Early Evaluation of Rehabilitated Rain Gardens and Monitoring Optimization Via Sampling Interval Modifications

Abstract

As the environment is subject to rapid urbanization, the effective management of stormwater runoff has become an imperative component of public safety and sustainable development. The implementation of green stormwater infrastructure (GSI) has become a popular tool to help municipalities reach their water quality and quantity goals. The purpose of this research is to investigate the performance and monitoring strategy of two rehabilitated rain gardens located along I-95 in Philadelphia, PA. These two independent linear bioswales were previously designated as underperforming systems due to poor infiltration and were subsequently redesigned and reconstructed. This work considers the reconstruction process and visual observations of site performance to make conclusions about the effectiveness of the redesign. Initial observations show that there are adverse conditions, resulting from construction errors and lack of maintenance, that may compromise the performance of these sites if left unaddressed.

These stormwater management practices (SMPs) were thoroughly instrumented with hydrologic sensors to allow for quantitative evaluations of SMP performance. However, this data was not collected in time for use in the rehabilitation analysis. Data collected from nearby sites was used to investigate previously observed sources of GSI failure. Rainfall and flow data from a nearby site were combined to investigate highway surface runoff capture and the quantification of effective drainage areas (EDAs). It was found that the calculated EDA entering a highway surface inlet was consistently below the ArcGIS delineated drainage area, suggesting high rates of bypass. The source of variation in EDA was unclear, as linear regressions between the EDA and storm characteristics (rainfall depth, duration, intensity, and temperature) showed no correlation. These findings suggest that highway drainage areas are dynamic and that this fluctuation in performance should be considered in GSI design.

The United States Environmental Protection Agency (USEPA) Stormwater Management Model (SWMM) was used to mimic the effect of modifying hydrologic sensor sampling interval on perceptions of GSI performance. It was found that modifying the sampling interval does not affect the modeled inflow volume but does change modeled inflow peak rates and peak rate reduction through an SMP. The degree by which modeled peak inflows differed varied by each sampling interval comparison, but all linear regressions showed that increasing the sampling interval results in an underestimation of modeled peak inflow rates into an SMP. A key finding using local rainfall data was that using a 5-minute sampling interval results in a simulated peak inflow rate that is, on average, 79% of the peak rate simulated by a 1-minute sampling interval (n = 23, R² = 0.98). Modeling the peak rate reduction through an SMP at different sampling intervals showed that calculated peak rate reductions are sensitive to the sampling interval. Predictions were not always consistent as the sampling interval was changed, but linear regressions showed that longer sampling intervals, i.e., simulations with more frequent sampling, predicted higher peak rate reductions on average. These findings highlight the importance of using the shortest sampling interval possible and the need for regulated, consistent sampling intervals and modeling time steps when models are used for compliance purposes.

The opinions presented in this publication are those of the authors and do not necessarily express the opinions of the Pennsylvania Department of Transportation. Reference in this report to any commercial product, process, or service, or the use of any trade, firm, or corporation name is for general informational purposes only and does not constitute an endorsement or certification of any kind by the authors. This project is a research initiative of the Villanova Center for Resilient Water Systems.