Assessing Crown Fire Potential in Coniferous Forests of Western North America: A Critique of Current Approaches and Recent Simulation Studies. Cruz, M. G. & Alexander, M. E. 19(4):377+.
Assessing Crown Fire Potential in Coniferous Forests of Western North America: A Critique of Current Approaches and Recent Simulation Studies [link]Paper  doi  abstract   bibtex   
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 Management Analyst (FMAPlus®), BehavePlus) based on separate implementations or direct integration of Rothermel's surface and crown rate of fire spread models with Van Wagner's crown fire transition and propagation models are shown to have a significant underprediction bias when used in assessing potential crown fire behaviour in conifer forests of western North America. The principal sources of this underprediction bias are shown to include: [::(i)] incompatible model linkages; [::(ii)] use of surface and crown fire rate of spread models that have an inherent underprediction bias; and [::(iii)] reduction in crown 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. [\n] These sources are described and documented in detail based on comparisons with experimental fire and wildfire observations and on separate analyses of model components. The manner in which the two primary canopy fuel inputs influencing crown fire initiation (i.e. foliar moisture content and canopy base height) is handled in these simulation studies and the meaning of Scott and Reinhardt's two crown fire hazard indices are also critically examined. [Excerpt: Summary and concluding remarks] The ready availability of a multitude of fire modelling systems in the US in recent years has led to their widespread use in numerous simulation studies aimed at assessing various fire behaviour characteristics associated with specific fuel complex structures, including the propensity for crown fire initiation and spread (McHugh 2006). The results of these simulations, often aimed at evaluating fuel treatment effectiveness, are in turn utilised in a whole host of applications (e.g. Scott 2003; Fiedler et al. 2004; Skog et al. 2006; Johnson et al. 2007; Finkral and Evans 2008; Huggett et al. 2008; Johnson 2008; Reinhardt et al. 2010) and thus have significant implications for public and wildland firefighter safety, community fire protection, fire management policy-making, and forest management practices. As Cheney (1981) has noted, 'The reality of fire behaviour predictions is that overestimates can be easily readjusted without serious consequences; underestimates of behaviour can be disastrous both to the operations of the fire controller and the credibility of the person making the predictions'. [\n] A critical review of several of these simulation studies, as documented here, has found that the results are often unrealistic for a variety of reasons. It's recognised that the authors of these studies commonly point out the limitations of the models and modelling systems being used through a customary disclaimer concerning the unknowns regarding crown fire behaviour (e.g. Stephens et al. 2009). Nevertheless, the fact that the fuel treatment evaluation studies referenced here are based on modelling systems that utilised model linkages for gauging potential crown fire behaviour that have not previously undergone any form of performance evaluation against independent datasets or any empirical observations should be of concern. There appears, however, to be an aversion within an element of the fire research community to do so (e.g. Scott and Reinhardt 2001; Scott 2006; Stephens et al. 2009). Nevertheless, such testing is now generally regarded as a basic tenet of modern-day model development and evaluation (Jakeman et al. 2006). [\n] Fire modelling systems like NEXUS (Scott and Reinhardt 2001), FFE-FVS (Reinhardt and Crookston 2003), FARSITE (Finney 2004), FMAPlus (Carlton 2005), FlamMap (Finney 2006), and BehavePlus (Andrews et al. 2008) that are based on separate implementations or linkages between Rothermel's (1972, 1991) rate of fire spread models and Van Wagner's (1977, 1993) crown fire transition and propagation models have been shown to have a marked underprediction bias when used to assess potential crown fire behaviour. What has been allowed to evolve is a family of modelling systems composed of independently developed, linked models that were never intended to work together, are sometimes based on very limited data, and may propagate errors beyond acceptable limits. [\n] We have documented here the sources of the bias based on empirical evidence in the form of published experimental fire and wildfire datasets. By analysing model linkages and components, we have described the primary sources of such bias, namely: (1) incompatible model linkages; (2) use of surface and crown fire rate of spread models that have an inherent underprediction bias; and (3) reduction in crown fire rate of spread based on use of unsubstantiated CFB functions. The use of uncalibrated, custom fuel models to represent surface fuelbeds is considered another potential source of bias. [\n] Our analysis has also shown that the crown fire initiation underprediction bias inherent in all of these fire modelling systems could possibly be rectified by modifying the method used to calculate the surface fireline intensity for the purposes of assessing crown fire initiation potential, namely using Nelson's (2003) model to estimate tr in place of Anderson's model (1969). Other modelling systems exist for predicting the likelihood of crown fire initiation and other aspects of crown fire behaviour (Alexander et al. 2006; Cruz et al. 2006b, 2008). Mitsopoulos and Dimitrakopoulos (2007) have, for example, made extensive use of this suite of models in their assessment of crown fire potential in Aleppo pine (Pinus halepensis) forests in Greece. These systems are based on models that have undergone performance evaluations against independent datasets and been shown to be reasonably reliable (Cruz et al. 2003b, 2004, 2006b; Cronan and Jandt 2008). Resolving the underprediction bias associated with predicting active crown fire rate of spread inherent in the Rothermel (1991) model would require substantial changes, including a reassessment of the use of a CFB function, if not complete replacement with a more robust empirically developed model (Cruz et al. 2005) that has been extensively tested (Alexander and Cruz 2006) or a physically based one that has undergone limited testing (Butler et al. 2004). [\n] Alexander (2007) has emphasised that assessments of wildland fire potential involving simulation modelling must be complemented with fire behaviour case study knowledge and by experienced judgment. This review has revealed an overwhelming need for the research users of fire modelling systems to be grounded in the theory and proper application of such tools, including a solid understanding of the assumptions, limitations and accuracy of the underlying models as well as practical knowledge of the subject phenomena (Brown and Davis 1973; Albini 1976; Alexander 2009a, 2009b).
@article{cruzAssessingCrownFire2010,
  title = {Assessing Crown Fire Potential in Coniferous Forests of Western {{North America}}: A Critique of Current Approaches and Recent Simulation Studies},
  author = {Cruz, Miguel G. and Alexander, Martin E.},
  date = {2010},
  journaltitle = {International Journal of Wildland Fire},
  volume = {19},
  pages = {377+},
  issn = {1049-8001},
  doi = {10.1071/wf08132},
  url = {https://doi.org/10.1071/wf08132},
  abstract = {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 Management Analyst (FMAPlus®), BehavePlus) based on separate implementations or direct integration of Rothermel's surface and crown rate of fire spread models with Van Wagner's crown fire transition and propagation models are shown to have a significant underprediction bias when used in assessing potential crown fire behaviour in conifer forests of western North America. The principal sources of this underprediction bias are shown to include: 

