Stormwater Control Measure Performance: Predicting Behavior Under Climate Change Using Historical Data

Abstract Submission: Historically, studies and policies surrounding stormwater control measure (SCM) effectiveness have relied on static rain data to determine expected removal. As climate change impacts accelerate, the frequency, depth, and intensity of rainfall events will change; rainfall is expected to become more intense and volatile. These predictions warrant research into pollutant removal under expected conditions. This study seeks to use historical data to examine the effectiveness of pollutant removal in SCMs across the United States in removing Total Nitrogen, Total Phosphorous, Total Suspended Solids, Copper, and Zinc during high intensity storm events that may mimic the storm types that will be seen as rainfall patterns become more intense. This study examines bioretention and grassed swales, due to their prevalence within stormwater systems and depth of data available. Additionally, bioretention systems are not very susceptible to scour while grassed swales are, providing insight on how scour affects pollutant removal. To investigate, the influent and effluent pollutant data and rainfall depth data was extracted from the International Stormwater BMP Database. This data was then analyzed with percent removal as a function of rainfall depth. Findings of this study suggest that assumed correlations between pollutants could be inaccurate under high-intensity conditions. While most state manuals assume a relationship between total phosphorous and total suspended solid removal, these findings suggest otherwise. Additionally, the findings suggest that scour becomes concerning in BMPs that do not contain standing water or have a ponding depth to still water. Future work includes further analyzing assumed correlations between pollutants, expanding BMP types to further investigate effects of scour, and exploring percent removal as related to rainfall intensity.

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