Thermal impact of typical high-density residential, industrial, and commercial land uses is a major concern for the health of aquatic life in urban watersheds, especially in smaller, cold, and coolwater streams. This is the first study of its kind that provides simple easy-to-use equations, developed using gene expression programming (GEP) that can guide the assessment and the design of urban stormwater management systems to protect thermally sensitive receiving streams. We developed 3 GEP models using data collected during 3 years (2009-2011) from 4 urban catchments; the first GEP model predicts event mean temperature at the inlet of the pond; the second model predicts the stormwater temperature at the outlet of the pond; and the third model predicts the temperature of the stormwater after flowing through a cooling trench and before discharging to the receiving stream. The new models have high correlation coefficients of 0.90-0.94 and low prediction uncertainty of less than 4% of the median value of the predicted runoff temperatures. Sensitivity analysis shows that climatic factors have the highest influence on the thermal enrichment followed by the catchment characteristics and the key design variables of the stormwater pond and the cooling trench. The general method presented here is easily transferable to other regions of the world (but not necessarily the exact equations developed here); also through sensitivity and parametric analysis, we gained insight on the key factors and their relative importance in modelling thermal enrichment of urban stromwater runoff.
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.
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