[::(i)] incompatible model linkages; 

[::(ii)] use of surface and crown fire rate of spread models that have an inherent underprediction bias; and 

[::(iii)] reduction in crown 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. 

[\textbackslash n] These sources are described and documented in detail based on comparisons with experimental fire and wildfire observations and on separate analyses of model components. The manner in which the two primary canopy fuel inputs influencing crown fire initiation (i.e. foliar moisture content and canopy base height) is handled in these simulation studies and the meaning of Scott and Reinhardt's two crown fire hazard indices are also critically examined.

[Excerpt: Summary and concluding remarks] 

The ready availability of a multitude of fire modelling systems in the US in recent years has led to their widespread use in numerous simulation studies aimed at assessing various fire behaviour characteristics associated with specific fuel complex structures, including the propensity for crown fire initiation and spread (McHugh 2006). The results of these simulations, often aimed at evaluating fuel treatment effectiveness, are in turn utilised in a whole host of applications (e.g. Scott 2003; Fiedler et al. 2004; Skog et al. 2006; Johnson et al. 2007; Finkral and Evans 2008; Huggett et al. 2008; Johnson 2008; Reinhardt et al. 2010) and thus have significant implications for public and wildland firefighter safety, community fire protection, fire management policy-making, and forest management practices. As Cheney (1981) has noted, 'The reality of fire behaviour predictions is that overestimates can be easily readjusted without serious consequences; underestimates of behaviour can be disastrous both to the operations of the fire controller and the credibility of the person making the predictions'.

