Fire Behavior Experiments in Mixed Fuel Complexes

1993 ◽  
Vol 3 (1) ◽  
pp. 45 ◽  
Author(s):  
EA Catchpole ◽  
WR Catchpole ◽  
RC Rothermel
Keyword(s):  
Fire Behavior ◽  
Mixed Fuel ◽  
Rate Of Spread ◽  
Fire Experiments

A series of laboratory fire experiments were conducted in fuel beds consisting of either excelsior (wood wool), or 6.35 mm sticks, or a mixture of these. Tests were done both with and without wind. Various characteristics of the fire, including rate of spread and fireline intensity, were compared with predictions from the Rothermel model. The behavior of the mixed fuel fires, compared to that of the fires in the constituent fuels, did not agree well with the predictions of the model. We conclude that, in order to model the behavior of fire in mixed fuels, the Rothermel model needs modification.

Slope and Fuel Load Effects on Fire Behavior: Laboratory Experiments in Pine Needles Fuel Beds

1995 ◽  
Vol 5 (3) ◽  
pp. 153 ◽  
Author(s):  
JL Dupuy
Keyword(s):  
Laboratory Experiments ◽  
Pinus Halepensis ◽  
Fire Behavior ◽  
Fuel Load ◽  
Fire Behaviour ◽  
Rate Of Spread ◽  
Load Effects ◽  
Heat Transfers ◽  
Different Levels ◽  
Fire Experiments

Laboratory fire experiments were conducted in both Pinus pinaster and Pinus halepensis litters in order to investigate the effect of slope on fire behaviour for different levels of fuel load. Simulated slopes ranged between -30 degrees and +30 degrees. The results are reported in terms of rate of spread and rate of mass loss when observed fire was quasi-steady. Upslope fires were observed, on the present devices, to be unsteady, and their flame to be three-dimensionnal, when slope and fuel load exceeded certain limits. The heat transfers involved in the explanation of the observed behaviours are discussed, especially on the base of the quite different results obtained in the two tested fuel. beds.

Instantaneous spatio-temporal rate of spread of fast spreading wildfires - a new approach from visible and thermal image processing

2021 ◽  
Author(s):  
Benjamin Schumacher ◽  
Katharine Melnik ◽  
Marwan Katurji ◽  
Veronica Clifford ◽  
Jiawei Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
New Zealand ◽  
Stand Structure ◽  
Fire Severity ◽  
Thermal Infrared ◽  
Fire Spread ◽  
New Approach ◽  
Rate Of Spread ◽  
Fast Spreading ◽  
Fire Experiments

