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 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 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 PROPAGATOR: An Operational Cellular-Automata Based Wildfire Simulator

Fire ◽  
2020 ◽  
Vol 3 (3) ◽  
pp. 26
Author(s):  
Andrea Trucchia ◽  
Mirko D’Andrea ◽  
Francesco Baghino ◽  
Paolo Fiorucci ◽  
Luca Ferraris ◽  
...  
Keyword(s):  
Moisture Content ◽  
Fire Spread ◽  
Fire Fighting ◽  
Fuel Moisture ◽  
Time Step ◽  
Ignition Point ◽  
The Real ◽  
Fire Propagation ◽  
Spread Model ◽  
Fuel Moisture Content

PROPAGATOR is a stochastic cellular automaton model for forest fire spread simulation, conceived as a rapid method for fire risk assessment. The model uses high-resolution information such as topography and vegetation cover considering different types of vegetation. Input parameters are wind speed and direction and the ignition point. Dead fine fuel moisture content and firebreaks—fire fighting strategies can also be considered. The fire spread probability depends on vegetation type, slope, wind direction and speed, and fuel moisture content. The fire-propagation speed is determined through the adoption of a Rate of Spread model. PROPAGATOR simulates independent realizations of one stochastic fire propagation process, and at each time-step gives as output a map representing the probability of each cell of the domain to be affected by the fire. These probabilities are obtained computing the relative frequency of ignition of each cell. The model capabilities are assessed by reproducing a set of past Mediterranean fires occurred in different countries (Italy and Spain), using when available the real fire fighting patterns. PROPAGATOR simulated such scenarios with affordable computational resources and with short CPU-times. The outputs show a good agreement with the real burned areas, demonstrating that the PROPAGATOR can be useful for supporting decisions in Civil Protection and fire management activities.

scholarly journals Recent advances and applications of WRF–SFIRE

2014 ◽  
Vol 14 (10) ◽  
pp. 2829-2845 ◽  
Author(s):  
J. Mandel ◽  
S. Amram ◽  
J. D. Beezley ◽  
G. Kelman ◽  
A. K. Kochanski ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Atmospheric Chemistry ◽  
Wrf Model ◽  
Fire Spread ◽  
Fuel Moisture ◽  
Management Tools ◽  
Ignition Point ◽  
Recent Advances ◽  
Operational Testing ◽  
Spread Model

Abstract. Coupled atmosphere–fire models can now generate forecasts in real time, owing to recent advances in computational capabilities. WRF–SFIRE consists of the Weather Research and Forecasting (WRF) model coupled with the fire-spread model SFIRE. This paper presents new developments, which were introduced as a response to the needs of the community interested in operational testing of WRF–SFIRE. These developments include a fuel-moisture model and a fuel-moisture-data-assimilation system based on the Remote Automated Weather Stations (RAWS) observations, allowing for fire simulations across landscapes and time scales of varying fuel-moisture conditions. The paper also describes the implementation of a coupling with the atmospheric chemistry and aerosol schemes in WRF–Chem, which allows for a simulation of smoke dispersion and effects of fires on air quality. There is also a data-assimilation method, which provides the capability of starting the fire simulations from an observed fire perimeter, instead of an ignition point. Finally, an example of operational deployment in Israel, utilizing some of the new visualization and data-management tools, is presented.

A comparison of three approaches for simulating fine-scale surface winds in support of wildland fire management. Part I. Model formulation and comparison against measurements

2014 ◽  
Vol 23 (7) ◽  
pp. 969 ◽  
Author(s):  
Jason M. Forthofer ◽  
Bret W. Butler ◽  
Natalie S. Wagenbrenner
Keyword(s):  
Fire Management ◽  
Prediction Models ◽  
Wildland Fire ◽  
Surface Wind ◽  
Field Measurements ◽  
Fire Spread ◽  
Neutral Stability ◽  
Wind Fields ◽  
Forecast Models ◽  
Wildland Fire Management

