scholarly journals Modelling the fire propagation from the fuel bed to the lower canopy of ornamental species used in wildland–urban interfaces

2019 ◽  
Vol 28 (2) ◽  
pp. 113 ◽  
Author(s):  
L. Terrei ◽  
A. Lamorlette ◽  
A. Ganteaume
Keyword(s):  
Mass Loss ◽  
Ignition Temperature ◽  
Maximum Temperature ◽  
Fuel Moisture ◽  
Fire Propagation ◽  
Recorded Data ◽  
Fuel Moisture Content ◽  
Ornamental Species ◽  
Over Time ◽  
Geometric Definition

South-eastern France is strongly affected by wildfires mostly occurring in the wildland–urban interfaces (WUIs). A WUI fire is often initiated in dead surface fuel, then can propagate to shrubs and trees when the lower canopy is close to (or touches) the ground. Whereas a previous study assessed the fire propagation from the fuel bed to the lower canopy of different species used as ornamental vegetation in this region, the objectives of the current work consisted of checking if the modelling of this fire propagation was possible using WFDS (Wildland–Urban Interface Fire Dynamical Simulator) in comparing experimental and modelling results. Experimental and modelling constraints (i.e. branch geometric definition, branch motion due to convection) showed differences in some of the recorded data (such as time to ignition, ignition temperature, mass loss and maximum temperature), but comparisons of variation in mass loss and temperature over time showed that modelling the fire propagation at the scale of a branch was possible if the branch fuel-moisture content remained lower than 25%. For both experiments and modelling, the ranking of species according to their branch flammability highlighted identical groups of species.

Combustion and Nutrient Losses During Laboratory Burns

1995 ◽  
Vol 5 (1) ◽  
pp. 1 ◽  
Author(s):  
D Gillon ◽  
V Gomendy ◽  
C Houssard ◽  
J Marechal ◽  
JC Valette
Keyword(s):  
Moisture Content ◽  
Fuel Consumption ◽  
Fire Spread ◽  
Fuel Load ◽  
Maximum Temperature ◽  
Fuel Moisture ◽  
Nutrient Losses ◽  
Constant Moisture Content ◽  
Fuel Moisture Content ◽  
Experimental Burns

The aim of this study was to assess the effects on combustion characteristics, and their consequences on nutrient losses, of (1) the change in load and packing ratio of the fuel bed, and (2) the change in fuel moisture content. Eighty-one experimental burns were carried out, on a test bench in the laboratory; the fuel was composed of needles and twigs of Pinus pinaster. Two levels of fuel load an dpacking ratio (8t ha-1 needles, packing ratio of 0.040; and 16t ha-1 twigs and needles, packing ratio of 0.066) were compared at constant moisture content (6%); and four levels of moisture content(6%, 12%, 24% and 30% dry weight) were compared at constant fuel load (8t ha-1 needles). At constant moisture content, an increase in the load and packing ratio of the fuel bed led to an increase in the height of flames and in the maximum temperature 25 cm above the fuel bed, in the duration of the rise in temperatures within the fuel, and in the fireline intensity. Conversely, the rate of fire spread decreased. At constant fuel load, an increase in the moisture content of the fuel led to a decrease in the rate of fire spread, in the flame height and the maximum temperature 25 cm above the fuel bed, and in the fireline intensity. In contrast, the maximum temperatures reached within the fuel, when the flaming front was continuous, did not significantly change with varying fuel loads or fuel moisture contents. The percentage fuel consumption was always high, more than 80%, but it significantly decreased with increasing fuel load and packing ratio and with increasing moisture content. Total losses of N, S, and K significantly decreased with increasing fuel load and packing ratio, with increasing moisture content and with decreasing percentage fuel consumption. Losses in P only significantly decreased with increasing fuel load and packing ratio. Losses in Mg and Ca were not significantly affected by fuel load, moisture content. or percentage consumption. An attempt was made to separate volatile from particulate losses, based on the assumption that all the losses of Ca were in particulate form. Whereas losses in particulate form remained relatively constant, losses of nutrients in volatile form seem to have been related to the percentage fuel consumption. Even if these experimental burns were of low intensity (40 to 56 kW m-1), their impact, in terms of lethal temperatures and nutrient losses, was not negligible, particularly for N and P. The increasing fireline intensity with increasing fuel load was not accompanied by an enhancement in the proportion of nutrient losses. In the same way, the strong decrease in fireline intensity with increasing fuel moisture content led only to a slight decrease in some nutrient losses. It was through their effect on the percentage fuel consumption that fuel load or moisture content modified the nutrient losses, particularly volatile losses.

