Wavelet Analysis of Streamflow Variability in Canadian Rivers as a Response to Influences of Large-Scale Climate Indices

Friday, 19 December 2014
Deasy Nalley, Jan F Adamowski and Bahaa Khalil, McGill University, Montreal, QC, Canada
The influences of large-scale climate indices on hydrological systems have been documented in different parts of the world. Due to the non-stationary characteristics of hydro-climatic data, spectral analysis techniques that involve the use of wavelet transforms are very useful in extracting time-frequency information of such data, as well as in investigating the relationship between large-scale climate indices and hydrological variables (such as streamflow). It is important to identify predictable patterns in these climate indices, along with their influences on water resources, in order to reduce uncertainties associated with the effects of climate variability. This study aims to describe the monthly streamflow variability across Canadian rivers as a response to the influences of three main climate indices affecting the North American climate: the El-Niño Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), and North Atlantic Oscillation (NAO). Data from a total of 111 Reference Hydrometric Basin Network (RHBN) stations in Canada with a minimum length of 40 years were analyzed using the Continuous Wavelet Transform (CWT) and Wavelet Coherence (WTC). The results of the wavelet analysis showed that wavelet transforms were able to describe streamflow variability at the intra-annual, inter-annual, and inter-decadal scales as a response to the ENSO, PDO, and NAO indices. Spearman Correlations were then used to determine the lag time of streamflow response to these climate indices. The WTC spectra indicate that the influences of the ENSO and NAO indices occur at the 2-6-year time scales, and the influences of the PDO index are more apparent at time scales of up to 8 years and greater than 16 years. The results obtained in this study can assist in regional water supply management, as well as in improving long term flow forecasting.