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Sewage systems are built to carry contaminated wastewater from domestic discharge points to collection points for treatment. However, their capacity can be reduced by obstructions, displaced pipe joints, or broken pipes, creating abnormal hydraulic conditions. Wastewater overflows are a potential consequence of these abnormal conditions, which pose a direct threat to the environment and human health. This paper describes a permanent continuous monitoring system of a real sewage network using ultrasonic water level sensors for the purpose of blockage/choke detection, as installed in the suburb of Stonyfell, South Australia. From 62 available data sets collected over 1 year, two distinctive features of growing chokes were identified, including irregular peaks and durations that the water level remains irregularly high in the sewer maintenance holes. An early choke detection method was formulated based on the later feature, which continuously scans the near-real-time data to find time periods containing these abnormal water levels. Application of the methodology showed that the proposed method is effective in detecting possible chokes and overflow events before they occur. In most cases, the warning from the first detection is early enough to allow proactive maintenance attendance to be scheduled by the water utility. [ABSTRACT FROM AUTHOR]

Uncontrolled pipe breaks are a challenge for water utilities all over the world. This paper describes a technique that enables pipe cracks to be identified at an early stage before they become uncontrolled breaks by utilizing a permanent acoustic monitoring system as part of a smart water network. Multiple acoustic features are selected and extracted from recorded wave files that are associated with proactive repair and uncontrolled pipe break events. The extracted acoustic features and the associated wave file labels (as either crack/leak noise or no crack/leak noise) are used to train a support vector machine model. The trained model has been operationalized in the South Australia Water Corporation's smart water network analytics platform to process incoming new acoustic wave files in a near-real-time manner. If the acoustic wave file is classified as a pipe crack/leak, an alarm is sent to an investigation crew such that leak localization can be conducted and repairs started. The successful detection of multiple pipe cracks/leaks by the developed model after its implementation proves that it is an effective tool to enable proactive management of pipe breaks in water distribution systems. [ABSTRACT FROM AUTHOR]

Managing pipe breaks in water supply networks has been a challenge for water utilities around the world. In order to transform from reactive to proactive management of pipe breaks, South Australia Water Corporation (SA Water) has invested more than 4 million dollars to set up a Smart Water Network (SWN), in the central business district (CBD) of Adelaide. The network includes 305 permanent accelerometers that continuously monitor the 'noise level' in the water network. When a new pipe crack forms, the baseline noise level of the continuous measurements increases (relative to 'normal' network and other 'environmental' diurnal noise variations). This research develops a technique for automated pipe crack detection by detecting baseline changes in continuous noise measurements from the accelerometers. The developed technique is validated by field data from Adelaide SWN, and results confirm that the developed technique can achieve cost-effective early pipe crack detection and uncontrolled main break prevention. [ABSTRACT FROM AUTHOR]

Stochastic rainfall modelling is a commonly used technique for evaluating the impact of flooding, drought, or climate change in a catchment. While considerable attention has been given to the development of stochastic rainfall models (SRMs), significantly less attention has been paid to developing methods to evaluate their performance. Typical evaluation methods employ a wide range of rainfall statistics. However, they give limited understanding about which rainfall statistical characteristics are most important for reliable streamflow prediction. To address this issue a formal evaluation framework is introduced, with three key features: (i) streamflow-based, to give a direct evaluation of modelled streamflow performance, (ii) virtual, to avoid the issue of confounding errors in hydrological models or data, and (iii) targeted, to isolate the source of errors according to specific sites and seasons. The virtual hydrological evaluation framework uses two types of tests, integrated tests and unit tests, to attribute deficiencies that impact on streamflow to their original source in the SRM according to site and season. The framework is applied to a case study of 22 sites in South Australia with a strong seasonal cycle. In this case study, the framework demonstrated the surprising result that apparently "good" modelled rainfall can produce "poor" streamflow predictions, whilst "poor" modelled rainfall may lead to "good" streamflow predictions. This is due to the representation of highly seasonal catchment processes within the hydrological model that can dampen or amplify rainfall errors when converted to streamflow. The framework identified the importance of rainfall in the "wetting-up" months (months where the rainfall is high but streamflow low) of the annual hydrologic cycle (May and June in this case study) for providing reliable predictions of streamflow over the entire year despite their low monthly flow volume. This insight would not have been found using existing methods and highlights the importance of the virtual hydrological evaluation framework for SRM evaluation. [ABSTRACT FROM AUTHOR]

This paper presents a strategy for diagnosing and interpreting hydrological nonstationarity, aiming to improve hydrological models and their predictive ability under changing hydroclimatic conditions. The strategy consists of four elements: (i) detecting potential systematic errors in the calibration data; (ii) hypothesizing a set of 'nonstationary' parameterizations of existing hydrological model structures, where one or more parameters vary in time as functions of selected covariates; (iii) trialing alternative stationary model structures to assess whether parameter nonstationarity can be reduced by modifying the model structure; and (iv) selecting one or more models for prediction. The Scott Creek catchment in South Australia and the lumped hydrological model GR4J are used to illustrate the strategy. Streamflow predictions improve significantly when the GR4J parameter describing the maximum capacity of the production store is allowed to vary in time as a combined function of: (i) an annual sinusoid; (ii) the previous 365 day rainfall and potential evapotranspiration; and (iii) a linear trend. This improvement provides strong evidence of model nonstationarity. Based on a range of hydrologically oriented diagnostics such as flow-duration curves, the GR4J model structure was modified by introducing an additional calibration parameter that controls recession behavior and by making actual evapotranspiration dependent only on catchment storage. Model comparison using an information-theoretic measure (the Akaike Information Criterion) and several hydrologically oriented diagnostics shows that the GR4J modifications clearly improve predictive performance in Scott Creek catchment. Based on a comparison of 22 versions of GR4J with different representations of nonstationarity and other modifications, the model selection approach applied in the exploratory period (used for parameter estimation) correctly identifies models that perform well in a much drier independent confirmatory period. [ABSTRACT FROM AUTHOR]

Abstract An artificially destratified reservoir was simulated with the freshwater phytoplankton model PROTECH (Phytoplankton Responses To Environmental Change). The chosen site for validation was a highly managed drinking water supply reservoir (Myponga Reservoir, South Australia). Chemical dosing using copper sulphate (CuSO4 ) and artificial mixing via an aerator and two raft-mounted mechanical surface mixers (hereafter referred to as surface mixers) are used at Myponga to manage water quality, in particular the threat of cyanobacteria growth. The phytoplankton community was adequately modelled and showed that the community was dominated by species tolerant of low light doses (R-type strategists). The light limitation in the water body was found to be the controlling factor on phytoplankton succession. Subsequently, small fast-growing species and larger motile phytoplankton (C and CS-type, respectively) do not have the opportunity to dominate under all simulated conditions, diminishing the need for CuSO4 dosing. These simulations demonstrated that the individual and combined impact of the management strategies reduces the total algal biomass, but have minimal effect upon phytoplankton functional-type succession, and R-type species continued to dominate under all simulated scenarios. It was concluded that, due to the light-limitation and current nutrient availability in Myponga Reservoir, the probability of persistent populations of undesirable scum-forming cyanobacteria is minimal, even in the absence of artificial control. [ABSTRACT FROM AUTHOR]