Improved Calibration of a Large Fuel Burnout Model

1997 ◽  
Vol 7 (1) ◽  
pp. 21 ◽  
Author(s):  
FA Albini ◽  
ED Reinhardt
Keyword(s):  
Computer Simulation ◽  
Moisture Content ◽  
Fuel Consumption ◽  
Influence Factor ◽  
Fuel Moisture ◽  
Fuel Loading ◽  
Fuel Moisture Content

Calibration and testing of a computer simulation of the burning of large woody natural fuels has been presented previously in this journal. This note describes an improved calibration of the model for better prediction of fuel loading reductions. Using the same data as before, fuel consumption predictions are improved substantially by making the area influence factor a function of fuel moisture content.

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.

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

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.

Effects of distribution of bulk density and moisture content on shrub fires

2013 ◽  
Vol 22 (5) ◽  
pp. 625 ◽  
Author(s):  
Ambarish Dahale ◽  
Selina Ferguson ◽  
Babak Shotorban ◽  
Shankar Mahalingam
Keyword(s):  
Moisture Content ◽  
Spatial Variation ◽  
Bulk Density ◽  
Solid Fuel ◽  
Numerical Solutions ◽  
Propagation Speed ◽  
Fire Spread ◽  
Fuel Moisture ◽  
Combustion Heat ◽  
Fuel Moisture Content

Formulation of a physics-based model, capable of predicting fire spread through a single elevated crown-like shrub, is described in detail. Predictions from the model, obtained by numerical solutions to governing equations of fluid dynamics, combustion, heat transfer and thermal degradation of solid fuel, are found to be in fairly good agreement with experimental results. In this study we utilise the physics-based model to explore the importance of two parameters – the spatial variation of solid fuel bulk density and the solid fuel moisture content – on the burning of an isolated shrub in quiescent atmosphere. The results suggest that vertical fire spread rate within an isolated shrub and the time to initiate ignition within the crown are two global parameters significantly affected when the spatial variation of the bulk density or the variation of fuel moisture content is taken into account. The amount of fuel burnt is another parameter affected by varying fuel moisture content, especially in the cases of fire propagating through solid fuel with moisture content exceeding 40%. The specific mechanisms responsible for the reduction in propagation speed in the presence of higher bulk densities and moisture content are identified.

Modelling fine fuel moisture content and the likelihood of fire spread in blue gum (Eucalyptus globulus) litter

2014 ◽  
pp. 353-359
Author(s):  
Anita Pinto ◽  
Juncal Espinosa-Prieto ◽  
Carlos Rossa ◽  
Stuart Matthews ◽  
Carlos Loureiro ◽  
...  
Keyword(s):  
Moisture Content ◽  
Eucalyptus Globulus ◽  
Fire Spread ◽  
Fuel Moisture ◽  
Fuel Moisture Content ◽  
Blue Gum

scholarly journals SAR-enhanced mapping of live fuel moisture content

2020 ◽  
Vol 245 ◽  
pp. 111797 ◽  
Author(s):  
Krishna Rao ◽  
A. Park Williams ◽  
Jacqueline Fortin Flefil ◽  
Alexandra G. Konings
Keyword(s):  
Moisture Content ◽  
Fuel Moisture ◽  
Live Fuel Moisture ◽  
Fuel Moisture Content
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