Stormwater management wet ponds are generally very shallow and hence significantly increase runoff temperatures in discharge waters during summer months. These increases in temperatures adversely affect receiving urban stream ecosystems. Monitoring results for three summers (2009 – 2011) from three stormwater management ponds in the cities of Guelph and Kitchener, Ontario, Canada are employed to advance our knowledge of key design parameters that influence thermal enrichment of stormwater discharges.
An artificial neural network (ANN) model developed to predict the event mean temperature at the pond outlet (EMTO) using collected monitoring data. The ANN model explains 99% of the variability in EMTO. Sensitivity analyses show that increasing the permanent pond volume from 2000 m³ to 4000 m³, while keeping all other parameters constant, results in an average increase of 5°C in EMTO. Therefore, ponds with larger permanent pool volumes tend to release the warmer water from the ponds, with less thermal interaction with the fresh event runoff at the pond inlet. Similarly, sensitivity analysis using ANN model for travel path ratio (TPR) reveals that increasing the travel path ratio using baffles results in increasing EMTO. For instance, increasing the TPR from 0.6 to 1.2 m leads to an average increase of 6°C in event mean temperature at the pond outlet. Hence, larger volume ponds and ponds with elongated travel path tend to discharge more of the warmer water that is residing in the pond than the cooler, fresh event runoff. However, increasing the average pond depth from 0.6 m to 1.0 m results in up to 9°C decrease in EMTO; therefore, design of ponds with average depths greater than 1.2 m can result in significant decreases in EMTO when using bottom-draw structures.
The findings describe in the paper can lead to design of deeper ponds with bottom-draw outlets and smaller travel path ratio, although care must be taken in ensure that the implications of ponds to meet the essential requirements according to MOE guidelines.