<p>The rate of spread (ROS) of wildfires is an important parameter for understanding fire-atmospheric interactions and developing fire-spread models, but it is also vital for firefighting operations to ensure the safety of firefighters (Plucinski 2017, Stow 2019). Spatial ROS observations are usually carried out by using visible and thermal satellite imagery of wildfires estimating the ROS on a time scale of hours to days for large fires (>100 ha) or repeated passing with an airborne thermal infrared imager for higher spatial and temporal resolution (Viedma et al. 2015, Stow 2014). For fire experiments in highly controlled conditions like laboratory fires or during light fuel prescribed burns, ROS estimation usually involves lag-correlation of temperature point measurements (Finney 2010, Johnston 2018). However, these methodologies are not applicable to fast-spreading grass or bush fires because of their temporal and spatial limitations. Instantaneous spatial ROS of these fires is needed to understand rapid changes in connection with the three major drivers of the fire: fuel, topography and atmospheric forcings.</p><p>We are presenting a new approach towards a spatial ROS product which includes newly developed image tracking methods based on thermal and visible imagery collected from unmanned aerial vehicles to estimate instantaneous, spatial ROS of fast spreading grass or bush fires. These techniques were developed using imagery from prescribed wheat-stubble burns carried out in Darfield, New Zealand in March 2018 (Finney 2018). Results show that both the visible and thermal tracking techniques produce similar mean ROS; however they differ in limitations and advantages. The visible-spectrum tracking method clearly identifies the flaming zone and provides accurate ROS measurements especially at the fire front. The thermal tracking technique is superior when resolving dynamics and ROS within the flaming zone because it resolves smaller scale structures within the imagery.</p><p> </p><p>References:</p><p>Finney, M. et al. 2010: An Examination of Fire Spread Thresholds in Discontinuous Fuel Beds.” International Journal of Wildland Fire, 163–170.</p><p>Finney, M. et al. 2018: New Zealand prescribed fire experiments to test convective heat transfer in wildland fires. In Advances in Forest Fire Research, Imprensa da Universidade de Coimbra: Coimbra, 2018.</p><p>Johnston, J. M., et al. 2018:  Flame-Front Rate of Spread Estimates for Moderate Scale Experimental Fires are Strongly Influenced by Measurement Approach. Fire 1: 16–17</p><p>Plucinski M., et al. 2017: Improving the reliability and utility of operational bushfire behaviour predictions in Australian vegetation. Environmental Modelling & Software 91, 1-12.</p><p>Stow, D., et al. 2014: Measuring Fire Spread Rates from Repeat PassAirborne Thermal Infrared Imagery. Remote Sensing Letters 5: 803–881.</p><p>Stow, D., et al. 2019: Assessing uncertainty and demonstrating potentialfor estimating fire rate of spread at landscape scales based on time sequential airbornethermal infrared imaging, International Journal of Remote Sensing, 40:13, 4876-4897</p><p>Viedma, O., et al. 2015:  Fire Severity in a Large Fire in a Pinus Pinaster Forest Is Highly Predictable from Burning Conditions, Stand Structure, and Topography. Ecosystems18: 237–250.</p>

scholarly journals Meteorological Conditions Conducive to the Rapid Spread of the Deadly Wildfire in Eastern Attica, Greece

2019 ◽  
Vol 100 (11) ◽  
pp. 2137-2145 ◽  
Author(s):  
K. Lagouvardos ◽  
V. Kotroni ◽  
T. M. Giannaros ◽  
S. Dafis
Keyword(s):  
Fire Behavior ◽  
Fire Spread ◽  
High Rate ◽  
Contributing Factors ◽  
Suburban Areas ◽  
Rate Of Spread ◽  
Rapid Spread ◽  
Westerly Flow ◽  
Downslope Flow ◽  
Extreme Fire

AbstractOn 23 July 2018, Attica, Greece, was impacted by a major wildfire that took place in a wildland–urban interface area and exhibited extreme fire behavior, characterized by a very high rate of spread. One-hundred civilian fatalities were registered, establishing this wildfire as the second-deadliest weather-related natural disaster in Greece, following the heat wave of July 1987. On the day of the deadly wildfire, a very strong westerly flow was blowing for more than 10 h over Attica. Wind gusts up to 30–34 m s−1 occurred over the mountainous areas of Attica, with 20–25 m s−1 in the city of Athens and surrounding suburban areas. This strong westerly flow interacted with the local topography and acted as downslope flow over the eastern part of Attica, with temperatures rising up to 39°C and relative humidity dropping to 19% prior to the onset of the wildfire. These weather elements are widely acknowledged as the major contributing factors to extreme fire behavior. WRF-SFIRE correctly predicted the spatiotemporal distribution of the fire spread and demonstrated its utility for fire spread warning purposes.

