Fire Behavior Prediction Research Articles

Empirical-based models for predicting head-fire rate of spread in Australian fuel types

SummaryThe knowledge of a free-burning fire’s potential rate of spread is critical for safe and effective bushfire control and use. A number of models for predicting the head-fire rate of spread in various types of Australian vegetation have been developed over the past 60 years or so since Alan G. McArthur began his pioneering research into bushfire behaviour. Most of the major Australian vegetation types have had more than one model developed for operational use. These include grassland, shrubland, both dry and wet eucalypt forests, and pine plantation fuel types. A better understanding of the technical basis for each of these models and their utility is essential for the correct selection and application of the most appropriate models. This review provides a systematic overview of 22 models of the rate of fire spread and their applicability in prescribed burning and wildfire operations.Background information and a description of each model is given. This includes information on the data used in the model development that defines the bounds of its application. The mathematical equations that represent each model are given along with a discussion of model form and behaviour, the main input variables and their influence, and evaluations of model performance undertaken to date.This review has enabled the identification of those models that constitute the current state of knowledge with respect to bushfire behaviour science in Australia. We recommend the models that should underpin best practice in the near term in the operational prediction of fire behaviour and those that should no longer be used, and provide reasons for these recommendations.

Burn or rot: leaf traits explain why flammability and decomposability are decoupled across species

SummaryIn fireprone ecosystems, two important alternative fates for leaves are burning in a wildfire (when alive or as litter) or they get consumed (as litter) by decomposers. The influence of leaf traits on litter decomposition rate is reasonably well understood. In contrast, less is known about the influence of leaf traits on leaf and litter flammability. The aim of this study was twofold: (i) to determine which morphological and chemical leaf traits drive flammability and (ii) to determine whether different (combinations of) morphological and chemical leaf traits drive interspecific variation in decomposition and litter flammability and, in turn, help us understand the relationship between decomposability and flammability.To explore the relationships between leaf traits and flammability of individual leaves, we used 32 evergreen perennial plant species from eastern Australia in standardized experimental burns on three types of leaf material (i.e. fresh, dried and senesced). Next, we compared these trait–flammability relationships to trait–decomposability relationships as obtained from a previous decomposition experiment (focusing on senesced leaves only).Within the three parameters of leaf flammability that we measured, interspecific variation in time to ignition was mainly explained by specific leaf area and moisture content. Flame duration and smoulder duration were mostly explained by leaf dry mass and to a lesser degree by leaf chemistry, namely, nitrogen, phosphorus and tannin concentrations.The variation in the decomposition constant across species was unrelated to our measures of flammability. Moreover, different combinations of morphological and chemical leaf properties underpinned the interspecific variation in decomposability and flammability. In contrast to litter flammability, decomposability was driven by lignin and phosphorus concentrations.The decoupling of flammability and decomposability leads to three possible scenarios for species’ influence on litter fates: (i) fast‐decomposing species for which flammability is irrelevant because there will not be enough litter to support a fire; (ii) species with slow‐decomposing leaves and a high flammability; and (iii) species with slow‐decomposing leaves and a low flammability. We see potential for making use of the decoupled trait–decomposition–flammability relationships when modelling carbon and nutrient fluxes. Including information on leaf traits in models can improve the prediction of fire behaviour. We note that herbivory is another key fate for leaves, but this study was focused on fire and decomposition.

Developing Custom Fire Behavior Fuel Models for Mediterranean Wildland–Urban Interfaces in Southern Italy

The dramatic increase of fire hazard in wildland-urban interfaces (WUIs) has required more detailed fuel management programs to preserve ecosystem functions and human settlements. Designing effective fuel treatment strategies allows to achieve goals such as resilient landscapes, fire-adapted communities, and ecosystem response. Therefore, obtaining background information on forest fuel parameters and fuel accumulation patterns has become an important first step in planning fuel management interventions. Site-specific fuel inventory data enhance the accuracy of fuel management planning and help forest managers in fuel management decision-making. We have customized four fuel models for WUIs in southern Italy, starting from forest classes of land-cover use and adopting a hierarchical clustering approach. Furthermore, we provide a prediction of the potential fire behavior of our customized fuel models using FlamMap 5 under different weather conditions. The results suggest that fuel model IIIP (Mediterranean maquis) has the most severe fire potential for the 95th percentile weather conditions and the least severe potential fire behavior for the 85th percentile weather conditions. This study shows that it is possible to create customized fuel models directly from fuel inventory data. This achievement has broad implications for land managers, particularly forest managers of the Mediterranean landscape, an ecosystem that is susceptible not only to wildfires but also to the increasing human population and man-made infrastructures.