For this study three types of wind models have been defined for simulating surface wind flow in support of wildland fire management: (1) a uniform wind field (typically acquired from coarse-resolution (~4km) weather service forecast models); (2) a newly developed mass-conserving model and (3) a newly developed mass and momentum-conserving model (referred to as the momentum-conserving model). The technical foundation for the two new modelling approaches is described, simulated surface wind fields are compared to field measurements, and the sensitivity of the new model types to mesh resolution and aspect ratio (second type only) is discussed. Both of the newly developed models assume neutral stability and are designed to be run by casual users on standard personal computers. Simulation times vary from a few seconds for the mass-conserving model to ~1h for the momentum-conserving model using consumer-grade computers. Applications for this technology include use in real-time fire spread prediction models to support fire management activities, mapping local wind fields to identify areas of concern for firefighter safety and exploring best-case weather scenarios to achieve prescribed fire objectives. Both models performed best on the upwind side and top of terrain features and had reduced accuracy on the lee side. The momentum-conserving model performed better than the mass-conserving model on the lee side.

scholarly journals Adaptive Forest Fire Spread Simulation Algorithm Based on Cellular Automata

Forests ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1431
Author(s):  
Liyang Sun ◽  
Congcong Xu ◽  
Yanglangxing He ◽  
Yanjun Zhao ◽  
Yuan Xu ◽  
...  
Keyword(s):  
Cellular Automata ◽  
Forest Fire ◽  
Forest Fires ◽  
Fire Spread ◽  
Time Step ◽  
Simulation Accuracy ◽  
Change Rate ◽  
Spread Model ◽  
Speed Change ◽  
Actual Fire

The popular simulation process that uses traditional cellular automata with a fixed time step to simulate forest fire spread may be limited in its ability to reflect the characteristics of actual fire development. This study combines cellular automata with an existing forest fire model to construct an improved forest fire spread model, which calculates a speed change rate index based on the meteorological factors that affect the spread of forest fires and the actual environment of the current location of the spread. The proposed model can adaptively adjust the time step of cellular automata through the speed change rate index, simulating forest fire spread more in line with the actual fire development trends while ensuring accuracy. When used to analyze a forest fire that occurred in Mianning County, Liangshan Prefecture, Sichuan Province in 2020, our model exhibited simulation accuracy of 96.9%, and kappa coefficient of 0.6214. The simulated fire situation adapted well to the complex and dynamic fire environment, accurately depicting the detailed fire situation. The algorithm can be used to simulate and predict the spread of forest fires, ensuring the accuracy of spread simulation and helping decision makers formulate reasonable plans.

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.

A comparison of three approaches for simulating fine-scale surface winds in support of wildland fire management. Part II. An exploratory study of the effect of simulated winds on fire growth simulations

2014 ◽  
Vol 23 (7) ◽  
pp. 982 ◽  
Author(s):  
Jason M. Forthofer ◽  
Bret W. Butler ◽  
Charles W. McHugh ◽  
Mark A. Finney ◽  
Larry S. Bradshaw ◽  
...  
Keyword(s):  
Fire Management ◽  
Wildland Fire ◽  
Fire Spread ◽  
Fire Growth ◽  
Conservation Of Mass ◽  
Area Of Interest ◽  
Support Tools ◽  
Fire Planning ◽  
Fine Resolution ◽  
Wildland Fire Management

The effect of fine-resolution wind simulations on fire growth simulations is explored. The wind models are (1) a wind field consisting of constant speed and direction applied everywhere over the area of interest; (2) a tool based on the solution of the conservation of mass only (termed mass-conserving model) and (3) a tool based on a solution of conservation of mass and momentum (termed momentum-conserving model). Fire simulations use the FARSITE fire simulation system to simulate fire growth for one hypothetical fire and two actual wildfires. The momentum-conserving model produced fire perimeters that most closely matched the observed fire spread, followed by the mass-conserving model and then the uniform winds. The results suggest that momentum-conserving and mass-conserving models can reduce the sensitivity of fire growth simulations to input wind direction, which is advantageous to fire growth modellers. The mass-conserving and momentum-conserving wind models may be useful for operational use as decision support tools in wildland fire management, prescribed fire planning, smoke dispersion modelling, and firefighter and public safety.

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