1. Introduction
Rapid urbanization has resulted in increased stormwater runoff flowrates and volumes. The management of this increased urban stormwater runoff is a growing concern for catchment managers, considering its harmful effects for receiving water bodies. In the literature [
1,
2,
3], there is a consensus that stormwater discharge from residential catchments is a major pollutant source to receiving surface bodies. Urban stormwater carries urban pollutants with it to the catchment outlet. The urban pollutants in general consist of sediments, oxygen-demanding substances, heavy metals, organics, bacteria, viruses, nutrients, litter, and natural organic matter [
4]. Total nitrates (TN) Total Phosphorous (TP), and TSS (Total suspended solids or sediment) are pollutants of particular concern to many proponents of policy for urban stormwater. Excessive nutrients in the form of TP and TN loading in receiving waters can lead to cultural eutrophication and algal proliferation [
5]. TSS and nutrients can damage the ecology of receiving waters—for example, coastal reefs, which support a variety of fish, molluscs, and seastars [
6]. TSS can also reduce the hydraulic efficiency of receiving waters as sediment continuously accumulates. In Australia, urban stormwater is conveyed by a separate drainage system. This means that stormwater is conveyed to receiving water bodies and typically discharges from catchments without treatment.
Urban planners and policymakers need economical and feasible solutions to this problem. Reducing discharge of stormwater runoff and pollutants from catchments before discharge to receiving waters is one way of managing the harmful effects of urban stormwater runoff [
7]. For this, different studies, for example, Todeschini et al. [
8], have attempted to characterize the performance of different management options to improve the quality of runoff discharges from the catchment to protect the quality of receiving water bodies. Green spaces provide natural infiltration losses and depression storages to hold rainfall runoff. However, with rapid urbanization, there are limited green spaces available in cities for limiting outflows from catchments [
9]. Urban planners worldwide are adopting a variety of approaches to mitigate problems with urban stormwater. These approaches are applied all over the world with different names. For example, in UK Sustainable Urban Drainage Systems (SUDS), in the USA Best Management Practices (BMP), and in China “sponge cities” [
10], while the current study, based in Australia, adopts the term Water Sensitive Urban Design (WSUD). These approaches to urban design have shown tremendous potential, and the literature has reported successful case studies for managing urban stormwater [
7]. Infiltration systems are one of the constructed forms of WSUD measure for reducing runoff and pollutant volume. Infiltration systems cover broad array of devices ranging from source control devices (e.g., leaky wells) to more catchment scale devices like bioretention systems [
11].
One popular method of retrofitting established catchments with infiltration systems is the use of distributed infiltration systems, for example, leaky wells [
12], soakaways [
13], and bioretention systems [
14]. In this strategy, infiltration systems are distributed over the catchment to intercept contributing impervious area. These devices, due to their small footprint, can easily be retrofitted in an existing catchment and have a demonstrated potential to reduce runoff volumes from urban catchments [
14]. Transport of stormwater pollutants is the function of runoff outflows from the catchment [
15]. By reducing runoff volume, distributed infiltration systems can also contribute to reducing the transport of associated pollutant loads proceeding downstream thus providing solution to protect receiving water bodies. Literature has reported their effectiveness in reducing stormwater runoff, e.g., Locatelli, Mark, Mikkelsen, Arnbjerg-Nielsen, Deletic, Roldin, and Binning [
11]. However limited research is available, which has drawn conclusions from the monitored field data, regarding the efficacy of these devices to manage urban runoff and water quality at the catchment scale [
16].