Experimental Study of Fire Behavior in a Depressurized Aircraft Cargo Compartment

2017 ◽  
Author(s):  
Cong Li ◽  
Yina Yao ◽  
Zhenxiang Tao ◽  
Rui Yang
Keyword(s):  
Burning Rate ◽  
Oscillation Frequency ◽  
Dynamic Pressure ◽  
Flame Temperature ◽  
Fire Behavior ◽  
Federal Aviation Administration ◽  
Pressure Control ◽  
Rate Oscillation ◽  
Pressure Control System ◽  
Fire Experiments

To analyze the fire behavior in the dynamic pressure environment, a series of n-heptane pool fire experiments were conducted in an 8.11m × 4.16m × 1.67m simulated aircraft cargo compartment. The compartment is capable of mimicking flight environment from taking off to landing of the aircraft according to the standards of Federal Aviation Administration (FAA) by a pressure control system. Pool fires with 30cm diameter were tested under the dynamic pressure from 101kPa to 45kPa with various depressurization rates of 10kPa/min, 15kPa/min, 20kPa/min and 25kPa/min. Fire behavior such as burning rate, oscillation frequency and flame temperature were analyzed. The results revealed that the dynamic pressure influences the burning rate not only during the depressurization stage but also after depressurization. The oscillation frequency increases with the pressure decrease but has no relationship with depressurization rate. The flame temperature at different heights shows various tendencies with pressure.

scholarly journals Deriving Fire Behavior Metrics from UAS Imagery

Fire ◽  
2019 ◽  
Vol 2 (2) ◽  
pp. 36 ◽  
Author(s):  
Christopher J. Moran ◽  
Carl A. Seielstad ◽  
Matthew R. Cunningham ◽  
Valentijn Hoff ◽  
Russell A. Parsons ◽  
...  
Keyword(s):  
Fire Behavior ◽  
Unmanned Aerial Systems ◽  
Prescribed Fires ◽  
Rate Of Spread ◽  
Complex Interactions ◽  
Sensing Platform ◽  
Fire Environment ◽  
Rotor Wing ◽  
Heavy Tailed ◽  
Aerial Systems

The emergence of affordable unmanned aerial systems (UAS) creates new opportunities to study fire behavior and ecosystem pattern—process relationships. A rotor-wing UAS hovering above a fire provides a static, scalable sensing platform that can characterize terrain, vegetation, and fire coincidently. Here, we present methods for collecting consistent time-series of fire rate of spread (RoS) and direction in complex fire behavior using UAS-borne NIR and Thermal IR cameras. We also develop a technique to determine appropriate analytical units to improve statistical analysis of fire-environment interactions. Using a hybrid temperature-gradient threshold approach with data from two prescribed fires in dry conifer forests, the methods characterize complex interactions of observed heading, flanking, and backing fires accurately. RoS ranged from 0–2.7 m/s. RoS distributions were all heavy-tailed and positively-skewed with area-weighted mean spread rates of 0.013–0.404 m/s. Predictably, the RoS was highest along the primary vectors of fire travel (heading fire) and lower along the flanks. Mean spread direction did not necessarily follow the predominant head fire direction. Spatial aggregation of RoS produced analytical units that averaged 3.1–35.4% of the original pixel count, highlighting the large amount of replicated data and the strong influence of spread rate on unit size.

Estimation of Byram’s Fire Intensity and Rate of Spread from Spaceborne Remote Sensing Data in a Savanna Landscape

Fire ◽  
2021 ◽  
Vol 4 (4) ◽  
pp. 65
Author(s):  
Gernot Ruecker ◽  
David Leimbach ◽  
Joachim Tiemann
Keyword(s):  
Remote Sensing ◽  
Fuel Consumption ◽  
Spatial Resolution ◽  
Fire Behavior ◽  
Remote Sensing Data ◽  
Fire Intensity ◽  
Rate Of Spread ◽  
Radiative Power ◽  
Sentinel 2 ◽  
Fire Radiative Power

