Entity

Time filter

Source Type

Bridgewater, Australia

Berghout B.,Hunter Water Corporation | Henley B.J.,Hunter Water Corporation | Henley B.J.,University of Melbourne | Kuczera G.,University of Newcastle
The Art and Science of Water - 36th Hydrology and Water Resources Symposium, HWRS 2015 | Year: 2015

Time series simulation of reservoir behaviour using synthetically generated streamflow and rainfall data is an integral part of calculating drought risks for many water utilities in Australia. Two key parameters that are required for the generation of the synthetic climate data are the mean and standard deviation of the annual data for each site, these being estimated from historic data. Stedinger and Taylor (1982) explored the impact of uncertainty in these parameters on the simulation of reservoir behaviour and the size of reservoir that would be required in order to maintain a specified target release for a 50 year sequence of inflow. The contemporary focus of drought risk assessments is different and the decision making criteria now require substantially more computational effort. Today, the focus of the assessments is generally on how much water can be supplied from a given set of infrastructure for a given set of reliability targets, rather than on how large the infrastructure needs to be to meet the reliability target. Further, the reliability targets are commonly expressed in terms of low frequency events, which means that many replicates are required per assessment to estimate these low risks. In this paper the impact of uncertainty in the mean and standard deviation of the historic climate data is explored in terms of its impact on a contemporary calculation of system yield. It is found that the uncertainty in these parameters has an appreciable impact on uncertainty associated with the estimated system yield. © 2015, Engineers Australia. All rights reserved. Source


Williams B.J.,University of Newcastle | Cole B.,Hunter Water Corporation
Ecological Modelling | Year: 2013

A Bayesian network model of Anabaena blooms in Grahamstown Dam, near Newcastle, Australia is described. This model meets the criteria of being decision-focused, data driven, transparent, and capable of being used by non-expert modellers. Monitored data were arranged in a consistently formatted database from which the model could 'learn' probabilistic relationships between model elements such as pumped nutrient load, lake water column nutrient concentrations, and Anabaena concentrations. This 'minimal model' produced useful insights into ecosystem relationships and provided a basic model for later development. Subsequent modelling and elicitation of conditional probabilities from experts strengthened components of the model for which there is little data available. The approach to incorporating elicited data is described and some simple scenario testing is also presented. Management outcomes resulting from application of the model are presented. © 2012 Elsevier B.V. Source


Asquith E.A.,University of Newcastle | Evans C.A.,University of Newcastle | Geary P.M.,University of Newcastle | Dunstan R.H.,University of Newcastle | Cole B.,Hunter Water Corporation
Journal of Water Supply: Research and Technology - AQUA | Year: 2013

The secondary metabolites geosmin and 2-methylisoborneol (2-MIB) provide soil with its characteristic earthy-musty odour, being notably produced by the abundant spore-forming filamentous bacterial genus Streptomyces, among other Actinobacteria. Taste and odour (T&O) problems attributed to these compounds affect drinking water supplies worldwide, often occurring sporadically and untraced to their biological origins. A number of prokaryotic and eukaryotic organisms are recognised geosmin and 2-MIB producers in aquatic environments. However, the focus of this paper is to assess the potential contribution of Actinobacteria to this water quality issue. To date, the aquatic ecology of these bacteria remains poorly understood and debate surrounds whether they exist solely as dormant spores of terrestrial origin or are capable of growing and biosynthesising these odourous compounds in aquatic environments. The Actinobacteria which are known to produce geosmin and 2-MIB are identified and a critical assessment of habitats within aquatic environments in which they may be metabolically active residents and thus potential sources of T&O is provided. Current understandings of the chemical ecology and biosynthetic pathways of geosmin and 2-MIB, as well as the conditions under which these secondary metabolites are produced by Streptomyces, are reviewed. © IWA Publishing 2013. Source


Pudasaini M.S.,Hunter Water Corporation | Shrestha S.P.,University of Western Sydney
Australian Journal of Water Resources | Year: 2010

Kinetic energy of a rainfall event is determined by its intensity. However, the effective kinetic energy reaching a soil surface that is responsible for detachment and transportation of soil particles is often less than the total kinetic energy of the rainfall event. This is because of the cushioning effect a film of water provides. Therefore it is essential to account for the loss in kinetic energy of a rainfall event and incorporate it in simulation models to accurately estimate soil erosion. This paper proposes a logarithmic energy loss model to estimate kinetic energy of rainfall reaching the soil surface. The model accounts for the depth of shallow overland flow and rainfall intensity. The empirical model was established through the set of data obtained from a rainfall simulation experimental setup consisting of a laboratonfscale tilting hydraulic flume, rainfall simulator and a series of sensitive piezoelectric force transducers. Slope variations were simulated by mechanically tilting the flume between 0° and 15°. Responses captured by the transducers in the form of voltage and pulses were analysed to establish the empirical model. The high Nash efficiency (E = 090) suggests the reliability of the empirical model and its potential for applications in soil erosion modelling. © Institution of Engineers Australia, 2011. Source


Mortazavi-Naeini M.,University of Newcastle | Kuczera G.,University of Newcastle | Kiem A.S.,University of Newcastle | Cui L.,University of Newcastle | And 3 more authors.
Environmental Modelling and Software | Year: 2015

Urban bulk water systems supply water with high reliability and, in the event of extreme drought, must avoid catastrophic economic and social collapse. In view of the deep uncertainty about future climate change, it is vital that robust solutions be found that secure urban bulk water systems against extreme drought. To tackle this challenge an approach was developed integrating: 1) a stochastic model of multi-site streamflow conditioned on future climate change scenarios; 2) Monte Carlo simulation of the urban bulk water system incorporated into a robust optimization framework and solved using a multi-objective evolutionary algorithm; and 3) a comprehensive decision space including operating rules, investment in new sources and source substitution and a drought contingency plan with multiple actions with increasingly severe economic and social impact. A case study demonstrated the feasibility of this approach for a complex urban bulk water supply system. The primary objective was to minimize the expected present worth cost arising from infrastructure investment, system operation and the social cost of "normal" and emergency restrictions. By introducing a second objective which minimizes either the difference in present worth cost between the driest and wettest future climate change scenarios or the present worth cost for driest climate scenario, the trade-off between efficiency and robustness was identified. The results show that a significant change in investment and operating strategy can occur when the decision maker expresses a stronger preference for robustness and that this depends on the adopted robustness measure. Moreover, solutions are not only impacted by the degree of uncertainty about future climate change but also by the stress imposed on the system and the range of available options. © 2015 Elsevier Ltd. Source

Discover hidden collaborations