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.

Fire modelling in Tasmanian buttongrass moorlands. III. Dead fuel moisture

2001 ◽  
Vol 10 (2) ◽  
pp. 241 ◽  
Author(s):  
Jon B. Marsden-Smedley ◽  
Wendy R. Catchpole
Keyword(s):  
Prediction Model ◽  
Regression Model ◽  
Fire Management ◽  
Prediction Models ◽  
Dew Point ◽  
Seasonal Effects ◽  
Experimental Program ◽  
Fuel Moisture ◽  
Fire Behaviour ◽  
Fire Modelling

An experimental program was carried out in Tasmanian buttongrass moorlands to develop fire behaviour prediction models for improving fire management. This paper describes the results of the fuel moisture modelling section of this project. A range of previously developed fuel moisture prediction models are examined and three empirical dead fuel moisture prediction models are developed. McArthur’s grassland fuel moisture model gave equally good predictions as a linear regression model using humidity and dew-point temperature. The regression model was preferred as a prediction model as it is inherently more robust. A prediction model based on hazard sticks was found to have strong seasonal effects which need further investigation before hazard sticks can be used operationally.

scholarly journals Fire Behaviour Observation in Shrublands in Nova Scotia, Canada and Assessment of Aids to Operational Fire Behaviour Prediction

Fire ◽  
2020 ◽  
Vol 3 (3) ◽  
pp. 34
Author(s):  
Anne-Claude Pepin ◽  
Mike Wotton
Keyword(s):  
Nova Scotia ◽  
Moisture Content ◽  
Bulk Density ◽  
Prediction Models ◽  
Seasonal Effect ◽  
Fuel Type ◽  
Fire Behaviour ◽  
Fire Weather Index ◽  
Rate Of Spread ◽  
Wide Range

Parks Canada, in collaboration with Nova Scotia Lands and Forests and Natural Resources Canada, documented shrub fire behaviour in multiple plots burned over two periods: a spring period in June 2014 and a summer period in July 2017. The study area, located within Cape Breton Highlands National Park, comprised fifteen burn units (20 m by 20 m in size). Each unit was ignited by line ignition and burned under a wide range of conditions. Pre-burn fuel characteristics were measured across the site and used to estimate pre-fire fuel load and post-fire fuel consumption. This fuel complex was similar to many flammable shrub types around the world, results show that this shrub fuel type had high elevated fuel loads (3.17 ± 0.84 kg/m2) composed of exposed live and dead stunted black spruce as well as ericaceous shrubs, mainly Kalmia angustifolia (evergreen) and Rhodora canadensis (deciduous). Data show that the dead moisture content in this fuel complex is systematically lower than expected from the traditional relationship between FFMC and moisture content in the Canadian Fire Weather Index System but was statistically correlated with Equilibrium Moisture Content. A significant inverse relationship between bulk density and fire rate of spread was observed as well as a clear seasonal effect between the spring burns and the summer burns, which is likely attributable to the increase in bulk density in the summer. Unlike most shrub research, wind and dead moisture content did not have a statistically significant association with fire spread rates. However, we believe this to be due to noise in wind data and small dataset. Rate of spread as high as 14 m/min and flame lengths over 4 m were recorded under Initial Spread Index values of 6.4 and relative humidity of 54%. A comparison with a number of well-known shrubland spread rate prediction models was made. An aid to operational fire prediction behaviour is proposed, using a fuel type from the Canadian Fire Prediction System (O-1b) and a modified estimate of fuel moisture of the elevated fuel in the fuel complex.

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.

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.