Forest Fire Propagation Prediction Based on Overlapping DDDAS Forecasts

Forest fire devastate every year thousand of hectares of forest around the world. Fire behavior prediction is a useful tool to aid coordination and management of human and mitigation resources when fighting against these kind of hazards. Any fire spread forecast system requires to be fitted with different kind of data with a high degree of uncertainty, such as for example, me- teorological data and vegetation map among others. The dynamics of this kind of phenomena requires to develop a forecast system with the ability to adapt to changing conditions. In this work two different fire spread forecast systems based on the Dynamic Data Driven Application paradigm are applied and an alternative approach based on the combination of both predictions is presented. This new method uses the computational power provided by high performance computing systems to deliver the predictions under strict real time constraints.

Estimating the heat transfer to an organic soil surface during crown fire

The Peatland Smouldering and Ignition (PSI) model was developed to quantify the heat transfer from a wildfire to an organic soil or moss surface in a Sphagnum–black spruce peatland. The Canadian Fire Behaviour Prediction system was used as a basis for the relationship between wind speed and rate of spread. Convection, conduction, and radiation processes were modelled before and during the arrival of the flaming front. The net heat flux to the soil from fire varied between 1.1 and 8.6 MJ m–2, with moderate-intensity fires transferring more energy to the surface compared with higher-intensity fires under higher winds. Radiative heat transfer to the soil surface both before the fire’s arrival and within the flaming front were the primary mechanisms of energy gain to the peatland surface. The role of convective and conductive cooling was no greater than 30% of gross energy gain. Peatland surface ignition in hummock and hollow microforms was modelled under normal and drought conditions. Hollow microforms dried out significantly during the course of a summer and increased their ignition vulnerability. Small-scale changes in slope and aspect of the peatland surface increased the amount of heat transferred by radiation by up to 30%, allowing some areas of higher soil moisture content to ignite. While no direct model validation is available, model outputs showing the preferential combustion of lichen and feathermoss, and the lack of ignition in Sphagnum in all but the most severe drought generally mimic observed ignitions patterns. The modelled peak of net energy input to the surface occurred at moderate wind speeds, suggesting that high-intensity fires do not necessarily lead to greater energy transfer and risk of smouldering combustion.

Using a prescribed fire to test custom and standard fuel models for fire behaviour prediction in a non‐native, grass‐invaded tropical dry shrubland

AbstractQuestionsDo fuel models developed for North American fuel types accurately represent fuel beds found in non‐native, grass‐invaded tropical dry shrublands? Do standard or custom fuel models used in fire behaviour models with in situ or remote automated weather stations (RAWS) measured fuel moistures affect the accuracy of predicted fire behaviour in grass‐invaded tropical shrublands?LocationHawai'i Volcanoes National Park, Hawai'i, USA.MethodsPre‐fire fuel loads of coarse woody debris, live herbaceous and live woody fuel loads were quantified with Brown's transects and biomass sampling to create a custom fuel model for non‐native grass‐invaded tropical dry shrublands in Hawai'i. In situ fuel moistures were quantified using oven‐dried vegetation samples, and compared to RAWS estimated fuel moistures. Fire behaviour was recorded on a stationary video camera to quantify flame length (FL) and rate of spread (ROS). Observed fire behaviour was compared to BehavePlus predicted fire behaviour parameterized with both standard and customized fuel models, and in situ and RAWS‐based estimates of fuel moisture.ResultsThe custom fuel model and measured fuel moistures performed better than most standard models, but over‐predicted actual ROS and top decile FL by 29% and 26%, respectively. The best match between observed and modelled fire behaviour came from a standard fuel model for shrublands with a grassy matrix (23% under‐prediction for ROS and 9% under‐prediction for FL) using measured fuel moistures. Using fuel moistures and wind speeds estimated from the nearest RAWS station (5 km from the fire) substantially decreased prediction accuracy of the custom fuel model and increased its relative error to 71% over‐prediction of ROS and 45% over‐prediction of FL.ConclusionsFire behaviour in at least some tropical fuel beds can be accurately modelled using certain standard or custom fuel models. Standard fuel models should not be applied uncritically to systems outside of North America, as our comparison showed widely ranging accuracy across six standard models. In addition, the current reliance on RAWS data for meteorological inputs to predict fire behaviour in the tropics, especially in the US‐affiliated tropical Pacific, must be used with caution. Instead, field‐measured fuel moistures should be used when possible.

