Modeling stormwater management pond thermal impacts during storm events

Abstract

Discharges of warm water from shallow stormwater management ponds during summer months into receiving headwater streams pose a risk to aquatic ecosystems in urban watersheds, especially in cool- and cold-water streams. Physically based models such as MINUHET have been used to assess the effects of thermal loading on receiving streams. However, these models require a significant amount of data and are complex to calibrate and use for the optimized designs that ensure protection of vulnerable aquatic life in urban stream ecosystems. This research introduces a user friendly and more accurate machine learning method to predict thermal profiles in stormwater ponds and the pond outlet discharge temperature to the receiving streams during storm events. Monitoring data were collected between 2014 and 2016 from three stormwater ponds in southern Ontario to train and test the new model. The new model is used to demonstrate the degree to which cooling effects can be achieved from remedial actions such as increasing the pond depth by dredging one meter and if a bottom-draw outlet is used instead of a mid-depth draw outlet. The methods presented in this study can be used to support an improved stormwater management pond design guidelines based on the key design objective that the thermal regime of the stormwater pond outlet discharge water must match the thermal regime of the receiving stream. The main objective is to avoid any discernable warming of the stream due to the stormwater pond discharge under a range of summer temperatures and storm event sizes.