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.

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).

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).

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.