A Comparative Analysis of EO-1 Hyperion, Quickbird and Landsat TM Imagery for Fuel Type Mapping of a Typical Mediterranean Landscape

Forest fires constitute a natural disturbance factor and an agent of environmental change with local to global impacts on Earth’s processes and functions. Accurate knowledge of forest fuel extent and properties can be an effective component for assessing the impacts of possible future wildfires on ecosystem services. Our study aims to evaluate and compare the spectral and spatial information inherent in the EO-1 Hyperion, Quickbird and Landsat TM imagery. The analysis was based on a support vector machine classification approach in order to discriminate and map Mediterranean fuel types. The fuel classification scheme followed a site-specific fuel model within the study area, which is suitable for fire behavior prediction and spatial simulation. The overall accuracy of the Quickbird-based fuel type mapping was higher than 74% with a quantity disagreement of 9% and an allocation disagreement of 17%. Both classifications from the Hyperion and Landsat TM fuel type maps presented approximately 70% overall accuracy and 16% allocation disagreement. The McNemar’s test indicated that the overall accuracy differences between the three produced fuel type maps were not significant (p < 0.05). Based on both overall and individual higher accuracies obtained with the use of the Quickbird image, this study suggests that the high spatial resolution might be more decisive than the high spectral resolution in Mediterranean fuel type mapping.

Canopy Fuel Load Mapping of Mediterranean Pine Sites Based on Individual Tree-Crown Delineation

This study presents an individual tree-crown-based approach for canopy fuel load estimation and mapping in two Mediterranean pine stands. Based on destructive sampling, an allometric equation was developed for the estimation of crown fuel weight considering only pine crown width, a tree characteristic that can be estimated from passive imagery. Two high resolution images were used originally for discriminating Aleppo and Calabrian pines crown regions through a geographic object based image analysis approach. Subsequently, the crown region images were segmented using a watershed segmentation algorithm and crown width was extracted. The overall accuracy of the tree crown isolation expressed through a perfect match between the reference and the delineated crowns was 34.00% for the Kassandra site and 48.11% for the Thessaloniki site, while the coefficient of determination between the ground measured and the satellite extracted crown width was 0.5. Canopy fuel load values estimated in the current study presented mean values from 1.29 ± 0.6 to 1.65 ± 0.7 kg/m2 similar to other conifers worldwide. Despite the modest accuracies attained in this first study of individual tree crown fuel load mapping, the combination of the allometric equations with satellite-based extracted crown width information, can contribute to the spatially explicit mapping of canopy fuel load in Mediterranean areas. These maps can be used among others in fire behavior prediction, in fuel reduction treatments prioritization and during active fire suppression.

Dead fuel moisture research: 1991–2012

The moisture content of dead fuels is an important determinant of many aspects of bushfire behaviour. Understanding the relationships of fuel moisture with weather, fuels and topography is useful for fire managers and models of fuel moisture are an integral component of fire behaviour models. This paper reviews research into dead fuel moisture for the period 1991–2012. The first half of the paper deals with experimental investigation of fuel moisture including an overview of the physical processes that affect fuel moisture, laboratory measurements used to quantify these processes, and field measurements of the dependence of fuel moisture on weather, vegetation structure and topography. The second set of topics examine models of fuel moisture including empirical models derived from field measurements, process-based models of vapour exchange and fuel energy and water balance, and experimental testing of both types of models. Remaining knowledge gaps and future research problems are also discussed. Opportunities for exciting research in the future exist for basic fuel moisture processes, developing new methods for applying models to fire behaviour prediction, and linking fuel moisture and weather forecast models.

