Modelling water quality in the Peace, Athabasca, and Slave River Systems represents some fundamental challenges. These are large complex systems that are relatively oligotrophic, located at relatively high latitudes, and experience highly seasonal environmental fluctuations. This report summarizes the major modelling projects undertaken by the NRBS, provides a critical summary of major results, and makes recommendations for future work. Section 1.0 describes the scope of the problem of model water quality in the Northern River Basins and provides a summary of the models used by NRBS to predict key water quality variables. Section 2.0 provides a general overview of the utility and shortcomings of models of water quality with the goal of establishing key criteria for assessing the successes or failures of models developed by NRBS. Section 3.0 summarizes the key findings of NRBS models and evaluates the modellingresultsagainstthecriteriaoutlinedinSection2.0. Section4.0presentsaseriesof recommendations along with strategic suggestions for future work in the modelling of water quality in the Northern River Basins.
In Section 4.0, specific recommendations for modelling dissolved oxygen, transport and fate of contaminants, and distribution of contaminants in the food chain are summarized. General recommendations for future work include: 1) The development of predictive transport models for the Northern Rivers is of paramount importance for future progress. 2) Models need to be developed to predict the impact on the biota of changes in water quality. 3) A process-oriented database needs to be created and maintained for the modelling efforts in the Northern Rivers. 4) Management objectives with regard to scale of prediction need to be clarified, and these goals have to be carefully evaluated with respect to data availability. 5) More emphasis has to be placed on models of water quality that can be adapted to evaluate changes in water quality broughtaboutbychangesintheprocesstechnologyofthepulpandpaperindustry. 6)Modelling teams need to be established, drawing experts from government, industry, and universities, to tackle the difficult interdisciplinary problems associated with the development of predictive water quality models.