Modeling and Estimating Post-Earthquake Fire Spread

2012 ◽  
Vol 28 (2) ◽  
pp. 795-810 ◽  
Author(s):  
Geoff Thomas ◽  
David Heron ◽  
Jim Cousins ◽  
Mairéad de Róiste
Keyword(s):  
Fire Spread ◽  
Spontaneous Ignition ◽  
Broken Windows ◽  
Conditional Probabilities ◽  
Existing Buildings ◽  
Spread Model ◽  
Building Information ◽  
Piloted Ignition ◽  
Further Development ◽  
Fire Spread Model

This paper describes the development of a GIS-based dynamic fire-spread model, with seven distinct modes of fire spread: direct contact, spontaneous ignition of claddings, piloted ignition of claddings, spontaneous ignition through windows, piloted ignition through broken windows, fire spread via non-fire-rated roofs and branding. All except the first two modes include in-built probabilities, but these can be selected individually and given user-defined values. Fire spread modes can be added to the model or altered to suit available building information. Critical details of buildings are obtained from an existing-buildings database, street surveys, or deduced using conditional probabilities from available data. Results show that comparison with actual fires is reasonable. The model could be extended with further development for use as a real time firefighting tool.

scholarly journals Coupled atmosphere-wildland fire modeling with WRF-Fire version 3.3

2011 ◽  
Vol 4 (1) ◽  
pp. 497-545 ◽  
Author(s):  
J. Mandel ◽  
J. D. Beezley ◽  
A. K. Kochanski
Keyword(s):  
Level Set Method ◽  
Level Set ◽  
Wildland Fire ◽  
Surface Wind ◽  
Numerical Algorithms ◽  
Fire Spread ◽  
Weather Research And Forecasting ◽  
Time Step ◽  
Spread Model ◽  
Fire Spread Model

Abstract. We describe the physical model, numerical algorithms, and software structure of WRF-Fire. WRF-Fire consists of a fire-spread model, implemented by the level-set method, coupled with the Weather Research and Forecasting model. In every time step, the fire model inputs the surface wind, which drives the fire, and outputs the heat flux from the fire into the atmosphere, which in turn influences the atmosphere. The level-set method allows submesh representation of the burning region and flexible implementation of various kinds of ignition. WRF-Fire is distributed as a part of WRF and it uses the WRF parallel infrastructure for parallel computing.

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.

FIRE SPREAD MODEL FOR A LINEAR FRONT IN A HORIZONTAL SOLID POROUS FUEL BED IN STILL AIR

2004 ◽  
Vol 176 (2) ◽  
pp. 135-182 ◽  
Author(s):  
G. C. VAZ ◽  
J. C. S. ANDRÉ ◽  
D. X. VIEGAS
Keyword(s):  
Fire Spread ◽  
Spread Model ◽  
Fire Spread Model

scholarly journals Fire spread model for old towns based on cellular automaton

2008 ◽  
Vol 13 (5) ◽  
pp. 736-740 ◽  
Author(s):  
Nan Gao ◽  
Wenguo Weng ◽  
Wei Ma ◽  
Shunjiang Ni ◽  
Quanyi Huang ◽  
...  
Keyword(s):  
Cellular Automaton ◽  
Fire Spread ◽  
Spread Model ◽  
Fire Spread 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.

scholarly journals Coupled Fire–Atmosphere Simulations of the Rocky River Fire Using WRF-SFIRE

2016 ◽  
Vol 55 (5) ◽  
pp. 1151-1168 ◽  
Author(s):  
Mika Peace ◽  
Trent Mattner ◽  
Graham Mills ◽  
Jeffrey Kepert ◽  
Lachlan McCaw
Keyword(s):  
Fire Behavior ◽  
South Australia ◽  
Simulation Models ◽  
Fire Spread ◽  
Fire Plume ◽  
Spread Model ◽  
Mesoscale Convective ◽  
Rayleigh Benard ◽  
Fire Spread Model ◽  
Extreme Fire

AbstractThe coupled atmosphere–fire spread model “WRF-SFIRE” has been used to simulate a fire where extreme fire behavior was observed. Tall flames and a dense convective smoke column were features of the fire as it burned rapidly up the Rocky River gully on Kangaroo Island, South Australia. WRF-SFIRE simulations of the event show a number of interesting dynamical processes resulting from fire–atmosphere feedback, including the following: fire spread was sensitive to small changes in mean wind direction; fire perimeter was affected by wind convergence resulting from interactions between the fire, atmosphere, and local topography; and the fire plume mixed high-momentum air from above a strong subsidence inversion. At 1-min intervals, output from the simulations showed fire spread exhibiting fast and slow pulses. These pulses occurred coincident with the passage of mesoscale convective (Rayleigh–Bénard) cells in the planetary boundary layer. Simulations show that feedback between the fire and atmosphere may have contributed to the observed extreme fire behavior. The findings raise questions as to the appropriate information to include in meteorological forecasts for fires as well as future use of coupled and uncoupled fire simulation models in both operational and research settings.

