Numerical Prediction of the Fire Spread in Vegetative Fuels Using NISTWFDS Model

2015 ◽  
Vol 20 ◽  
pp. 177-192 ◽  
Author(s):  
Hadj Miloua
Keyword(s):  
Forest Fires ◽  
Wildland Fire ◽  
Radiation Heat Transfer ◽  
Fire Behavior ◽  
Reaction Diffusion ◽  
Fire Spread ◽  
Three Dimensions ◽  
Forest Stands ◽  
Fire Dynamics ◽  
Wind Climate

Current study focuses to the application of an advanced physics-based (reaction–diffusion) fire behavior model to the fires spreading through surface vegetation such as grasslands and elevated vegetation such as trees present in forest stands. This model in three dimensions, called Wildland Fire Dynamics Simulator WFDS, is an extension, to vegetative fuels, of the structural FDS developed at NIST. For simplicity, the vegetation was assumed to be uniformly distributed in a tree crown represented by a well defined geometric shape. This work on will focus on predictions of thermal function such as the radiation heat transfer and and thermal function for diverse cases of spatial distribution of vegetation in forest stands. The influence of wind, climate characteristics and terrain topography will also be used to extend and validate the model. The results obtained provide a basis to carry out a risk analysis for fire spread in the studied vegetative fuels in the Mediterranean forest fires.

A preliminary study of wildland fire pattern indicator reliability following an experimental fire

2017 ◽  
Vol 35 (5) ◽  
pp. 359-378 ◽  
Author(s):  
Albert Simeoni ◽  
Zachary C Owens ◽  
Erik W Christiansen ◽  
Abid Kemal ◽  
Michael Gallagher ◽  
...  
Keyword(s):  
Wildland Fire ◽  
Fire Behavior ◽  
Field Studies ◽  
Fire Spread ◽  
Environmental Data ◽  
Fire Investigation ◽  
Experimental Protocol ◽  
Fire Dynamics ◽  
Preliminary Study ◽  
Infrared Cameras

An experimental fire was conducted in 2016, in the Pinelands National Reserve of New Jersey, to assess the reliability of the fire pattern indicators used in wildland fire investigation. Objects were planted in the burn area to support the creation of the indicators. Fuel properties and environmental data were recorded. Video and infrared cameras were used to document the general fire behavior. This work represents the first step in the analysis by developing an experimental protocol suitable for field studies and describing how different fire indicators appeared in relation to fire behavior. Most of the micro- and macroscale indicators were assessed. The results show that some indicators are highly dependent on local fire conditions and may contradict the general fire spread. Overall, this study demonstrates that fire pattern indicators are a useful tool for fire investigators but that they must be interpreted through a general analysis of the fire behavior with a good understanding of fire dynamics.

scholarly journals Wildland Fire Behaviour Case Studies and Fuel Models for Landscape-Scale Fire Modeling

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
Paul-Antoine Santoni ◽  
Jean-Baptiste Filippi ◽  
Jacques-Henri Balbi ◽  
Frédéric Bosseur
Keyword(s):  
Case Studies ◽  
Fire History ◽  
Spatial Information ◽  
Wildland Fire ◽  
Meteorological Data ◽  
Fire Behavior ◽  
Fire Spread ◽  
Landscape Scale ◽  
Three Dimensions ◽  
Fuel Models

This work presents the extension of a physical model for the spreading of surface fire at landscape scale. In previous work, the model was validated at laboratory scale for fire spreading across litters. The model was then modified to consider the structure of actual vegetation and was included in the wildland fire calculation system Forefire that allows converting the two-dimensional model of fire spread to three dimensions, taking into account spatial information. Two wildland fire behavior case studies were elaborated and used as a basis to test the simulator. Both fires were reconstructed, paying attention to the vegetation mapping, fire history, and meteorological data. The local calibration of the simulator required the development of appropriate fuel models for shrubland vegetation (maquis) for use with the model of fire spread. This study showed the capabilities of the simulator during the typical drought season characterizing the Mediterranean climate when most wildfires occur.

