A near real-time early flood warning system for dam safety in Western Australia

Authors: Hamilton, J; Hookey, G; Singleton, R; Badrzadeh, H Download Paper Open access courtesy of:

DOI https://doi.org/10.36487/ACG_repo/2615_92

Cite As:
Hamilton, J, Hookey, G, Singleton, R & Badrzadeh, H 2026, 'A near real-time early flood warning system for dam safety in Western Australia', in AB Fourie, G Boggs, J Heyes & M Tibbett (eds), Mine Closure 2026: Proceedings of the 19th International Conference on Mine Closure, Australian Centre for Geomechanics, Perth, pp. 1-14, https://doi.org/10.36487/ACG_repo/2615_92



Abstract:
This paper presents an early warning and flood intelligence system developed for Ophthalmia Dam, which is a BHP-operated water storage facility in Western Australia. The system was originally developed to support flood preparedness and overtopping risk management at Jimblebar Wye and Ophthalmia Dam. However, the system is adaptable to a range of asset life cycle stages, in particular operated assets undergoing progressive closure, final closure readiness, and post-closure monitoring and maintenance. Regular monitoring and flood modelling validation during the operational period of an asset can inform closure planning and frame decision-making around flood protection design criteria. The system enhances asset awareness by integrating a live hydrometeorological monitoring network with an adaptive catchment response model. By automatically processing rainfall and water level sensor data into the model, the system provides continuous oversight that reduces the need for frequent physical site inspections. The system includes probabilistic risk modes that can be used to evaluate a range of upstream catchment responses. This is essential for defining and continuously refining the magnitude of events associated with closure design criteria (e.g. the flows resulting from a specific rainfall event). Hydrographs in the model are user tuneable and can evolve as more storm data is gathered. A decision support interface within the model also provides an intuitive visual output of predicted water level trajectories and alert threshold envelopes. This enables remote teams to manage flood preparedness and safety risks from a centralised location. Adaptive learning features also provide event-based adjustments to rainfall loss parameters. This allows the predictive model to refine accuracy of estimated flood hydrographs as post-closure data are observed over time. This paper outlines the system architecture and the operational outcomes of deploying data-enabled infrastructure to manage residual risk. The work demonstrates how data automation and predictive analytics can strengthen the resilience of closed mine sites.

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