Green Stormwater Infrastructure Research Articles

Retrofitting urban land through integrative, subsoils-based planning of green stormwater infrastructure: a research framework

Abstract We present a research framework that integrates native subsoil performance and surface retrofitting into coordinated green stormwater infrastructure (GSI) planning. This framework provides communities a strategy to move beyond opportunistic GSI, which can be limited to capturing marginal amounts of stormwater, toward more impactful, coordinated GSI planning that restores the lost hydrologic functioning of the pre-development landscape. We create this framework by establishing critical performance-based relationships among four variables: (1) saturated hydraulic conductivity of native subsoils (∼upper 2 m below urban compaction and fill); (2) GSI design depth for both rain gardens and permeable pavement (in increments of 6″ from 12–30″ for planted and paved GSI); (3) loading ratio, defined as the ratio of GSI retrofit area to upstream impervious surface runoff area (from 1:2 to 1:5 for planted GSI; and 1:1 and direct infiltration for paved GSI); and (4) design storms (rainfall quantity up to 5-inches over 2 h and 24 h durations). We model the four variables using GSI models (built in the US Environmental Protection Agency’s Storm Water Management Model) and reliability analysis, a risk-assessment method adapted to characterize the reliability of GSI in response to varying stormwater runoff loading. The outcome of the modeling is a set of fragility curves and design prototypes, adjustable to catchment and sub-catchment scales, to assist municipalities in early funding and investment decisions to retrofit urbanized land through GSI. We also share two piloted applications in which we use the research framework within the Chicago-Calumet region in Illinois, USA, to conduct site-specific subsoil sampling and determinations of saturated hydraulic conductivity and to develop urban-scale GSI retrofit scenarios. Our framework is transferable to other urban regions, and particularly useful where a lack of integrating native subsoil performance into GSI design hinders decision-making, coordinated GSI planning at scale, and achieving high runoff reduction targets.

Evaluation of ammonia and nitrate distribution and reduction within stormwater green infrastructure with different woody plants under multiple influencing factors

The impact of stormwater green infrastructures (GIs) with different woody plants on nitrogen (N) distribution is still poorly understood. Laboratory experiments were conducted for GIs without or with Sophora japonica and Malus baccata to investigate the distribution of NH3–N and NO3–N. The test data was utilized to calibrate and validate the HYDRUS-2D. The validated model was subsequently used to analyze the distribution of NH3–N and NO3–N within the different GIs under three different rainfall conditions: inflow/runoff pollutant concentration, rainfall recurrence interval (runoff amount of a rainfall event), and number of dry days (during which no rainwater infiltrates into the soil). The average NH3–N and NO3–N concentrations in the upper soil (0–30 cm) of the GIs were about 4.8 and 2.4 times those of the lower layer (30–60 cm). Compared to the control (Vc), the average NH3–N concentrations in soil with Sophora japonica (Vs) and Malus baccata (Vm) decreased by 15.8% and 35.1% while those of NO3–N decreased by 15.5% and 27.2%, respectively. Degrees of influence by the three factors on the average soil NH3–N and NO3–N concentrations were inflow concentration > number of dry days > recurrence interval. The number of dry days was the smallest influence factor for the overflow N load while the inflow concentration was the most significant influence factor for the outflow, bio-utilization, and soil nitrogen loads. Compared to the control, outflow (groundwater recharge) loads of NO3–N from the Vs and Vm increased by 14.0–16.6% and 3.7–6.8%, respectively under different conditions. The overflow (runoff) loads from Vs and Vm decreased by 16.8–36.3% and 6.6%–8.4%, respectively. A multiple regression equation was used to establish a quantitative coupling relationship between N pollutant load reduction rates and influence factors (R2 ≥ 0.83). This relationship can be used to estimate the runoff treatment effectiveness of green infrastructure on target pollutants.