This lack of knowledge means urban planners can be hesitant to prescribe infiltration measures to reduce runoff and meet water quality targets [
16]. As an alternative to field studies, hydrological modeling offers cost-effective way of establishing the usefulness of distributed leaky well systems [
17]. In this domain, use of simple stochastic hydrological models like the Model for Urban Stormwater Improvement Conceptualization (MUSIC) [
18], a commercial software developed for modeling WSUD devices and Australian catchments, can prove useful to quantify catchment outflows and the performance of constructed WSUD devices which may be implemented. In Australia, design practitioners have used MUSIC to support the implementation of WSUD systems in the catchments. Default input values for pollutant concentrations according to land use in MUSIC are based on the findings of an extensive review of stormwater quality in urban catchments performed by Duncan [
19], and more localized parameters are also available [
20]. Another issue specific to modeling of distributed system is that the modeling of a large number of distributed systems individually can become a very tiresome and complex process. In MUSIC, large numbers of WSUD systems, for example, rainwater tanks [
21], can be aggregated to form a lumped model, where multiple systems are represented as a single larger device. However, aggregation causes issues; becasue when a large quantity of devices is aggregated in MUSIC, the model has been reported to falsely exaggerate their performance levels. Elliott, Trowsdale, and Wadhwa [
21] has discussed the details of problems associated with the aggregation of infiltration devices. While the modeling of each individual system is tiresome, the conclusion drawn from the research [
21] is that aggregation of devices should be within the model limitations, so as not to affect the performance of distributed infiltration devices.
Research Objectives
The focus of this study is to characterize the stormwater quality of a residential urban catchment in South Australia, of which little has been reported with respect to individual land uses. The information will serve the purpose of informing policy makers regarding the estimated urban pollutants, a typical catchment discharge during a storm. Further the study will place the case study catchment in the context of the national and international averages for residential stormwater pollution. This will explain that if the catchment is indeed a representative of typical Australian catchment in terms of pollutant discharges. The study planned to achieve this through comparison of stormwater quality from study catchment against reported national and international averages of pollutant concentrations in stormwater runoff discharges from urban catchments.
An additional goal of the study is to demonstrate the effectiveness of MUSIC to simulate runoff from the catchment by carrying out statistical evaluation. This evaluation has not been widely reported in the literature. This information will benefit potential users of MUSIC to consider automated calibration before applying MUSIC in practice. Further, results could inform the readers regarding representing the smaller curbside leaky well systems in MUSIC. This will also include discussion regarding the appropriate level of aggregation for modeling curbside leaky well systems, without falsely simulating their performance to reduce the stormwater pollutant loading. In doing so, the study established the capability of MUSIC to characterize the stormwater quality from urban catchment. Based on the MUSIC simulations, the study also aims to inform reader regarding the efficiency of curbside leaky well systems to reduce the annual pollutant load from the catchment outflows. The results of the study will inform policymakers regarding the potential of distributed infiltration measures to reduce pollutant loads.
The results of this study will provide important information to planners regarding the current state of catchment stormwater quality, how it is represented in common decision-making tools and how it may be improved. The results for the case study area considered are intended to contribute to ongoing development of effective policy regarding urban stormwater more broadly and improve the design of distributed stormwater systems.
2. Materials and Methods
The methods described here cover the procedure to extract data and develop a MUSIC model of the case study catchment (
Figure 1). The methods will describe field data and laboratory testing programs and the methods used to derive a calibrated and verified MUSIC model of a case study catchment. The methodology will also describe model calibration method. In addition, the modeling section will provide insight to modeling of distributed curbside infiltration systems.
Figure 1 provides with an overview of the methodology adopted to achieve the objectives of this study.
2.1. Catchment and Curbside Leaky Well Description
Field investigations and monitoring were conducted in a 17.45 ha urbanized sub-catchment comprising of 108 residential allotments and road infrastructure in Hawthorn, an inner suburb of Adelaide, the capital city of South Australia (
Figure 2). The catchment consisted of established homes on well vegetated allotments. The average allotment size was 700 m
2. The local climate is semi-arid, with most of the average annual rainfall of 541 mm falling between late autumn and early spring (May to September). Approximately 80% of precipitation typically falls at intensities of less than 4 mm/h. Average annual potential evapotranspiration was 1500 mm [
22] with hot, dry summers (December to February). The catchment terrain grades evenly in a westerly direction with a gradient of 0.5%. The catchment had a separate sewer and stormwater drainage system, as is usual in Australia. The catchment followed the standard curb and gutter stormwater collection practices. Each house in the catchment discharged to the street gutter and stormwater was carried along the street leading to a catch basin at the end of the street to the underground stormwater pipe network. Stormwater drainage pipes ranged from 225 mm to 450 mm in diameter.
