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Illicit discharges in urban stormwater drains are a major environmental concern that deteriorate downstream waterway health. Conventional detection methods such as stormwater drain visual inspection and dye testing have fundamental drawbacks and limitations which can prevent easy location and elimination of illegal discharges in a catchment. We deployed 22 novel low-cost level, temperature and conductivity sensors across an urban catchment in Melbourne for a year to monitor the distributed drainage network, thereby detecting likely illicit discharges ranging from a transitory flow with less than 10 minutes to persistent flows lasting longer than 20 hours. We discuss rapid deployment methods, real-time data collection and online processing. The ensemble analysis of all dry weather flow data across all sites indicates that: (i) large uncertainties are associated with discharge frequency, duration, and variation in water quality within industrial and residential land uses; (ii) most dry weather discharges are intermittent and transient flows which are difficult to detect and not simply due to cross-connections with the sewerage network; (iii) detectable diurnal discharge patterns can support mitigation efforts, including policies and regulatory measures (e.g., enforcement or education) to protect receiving waterways; and, (iv) that it is possible to cost effectively isolate sources of dry weather pollution using a distributed sensor network.

Planning for future urban development and water infrastructure is uncertain due to changing human activities and climate. To quantify these changes, we need adaptable and fast models that can reliably explore scenarios without requiring extensive data and inputs. While such models have been recently considered for urban development, they are lacking for stormwater pollution assessment. This work proposesa novel Future Urban Stormwater Simulation (FUSS) model, utilizing a previously developed urban planning algorithm (UrbanBEATS) to dynamically assess pollution changes in urban catchments. By using minimal input data and adding stochastic point-source pollution to the buildup/wash-off approach, this study highlights calibration and sensitivity analysis of flow and pollution modules, across the range of common stormwater pollutants. The results highlight excellent fit to measured values in a continuous rainfall simulation for the flow model, with one significant calibration parameter. The pollution model was more variable, with TSS, TP and Pb showing high model efficiency, while TN was predicted well only across event-based assessment. The work further explores the framework for the model application in future pollution assessment, and points to the future work aiming to developing land-use dependent model parameter sets, to achieve flexibility for model application across varied urban catchments.

Greywater is an alternative water resource, having many favourable characteristics over stormwater or wastewater. In particular, it can be supplied consistently and generated close to desirable end-use applications. Although greywater is considered to contain a low level of contamination, treatment is still required before the end-use, and passive filtration such as biofiltration became a popular option due to its low maintenance and low energy consumption. To this end, a laboratory experiment was conducted with synthetic greywater over 12 months to test the influence of operational characteristics and design factors on pathogen removal from greywater. The role of plants types, saturated zone, and the absence/inclusion of antimicrobial material were tested in a total of 80 biofilter columns. The results showed that the biofilters obtained a median E. coli logarithmic removal of 3.0 across the tested configurations and produced quality of water that meets indoor non-potable use standard (NRMMC, 2006). Further, the infiltration rate and event size were found to significantly influence E. coli removal in greywater biofilters (ANOVA, p < 0.05). The presence of antimicrobial media also significantly improved E. coli removal (up to 1.4 log) compared to non-amended media (ANOVA, p < 0.05). Vegetation also played a significant role in removing E. coli (ANOVA < 0.05). This study successfully suggested the great potential for the application of biofilters to greywater treatment and identified key design factors for consideration prior to implementation.

Greywater and stormwater have received significant attention due to increasing water scarcity. Passive filtration such as biofiltration has been a popular treatment method with its low energy input and environmental friendliness. However, pathogen removal capacity needs improvement to achieve safe water quality. In this study, a prebiotic chemistry inspired copolymer based on aminomalononitrile and 3,4,5-trihydroxybenzaldehyde (AMNT30) was introduced to develop antimicrobial media for passive filtration. The AMNT30 polymer provided an adhesive coating on zeolite substrates following a spontaneous polymerisation process at room temperature. AMNT30 coated media were investigated for metal loading capacity, surface morphology, E. coli removal and metal leaching after filtration of different water sources (i.e. stormwater, greywater, and deionised water) at low/high conductivity. The coating enhanced metal ion loading on the surface and demonstrated that >8 log reduction of E. coli can be achieved for silver loaded materials compared to a 1 log reduction for copper loaded materials. The coating also increased the stability of the metals on the media irrespective of inflow characteristics. This study provided the first example using AMNT30 to create antimicrobial water purification media. It is expected that this technology will find applications in the water treatment industry.

