Ana Deletic

The first flush load of urban surface runoff

The treatment of urban surface runoff is steadily becoming common practice. Good management of treatment works requires an understanding of the first flush phenomenon of wet weather flow in urban drainage systems. The objective of this paper is to analyse evidence for the existence and nature of the first flush load of pollution input into drainage systems, using data on suspended solids, conductivity, pH, and temperature of storm surface runoff. The data were collected continuously for almost a year for single road inlets at two urban asphalt catchments (in Belgrade, Yugoslavia and Lund, Sweden). Cumulative load curves were constructed for all monitored water quality characteristics, and the pollution load carried by the first 20% of runoff, FF20, was calculated for each recorded event. FF20 values were analysed using standard statistical methods, including multiple regression. Only slight first flush effects for suspended solids and conductivity of storm runoff were observed at both catchments. No first flush effect was recorded for pH or temperature. The rainfall and runoff characteristics which influence the first flush phenomenon are different at each catchment studied, although the catchments have very similar characteristics. It was concluded that: (1) the first flush, if strongly present at the end of a drainage system, is not generated by the first flush of pollution input; (2) regression curves are not very reliable for prediction of the first flush load of pollution input into drainage systems.

Taking the “Waste” Out of “Wastewater” for Human Water Security and Ecosystem Sustainability

Humans create vast quantities of wastewater through inefficiencies and poor management of water systems. The wasting of water poses sustainability challenges, depletes energy reserves, and undermines human water security and ecosystem health. Here we review emerging approaches for reusing wastewater and minimizing its generation. These complementary options make the most of scarce freshwater resources, serve the varying water needs of both developed and developing countries, and confer a variety of environmental benefits. Their widespread adoption will require changing how freshwater is sourced, used, managed, and priced.

Modelling of water and sediment transport over grassed areas

Abstract Grassed areas, such as grass filter strips (GFS) and grassed swales, have been used extensively for the protection of surface water against erosion and polluted runoffs. A mathematical model of sediment transport in runoff over grass has been developed, allowing an assessment of the sediment removal efficiency of GFS and swales for non-submerged flow conditions. The model is one-dimensional and simulates two main processes: (1) generation of runoff and (2) sediment transport. The modified Green–Ampt model is used for assessment of infiltration, while a kinematic wave model is used for simulation of overland flow. A transport equation is used for modelling of sediment movement through grass, where sediment deposition is assessed by the method developed at the University of Aberdeen. The model does not simulate particle trapping in a pond (which may develop upstream from a GFS), re-suspension of particles already deposited, or infiltration of particles into soil. For a known particle size distribution of inflow sediment, the model is capable of predicting the particle size distribution of the outflow sediment. The model was developed for modelling single rain events. However, it could be also applied for a sequence of rain events assuming that the initial soil wetness is constant. An example of model application in an urban area is presented in the paper, along with sensitivity analysis of the main model results.

Comparison of different uncertainty techniques in urban stormwater quantity and quality modelling

Urban drainage models are important tools used by both practitioners and scientists in the field of stormwater management. These models are often conceptual and usually require calibration using local datasets. The quantification of the uncertainty associated with the models is a must, although it is rarely practiced. The International Working Group on Data and Models, which works under the IWA/IAHR Joint Committee on Urban Drainage, has been working on the development of a framework for defining and assessing uncertainties in the field of urban drainage modelling. A part of that work is the assessment and comparison of different techniques generally used in the uncertainty assessment of the parameters of water models. This paper compares a number of these techniques: the Generalized Likelihood Uncertainty Estimation (GLUE), the Shuffled Complex Evolution Metropolis algorithm (SCEM-UA), an approach based on a multi-objective auto-calibration (a multialgorithm, genetically adaptive multi-objective method, AMALGAM) and a Bayesian approach based on a simplified Markov Chain Monte Carlo method (implemented in the software MICA). To allow a meaningful comparison among the different uncertainty techniques, common criteria have been set for the likelihood formulation, defining the number of simulations, and the measure of uncertainty bounds. Moreover, all the uncertainty techniques were implemented for the same case study, in which the same stormwater quantity and quality model was used alongside the same dataset. The comparison results for a well-posed rainfall/runoff model showed that the four methods provide similar probability distributions of model parameters, and model prediction intervals. For ill-posed water quality model the differences between the results were much wider; and the paper provides the specific advantages and disadvantages of each method. In relation to computational efficiency (i.e. number of iterations required to generate the probability distribution of parameters), it was found that SCEM-UA and AMALGAM produce results quicker than GLUE in terms of required number of simulations. However, GLUE requires the lowest modelling skills and is easy to implement. All non-Bayesian methods have problems with the way they accept behavioural parameter sets, e.g. GLUE, SCEM-UA and AMALGAM have subjective acceptance thresholds, while MICA has usually problem with its hypothesis on normality of residuals. It is concluded that modellers should select the method which is most suitable for the system they are modelling (e.g. complexity of the models structure including the number of parameters), their skill/knowledge level, the available information, and the purpose of their study.

