Journal of Sustainable Water in the Built Environment

Forum papers are thought-provoking opinion pieces or essays founded in fact, sometimes containing speculation, on a civil engineering topic of general interest and relevance to the readership of the journal. The views expressed in this Forum article do not necessarily reflect the views of ASCE or the Editorial Board of the journal.

Urban runoff volumes and flow peaks are likely to increase in the future owing to climate change–driven effects on rainfall and continued urbanization. Actionable planning estimates that anticipate these impacts are needed to assess stormwater management infrastructure requirements and to minimize impacts on ecosystem services. This study presents a planning-level simple flow simulation tool and quantifies benefits of green stormwater management practices in small watersheds. Flow simulation was performed using a curve number–based watershed model (CWM). A portfolio approach was used to assess cost-optimal stormwater adaptation pathways considering a suite of alternative practices including both gray and green infrastructure. The CWM provides actionable information for medium to highly urbanized watersheds with percent bias less than 30% for highly urbanized watersheds. Considering projected future stormwater needs, analysis of multiple stormwater management approaches showed that green stormwater management alternatives are less cost-optimal than gray infrastructure at small watershed scales. These results suggest the possible use of CWM for quick planning-level flow estimates and analysis of more green practices for cost-optimal alternatives.

New residential landscapes in Florida are often installed on sandy fill soil that has been compacted (bulk densities $>1.60\mathrm{g}/{\mathrm{cm}}^3$) during the home construction process. The goal of this study was to determine whether incorporating compost into new residential lawns could reduce stormwater runoff volumes and nutrient loadings. Before landscape installation, one of three treatments (null, tilled, and tilled with compost) were applied to the lawn area (60% of landscape) and after landscape installation was completed, approximately half of the lots were topdressed. Overall, drainage areas with compost-amended soils produced 2.5% less runoff than tilled soils, but did not produce less runoff than null soils. However, considering variability across storm events, amending soils resulted in lower event runoff depths compared with tilled and null treatments, with effective curve numbers of 77, 88, and 82, respectively. Neither total nitrogen (TN) nor total phosphorus (TP) concentrations or loadings were significantly different based on treatment or topdressing. Annualized TN loading rates were highest for tilled soils ($5.14\mathrm{kg}\mathrm{N}/\mathrm{ha}/\mathrm{year}$) and lowest for composted soils ($4.21\mathrm{kg}\mathrm{N}/\mathrm{ha}/\mathrm{year}$). Tilled soils produced the greatest annualized phosphorus loading rates ($1.18\mathrm{kg}\mathrm{P}/\mathrm{ha}/\mathrm{year}$), with null soils producing the lowest loading rates ($0.73\mathrm{kg}\mathrm{P}/\mathrm{ha}/\mathrm{year}$). Results of this study indicate that amending compacted fill soils with compost prior to landscape installation decreased runoff volumes without increasing nutrient concentrations.

Bioretention areas are a common form of green stormwater infrastructure (GSI). There is significant research on the performance of individual bioretention cells, but the watershed-scale benefits of GSI are still unclear. Furthermore, differences in bioretention design and rainfall patterns make it difficult to compare results between studies. We used the Storm Water Management Model (SWMM) to assess the effects of bioretention size, soil infiltration rate, storm size, and climate on the watershed-scale performance of GSI. We first divided the contiguous US into 10 rainfall regions based on similarities in precipitation amount, intensity, and other storm characteristics. We then modeled the effects of bioretention areas in a single watershed under these different rainfall regimes. Bioretention areas did provide watershed-scale benefits, although performance declined as (1) bioretention areas became smaller, (2) soil infiltration rates decreased, and (3) precipitation depth increased. High-intensity rainfall was the primary cause of outflow from bioretention areas, although back-to-back storm events also caused outflow in some climates. There were some clear discrepancies between subbasin-scale and watershed-scale GSI performance. Generally, runoff volume reduction was greater when measured at the subbasin scale. Peak flow reduction, however, was greater at the watershed-scale, likely because bioretention areas changed the shape of subbasin runoff hydrographs, leading to watershed-scale peak flow reduction that was greater than the sum of the parts. We provide recommendations for design, management, and future research to help advance effective application of GSI for achieving watershed-scale hydrologic benefits.