Adelaide has cooler temperate waters in its coastlines, which offer a conducive environment for the growth of macroalgae. In fact, Adelaide coastline supports 30–40 percent of total macroalgae species exists in the world [
6]. Nutrients—the source of nitrogen, can cause algal blooms and epiphyte growth on seagrass, leading to loss of seagrass. Seagrass meadows are of fundamental importance to the ecosystem in Gulf St Vincent [
6]. They bind the sediments and provide nurseries and safe habitat for marine organisms. Loss of seagrass will also result in erosion of beach soil, resulting in degradation of coastline. MacDonald, Ardeshiri, Rose, Russell, and Connell [
6] reported the loss of one-third of original sea grass in over 80 years due to continuous expansion of urban areas. Similarly, discharges of high levels of suspended solids into the coastal waters increase turbidity levels contributing to poor recreational water quality and may result in beach closures. It is understood that stormwater nutrients, turbidity and sediments may have been a contributing factor to seagrass die-off. Stormwater is the major contributor (67%) to sediment load discharged into the coastal environment. Due to these mentioned issues associated with stormwater, it is acknowledged as having an adverse impact on receiving waters. However, it is not considered financially viable to discard the standard practice of discharging stormwater without treatment to receiving water bodies. Based on the problems associated with urban stormwater quality, this study has attempted to describe the stormwater quality in typical Australian residential catchment in Hawthorn, an inner suburb of Adelaide, South Australia.
2.2. Monitoring Equipment
In December 2015, the catchment was equipped with monitoring instruments to measure the quantity and quality of stormwater discharge. A tipping bucket rain gauge (TB3, Hyquest Solutions, Warwick Farm, NSW, Australia) was used to collect rainfall data in the case study catchment at a one-minute resolution in increments of 0.2 mm. An area-velocity flow meter (Starflow Ultrasonic Doppler Instrument, Model 6526, Unidata Pty Ltd., O’Connor, WA, Australia) was installed in the 450 mm diameter concrete drainpipe at the catchment outlet, measuring stormwater runoff flow rate and volume from the 17.45 ha catchment. Depth measurement accuracy was ±1 mm; flow velocity accuracy was ±1 mm/s. Rain and flow data collection started on 21 December 2015 and was ongoing at the time of writing.
Water quality of runoff form the catchment was measured using an autosampler (Water Data Services flow proportional composite sampler, Adelaide, Australia) including a peristaltic pump connected to a sample hose and composite sample tub of 60 L capacity, for the collection of samples for water quality analysis. The sampler was connected to a controlled programmed to collect 500 mL samples for every 5000 L of discharge in the drainpipe. The typical depth for sample extractions was set to 50 (mm) to 150 (mm). At the conclusion of a storm, the tub contains a composite water quality sample proportional to the flow. Analysis of the composite sample provides a flow weighted mean concentration of each pollutant and combined with runoff volume, enables the determination of event pollutant load.
2.3. Curbside Leaky Well Installation
The installation of curbside leaky well systems was part of the City of Mitcham initiative to reduce runoff volume from the catchment, to protect downstream receiving water bodies. Curbside leaky wells were installed in street verges—local government green space between the road and footpath. The depth of leaky wells varied from 820 to 1400 mm with width of 460 mm occupying total surface area of 120 mm
2. In total 181 leaky wells were installed, amounting to 10 systems per hectare of catchment. The TREENET curbside inlet flow capture device consists of a slotted face plate and a PVC pipe fitting that are cast into the concrete of the curb and gutter (
Figure 3). The plate has been designed to restrict the inflow of leaves and other litter through to the leaky well. The design of the inlet included a shallow basin in the gutter which created a small pool from which the water flowed into the inlet. Reduced flow velocity due to greater cross-sectional area in the gutter established an eddy in the pool which deposited larger sediments away from the capture slot. In this way, clogging of the inlet may be reduced and routine mechanized street sweeping used to remove deposited sediment [
23].