The influence of design parameters on clogging of stormwater biofilters: A large-scale column study

A large-scale laboratory study was conducted to test the influence of design and operating conditions on the lifespan of stormwater biofilters. The evolution of hydraulic conductivity over time was studied in relation to a number of key design parameters (media type, filter depth, vegetation type, system sizing, etc). The biofilters were observed to clog over time, with average hydraulic conductivity decreasing by a factor of 3.6 over the 72 weeks of testing. The choice of plant species appears to have a significant effect on the rate of decrease in permeability, with plants with thick roots (e.g. Melaleuca) demonstrating an ability to maintain permeability over time. Other species studied, with finer roots, had no such beneficial effects. As expected, small systems relative to their catchment (and thus which are subjected to high loading rates) are more prone to clogging, as increases in hydraulic and sediment loading can lead to extremely low hydraulic conductivities. Sizing and the appropriate choice of vegetation are thus key elements in design because they can limit clogging, and therefore, indirectly increase annual load treated by limiting the volume of water bypassing the system.

Uncertainties in stormwater E. coli levels

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 events 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.

Intra-event variability of Escherichia coli and total suspended solids in urban stormwater runoff

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 catchments 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.

Techniques for water and wastewater management: a review of techniques and their integration in planning

Abstract This paper presents a review of techniques in wastewater management and a discussion as to how they can be integrated in future water-planning issues. Three technical areas are dealt with: rainwater management, domestic wastewater management, and water and waste re-use. Each approach is reviewed from a technical perspective with a further commentary on economic and social factors that underpin the different techniques that each approach has to offer. This is followed by a discussion on how integrated assessment can lead to the design and implementation of more sustainable approaches to wastewater management.

The enabling institutional context for integrated water management: Lessons from Melbourne

There is widespread international acceptance that climate change, demographic shifts and resource limitations impact on the performance of water servicing in cities. In response to these challenges, many scholars propose that a fundamental move away from traditional centralised infrastructure towards more integrated water management is required. However, there is limited practical or scholarly understanding of how to enable this change in practice and few modern cities have done so successfully. This paper addresses this gap by analysing empirical evidence of Melbournes recent experience in shifting towards a hybrid of centralised and decentralised infrastructure to draw lessons about the institutional context that enabled this shift. The research was based on a qualitative single-case study, involving interviews and envisioning workshops with urban water practitioners who have been directly involved in Melbournes water system changes. It was found that significant changes occurred in the cultural-cognitive, normative and regulative dimensions of Melbournes water system. These included a shift in cultural beliefs for the water profession, new knowledge through evidence and learning, additional water servicing goals and priorities, political leadership, community pressure, better coordinated governance arrangements and strong market mechanisms. The paper synthesises lessons from the case study that, with further development, could form the basis of prescriptive guidance for enabling the shift to new modes of water servicing to support more liveable, sustainable and resilient outcomes for future cities.

Performance and sensitivity analysis of stormwater models using a Bayesian approach and long-term high resolution data

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.