Miguel G. Cruz

Assessing crown fire potential in coniferous forests of western North America: a critique of current approaches and recent simulation studies

To control and use wildland fires safely and effectively depends on creditable assessments of fire potential, including the propensity for crowning in conifer forests. Simulation studies that use certain fire modelling systems (i.e. NEXUS, FlamMap, FARSITE, FFE-FVS (Fire and Fuels Extension to the Forest Vegetation Simulator), Fuel Manage- ment Analyst (FMAPlus), BehavePlus) based on separate implementations or direct integration of Rothermels surface andcrownrateoffirespread modelswithVanWagnerscrownfiretransitionandpropagationmodelsareshowntohavea significant underprediction bias when used in assessing potential crown fire behaviour in conifer forests of western North America.Theprincipal sourcesof thisunderprediction biasareshowntoinclude:(i)incompatible modellinkages;(ii)use of surfaceand crownfire rateof spreadmodels that havean inherent underprediction bias;and (iii)reductionincrown fire rate of spread based on the use of unsubstantiated crown fraction burned functions. The use of uncalibrated custom fuel models to represent surface fuelbeds is a fourth potential source of bias. These sources are described and documented in detail based on comparisons with experimental fire and wildfire observations and on separate analyses of model components.Themannerinwhichthetwoprimarycanopyfuelinputsinfluencingcrownfireinitiation(i.e.foliarmoisture contentandcanopybaseheight)ishandledinthesesimulationstudiesandthemeaningofScottandReinhardtstwocrown fire hazard indices are also critically examined.

Assessing canopy fuel stratum characteristics in crown fire prone fuel types of western North America

Application of crown fire behavior models in fire management decision-making have been limited by the difficulty of quantitatively describing fuel complexes, specifically characteristics of the canopy fuel stratum. To estimate canopy fuel stratum characteristics of four broad fuel types found in the western United States and adjacent areas of Canada, namely Douglas-fir, ponderosa pine, mixed conifer, and lodgepole pine forest stands, data from the USDA Forest Services Forest Inventory and Analysis (FIA) database were analysed and linked with tree-level foliage dry weight equations. Models to predict canopy base height (CBH), canopy fuel load (CFL) and canopy bulk density (CBD) were developed through linear regression analysis and using common stand descriptors (e.g. stand density, basal area, stand height) as explanatory variables. The models developed were fuel type specific and coefficients of determination ranged from 0.90 to 0.95 for CFL, between 0.84 and 0.92 for CBD and from 0.64 to 0.88 for CBH. Although not formally evaluated, the models seem to give a reasonable characterization of the canopy fuel stratum for use in fire management applications.

Interdependencies between flame length and fireline intensity in predicting crown fire initiation and crown scorch height

This state-of-knowledge review examines some of the underlying assumptions and limitations associated withtheinter-relationships amongfourwidelyuseddescriptorsofsurfacefirebehaviourandpost-fireimpacts inwildland fire science and management, namely Byrams fireline intensity, flame length, stem-bark char height and crown scorch height. More specifically, the following topical areas are critically examined based on a comprehensive review of the pertinent literature:(i)estimatingfirelineintensityfrom flamelength;(ii)substitutingflamelengthforfirelineintensityin Van Wagners crown fire initiation model; (iii) the validity of linkages between the Rothermel surface fire behaviour and Van Wagners crown scorch height models; (iv) estimating flame height from post-fire observations of stem-bark char height; and (v) estimating fireline intensity from post-fire observations of crown scorch height. There has been an overwhelming tendency within the wildland fire community to regard Byrams flame length-fireline intensity and Van Wagners crown scorch height-fireline intensity models as universal in nature. However, research has subsequently shown that such linkages among fire behaviour and post-fire impact characteristics are in fact strongly influenced by fuelbed structure, thereby necessitating consideration of fuel complex specific-type models of such relationships. Additional keywords: fire behaviour, fire impacts, fire modelling, first-order fire effects, flame angle, flame depth, flame-front residence time, ignition pattern, stem-bark char height, surface fire.