Long term planning of urban water infrastructure requires acknowledgement that transitions in the water system are driven by changes in the urban environment, as well as societal dynamics. Inherent to the complexity of these underlying processes is that the dynamics of a system's evolution cannot be explained by linear cause-effect relationships and cannot be predicted under narrow sets of assumptions. Planning therefore needs to consider the functional behaviour and performance of integrated flexible infrastructure systems under a wide range of future conditions. This paper presents the first step towards a new generation of integrated planning tools that take such an exploratory planning approach. The spatially explicit model, denoted DAnCE4Water, integrates urban development patterns, water infrastructure changes and the dynamics of socio-institutional changes. While the individual components of the DAnCE4Water model (i.e. modules for simulation of urban development, societal dynamics and evolution/performance of water infrastructure) have been developed elsewhere, this paper presents their integration into a single model. We explain the modelling framework of DAnCE4Water, its potential utility and its software implementation. The integrated model is validated for the case study of an urban catchment located in Melbourne, Australia.

The public health risks associated with pathogens in urban stormwater have been well established, making it necessary to ensure adequate treatment of the stormwater before it is discharged into recreational water bodies or is harvested for reuse. Biofilters, also known as stormwater bioretention systems or raingardens, have shown promising, yet variable, results in reducing indicator bacteria in stormwater. Different biofilter design elements, such as filter media composition and vegetation type, have been found to cause this variable removal performance. Although plants play a key role in the treatment of pollutants, relatively little work has been conducted to understand the importance of interactions between vegetation and the biofilter microbial community on fecal microbial removal. A laboratory-scale biofilter experiment was conducted using Escherichia coli as the test fecal microorganism. Biofilter columns with differing soil media and vegetation types were dosed over a two month span, during which inflow and outflow samples were collected to evaluate system performance. The columns were then decommissioned to collect rhizosphere and bulk soil samples. Root exudates were extracted and used in an E. coli survival study to evaluate their contribution to system performance. The study demonstrated that the antagonistic effects of root exudates/rhizosphere microbes and Cu2+ exchanged zeolite antimicrobial filter media adversely impact the survival of E. coli retained within stormwater biofilters. Furthermore, leaf and flower/seed extracts of L. continentale showed some potential antibacterial activity against E. coli. This work supports the concept that natural processes in biological systems can deliver effective results in the removal of fecal microorganisms, and should be promoted to the extent possible in stormwater green infrastructure.

Urban estuaries around the world are experiencing contamination from diffuse and point sources, which increases risks to public health. To mitigate and manage risks posed by elevated levels of contamination in urban waterways, it is critical to identify the primary water sources of contamination within catchments. Source tracking using microbial community fingerprints is one tool that can be used to identify sources. However, results derived from this approach have not yet been evaluated using independent datasets. As such, the key objectives of this investigation were: (1) to identify the major sources of water responsible for bacterial loadings within an urban estuary using microbial source tracking (MST) using microbial communities; and (2) to evaluate this method using a 3-dimensional hydrodynamic model. The Yarra River estuary, which flows through the city of Melbourne in South-East Australia was the focus of this study. We found that the water sources contributing to the bacterial community in the Yarra River estuary varied temporally depending on the estuary's hydrodynamic conditions. The water source apportionment determined using microbial community MST correlated to those determined using a 3-dimensional hydrodynamic model of the transport and mixing of a tracer in the estuary. While there were some discrepancies between the two methods, this investigation demonstrated that MST using bacterial community fingerprints can identify the primary water sources of microorganisms in an estuarine environment. As such, with further optimization and improvements, microbial community MST has the potential to become a powerful tool that could be practically applied in the mitigation of contaminated aquatic systems.

Knowledge of pathogen removal in stormwater biofilters (also known as stormwater bioretention systems or rain gardens) has predominately been determined using bacterial indicators, and the removal of reference pathogens in these systems has rarely been investigated. Furthermore, current understanding of indicator bacteria removal in these systems is largely built upon laboratory-scale work. This paper examines whether indicator organism removal from urban stormwater using biofilters in laboratory settings are representative of the removal of pathogens in field conditions, by studying the removal of Escherichia coli (a typical indicator microorganism) and Campylobacter spp. (a typical reference pathogen) from urban stormwater by two established field-scale biofilters. It was found that E. coli log reduction was higher than that of Campylobacter spp. in both biofilters, and that there was no correlation between E. coli and Campylobacter spp. log removal performance. This confirms that E. coli behaves significantly differently to this reference pathogen, reinforcing that single organisms should not be employed to understand faecal microorganism removal in urban stormwater treatment systems. The average reduction in E. coli from only one of the tested biofilters was able to meet the log reduction targets suggested in the current Australian stormwater harvesting guidelines for irrigating sports fields and golf courses. The difference in the performance of the two biofilters is likely a result of a number of design and operational factors; the most important being that the biofilter that did not meet the guidelines was tested using extremely high influent volumes and microbial concentrations, and long antecedent dry weather periods. As such, the E. coli removal performances identified in this study confirmed laboratory findings that inflow concentration and antecedent dry period impact overall microbial removal. In general, this paper emphasizes the need for the valida