2.4. Description of Data Collection
Rainfall and runoff were recorded in one-minute resolution. However, for this study, rainfall and runoff data were manipulated to produce data in six-minute resolution. This was required to construct a model of the catchment using MUSIC version 6.1. Data up to December 2016 illustrated catchment behavior without curbside leaky wells (preinstallation). In this time, six water quality samples were obtained. One of these samples was not included in the analysis due to construction activities in the catchment during the time of sampling causing an unusually high level of sediment.
2.5. Catchment Model Development
In this study, we have developed a model of the case study catchment using MUSIC Version 6.1 [
18] model to meet our study objectives. The details of MUSIC are provided in program documentation [
18]. Here, we briefly describe its main feature to provide context to the model building for this current catchment. In MUSIC, the modeler can represent the catchment using number of source nodes. However, the ability of MUSIC to represent the drainage network is not as developed as other models more focused on hydraulic conveyance, e.g., EPA SWMM. Source nodes, used to represent a subcatchment, have homogenous soil properties, impervious cover, and pollutants generation. Each source node also has groundwater reservoir option as well.
The runoff predictions are based on user defined rainfall and evapotranspiration data. Each sub catchment, when its infiltration capacity is exceeded, discharges to the outlet through links.
2.5.1. Initial Parameter Section
Initially, we constructed a model of the case study catchment with eight subcatchments. The decision to represent the catchment with eight nodes (lumping lots together) as opposed to 108 nodes representing each home was made to reduce model runtime (35 min to 3 min) and complexity. There are three main parameters in MUSIC to characterize a sub-catchment: total area, imperviousness, and perviousness. We aggregated total area as the sum of lot area in each node, which were part of the aggregated subcatchment. For imperviousness and perviousness, we took the weighted average for individual house in the aggregated catchment. The values for these parameters were directly obtained from investigating aerial photography in a graphical information system. Next, we estimated the rainfall threshold parameter based on calibration, seeking to ensure the commencement of runoff flow correctly during the storm. The rainfall threshold parameter was found to be influential in describing the shape and timing of the hydrograph. The model parameters adopted to construct the case study catchment relied heavily on SA Guidelines for MUSIC modeling [
20], specific for Adelaide region. The ground water contribution in the model were ignored as only surface runoff flow data was available.
Table 1 contains the final set of parameters adopted to represent the runoff generation capacity of the catchment. The rainfall–runoff parameters, in particular, soil parameter values, we selected for calibrations was based on suggestion of SA Guidelines for MUSIC modeling [
20], to calibrate these values in the presence of available flow data. The model was calibrated to preinstallation runoff flow data. It is because that focus of the study is to characterize the stormwater quality of the catchment without treatment measures. This will allow the review of total pollutant generation without the treatment measures, which can then be compared with future development scenarios.
2.5.2. Automated Calibration
We used Parameter estimation software (PEST) version 17 [
24] to calibrate runoff generating parameters of the catchment. PEST is an automated method of estimating the parameters, with focus to reduce the objective function based on finding the local optima. PEST uses a gradient-based linear approach in finding local optima by using the Gauss–Marquardt–Levenberg method [
24]. We established the MUSIC/PEST interaction by running the basic MUSIC input file (msf) from the command line. In addition, MUSIC configuration file (mcf) was also prepared and provided in the executable command in PEST. The configuration file defines the data, to be extracted from MUSIC outputs, and also the location where these outputs will be written. The outputs from MUSIC include time series, mean annual loads, node water balance, and statistics, which can be extracted from any node and then written to a text file. The PEST algorithm changes the initial, user-defined calibration parameters in a MUSIC input file over successive model runs to optimize the fitness of the model to observed data. In doing so, it created a new MUSIC input file with the calibrated set of parameter values. In this calibration we adopted the Taylor series expansion to linearize the process. In this method, the partial derivatives from each model run are evaluated with respect to every parameter change after each iteration. The outputs from this iteration are the current optimal set of parameters. PEST then compares the parameter set to that of optimal set as obtained from previous iterations. If three iterations passed without significantly lowering the objective function PEST, then terminates the estimation process. PEST offered options for users to provide weight to the individual events. Doing this, the objective function is influenced by these weighted events. Therefore, PEST focuses on getting those values right to lower the objective function. Objective function in PEST is the difference in model prediction and weighted observed runoff flow at any point in time. Equation (1) provides the mathematical form of the objective functions as used in PEST.
where ϕ = objective function,
Ot = Weighted observed runoff flow at time
t, Rt = residuals from weighted observed, and simulated runoff flows in time
t.
