Evaluating Watershed-Scale Hydrologic Impacts of Green Stormwater Infrastructure Across Three U.S. Urban Climates

Nayeon Kwak; Virginia Smith; Kelly Good

Urban stormwater management increasingly relies on green stormwater infrastructure (GSI) to mitigate runoff and restore hydrologic function in developed watersheds. However, quantifying the influence of GSI at the watershed scale remains difficult, as most existing studies have focused on small subdivisions or isolated installations. This ongoing project seeks to characterize how the hydrologic response of urban watersheds to GSI implementation varies with factors such as climate, geography, and seasonality, and evaluate the potential of gridded precipitation products to enhance the replicability and scalability of urban hydrology assessments. This work develops a transferable, data-driven approach to evaluate the hydrologic impacts of GSI across three U.S. urban watersheds—in Philadelphia, Atlanta, and Austin—representing diverse climates and physiographic settings where GSI has been extensively implemented. To improve the accuracy and reproducibility of rainfall inputs, the study compares gridded precipitation datasets with point gage observations and assess how data source influences hydrologic interpretation. Precipitation data are paired with USGS streamflow records to examine hydrograph features and temporal changes in watershed response over the past decade, during which substantial GSI implementation has occurred in each watershed. Both upstream and downstream subwatersheds are analyzed to explore spatial variability in flow response and potential GSI influence. Findings are expected to provide an approach for consistent, city-scale evaluation of GSI performance and to improve understanding of how distributed green infrastructure collectively shapes urban watershed behavior.

Locating and Quantifying Inflow and Infiltration Using Distributed Temperature Sensing

 Omar Hegazy, Walter McDonald

Rainfall-derived inflow and infiltration (I/I) can overwhelm sanitary sewer systems, yet conventional monitoring provides limited information on where wet-weather inputs enter or how much flow they contribute. This study evaluates distributed temperature sensing (DTS) as a high-resolution approach for identifying and quantifying I/I along a 365-m residential sewer. Temperature was measured at 1-m spatial and 3-min temporal resolution during three summer storm events. A coupled mass–energy balance and nonlinear optimization framework estimated time-varying I/I temperature and distributed flow contributions while matching a downstream reference hydrograph. The optimized model reproduced the timing and magnitude of wet-weather flow, and inferred temperatures followed physically plausible patterns of initial warming followed by cooling and stabilization. Results also showed that I/I was concentrated at discrete locations, particularly within the downstream portion of the monitored reach. Ongoing machine-learning work extends this framework toward automated classification of wastewater and I/I signals for sewer-system screening and targeted rehabilitation.

Assessing Active and Passive Sampling Techniques for Emerging Contaminants Using Nested Watershed Monitoring

Henry Kibuye; Tamie Veith; Tyler Groh; Heather Preisendanz

Even at trace levels, emerging contaminants (ECs) in surface water resources pose potential adverse ecological and human health impacts. The assessment of monitoring approaches and sampling methods used in tracking spatial-temporal patterns of ECs is key to designing efficient monitoring networks. In this study, a nested watershed monitoring approach was implemented to quantify 24 ECs over the 2023-2024 growing seasons at five sampling sites within the Halfmoon Creek watershed of the Chesapeake Bay basin. Using grab sampling and polar organic chemical integrative samplers (POCIS), both active and passive stream samples were collected every two weeks to (1) compare the effectiveness of the methods in documenting spatial-temporal patterns of ECs, and (2) examine the relationship between estimated POCIS time-weighted average (TWA) and grab sample concentrations. POCIS sampling detected ECs at equal or higher frequencies than grab sampling did, while grab sampling showed higher seasonal variability in concentrations. Atrazine, simazine, clothianidin, and caffeine were the most frequently detected ECs, present in at least 68% of both grab and POCIS samples. Nested watershed monitoring enabled the identification of hotspot sites, and multivariate analysis indicated that POCIS captured greater differences in EC profiles between sites than grab samples. Neutral ECs at environmental pH showed greater POCIS sorption and had higher estimated POCIS TWA than grab samples concentrations. These results highlight the importance of multiple-site monitoring and utilizing both grab and passive sampling for comprehensive water quality assessment. Additionally, the protonation state of target compounds at environmental pH should be considered when selecting passive samplers.