Biological clogging of filtration and infiltration systems has been acknowledged as a significant problem in the case of wastewater systems. However, scant research has been conducted on biological clogging in storm water filtration and infiltration systems, with the main hypothesis being that biological clogging is insignificant due to the low level of organics present in storm water. This article tested that hypothesis, using a laboratory-based approach. Five replicates of each zeolite-based filter design were dosed with storm water manifesting the following forms: (1) typical storm water (base case); (2) storm water containing very high nutrient concentrations to accelerate biological clogging; (3) typical storm water that was sterilized to suppress biological clogging; and (4) typical storm water with the addition of chlorine to suppress biological activity. The hydraulic performances of these four configurations were monitored over time until the systems were fully clogged. Loss on ignition (LoI) and microscopic analysis of accumulated material within the top layers of the filters were undertaken in an attempt to assess the level of organic matter present in clogging layers of the filters. It was found that all configurations performed differently in comparison to the base case, which represents the most likely set of operational conditions in the field. For instance, the chlorinated filters treated about 30% more storm water compared with the base case. Columns dosed with sterilized storm water treated almost the same volume of storm water but removed a greater quantity of sediment with higher treatment efficiency. Columns dosed with a high level of nutrients clogged more quickly than in the base case. Results of loss on ignition partially confirmed these findings. However, results of microbial cell counts provided tenuous evidence of biological clogging being present. Although the evidence was not overpowering, the variations observed in this study suggest

Assessing uncertainties in models due to different sources of errors is crucial for advancing urban drainage modelling practice. This paper explores the impact of input and calibration data errors on the parameter sensitivity and predictive uncertainty by propagating these errors through an urban stormwater model (rainfall runoff model KAREN coupled with a build-up/wash-off water quality model). Error models were developed to disturb the measured input and calibration data to reflect common systematic and random uncertainties found in these types of datasets. A Bayesian approach was used for model sensitivity and uncertainty analysis. It was found that random errors in measured data had minor impact on the model performance and sensitivity. In general, systematic errors in input and calibration data impacted the parameter distributions (e.g. changed their shapes and location of peaks). In most of the systematic error scenarios (especially those where uncertainty in input and calibration data was represented using 'best-case' assumptions), the errors in measured data were fully compensated by the parameters. Parameters were unable to compensate in some of the scenarios where the systematic uncertainty in the input and calibration data were represented using extreme worst-case scenarios. As such, in these few worst case scenarios, the model's performance was reduced considerably.

Biofilters, also known as bioretention areas or raingardens, are an effective treatment option for the removal of various pollutants from stormwater. However, they show variable treatment efficiency for the removal of indicator bacteria, and the operational and design factors which impact this variability are largely unknown. This study uses a laboratory scale column set-up to explore how Escherichia coli (the chosen indicator organism) removal in the stormwater biofilters is impacted by: plant presence and species type, the presence of a submerged zone (SZ), and operational conditions (duration of dry periods and changes over the initial stages of the system's life-span). Vegetation selection was found to be important for E. coli removal and the highly performing plant species were associated with lower infiltration rates. Based on the current results, a biofilter planted with Leptospermum continentale, Melaleuca incana or Palmetto buffalo and comprising a SZ can be recommended for improved E. coli removal. Inclusion of SZ was found to generally enhance the removal performance; which may be explained by the contribution of microbial processes that are happening within the SZ (such as predation/competition and natural die-off). Results also suggest that the E. coli removal performance is reduced after a significant dry period, while the overall removal performance improves over time as systems mature.

Porous pavements are easily retrofitted, and effective in improving water quality and hydrology, but prone to clogging. Despite being a major determinant in the lifespan of porous pavements, there is limited information on the physical clogging processes through these systems. The aim of this study was to understand the main physical processes that govern physical clogging and develop a simple black-box model that predicts physical clogging. The key variables that were hypothesised to influence clogging were pavement design and climate characteristics. Two compressed time scale laboratory experiments were conducted over 3. years on three common porous pavement types; monolithic porous asphalt, modular Hydrapave and monolithic Permapave. Pavement design was found to be an important role in clogging. Permapave did not clog even after 26. years of operation in simulated sub-tropical Brisbane (Australia) climate while porous asphalt and Hydrapave clogged after just 12. years, from surface clogging and geotextile clogging, respectively. Each system was tested using two different dosing patterns: (1) continual wetting with no dry periods and (2) variable inflow rates with drying periods (i.e. representing more natural conditions). The latter dosing method approximately doubled the lifespan of all systems suggesting the influence of climate conditions on clogging. Clogging was found to be highly correlated with cumulative volume and flow rate. A simple black-box regression model that predicts physical clogging was developed as a function of cumulative volume and Brisbane climatic conditions. However it is very likely that the shape of this regression is general, and that it could be calibrated for different climates in the future.