Mountains Forest Fire Spread Simulator Based on Geo-Cellular Automaton Combined With Wang Zhengfei Velocity Model

The aim of this paper is to propose a more practical mountain fire spread model for fire behavior prediction and management in Southwest forest area of China. These areas are covered mainly with spatial heterogeneous flammable forest and are characterized by undulating terrain and steep slopes. This model can produce more accurate fire propagation maps by combining CA (Cellular Automaton) framework with Wang Zhengfei fire physical velocity model in fine scale. Considering the inherent uncertainties of fire propagation, the model has been built on multi-dimension geophysical and environmental components and also sound knowledge of fire spread physical mechanism. Regarding small fuel patches as spatial homogenous cells, this approach makes it easier to generate higher level complex fire behavior maps from CA simple local rules and local behavior integrated with high resolution vegetation images, fine scale terrain maps and surface wind field. Because the model focuses primarily on the study of surface fire front propagation behavior, it attempts to simplify complex fuel modeling. Additionally, this Wang-Geophysical-CA model is able to analyze the time series spatial pattern of fire-front spread and model local behavior instead of the final fire spread pattern of the conventional approach. In this work, not only single influence verification tests have been made, but also simulation tests with multiple influences are carried out to demonstrate the capability of the model with fine scale vegetation maps, surface wind field, terrain, moisture content and man-made structures. Consequently, it is believable that the model predictions are in good agreement with experimental data for steady-state fire simulation. The proposed model helps to gain a greater understanding of the fire front spread local behavior and can quickly generate a sequence of complex fire front contours. It enables local managers to plan practical fire prevention activities in Southwest forest area of China as well as improve fire management skills, and will enhance the effectiveness of fire fighting strategies.

Adaptive prediction of forest fire behavior on the basis of recurrent neural networks

A method of modeling a dynamic process on the Earth surface, for instance, a forest fire, with the use of a recurrent neural network is proposed. The learning process of the neural network, similar to the process of data assimilation in GIS technologies, is described. A method of acceleration of neural network learning by using the Kalman filtration is proposed. The efficiency of its application is analyzed, and the neural network parameters at which it is reasonable to use the Kalman filter are determined.

LANDFIRE – A national vegetation/fuels data base for use in fuels treatment, restoration, and suppression planning

LANDFIRE is the working name given to the Landscape Fire and Resource Management Planning Tools Project (http://www.landfire.gov). The project was initiated in response to mega-fires and the need for managers to have consistent, wall-to-wall (i.e., all wildlands regardless of agency/ownership), geospatial data, on vegetation, fuels, and terrain to support use of fire behavior and effects prediction systems in guiding policy and management decisions. Base layers were created in a 5-year program of research and development ending in 2009, with processes in place to periodically update fuel and vegetation layers in response to anthropogenic and natural disturbances. LANDFIRE has been institutionalized as the primary data source for modeling activities aimed at meeting the goals of the United States’ National Cohesive Wildland Fire Management Strategy, and the data are available on-line to any user for conducting landscape analyses. Data access and use are high and expected to grow with the increasing scope and complexity of wildland fire management, thus requiring continued LANDFIRE improvements and updates.

Research and development supporting risk-based wildfire effects prediction for fuels and fire management: status and needs

Wildland fire management has moved beyond a singular focus on suppression, calling for wildfire management for ecological benefit where no critical human assets are at risk. Processes causing direct effects and indirect, long-term ecosystem changes are complex and multidimensional. Robust risk-assessment tools are required that account for highly variable effects on multiple values-at-risk and balance competing objectives, to support decision making. Providing wildland fire managers with risk-analysis tools requires a broad scientific foundation in fire behaviour and effects prediction as well as high quality computer-based tools and associated databases. We outline a wildfire risk-assessment approach, highlight recent developments in fire effects science and associated research needs, and recommend developing a comprehensive plan for integrated advances in wildfire occurrence, behaviour and effects research leading to improved decision support tools for wildland fire managers. We find that the current state of development in fire behaviour and effects science imposes severe limits on the development of risk-assessment technology. In turn, the development of technology has been largely disconnected from the research enterprise, resulting in a confusing array of ad hoc tools that only partially meet decision-support needs for fuel and fire management. We make the case for defining a common risk-based analytic framework for fire-effects assessment across the range of fire-management activities and developing a research function to support the framework.

