A Review of Fine Fuel Moisture Modelling

1991 ◽  
Vol 1 (4) ◽  
pp. 215 ◽  
Author(s):  
NR Viney
Keyword(s):  
Moisture Content ◽  
Liquid Water ◽  
Future Research ◽  
Fuel Moisture ◽  
Fire Behaviour ◽  
Fuel Temperature ◽  
Moisture Change ◽  
Forest Fuels ◽  
Water Models ◽  
Prediction Systems

Models describing the moisture content of forest fuels are an integral component of most fire behaviour prediction systems. In this paper, models of all aspects of moisture change in fine, dead, surface litter are examined and reviewed. Included are models describing the changes in moisture content associated with isother mal vapour exchange by sorption processes, and the effects of precipitation and condensation of liquid water. Models for predicting fuel temperature and humidity, and equilibrium moisture content are also assessed. Critical reviews of the assumptions underlying each model are made, and points of comparison and contrast explored. Some recommendations for future research are suggested.

Modifying the Canadian Fine Fuel Moisture Code for masticated surface fuels

2015 ◽  
Vol 24 (1) ◽  
pp. 79 ◽  
Author(s):  
T. J. Schiks ◽  
B. M. Wotton
Keyword(s):  
Moisture Content ◽  
Prescribed Burning ◽  
Small Diameter ◽  
Management Technique ◽  
Fuel Moisture ◽  
Fuel Temperature ◽  
Weather Index ◽  
Fire Weather Index ◽  
Forest Fuels ◽  
Mean Error

Mechanical mastication is a fuel management technique that disrupts the vertical continuity of forest fuels by shredding of trees and understory vegetation into a highly compacted surface fuel bed. Despite the increasing application of mastication to manage wildfire risk, there is little information to date on fuel moisture in masticated fuels and optimal ignition patterns for prescribed burning. We investigated the applicability of the Fine Fuel Moisture Code (FFMC), a component of the Canadian Fire Weather Index (FWI) System, in tracking the diurnal and day-to-day changes in masticated surface fuel moisture, and developed a calibration of the standard conversion between moisture content and FFMC via regression modelling. We also proposed several modifications to the FFMC model (including a solar radiation driven fuel temperature) to better estimate the fuel-specific parameters of small diameter (<1 cm) masticated surface fuels. Model validation was performed using destructive moisture content observations from a mastication treatment in west-central Alberta, Canada. A calibrated form of the moisture content to FFMC conversion produced mean error of –2.3% moisture content, and closely resembled previous FWI System calibrations for fast drying surface fuels. Our modified FFMC-based model fit well with field observations, and was capable of producing mean error of 1.0% moisture content. The fast drying that we observed highlights the need to better understand moisture dynamics of masticated fuel beds.

Evaluation of the predictive capacity of dead fuel moisture models for Eastern Australia grasslands

2016 ◽  
Vol 25 (9) ◽  
pp. 995 ◽  
Author(s):  
Miguel G. Cruz ◽  
Susan Kidnie ◽  
Stuart Matthews ◽  
Richard J. Hurley ◽  
Alen Slijepcevic ◽  
...  
Keyword(s):  
Moisture Content ◽  
Process Model ◽  
Prediction Models ◽  
Percentage Error ◽  
Fuel Moisture ◽  
Fire Behaviour ◽  
Predictive Capacity ◽  
Eastern Australia ◽  
Physically Based ◽  
Community Information

The moisture content of dead grass fuels is an important input to grassland fire behaviour prediction models. We used standing dead grass moisture observations collected within a large latitudinal spectrum in Eastern Australia to evaluate the predictive capacity of six different fuel moisture prediction models. The best-performing models, which ranged from a simple empirical formulation to a physically based process model, yield mean absolute errors of 2.0% moisture content, corresponding to a 25–30% mean absolute percentage error. These models tended to slightly underpredict the moisture content observations. The results have important implications for the authenticity of fire danger rating and operational fire behaviour prediction, which form the basis of community information and warnings, such as evacuation notices, in Australia.

Empirical modelling of surface fire behaviour in maritime pine stands

2009 ◽  
Vol 18 (6) ◽  
pp. 698 ◽  
Author(s):  
Paulo M. Fernandes ◽  
Hermínio S. Botelho ◽  
Francisco C. Rego ◽  
Carlos Loureiro
Keyword(s):  
Wind Speed ◽  
Moisture Content ◽  
Fire Spread ◽  
Fire Intensity ◽  
Fuel Moisture ◽  
Fire Behaviour ◽  
Spread Rate ◽  
Surface Fire ◽  
Rate Of Spread ◽  
Fuel Moisture Content

An experimental burning program took place in maritime pine (Pinus pinaster Ait.) stands in Portugal to increase the understanding of surface fire behaviour under mild weather. The spread rate and flame geometry of the forward and backward sections of a line-ignited fire front were measured in 94 plots 10–15 m wide. Measured head fire rate of spread, flame length and Byram’s fire intensity varied respectively in the intervals of 0.3–13.9 m min–1, 0.1–4.2 m and 30–3527 kW m–1. Fire behaviour was modelled through an empirical approach. Rate of forward fire spread was described as a function of surface wind speed, terrain slope, moisture content of fine dead surface fuel, and fuel height, while back fire spread rate was correlated with fuel moisture content and cover of understorey vegetation. Flame dimensions were related to Byram’s fire intensity but relationships with rate of spread and fine dead surface fuel load and moisture are preferred, particularly for the head fire. The equations are expected to be more reliable when wind speed and slope are less than 8 km h–1 and 15°, and when fuel moisture content is higher than 12%. The results offer a quantitative basis for prescribed fire management.

