Estimating fuel response time and predicting fuel moisture content from field data

2001 ◽  
Vol 10 (2) ◽  
pp. 215 ◽  
Author(s):  
E. A. Catchpole ◽  
W. R. Catchpole ◽  
N.R.Viney ◽  
W. L. McCaw ◽  
J. B. Marsden-Smedley
Keyword(s):  
Moisture Content ◽  
Response Time ◽  
Data Sets ◽  
Fuel Moisture ◽  
Fuel Temperature ◽  
Broad Agreement ◽  
Governing Differential Equation ◽  
Using Data ◽  
Fuel Moisture Content ◽  
Western Australian

We develop a method for estimating equilibrium moisture content (EMC) and fuel moisture response time, using data collected for Eucalyptus twig litter. The method is based on the governing differential equation for the diffusion of water vapour from the fuel, and on a semi-physical formulation for EMC (Nelson 1984), based on the change in Gibbs free energy, which estimates the EMC as a function of fuel temperature and humidity. We then test the model on data collected in Western Australian mallee shrubland and in Tasmanian buttongrass moorland. This method is more generally applicable than those described by Viney and Catchpole (1991) and Viney (1992). The estimates of EMC and response time are in broad agreement with laboratory-based estimates for similar fuels (Anderson 1990a ; Nelson 1984). The model can be used to predict fuel moisture content by a book-keeping method. The predictions agree wellwith the observations for all three of our data sets.

Predicting the elevated dead fine fuel moisture content in gorse (Ulex europaeus L.) shrub fuels

2009 ◽  
Vol 39 (12) ◽  
pp. 2355-2368 ◽  
Author(s):  
Stuart A.J. Anderson ◽  
Wendy R. Anderson
Keyword(s):  
Moisture Content ◽  
Response Time ◽  
Equilibrium Moisture Content ◽  
Fire Danger ◽  
Weather Data ◽  
Fuel Moisture ◽  
Fire Weather Index ◽  
Ulex Europaeus ◽  
Fuel Layer ◽  
Fuel Moisture Content

Methods were developed to predict the moisture content of the elevated dead fine fuel layer in gorse ( Ulex europaeus L.) shrub fuels. This layer has been observed to be important for fire development and spread in these fuels. The accuracy of the Fine Fuel Moisture Code (FFMC) of the Canadian Fire Weather Index System to predict the moisture content of this layer was evaluated. An existing model was used to determine the response time and equilibrium moisture content from field data. This response time was incorporated into a bookkeeping model, combining the FFMC and this response time–equilibrium moisture content model. The FFMC poorly predicted the elevated dead fuel moisture content in gorse fuels, and attempts to improve its accuracy through regression modelling were unsuccessful. The response time of the elevated dead fine fuel layer was very fast (38–77 min) and has important implications for fire danger rating. The bookkeeping approach was the most promising method to predict elevated dead fuel moisture content. A limitation was the inability to model fuel-level meteorology. However, this model warrants further validation and extension to other shrub fuels and could be incorporated into existing fire danger rating systems that can utilize hourly weather data.

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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