Rapid Assessment and Long-Term Monitoring of Green Stormwater Infrastructure with Citizen Scientists

Green stormwater infrastructure (GSI) has emerged as a promising decentralized management approach to urban stormwater challenges. A lack of data about GSI performance interferes with widespread adoption of GSI. A citizen science program that benefits researchers, lay scientists, and municipalities offers a way to provide these lacking data. We have developed an open-source, transferable green infrastructure rapid assessment (GIRA) protocol for studying the performance of GSI with citizen scientists. This protocol has been tested in six North American cities (New York City, Toronto, Vancouver, Chicago, San Francisco, and Buffalo). In this research we define the performance of GSI in varying geographic, climatic, and maintenance conditions with the intent to create technological, institutional, and management solutions to urban stormwater problems. The GIRA protocol was used by citizen scientists to assess the physical properties and capabilities of bioswales, while small, affordable Green Infrastructure Sensors Boxes (GIBoxes) were used to determine longer-term function across several rain events. Our results indicate that teams of citizen scientists can be effective for collecting and archiving widespread information on the post-installation function of GSI. The effort also showed that citizen scientists had changes in understanding of urban stormwater challenges and the role that GSI can play in solving these problems. We explore the multiple benefits to knowledge, participants, and municipal partners as a result of this research.

Reading the Green Landscape: Public Attitudes toward Green Stormwater Infrastructure and the Perceived Nonmonetary Value of Its Co-Benefits in Three US Cities

AbstractGreen stormwater infrastructure mirrors natural hydrologic processes and is presented as an alternative or complement to traditional gray stormwater infrastructure, which uses concrete chan...

Influence of soil characteristics and metal(loid)s on antibiotic resistance genes in green stormwater infrastructure in Southern California

The synergetic effects of metal(loid)s and soil characteristics on bacterial antibiotic resistance genes (ARGs) in green stormwater infrastructure (GSI) has been relatively understudied. Surface soil samples from six GSIs in Southern California over three time periods were assessed for selected ARGs, class 1 integron-integrase genes (intI1), 16S rRNA genes, and bioavailable and total concentrations of nine metal(loid)s, to investigate the relationships among ARGs, soil characteristics, and co-occurring metal(loid)s. Significant correlations existed among relative gene abundances (sul1, sul2, tetW, and intI1), total metal(loid)s (arsenic, copper, lead, vanadium, and zinc), and bioavailable metal(loid) (arsenic) (r = 0.29–0.61, padj < 0.05). Additionally, soil texture, organic matter, and nutrients within GSI appeared to be significantly correlated with relative gene abundances of sul1, sul2, and tetW (r = −0.57 to 0.59, padj < 0.05). Multiple regression models significantly improved the estimation of ARGs in GSI when considering multiple effects of soil characteristics and metal(loid)s (r = 0.74, padj < 0.001) compared to correlation results. Total arsenic was a significant (positive) correlate in all the regression models of relative gene abundances. This work provides new insights into co-dependencies between GSI ARGs and co-occurring metal(loid)s, indicating the need for risk assessment of metal(loid)-influenced ARG proliferation.

Efficiencies of indigenous South African plant biofilters for urban stormwater runoff water quality improvement with a focus on nutrients and metals

Abstract In South Africa, urban activities contribute high levels of pollution to rivers and groundwater via stormwater runoff. In reducing urban stormwater loads of engineered plant biofiltration, an effective and self-sustaining component of green infrastructure is a treatment option. The country's extensive natural biodiversity offers untapped potential of indigenous species' use in plant biofilters. This paper presents the findings of a plant biofilter column experiment, which investigated the performance of nine indigenous plant species under varied urban stormwater pollutant load strengths. Average significant loads of dissolved Cd (&amp;gt;98%), Cu (&amp;gt;84%), Pb (&amp;gt;99%) and Zn (&amp;gt;95%), as well as NH3-N (&amp;gt;93%), were removed by the plant biofilters, whereas the removal of -N (−37 to 79%) and -P (−81 to 63%) was more variable. Biofilters equipped with indigenous plant species were on average at least 11% more efficient than unvegetated soil in the removal of urban nutrient and metal pollutants. Over time, planted biofilters improved nutrient and metal removal efficiencies. The results support the inclusion of indigenous plants in biofilters within urban stormwater green infrastructure initiatives. Further research to inform plant biofilter design practicalities and assess plant biofilter performance in the field is warranted.