Fire behavior is well described by a fire’s direction, rate of spread, and its energy release rate. Fire intensity as defined by Byram (1959) is the most commonly used term describing fire behavior in the wildfire community. It is, however, difficult to observe from space. Here, we assess fire spread and fire radiative power using infrared sensors with different spatial, spectral and temporal resolutions. The sensors used offer either high spatial resolution (Sentinel-2) for fire detection, but a low temporal resolution, moderate spatial resolution and daily observations (VIIRS), and high temporal resolution with low spatial resolution and fire radiative power retrievals (Meteosat SEVIRI). We extracted fire fronts from Sentinel-2 (using the shortwave infrared bands) and use the available fire products for S-NPP VIIRS and Meteosat SEVIRI. Rate of spread was analyzed by measuring the displacement of fire fronts between the mid-morning Sentinel-2 overpasses and the early afternoon VIIRS overpasses. We retrieved FRP from 15-min Meteosat SEVIRI observations and estimated total fire radiative energy release over the observed fire fronts. This was then converted to total fuel consumption, and, by making use of Sentinel-2-derived burned area, to fuel consumption per unit area. Using rate of spread and fuel consumption per unit area, Byram’s fire intensity could be derived. We tested this approach on a small number of fires in a frequently burning West African savanna landscape. Comparison to field experiments in the area showed similar numbers between field observations and remote-sensing-derived estimates. To the authors’ knowledge, this is the first direct estimate of Byram’s fire intensity from spaceborne remote sensing data. Shortcomings of the presented approach, foundations of an error budget, and potential further development, also considering upcoming sensor systems, are discussed.

Mediterranean fuel models and potential fire behaviour in Greece

2002 ◽  
Vol 11 (2) ◽  
pp. 127 ◽  
Author(s):  
A. P. Dimitrakopoulos
Keyword(s):  
Fire Behavior ◽  
Pine Forests ◽  
Fuel Load ◽  
Average Height ◽  
Litter Layer ◽  
Data Set ◽  
Rate Of Spread ◽  
Fuel Models ◽  
Tall Shrub ◽  
Mediterranean Pine

The Mediterranean vegetation types of Greece were classified into typical fuel models by measuring the following fuel parameters in 181 representative natural fuel complexes: 1-h, 10-h, 100-h and 1000-h fuel loads; foliage load; litter load and depth; total fuel load; average height and soil cover of the herbaceous, small shrub (up to 0.5 m) and tall shrub (0.5-3.0 m) vegetation layers. The data set was statistically analysed by a two-stage clustering procedure that produced seven distinct fuel models: two for evergreen-sclerophyllous shrublands (maquis), one for kermes oak shrublands, two for phrygana, one for grasslands and one for the litter layer of Mediterranean pine forests. The indicative range (upper and lower limit) of potential fire behavior for every fuel model was calculated with the BEHAVE fire behavior prediction system, using as inputs the specific fuel parameter values of every model. The shrubland fuel models resulted in fires with high intensity and rate of spread, while the phrygana and grassland models in fast fires of medium to low intensity. The litter layer of the pine forests provided the least severe burning conditions.

Examining fire behavior in mesquite - acacia shrublands

2005 ◽  
Vol 14 (2) ◽  
pp. 131 ◽  
Author(s):  
Tamara J. Streeks ◽  
M. Keith Owens ◽  
Steve G. Whisenant
Keyword(s):  
Flame Temperature ◽  
Fire Behavior ◽  
Fire Spread ◽  
Flame Length ◽  
South Texas ◽  
Fire Intensity ◽  
Rate Of Spread ◽  
Vegetation Shift ◽  
Naturally Occurring ◽  
Behavior Systems

The vegetation of South Texas has changed from mesquite savanna to mixed mesquite–acacia (Prosopis–Acacia) shrubland over the last 150 years. Fire reduction, due to lack of fine fuel and suppression of naturally occurring fires, is cited as one of the primary causes for this vegetation shift. Fire behavior, primarily rate of spread and fire intensity, is poorly understood in these communities, so fire prescriptions have not been developed. We evaluated two current fire behavior systems (BEHAVE and the CSIRO fire spread and fire danger calculator) and three models developed for shrublands to determine how well they predicted rate of spread and flame length during three summer fires within mesquite–acacia shrublands. We also used geostatistical analyses to examine the spatial pattern of net heat, flame temperature and fuel characteristics. The CSIRO forest model under-predicted the rate of fire spread by an average of 5.43 m min−1 and over-predicted flame lengths by 0.2 m while the BEHAVE brush model under-predicted rate of spread by an average of 6.57 m min−1 and flame lengths by an average of 0.33 m. The three shrubland models did not consistently predict the rate of spread in these plant communities. Net heat and flame temperature were related to the amount of 10-h fuel on the site, but were not related to the cover of grasses, forbs, shrubs, or apparent continuity of fine fuel. Fuel loads were typical of South Texas shrublands, in that they were uneven and spatially inconsistent, which resulted in an unpredictable fire pattern.