Predicting continuous variation in forest fuel load using biophysical models: a case study in south-eastern Australia

The increasing potential for wildfires in Mediterranean-type landscapes has resulted in pressure to mitigate fire threats. This is typically achieved by strategic reduction of fuel. To prioritise fuel management, it is necessary to understand vegetation dynamics and the relationships between plants and fuel. As the direct measurement of fuel in the field is labour intensive, mapped vegetation classes are typically used as to estimate fuel load. As vegetation properties vary continuously, the error in such estimates can be high. Remotely sensed and biophysical data are commonly used for vegetation classification, but rarely for estimating fuel load. This study investigated how fuel load varied with vegetation composition in an Australian woodland and assessed the potential for using biophysical models to create continuous estimates. Fuel was found to be influenced by species abundance, with some species having a greater contribution to load than others. Fuel was found to be somewhat predictable, with quantities related to fire history and several other biophysical variables. Models were applied to create continuous maps of fuel load; these provided a more precise representation of fuel variation than using discrete classes. Improved maps have the potential to facilitate improved prediction of fire behaviour and assist targeted fuel management.

4변형 인자에 의한 고강도콘크리트의 내화성능 평가

A numerical model considering the internal vaporization and the creep effect, in the form of a analytical program, for tracing the behavior of high strength concrete(HSC) members exposed to fire is presented. The two stages, i.e., spalling procedure and fire resistance time, associated with the thermal, moisture flow, creep and structural analysis, for the prediction of fire resistance behavior are explained. The use of the analytical program for tracing the response of HSC member from the initial pre-loading stage to collapse, due to fire, is demonstrated. Moisture evaporates, when concrete is exposed to fire, not only at concrete surface but also at inside the concrete to adjust the equilibrium and transfer properties of moisture. Finite element method is employed to facilitate the moisture diffusion analysis for any position of member, so that the prediction method of the moisture distribution inside the concrete members at fire is developed. The validity of the numerical model used in this program is established by comparing the predictions from this program with results from others fire resistance tests. The analytical program can be used to predict the fire resistance of HSC members for any value of the significant parameters, such as load, sectional dimensions, member length, and concrete strength.

Describing wildland surface fuel loading for fire management: a review of approaches, methods and systems

Wildland fuelbeds are exceptionally complex, consisting of diverse particles of many sizes, types and shapes with abundances and properties that are highly variable in time and space. This complexity makes it difficult to accurately describe, classify, sample and map fuels for wildland fire research and management. As a result, many fire behaviour and effects software prediction systems use a generalised description of fuels to simplify data collection and entry into various computer programs. There are several major fuel description systems currently used in the United States, Canada and Australia, and this is a source of confusion for many in fire management. This paper (1) summarises the challenges of describing fuels, (2) contrasts approaches (association, classification and abstraction) for developing fuel description systems and (3) discusses possible future directions in wildland fuel description and science to transition to a universal fuel description system. Most discussion centres on surface fuel loadings as the primary descriptive characteristic. This synthesis paper is intended to provide background for understanding surface fuel classification and description systems and their use in simulating fire behaviour and effects, quantifying carbon inventories and evaluating site productivity.

Coupled slope and wind effects on fire spread with influences of fire size: a numerical study using FIRETEC

Wind and slope are commonly accepted to be major environmental factors affecting the manner in which wildfires propagate. Fire-line width has been observed as having a significant effect on fire behaviour in some experimental fires. Most wildfire behaviour models and fire behaviour prediction systems take wind and slope effects into account when determining the rate of spread, but do not take into account the influence of fire width on the coupled effects of slope and wind. In the present study, the effect of topographic slope on rate of spread under weak (1 m s–1), moderate (5 m s–1) and strong (12 m s–1) wind speeds is investigated for two different initial fire widths (20 and 50 m) in a typical Mediterranean garrigue, using the coupled atmosphere–fire HIGRAD-FIRETEC model. The results show non-trivial combined effects and suggest a strong effect of fire width under low-wind conditions, especially for steep slopes. Simulated spread rates were compared with predictions of existing models of operational systems and a reasonable agreement was found. Additional exploratory simulations of fire behaviour in small canyons are provided. These simulations show how combined effects of wind, slope and fire-front size can induce different fire behaviours that operational models could fail to predict and provide insight that could be valuable for analysis of blow-up fires. These preliminary results also suggest that 3D physically based models could be used in the future to investigate how operational models can include non-local effects of fire propagation.

