Risk criticality model for bushfire threatened water distribution networks

Background Water authorities commonly use vulnerability assessments to evaluate asset bushfire risk for large or key facilities; however, this traditional approach ignores the vast array of distributed component assets (e.g. pipes, air valves, etc.). Aims To address this, a new risk criticality model was developed to correlate geographic information system (GIS)-referenced bushfire intensity data with water distribution asset data to determine risk criticality for each component asset. Methods Fire agencies and water operators were consulted to incorporate the available GIS-referenced bushfire behaviour and water network asset data. During this consultation, asset risk consequence ratings were established as a reference source from which GIS software iteratively quantified asset critical bushfire risk. Key results The model successfully demonstrated its application through a case study providing the water operator with enhanced risk assessment and planning data, and highlighting its capacity to inform and strengthen bushfire risk management. Conclusions The model provides the water sector with a practical tool, aligned to their risk appetite, which can be periodically updated as new fire behaviour and water asset data becomes available. Implications The framework developed for water assets also demonstrated its adaptability and potential for application by other utilities and represents a significant step towards informed climate resilient design.

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Publication Details

Journal
International Journal of Wildland Fire
Published
2026-11-01
DOI
https://doi.org/10.1071/wf26167
Primary Topic
Infrastructure Resilience and Vulnerability Analysis
Type
article
Field-Weighted Citation Impact
0.00

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article

Risk criticality model for bushfire threatened water distribution networks

Vaughan Mitchell, Ellen M. Moon, Wenyan Wu, Jinzhe Gong
1 links
International Journal of Wildland Fire
Infrastructure Resilience and Vulnerability Analysis
article

Risk criticality model for bushfire threatened water distribution networks

Vaughan Mitchell, Ellen M. Moon, Wenyan Wu, Jinzhe Gong
article en

Abstract

Background Water authorities commonly use vulnerability assessments to evaluate asset bushfire risk for large or key facilities; however, this traditional approach ignores the vast array of distributed component assets (e.g. pipes, air valves, etc.). Aims To address this, a new risk criticality model was developed to correlate geographic information system (GIS)-referenced bushfire intensity data with water distribution asset data to determine risk criticality for each component asset. Methods Fire agencies and water operators were consulted to incorporate the available GIS-referenced bushfire behaviour and water network asset data. During this consultation, asset risk consequence ratings were established as a reference source from which GIS software iteratively quantified asset critical bushfire risk. Key results The model successfully demonstrated its application through a case study providing the water operator with enhanced risk assessment and planning data, and highlighting its capacity to inform and strengthen bushfire risk management. Conclusions The model provides the water sector with a practical tool, aligned to their risk appetite, which can be periodically updated as new fire behaviour and water asset data becomes available. Implications The framework developed for water assets also demonstrated its adaptability and potential for application by other utilities and represents a significant step towards informed climate resilient design.

International Journal of Wildland FireVol. 35(11)
Deakin University (AU), The University of Melbourne (AU)
Department of Energy, Environment and Climate Action
Clean water and sanitation
Openalex Percentile: Top 19%
Infrastructure Resilience and Vulnerability Analysis
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Risk criticality model for bushfire threatened water distribution networks — Vaughan Mitchell, Ellen M. Moon, et al. · International Journal of Wildland Fire (2026) | TGRS Research Map | TGRS