PhD Oral Defence: Wavelet transform-based methods to analyze variability of hydrological data
PhD Oral Defence of Deasy Nalley, Bioresource Engineering
Time series analyses performed on precipitation and streamflow data provide information regarding the evolution of climatic changes and variability. Quantifying these changes is challenging due to unavailability of data and the existence of multiple periodicities in the data that may be associated with large-scale climate oscillations. This thesis focuses on the development and applications of innovative wavelet transform (WT)-based approaches for applications in analyzing the influence of climate oscillations on streamflow and precipitation data. The precipitation and streamflow data used are obtained from stations in Canada; the climate oscillations included in the analysis are El Niño-Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO) and the Pacific Decadal Oscillation (PDO).
By combining continuous WT, discrete WT and non-parametric statistical approaches, this thesis demonstrates the utility and advantages of WT-based methods in two main areas: 1) the multiscale influences of large-scale climate oscillations (individually and simultaneously) on streamflow and precipitation data; and 2) improving the performance of several record extension methods by incorporating WT as a pre-processing technique. Collectively, the results obtained enhance the understanding of variability in streamflow and precipitation over many areas in Canada. This is useful for improving forecasting in operational hydrology and for sustainable management of water resources as a whole.