Creation of a Dynamic Risk Ranking Methodology for Multiple Storm Events and its Application to the City of Edmonton

Abstract

Globally, municipalities have been experiencing flooding events caused by riverine, coastal, or urban flooding and have applied both many and varied mitigation measures. In most flood mitigation cases, financial investment for mitigation improvements was approved post flooding. This reactive rather than proactive approach tended to focus mainly on financial consequences. This thesis is based on the risk ranking methodology that was part of EPCOR’s Stormwater Integrated Resources Planning that was created for the City of Edmonton as part of their flood management plan. It utilizes a grid-based risk methodology of likelihood and consequence events to determine investment focus. This provides direction in order to mitigate flood damage before a severe weather event causing property loss and/or impacts in a community. Rather than flood mitigation projects initiated solely on a financial cost benefit basis, Health and Safety, Environment, and Social factors were also included in the grid to generate a multi-faceted consequence approach. Climate change is causing severity and frequency of storm events to increase, which are effects that are also considered within the risk determination. This is a disaggregated approach to assessing risk and has the ability to identify and implement incremental improvements as the study area changes. The risk assessment framework was formulated and applied to the City of Edmonton’s watershed and sewershed basins and validated with past known flooding events. The results were then applied to a Geospatial map of the City of Edmonton to provide a mapping representation of the risk throughout the City. The visual representation provided users a tool to prioritize future areas for flood mitigation programs. The dynamic capability allows for the user to apply flood mitigation projects to the study area and see the risk difference it would provide before it is physically implemented. Through the dedication of regular updates and the ability to add new risk criteria the model is a continuously evolving risk model.