Beal, L, Miller, S, Klakovich, J, Askar, A, Kimball, B, Persico, A & Ray, B 2025, 'A probabilistic framework for assessing groundwater remediation at a legacy mine', in S Knutsson, AB Fourie & M Tibbett (eds), Mine Closure 2025: Proceedings of the 18th International Conference on Mine Closure, Australian Centre for Geomechanics, Perth, pp. 1-16, https://doi.org/10.36487/ACG_repo/2515_65
(https://papers.acg.uwa.edu.au/p/2515_65_Beal/)
Abstract: This study demonstrates the advantages of a probabilistic groundwater flow and transport model over a deterministic approach for evaluating the long-term effectiveness of a mine closure groundwater remediation program. While addressing inherent uncertainties, the model uses chloride as a conservative tracer to predict contaminant migration to down-gradient compliance points under two scenarios: future groundwater extraction based on corrective actions informed by the deterministic model and no action.
An automated three-step workflow is employed: 
Regional and site-scale hydrogeological models are constructed using MODFLOW-6.
The analysis reveals that planned pumping rates derived from the deterministic model are unlikely to be sustainable due to limitations in aquifer hydraulic properties. The probabilistic model identifies maximum sustainable pumping rates which vary over time, are lower than the planned pumping rates, and assesses groundwater quality sensitivity to pumping variations. While the groundwater extraction program is generally effective in suppressing contaminant concentrations during operation, results indicate concentrations rebound near or above regulatory limits after the extraction program is complete, likely due to residual mass stored in the vadose zone that is released when groundwater levels stabilise. 
This work underscores the importance of probabilistic modelling for capturing inherent uncertainties in groundwater remediation predictions. This robust, risk-informed framework enhances mine closure planning at any stage of mining, empowering stakeholders to make informed decisions and improve long-term groundwater resource protection. The probabilistic approach provides a more realistic assessment of potential risks and allows for more effective management strategies compared to traditional deterministic methods.

Keywords: groundwater modelling, probabilistic analysis, mine remediation