scholarly journals Non-Additive Effects of Forest Litter on Flammability

Fire ◽  
2020 ◽  
Vol 3 (2) ◽  
pp. 12 ◽  
Author(s):  
Angela G. Gormley ◽  
Tina L. Bell ◽  
Malcolm Possell
Keyword(s):  
Vegetation Type ◽  
Forest Litter ◽  
Flame Height ◽  
Fuel Load ◽  
Additive Effects ◽  
Forest Types ◽  
Fuel Component ◽  
Hazard Reduction ◽  
Sclerophyll Forest ◽  
Optimisation Methods

Forest litter is a fuel component that is important for the propagation of fire. Data describing fuel load, structure and fuel condition were gathered for two sites of Sydney Coastal Dry Sclerophyll Forest, a common vegetation type in the Sydney Basin, Australia. Surface litter from the sites was sorted into its constituent components and used to establish which component or mixture of components were the most flammable using several metrics. A general blending model was used to estimate the effect the different mixtures had on the response of the flammability metrics and identify non-additive effects. Optimisation methods were applied to the models to determine the mixture compositions that were the most or least flammable. Differences in the flammability of the two sites were significant and were driven by Allocasuarina littoralis. The presence of A. littoralis in litter mixtures caused non-additive effects, increasing the rate of flame spread and flame height non-linearly. We discuss how land managers could use these models as a tool to assist in prioritising areas for hazard reduction burns and how the methodology can be extended to other fuel conditions or forest types.

Determinants of spatial variation in fire return period in a semiarid African savanna

2011 ◽  
Vol 20 (4) ◽  
pp. 540 ◽  
Author(s):  
T. G. O'Connor ◽  
C. M. Mulqueeny ◽  
P. S. Goodman
Keyword(s):  
Spatial Variation ◽  
Return Period ◽  
Vegetation Type ◽  
Fire Frequency ◽  
Fuel Load ◽  
Annual Rainfall ◽  
Vegetation Types ◽  
Terrestrial Vegetation ◽  
African Savanna ◽  
Data Set

Fire pattern is predicted to vary across an African savanna in accordance with spatial variation in rainfall through its effects on fuel production, vegetation type (on account of differences in fuel load and in flammability), and distribution of herbivores (because of their effects on fuel load). These predictions were examined for the 23 651-ha Mkuzi Game Reserve, KwaZulu-Natal, based on a 37-year data set. Fire return period varied from no occurrence to a fire every 1.76 years. Approximately 75% of the reserve experienced a fire approximately every 5 years, 25% every 4.1–2.2 years and less than 1% every 2 years on average. Fire return period decreased in relation to an increase in mean annual rainfall. For terrestrial vegetation types, median fire return periods decreased with increasing herbaceous biomass, from forest that did not burn to grasslands that burnt every 2.64 years. Fire was absent from some permanent wetlands but seasonal wetlands burnt every 5.29 years. Grazer biomass above 0.5 animal units ha–1 had a limiting influence on the maximum fire frequency of fire-prone vegetation types. The primary determinant of long-term spatial fire patterns is thus fuel load as determined by mean rainfall, vegetation type, and the effects of grazing herbivores.

scholarly journals Determining Firebrand Generation Rate Using Physics-Based Modelling from Experimental Studies through Inverse Analysis

Fire ◽  
2022 ◽  
Vol 5 (1) ◽  
pp. 6
Author(s):  
Amila Wickramasinghe ◽  
Nazmul Khan ◽  
Khalid Moinuddin
Keyword(s):  
Wind Velocity ◽  
Forest Fire ◽  
Inverse Analysis ◽  
Vegetation Type ◽  
Experimental Studies ◽  
Generation Rate ◽  
Fuel Load ◽  
Potential Threat ◽  
Collection Data ◽  
Vegetation Species

Firebrand spotting is a potential threat to people and infrastructure, which is difficult to predict and becomes more significant when the size of a fire and intensity increases. To conduct realistic physics-based modeling with firebrand transport, the firebrand generation data such as numbers, size, and shape of the firebrands are needed. Broadly, the firebrand generation depends on atmospheric conditions, wind velocity and vegetation species. However, there is no experimental study that has considered all these factors although they are available separately in some experimental studies. Moreover, the experimental studies have firebrand collection data, not generation data. In this study, we have conducted a series of physics-based simulations on a trial-and-error basis to reproduce the experimental collection data, which is called an inverse analysis. Once the generation data was determined from the simulation, we applied the interpolation technique to calibrate the effects of wind velocity, relative humidity, and vegetation species. First, we simulated Douglas-fir (Pseudotsuga menziesii) tree-burning and quantified firebrand generation against the tree burning experiment conducted at the National Institute of Standards and Technology (NIST). Then, we applied the same technique to a prescribed forest fire experiment conducted in the Pinelands National Reserve (PNR) of New Jersey, the USA. The simulations were conducted with the experimental data of fuel load, humidity, temperature, and wind velocity to ensure that the field conditions are replicated in the experiments. The firebrand generation rate was found to be 3.22 pcs/MW/s (pcs-number of firebrands pieces) from the single tree burning and 4.18 pcs/MW/s in the forest fire model. This finding was complemented with the effects of wind, vegetation type, and fuel moisture content to quantify the firebrand generation rate.