The Yarra River flows through the city of Melbourne in southeast Australia. The estuary of the river is in the centre of the city, and so is an area of recreational activities with strict water quality requirements to limit human health risks. These requirements are based on ensuring that the measured E. coli concentrations are below certain values. The objectives of the work presented in this paper were (i) to identify possible patterns in spatial and temporal variability of E. coli concentrations along the Yarra River, (ii) to understand the relationship between E. coli concentrations and other pollutants along the Yarra River, and (iii) to quantify the importance of stormwater drains in determining the magnitude of E. coli levels in the estuary. Two long-term water quality data sets, including E. coli concentrations, along the estuary were analysed: one data set comprises approximately monthly data collected in the period 1994-2010 at six sites, and the other is composed by weekly samples in 12 sites collected in 2005-2010. Additionally, data from two major stormwater drains were analysed. Median values of E. coli concentrations were observed to increase further downstream with concentrations showing larger variability in the estuary. Inter-annual and seasonal patterns were evident in the upper catchment, although differences were sometimes not statistically significant; in the more urbanised part of the catchment, including the estuary, inter-annual and seasonal variations were no longer present. A multiple linear regression analysis showed that the E. coli concentrations along the river were positively correlated with phosphorus. Estimates of loads from the streams and some of the drains supplying the estuary suggest that stormwater drains might be a considerable source of E. coli contamination especially during wet-weather flow; however, the loads from the drains appear to be at least one order of magnitude lower than the inputs of E. coli from upstream of

Understanding microorganism levels in urban runoff is essential for appropriate storm-water management, especially when storm-water is utilized by humans. An Escherichia coli monitoring program was conducted at four urban catchments in Melbourne, Australia. Simple and multiple correlation analyses were conducted that related climatic, rainfall, storm-water runoff, and other water-quality characteristics with the event mean concentrations (EMCs) of E. coli. Also, several existing and modified storm-water-quality models were tested against the measured storm-water E. coli levels to determine whether existing storm-water models could be used for prediction. The key findings are that source, growth, and removal of microorganisms are more important than wash-off and transport processes during wet weather since the most influential factors for E. coli EMC levels are (1) antecedent catchment conditions, such as vapor pressure prior to an event; (2) nutrient levels in storm water, such as ammonium, total phosphorus, and total nitrogen levels. However, rainfall intensity, which was found to be the most important transport-related variable (although less significant than the aforementioned build-up parameters), may also be considered in EMC prediction.

Stormwater biofilters are not currently optimised for pathogen removal since the behaviour of these pollutants within the stormwater biofilters is poorly understood. Modelling is a common way of optimising these systems, which also provides a better understanding of the major processes that govern the pathogen removal. This paper provides an overview of a laboratory-scale study that investigated how different design and operational conditions impact pathogen removal in the stormwater biofilters. These data were then used to develop a modelling tool that can be used to optimise the design and operation of the stormwater biofilters. The model uses continuous simulations where adsorption and desorption were dominant during wet weather periods and first order die-off kinetics were significant in dry periods between the wet weather events. Relatively high Nash Sutcliffe Efficiencies (>0.5) indicate that the calibrated model is in good agreement with observed data and the optimised model parameters were comparable with values reported in the literature. The model's sensitivity is highest towards the adsorption process parameter followed by the die-off and desorption rate parameters, which implies that adsorption is the governing process of the model. Vegetation is found to have an impact on the wet weather processes since the adsorption and desorption parameters vary significantly with the different plant configurations. The model is yet to be tested against field data and needs to be improved to represent the effect of some other biofilter design configurations, such as the inclusion of the submerged zone.