Impact of Green Stormwater Infrastructure Age and Type on Water Quality

Green stormwater infrastructure (GSI) has become increasingly common to mitigate urban stormwater runoff. However, there is limited research on the impact of age and type of GSI. This study evaluated nutrient and metals concentrations in the soil water of five different GSI systems located at the University of Portland in Portland, Oregon. The GSI systems included a bioretention curb extension (part of Portland’s Green Street project), a bioretention basin, a bioretention planter, an infiltration basin, and a bioswale ranging in age from 2 to 11 years. Samples were taken from each system during rain events over a 10-month period and analyzed for copper (Cu), zinc (Zn), phosphate (PO43−), and total phosphorus (TP). Copper and zinc concentrations were found to be impacted by GSI age, with lower concentrations in older systems. The same trend was not found with PO43− and TP, where almost all GSI systems had soil water concentrations much higher than average stormwater concentrations. Age likely played a role in phosphorus soil water concentrations, but other factors such as sources had a stronger influence. Phosphorus is likely coming from the compost in the soil mix in addition to other sources in runoff. This study shows that GSI systems can be effective for copper and zinc, but changes to the soil mix design are needed to reduce high levels of PO43− and TP in soil water.

Three-Dimensional Numerical Modeling of Urban Green Stormwater Infrastructure Curb Inlets

AbstractThe current equations for curb inlet opening sizing often inaccurately estimate the capture efficiency of small urban green stormwater infrastructure (GSI) curb inlets. Most of these equati...

How well do stormwater green infrastructure respond to changing climatic conditions?

Urban green infrastructure (GI) such as biofiltration systems (also known as bioretention, biofilters, rain gardens) are being increasingly implemented in different parts of the world to enhance urban greening whilst controlling stormwater pollution. Treatment effectiveness has been shown to be reliant upon the surrounding climate, including rainfall patterns (e.g. length of wet and dry weather) and temperature. Plant species play a key role in treatment, yet, the response of different plant species to pollutant removal under different climatic and environmental conditions is still not fully known. This information is pertinent to ensure sustained biofilter performance under a range of conditions. This study investigates the change in biofilter nutrient and E. coli removal performance in the presence of four plant species (Canna indica, Carex appressa, Ginkgo biloba and Miscanthus sinensis) with distinct above and below ground characteristics, when exposed to wet weather, dry weather and cold weather conditions. The extent of the decline in nutrient treatment performance following extended drying and under cold conditions was found to be species-specific. Miscanthus sinensis was the best performer and was also found to be cold-resistant. With the exception of Miscanthus sinensis, all plant species were more impacted by the cold temperature (decrease from 70% to 45% TN removal on average; <1 to 30% decrease across species) compared to 4-weeks drying (decrease from 60% to 54% TN removal on average; <1–16% decrease across species) in systems equipped with a submerged zone. In contrast, the 4-weeks drying (E. coli decrease from 2.60 to 1.08 log removal on average) had a more pronounced effect on E. coli removal performance compared to the cold conditions (E. coli decrease from 1.70 to 1.38 log removal on average). This study shows that plant species relative contribution to pollutant removal is pollutant-specific and varies under different climatic conditions. A mix of plant species may ensure higher level of system resilience under a variable climate. The study also highlights the need to understand more about the microbial ecology of these plant systems to optimise both nutrient and pathogen removal for sustained long term performance.

Conceptual ex ante simulation for Green Stormwater Infrastructure Adoption on private property using agent-based modeling.

This paper presents the Green Stormwater Infrastructure Social Spatial Adoption (G-SSA) model, a cellular automata agent-based model that simulates the behavior of private property owners responding to incentives to adopt on-site green stormwater infrastructure (GSI). Concepts such as small-world social networks, opinion dissemination, and diffusion of innovation are used to capture dynamic social, spatial, and temporal aspects of green stormwater infrastructure adoption. Demographic information, site constraints, GSI practice type and costs, and financial incentive information are integrated into modeling rules that influence adoption dynamics. A methodology is presented that describes how these concepts have been translated into an agent-based modeling platform that provides the opportunity to explore modeling dynamics and output. Model output confirms the viability of the methodology and produces results that inform future efforts to explore the G-SSA model. PRACTITIONER POINTS: Agent-based modeling (ABM) can evaluate the expected impact of market-based approaches for green stormwater infrastructure (GSI) adoption and O&M services. ABM can simulate years of implementation of GSI program incentives, which vary from stormwater fee reduction to subsidy payments to tradable credits revenues generated. Publicly available demographic data combined with behavioral economic relationships can build models to evaluate how municipalities can meet regulatory goals for urban retrofits using market-based approaches to encourage GSI adoption.