scholarly journals A comparison of bushfire fuel hazard assessors and assessment methods in dry sclerophyll forest near Sydney, Australia

2012 ◽  
Vol 21 (6) ◽  
pp. 755 ◽  
Author(s):  
Penny J. Watson ◽  
Sandra H. Penman ◽  
Ross A. Bradstock
Keyword(s):  
Hazard Assessment ◽  
Assessment Method ◽  
Assessment Methods ◽  
Urban Fringe ◽  
The Other ◽  
Fuel Load ◽  
Forest Sites ◽  
Sclerophyll Forest ◽  
Hazard Assessments ◽  
Sydney Australia

Over the last decade, fire managers in Australia have embraced the concept of ‘fuel hazard’, and guides for its assessment have been produced. The reliability of these new metrics, however, remains to be determined. This study compared fuel hazard ratings generated by five assessment teams using two Australian hazard assessment methods, in two dry sclerophyll forest sites on Sydney’s urban fringe. Attributes that underpin hazard scores, such as cover and height of various fuel layers, were also assessed. We found significant differences between teams on most variables, including hazard scores. These differences were more apparent when fuel hazard assessments focussed on individual fuel layers than when teams’ assessments were summarised into an overall fuel hazard score. Ratings of surface (litter) fuel hazard were higher when one assessment method was used than when assessors employed the other; however, ratings of elevated (shrub) and bark fuel hazard were relatively consistent across assessment methods. Fuel load estimates based on the two hazard assessment methods differed considerably, with differences between teams also significant. Inconsistency in scoring fuel hazard may lead to discrepancies in a range of management applications, which in turn may affect firefighting safety and effectiveness.

A Study on Complexity of Major Vegetation Types in China

2013 ◽  
Vol 864-867 ◽  
pp. 2459-2462
Author(s):  
Zong Han Li ◽  
Hua Yong Zhang ◽  
Fei Li ◽  
Xiang Xu
Keyword(s):  
Fractal Dimension ◽  
Vegetation Type ◽  
Alpine Vegetation ◽  
Vegetation Types ◽  
Forest Types ◽  
Vegetation Map ◽  
Tropical Mountains ◽  
The Relationship ◽  
Vegetation Patches ◽  
Dimension Index

In this study, fractal dimension index is applied to describe the complexity of 11 vegetation groups and 5 needle-leaf forest vegetation types in China. Basing on the Vegetation Map of China, we calculate the perimeter and area of vegetation patches with the software ArcGis. The relationship between perimeter and area is established for each vegetation group and vegetation type, and the corresponding fractal dimension index is estimated. The results show that, among the 11 vegetation groups, the Alpine vegetation is the most complex vegetation. In the 5 needle-leaf forest types, the subtropical and tropical mountains needle-leaf forest is the most complex vegetation. It seems that the complexity of vegetation is associated with altitude. The topography may be responsible for the complexity at different scales.

Bare soil and rill formation following wildfires, fuel reduction treatments, and pine plantations in the southern Sierra Nevada, California, USA

2010 ◽  
Vol 19 (4) ◽  
pp. 478 ◽  
Author(s):  
Neil H. Berg ◽  
David L. Azuma
Keyword(s):  
Sierra Nevada ◽  
Vegetation Type ◽  
Fire Severity ◽  
Reference Conditions ◽  
Bare Soil ◽  
Fuel Load ◽  
Load Reduction ◽  
Fuel Reduction ◽  
Management Options ◽  
Burned Areas

