaluations.

scholarly journals Calibration of FARSITE fire area simulator in Iranian northern forests

2014 ◽  
Vol 2 (9) ◽  
pp. 6201-6240 ◽  
Author(s):  
R. Jahdi ◽  
M. Salis ◽  
A. A. Darvishsefat ◽  
F. J. Alcasena Urdiroz ◽  
V. Etemad ◽  
...  
Keyword(s):  
Vegetation Type ◽  
Fire Spread ◽  
Physical Models ◽  
Northern Iran ◽  
Burned Areas ◽  
Risk Monitoring ◽  
Spread Model ◽  
Fuel Models ◽  
And Behavior ◽  
In Fire

Abstract. Wildfire simulators based on empirical or physical models need to be locally calibrated and validated when used under conditions that differ from those where the simulators were originally developed. This study aims to calibrate FARSITE fire spread model considering a set of recent wildfires occurred in Northern Iran forests. Site specific fuel models in the study areas were selected by sampling the main natural vegetation type complexes and assigning standard fuel models. Overall, simulated fires presented reliable outputs that accurately replicated the observed fire perimeters and behavior. Standard fuel models of Scott and Burgan (2005) afforded better accuracy in the simulated fire perimeters than the standard fuel models of Anderson (1982). The best match between observed and modeled burned areas was observed on herbaceous type fuel models. Fire modeling showed a high potential for estimating spatial variability in fire spread and behavior in the study areas. This work represents a first step in the application of fire spread modeling on Northern Iran for wildfire risk monitoring and management.

Reformulation of Rothermel’s wildland fire behaviour model for heterogeneous fuelbedsThis article is one of a selection of papers published in the Special Forum on the Fuel Characteristic Classification System.

2007 ◽  
Vol 37 (12) ◽  
pp. 2438-2455 ◽  
Author(s):  
David V. Sandberg ◽  
Cynthia L. Riccardi ◽  
Mark D. Schaaf
Keyword(s):  
Wind Speed ◽  
Environmental Conditions ◽  
Classification System ◽  
Fire Behavior ◽  
Fire Spread ◽  
Fuel Moisture ◽  
Fire Behaviour ◽  
Spread Model ◽  
Fuel Characteristic ◽  
Fire Spread Model

The Fuel Characteristic Classification System (FCCS) includes equations that calculate energy release and one-dimensional spread rate in quasi-steady state fires in heterogeneous but spatially-uniform wildland fuelbeds, using a reformulation of the widely used Rothermel fire spread model. This reformulation provides an automated means to predict fire behavior under any environmental conditions in any natural, modified, or simulated wildland fuelbed. The formulation may be used to compare potential fire behavior between fuelbeds that differ in time, space, or as a result of management, and provides a means to classify and map fuelbeds based on their expected surface fire behavior under any set of defined environmental conditions (i.e., effective wind speed and fuel moisture content). Model reformulation preserves the basic mathematical framework of the Rothermel fire spread model, reinterprets data from two of the original basic equations in his model, and offers a new conceptual formulation that allows the direct use of inventoried fuel properties instead of stylized fuel models. Alternative methods for calculating the effect of wind speed and fuel moisture, based on more recent literature, are also provided. This reformulation provides a framework for the incremental improvement in quantifying fire behaviour parameters in complex fuelbeds and for modeling fire spread.

Evaluation of FARSITE simulator in Mediterranean maquis

2007 ◽  
Vol 16 (5) ◽  
pp. 563 ◽  
Author(s):  
Bachisio Arca ◽  
P. Duce ◽  
M. Laconi ◽  
G. Pellizzaro ◽  
M. Salis ◽  
...  
Keyword(s):  
Weather Conditions ◽  
Structural Heterogeneity ◽  
Fire Spread ◽  
Mediterranean Ecosystems ◽  
Rate Of Spread ◽  
Fuel Model ◽  
Fuel Models ◽  
Mediterranean Maquis ◽  
Meteorological Input ◽  
Accuracy Of Results

