10. Long-term data tools: Decision support for growth management

The final stage in this project would be the creation of a decision-support system to help inform municipalities and the Province whether areas in which growth has been assigned have adequate infrastructure to service it, whether local sources of drinking water are adequate to supply the water needed, and whether receiving water bodies have sufficient assimilative capacity to process the increased effluent loads caused by growth.

A final deliverable in Phase 3 would be a suite of tools, including an environmental management model that accounts for the impacts of urban growth, and a Decision Support System (DSS) that contributes to effective growth planning and water management decision-making in the GGH. These tools will embody the understanding derived from and formalized during this data scan and optimize water management in the face of often-competing stakeholder demands on the available water resources.

The DSS will rely on meteorological, hydrometric, and operational data, potentially with the addition of numerical models of the hydrologic and hydraulic characteristics of the watersheds through which a waterway runs. This system would allow for monitoring of physical variables within the waterway and model the outcomes of different potential growth decisions.

In addition, the DSS could predict the impacts of potential future short- and long-term climate scenarios (for example, wetter-than-normal, drier-than-normal, and average seasonal conditions). The DSS will make it possible to compare the physical characteristics of the system with different stakeholder needs, such as growth, water quality and assimilative capacity, flooding and shoreline protection (where applicable), and the preservation of aquatic habitats.

It is anticipated that the DSS will use advanced, scenario-based assessments to evaluate the different options available to planners and to identify a preferred planning or management option. A key advantage of this approach is that it allows different stakeholder weightings to be identified and agreed upon ahead of time, enhancing public transparency in the decision-making process. Of course, these weightings will change over time in response to the varying seasonal and cyclical needs of different stakeholders, and to changing physical factors, so the DSS needs to be sufficiently robust to handle these variations in weighting, and to allow for appropriate operational changes in response to this.

An automated data system that can handle the diverse array of data inputs identified during Phase 1 of this study would be able to run numerical models or look up previously modelled scenarios as appropriate, evaluate system responses to different management options (such as varying and multiple annual cycle models), incorporate different stakeholder needs, and suggest a preferred planning outcome for growth and water management in the GGH.

Servicing decisions are, however, complex and the results of the proposed decision-support system would be weighed alongside other factors, such as the protection of water resource systems or natural heritage features, cultural values and indigenous interests, the need to focus growth where existing infrastructure can be maximized, and the financial costs of new infrastructure needed to support growth.