Improving the reliability and utility of operational bushfire behaviour predictions in Australian vegetation. (May 2017)

• Record Type: Journal Article
• Authors: Plucinski, Matt P.; Sullivan, Andrew L.; Rucinski, Chris J.; Prakash, Mahesh
• Is Part Of: Environmental modelling & software. Volume 91(2017)
• Page Start: 1
• Page End: 12
• Publication Date: 2017-05
• Subjects: Wildfire -- Wildland fire -- Decision support system -- Software -- Fire spread -- Amicus; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2017.01.019
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Fire behaviour and spread predictions guides suppression strategies and public warnings during wildfires. The scientific understanding of fire behaviour forms the core of these predictions, but is incomplete, and expert judgement and experience are required to augment the evidence based knowledge. Amicus is a new decision support system that implements contemporary, published and operationalised bushfire behaviour models (e.g. rate of spread, flame height, fireline intensity, spotting distance) in the Australian bushfire context. It enables the inclusion of expert judgement and local knowledge, allows users to analyse temporal trends and uncertainty in inputs, and facilitates reliable and practical predictions. This paper provides a comprehensive overview of Amicus, including its operation and functionality, identifies the boundaries of the current understanding of fire science, discusses the major limitations in existing knowledge, and provides a framework for allowing deterministic and anecdotal/local knowledge to be incorporated into formal fire behaviour predictions. Highlights: Fire behaviour predictions inform suppression strategies and public warnings. Expert judgement and experience can augment fire science. Amicus combines science and expert knowledge for robust transparent predictions. Amicus highlights operational domains and the limits of model reliability. Users can investigate the impact of uncertainty in input data on outputs.