scholarly journals A Deep Learning Approach to Downscale Geostationary Satellite Imagery for Decision Support in High Impact Wildfires

Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 294
Author(s):  
Nicholas F. McCarthy ◽  
Ali Tohidi ◽  
Yawar Aziz ◽  
Matt Dennie ◽  
Mario Miguel Valero ◽  
...  
Keyword(s):  
Deep Learning ◽  
Decision Support ◽  
Real Time ◽  
Forest Fires ◽  
Wildland Fire ◽  
Fire Spread ◽  
Low Earth Orbit ◽  
Practical Implementation ◽  
Spatiotemporal Resolution ◽  
Active Fire

Scarcity in wildland fire progression data as well as considerable uncertainties in forecasts demand improved methods to monitor fire spread in real time. However, there exists at present no scalable solution to acquire consistent information about active forest fires that is both spatially and temporally explicit. To overcome this limitation, we propose a statistical downscaling scheme based on deep learning that leverages multi-source Remote Sensing (RS) data. Our system relies on a U-Net Convolutional Neural Network (CNN) to downscale Geostationary (GEO) satellite multispectral imagery and continuously monitor active fire progression with a spatial resolution similar to Low Earth Orbit (LEO) sensors. In order to achieve this, the model trains on LEO RS products, land use information, vegetation properties, and terrain data. The practical implementation has been optimized to use cloud compute clusters, software containers and multi-step parallel pipelines in order to facilitate real time operational deployment. The performance of the model was validated in five wildfires selected from among the most destructive that occurred in California in 2017 and 2018. These results demonstrate the effectiveness of the proposed methodology in monitoring fire progression with high spatiotemporal resolution, which can be instrumental for decision support during the first hours of wildfires that may quickly become large and dangerous. Additionally, the proposed methodology can be leveraged to collect detailed quantitative data about real-scale wildfire behaviour, thus supporting the development and validation of fire spread models.

Review of Basic Models in Fire Dynamics

2000 ◽  
Author(s):  
Hyeong-Jin Kim ◽  
David G. Lilley
Keyword(s):  
Fire Behavior ◽  
Fire Spread ◽  
Scientific Understanding ◽  
Fire Dynamics ◽  
Topic Area ◽  
Background Theory ◽  
Burning Rates ◽  
Main Components ◽  
Structural Fires ◽  
In Fire

Abstract The ultimate goal of this study is to improve scientific understanding of fire behavior leading to flashover in structural fires. This document summarizes important information in five topic areas: burning rates, radiant ignition, fire spread rates, ventilation limit imposed by size of opening, and flashover criteria. These are the main components related to the scientific understanding of the fire growth and flashover problem involved in real-world structural fires. Within each topic area, there are four subsections dealing with background, theory, comments, and references. Main components of the study are to develop improved mathematical simulations so as to improve the accuracy of theoretical calculation and to develop and extend the range of knowledge and modeling capability so as to extend the range of available experimental data.

scholarly journals Advancing the Science of Wildland Fire Dynamics Using Process-Based Models

Fire ◽  
2018 ◽  
Vol 1 (2) ◽  
pp. 32 ◽  
Author(s):  
Chad Hoffman ◽  
Carolyn Sieg ◽  
Rodman Linn ◽  
William Mell ◽  
Russell Parsons ◽  
...  
Keyword(s):  
Wildland Fire ◽  
Fire Behavior ◽  
Physical World ◽  
Empirical Models ◽  
Discovery Process ◽  
Fire Dynamics ◽  
Synergistic Approach ◽  
Measurement Strategies ◽  
Prior Experiences ◽  
Process Based Models

