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Stormwater management: municipal policies in Ontario for managing rain where it falls through green infrastructure

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posted on 25.05.2021, 07:14 by Wayne Savio Coutinho
This research explores the state of practice for managing rain where it falls in southern Ontario through green infrastructure policies. A literature review and first hand experience from municipalities provided the understanding of the issues to wide‐spread adoption. Stormwater runoff is a significant issue within urban settings, contributing to localized riverine and/or basement flooding that impacts municipal infrastructure, residences, and environmental quality of waterways. Traditional grey infrastructure, an engineered approach of collection and treatment facilities, addresses concerns with combined sewer overflow (CSO), but is not an effective system for Stormwater Management (SWM), evident by the increase in flooding and pollution from intensified rain events, with climate change. Supported by evidence in published literature over the last decade, Low Impact Development (LID) principles have demonstrated effective results for cold climates, provided life costs‐analysis, and a planning framework to determine suitable placement for installations. Co-benefits of Green Stormwater Infrastructure (GSI) are especially valued such as building great communities to live in with "high functioning" urban green spaces. This evidence suggests that managing stormwater runoff locally is a more cost effective and sustainable than end‐of‐pipe solutions. The study revealed best practices and lessons learned from municipalities implementing GSI for the Right‐of‐Way (ROW) though “Green Streets” and on private lands through LID to manage rain where it falls. In Ontario, GSI is at an early adoption stage. The findings support a planning rationale for a coordinated approach to implement, finance, and operate GSI programs for both private lands and in the public ROW for SWM.





Master of Planning


Urban Development

Granting Institution

Ryerson University

LAC Thesis Type


Thesis Advisor

Nina‐Marie Lister