This study presents results on the assessment of the application of a Bayesian approach to evaluate the sensitivity and uncertainty associated with urban rainfall-runoff models. The software MICA was adopted, in which the prior information about the parameters is updated to generate the parameter posterior distribution. The likelihood function adopted in MICA assumes that the residuals between the measured and modelled values have a normal distribution. This is a trait of many uncertainty/ sensitivity procedures. This study compares the results from three different scenarios: (i) when normality of the residuals was checked but if they were not normal then nothing was done (unverified); (ii) normality assumption was checked, verified (using data transformations) and a weighting strategy was used that gives more importance to high flows; and (iii) normality assumption was checked and verified, but no weights were applied. The modelling implications of such scenarios were analysed in terms of model efficiency, sensitivity and uncertainty assessment. The overall results indicated that verifying the normality assumption required the models to fit a wider portion of the hydrograph, allowing a more detailed inspection of parameters and processes simulated in both models. Such an outcome provided important information about the advantages and limitations of the models' structure.

If faecal microorganisms survive for extended periods in river bed sediments, then this might be a critical source of faecal microorganisms in the water column of contaminated streams that is difficult to remediate. To determine the ability of bacteria to survive in a historically-contaminated urban stream, viable counts of faecal indicator bacteria and pathogens were conducted in the bed and bank sediments of the Yarra River estuary, Melbourne, Australia. In a first stage, core samples were collected at three locations along the Yarra River on a monthly basis and the viable counts of E. coli, the traditional faecal indicator bacteria, was monitored over time in the water column and the bed sediments. In a second stage, viable counts of E. coli and the bacterial pathogen, Campylobacter, were monitored over time at different depths in the cores that were incubated in the laboratory. In a third stage samples were collected at different location along the banks of the river and tested for E. coli and Campylobacter. Across all studies, the results show that E. coli and Campylobacter can survive in the bed and bank sediments of the Yarra River estuary. E. coli survival outside a warm blood animal host have already been widely established in the literature and raises concern, however the survival of Campylobacter outside of a host has always been considered highly unlikely and therefore is even more concerning, because it could cause significant health risks to users of the estuary if resuspended. Also this study has shown that the high salinity of the estuary had little effect on E. coli survival. The growth dynamics of both E. coli and Campylobacter are complex and depend on a number of parameters, so further work is required to determine what chemical and biological factors minimize this source of pathogenic bacteria in historically-contaminated stream sediments.

Knowledge of pathogen removal in stormwater biofilters has predominately been extrapolated from bacterial indicators; indeed, the removal of actual pathogens in these systems has not yet been studied. Furthermore, there is no knowledge on as to how the pathogen removal capacity varies in biofilters with different design parameters (e.g. media composition, inclusion of submerged zone). Hence, this study presents the performance of two field biofilter cells (with different designs) dosed with semi synthetic stormwater in removing ten faecal microorganisms, including: indicators and pathogenic bacteria, protozoa and virus. A net reduction in concentrations was observed for all pathogens and indicators, but the removal performance varied between bacteria, protozoa and viruses. Interestingly, the behaviour of the chosen bacterial pathogens was comparable to the bacterial indicators, whereas protozoan and viral pathogen removal were lower than their corresponding indicators. Biofilters containing a submerged zone generally had higher removal.

Biofilters are often used as treatment systems for urban stormwater runoff and have been shown to efficiently reduce nutrient loads. However, there is limited knowledge on how biofilters perform reduction of micro-pollutants in stormwater. This topic is of high importance if we are to harvest stormwater for human uses and in particular treat it to potable standards. This study investigated the micro-pollutant removal efficiency of two field-scale stormwater biofilters dosed with seminature stormwater, which included some key stormwater micro-pollutants (THMs, PAHs, TPHs, phthalates, halogenated phenols, glyphosate, and triazines). The results show excellent removal of PAHs, TPHs, phthalates and halogenated phenols, while there are variable levels of removal of glyphosate, triazines and chloroform. The outflow concentrations varied with the total pore volume over three tests, with the two cells having very different removal efficiencies. This preliminary study confirmed that in-situ challenge tests could be carried out to validate stormwater harvesting biofilters. It also suggests that design characteristics have a large impact on micropollutant removal, and a targeted lab study is needed to optimise biofilter design for this important function.

The current state of knowledge regarding uncertainties in urban drainage models is poor. This is in part due to the lack of clarity in the way model uncertainty analyses are conducted and how the results are presented and used. There is a need for a common terminology and a conceptual framework for describing and estimating uncertainties in urban drainage models. Practical tools for the assessment of model uncertainties for a range of urban drainage models are also required to be developed. This paper, produced by the International Working Group on Data and Models, which works under the IWA/IAHR Joint Committee on Urban Drainage, is a contribution to the development of a harmonised framework for defining and assessing uncertainties in the field of urban drainage modelling. The sources of uncertainties in urban drainage models and their links are initially mapped out. This is followed by an evaluation of each source, including a discussion of its definition and an evaluation of methods that could be used to assess its overall importance. Finally, an approach for a Global Assessment of Modelling Uncertainties (GAMU) is proposed, which presents a new framework for mapping and quantifying sources of uncertainty in urban drainage models.