As scientists and managers seek to understand fire behavior in conditions that extend beyond the limits of our current empirical models and prior experiences, they will need new tools that foster a more mechanistic understanding of the processes driving fire dynamics and effects. Here we suggest that process-based models are powerful research tools that are useful for investigating a large number of emerging questions in wildland fire sciences. These models can play a particularly important role in advancing our understanding, in part, because they allow their users to evaluate the potential mechanisms and interactions driving fire dynamics and effects from a unique perspective not often available through experimentation alone. For example, process-based models can be used to conduct experiments that would be impossible, too risky, or costly to do in the physical world. They can also contribute to the discovery process by inspiring new experiments, informing measurement strategies, and assisting in the interpretation of physical observations. Ultimately, a synergistic approach where simulations are continuously compared to experimental data, and where experiments are guided by the simulations will profoundly impact the quality and rate of progress towards solving emerging problems in wildland fire sciences.

scholarly journals Pixel-level statistical analyses of prescribed fire spread

2019 ◽  
Vol 49 (1) ◽  
pp. 18-26 ◽  
Author(s):  
M. Currie ◽  
K. Speer ◽  
J.K. Hiers ◽  
J.J. O’Brien ◽  
S. Goodrick ◽  
...  
Keyword(s):  
Prescribed Fire ◽  
Wildland Fire ◽  
Spatial Scales ◽  
Three Dimensional ◽  
Fire Spread ◽  
Infrared Images ◽  
Fire Dynamics ◽  
Efficient Tool ◽  
Ca Model ◽  
Turbulent Process

Wildland fire dynamics are a complex three-dimensional turbulent process. Cellular automata (CA) is an efficient tool to predict fire dynamics, but the main parameters of the method are challenging to estimate. To overcome this challenge, we compute statistical distributions of the key parameters of a CA model using infrared images from controlled burns. Moreover, we apply this analysis to different spatial scales and compare the experimental results with a simple statistical model. By performing this analysis and making this comparison, several capabilities and limitations of CA are revealed.

scholarly journals Fire spread across pine needle fuel beds: characterization of temperature and velocity distributions within the fire plume

2004 ◽  
Vol 13 (1) ◽  
pp. 37 ◽  
Author(s):  
Thierry Marcelli ◽  
Paul A. Santoni ◽  
Albert Simeoni ◽  
Eric Leoni ◽  
Bernard Porterie
Keyword(s):  
Forest Fires ◽  
Fire Behavior ◽  
Thermal Fluctuation ◽  
Fire Spread ◽  
Reconstruction Method ◽  
Pine Needle ◽  
Fire Plume ◽  
Two Phase ◽  
Experimental Apparatus

The aim of this article is twofold. First, it concerns the improvement of knowledge on the fundamental physical mechanisms that control the propagation of forest fires. To proceed, an experimental apparatus was designed to study, in laboratory conditions, the flame of a fire spreading across a pine needle fuel bed. Characterization of temperature was managed by using a reconstruction method based on a double thermocouple probe technique developed recently. The vertical gas velocity distribution was derived from the previous reconstructed signals by measuring the transit time of a thermal fluctuation between two points of the flow. Second, the experimental data were used for the testing of a physical two-phase model of forest fire behavior in which the decomposition of solid fuel constituting a forest fuel bed as well as the multiple interactions with the gas phase are represented.

scholarly journals Coupling Terrestrial Laser Scanning with 3D Fuel Biomass Sampling for Advancing Wildland Fuels Characterization

2019 ◽  
Author(s):  
Eric Rowell ◽  
E. Louise Loudermilk ◽  
Christie Hawley ◽  
Scott Pokswinski ◽  
Carl Seielstad ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Wildland Fire ◽  
Fire Behavior ◽  
Terrestrial Laser Scanning ◽  
Sampling Technique ◽  
Fire Effects ◽  
Three Dimensions ◽  
Fine Scale ◽  
Field Sampling ◽  
Volume Porosity

