Hydrological variables such as rainfall and streamfiow vary at a range of temporal scales, from short term (diurnal and seasonal) to the inter annual time scales associated with the El Nino - Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) phenomena, to even longer time scales such as those linked to the Pacific (inter-) Decadal Oscillation (PDO). This temporal variability poses a significant challenge to hydrologists and water resource managers, since a failure to take such variability into account can lead to an underestimation of the likelihood of droughts and sequences of above average rainfall, which in turn has important implications for the design and operation of reservoirs for hydroelectricity generation, irrigation and municipal water supply. Understanding and accounting for this variability through well designed prediction systems is thus an important part of improving the planning, management and operation of complex water resources systems. This thesis outlines the application of two statistical techniques: wavelets and independent component analysis, to identify sources of hydrological variability, and then use this information to probabilistically generate multivariate seasonal forecasts or develop extended synthetic sequences of hydrological time series. The research is divided into four main parts. The first part outlines an application of the method of wavelets to analyse sources of Australian rainfall variability, and shows that there are coherent regions of variability in addition to the ENSO phenomenon that should be considered when developing seasonal forecasts. The second part examines the capability of three component extraction techniques: principal component analysis (PCA), Varimax and independent component analysis (ICA), in identifying and interpreting modes of variability in the global sea surface temperature dataset. The third part outlines a new technique that uses ICA to factorise multivariate reservoir inflow time series into a set of independent univariate time series, so that univariate methods can be used to develop multivariate synthetic sequences and probabilistic seasonal forecasts. Finally, the fourth part synthesises the previous three parts by demonstrating a wavelets- and correlation-based methodology for assessing sources of climate variability, and then using ICA to generate probabilistic multivariate seasonal forecasts of reservoir inflows that form part of Sydney's water supply system.
|Creators||Westra, Seth Pieter, Civil & Environmental Engineering, Faculty of Engineering, UNSW|
|Publisher||Awarded by:University of New South Wales. Civil & Environmental Engineering|
|Source Sets||Australiasian Digital Theses Program|
|Rights||Copyright Westra Seth Pieter., http://unsworks.unsw.edu.au/copyright|
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