Abstract

The capacity to collect meaningful data to estimate stormwater runoff water quality and subsequent system removal performance is key to selecting the appropriate solutions to protect water resources. Historically, grab sampling and automated composite sampling approaches have been used with training and comprehensive quality assurance protocols to produce defensible data. Innovative approaches use real-time ultraviolet-visual spectrometry (UV-Vis) can provide a powerful tool for understanding pollutant loading regionally. This study found that real-time UV-Vis sensing is a potential new tool for understanding stormwater effluent pollutant dynamics. Researchers compared data from real-time sensing using UV-Vis spectrometers to develop calibration curves for predicting pollutant concentrations in stormwater flows. Results from paired laboratory data and raw spectral data established calibrations for the stormwater runoff composition and support further investigations of the use of this technology to predict in situ concentrations of total Kjeldahl nitrogen (TKN), dissolved organic carbon (DOC), total phosphorus (TP), total suspended solids (TSS), total nitrogen (TN), and nitrate as nitrogen (${\mathrm{NO}}_3-\mathrm{N}$) resulted in robust models with $R^2$ values in the range 0.99–0.93. Using partial least squares regression (PLSR) methods, the study demonstrated a strong correlation between concentrations generated by the raw absorbance data across the full available spectrum (220 to 730 nm). These results indicate the potential for developing specific stormwater calibration curves for pollutants of interest representative of stormwater runoff. Collectively, these results indicate that real-time UV-Vis spectrometers can redefine stormwater control monitoring by potentially delivering more accurate, more repeatable laboratory quality data instantaneously, with greater efficiency.

Practical Applications

Results from single grab samples of stormwater events are insufficient to develop estimates of storm event mean concentrations (EMCs). Commonly grab samples are taken during the rising limb of a hydrograph and used to characterize first flush phenomena. It is now known that first flush is a simple way to think about pollutant build-up and wash-off dynamics; however, it is a more theoretical concept because actual runoff concentrations for different pollutants vary due to many watershed and pollutant characteristics. Analyzing individual samples may be helpful to identify trends in loading rates of various constituents over a storm event and their changing intensities, but this significantly adds to laboratory costs and personnel hours to manage and process each event. Composite samples, on the other hand, offer affordable analytics and management but represent average pollutant concentrations that cannot be further discretized. These methods coupled with the difficulty of predicting dynamic storm variations in real time necessary to appropriately sample the storm leads to masked representation of what is happening in real time. The results of this paper introduce a real-time analytical method for stormwater chemistry assessment that bridges many of the contemporary sampling pitfalls. Regressions were within typical acceptable ranges allowed in stormwater chemistry analytical results from certified labs. These results encourage advancing the use of these technologies with greater efficiency and at a lower overall cost than conventionally available methodologies.

Over the last few decades, there has been an increased demand for resilient low-impact development (LID) systems for stormwater management. During extreme uncertain events, a resilient LID system is expected not only to handle immediate stressors but also to rapidly adapt through changing and regulating itself to ensure continuous functionality. This study presents a new resilience quantification approach applicable to different LID systems. To demonstrate its utility, the developed approach was applied on a bioretention system. A set of equations for the LID system’s functionality was developed, integrating an analytical probabilistic approach (APA) and the stormwater management model (SWMM) continuous simulation output. These equations were subsequently used to evaluate resilience indices such as robustness, rapidity, serviceability, and the LID system’s reliability for different LID area ratios and surface depression storage depths. Both APA and SWMM exhibited similar resilience index values of 0.66–1.0 and 0.73–1.0, respectively. The overall reliability index values ranged from 60.50% to 100% when using SWMM and 56.67% to 100% when using APA, reflecting their consistency in predicting excellent system performance throughout the simulation period. However, the average rapidity index value prediction with APA was lower compared to SWMM. This slight variation was due to event-by-event hydrological simulation in APA, unlike the time step-by-time step continuous simulations in SWMM. The developed approach and findings of this study provide policy-makers with a consistent methodology to design resilient LID systems and empower decision-makers to strategize investment focused on optimized LID resilience-based designs.

