A flexible modeling framework for hydraulic and water quality performance assessment of stormwater green infrastructure

Insights into the transport and bio-degradation of dissolved inorganic nitrogen in the biochar-pyrite amended stormwater biofilter using dynamic modeling

The stormwater biofilter is a prevailing green infrastructure for urban stormwater management, but it is less effective in dissolved nitrogen removal, especially for nitrate. The mechanism that governs the nitrate leaching and performance stability of stormwater biofilters is poorly understood. In this study, a water quality model was developed to predict the ammonium and nitrate dynamics in a biochar-pyrite amended stormwater biofilter. The transport of dissolved nitrogen species was described by advection-dispersion models. The kinetics of adsorption and pyrite-based autotrophic denitrification are included in the model and simulated with a steady-state saturated flow. The model was calibrated and validated using eleven storm events. The modeling results reveal that the contribution of pyrite-based autotrophic denitrification to nitrate leaching alleviation improves with the increased drying duration. The nitrate removal efficiency was affected by a series of design parameters. Pyrite filling rate has a minor effect on nitrate removal promotion. Service area ratio and submerged zone depth are the key parameters to prevent nitrate leaching, as they influence the emergence and discharge time of nitrate breakthrough. The high inflow volume (high service area ratio) and small submerged zone can lead to earlier and increased discharge of peak nitrate otherwise the peak nitrate could be retained in the submerged zone and denitrified during the drying period. The developed mechanistic model provides a useful tool to evaluate the treatment ability of stormwater biofilters under varying conditions and offers a guideline for biofilter design optimization.

Practice makes the model: A critical review of stormwater green infrastructure modelling practice

Green infrastructures (GIs) have in recent decades emerged as sustainable technologies for urban stormwater management, and numerous studies have been conducted to develop and improve hydrological models for GIs. This review aims to assess current practice in GI hydrological modelling, encompassing the selection of model structure, equations, model parametrization and testing, uncertainty analysis, sensitivity analysis, the selection of objective functions for model calibration, and the interpretation of modelling results. During a quantitative and qualitative analysis, based on a paper analysis methodology applied across a sample of 270 published studies, we found that the authors of GI modelling studies generally fail to justify their modelling choices and their alignments between modelling objectives and methods. Some practices, such as uncertainty analysis, were also found to be limited, despite their necessity being widely acknowledged by the scientific community and their application in other fields. In order to improve current GI modelling practice, the authors suggest the following: i) a framework, called STAMP, designed to promote the standardisation of the documentation of GI modelling studies, and ii) improvements in modelling tools for facilitating good practices, iii) the sharing of data for better model testing, iv) the evaluation of the suitability of hydrological equations for GI application, v) the publication of clear statements regarding model limitations and negative results.

Effectiveness of Design and Implementation Alternatives for Stormwater Control Measures Modeled at the Watershed Scale

To evaluate the effectiveness of dispersed stormwater control measures (SCMs), it is important to consider groundwater-surface water interactions and their consequences for stream hydrologic responses relevant to channel geomorphic stability and ecology. This study aimed to evaluate the effectiveness of different SCM design scenarios and implementation alternatives on exceedance levels and volumes of streamflow at the watershed scale. For this purpose, a process-based block-connector model of Sligo Creek, an urban watershed (29 km2) in the suburbs of Washington, DC, was used to study the effects of SCM system design on the stream hydrograph. The watershed has 34% impervious area (IA), which was discretized into 14 similar-sized subwatersheds, each consisting of pervious and impervious surface areas. Each subwatershed was compartmentalized with the representative overland flow, unsaturated flow, groundwater blocks, and links to main channel segments. The model was calibrated and validated to existing conditions using a 3-year time series of observed flow data. Afterward, a predevelopment simulation was configured. Three SCM unit designs and IA diversions through the SCM retrofit system were simulated. The three unit design scenarios represented a simple pond with surface storage and overflow or SCMs that infiltrate with an engineered soil layer and with or without an underdrain pipe. Differences among the model simulations were evaluated using flow exceedance probability curves. The area of the SCM system was modeled as 5% of the IA retrofit. Three implementation levels, including 10%, 50%, and 90% of the IA diverted through SCMs, were considered for each SCM unit design. The results showed that at least a 50% retrofit of runoff from IA watershedwide would be needed to achieve similar predevelopment base flows and peak flows. Intermediate flows could not be matched but were closest for the infiltration with the underdrain pipe design scenario. It was also found that concentrating the SCMs in the lower portion of the watershed resulted in more effectively achieving the predeveloped exceedance curves than uniform SCM implementation. The results are relevant to planning-level decisions that depend on effectiveness predictions of different SCM unit designs and implementation alternatives in developed watersheds.

Microplastics retained in stormwater control measures: Where do they come from and where do they go?

