Optimal Source Control in Urban Water Cycle Management. Major opportunities exist to improve the management of the urban water cycle by better use of source control technology such as the use of rainwater tanks and water-sensitive design. This program will optimise the use of this technology at three scales, allotment, subdivision and regional, using genetic algorithms, parallel computing and shadow pricing. The aim is to minimise community lifecycle costs subject to sustainable use of ecosystem ....Optimal Source Control in Urban Water Cycle Management. Major opportunities exist to improve the management of the urban water cycle by better use of source control technology such as the use of rainwater tanks and water-sensitive design. This program will optimise the use of this technology at three scales, allotment, subdivision and regional, using genetic algorithms, parallel computing and shadow pricing. The aim is to minimise community lifecycle costs subject to sustainable use of ecosystems and maintenance of public health standards. The benefits include national savings of the order of $2 billion and significantly reduced demand on water supply and stormwater infrastructure and its supporting ecosystems.Read moreRead less
A Stochastic Spatial Rainfall Model for Engineering Risk Assessment. Current Australian hydrologic design practice is moving towards use of continuous simulation to more accurately evaluate the performance of the water-related infrastructure for managing floods and droughts. A major impediment is the inability to simulate the temporal and spatial variability of rainfall. This project aims to develop a stochastic rainfall model that will simulate long records of representative six-minute duration ....A Stochastic Spatial Rainfall Model for Engineering Risk Assessment. Current Australian hydrologic design practice is moving towards use of continuous simulation to more accurately evaluate the performance of the water-related infrastructure for managing floods and droughts. A major impediment is the inability to simulate the temporal and spatial variability of rainfall. This project aims to develop a stochastic rainfall model that will simulate long records of representative six-minute duration rainfall throughout the target region. The proposal introduces a three-level hierarchical model of space-time rainfall building on experience of a point rainfall model developed in previous ARC research. Practical issues dealing with data quality and validation will also be addressed.Read moreRead less
Modelling long-term hydrological persistence using hidden state Markov models. Long-term climatic persistence has a pronounced effect on engineering risk assessment of drought and flood severity. Accurate risk assessment is essential for economic design of water resource and flood defence infrastructure. A new, physically realistic, framework for stochastic modelling of persistence is developed, in which the probability distributions of hydrological variables depend on underlying climatic states ....Modelling long-term hydrological persistence using hidden state Markov models. Long-term climatic persistence has a pronounced effect on engineering risk assessment of drought and flood severity. Accurate risk assessment is essential for economic design of water resource and flood defence infrastructure. A new, physically realistic, framework for stochastic modelling of persistence is developed, in which the probability distributions of hydrological variables depend on underlying climatic states. These states are not directly observable, and occasionally change in a random manner. The research program, involving three PhD projects, will develop: estimation techniques and software using climate indices and multi-site data; a new approach to flood risk regionalisation; and seasonal rainfall forecasting methods.Read moreRead less
A stochastic space-time model of rainfall fields in large heterogeneous regions. The extreme temporal and spatial variability of Australia's rainfall affects the quantity and quality of its water resources, the productivity of its agricultural systems, and its aquatic and terrestrial ecosystems. Given the impact of extreme events such as droughts and floods and given the massive investment in water-related infrastructure, evaluation of such risks is an issue of national economic, social and envi ....A stochastic space-time model of rainfall fields in large heterogeneous regions. The extreme temporal and spatial variability of Australia's rainfall affects the quantity and quality of its water resources, the productivity of its agricultural systems, and its aquatic and terrestrial ecosystems. Given the impact of extreme events such as droughts and floods and given the massive investment in water-related infrastructure, evaluation of such risks is an issue of national economic, social and environmental significance. Stochastic space-time rainfall models enable rainfall and climatic variability to be quantified, simulated over arbitrarily long periods, and risks assessed. This research will provide software and the development of rainfall modelling frameworks for large river basins such as the Murray-Darling.Read moreRead less
A Bayesian Hierarchical Approach for Simulating Multi-time Scale Hydrological Variability for Water Resource Planning. Assessments of future drought risks are dependent on simulations of hydrological inputs provided by stochastic models. The current models are limited to simulating variability at a single time scale using only local observed hydrological data. This data has only limited information on the long-term climate variability which is the cause of long-term severe droughts. The proposed ....A Bayesian Hierarchical Approach for Simulating Multi-time Scale Hydrological Variability for Water Resource Planning. Assessments of future drought risks are dependent on simulations of hydrological inputs provided by stochastic models. The current models are limited to simulating variability at a single time scale using only local observed hydrological data. This data has only limited information on the long-term climate variability which is the cause of long-term severe droughts. The proposed research will develop a new Bayesian framework for simulating multi-time scale variability in hydrological data. This will enable the dynamic processes which simulate long-term variability to be identified using auxiliary information in an uncertainty framework. This will provide water resource planners with more accurate assessments of long-term drought risks.
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