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.

scholarly journals Corrigendum to: Estimation of surface dead fine fuel moisture using automated fuel moisture sticks across a range of forests worldwide

2020 ◽  
Vol 29 (6) ◽  
pp. 560
Author(s):  
Jane G. Cawson ◽  
Petter Nyman ◽  
Christian Schunk ◽  
Gary J. Sheridan ◽  
Thomas J. Duff ◽  
...  
Keyword(s):  
Moisture Content ◽  
Measurement Techniques ◽  
Field Measurements ◽  
Future Research ◽  
Fuel Moisture ◽  
Potential Solution ◽  
Forest Types ◽  
Wildfire Management ◽  
Forest Fuel ◽  
Fuel Moisture Content

Field measurements of surface dead fine fuel moisture content (FFMC) are integral to wildfire management, but conventional measurement techniques are limited. Automated fuel sticks offer a potential solution, providing a standardised, continuous and real-time measure of fuel moisture. As such, they are used as an analogue for surface dead fine fuel but their performance in this context has not been widely evaluated. We assessed the ability of automated fuel sticks to predict surface dead FFMC across a range of forest types. We combined concurrent moisture measurements of the fuel stick and surface dead fine fuel from 27 sites (570 samples), representing nine broad forest fuel categories. We found a moderate linear relationship between surface dead FFMC and fuel stick moisture for all data combined (R2=0.54), with fuel stick moisture averaging 3-fold lower than surface dead FFMC. Relationships were typically stronger for individual forest fuel categories (median R2=0.70; range=0.55–0.87), suggesting the sticks require fuel-specific calibration for use as an analogue of surface dead fine fuel. Future research could identify fuel properties that will enable more generalised calibration functions.

Live fuel moisture content and leaf ignition of forest species in Andean Patagonia, Argentina

2015 ◽  
Vol 24 (3) ◽  
pp. 340 ◽  
Author(s):  
Lucas O. Bianchi ◽  
Guillermo E. Defossé
Keyword(s):  
Moisture Content ◽  
Environmental Variables ◽  
Mediterranean Ecosystems ◽  
Fuel Moisture ◽  
Fire Behaviour ◽  
Black Poplar ◽  
Nothofagus Antarctica ◽  
Live Fuel Moisture ◽  
Ignition Characteristics ◽  
Fuel Moisture Content

Wildfires are common from summer to early fall in Patagonian forests of Argentina. Live fuel moisture content (LFMC) and leaf ignition are important factors for understanding fire behaviour. In this study, we determined seasonal LFMC and leaf ignition of some key fire-prone species of these forests, and their relationships with environmental variables. Species investigated were the native trees ñire (Nothofagus antarctica) and cypress (Austrocedrus chilensis), the understorey tree-like radal (Lomatia hirsuta) and laura (Schinus patagonicus), the bamboo caña colihue (Chusquea culeou), and the non-native black poplar (Populus nigra). LFMC differed among species, with caña colihue having lower values (LFMC <100%); ñire, laura, cypress, and radal having medium values (110–220%); and black poplar, upper values (>220%). Ignition characteristics differed among species (caña colihue > ñire > radal > cypress > laura > black poplar) and were inversely related to LFMC. Correlations between LFMC and environmental variables were highly significant for caña colihue, significant for ñire, radal, and laura, and weakly significant or non-significant for cypress and black poplar. These results contribute to our understanding of fire behaviour, and validate the fuel typology for Patagonian forests. At the same time, they add some useful knowledge for comparison with other fire-prone Mediterranean ecosystems around the world.

Fuel-related fire-behaviour relationships for mixed live and dead fuels burned in the laboratory

2017 ◽  
Vol 47 (7) ◽  
pp. 883-889 ◽  
Author(s):  
Carlos G. Rossa ◽  
Paulo M. Fernandes
Keyword(s):  
Moisture Content ◽  
Fire Spread ◽  
Fuel Load ◽  
Experimental Program ◽  
Structural Parameter ◽  
Fuel Moisture ◽  
Fuel Type ◽  
Fire Behaviour ◽  
Spread Rate ◽  
Fuel Moisture Content