Stormwater control measures (SCM) can remove and accumulate microplastics and may serve as a long-term source of microplastics for groundwater pollution because of their potential for downward mobility in subsurface. Furthermore, the number of microplastics accumulated in SCM may have been underestimated as the calculation typically only accounts for microplastics accumulated via episodic stormwater loading and ignores microplastic accumuation via continuous atmospheric deposition. To evaluate the source pathways of accumulated microplastics and their potential for downward mobility to groundwater, we analyzed spatial distributions of microplastics above ground on the canopy around SCM and below ground in the subsurface in and outside the boundaries of fourteen SCM in Los Angeles. Using an exponential model, we link subsurface retardation of microplastics to the median particle size of soil (D₅₀) and land use. Despite receiving significantly more stormwater, microplastic concentrations in SCM at surface depth or subsurface depth were not significantly different from the concentration at the same depth outside the SCM. Similar concentration in and outside of SCM indicates that stormwater is not the sole source of microplastics accumulated in SCM. The high concentration of microplastics on leaves of vegetation in SCM confirms that the contribution of atmospheric deposition is significant. Within and outside the SCM boundary, microplastics are removed within the top 5 cm of the subsurface, and their concentration decreases exponentially with depth, indicating limited potential for groundwater pollution from the microplastics accumulated in SCM. Outside the SCM boundary, the subsurface retardation coefficient decreases with increases in D₅₀, indicating straining of microplastics as the dominant removal mechanism. Inside the boundary of SCM, however, the retardation coefficient was independent of D₅₀, implying that microplastics could have either moved deeper into the filter layer in SCM or that compost, mulch, or organic amendments used in the filter media were pre-contaminated with microplastics. Overall, these results provide insights on microplastics source, accumulation, and downward mobility in SCM.

Statistical modelling of hydrological performance in a suite of green infrastructure practices

Statistical modelling procedures (feature selection in conjunction with multiple linear regressions) were applied to determine the performance of a suite of stormwater green infrastructures (GIs) installed at the Belknap Campus of the University of Louisville. Two separate multiple linear regression models (MLRMs) were developed and calibrated to estimate the reductions of the flow regime parameters (flow volume and peak flow rates) within the down-gradient combined sewer system (CSS). The developed MLRMs showed that wet-weather-related CSS flow was mitigated post implementation of the stormwater GIs. At the down-gradient combined sewer flow-monitoring site, the average reduction rates of flow volume and the peak flow were estimated to be 22 and 63% per rainfall event, respectively. Unlike the black-box nature of most machine-learning techniques, the MLRM has the advantage of showing the unique statistical relationship between the rainfall features and the investigated CSS flow parameters. The results from this study indicate that proper statistic modelling can be applied effectively to evaluate the hydrological performance of stormwater management practices when lacking instrumentation and having limited drainage or sewer information.

Nature-based solutions efficiency evaluation against natural hazards: Modelling methods, advantages and limitations

Nature-based solutions (NBS) for hydro-meteorological risks (HMRs) reduction and management are becoming increasingly popular, but challenges such as the lack of well-recognised standard methodologies to evaluate their performance and upscale their implementation remain. We systematically evaluate the current state-of-the art on the models and tools that are utilised for the optimum allocation, design and efficiency evaluation of NBS for five HMRs (flooding, droughts, heatwaves, landslides, and storm surges and coastal erosion). We found that methods to assess the complex issue of NBS efficiency and cost-benefits analysis are still in the development stage and they have only been implemented through the methodologies developed for other purposes such as fluid dynamics models in micro and catchment scale contexts. Of the reviewed numerical models and tools MIKE-SHE, SWMM (for floods), ParFlow-TREES, ACRU, SIMGRO (for droughts), WRF, ENVI-met (for heatwaves), FUNWAVE-TVD, BROOK90 (for landslides), TELEMAC and ADCIRC (for storm surges) are more flexible to evaluate the performance and effectiveness of specific NBS such as wetlands, ponds, trees, parks, grass, green roof/walls, tree roots, vegetations, coral reefs, mangroves, sea grasses, oyster reefs, sea salt marshes, sandy beaches and dunes. We conclude that the models and tools that are capable of assessing the multiple benefits, particularly the performance and cost-effectiveness of NBS for HMR reduction and management are not readily available. Thus, our synthesis of modelling methods can facilitate their selection that can maximise opportunities and refute the current political hesitation of NBS deployment compared with grey solutions for HMR management but also for the provision of a wide range of social and economic co-benefits. However, there is still a need for bespoke modelling tools that can holistically assess the various components of NBS from an HMR reduction and management perspective. Such tools can facilitate impact assessment modelling under different NBS scenarios to build a solid evidence base for upscaling and replicating the implementation of NBS.