Accelerated erosion commonly occurs after wildfires on forested lands. As burned areas recover, erosion returns towards prefire rates depending on many site-specific characteristics, including fire severity, vegetation type, soil type and climate. In some areas, erosion recovery can be rapid, particularly where revegetation is quick. Erosion recovery is less well understood for many fuel load reduction treatments. The rate of post-disturbance erosion recovery affects management options for forested lands, particularly when considering the combined ramifications of multiple disturbances on resource recovery rates (i.e. cumulative watershed effects). Measurements of percentage bare soil and rilling on over 600 plots in the southern Sierra Nevada with slopes less than 75% and within 1 km of roads were made between 2004 and 2006. Results suggest that after high-, moderate- or low-severity wildfire, rilling was seldom evident more than 4 years after fire. Percentage bare soil generally did not differ significantly between reference plots and wildfire plots greater than 6 years old. Little rilling was evident after treatment with a variety of fuel reduction techniques, including burning of machine- and hand-piled fuel, thinning, mastication, and crushing. Percentage bare soil at the fuel load reduction treatment plots also did not differ significantly from reference conditions. Percentage bare soil at pine plantation plots was noticeably higher than at reference sites.

scholarly journals A VOXEL-BASED METHOD TO ESTIMATE NEAR-SURFACE AND ELEVATED FUEL FROM DENSE LIDAR POINT CLOUD FOR HAZARD REDUCTION BURNING

2020 ◽  
Vol VI-3/W1-2020 ◽  
pp. 3-10
Author(s):  
J. Barton ◽  
B. Gorte ◽  
M. S. R. S. Eusuf ◽  
S. Zlatanova
Keyword(s):  
Performance Testing ◽  
Decision Support Tool ◽  
Airborne Lidar ◽  
Fuel Load ◽  
Management Technique ◽  
Near Surface ◽  
Support Tool ◽  
Hazard Reduction ◽  
The Face ◽  
Dry Heating

Abstract. Drastic changes in the climate has revised the face of disaster management: it is contributing to abnormal intensity, frequency and duration of extreme weather and climate events. The year 2020 started with more than 100 fires burning across Australia. Hazard reduction burning has become a resolute and primary land management technique that contribute to the reduction of bushfire severity. One of the key variables to consider for this application is fuel load, as the accumulation of vegetation in a forest profile affects the intensity of the burn. Conventionally, fuel loads are measured by manually cutting the vegetation and physically measuring the quantity after dry heating. This process is expensive, and time consuming. There is an opportunity for these techniques to be digitised and automated to give results in a timely manner and work as a decision support tool for practitioners. This paper proposes a voxel-based approach that can be used for estimating fuel load and percentage cover of the vegetation, at the elevated and near-surface fuel/vegetation layer as a method to augment manual estimation. We use an airborne LiDAR pointcloud dataset of Vermont Place Park, Newcastle, Australia to test the method. The preliminary inspection of the results confirms the technique that can approximate conventional manual method. Next steps include performance testing including more dataset to derive quantitative measures on the approach.

Got to burn to learn: the effect of fuel load on grassland fire behaviour and its management implications

2018 ◽  
Vol 27 (11) ◽  
pp. 727 ◽  
Author(s):  
Miguel G. Cruz ◽  
Andrew L. Sullivan ◽  
James S. Gould ◽  
Richard J. Hurley ◽  
Matt P. Plucinski
Keyword(s):  
Fire Spread ◽  
Flame Height ◽  
Fuel Load ◽  
Limiting Factor ◽  
Fire Behaviour ◽  
Load Effect ◽  
Fire Propagation ◽  
Rate Of Spread ◽  
Eastern Australia ◽  
Modelling Assumptions

The effect of grass fuel load on fire behaviour and fire danger has been a contentious issue for some time in Australia. Existing operational models have placed different emphases on the effect of fuel load on model outputs, which has created uncertainty in the operational assessment of fire potential and has led to end-user and public distrust of model outcomes. A field-based experimental burning program was conducted to quantify the effect of fuel load on headfire rate of spread and other fire behaviour characteristics in grasslands. A total of 58 experimental fires conducted at six sites across eastern Australia were analysed. We found an inverse relationship between fuel load and the rate of spread in grasslands, which is contrary to current, untested, modelling assumptions. This result is valid for grasslands where fuel load is not a limiting factor for fire propagation. We discuss the reasons for this effect and model it to produce a fuel load effect function that can be applied to operational grassfire spread models used in Australia. We also analyse the effect of fuel load on flame characteristics and develop a model for flame height as a function of rate of fire spread and fuel load.

scholarly journals VOLUME ESTIMATION OF FUEL LOAD FOR HAZARD REDUCTION BURNING: FIRST RESULTS TO A VOXEL APPROACH

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1199-1206
Author(s):  
M. S. R. S. Eusuf ◽  
J. Barton ◽  
B. Gorte ◽  
S. Zlatanova
Keyword(s):  
Global Warming ◽  
Land Management ◽  
Management Practices ◽  
Volume Estimation ◽  
Fuel Load ◽  
Hazard Reduction ◽  
Fuel Loads ◽  
First Results ◽  
Conventional Methods