Integrating Urban Agriculture and Stormwater Management in a Circular Economy to Enhance Ecosystem Services: Connecting the Dots

Due to the rapid urbanization in the context of the conventional linear economy, the vulnerability of the urban ecosystem to climate change has increased. As a result, connecting urban ecosystem services of different urban land uses is imperative for urban sustainability and resilience. In conventional land use planning, urban agriculture (UA) and urban stormwater management are treated as separate economic sectors with different-disconnected-ecosystem services. Furthermore, few studies have synthesized knowledge regarding the potential impacts of integration of UA and stormwater green infrastructures (GIs) on the quantity and quality of urban ecosystem services of both economic sectors. This study provides a detailed analysis of the imperative question—how should a city integrate the developments of both urban agriculture and stormwater green infrastructure to overcome barriers while enhancing the ecosystem services? To answer this question, we conducted an extensive literature review. The results show that integrating UA with GIs can enhance urban food production while protecting urban water quality. This paper provides an initial context and mechanisms for future researchers and city planners regarding the manner in which the synergies between UA and stormwater GIs can create greater value for the wellbeing of urban ecosystems and resilience in the circular economy.

Engineering gentrification: urban redevelopment, sustainability policy, and green stormwater infrastructure in Minneapolis

ABSTRACT Cities increasingly turn to green stormwater infrastructure (GSI) to improve water quality and mitigate flooding risk, yet like other forms of green infrastructure, early research suggests GSI may contribute to ‘green gentrification,’ in which greening increases housing costs, drives gentrification and displacement, and deepens inequalities. Using a spatially explicit mixed-methods approach, I interrogate the relationship between GSI and gentrification in Minneapolis, MN, a city characterized by deep racial inequalities potentially exacerbated by green gentrification. From 2000-2015, census tracts that gentrified received, on average, more GSI projects, more funding per project, and more funding overall. Gentrified tracts received five times more GSI funding than low-income tracts that did not gentrify. Buffer analysis reveals that, adjacent to GSI, rent prices and the college-educated share of the population increased at rates significantly higher than the city average. Quantitative and qualitative analysis of institutions providing and receiving GSI funding indicates that inter-governmental collaborations between watershed governing bodies and city government direct GSI funding to gentrifying areas, where GSI aid in and legitimize the aesthetic transformation of gentrifying neighborhoods. When enmeshed in neighborhood recapitalization via green gentrification, GSI may ultimately deepen environmental inequalities, highlighting the need for planning and policies that proactively mitigate gentrification risks.

Evaluating the effect of city ordinances on the implementation and performance of green stormwater infrastructure (GSI)

The replacement of natural pervious surfaces with impervious surfaces due to urbanization, construction, and development causes excess stormwater runoff and results in cities experiencing localized flooding events. The installation of green stormwater infrastructure (GSI) is one way of reducing flooding events and preventing downstream erosion and damage. In this study, computer rainfall-runoff simulations were performed to analyze GSI's effectiveness in mitigating stormwater runoff when applied to sites with different soil types and for which different design storms were established by regulation. A mixed-use development site was used as a hypothetical site on which to perform the analysis. The study applied the same design to six small- to medium-sized cities in the southeastern United States with different design storm magnitudes. The cities’ ordinances were reviewed, and none required GSI. Therefore, this study revised some of the stormwater management requirements to stress GSI implementation, and then stormwater modeling was conducted to see how regulatory changes would affect runoff. The HydroCAD stormwater modeling tool was used to perform hydrologic simulations for the hypothetical building site in each of the six cities using the design storms and small storms of the cities. Even though GSI has been commonly implemented in large cities, small and medium-sized cities can also prevent excess stormwater by incorporating GSI in their ordinances for new developments and site retrofits. Based on the hydrologic simulation results, municipalities with lower magnitude design storms and low infiltration soils have the most to benefit from GSI and could benefit from ordinances requiring GSI. For smaller, more frequent storms, GSI alone can meet the pre-development peak flow requirements.

