Wavelet-based trend analysis of hydrological processes at different timescales

Abstract

This study analyzes involved trends in stream flow and precipitation data at monthly, seasonal and annual timescales observed at six precipitation and four stream flow stations of Tampa Bay using non-parametric Mann–Kendall (MK) and discrete wavelet transform (DWT) methods. The MK test and sequential MK analysis were applied to different combinations of DWT after removing the effect of significant lag-1 serial correlation to calculate components responsible for trend of the time series. Also, the sequential MK test was used to find the starting point of changes in annual time series. The results showed that negative trend is prevalent in the case study; generally, short-term periods were important in the involved trend at original time series. Thus, the precipitation data at three scales showed short-term periods of 2 months, 6 months and 2 years in monthly, seasonal and annual scales, respectively. In the greatest stream-flow time series at three timescales, wavelet-based detail at level 2 plus the approximations time series was conceded as the dominant periodic component. Finally, the results of Sen's trend analysis, applied to the original annual time series, also confirmed the results of the proposed wavelet-based MK test in most cases.