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.

scholarly journals Coupled atmosphere-wildland fire modeling with WRF 3.3 and SFIRE 2011

2011 ◽  
Vol 4 (3) ◽  
pp. 591-610 ◽  
Author(s):  
J. Mandel ◽  
J. D. Beezley ◽  
A. K. Kochanski
Keyword(s):  
Wildland Fire ◽  
Surface Wind ◽  
Wrf Model ◽  
Coupled Model ◽  
Numerical Algorithms ◽  
Fire Spread ◽  
Fire Modeling ◽  
Time Step ◽  
Spread Model ◽  
Fine Resolution

Abstract. We describe the physical model, numerical algorithms, and software structure of a model consisting of the Weather Research and Forecasting (WRF) model, coupled with the fire-spread model (SFIRE) module. 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. SFIRE is implemented by the level set method, which allows a submesh representation of the burning region and a flexible implementation of various kinds of ignition. The coupled model is capable of running on a cluster faster than real time even with fine resolution in dekameters. It is available as a part of the Open Wildland Fire Modeling (OpenWFM) environment at http://openwfm.org, which contains also utilities for visualization, diagnostics, and data processing, including an extended version of the WRF Preprocessing System (WPS). The SFIRE code with a subset of the features is distributed with WRF 3.3 as WRF-Fire.

scholarly journals Physical parametrisation of fire-spotting for operational fire spread models: response analysis with a model based on the Level Set Method

2018 ◽  
Author(s):  
Inderpreet Kaur ◽  
Anton Butenko ◽  
Gianni Pagnini
Keyword(s):  
Level Set Method ◽  
Level Set ◽  
Discrete Event ◽  
Fire Spread ◽  
Response Analysis ◽  
System Specification ◽  
Time Step ◽  
Processing Scheme ◽  
Model Based ◽  
The Many

Abstract. Fire-spotting is often responsible for a dangerous flare up in the wildfire and causes secondary ignitions isolated from the primary fire zone leading to perilous situations. In this paper a complete physical parametrisation of fire-spotting is presented within a formulation aimed to include random processes into operational fire spread models. This formulation can be implemented into existing operational models as a post-processing scheme at each time step, without calling for any major changes in the original framework. In particular, the efficacy of this formulation has already been shown for wildfire simulators based on an Eulerian moving interface method, namely the Level Set Method (LSM) that forms the baseline of the operational software WRF-SFIRE, and for wildfire simulators based on a Lagrangian front tracking technique, namely the Discrete Event System Specification (DEVS) that forms the baseline of the operational software FOREFIRE. The simple and computationally less expensive parametrisation includes the important parameters necessary for describing the landing behavior of the firebrands. The results from different simulations with a simple model based on the LSM highlight the response of the parametrisation to varying fire intensities, wind conditions and different firebrand radii. The contribution of the firebrands towards increasing the fire perimeter varies according to different concurrent conditions and the simulation results prove to be in agreement with the physical processes. Among the many rigorous approaches available in literature to model the firebrand transport and distribution, the approach presented here proves to be simple yet versatile for application to operational fire spread models.

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.

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.

Examination of the wind speed limit function in the Rothermel surface fire spread model

2013 ◽  
Vol 22 (7) ◽  
pp. 959 ◽  
Author(s):  
Patricia L. Andrews ◽  
Miguel G. Cruz ◽  
Richard C. Rothermel
Keyword(s):  
Wind Speed ◽  
Wildland Fire ◽  
Fire Spread ◽  
Speed Limit ◽  
Limit Function ◽  
Surface Fire ◽  
Rate Of Spread ◽  
Fire Modelling ◽  
Spread Model ◽  
Fire Spread Model

The Rothermel surface fire spread model includes a wind speed limit, above which predicted rate of spread is constant. Complete derivation of the wind limit as a function of reaction intensity is given, along with an alternate result based on a changed assumption. Evidence indicates that both the original and the revised wind limits are too restrictive. Wind limit is based in part on data collected on the 7 February 1967 Tasmanian grassland fires. A reanalysis of the data indicates that these fires might not have been spreading in fully cured continuous grasslands, as assumed. In addition, more recent grassfire data do not support the wind speed limit. The authors recommend that, in place of the current wind limit, rate of spread be limited to effective midflame wind speed. The Rothermel model is the foundation of many wildland fire modelling systems. Imposition of the wind limit can significantly affect results and potentially influence fire and fuel management decisions.

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.

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