Future directions for regional water balance modelling to inform mine site rehabilitation

Authors: Sims, A; Guthrie, E; Morden, R; Hardie, R Download Paper Open access courtesy of:

DOI https://doi.org/10.36487/ACG_repo/2615_96

Cite As:
Sims, A, Guthrie, E, Morden, R & Hardie, R 2026, 'Future directions for regional water balance modelling to inform mine site rehabilitation', 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. 1-12, https://doi.org/10.36487/ACG_repo/2615_96



Abstract:
Dynamic water demands, climate variability and climate change create deep uncertainty in assessing water use impacts for mine rehabilitation, yet water balance modelling for closure environmental impact assessments is still commonly deterministic. Typical practice pairs a small number of fixed demand assumptions and closure arrangements with a small number of climate scenarios, often applied as uniform scaling of historical streamflows. This method does not represent the inherently uncertain sequencing of future wet and dry periods, or treat rainfall, temperature and streamflow as evolving through time, and can misrepresent how a flow regime responds to climate change. These limitations matter because environmental values respond to a flow regime, and extraction demand can evolve in response to climate and changing urban and rural consumptive uses. This paper describes an innovative stochastic modelling framework designed to address these issues. The framework couples dynamic water demands that change over time and respond to climate with stochastic climate sequences to represent natural variability. Climate change is incorporated as a plausible range of end-state changes in rainfall and temperature, implemented through time-dependent scaling of model inputs so that change is progressive rather than constant. Critically, scaling is event-specific, allowing low flows and high flows to respond differently under each plausible climate future. Large ensembles of simulations are then run in a Monte Carlo style method to quantify the distribution of outcomes and the likelihood of environmental performance thresholds being exceeded. We demonstrate this modelling method using the Yallourn mine rehabilitation project in Victoria’s Latrobe Valley, Australia, as a case study. This study shows how the method improves attribution of impacts to climate versus operational decisions, supports testing of alternative project strategies, and provides a more transparent, automated and updateable evidence base for closure planning under uncertainty.

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