scholarly journals Georeferencing Oblique Aerial Wildfire Photographs: An Untapped Source of Fire Behaviour Data

Fire ◽  
2021 ◽  
Vol 4 (4) ◽  
pp. 81
Author(s):  
Henry Hart ◽  
Daniel D. B. Perrakis ◽  
Stephen W. Taylor ◽  
Christopher Bone ◽  
Claudio Bozzini
Keyword(s):  
Fire Behavior ◽  
Fire Spread ◽  
Behavior Prediction ◽  
Empirical Relationships ◽  
Rate Of Spread ◽  
Front Position ◽  
Fire Front ◽  
Initial Work ◽  
The Mean ◽  
Behaviour Data

In this study, we investigate a novel application of the photogrammetric monoplotting technique for assessing wildfires. We demonstrate the use of the software program WSL Monoplotting Tool (MPT) to georeference operational oblique aerial wildfire photographs taken during airtanker response in the early stages of fire growth. We located the position of the fire front in georeferenced pairs of photos from five fires taken 31–118 min apart, and calculated the head fire spread distance and head fire rate of spread (HROS). Our example photos were taken 0.7 to 4.7 km from fire fronts, with camera angles of incidence from −19 to −50° to image centre. Using high quality images with detailed landscape features, it is possible to identify fire front positions with high precision; in our example data, the mean 3D error was 0.533 m and the maximum 3D error for individual fire runs was less than 3 m. This resulted in a maximum HROS error due to monoplotting of only ~0.5%. We then compared HROS estimates with predictions from the Canadian Fire Behavior Prediction System, with differences mainly attributed to model error or uncertainty in weather and fuel inputs. This method can be used to obtain observations to validate fire spread models or create new empirical relationships where databases of such wildfire photos exist. Our initial work suggests that monophotogrammetry can provide reproducible estimates of fire front position, spread distance and rate of spread with high accuracy, and could potentially be used to characterize other fire features such as flame and smoke plume dimensions and spotting.

Development and testing of models for predicting crown fire rate of spread in conifer forest stands

2005 ◽  
Vol 35 (7) ◽  
pp. 1626-1639 ◽  
Author(s):  
Miguel G Cruz ◽  
Martin E Alexander ◽  
Ronald H Wakimoto
Keyword(s):  
Bulk Density ◽  
Fire Behavior ◽  
Fire Regimes ◽  
Management Decision ◽  
Crown Fire ◽  
Conifer Forest ◽  
Data Set ◽  
Rate Of Spread ◽  
Spread Model ◽  
Canopy Bulk Density

The rate of spread of crown fires advancing over level to gently undulating terrain was modeled through nonlinear regression analysis based on an experimental data set pertaining primarily to boreal forest fuel types. The data set covered a significant spectrum of fuel complex and fire behavior characteristics. Crown fire rate of spread was modeled separately for fires spreading in active and passive crown fire regimes. The active crown fire rate of spread model encompassing the effects of 10-m open wind speed, estimated fine fuel moisture content, and canopy bulk density explained 61% of the variability in the data set. Passive crown fire spread was modeled through a correction factor based on a criterion for active crowning related to canopy bulk density. The models were evaluated against independent data sets originating from experimental fires. The active crown fire rate of spread model predicted 42% of the independent experimental crown fire data with an error lower then 25% and a mean absolute percent error of 26%. While the models have some shortcomings and areas in need of improvement, they can be readily utilized in support of fire management decision making and other fire research studies.

Sign in / Sign up

Export Citation Format

Share Document