[\textbackslash n] A critical review of several of these simulation studies, as documented here, has found that the results are often unrealistic for a variety of reasons. It's recognised that the authors of these studies commonly point out the limitations of the models and modelling systems being used through a customary disclaimer concerning the unknowns regarding crown fire behaviour (e.g. Stephens et al. 2009). Nevertheless, the fact that the fuel treatment evaluation studies referenced here are based on modelling systems that utilised model linkages for gauging potential crown fire behaviour that have not previously undergone any form of performance evaluation against independent datasets or any empirical observations should be of concern. There appears, however, to be an aversion within an element of the fire research community to do so (e.g. Scott and Reinhardt 2001; Scott 2006; Stephens et al. 2009). Nevertheless, such testing is now generally regarded as a basic tenet of modern-day model development and evaluation (Jakeman et al. 2006).

[\textbackslash n] Fire modelling systems like NEXUS (Scott and Reinhardt 2001), FFE-FVS (Reinhardt and Crookston 2003), FARSITE (Finney 2004), FMAPlus (Carlton 2005), FlamMap (Finney 2006), and BehavePlus (Andrews et al. 2008) that are based on separate implementations or linkages between Rothermel's (1972, 1991) rate of fire spread models and Van Wagner's (1977, 1993) crown fire transition and propagation models have been shown to have a marked underprediction bias when used to assess potential crown fire behaviour. What has been allowed to evolve is a family of modelling systems composed of independently developed, linked models that were never intended to work together, are sometimes based on very limited data, and may propagate errors beyond acceptable limits.

[\textbackslash n] We have documented here the sources of the bias based on empirical evidence in the form of published experimental fire and wildfire datasets. By analysing model linkages and components, we have described the primary sources of such bias, namely: (1) incompatible model linkages; (2) use of surface and crown fire rate of spread models that have an inherent underprediction bias; and (3) reduction in crown fire rate of spread based on use of unsubstantiated CFB functions. The use of uncalibrated, custom fuel models to represent surface fuelbeds is considered another potential source of bias.

[\textbackslash n] Our analysis has also shown that the crown fire initiation underprediction bias inherent in all of these fire modelling systems could possibly be rectified by modifying the method used to calculate the surface fireline intensity for the purposes of assessing crown fire initiation potential, namely using Nelson's (2003) model to estimate tr in place of Anderson's model (1969). Other modelling systems exist for predicting the likelihood of crown fire initiation and other aspects of crown fire behaviour (Alexander et al. 2006; Cruz et al. 2006b, 2008). Mitsopoulos and Dimitrakopoulos (2007) have, for example, made extensive use of this suite of models in their assessment of crown fire potential in Aleppo pine (Pinus halepensis) forests in Greece. These systems are based on models that have undergone performance evaluations against independent datasets and been shown to be reasonably reliable (Cruz et al. 2003b, 2004, 2006b; Cronan and Jandt 2008). Resolving the underprediction bias associated with predicting active crown fire rate of spread inherent in the Rothermel (1991) model would require substantial changes, including a reassessment of the use of a CFB function, if not complete replacement with a more robust empirically developed model (Cruz et al. 2005) that has been extensively tested (Alexander and Cruz 2006) or a physically based one that has undergone limited testing (Butler et al. 2004).

[\textbackslash n] Alexander (2007) has emphasised that assessments of wildland fire potential involving simulation modelling must be complemented with fire behaviour case study knowledge and by experienced judgment. This review has revealed an overwhelming need for the research users of fire modelling systems to be grounded in the theory and proper application of such tools, including a solid understanding of the assumptions, limitations and accuracy of the underlying models as well as practical knowledge of the subject phenomena (Brown and Davis 1973; Albini 1976; Alexander 2009a, 2009b).},
  keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-13706015,~to-add-doi-URL,canada,comparison,conifers,model-comparison,modelling-uncertainty,prediction-bias,rothermel,simulation,software-uncertainty,uncertainty,united-states,wildfires},
  number = {4}
}
Downloads: 0
About
Documentation
Keywords
Search
Users
Terms and Conditions of Use
Privacy Policy
Sitemap
© BibBase.org 2021