Numerical modelling of oil-sands tailings dam breach runout and overland flow.

Tailings dams, used for containing the residue of mining processes, are very important elements of the Alberta oil-sands industry in Canada. Potential breach of any of these dams can have catastrophic impact on the environment, economy and human health and safety. Therefore, understanding the after-breach processes is a crucial step in hazard analysis and response planning. This paper studies the potential consequence of a hypothetical oil-sands tailings dam breach by performing numerical simulations of the runout and non-Newtonian overland flow of tailings, including the resulting flooding condition and subsequent spill to nearby water bodies. A non-Newtonian dam-breach model with a visco-plastic rheological relationship is used for this purpose. The model is first validated using the 2014 Mount Polley tailings dam breach in British Columbia, before its application to investigate the flooding volume, extent, and downstream hydrograph of a hypothetical breach from a selected oil-sands tailings dam. The validation results show that the model is able to reproduce the flooding extent and water level variation (due to breach wave) at a downstream lake. The oil-sands tailings spill simulation study demonstrated the importance of considering the non-Newtonian behaviour of tailings materials as the non-Newtonian approach resulted in twice as long flood travel time and slightly less spill volume to the downstream river (i.e. Lower Athabasca River) as that of a Newtonian fluid (i.e. water). The results are also found to be highly sensitive to the rheological parameters of the tailings materials such as their viscosity and yield stress that need to be determined through proper calibration.

Modelling the potential effects of Oil-Sands tailings pond breach on the water and sediment quality of the Lower Athabasca River.

Within the Oil-Sands industry in Alberta, Canada, tailings ponds are used as water recycling and tailings storage facilities (TSF) for mining activities. However, there could be possible circumstances under which a sudden breach of an embankment confining one of the TSFs may occur. Such a tailings pond breach would result in a sudden release of a huge volume of Oil Sands process-affected water (OSPW) and sediment slurry containing substantial amount of chemical constituents that would follow the downstream drainage paths and subsequently enter into the Lower Athabasca River (LAR). This study investigates the implications of OS tailings release on the water and sediment quality of the LAR by simulating the fate of sediment and associated chemicals corresponding to a hypothetical breach and release scenarios from a select set of tailings ponds using a two-dimensional hydrodynamic and constituent transport model. After predicting the total volume, time evolution and concentration of sediment and associated chemicals (metals, polycyclic aromatic hydrocarbons (PAHs) and naphthenic acids (NAs)) reaching the LAR, the transport and deposition of these materials within the study reach is simulated. The results show that, depending on tailings release locations, between 40 and 70% of the sediment and associated chemicals get deposited onto the river bed of the 160 km study reach while the rest leaves the study domain during the first three days following the release event. These sediment/chemicals deposited during the initial spill may also have long-term effects on the water quality and aquatic ecosystem of the river and the downstream delta. However, care has to be taken in interpreting the results as further analysis has shown that the outcomes of such model simulations are very sensitive to the various underlying assumptions as well as the values assigned to some model parameters representing the physical properties of the tailings material.

An integrated numerical framework for water quality modelling in cold-region rivers: A case of the lower Athabasca River.

There is a great deal of interest to determine the state and variations of water quality parameters in the lower Athabasca River (LAR) ecosystem, northern Alberta, Canada, due to industrial developments in the region. As a cold region river, the annual cycle of ice cover formation and breakup play a key role in water quality transformation and transportation processes. An integrated deterministic numerical modelling framework is developed and applied for long-term and detailed simulation of the state and variation (spatial and temporal) of major water quality constituents both in open-water and ice covered conditions in the lower Athabasca River (LAR). The framework is based on the a 1D and a 2D hydrodynamic and water quality models externally coupled with the 1D river ice process models to account for the cold season effects. The models are calibrated/validated using available measured data and applied for simulation of dissolved oxygen (DO) and nutrients (i.e., nitrogen and phosphorus). The results show the effect of winter ice cover on reducing the DO concentration, and a fluctuating temporal trend for DO and nutrients during summer periods with substantial differences in concentration between the main channel and flood plains. This numerical frame work can be the basis for future water quality scenario-based studies in the LAR.

Temporal neural networks for downscaling climate variability and extremes.

This paper presents an application of temporal neural networks for downscaling global climate models (GCMs) output. Because of computational constraints, GCMs are usually run at coarse grid resolution (in the order of 100s of kilometres) and as a result they are inherently unable to present local sub-grid scale features and dynamics. Consequently, outputs from these models cannot be used directly in many climate change impact studies. This research explored the issues of 'downscaling' the outputs of GCMs using a temporal neural network (TNN) approach. The method is proposed for downscaling daily precipitation and temperature series for a region in northern Quebec, Canada. The downscaling models are developed and validated using large-scale predictor variables derived from the National Center for Environmental Prediction (NCEP) reanalysis data set. The performance of the temporal neural network downscaling model is also compared to a regression-based statistical downscaling model with emphasis on their ability in reproducing the observed climate variability and extremes. The downscaling results for the base period (1961-2000) suggest that the TNN is an efficient method for downscaling both daily precipitation as well as daily maximum and minimum temperature series. Furthermore, the different model test results indicate that the TNN model mostly outperforms the statistical models for the downscaling of daily precipitation extremes and variability.