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Water quality models can be used for impact assessments and operational analyses of mining projects to predict constituent concentrations in typical mine facilities (e.g. tailings water impoundments or pit lakes) or within a receiving aquatic environment affected by mine effluent. These models depend on water source loadings (i.e., mine and natural waters), which may be characterized through monitoring programs that include flow measurements and water samples analysed for various constituents. However, water samples may be limited in number and therefore represent only a subset of the range of possible concentrations from the source loadings. A probabilistic water quality modelling approach is presented in this paper and is intended to address these cases with limited data. This approach entails the generation of time series of source concentrations from probability distributions fitted to observed water samples. The length of the generated time series can be long (e.g., 50 years or more), so that the water chemistry predicted from the model may encompass a large number of combinations of climatic, chemical loading and flow conditions. An approach to characterise the variability of the parameters (i.e., mean and standard deviation) of the fitted probability distributions is then presented in this paper, and can be incorporated in the probabilistic modelling formulation to address uncertainty in water quality predictions. From this characterization, the model can be used to establish confidence bands on water quality predictions as part of an uncertainty analysis. Incorporation of climate scenarios to assess the impact of climate change to predicted constituent concentrations will also be discussed. The modelling approach is briefly demonstrated with data and results from the environmental impact assessments of an oil sands mining development in Northern Alberta, Canada. The results presented herein illustrate the development of uncertainty bands around the results produced as part of the impact assessment (EIA). The results also show the uncertainty bands developed under different climate scenario as part of a climate change analysis. These bands, when compared to those developed for the uncertainty analysis under the EIA climate, show the possible shift in concentrations that may occur as a result of different climate conditions affecting the aquatic environment.

Probabilistic modelling applied to the mining industry to address water quality uncertainty