Development and Application of a SWMM-Based Simulation Model for Municipal Scale Hydrologic Assessments

There is growing interest for the installation of green stormwater infrastructure (GSI) to improve stormwater control, increase infiltration of stormwater, and improve receiving water body quality. Planning level tools are needed to inform municipal scale decisions on the type and extent of GSI to apply. Here, a modified methodology is developed for the EPA Storm Water Management Model (SWMM) to create SWMM for Low Impact Technology Evaluation (SWWM-LITE) that enables municipal scale assessment of stormwater control measure (SCM) performance with minimal input data requirements and low processing time. Hydrologic outputs of SWMM-LITE are compared to those for SWMM and the National Stormwater Calculator (SWC) to assess the performance of SWMM-LITE. Three scenarios including the baseline without SCMs and the installation of varying SCMs were investigated. Across the three scenarios, SWMM-LITE estimates of annual average hydrologic performance (runoff, infiltration, and evaporation) were within +/−0.1% of estimates from a rigorously developed SWMM model in the City of Fort Collins, CO, for an evaluation of 30 years of continuous simulation. Analysis conducted for 2 year (y), 10 y, and 100 y storm events showed less than +/−2.5% difference between SWMM and SWMM-LITE hydrologic outputs. SWC provided reasonable estimates of hydrologic parameters for the case study area, but was designed for site level analyses of performance of SCMs rather than on the municipal scale. A sensitivity analysis revealed that the most sensitive parameters were primarily consistent for the SWMM-LITE and the complete SWMM. SWMM-LITE has low input data requirements and processing time and can be applied for assessing the hydrologic performance of SCMs to inform planning level decisions.

Assessing the Use of Dual-Drainage Modeling to Determine the Effects of Green Stormwater Infrastructure on Roadway Flooding and Traffic Performance

Green stormwater infrastructure (GSI) is increasingly used to reduce stormwater input to the subsurface stormwater network. This work investigated how GSI interacts with surface runoff and stormwater structures to affect the spatial extent and distribution of roadway flooding and subsequent effects on the performance of the traffic system using a dual-drainage model. The model simulated roadway flooding using PCSWMM (Personal Computer Stormwater Management Model) in Harvard Gulch, Denver, Colorado, and was then used in a microscopic traffic simulation using the Simulation of Urban Mobility Model (SUMO). We examined the effect of converting between 1% and 5% of directly connected impervious area (DCIA) to bioretention GSI on roadway flooding. The results showed that even for 1% of DCIA converted to GSI, the extent and mean depth of roadway flooding was reduced. Increasing GSI conversion further reduced roadway flooding depth and extent, although with diminishing returns per additional percentage of DCIA converted to GSI. Reduced roadway flooding led to increased average vehicle speeds and decreased percentage of roads impacted by flooding and total travel time. We found diminishing returns in the roadway flooding reduction per additional percentage of DCIA converted to GSI. Future work will be conducted to reduce the main limitations of insufficient data for model validation. Detailed dual-drainage modeling has the potential to better predict what GSI strategies will mitigate roadway flooding.

Valuing stakeholder preferences for environmental benefits of stormwater ponds: Evidence from choice experiment

With population growth driving urban expansion in many cities in the United States, there is a need for a sustainable way to manage stormwater. Green stormwater infrastructure (GSI) is considered an innovative way to handle stormwater because of its potential to provide multiple ecosystem services (ES) beyond flooding reduction. However, there is limited research regarding the society's perceived value for GSI practices' co-benefits. This study utilized stated-preference data obtained from a choice experiment in an online survey of 1159 South Carolina (SC) residents to estimate a monetary value for the ES provided by wet detention ponds— the most widely adopted stormwater practice in coastal counties of SC. The benefits examined are flooding reduction, water quality, wildlife habitat, recreation, and scenic beauty. The data were analyzed using a Mixed logit formulation. Considering the differences across the state, the model was estimated separately for five counties. Findings indicate that residents are willing to pay $13.8 to $37.8 annually for a 50% improvement in pollutant removal efficiency of ponds in addition to their current stormwater fee. Also, they are willing to pay $12.5 to $42.9 per year for the nearest pond to have buffer vegetation and wildlife. They are also likely to pay $5 to $22.5 for ponds to contribute to their neighborhood's scenic beauty. Furthermore, the results indicate that respondents from three counties are willing to pay $5.4 to $13.2 for a 50% improvement in flooding reduction, while those from two counties are likely to pay $3.9 to $4.9 for ponds to have recreational benefits. The findings of the study could help stormwater managers in designing their stormwater management programs, especially for better evaluation of stormwater utility fees.

