4. Conclusion(s)

This study explored the usefulness of continuous wavelet analysis in the investigation of salinity intrusion. The study summarized CWT, WTC, XWT, and PWC approaches and applied them in the analysis of the impact of forcing variables on the seawater flux in Sakai Channel. The study revealed fundamental characteristics in the variation of forcing parameters and seawater flux, as well as their interactions. The only constraint in this study was a high computation time due to 1000 Monte Carlo simulation runs.

The CWT results show that the seawater flux and the tide level have regular oscillations in the 12-h and 1-day period band, indicating that the influence of astronomical tides is dominant. River discharge from the Hii River does not exhibit any periodical variations due to the irregularity of precipitation and the controlled release from upstream reservoirs. Atmospheric pressure exhibits a continuous high power (lasting over a month) with a period range from 16 day to 1-year. East–West (wx) and North–South (wy) wind velocity components show irregular oscillations with periods between 2 and 16 days.

WTC, XWT, and PWC revealed the influence of tide level, river discharge, atmospheric pressure, and wind velocity on seawater flux. WTC, XWT, and PWC showed that tides are consistently influential on the seawater flux in the 0.5- and 1-day period band. River discharge influenced seawater flux after heavy rains or water releases from upstream reservoirs. Atmospheric pressure and wind velocity occasionally influence seawater flux at Nakaura Watergate and may have an indirect influence on salinity transport through their effect on sea surface elevation. High drops of atmospheric pressure occasionally resulted in an increased tide level. This study reiterated the importance of tides in the transport of seawater in and out of Lakes Shinji and Nakaumi.

To conclude, the wavelet analysis of seawater intrusion studies proved useful. Wavelet coherence is helpful in the study of relationships between two time series. Partial wavelet coherence reveals the relationship between two time series after removing the effect of other time series. This is very useful when a dependent variable is under the influence of two or more variables. Wavelet analysis performs spectral analysis in frequency-time domain, revealing time-varying relationships across frequencies.