Sediment levels are important for environmental health risk assessments of surface water bodies, while faecal pollution can introduce significant public health risks for users of these systems. Urban stormwater is one of the largest sources of contaminants to surface waters, yet the fate and transport of these contaminants (especially those microbiological) have received little attention in the literature. Stormwater runoff from five urbanized catchments were monitored for pathogen indicator bacteria and total suspended solids in two developed countries. Multiple discrete samples were collected during each storm event, allowing an analysis of intra-event characteristics such as initial concentration, peak concentration, maximum rate of change, and relative confidence interval. The data suggest that a catchment's area influences pollutant characteristics, as larger catchments have more complex stormwater infrastructure and more variable pollutant sources. The variability of total suspended solids for many characteristics was similar to Escherichia coli, indicating that the variability of E. coli may not be substantially higher than that of other pollutants as initially speculated. Further, variations in E. coli appeared to be more commonly correlated to antecedent climate, while total suspended solids were more highly correlated to rainfall/runoff characteristics. This emphasizes the importance of climate on microbial persistence and die off in urban systems. Discrete intra-event concentrations of total suspended solids and, to a lesser extent E. coli, were correlated to flow, velocity, and rainfall intensity (adjusted by time of concentrations). Concentration changes were found to be best described by adjusted rainfall intensity, as shown by other researchers. This study has resulted in an increased understanding of the magnitude of intra-event variations of total suspended solids and E. coli and what physical and climatic parameters influence these variations.

There is a need to develop effective stormwater filters for passive (without any addition of chemicals or energy) and effective removal of pathogens in order to mainstream stormwater harvesting. This study focuses on the development of coated granular activated carbon (GAC) filtration material in order to develop filters for effective removal of pathogens from urban stormwater. Several laboratory trials were performed to gauge the effectiveness of the filters, which use a mixture of the zinc-sulphate-heptahydrate coated GAC and sand, on the removal of Escherichia coli (E. coli) from seminatural stormwater. On average, a 98% removal of the inflow concentration of E. coli was achieved. Furthermore, there was also an improvement of approximately 25% in the removal of phosphorous. However, it was found that the treated material was leaching zinc. It was important to determine whether the observed removal of E. coli was indirectly caused by the sampling methodology. The results showed that the inactivation of the E. coli in the collected sample was small compared with the inactivation which actually occurred within the filter. This provides much promise to the filter, but the presence of zinc in the outflow demonstrates the need for further investigation into the stabilisation of the coating process.

Biofilters are common, low energy technologies used for the treatment of urban stormwater. While they have shown promising results for the removal of stormwater microorganisms, certain factors affect their performance. Hence, this study investigated the effects of particle-microbial interaction, inflow concentration, antecedent microbial levels and plant species on microbial removal capacity. A biofilter column study was set up to evaluate removal performance and a sequential filtration procedure was used to estimate microbial partitioning. The columns were dosed with different concentrations of free phase Escherichia coli only and E. coli mixed with stormwater sediment. Results indicate that the microbial removal is significantly affected by inflow concentration and antecedent microbial levels. Leaching was only observed when a relatively low inflow concentration event occurred within a short period after a very high inflow concentration event. Finally, Lomandra longifolia showed better removal compared with Carex appressa.

An experimental study was undertaken by Monash University to develop and test enviss™ stormwater treatment and harvesting technologies - non-vegetated filtration systems with an extremely low footprint. This paper focuses on the water quality and hydraulic performance of two systems tested over a 'year' of operation in a Melbourne climate: (1) REUSE enviss™ filters, designed for stormwater harvesting systems for non-potable supply substitution, and (2) WSUD enviss™ filters, developed to treat urban stormwater prior to discharge to downstream systems. The presence of chlorine as a disinfection agent proved to be very efficient for the removal of microorganisms in REUSE enviss™ filters. WSUD enviss™ filters had the benefit of providing an elevated nutrient treatment performance, due to an extended depth of filter media. However, nutrient outflow concentrations (total nitrogen (TN) in particular) were found to increase during the testing period. Also, extended dry weather periods were found to have a detrimental effect on the treatment performance of almost all pollutants for both filters (nutrients, Escherichia coli and heavy metals). Although hydraulic conductivity results indicated two or three sediment trap replacements per year are required to maintain filtration rates, it is expected that the compressed loading rate schedule overestimated this maintenance frequency.