Abstract. In 2019/20 over 100 severe bushfires burned across the continent of Australia. The severity of these fires was exacerbated by many factors, including macroclimatic effects of global warming and, at the meso and micro scales, land management practices. The bushfire phenomenon cannot be stopped, however better management practices can help counter the increasing severity of fires. Hazard reduction burning is a method where certain vegetation is deliberately burned under controlled circumstances to thin the fuel to reduce the severity of bushfires. Fuel load is an important parameter to assess when hazard reduction burning, as the accumulation of vegetation in a forest profile affects the intensity of the burn. Conventional methods of measuring fuel load are time consuming and costly, and therefore it becomes increasingly important to investigate automated approaches for assessing fuel loads. This paper provides an overview of hazard reduction burning while explaining the methods to quantify fuel load. Then the paper presents our voxel approach in estimating the volume of fuel loads. The first results regarding different voxel resolutions are reported and analysed. This paper concludes with future steps and developments.

Relating fuel loads to overstorey structure and composition in a fire-excluded Sierra Nevada mixed conifer forest

2015 ◽  
Vol 24 (4) ◽  
pp. 484 ◽  
Author(s):  
Jamie M. Lydersen ◽  
Brandon M. Collins ◽  
Eric E. Knapp ◽  
Gary B. Roller ◽  
Scott Stephens
Keyword(s):  
Sierra Nevada ◽  
Predictive Models ◽  
Vegetation Type ◽  
Fuel Load ◽  
Small Scale ◽  
Conifer Forest ◽  
Fire Behaviour ◽  
Mixed Conifer ◽  
Fuel Loads ◽  
Mixed Conifer Forest

Although knowledge of surface fuel loads is critical for evaluating potential fire behaviour and effects, their inherent variability makes these difficult to quantify. Several studies relate fuel loads to vegetation type, topography and spectral imaging, but little work has been done examining relationships between forest overstorey variables and surface fuel characteristics on a small scale (<0.05 ha). Within-stand differences in structure and composition would be expected to influence fuel bed characteristics, and thus affect fire behaviour and effects. We used intensive tree and fuel measurements in a fire-excluded Sierra Nevada mixed conifer forest to assess relationships and build predictive models for loads of duff, litter and four size classes of downed woody fuels to overstorey structure and composition. Overstorey variables explained a significant but somewhat small percentage of variation in fuel load, with marginal R2 values for predictive models ranging from 0.16 to 0.29. Canopy cover was a relatively important predictor for all fuel components, although relationships varied with tree species. White fir abundance had a positive relationship with total fine woody fuel load. Greater pine abundance was associated with lower load of fine woody fuels and greater load of litter. Duff load was positively associated with total basal area and negatively associated with oak abundance. Knowledge of relationships contributing to within-stand variation in fuel loads can increase our understanding of fuel accumulation and improve our ability to anticipate fine-scale variability in fire behaviour and effects in heterogeneous mixed species stands.

scholarly journals An Empirical Model for the Effect of Wind on Fire Spread Rate

Fire ◽  
2018 ◽  
Vol 1 (2) ◽  
pp. 31 ◽  
Author(s):  
Carlos Rossa ◽  
Paulo Fernandes
Keyword(s):  
Empirical Model ◽  
Vegetation Type ◽  
Fire Spread ◽  
Fuel Load ◽  
Wind Effect ◽  
High Moisture ◽  
Independent Laboratory ◽  
Live Fuels ◽  
Released Energy ◽  
Good Agreement

Predicting wind-driven rate of fire spread (RoS) has been the aim of many studies. Still, a field-tested model for general use, regardless of vegetation type, is currently lacking. We develop an empirical model for wind-aided RoS from laboratory fires (n = 216), assuming that it depends mainly on fire-released energy and on the extension of flame over the fuel bed in still air, and that it can be obtained by multiplying RoS in no-wind and no-slope conditions by a factor quantifying the wind effect. Testing against independent laboratory and field data (n = 461) shows good agreement between observations and predictions. Our results suggest that the fuel bed density effect detected by other work may be a surrogate for the amount of fuel involved in combustion, which depends on fuel load. Because RoS under windless conditions is unaffected by fuel load, the involved mechanisms differ from wind-aided propagation. Compared to shallow fuel beds, the wind effect is usually modest in deep vegetation, because tall fuel complexes are dominated by live fuels (high moisture content) and flames extend less above the vegetation when fuel moisture is high. The present work warrants further inspection in a broader range of field conditions.

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