FireStation — an integrated software system for the numerical simulation of fire spread on complex topography

Abstract A software system aimed at the simulation of fire spread over complex topography is presented. The software implements a semi-empirical model for fire rate of spread, which takes as input local terrain slope, parameters describing fuel properties as well as the wind speed and direction. Fire shape is described with recourse to an ellipse-type model. Two different models are implemented for the simulation of the wind field. Both these models predict wind velocity and direction based on local observation taken at meteorological stations. The whole system was developed under a graphical interface, aiming at a better ease of use and output readability so as to facilitate its application under operational conditions. This work describes the mathematical models employed, provides an overview of the graphical interface and presents the results of some simulations tested against experimental data.

Predicting the ignition of crown fuels above a spreading surface fire. Part I: model idealization

A model was developed to predict the ignition of forest crown fuels above a surface fire based on heat transfer theory. The crown fuel ignition model (hereafter referred to as CFIM) is based on first principles, integrating: (i) the characteristics of the energy source as defined by surface fire flame front properties; (ii) buoyant plume dynamics; (iii) heat sink as described by the crown fuel particle characteristics; and (iv) energy transfer (gain and losses) to the crown fuels. Fuel particle temperature increase is determined through an energy balance relating heat absorption to fuel particle temperature. The final model output is the temperature of the crown fuel particles, which upon reaching ignition temperature are assumed to ignite. CFIM predicts the ignition of crown fuels but does not determine the onset of crown fire spread per se. The coupling of the CFIM with models determining the rate of propagation of crown fires allows for the prediction of the potential for sustained crowning. CFIM has the potential to be implemented in fire management decision support systems.

Plant flammability experiments offer limited insight into vegetation-fire dynamics interactions

Flammability is the general ability of vegetation (fuel) to burn (Gill & Zylstra, 2005). The concept of flammability can be narrowed down to distinct aspects of combustion as gauged by a number of metrics (White & Zipperer, 2010). In this respect, Anderson (1970) proposed flammability to consist of ignitibility (ease of ignition), sustainability (how well combustion will proceed) and combustibility (velocity or intensity of combustion), and Martin et al. (1994) further added consumability, the amount of combusted fuel. Flammability is experimentally assessed by burning fuels in the laboratory, either in the form of discrete elements (e.g. a leaf, a twig) for which the concept has been coined, or as a fuel bed, a homogeneous or heterogeneous assemblage of individual units. In the real world (the community or stand level), flammability should be translated in terms of fire behaviour characteristics, for example, rate of fire spread and fire intensity (Anderson, 1970). However, because flammability is a broad, allencompassing idea, its usage and appraisal know no scale boundaries and can even extend to the ecosystem level. It is notable that the scientific and practical relevance of vegetation flammability have been dismissed (Dickinson & Johnson, 2001), possibly because the term has been used less rigorously in fire ecology than in combustion science. Flammability studies have been carried out for various purposes. A recent trend is to examine plant flammability descriptors as fire-adaptive traits (Schwilk, 2003; Scarff & Westoby, 2006; Cowan & Ackerly, 2010; Saura-Mas et al., 2010; Pausas et al., 2012) in relation to the hypothesis that flammability has evolved to confer fitness in fire-prone environments (Bond & Midgley, 1995). None of these studies or, for that matter, any other flammability-related research thoroughly questions to what extent flammability experiments are adequate surrogates for real-world, full-scale fire behaviour and dynamics. White & Zipperer (2010) provide a thorough description and comparison of the methods used to assess flammability. Here we discuss the limitations of plant flammability tests regarding their ability to replicate real-world conditions, with an emphasis on shrub species. Scale limitations of plant flammability experiments

Uncertainty associated with model predictions of surface and crown fire rates of spread