In the last two decades, several models were developed to provide temporal and spatial variations of fire spread and behaviour. The most common models (i.e. BEHAVE and FARSITE) are based on Rothermel's original fire spread equation and describe fire spread and behaviour taking into account the influences of fuels, terrain and weather conditions. The use of FARSITE on areas different from those where the simulator was originally developed requires a local calibration to produce reliable results. This is particularly true for Mediterranean ecosystems, where plant communities are characterised by high specific and structural heterogeneity and complexity. To perform FARSITE calibration, an appropriate fuel model or the development of a specific custom fuel model is needed. In this study, FARSITE was employed to simulate three fire events in Mediterranean areas using different fuel models and meteorological input data, and the accuracy of results was analysed. A custom fuel model designed and developed for shrubland vegetation (maquis) provided realistic values of rate of spread, when compared with estimated values obtained using standard fuel models. Our results confirm that the use of both wind field data and appropriate custom fuel models are crucial to obtain reasonable simulations of wildfire events occurring on Mediterranean vegetation during the drought season.

High-resolution observations of combustion in heterogeneous surface fuels

2014 ◽  
Vol 23 (7) ◽  
pp. 1016 ◽  
Author(s):  
E. Louise Loudermilk ◽  
Gary L. Achtemeier ◽  
Joseph J. O'Brien ◽  
J. Kevin Hiers ◽  
Benjamin S. Hornsby
Keyword(s):  
High Resolution ◽  
Regression Tree ◽  
Fire Spread ◽  
Recognition Algorithm ◽  
Fine Scale ◽  
Imaging Data ◽  
Fire Behaviour ◽  
Spread Model ◽  
Fuel Heterogeneity ◽  
Fuel Characteristics

In ecosystems with frequent surface fires, fire and fuel heterogeneity at relevant scales have been largely ignored. This could be because complete burns give an impression of homogeneity, or due to the difficulty in capturing fine-scale variation in fuel characteristics and fire behaviour. Fire movement between patches of fuel can have implications for modelling fire spread and understanding ecological effects. We collected high resolution (0.8×0.8-cm pixels) visual and thermal imaging data during fire passage over 4×4-m plots of mixed fuel beds consisting of pine litter and grass during two prescribed burns within the longleaf pine forests of Eglin Air Force Base, FL in February 2011. Fuel types were identified by passing multi-spectral digital images through a colour recognition algorithm in ‘Rabbit Rules,’ an experimental coupled fire-atmosphere fire spread model. Image fuel types were validated against field fuel types. Relationships between fuel characteristics and fire behaviour measurements at multiple resolutions (0.8×0.8cm to 33×33cm) were analysed using a regression tree approach. There were strong relationships between fire behaviour and fuels, especially at the 33×33-cm scale (R2=0.40–0.69), where image-to-image overlap error was reduced and fuels were well characterised. Distinct signatures were found for individual and coupled fuel types for determining fire behaviour, illustrating the importance of understanding fire-fuel heterogeneity at fine-scales. Simulating fire spread at this fine-scale may be critical for understanding fire effects, such as understorey plant community assembly.

A two-dimensional model of fire spread across a fuel bed including wind combined with slope conditions

2002 ◽  
Vol 11 (1) ◽  
pp. 53 ◽  
Author(s):  
Frédéric Morandini ◽  
Paul A. Santoni ◽  
Jacques H. Balbi ◽  
João M. Ventura ◽  
José M. Mendes-Lopes
Keyword(s):  
Fire Management ◽  
Wildland Fire ◽  
Threshold Value ◽  
Fire Spread ◽  
Management Tool ◽  
Fire Behaviour ◽  
Dimensional Model ◽  
Proposed Model ◽  
Spread Model ◽  
Fire Spread Model

In a previous work (Santoni et al., Int. J. Wildland Fire, 2000, 9(4), 285–292), we proposed a twodimensional fire spread model including slope effects as another step towards our aim to elaborate a fire management tool. In the present study, we improve the model to include both wind conditions and wind combined with slope conditions. For this purpose the effect of wind and slope are considered similar, in the sense that they both force the flames to lean forward. However, this analogy remains acceptable only when flame tilt is below a threshold value. Simulation results are compared to experimental data under wind and no-slope conditions. The proposed model is able to describe the fire behaviour. Predictions of the model for wind and slope conditions are then considered and comparisons with observations are also provided.

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