Model calibration and sensitivity analysis of stormwater models are required to assess model performance; it is very unlikely that non-calibrated models will lead to reasonable results. The aim of this paper is to present results of the calibration and sensitivity analysis of the key parameters used in flow modelling by MUSIC and parameters of a simple stormwater quality model. The assessment of the models is undertaken using a Monte Carlo Markov Chain approach. We describe the models' performance, provide information on their sensitivity to parameters and also discuss the correlation between these parameters. This work will help practitioners to understand importance of the MUSIC parameters that they usually use without calibration. The information reported in the results will also help to guide future development of stormwater quality models and the data needed to support it.

Stormwater models are important tools in the design and management of urban drainage systems. Understanding the sources of uncertainty in these models and their consequences on the model outputs is essential so that subsequent decisions are based on reliable information. Model calibration and sensitivity analysis of such models are critical to evaluate model performance. The aim of this paper is to present the performance and parameter sensitivity of stormwater models with different levels of complexities, using the formal Bayesian approach. The rather complex MUSIC and simple KAREN models were compared in terms of predicting catchment runoff, while an empirical regression model was compared to a process-based build-up/wash-off model for stormwater pollutant prediction. A large dataset was collected at five catchments of different land-uses in Melbourne, Australia. In general, results suggested that, once calibrated, the rainfall/runoff models performed similarly and were both able to reproduce the measured data. It was found that the effective impervious fraction is the most important parameter in both models while both were insensitive to dry weather related parameters. The tested water quality models poorly represented the observed data, and both resulted in high levels of parameter uncertainty.

Accurate estimation of the levels of microorganisms in urban stormwater is needed for stormwater harvesting and to ensure that our streams and bays are safe for recreational uses. The aim of this research was to develop and test a simple urban stormwater microorganism model (Micro-Organism Prediction in Urban Stormwater - MOPUS) which is spatially lumped and coupled to a rainfall runoff model. The microorganism model has surface and subsurface components to simulate build-up and wash-off of microorganisms from the impervious surfaces of the catchment and the stormwater pipes, respectively. The rainfall-runoff model simulates processes from both pervious and impervious surfaces. Both models are conceptual and represent important processes in a simplified manner, thereby limiting the number of calibration coefficients (five for each model) while maintaining accuracy. The coupled model has been tested using a large Escherichia coli (E. coli) dataset collected from four urban catchments in Melbourne, Australia. For each catchment, around 20 well sampled pollutographs were available. Reasonably good predictions were obtained at each site for both instantaneous flow rates (Nash Sutcliffe E between 0.62 and 0.89) and E. coli concentrations (E= 0.25-0.45). Event E. coli peaks (E= 0.42-0.75), E. coli loads (E= 0.48-0.86) and event mean E. coli concentrations (E= 0.56-0.76) were also well estimated. In general, it has been demonstrated that, with further development and testing, MOPUS appears capable of reliable predictions of E. coli discharges from urban stormwater systems, allowing its use as a planning tool for urban catchments.

The complex nature of pollutant accumulation and washoff, along with high temporal and spatial variations, pose challenges for the development and establishment of accurate and reliable models of the pollution generation process in urban environments. Therefore, the search for reliable stormwater quality models remains an important area of research. Model calibration and sensitivity analysis of such models are essential in order to evaluate model performance; it is very unlikely that non-calibrated models will lead to reasonable results. This paper reports on the testing of three models which aim to represent pollutant generation from urban catchments. Assessment of the models was undertaken using a simplified Monte Carlo Markov Chain (MCMC) method. Results are presented in terms of performance, sensitivity to the parameters and correlation between these parameters. In general, it was suggested that the tested models poorly represent reality and result in a high level of uncertainty. The conclusions provide useful information for the improvement of existing models and insights for the development of new model formulations.

The management of stormwater pollution has placed particular emphasis on the first flush phenomenon. However, definition and current methods of analyses of the phenomena contain serious limitations, the most important being their inability to capture a possible impact of the event size (total event volume) on the first flush. This paper presents the development of a novel approach in defining and assessing the first flush that should overcome these problems. The phenomenon is present in a catchment if the decrease in pollution concentration with the absolute cumulative volume of runoff from the catchment is statistically significant. Using data from seven diverse catchments around Melbourne, Australia, changes in pollutant concentrations for Total Suspended Solids (TSS) and Total Nitrogen (TN) were calculated over the absolute cumulative runoff and aggregated from a collection of different storm events. Due to the discrete nature of the water quality data, each concentration was calculated as a flow-weighted average at 2mm runoff volume increments. The aggregated concentrations recorded in each increment (termed as a 'slice' of runoff) were statistically compared to each other across the absolute cumulative runoff volume. A first flush is then defined as the volume at which concentrations reach the 'background concentration' (i.e. the statistically significant minimum). Initial results clearly highlight first flush and background concentrations in all but one catchment supporting the validity of this new approach. Future work will need to address factors, which will help assess the first flush's magnitude and volume. Sensitivity testing and correlation with catchment characteristics should also be undertaken.

