Grants and Contributions:

Grant or Award spanning more than one fiscal year (2017-2018 to 2022-2023).

In urban areas stormwater ponds and constructed wetlands are commonly used to prevent flooding by temporarily storing surface runoff and then releasing it at a decreased rate. Stormwater ponds/wetlands also have another important role, improving water quality by removing pollutants such as sediment and nutrients. Water quality in Alberta Rivers has deteriorated in recent years due to increases in nutrient levels and suspended sediments and in Calgary stormwater runoff is a major source of these pollutants to the Bow and Elbow rivers. As a result there is an urgent need to understand how existing stormwater facilities perform in improving stormwater quality, and how to improve future designs and optimize their operations. However, the performance of stormwater ponds/wetlands varies greatly as a function of local conditions (e.g., season and climate, geological and soil conditions, land use and drainage characteristics), storm characteristics and the resulting hydrological conditions, and the design of each facility (e.g. physical dimensions, inflow/outflow design, plants and vegetation). A comprehensive research study is therefore needed to quantify the sediment and nutrient loadings to ponds/wetlands and to investigate the processes that govern their transport, deposition and cycling within the ponds/wetlands. To address this need we are proposing a 5-year project with the following major objectives: (a) Use a watershed model (e.g. SWMM) to investigate the sediment and nutrient loads entering Calgary's stormwater facilities; (b) Monitor suspended sediment transport and nutrient cycles in four existing ponds and wetlands for two years; (c) Investigate the physical and biogeochemical processes that govern suspended sediment transport and nutrient cycles in stormwater ponds using a three-dimensional hydrodynamic-water quality model (EFDC); (d) Develop improved design and operation guidelines based on the field monitoring data and computer modelling. This project has a strong HQP training component including five graduate students, two summer students and two post-doctoral fellows.x000D