Conversion of fuel moisture content values to ignition potential for integrated fire danger assessment

2004 ◽  
Vol 34 (11) ◽  
pp. 2284-2293 ◽  
Author(s):  
Emilio Chuvieco ◽  
Inmaculada Aguado ◽  
Alexandros P Dimitrakopoulos
Keyword(s):  
Moisture Content ◽  
Fire Danger ◽  
Assessment System ◽  
Fuel Moisture ◽  
Fire Propagation ◽  
Web Mapping ◽  
Danger Assessment ◽  
Fuel Moisture Content ◽  
In Fire ◽  
Fire Ignition

Fuel moisture content (FMC) estimation is a critical part of any fire danger rating system, since fuel water status is determinant in fire ignition and fire propagation. However, FMC alone does not provide a comprehensive assessment of fire danger, since other factors related to fire ignition (lightning, human factors) or propagation (wind, slope) also need to be taken into account. The problem in integrating all these factors is finding a common scale of danger rating that will make it possible to derive synthetic indices. This paper reviews the importance of FMC in fire ignition and fire propagation, as well as the most common methods of estimating FMC values. A simple method to convert FMC values to danger ratings is proposed, based on computing ignition potential from thresholds of moisture of extinction adapted to each fuel. The method has been tested for the Madrid region (central Spain), where a fire danger assessment system has been built. All the variables related to fire danger were integrated into a dedicated geographic information system and information provided to fire managers through a web mapping server.

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.

Flame characteristics, temperature - time curves, and rate of spread in fires propagating in a bed of Pinus pinaster needles

2003 ◽  
Vol 12 (1) ◽  
pp. 67 ◽  
Author(s):  
José M. C. Mendes-Lopes ◽  
João M. P. Ventura ◽  
José M. P. Amaral
Keyword(s):  
Wind Speed ◽  
Moisture Content ◽  
Pinus Pinaster ◽  
Flame Height ◽  
Fuel Moisture ◽  
Propagation Models ◽  
Fire Propagation ◽  
Rate Of Spread ◽  
Fuel Moisture Content ◽  
Flame Angle

An extensive set of experiments was carried out in order to collect data to validate fire propagation models being developed in the context of an European research project. The experiments were performed in a dedicated burning tray (2.0 m × 0.70 m working section), where wind velocity, fuel moisture content and slope were varied to study fire propagation in beds of Pinus pinaster needles. All the runs were videotaped and, from the recordings, information on flame geometry (i.e. flame height, flame length and flame angle) and rate of spread was obtained. Temperature measurements were also carried out by a small tower of six thermocouples at different heights above the fuel bed. Results show that headfire rate of spread increases steeply with wind speed for wind-driven fires but does not depend on wind speed for backing fire spread rates. Rate of spread increases slightly with slope for up-hill propagation, and is not slope dependent for down-hill cases. Rate of spread decreases when fuel moisture content increases. Flame angle and flame height are also dependent on wind velocity, slope, and fuel moisture content. The importance of temperature for fire propagation is discussed, emphasizing the role of radiation heat transfer in the process. Correlations between temperature and other indicators of fire behaviour (namely the rate of spread) are presented. Results are discussed and compared. The results obtained provide a good database for the assessment of fire propagation models.

scholarly journals A generic fuel moisture content attenuation factor for fire spread rate empirical models

2018 ◽  
Vol 27 (2) ◽  
pp. e009 ◽  
Author(s):  
Carlos G. Rossa
Keyword(s):  
Moisture Content ◽  
Attenuation Factor ◽  
A Priori ◽  
Fire Spread ◽  
Fuel Moisture ◽  
Ignition Energy ◽  
Spread Rate ◽  
Fire Propagation ◽  
Damping Effect ◽  
Fuel Moisture Content

Aim of study: To develop a fuel moisture content (FMC) attenuation factor for empirical forest fire spread rate (ROS) models in general fire propagation conditions.Methods: The development builds on the assumption that the main FMC-damping effect is a function of fuel ignition energy needs.Main results: The generic FMC attenuation factor was successfully used to derive ROS models from laboratory tests (n = 282) of fire spread in no-wind and no-slope, slope-, and wind-aided conditions. The ability to incorporate the FMC attenuation factor in existing field-based ROS models for shrubland fires and grassland wildfires (n = 123) was also positively assessed.Research highlights: Establishing a priori the FMC-effect in field fires benefits the proper assessment of the remaining variables influence, which is normally eluded by heterogeneity in fuel bed properties and correlated fuel descriptors.

