Moving from Monitoring to Metering: Benefits of Real-Time Flow Data in Stormwater Management

Our cities are built over thousands of miles of hidden stormwater infrastructure. Stormwater performance data is infrequently and informally collected, meaning we often do not know what is going on in pipes until something goes wrong. Flow datasets are typically collected from monitoring projects that are temporary, limited, and sparse. Stormwater utilities are at the cusp of the same data revolution that electric, gas, and water utilities undertook: system-wide metering. Technology advances have made dense, real-time flow sensor networks possible and affordable. Moving beyond traditional flow monitoring, real-time flow metering gives rise to new data-driven BMPs and insights to help cities to understand their systems. Two case studies will be presented from Jersey City, NJ, and Norfolk, VA, demonstrating the broad applicability of flow metering as a stormwater BMP. Jersey City Municipal Utilities Authority installed a real-time flow sensor network to track and quantify combined sewage overflows as they occurred. The network featured multiple sensors at different locations in each outfall to determine the timing and volume of CSOs. Over a six-month study, direct measurements found 42 CSO days with a total discharge of 16 MG, compared to a SWMM model prediction of 21 CSO days and 10 MG of discharge (Fig 1). The differences detected in modeled and measured volumes are notable because JCMUA is required to report release volumes, and their Long Term Control Plan is developed for a specific volume. Flow metering in Jersey City is ongoing with a third network expansion planned for 2021. The City of Norfolk uses a real-time network to track tidal backflow in the storm system and separate contributions from tailwater and stormwater. Dense sensor networks were deployed in two flood-prone sewersheds. One objective was to evaluate overall capacity trends. Fig 2 shows the 30-day average capacity in one sewershed, which was over 50% full. The network delineated the exact inland extent of tailwater here. An algorithm was developed to separate stormwater from tidal flows in real-time. Fig 3 shows measured depth at a location with tidal influence, and Fig 4 shows the stormwater flows separated by algorithm. Findings from Jersey City and Norfolk show that using flow sensor networks to capture real-time data is critical to assessing system performance, is applicable to variable settings and objectives, and that real-time data itself can be considered a stormwater BMP.