Green roofs are implemented for providing urban ecosystem services like stormwater management, but they have also been identified as a potentially significant source of phosphorus in the runoff, which may cause concern for downstream water quality. A multiyear evaluation of phosphorus in green roof discharge and alternatives for phosphorus mitigation was conducted using 32 pilot-scale experimental green roofs. The extensive green roofs were made from nonproprietary base substrates [90 volumetric percentage (%v/v) lightweight aggregate (either pumice or expanded clay) and $10\%\mathrm{v}/\mathrm{v}$ compost] and planted with a variety of sedum species. The roofs were initially constructed in 2017, and 38 storm events were sampled over four growing seasons for precipitation, runoff volumes, and total phosphorus (TP) event mean concentrations (EMCs) in discharge from the experimental extensive green roofs and a reference roof. All extensive green roofs tested were a source of elevated TP EMCs compared with the reference roof for the entire monitoring period. The green roofs initially exhibited high TP EMCs and variability, ranging from 0.46 to $0.89\mathrm{mg}/\mathrm{L}$ (first and third quartiles), compared with the reference roof (90% of TP EMCs were below $0.05\mathrm{mg}/\mathrm{L}$). Green roof TP EMCs decreased to 0.11 and $0.19\mathrm{mg}/\mathrm{L}$ (first and third quartiles) by the fourth growing season. A combination of measurement and modeling determined that a net reduction in annual cumulative TP mass discharged from the green roofs compared with the reference roof may be achieved because of the stormwater retention capacity of the green roofs. Amending materials, including zeolite, wood-derived biochar, and oat hull–derived biochar, were tested as a downstream permeable reactive barrier or as an additive in the base substrate (zeolite only). A zeolite addition of $20\%\mathrm{v}/\mathrm{v}$ resulted in lower TP EMCs discharged than the base green roofs (without amendments). None of the other materials tested showed TP mitigating effects. There was no difference in TP EMCs among base green roofs after one growing season. The study highlights the initial excessive phosphorus content of the substrates exceeds the needs of sedum species and results in rapid phosphorus release in runoff discharged from green roofs.

Advances in green stormwater infrastructure (GSI) and extensive implementation of GSI in urban areas warrant further research by municipalities on different aspects of GSI systems (e.g., planning, modeling, design, construction, monitoring, and maintenance) to enhance operational efficiencies, reduce the overall cost of construction and maintenance, and increase the resilience of these systems. However, because of a limited budget and resources for GSI research at the municipal level, a multicriteria decision analysis (MCDA) method is needed to aid the prioritization of research needs using an array of technical, economic, social, and environmental considerations (criteria). To address this need, a hybrid, multicriteria, group decision making approach was used to inform the prioritization of GSI research needs. In this approach, the relative weights of decision criteria are determined using the Delphi method on the basis of input from multiple decision makers (DMs). Additionally, ranking the research topics is performed using a hierarchical simple additive weighting (SAW) method. The application of the hybrid MCDA method was shown in prioritizing GSI research needs to support a division within the Philadelphia Water Department’s GSI program for which a long- and short-list of GSI research topics, containing 67 and 13 items, respectively, and a hierarchy of multidisciplinary decision criteria, containing 39 items, were developed. Sensitivity analyses were conducted to partially address the uncertainties in the decision-making process. The presented method is simple (applicable to real case studies) yet systematic (adjustable in different geographic locations) and supports group decision making, making it suitable for adoption by urban GSI entities as a decision-support tool for prioritizing existing needs and developing future roadmaps for GSI research.

The substrate is the key component of a green roof system that directly influences the system’s stormwater mitigation performance; however, it is also identified as a potentially significant source of contaminants. It is interesting whether substrate phosphorus discharge behavior is predictable and, if so, whether the study of substrate phosphorus leaching behavior is representative of the green roof system performance. The substrates tested were extracted from a 4-year-old extensive green roof and a 1-year-old proprietary substrate from an agricultural (Ag) green roof. The column test for the extensive green roof substrate (comprised of 90% v/v pumice and 10% v/v compost) was designed to simulate 3 years of precipitation in a 3-month period, based on field monitoring of rainfall from 2018 to 2020, and assuming precipitation occurred equally each month. Double the amount of precipitation was applied to the Ag substrate to make up for the shorter duration field exposure before the laboratory experiment. Total phosphorus (TP) concentrations in the permeate from the extensive and the Ag substrates showed exponential decreases with cumulative water flow. The decreasing rates of TP concentrations were similar regardless of the initial phosphorus in the two different substrates. Field monitoring indicated that the TP discharge from the newly built green roof behaved differently compared with its substrate in the laboratory. However, substrate tests could be an extension of field monitoring to estimate the phosphorus discharge for aged green roofs.