Linking Stream Network Process Models to Robust Data Management Systems for the Purpose of Land-Use Decision Support

PI: Bruce MacVicar, University of Waterloo

Co-I's: Simon Courtenay; Stephen Murphy; Paulo Alencar; Don Cowan, University of Waterloo

The proposal is to develop a digital platform to improve the science, communications, and outcomes surrounding decision making in surface water channel networks. We addressed reviewers concerns from the LOI by reducing the scope and budget of the project to match the recommendations, better describing the specific decision making capabilities, clarifying the links between the proposed system and the GWF cloud, and focussing on four case studies where we have extensive experience. The proposed system is called the Stream Adaptive Management Environment or ‘SAME’. Its basic purpose is to combine monitoring and modelling efforts with a data management platform created by the Computer Systems Group at UW that supports environmental decision making (iEnvironment). Such research is aligned with Pillar 2 of the Global Water Futures (GWF) initiative – Developing Big Data and Decision Support Systems. The proposed strategy builds on previous efforts by incorporating two large databases with field monitoring and surface water modelling results; reusing existing monitoring, modelling, user interface, and access control tools; maintaining relations with active partners that include municipalities and conservation authorities across Ontario; extending analysis tools developed as part of an NSERC funded Strategic Project Grant; and leveraging funding for platform development. Secured funding through CANARIE, a non-profit Canadian corporation with a mandate to advance Canada’s knowledge and innovation infrastructure, will support the development of iEnvironment. Three-year funding from the Global Water Futures initiative would be used to develop modules that will allow research groups in river hydraulics and aquatic ecology to connect their work with this system to build an adaptive management platform in SAME. The new work would also ingest a range of data streams and allow users to access the data and analysis in the form of maps, tables and tailored report cards. Specifically supported decisions in the near term would include the evaluation of alternative development scenarios, best management practices, stream restoration, and assessment of risk due to predictive uncertainty and climate change. Case studies include Wilket, Morningside and Ganatsekaigon Creeks in Toronto and Blair Creek in Kitchener. The seven year vision for the project is that other groups connected to the Global Water Futures Initiative will develop modules and build the capacity of the platform to provide a trans-disciplinary decision support system for other questions related to cumulative effects, risk, and the management of surface water networks.