PEST also estimate the partial derivatives of model outputs at each iteration using central finite differences. PEST then estimates the sensitiveness of each parameter as by product based on these derivatives.
In this calibration, we assigned more weight to the representative flow events and assigned “0” weight to runoff flow events which arise as result of extreme events and to no-flow events. It is important to simulate every day storm events correctly as they are the source of the majority of the runoff producing storms and therefore have cumulative effect on total runoff of the simulated series. Due to impact of runoff volume influence in simulating the pollutant, it was deemed important to model the catchment with focus to predict runoff volume correctly. We adopted the option of log-transformation of parameter values during the inversion process. It is because adopting logarithmic transformation of model parameters, increase the ability of PEST to hold its linearity approximation in case of nonlinear problems [
24].
At the end of inversion process, PEST provides the optimized set of parameter values rather than its logarithmic values. PEST also provides as an output 95% confidence limits of optimized parameter values. PEST also provides as an output 95% confidence limits of optimized parameter values [
24]. Only few prior studies, for example, Dotto et al. [
25] have reported the uses of PEST for calibrating MUSIC. Five parameters were calibrated to meet the runoff flow series in PEST.
2.6. Model Evaluation
We evaluated the catchment model calibration and validation following similar techniques as recommended by the ASCE Task Committee [
26], i.e., first by visual inspection of graphical outputs, then by comparing statistical indices for the continuous runoff series, and finally by computing goodness-of-fit statistics for individual events extracted from the continuous simulated flow series. The ASCE task committee [
26] recommended that to assess the performance of a continuous simulation model, total percentage error in volume (PEV), Nash Sutcliffe Efficiency (NSE), and the coefficient of gain from daily mean should be reported to provide an indication of the fit of the continuous model simulation following the visual comparison of predicted and observed runoff hydrographs.
The PEV shows percentage differences in runoff volumes when compared to the observed runoff series. A value closer to zero indicates a better model fit. A negative value indicates that the model underpredicts the runoff volume, and a positive sign indicates the opposite. Criteria for assessing the catchment model performance were based on evaluating the model performance using NSE values against the criteria of ‘good’ models as developed by Moriasi et al. [
27], based on review of different hydrological models. They evaluated the catchment models for flow predictions, with NSE > 0.80 as very good and with 0.70 < NSE ≤ 0.80, as good. Models with values 0.50 < NSE ≤ 0.70 were termed satisfactory, however a model with NSE < 0.50 was considered unsatisfactory for use in advanced studies.
2.7. Infiltration Systems Modeling
MUSIC was then employed to simulate the curbside leaky well systems using infiltration system node. The parameters for infiltration systems are based on geometry of the installed leaky well systems and as provided in
Figure 2 of this document. The high bypass flowrate was based on laboratory trials (
Figure 4) to understand the limitation of curbside leaky well systems to intercept the approach runoff based on corresponding flowrate. We constructed the full-scale road and curb model, similar to installed curbside leaky well systems at the NATA-accredited hydraulic laboratory (Australian Flow Management Group, University of South Australia) in University of South Australia. We measured the approach flowrate using the calibrated electromagnetic flow meter. using the services of a. The approach flows considered were 0.5 L/s, to 5 L/s, in increments of 0.5 L/s. The gradients considered were 0%, 0.5%, 0.8%, 1%, 1.2%, 1.5%, 2%, 2.5%, and 5%. We estimated the capture efficiency by measuring the time to fill a 20 L gradated bucket. The capture flow was measured three times for each slope/approach flow rate and the mean was reported (
Figure 4).