The degree of accuracy in model predictions of rate of spread in wildland fires is dependent on the models applicability to a given situation, the validity of the models relationships, and the reliability of the model input data. On the basis of a compilation of 49 fire spread model evaluation datasets involving 1278 observations in seven different fuel type groups, the limits on the predictability of current operational models are examined. Only 3% of the predictions (i.e. 35 out of 1278) were considered to be exact predictions according to the criteria used in this study. Mean percent error varied between 20 and 310% and was homogeneous across fuel type groups. Slightly more than half of the evaluation datasets had mean errors between 51 and 75%. Under-prediction bias was prevalent in 75% of the 49 datasets analysed. A case is made for suggesting that a ?35% error interval (i.e. approximately one standard deviation) would constitute a reasonable standard for model performance in predicting a wildland fires forward or heading rate of spread. We also found that empirical-based fire behaviour models developed from a solid foundation of field observations and well accepted functional forms adequately predicted rates of fire spread far outside of the bounds of the original dataset used in their development. We examined error statistics of operational wildland fire spread models.We compiled 49 fire spread model evaluation datasets involving 1278 observations.Mean percent error varied between 20 and 310% and was homogeneous across fuel type groups.The analysis suggests that a ?35% error interval is a reasonable standard for model adequacy.

Short communication: Are the applications of wildland fire behaviour models getting ahead of their evaluation again?

Evaluation is a crucial component for model credibility and acceptance by researchers and resource managers. The nature and characteristics of free-burning wildland fires pose challenges to acquiring the kind of quality data necessary for adequate fire behaviour model evaluation. As a result, in some circles it has led to a research culture that tends to avoid evaluating model performance. Operational fire modelling systems commonly used in western North America have been shown to exhibit an underprediction bias when employed to determine the threshold conditions necessary for the onset of crowning and the associated spread rate of active crown fires in conifer forest stands. This pronouncement was made a few years ago after at least a decade of model misapplication in fire and fuel management simulation modelling stemming from a lack of model evaluation. There are signs that the same situation may be repeated with developing physics-based models that simulate potential wildland fire behaviour; these models have as yet undergone limited testing against observations garnered from planned and/or accidental wildland fires. We propose a broad co-operative project encompassing modellers and experimentalists is needed to define and acquire the benchmark fire behaviour data required for model calibration and evaluation.

Development of fuel models for fire behaviour prediction in maritime pine (Pinus pinaster Ait.) stands

A dataset of 42 experimental fires in maritime pine (Pinus pinaster Ait.) stands was used to develop fuel models to describe pine litter and understorey surface fuel complexes. A backtracking calibration procedure quantified the surface fuel bed characteristics that best explained the observed rate of fire spread. The study suggested the need for two distinct fuel models to adequately characterise the variability in fire behaviour in this fuel type. In these heterogeneous fuel beds the fuel models do not necessarily represent the inventoried average fuel conditions. Evaluation against the modelling data produced mean absolute errors of 0.8 and 0.6 m min–1 in rate of spread, respectively, for the litter and understorey fuel models, with little evidence of bias. The fuel models predicted the rate of spread of a validation dataset with comparable error. Comparison of the behaviour and evaluation statistics produced by the study fuel models with fuel models developed from inventoried fuel data alone revealed an improvement on model performance for the current study approach for the litter fuel model and comparable behaviour for the understorey one. We examined model behaviour through comparative analysis with models used operationally to predict fire spread in pine stands. Large departures from model behaviour essentially occur when the models are exercised outside the range of the model development dataset. The discrepancies in predicted fire behaviour were hypothesised to arise not from differences in fuel complex structure but from the selected functional relationships that determine the effect of wind and fuel moisture on rate of spread.

A generic, empirical-based model for predicting rate of fire spread in shrublands

A shrubland fire behaviour dataset was assembled using data from experimental studies in Australia, New Zealand, Europe and South Africa. The dataset covers a wide range of heathlands and shrubland species associations and vegetation structures. Three models for rate of spread are developed using 2-m wind speed, a wind reduction factor, elevated dead fuel moisture content and either vegetation height (with or without live fuel moisture content) or bulk density. The models are tested against independent data from prescribed fires and wildfires and found to predict fire spread rate within acceptable limits (mean absolute errors varying between 3.5 and 9.1 m min–1). A simple model to predict dead fuel moisture content is evaluated, and an ignition line length correction is proposed. Although the model can be expected to provide robust predictions of rate of spread in a broad range of shrublands, the effects of slope steepness and variation in fuel quantity and composition are yet to be quantified. The model does not predict threshold conditions for continuous fire spread, and future work should focus on identifying fuel and weather factors that control transitions in fire behaviour.