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Water quality remains a major issue in Canada. This paper reviews recent research on the impacts of urbanization, agriculture and forestry on water quality in Canada. Specific water quality issues such as mining, sewage treatment and waste treatment are not included in this paper. For each land use, a brief summary of the dominant processes linking runoff to water quality is provided and recent findings are summarized. With respect to urbanized watersheds, the relatively large proportion of impervious areas, lower vegetation cover and the presence of high-density drainage systems alter surface water routing and timing of peak flows. High concentrations of heavy metals are considered to be the most important water quality problem in urban runoff, but nutrients, pathogens, concentration of pharmaceuticals and water temperature also often contribute. In watersheds dominated by agricultural activities, overland flow is an important vector of pollutants, but subsurface flow such as macropore and tile-drain flo...
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Several businesses and industries rely on rainfall forecasts to support their day-to-day operations. To deal with the uncertainty associated with rainfall forecast, some meteorological organisations have developed products, such as ensemble forecasts. However, due to the intensive computational requirements of ensemble forecasts, the spatial resolution remains coarse. For example, Environment and Climate Change Canada’s (ECCC) Global Ensemble Prediction System (GEPS) data is freely available on a 1-degree grid (about 100 km), while those of the so-called High Resolution Deterministic Prediction System (HRDPS) are available on a 2.5-km grid (about 40 times finer). Potential users are then left with the option of using either a high-resolution rainfall forecast without uncertainty estimation and/or an ensemble with a spectrum of plausible rainfall values, but at a coarser spatial scale. The objective of this study was to evaluate the added value of coupling the Gibbs Sampling Disaggregation Model (GSDM) with ECCC products to provide accurate, precise and consistent rainfall estimates at a fine spatial resolution (10-km) within a forecast framework (6-h). For 30, 6-h, rainfall events occurring within a 40,000-km2 area (Quebec, Canada), results show that, using 100-km aggregated reference rainfall depths as input, statistics of the rainfall fields generated by GSDM were close to those of the 10-km reference field. However, in forecast mode, GSDM outcomes inherit of the ECCC forecast biases, resulting in a poor performance when GEPS data were used as input, mainly due to the inherent rainfall depth distribution of the latter product. Better performance was achieved when the Regional Deterministic Prediction System (RDPS), available on a 10-km grid and aggregated at 100-km, was used as input to GSDM. Nevertheless, most of the analyzed ensemble forecasts were weakly consistent. Some areas of improvement are identified herein.