2.8. Examining the Impacts of Aggregating Infiltration Systems on Stormwater Quality
We adopted a similar methodology for aggregating the infiltration system as reported by Elliott, Trowsdale, and Wadhwa [
21]. However, we deviated from their approach and did not alter the travel times in links. Initially, we modeled each infiltration system individually and connected them to eight sub-catchments. The runoff deficit from the pre-installation model was noted. Then, we aggregated the infiltration systems in eight nodes and connected to eight sub-catchments, each aggregated node to each sub-catchment. This is somewhat tantamount to providing the street scale system instead of distributed storages. Then, we provided only one node, by aggregating 181 wells into one node, placed at the end of catchment, to intercept runoff from all the catchments.
Figure 5 provides the summary of this process; we have used to select the aggregation model.
We then carried out one-way ANOVA to test the presence of notable differences in means of these three configurations. The decision to adopt the aggregated levels was based on the hypothesis that if there is no significant difference between runoff from aggregated and distributed modeling effort, then the model with street level aggregation will be adopted for further studies.
Table 2 summarizes the methodology adopted to convert each parameter into to represent aggregated nodes. The high flow bypass flowrate was based on prediction from PCSWMM model of the catchment for 4EY storms
2.9. Water Quality Modeling
Using the calibrated and evaluated model, we then simulated the stormwater quality of the catchment to understand the catchment capacity to generate TSS, TN, and TP. MUSIC does not require a large set of parameters to simulate event mean concentrations of TSS, TN, and TP, as its default parameters are based on extensive review of local urban stormwater quality as reported in Duncan [
19]. However, it must be mentioned here that most of these parameters are based on experimental trials from Brisbane and Melbourne. Land characteristics for different cities can impact the model outputs. Recently, Water Sensitive South Australia and Adelaide and Mount Lofty Ranges Natural Resources Management Board [
20] have published separate set of guidelines (SA Guidelines for MUSIC Modeling) for selecting input parameters for MUSIC models, considering local climate and geology of South Australia.
MUSIC provides two options for estimating pollutant concentrations: The first option is to use the default parameters based on land use type and selected region, and stochastically generating pollutant loads based on specified probability distribution. The pollutant generation in MUSIC is based either on using mean concentration or log-normally generated distribution. In this study, using default parameters provided in MUSIC for stochastic generation of pollutants, we adopted the log normal distribution to stochastically generate the pollutants loads from the catchment models, as per recommendations of SA Guidelines for MUSIC modeling. These default parameters are based on research reported by Duncan [
19]. Thus far. no study has reported the use of SA Guidelines for MUSIC modeling on residential scale catchment models in South Australia to estimate the pollutant loading in stormwater. The second option is to estimate pollutant concentration based on user-specified pollutant concentration in connection with simulated runoff data. Pollutant generation is stochastic and based on mean and standard deviation of data entered by the user to represent local conditions.
The accuracy of model to represent the pollutant generation capacity was assessed by running t-tests on predicted and observed mean values and standard deviations. The simulated statistics were obtained from MUSIC outputs, by using flow-based sub-sample statistics. This option was selected based on the need to exclude no flow influence from the timeseries. The hypothesis was tested that there is no difference in mean concentration of a particular pollutant as observed from the catchments in comparison to against simulated MUSIC sample-based statistics.
2.10. Data Analysis
The study took the logical approach of first plotting the available observed sample results from the catchment along with international averages for these pollutants as reported by Duncan [
19] and national averages as reported in Australian guidelines for water recycling [
28]. The plots were developed for pre-installation and post-installation pollutant generation. The study also reviewed stormwater pollutants at the catchment outlet with respect to water quality criteria for governing environmental values, according to water quality criteria for receiving water bodies as developed by Environment Protection Authority, SA [
29]. Note, however, that these guidelines in South Australia are only applicable to receiving water bodies as opposed to stormwater discharges from an urban catchment. The analysis of pollutants thus only provides an indication regarding the status of stormwater quality according to these ranges.
Table 3 provides the summary of average pollutant concentrations as retrieved from mentioned documents.