Assessment of trends in point rainfall using Continuous Wavelet Transforms

• A relatively new trend detection approach has been applied.
• Trends in original rainfall and its various periodic components were investigated.
• Dominant periodic components for the trends of the original series were identified.
• The sequential MK test was applied to find the trend turning points.

The existence of trends in hydro-climatic variables such as rainfall is an indication of potential climate variability and climate change and the identification of such trends in rainfall is essential for the planning and design of sustainable water resources. This study focuses on identifying existing trends in annual, seasonal and monthly rainfall at thirteen stations in the Onkaparinga catchment in South Australia during the period 1960–2010. A relatively new trend detection approach, which combines a Continuous Wavelet Transform (CWT) with the Mann Kendall (MK) test, was applied in this study. The original rainfall time series was decomposed to different periodic components using a CWT and then the MK test was applied to detect the trends. One station showed a statistically significant (at the 5% level) negative trend for annual rainfall. Winter rainfall exhibited significant positive trends at four stations. In the case of monthly rainfall, significant positive trends were observed in June (at seven stations), November (at one station) and December (at one station). The study showed that the periodic components might have significant trends even when there are no significant trends in the original data. The periodic component that dominates the trend in the original data varies from season to season. A sequential Mann–Kendall analysis was found useful for identifying the trend turning points. Most of the trends, whether positive or negative, started during the mid-1970s to mid-1980s. The technique developed in this study may also be applied for trend detection of other hydro-climatic variables in other catchments, particularly where temporal and spatial variabilities are high.