Methodology to simulate unsaturated zone hydrology in Storm Water Management Model (SWMM) for green infrastructure design and evaluation

Hydrologic models such as the USEPA Stormwater Management Model (SWMM) are commonly used to assess the design and performance of green infrastructure (GI). To accurately represent GI performance models used in design need to be able to address both the hydrology/hydraulics of the catchment and the GI unsaturated (vadose) zone hydrology. While hydrologic models, such as SWMM, address the need for catchment hydrology/hydraulics, they often simplify the unsaturated zone hydrology. This paper presents a methodology utilizing existing components of SWMM to represent unsaturated zone hydrology in an accessible format that does not require adjustments to the SWMM source code. The methodology simulated the unsaturated soil water movement by considering flow caused by differences of soil matric head and flow caused by gravity between soil layers with finite depth/length. The flow flux related to the soil matric head is a function of soil water diffusivity (D) and the soil moisture gradient, where D can be represented by a pump curve in SWMM. The flow flux related to gravity was controlled by unsaturated hydraulic conductivity (K) only and was also simulated by a pump. The methodology was compared to another variably saturated model, HYDRUS, with theoretical soils (with single layers of sand, loam, silt, and clay, as well as dual-layer scenarios). Field data was used to compare the methodology to HYDRUS and the SWMM LID (Low Impact Development) module. In all comparisons the presented methodology and HYDRUS delivered similar results for the vadose zone response to a storm event, while the LID module of SWMM exhibited slower water movement. The results showed that under natural conditions, the approximation of the presented methodology yielded satisfactory results to simulate flow through the unsaturated vadose zone.

Spatial analysis of landscape and sociodemographic factors associated with green stormwater infrastructure distribution in Baltimore, Maryland and Portland, Oregon

This study explores the spatial distribution of green stormwater infrastructure (GSI) relative to sociodemographic and landscape characteristics in Portland, OR, and Baltimore, MD, USA at census block group (CBG) and census tract scales. GSI density is clustered in Portland, while it is randomly distributed over space in Baltimore. Variables that exhibit relationships with GSI density are varied over space, as well as between cities. In Baltimore, GSI density is significantly associated with presence of green space (+), impervious surface coverage (+), and population density (-) at the CBG scale; though these relationships vary over space. At the census tract scale in Baltimore, a different combination of indicators explains GSI density, including elevation (+), population characteristics, and building characteristics. Spatial regression analysis in Portland indicates that GSI density at the CBG scale is associated with residents identifying as White (-) and well-draining hydrologic soil groups A and B (-). At both census tract and CBG scales, GSI density is associated with median income (-) and sewer pipe density (-). Hierarchical modelling of GSI density presents significant spatial dependence as well as group dependence implicit to Portland at the census tract scale. Significant results of this model retain income and sewer pipe density as explanatory variables, while introducing the relationship between GSI density and impervious surface coverage. Overall, this research offers decision-relevant information for urban resilience in multiple environments and could serve as a reminder for cities to consider who is inherently exposed to GSI benefits.

A Comprehensive Review of Low Impact Development Models for Research, Conceptual, Preliminary and Detailed Design Applications

This review compares and evaluates eleven Low Impact Development (LID) models on the basis of: (i) general model features including the model application, the temporal resolution, the spatial data visualization, the method of placing LID within catchments; (ii) hydrological modelling aspects including: the type of inbuilt LIDs, water balance model, runoff generation and infiltration; and (iii) hydraulic modelling methods with a focus on the flow routing method. Results show that despite the recent updates of existing LID models, several important features are still missing and need improvement. These features include the ability to model: multi-layer subsurface media, tree canopy and processes associated with vegetation, different spatial scales, snowmelt and runoff calculations. This review provides in-depth insight into existing LID models from a hydrological and hydraulic point of view, which will facilitate in selecting the best-suited model. Recommendations on further studies and LID model development are also presented.

Configuring Green Infrastructure for Urban Runoff and Pollutant Reduction Using an Optimal Number of Units

Green infrastructure (GI) has been regarded as an effective intervention for urban runoff reduction. Despite the growing interest in GI, the technical knowledge that is needed to demonstrate their advantages, cost, and performance in reducing runoff and pollutants is still under research. The present paper describes a framework that aims to obtain the optimal configuration of GI (i.e., the optimal number of units distributed within the catchment) for urban runoff reduction. The research includes an assessment of the performance of GI measures dealing with pollution load, peak runoff, and flood volume reduction. The methodological framework developed includes: (1) data input, (2) GI selection and placement, (3) hydraulic and water quality modelling, and (4) assessing optimal GI measures. The framework was applied in a highly urbanized catchment in Cali, Colombia. The results suggest that if the type of GI measure and its number of units are taken into account within the optimisation process, it is possible to achieve optimal solutions to reduce the proposed reduction objectives with a lower investment cost. In addition, the results also indicate a pollution load, peak runoff, and flood volume reduction for different return periods of at least 33%, 28%, and 60%, respectively. This approach could assist water managers and their stakeholders to assess the trade-offs between different GI.