AbstractThe spatial pattern of surface fuelbeds in fire-dependent ecosystems are rarely captured using long-standing fuel sampling methods. New techniques, both field sampling and remote sensing, that capture vegetation fuel type, biomass, and volume at super fine-scales (cm to dm) in three-dimensions (3D) are critical to advancing forest fuel and wildland fire science. This is particularly true for computational fluid dynamics fire behavior models that operate in 3D and have implications for wildland fire operations and fire effects research. This study describes the coupling of new 3D field sampling data with terrestrial laser scanning (TLS) data to infer fine-scale fuel mass in 3D. We found that there are strong relationships between fine-scale mass and TLS occupied volume, porosity, and surface area, which were used to develop fine-scale prediction equations using TLS across vegetative fuel types, namely grasses and shrubs. The application of this novel 3D sampling technique to high resolution TLS data in this study represents a major advancement in understanding fire-vegetation feedbacks in highly managed fire-dependent ecosystems.

Classifying and mapping fuels in central European forests

2021 ◽  
Author(s):  
Pia Labenski ◽  
Michael Ewald ◽  
Fabian Ewald Fassnacht
Keyword(s):  
Forest Fires ◽  
Fire Behavior ◽  
Fire Risk ◽  
Fire Spread ◽  
Fire Frequency ◽  
Extreme Weather Events ◽  
Central European ◽  
European Forests ◽  
First Results ◽  
Spread Dynamics

<p>In recent years, forest fires have become more frequent in central Europe. As the frequency and magnitude of future extreme weather events such as droughts are projected to increase, also the trend of increasing fire frequency in temperate forests is expected to continue. However, knowledge about fire behavior and spread dynamics in these forests is scarce. One of the key drivers of fire behavior is the availability of flammable vegetation, i.e. fuels. In the project ErWiN, we aim to describe the amount and distribution of fuels in different forest types in Southwestern Germany. Detailed field inventories of fuels in all vertical strata of the stands allow a first classification into different fuel types, which can be used in fire behavior simulations to obtain estimates of fire spread and intensity. In a further step, deep learning algorithms will be trained on recognizing these fuel types on GNSS located photos of forest stand situations to provide an efficient solution for mapping fuels in the field. By coupling field data with detailed remotely sensed information on forest structure obtained from airborne laserscanning, continuous fuel maps will be derived. Such fuel maps in turn allow landscape-scale analysis of fire behavior and can be useful in forest management decisions as well as in developing firefighting strategies. We thus hope to make a contribution to a better understanding of fuel-driven fire risk in central European forests and to facilitate the operational use of fire behavior models. In this contribution we present the concept developed in the ErWiN project and present first results obtained from the field survey of fuel types in Southwestern Germany.</p>

scholarly journals A Review of Fire Interactions and Mass Fires

2011 ◽  
Vol 2011 ◽  
pp. 1-14 ◽  
Author(s):  
Mark A. Finney ◽  
Sara S. McAllister
Keyword(s):  
Wildland Fire ◽  
Flame Temperature ◽  
Fire Behavior ◽  
Fire Spread ◽  
Velocity Pulsation ◽  
Interactive Behavior ◽  
Life Saving ◽  
Column Dynamics ◽  
Burning Rates ◽  
In Fire

The character of a wildland fire can change dramatically in the presence of another nearby fire. Understanding and predicting the changes in behavior due to fire-fire interactions cannot only be life-saving to those on the ground, but also be used to better control a prescribed fire to meet objectives. In discontinuous fuel types, such interactions may elicit fire spread where none otherwise existed. Fire-fire interactions occur naturally when spot fires start ahead of the main fire and when separate fire events converge in one location. Interactions can be created intentionally during prescribed fires by using spatial ignition patterns. Mass fires are among the most extreme examples of interactive behavior. This paper presents a review of the detailed effects of fire-fire interaction in terms of merging or coalescence criteria, burning rates, flame dimensions, flame temperature, indraft velocity, pulsation, and convection column dynamics. Though relevant in many situations, these changes in fire behavior have yet to be included in any operational-fire models or decision support systems.

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