Abstract Stormwater management wet ponds 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 to 2011) from four stormwater management ponds in the cities of Guelph and Kitchener, Ontario are employed to advance our knowledge of key design parameters that influence the thermal enrichment of stormwater discharges. An artificial neural network model was developed to predict the event mean temperature at the pond outlet. The artificial neural network model explains 99% of the variability in outlet event temperature. Sensitivity analyses show that increasing the permanent pond volume from 2 000 m³ to 4 000 m³ results in an average increase of 5 °C in outlet event mean temperature. Similarly, increasing the travel path ratio from 0.6 m to 1.2 m confirmed an average increase of 6 °C in outlet event mean temperature. In addition, ponds with average depths >1.0 m can result in significant decreases in pond outlet water temperature when using bottom draw structures. The results can lead to the promotion of the design of deeper ponds with bottom draw outlets and smaller travel path ratios. However, the implications of this approach on other performance criteria should be evaluated
