Modeling Hydrological Performance of SWM Pond – Integrating Real-time Water Level Data in Wet-weather Events and Design Scenario Comparisons

Throughout North America, Stormwater Management (SWM) ponds are used extensively to manage runoff in urban areas, providing peak flow reduction, increased detention time and improved water quality. Ponds are often designed under local design storm scenarios, but the performance and water level of SWM ponds can vary greatly and rapidly from theoretically derived design levels in real-time. This study leverage data generated by real-time monitoring pilot project in Vaughan. By comparing modeled water levels to real-time field measured values we are able to assess whether the theoretical model produced during the design phase of a SWM pond accurately reflects as-built performance and thus informing and generating new insights on how to best design stormwater infrastructure.

In partnership with the City of Vaughan and IOTICITI Networks Inc., a series of local rainfall and water level data were obtained from the ponds using real-time multi-probe sensors over the spring-fall seasons in 2022 and 2023. The original design model is re-constructed in Visual OTTHYMO for a representative SWM pond situated in the northeast of the City of Vaughan, in southern Ontario. Using the same model parameters, a new model is constructed, and single-event simulations are run using the local rainfall events. The resulting hydrographs from the model are converted to water level using the as-built stage-storage relationship of the pond and then compared with the field-measured water level using a linear regression model. A great deal of variation was observed between predicted and observed hydrologic response including peak flows magnitude and timing, and drawdown time. Overall, only three of eight modelled storms accurately reflected (R2 > 0.7) the observed conditions. The design model was found to both over and under predict peak water level within the SWM pond depending. The varied quality of model prediction may be due to the unique pattern of each rainfall event but more likely reflect the current hydrological functioning of the pond. Results from this work are intended to inform the City of the need to provide maintenance or management to certain ponds, and aid in decision-making for the design of future ponds. In the next steps of the project, the limitations of the model will be explored through a sensitivity analysis of how the model parameters may influence the results.