Smart metering founding a holistic evidence-based performance evaluation framework and demand forecasting model for diversified water supply schemes. The Australian water industry faces the challenge of catering for the potable water demand of a rapidly expanding population with reduced reliability on supply imposed by an increasingly variable climate. Diversified water supply schemes (DWSS) incorporating decentralised systems or reuse sources are touted as a means to handle the inherent weaknes ....Smart metering founding a holistic evidence-based performance evaluation framework and demand forecasting model for diversified water supply schemes. The Australian water industry faces the challenge of catering for the potable water demand of a rapidly expanding population with reduced reliability on supply imposed by an increasingly variable climate. Diversified water supply schemes (DWSS) incorporating decentralised systems or reuse sources are touted as a means to handle the inherent weaknesses of centralised urban water supply schemes by potentially drawing 30-50 per cent less demand on their reserves. This research study will provide evidence to support the implementation of best practice DWSS based on an evidence based holistic assessment of their performance considering potable water savings, capital and operation costs, energy demand, as well as environmental and community impacts.Read moreRead less
A novel and theoretically consistent method for correcting systematic errors in earth observation data and earth system model results. For a correct interpretation of satellite-based earth observation data and/or Earth system model results, it is very important that these data are free of systematic errors, commonly referred to as bias. It is well known that both these data sources are prone to a significant bias, which is currently neglected in many environmental impact and prediction studies. ....A novel and theoretically consistent method for correcting systematic errors in earth observation data and earth system model results. For a correct interpretation of satellite-based earth observation data and/or Earth system model results, it is very important that these data are free of systematic errors, commonly referred to as bias. It is well known that both these data sources are prone to a significant bias, which is currently neglected in many environmental impact and prediction studies. This project will present a method to develop models for these biases. A state update technique, the Ensemble Kalman Filter, will be adapted to correctly take into account bias in the merging of the two data sources. The project outcomes will be of high importance for long-term environmental studies, since these strongly rely on physically-based models and remote sensing data.Read moreRead less