Plant functional traits and viewer characteristics co-regulate cultural services provisioning by stormwater bioretention

Green stormwater infrastructure such as bioretention cells (BRCs) have the potential to influence human well-being through ecosystem services. Many services are regulated by vegetation, but only a handful (primarily regulating services) have been linked to specific plant functional traits that control their provisioning. Less attention has been paid to cultural services (e.g., aesthetics and sense of place, defined as people's attachment to and identification with natural elements that capture the unique character/identity of a place), such that we presently know little about the role plant traits play in regulating cultural services provisioning by BRCs. We address this knowledge gap by 1) quantifying aesthetic services and sense of place for 15 plants that are native to Southern California, U.S., and common in BRCs (human subjects surveys) and 2) the role of plant functional traits in regulating the provisioning of these services (supervised machine learning). Our survey demographic was civil and environmental engineering students in Southern California, who have grown up during a time of increased BRC implementation and are likely to play a role in their future design. Our study suggests that aesthetic services and sense of place in Southern California are regulated by different plant traits (leaf color for aesthetic services and maximum plant height for sense of place) and participant characteristics, particularly psychosocial characteristics like environmental worldviews (defined as an individual's perception of their environmental role in the world), which significantly influenced aesthetic services, but not sense of place. Reflecting these differences, only 4 of the 15 plant species evaluated were found to provide both cultural services well. This suggests that careful attention to plant selection is needed if we want BRCs to co-provide these services, enriching people's experiences with green stormwater infrastructure.

Care and safety in neighborhood preferences for vacant lot greenspace in legacy cities

We conducted a post-construction survey of neighborhood preferences for street scale urban greenspace designed as green stormwater infrastructure (GSI) on vacant residential lots in Detroit, a city where greenspace has potential to address structural inequities that characterize legacy cities and affect well-being. We investigated residents’ preferences related to their perceptions of microscale elements: immediately perceptible fine grain landscape characteristics of plants, landform, water, and structural materials. Our results suggest that microscale elements affect residents’ perceptions of care and safety, which are strongly related to preferences for landscape treatments on vacant lots near their homes. For each of two replicate pilot sites, we developed 15 alternative landscape treatments (including a control vacant lot). Across treatments, we varied microscale elements that could act as cues to care (CTC) or cues to safety (CTS). In a survey of all households within 250 m of the two sites, we measured residents’ perceptions of and preferences for alternative treatments. Among CTC, regular mowing was essential to preference, and low-growing shrubs and forbs with prominent flowers characterize the most preferred treatments. A CTS, bollards separating vacant lots from public access, were preferred for most treatments. Lots planted with many trees were preferred by a smaller percentage of residents, and their perceptions of care were less related to their preference. Overall, preferences are more well-explained by perceived care than by perceived safety. Furthermore, perceived care explains the effect of perceived safety on residents’ preferences for vacant lots near their homes.

Influences of time scale on green stormwater infrastructure’s effect on suspended solids in urban rainfall runoff

To investigate the influences of time scale on green stormwater infrastructure’s (GSI) effect on suspended solids (SS) in urban rainfall runoff, the control effect of GSI on SS in rainfall runoff was systematically explored based on three aspects of time scale – event, seasonal, annual. The Sponge City pilot area of Beijing, China, was selected as the study area. The results showed that under the condition of same rainfall duration and rainfall recurrence period, reduction of SS mass gradually decreased with the backward movement of rainfall peak. With regard to rainfall with small recurrence period, rainfall duration had little effect on the control effect of GSI. When the recurrence period was 10 yr and rainfall duration was 1, 2, and 3 h, the effect of GSI on SS percent reduction was 15.7%, 17.3%, and 19.1%, respectively, reflecting rainfall duration effect. Besides, with the extension of antecedent dry period, GSI effect on SS reduction first increased and then leveled off in the same recurrence period. In terms of seasonal change, the GSI effect on SS reduction showed high percent reduction in spring and winter (up to 45.5%), followed by autumn, with the lowest percent reduction in summer. On the annual time scale, GSI presented the strongest control effect in dry years (up to 45.0%), followed by normal years, and weakest in wet years.

Runoff Reduction by Four Green Stormwater Infrastructure Systems in a Shared Environment

AbstractGreen stormwater infrastructure (GI) imitates the hydrology of undeveloped land to mediate the impacts of stormwater runoff, but research is lacking that characterizes the performances of d...