Predicting 1-H Dead Fuel Moisture Content at Regional Scales Using Machine Learning from Himawari-8 Data

2021 ◽  
Author(s):  
Chunquan Fan ◽  
Binbin He ◽  
Peng Kong ◽  
Hao Xu ◽  
Qiang Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Moisture Content ◽  
Fuel Moisture ◽  
Fuel Moisture Content

Weight Loss and High-Protein, High-Fiber Diet Consumption Impact Blood Metabolite Profiles, Body Composition, Voluntary Physical Activity, Fecal Microbiota, and Fecal Metabolites of Adult Dogs

2021 ◽  
Author(s):  
Thunyaporn Phungviwatnikul ◽  
Anne H Lee ◽  
Sara E Belchik ◽  
Jan S Suchodolski ◽  
Kelly S Swanson
Keyword(s):  
Physical Activity ◽  
Weight Loss ◽  
Body Composition ◽  
Mass Loss ◽  
Fat Mass ◽  
Fecal Microbiota ◽  
Restricted Feeding ◽  
High Fiber ◽  
Voluntary Physical Activity ◽  
Over Time

Abstract Canine obesity is associated with reduced lifespan and metabolic dysfunction, but can be managed by dietary intervention. This study aimed to determine the effects of restricted feeding of a high-protein, high-fiber (HPHF) diet and weight loss on body composition, physical activity, blood metabolites, and fecal microbiota and metabolites of overweight dogs. Twelve spayed female dogs [age: 5.5±1.1 yr; body weight (BW): 14.8±2.0 kg, body condition score (BCS): 7.9±0.8] were fed a HPHF diet during a 4-wk baseline phase to maintain BW. After baseline (wk 0), dogs were first fed 80% of baseline intake and then adjusted to target 1.5% weekly weight loss for 24 wk. Body composition using dual-energy x-ray absorptiometry and blood samples (wk 0, 6, 12, 18, 24), voluntary physical activity (wk 0, 7, 15, 23), and fresh fecal samples for microbiota and metabolite analysis (wk 0, 4, 8, 12, 16, 20, 24) were measured over time. Microbiota data were analyzed using QIIME 2. All data were analyzed statistically over time using SAS 9.4. After 24 wk, dogs lost 31.2% of initial BW and had 1.43±0.73% weight loss per wk. BCS decreased (P<0.0001) by 2.7 units, fat mass decreased (P<0.0001) by 3.1 kg, and fat percentage decreased (P<0.0001) by 3.1 kg and 11.7% with weight loss. Many serum metabolites and hormones were altered, with triglycerides, leptin, insulin, C-reactive protein, and interleukin-6 decreasing (P<0.05) with weight loss. Relative abundances of fecal Bifidobacterium, Coriobacteriaceae UCG-002, undefined Muribaculaceae, Allobaculum, Eubacterium, Lachnospira, Negativivibacillus, Ruminococcus gauvreauii group, uncultured Erysipelotrichaceae, and Parasutterella increased (P<0.05), whereas Prevotellaceae Ga6A1 group, Catenibacterium, Erysipelatoclostridium, Fusobacterium, Holdemanella, Lachnoclostridium, Lactobacillus, Megamonas, Peptoclostridium, Ruminococcus gnavus group, and Streptococcus decreased (P<0.01) with weight loss. Despite the number of significant changes, a state of dysbiosis was not observed in overweight dogs. Fecal ammonia and secondary bile acids decreased, while fecal valerate increased with weight loss. Several correlations between gut microbial taxa and biological parameters were observed. Our results suggest that restricted feeding of a HPHF diet and weight loss promotes fat mass loss, minimizes lean mass loss, reduces inflammatory marker and triglyceride concentrations, and modulates fecal microbiota phylogeny and activity in overweight dogs.

scholarly journals Evaluating the Effects of Projected Climate Change on Forest Fuel Moisture Content

2014 ◽  
Vol 37 ◽  
pp. 65-69
Author(s):  
Kellen Nelson ◽  
Daniel Tinker
Keyword(s):  
Climate Change ◽  
Moisture Content ◽  
Stand Structure ◽  
Canopy Cover ◽  
Fire Season ◽  
Climate Change Scenarios ◽  
Fuel Moisture ◽  
Mature Forest ◽  
Forest Fuel ◽  
Fuel Moisture Content

Understanding how live and dead forest fuel moisture content (FMC) varies with seasonal weather and stand structure will improve researchers’ and forest managers’ ability to predict the cumulative effects of weather on fuel drying during the fire season and help identify acute conditions that foster wildfire ignition and high rates of fire spread. No studies have investigated the efficacy of predicting FMC using mechanistic water budget models at daily time scales through the fire season nor have they investigated how FMC may vary across space. This study addresses these gaps by (1) validating a novel mechanistic live FMC model and (2) applying this model with an existing dead FMC model at three forest sites using five climate change scenarios to characterize how FMC changes through time and across space. Sites include post-fire 24-year old forest, mature forest with high canopy cover, and mature forest affected by the mountain pine beetle with moderate canopy cover. Climate scenarios include central tendency, warm/dry, warm/wet, hot/dry, and hot/wet.

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