Classifying Method of Forest Fuels by Using MODIS Data——Demonstration in Hei Longjiang Province

Forest fuel is a key factor of three factors of Forest Inflammation Cycle Theory. It's the inner factor of forest fire. It is important for Forest Fire Prevention, such as Forest Fire Danger Rating Prediction, Forest Fire Behavior Prediction (FBP), Forest Fire Extermination and Forest Fire Salvation. A method for classifying forest fuels by integrating MODIS data and GIS technique is tested in Hei longjiang province. In studying, five forest fuels have been classified after serials process, such as deriving the Max Normalized Difference Vegetation Index (MNDVI) over sixteen days' data by using Multitemporal MODIS data, Analysising the MNDVI data by using Principal Components Analysis (PCA), Unsupervised classification method and Supervised classification method. It makes a strong base for further studying National forest fuels classification method by using modernistic information techniques.

Short- and Long-Term Effects on Fuels, Forest Structure, and Wildfire Potential from Prescribed Fire and Resource Benefit Fire in Southwestern Forests, USA

Prescribed and resource benefit fires are used to manage fuels in fire-prone landscapes in the Southwest. These practices, however, typically occur under different conditions, potentially leading to differences in fire behavior and effects. The objectives of this study were to investigate the effects of recent prescribed fires, resource benefit fires, and repeated fires in ponderosa pine forests, as well as recent resource benefit fires in pinyon-juniper woodlands. The Gila National Forest was the study area because it has a rich history of using fire as a restoration tool. In each vegetation type, fuels and stand structure were sampled using random plots stratified by burn severity in resource benefit fires. In ponderosa pine, sampling and analysis also included prescribed fire and areas subject to repeated resource benefit fires. To assess potential fire behavior, we used the crown fire behavior prediction model Nexus using ninetieth percentile weather conditions. In ponderosa pine forests, surface fuels were similar between prescribed fires and low severity resource benefit fires. However, resource benefit fires significantly reduced basal area, resulting in lower loading of canopy fuels and crown fire potential. Additionally, effects of resource benefit fire on stand structure and fuels seem to be sustained in areas that burned in two or three resource benefit fires in the last century. In pinyon-juniper woodlands, resource benefit low severity fires had no effect on surface or canopy fuel loads. Moderate severity resource benefit fires, on the other hand, did significantly reduce surface and canopy fuel loads. Results from this study are pertinent to fire and fuels managers throughout the southwestern United States who utilize prescribed and resource benefit fire to reduce fuel loads and restore historical forest conditions.

Potential crown fire behavior in Pinus pinea stands following different fuel treatments

Forest fires are permanent seasonal threats that have been accentuated in recent years by climate change. Taking this problem into consideration, forest policies must propose courses of action to mitigate the effects of fires on ecosystems and their surrounding population. The aim of this study is to evaluate the effectiveness silvicultural treatments of Pinus pinea stands at the polewood stage have on potential fire behavior. Evaluating fire behavior requires a characterization of the fuel at its two levels: surface and crown layers. Given the temporal costs of the latter characterization, field inventory has yielded an equation from which canopy fuel load can be obtained without cutting trees. A complete characterization of the fuel allows for a prediction of fire behavior for each silvicultural treatment and whether or not it will be virulent enough to transfer into crown fire. Polewood stands of P.pinea are susceptible to crown fires because canopy base height is usually overrun by dense and flammable undergrowth. Crown treatments, pruning and crown thinning do not contribute in themselves to eliminating crown fire susceptibility. This study raises the need for joint surface and crown fuel treatments to ensure the effectiveness of extinction efforts. Given current budgetary constraints, fuel treatments are limited to 23.40% of the study area for mitigating possible crown fire impacts on forested area that have a future leading role in the socioeconomic development of surrounding populations.