This paper presents the sensitivity analysis of a newly developed model which predicts microorganism concentrations in urban stormwater (MOPUS - MicroOrganism Prediction in Urban Stormwater). The analysis used Escherichia coli data collected from four urban catchments in Melbourne, Australia. The MICA program (Model Independent Markov Chain Monte Carlo Analysis), used to conduct this analysis, applies a carefully constructed Markov Chain Monte Carlo procedure, based on the Metropolis-Hastings algorithm, to explore the model's posterior parameter distribution. It was determined that the majority of parameters in the MOPUS model were well defined, with the data from the MCMC procedure indicating that the parameters were largely independent. However, a sporadic correlation found between two parameters indicates that some improvements may be possible in the MOPUS model. This paper identifies the parameters which are the most important during model calibration; it was shown, for example, that parameters associated with the deposition of microorganisms in the catchment were more influential than those related to microorganism survival processes. These findings will help users calibrate the MOPUS model, and will help the model developer to improve the model, with efforts currently being made to reduce the number of model parameters, whilst also reducing the slight interaction identified.

Although water-quality monitoring programs have been widely used to identify and understand the level of pollution in urban stormwater systems, these data are often used without due consideration of the inherent uncertainties contained within these measurements. This study focuses on the uncertainties associated with the monitored levels of Escherichia coli, a common microbial indicator, in urban stormwater. Four sites located in Melbourne, Australia, were used to assess the uncertainty of six stormwater flow and E. coli variables: (1) discrete E. coli concentration, (2) stormwater flow rate, (3) stormwater event volume, (4) event mean concentration (EMC) of E. coli (i.e. a flow-weighted average of an event's E. coli concentrations), (5) E. coli load for each measured event, and (6) site mean E. coli concentration (SMC) (i.e. a volume-weighted average of the E. coli EMCs). Uncertainties of discrete E. coli samples were greater than 30%, while the uncertainty in stormwater flow measurements averaged greater than 97%, mainly due to the high uncertainties in measurements of very low flows. Propagation of these uncertainties, through their respective formulas, found that E. coli EMC uncertainties varied between 10% and 52% and that uncertainties relating to SMC estimates ranged from 35% to 55%. These results show the importance of considering uncertainty when using monitored data sets for any application, including those relating to stormwater management decisions. Suggestions are made about how to increase the accuracies of E. coli monitoring in urban stormwater and how to balance the different sources of uncertainties so that the overall combined uncertainties are minimised while keeping costs at a minimum.

The harvesting of urban stormwater to supply non-potable water demands is emerging as a viable option, amongst others, as a means to augment increasingly stressed urban water supply systems. This paper investigates the sensitivity of an urban stormwater harvesting system's capacity-yield-reliability relationship to variations in the behaviour modelling method used, focusing on the storage and demand components of a single reservoir system. The aim is to enhance our understanding of the appropriate computational method for assessing such volumetric reliability/storage capacity relationships. Four reference scenarios were developed, based on two different climates and two different water demand patterns. A sensitivity analysis was conducted, which considered the following computational, storage and demand parameters: yield-spillage order, modelling time-step, length of rainfall record, initial storage volume, open/closed storage surface, dead storage volume, diurnal and weekly pattern of water demand, and inter-annual variability of seasonal water demand. It was found that several parameters had an insignificant impact on the estimation of volumetric reliability for the scenarios tested, whilst the three most significant parameters were: length of rainfall record, inter-annual variability of seasonal demand, and storage surface type. Recommendations about the minimum length of rainfall record used and the inclusion of both the inter-annual variability of seasonal demand and net evaporative losses in the case of an open store are made.

The development of a model that predicts the levels of microorganisms in urban stormwater will aid in the assessment of health risks when using stormwater for both recreational uses and as an alternate water resource. However, the development of such a model requires an understanding of the dominant processes that influence the behaviour of microorganisms in urban systems. Using simple and multiple regression analyses this paper determines the dominant processes which affect the inter-event variability of the microbial indicator Escherichia coli (E. coli) in four urbanised catchments. The results reveal that a number of antecedent climatic conditions, together with rainfall intensity, can significantly explain the inter-event variation in wet weather E. coli levels.