Indices for the evaluation of wildfire spread simulations using contemporaneous predictions and observations of burnt area

Duff, Thomas J.; Chong, Derek; Tolhurst, Kevin G.

doi:10.1016/j.envsoft.2016.05.005

Cited by 22 publications

(27 citation statements)

References 23 publications

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“…Objective metrics are necessary for a quantitative evaluation of model performance. To demonstrate the framework, we used indices of overlap to evaluate the fit of the simulation with each fire; the Area Difference Index (ADI, [65])-as a full index and decomposed into components of under and over-prediction. The ADI is a dimensionless ratio of the area incorrectly predicted to have been burnt relative to the correctly predicted area.…”

Section: Evaluation Metricsmentioning

confidence: 99%

Conditional Performance Evaluation: Using Wildfire Observations for Systematic Fire Simulator Development

Duff

Cawson

Cirulis

et al. 2018

Forests

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Faster than real-time wildland fire simulators are being increasingly adopted by land managers to provide decision support for tactical wildfire management and assist with strategic risk planning. These simulators are typically based on simple forward rate-of-spread algorithms that were predominantly developed using observations of experimental fires. Given their operational use, it is important that fire simulators be assessed in terms of their performance for their intended use; predicting the spatial progression of wildfires. However, the conditions under which wildfires occur cannot be easily replicated experimentally. We describe and demonstrate a method for use in model development, whereby a dataset comprised of wildfire case-studies is used for evaluating the predictive performance of fire simulators. Two different versions of the model PHOENIX RapidFire were assessed, one incorporating a novel algorithm that accounts fine-scale spatial variation in landscape dryness. Evaluation was done by comparing simulator predictions against contemporaneous observations of 9 different wildfires that occurred in Australia. Performance was quantified using the sum of the Area Difference Indices-a measure of prediction overlap calculated for each prediction/observation pair. The two versions of the model performed similarly, with the newer version being marginally (but not statistically significantly) better when outcomes were summarised across all fires. Despite this, it did not perform better in all cases, with three of the 9 fires better predicted using the original model. Wildfire evaluation datasets were demonstrated to provide valuable feedback for model development, however the limited availability of data means power is lacking for detailed comparisons. With increasingly extreme weather conditions resulting from climate change, conditions under which wildfires occur are likely to continue to extend well beyond those under which fire models algorithms were developed. Consequently, the adoption of improved methods for collecting and utilising wildfire data is critical to ensure fire simulators retain relevance.

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Section: Evaluation Metricsmentioning

confidence: 99%

Conditional Performance Evaluation: Using Wildfire Observations for Systematic Fire Simulator Development

Duff

Cawson

Cirulis

et al. 2018

Forests

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“…In [59], researchers derived simulated fire severity values using PHOENIX. PHOENIX Rapidfire was simulated to understand the fire extent and behavior of the Black Saturday fire [60].…”

Section: Phoenix Rapidfirementioning

confidence: 99%

Forest Fire Occurrence and Modeling in Southeastern Australia

Rahman¹,

Chang²,

Magill³

et al. 2018

Forest Fire

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Forest fire is one of the major environmental disturbances for the Australian continent. Identification of occurrence patterns of large fires, fire mapping, determination of fire spreading mechanisms, and fire effect modeling are some of the best measures to plan and mitigate fire effects. This chapter describes fire occurrence in New South Wales (Australia), the Australian National Bushfire Model Project (ANBMP), fire propagation modeling methods, the McArthur's model and current forest fire modeling approaches in the state of New South Wales of Australia. Among the established fire models, PHOENIX Rapidfire predicts fire spread and facilitates loss and damage assessments as the model considers many environmental and social variables. Two fire spread models, SPARK and Amicus, have been developed and facilitated fire spread mapping and modeling in Australia.

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“…Variants on this approach have been developed to assess fire risk through the use of custom fuel models and average seasonal weather conditions in southern Italy [75] and economic losses projected from fire exposure in Spain [48,76]. Calibrating model inputs, evaluating model uncertainty, and validating results (often on the basis of fire size distributions) remains a combination of art and science, although recent work is improving methods (e.g., [77,78]), and advances in fire spread theory might eventually lead to next-generation simulation models [57,58].…”

Section: Wildfire Risk Assessmentmentioning

confidence: 99%

Getting Ahead of the Wildfire Problem: Quantifying and Mapping Management Challenges and Opportunities

2016

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Wildfire is a global phenomenon that plays a vital role in regulating and maintaining many natural and human-influenced ecosystems but that also poses considerable risks to human populations and infrastructure. Fire managers are charged with balancing the short-term protection of human assets sensitive to fire exposure against the potential long-term benefits that wildfires can provide to natural systems and wildlife populations. The compressed decision timeframes imposed on fire managers during an incident are often insufficient to fully assess a range of fire management options and their respective implications for public and fire responder safety, attainment of land and resource objectives, and future trajectories of hazard and risk. This paper reviews the role of GIS-based assessment and planning to support operational wildfire management decisions, with a focus on recent and emerging research that pre-identifies anthropogenic and biophysical landscape features that can be leveraged to increase the safety and effectiveness of wildfire management operations. We use a case study from the United States to illustrate the development and application of tools that draw from research generated by the global fire management community.

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Indices for the evaluation of wildfire spread simulations using contemporaneous predictions and observations of burnt area

Cited by 22 publications

References 23 publications

Conditional Performance Evaluation: Using Wildfire Observations for Systematic Fire Simulator Development

Conditional Performance Evaluation: Using Wildfire Observations for Systematic Fire Simulator Development

Forest Fire Occurrence and Modeling in Southeastern Australia

Getting Ahead of the Wildfire Problem: Quantifying and Mapping Management Challenges and Opportunities

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