Fire Spread Model for Drum-Tower District of Tianjin Based on Cellular Automaton

2014 ◽  
Vol 568-570 ◽  
pp. 1987-1990
Author(s):  
Hong Po Wang ◽  
Hong Zhou
Keyword(s):  
Cellular Automaton ◽  
Numerical Simulations ◽  
Fire Spread ◽  
Commercial Culture ◽  
Historic Site ◽  
Evacuation Time ◽  
Time Dynamics ◽  
Influence Coefficients ◽  
Spread Model ◽  
Fire Spread Model

The Drum-Tower District is a historic site in the Inner place of Tianjin which represents the commercial culture,ecdemic culture,exotic culture and civilian culture of Tianjin. The buildings of this region are usually made of wood and other highly combustible materials. Once there be a fire, the influence will be very serious. The study of fire spread model Drum-Tower District is necessary. In the model of fire spread the cellular automaton rules were used. The effects of the different influence coefficients on the evacuation time were investigated, and the typical characteristics of space-time dynamics were analyzed. The numerical simulations show that the model is very good and can be used as a tool to predict fire.

scholarly journals Fitting a stochastic fire spread model to data

2019 ◽  
Vol 5 (1) ◽  
pp. 57-66
Author(s):  
X. Joey Wang ◽  
John R. J. Thompson ◽  
W. John Braun ◽  
Douglas G. Woolford
Keyword(s):  
Wildland Fire ◽  
Data Extraction ◽  
Fire Risk ◽  
Fire Spread ◽  
Small Scale ◽  
Intermediate Step ◽  
Spread Model ◽  
Recorded Data ◽  
Model Variability ◽  
Fire Spread Model

Abstract. As the climate changes, it is important to understand the effects on the environment. Changes in wildland fire risk are an important example. A stochastic lattice-based wildland fire spread model was proposed by Boychuk et al. (2007), followed by a more realistic variant (Braun and Woolford, 2013). Fitting such a model to data from remotely sensed images could be used to provide accurate fire spread risk maps, but an intermediate step on the path to that goal is to verify the model on data collected under experimentally controlled conditions. This paper presents the analysis of data from small-scale experimental fires that were digitally video-recorded. Data extraction and processing methods and issues are discussed, along with an estimation methodology that uses differential equations for the moments of certain statistics that can be derived from a sequential set of photographs from a fire. The interaction between model variability and raster resolution is discussed and an argument for partial validation of the model is provided. Visual diagnostics show that the model is doing well at capturing the distribution of key statistics recorded during observed fires.

scholarly journals Balancing uncertainty and complexity to incorporate fire spread in an eco-hydrological model

2017 ◽  
Vol 26 (8) ◽  
pp. 706 ◽  
Author(s):  
Maureen C. Kennedy ◽  
Donald McKenzie ◽  
Christina Tague ◽  
Aubrey L. Dugger
Keyword(s):  
Hydrological Model ◽  
Fire Regimes ◽  
Fire Spread ◽  
Fire Effects ◽  
Hydrologic Model ◽  
Simulation System ◽  
Model Assessment ◽  
Topographic Slope ◽  
Spread Model ◽  
Fire Spread Model

Wildfire affects the ecosystem services of watersheds, and climate change will modify fire regimes and watershed dynamics. In many eco-hydrological simulations, fire is included as an exogenous force. Rarely are the bidirectional feedbacks between watersheds and fire regimes integrated in a simulation system because the eco-hydrological model predicts variables that are incompatible with the requirements of fire models. WMFire is a fire-spread model of intermediate complexity designed to be integrated with the Regional Hydro-ecological Simulation System (RHESSys). Spread in WMFire is based on four variables that (i) represent known influences on fire spread: litter load, relative moisture deficit, wind direction and topographic slope, and (ii) are derived directly from RHESSys outputs. The probability that a fire spreads from pixel to pixel depends on these variables as predicted by RHESSys. We tested a partial integration between WMFire and RHESSys on the Santa Fe (New Mexico) and the HJ Andrews (Oregon State) watersheds. Model assessment showed correspondence between expected spatial patterns of spread and seasonality in both watersheds. These results demonstrate the efficacy of an approach to link eco-hydrologic model outputs with a fire spread model. Future work will develop a fire effects module in RHESSys for a fully coupled, bidirectional model.

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