A laboratory experimental program addressing fire spread in fuel beds composed of dead foliage litter and vertically placed quasi-live branches, representative of many natural fuel complexes, was carried out for either still-air or wind conditions. Fuel-bed characteristics, fire spread rate, flame geometry, and fuel consumption were assessed and empirical models for estimating several parameters were developed. Weighted fuel moisture content (18%–163%) provided good estimates of fire-behaviour characteristics and accounted for most of the variation in still-air and wind-driven spread rate (0.1–1.3 m·min−1). When predicting still-air fire spread rate, fuel height was the most relevant fuel-bed structural parameter and fuel type had significant influence, whereas for wind-driven spread, the effect of foliar fuel-bed density was dominant and fuel type became irrelevant. Flame length (0.4–2.2 m) increased from still-air to wind-assisted (8 km·h−1) fire spread, but its height remained constant. The fraction of total fuel load and mean woody fuel diameter consumed by fire were reasonably predicted from weighted fuel moisture content alone, but predictions for the latter variable improved substantially by adding foliar fuel load.

scholarly journals Estimation of surface dead fine fuel moisture using automated fuel moisture sticks across a range of forests worldwide

2020 ◽  
Vol 29 (6) ◽  
pp. 548
Author(s):  
Jane G. Cawson ◽  
Petter Nyman ◽  
Christian Schunk ◽  
Gary J. Sheridan ◽  
Thomas J. Duff ◽  
...  
Keyword(s):  
Moisture Content ◽  
Measurement Techniques ◽  
Field Measurements ◽  
Future Research ◽  
Fuel Moisture ◽  
Potential Solution ◽  
Forest Types ◽  
Wildfire Management ◽  
Forest Fuel ◽  
Fuel Moisture Content

Field measurements of surface dead fine fuel moisture content (FFMC) are integral to wildfire management, but conventional measurement techniques are limited. Automated fuel sticks offer a potential solution, providing a standardised, continuous and real-time measure of fuel moisture. As such, they are used as an analogue for surface dead fine fuel but their performance in this context has not been widely evaluated. We assessed the ability of automated fuel sticks to predict surface dead FFMC across a range of forest types. We combined concurrent moisture measurements of the fuel stick and surface dead fine fuel from 27 sites (570 samples), representing nine broad forest fuel categories. We found a moderate linear relationship between surface dead FFMC and fuel stick moisture for all data combined (R2=0.54), with fuel stick moisture averaging 3-fold lower than surface dead FFMC. Relationships were typically stronger for individual forest fuel categories (median R2=0.70; range=0.55–0.87), suggesting the sticks require fuel-specific calibration for use as an analogue of surface dead fine fuel. Future research could identify fuel properties that will enable more generalised calibration functions.

Effect of drying temperature on fuel moisture content measurements

2010 ◽  
Vol 19 (6) ◽  
pp. 800 ◽  
Author(s):  
Stuart Matthews
Keyword(s):  
Moisture Content ◽  
Dry Mass ◽  
Fuel Moisture ◽  
Laboratory Measurements ◽  
Fire Behaviour ◽  
Drying Temperature ◽  
Oven Drying ◽  
Fuel Moisture Content ◽  
Study Laboratory

Oven-drying of fuel samples is often used to determine fuel moisture content. In this study, laboratory measurements are used to demonstrate that drying temperature has a significant effect on the oven-dry mass of dead grass, pine and eucalyptus fuels. Differences between oven-dry masses of fuels dried at 60 and 105°C of up to 3.5% were measured. This is a large enough difference to have a significant effect on fire behaviour predictions. Samples should be dried at 105°C.

Why is the effect of live fuel moisture content on fire rate of spread underestimated in field experiments in shrublands?

2019 ◽  
Vol 28 (2) ◽  
pp. 127 ◽  
Author(s):  
F. Pimont ◽  
J. Ruffault ◽  
N. K. Martin-StPaul ◽  
J.-L. Dupuy
Keyword(s):  
Moisture Content ◽  
Field Experiments ◽  
Fire Season ◽  
Special Focus ◽  
Fuel Moisture ◽  
Fire Behaviour ◽  
Power Functions ◽  
Field Evidence ◽  
Live Fuel Moisture ◽  
Fuel Moisture Content

Live fuel moisture content (LFMC) influences fire activity at landscape scale and fire behaviour in laboratory experiments. However, field evidence linking LFMC to fire behaviour are very limited, despite numerous field experiments. In this study, we reanalyse a shrubland fire dataset with a special focus on LFMC to investigate this counterintuitive outcome. We found that this controversy might result from three causes. First, the range of experimental LFMC data was too moist to reveal a significant effect with the widespread exponential or power functions. Indeed, LFMC exhibited a strong effect below 100%, but marginal above this threshold, contrary to these functions. Second, we found that the LFMC significance was unlikely when the number of fire experiments was smaller than 40. Finally, an analysis suggested that 10 to 15% measurement error – arising from the estimation of environmental variables from field measurements – could lead to an underestimation by 30% of the LFMC effect. The LFMC effect in field experiments is thus stronger than previously reported in the range of LFMC occurring during the French fire season and in accordance with observations at different scales. This highlights the need to improve our understanding of the relationship between LFMC and fire behaviour to refine fire-danger predictions.

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