Estimating Fuel Moisture Response Times From Field Observations

1991 ◽  
Vol 1 (4) ◽  
pp. 211 ◽  
Author(s):  
NR Viney ◽  
EA Catchpole
Keyword(s):  
Moisture Content ◽  
Response Times ◽  
Continuous Process ◽  
Time Lag ◽  
Continuous Model ◽  
Step Change ◽  
Simple Relationship ◽  
Fuel Moisture ◽  
Content Model ◽  
Fuel Moisture Content

The response time of a fuel element is defined in terms of the response to a step change in moisture content, and is not measurable in the field. However the formulation of fine fuel moisture content as a continuous process leads to a simple relationship between the re- sponse time of the fuel and the time lag with respect to a diurnally varying equilibrium moisture content. This time lag is estimable from field data. This method is applied to obtain response times for fine litter in a EucaIyptus forest. The same formulation is applied to the discrete moisture content model of Fosberg and Deeming (1971) which is re-cast and solved as a continuous model.

scholarly journals Data assimilation of dead fuel moisture observations from remote automated weather stations

2016 ◽  
Vol 25 (5) ◽  
pp. 558 ◽  
Author(s):  
Martin Vejmelka ◽  
Adam K. Kochanski ◽  
Jan Mandel
Keyword(s):  
Data Assimilation ◽  
Moisture Content ◽  
Wildland Fire ◽  
Time Lag ◽  
Assessment System ◽  
Major Influence ◽  
Surface Model ◽  
Fuel Moisture ◽  
Weather Stations ◽  
Fuel Moisture Content

Fuel moisture has a major influence on the behaviour of wildland fires and is an important underlying factor in fire risk assessment. We propose a method to assimilate dead fuel moisture content (FMC) observations from remote automated weather stations (RAWS) into a time lag fuel moisture model. RAWS are spatially sparse and a mechanism is needed to estimate fuel moisture content at locations potentially distant from observational stations. This is arranged using a trend surface model (TSM), which allows us to account for the effects of topography and atmospheric state on the spatial variability of FMC. At each location of interest, the TSM provides a pseudo-observation, which is assimilated via Kalman filtering. The method is tested with the time lag fuel moisture model in the coupled weather-fire code WRF–SFIRE on 10-h FMC observations from Colorado RAWS in 2013. Using leave-one-out testing we show that the TSM compares favourably with inverse squared distance interpolation as used in the Wildland Fire Assessment System. Finally, we demonstrate that the data assimilation method is able to improve on FMC estimates in unobserved fuel classes.

Construction of empirical models for predicting Pinus sp. dead fine fuel moisture in NW Spain. I: Response to changes in temperature and relative humidity

2009 ◽  
Vol 18 (1) ◽  
pp. 71 ◽  
Author(s):  
Ana Daría Ruiz González ◽  
Jose Antonio Vega Hidalgo ◽  
Juan Gabriel Álvarez González
Keyword(s):  
Moisture Content ◽  
Time Lag ◽  
Decisive Role ◽  
Repeated Measurements ◽  
Fuel Moisture ◽  
North West ◽  
Fine Fuels ◽  
Fuel Particles ◽  
Fuel Moisture Content ◽  
Non Destructive

A statistical methodology is presented for developing moisture content models from repeated measurements made on non-destructive repeated measurements. Empirical vapour exchange models for dead fine fuels generated in Pinus radiata and P. pinaster stands are developed by using the methodology proposed. Experiments were carried out with five types of fuel particles (surface and aerial fine fuels) of the two species of pine, in Lugo (Galicia, north-west Spain). The samples of each fuel type were collected and placed inside an instrument shelter so that vapour exchange with the atmosphere was the only source of moisture in the fuels. Statistical criteria obtained from the residuals indicated that the fitted models were acceptable. The cross-validation results also confirmed the validity of the fitted models. The model underlined the decisive role played by the time lag in dead fine fuel moisture content variation.

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
Sign in / Sign up

Export Citation Format

Share Document