Journal of Data Science 2(2004), 149-163 Wavelet Analysis of Tide-affected Low Streamflows Series

Abstract: In certain rivers that drain very flat terrains in coastal areas, the streamflow series observed at a flow-gauging station may come under the direct influence of the backwater effects of tides. The phenomena may be negligible under conditions of high flows but can be critical under some extreme low-flow conditions. The errors in low flow estimation are large if a proper de-noising is not implemented to remove the effects of the tidal effects. Scrutinizing the hydrologic time series using a standard time-frequency do-main based Fourier transform methodology cannot resolve conclusively the sources of the noise. However, a new perspective can be obtained by using a wavelet transformation to analyze the time series in the time-scale domain. By using this approach, a case study involving a streamflow series observed at Kapit, Sarawak, Malaysia yielded conclusive evidence of the influence of tides at the flow-gauging site during the low flow period. Upon confirma-tion that the noise is indeed of tidal origin, the observed water level series was subjected to an appropriate wavelet-based de-noising procedure to de-rive a smoothed series. Then, together with an established rating curve, a de-noised discharge series could also be approximated. Low-flow quantiles were subsequently derived by fitting a suitable frequency distribution to the annual minimum series abstracted from the de-noised discharge series. The methodology presented illustrates the potential of using wavelet analysis methods in solving other similar problems