A discrete wavelet spectrum approach for identifying non-monotonic trends in hydroclimate data
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Abstract. The hydroclimatic process is changing non-monotonically and identifying its trends is a great challenge. Building on the discrete wavelet transform theory, we developed a discrete wavelet spectrum (DWS) approach for identifying non-monotonic trends in hydroclimate time series and evaluating their statistical significance. After validating the DWS approach using two typical synthetic time series, we examined annual temperature and potential evaporation over China from 19612013 and found that the DWS approach detected both the warming and the warming hiatus in temperature, and the reversed changes in potential evaporation. Further, the identified non-monotonic trends showed stable significance when the time series was longer than 30years or so (i.e. the widely defined climate timescale). The significance of trends in potential evaporation measured at 150stations in China, with an obvious non-monotonic trend, was underestimated and was not detected by the MannKendall test. Comparatively, the DWS approach overcame the problem and detected those significant non-monotonic trends at 380stations, which helped understand and interpret the spatiotemporal variability in the hydroclimatic process. Our results suggest that non-monotonic trends of hydroclimate time series and their significance should be carefully identified, and the DWS approach proposed has the potential for wide use in the hydrological and climate sciences.