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.

scholarly journals Dead Fuel Moisture Content (DFMC) Estimation Using MODIS and Meteorological Data: The Case of Greece

2021 ◽  
Vol 13 (21) ◽  
pp. 4224
Author(s):  
Eleni Dragozi ◽  
Theodore M. Giannaros ◽  
Vasiliki Kotroni ◽  
Konstantinos Lagouvardos ◽  
Ioannis Koletsis
Keyword(s):  
Moisture Content ◽  
Satellite Data ◽  
Spatial Scales ◽  
Meteorological Data ◽  
Water Vapor Pressure ◽  
Field Measurements ◽  
Fuel Moisture ◽  
Weather Station ◽  
Physically Based ◽  
Fuel Moisture Content

The frequent occurrence of large and high-intensity wildfires in the Mediterranean region poses a major threat to people and the environment. In this context, the estimation of dead fine fuel moisture content (DFMC) has become an integrated part of wildfire management since it provides valuable information for the flammability status of the vegetation. This study investigates the effectiveness of a physically based fuel moisture model in estimating DFMC during severe fire events in Greece. Our analysis considers two approaches, the satellite-based (MODIS DFMC model) and the weather station-based (AWSs DFMC model) approach, using a fuel moisture model which is based on the relationship between the fuel moisture of the fine fuels and the water vapor pressure deficit (D). During the analysis we used weather station data and MODIS satellite data from fourteen wildfires in Greece. Due to the lack of field measurements, the models’ performance was assessed only in the case of the satellite data by using weather observations obtained from the network of automated weather stations operated by the National Observatory of Athens (NOA). Results show that, in general, the satellite-based model achieved satisfactory accuracy in estimating the spatial distribution of the DFMC during the examined fire events. More specifically, the validation of the satellite-derived DFMC against the weather-station based DFMC indicated that, in all cases examined, the MODIS DFMC model tended to underestimate DFMC, with MBE ranging from −0.3% to −7.3%. Moreover, in all of the cases examined, apart from one (Sartis’ fire case, MAE: 8.2%), the MAE of the MODIS DFMC model was less than 2.2%. The remaining numerical results align with the existing literature, except for the MAE case of 8.2%. The good performance of the satellite based DFMC model indicates that the estimation of DFMC is feasible at various spatial scales in Greece. Presently, the main drawback of this approach is the occurrence of data gaps in the MODIS satellite imagery. The examination and comparison of the two approaches, regarding their operational use, indicates that the weather station-based approach meets the requirements for operational DFMC mapping to a higher degree compared to the satellite-based approach.

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.

Predicting hourly litter moisture content of larch stands in Daxinganling Region, China using three vapour-exchange methods

2015 ◽  
Vol 24 (1) ◽  
pp. 114 ◽  
Author(s):  
Ping Sun ◽  
Hongzhou Yu ◽  
Sen Jin
Keyword(s):  
Moisture Content ◽  
Forest Fires ◽  
Absolute Error ◽  
Model Parameters ◽  
Observation Data ◽  
Fuel Moisture ◽  
Fire Behaviour ◽  
Accuracy Requirement ◽  
Similar Accuracy ◽  
Direct Regression

Fuel moisture affects fuel ignition potential and fire behaviour. To accurately model fire behaviour, predict fuel ignition potential and plan fuel reduction, fuel moisture content must be assessed regularly and often. To establish models for Daxinganling Region, which has the most severe forest fires in China, hourly measurements were taken of moisture content in litter beds of larch stands sampled under different shading and slope conditions. Models were established using three vapour-exchange methods. The Nelson and Simard methods employed a direct timelag method using a timelag concept and the Nelson and Simard equilibrium moisture content (EMC) functions and estimating model parameters directly from fuel moisture and weather observation data in the field. The direct regression method used equations directly derived from linear regression of fuel moisture and field weather variation. The mean absolute error and mean relative error were determined for the Nelson (0.78%, 4.98%), Simard (1.04%, 5.57%) and direct regression (1.48%, 9.01%) methods. Only the models established using the direct timelag methods met the 1% accuracy requirement using either the Nelson or Simard EMC function, confirming the suitability and robustness of the direct timelag methods. The Simard and Nelson methods had similar accuracy, but Simard was more robust and only needed estimation of one parameter and hence is recommended for predicting litter moisture in this region.

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.

Sign in / Sign up

Export Citation Format

Share Document