Could porous pavements be a part of the urban water solution? With water demand in Australia approaching, and sometimes exceeding, limits of sustainability, there is a pressing need to find alternative water sources. At the same time, urban stormwater pollution remains a major environmental threat. These problems are particularly difficult in urban areas, due to space constraints. This project will test and refine porous pavement technology, which could help solve the 'urban water problem'. R ....Could porous pavements be a part of the urban water solution? With water demand in Australia approaching, and sometimes exceeding, limits of sustainability, there is a pressing need to find alternative water sources. At the same time, urban stormwater pollution remains a major environmental threat. These problems are particularly difficult in urban areas, due to space constraints. This project will test and refine porous pavement technology, which could help solve the 'urban water problem'. Replacing impervious areas with porous pavements will allow urban stormwater to be treated and harvested for re-use. Waterways will be protected from pollution, and the vast quantity of urban stormwater generated (similar to the total reticulated water supplied in Australia) can be harvested to sustain cities.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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