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A discrete wavelet spectrum approach for identifying non monotonic trends in hydroclimate data

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Figure

Figure 1. Technical flowchart for identification of the non-monotonic trends in a time series using the developed discrete wavelet spectrum(DWS) approach, where the discrete wavelet transform (DWT) method was used to decompose the time series and get sub-signal at eachdecomposition level (DL), and the reference discrete wavelet spectrum (RDWS) with certain confidence interval (CI) was used for theevaluation of significance.
Figure 2. Non-monotonic trends in the synthetic series S1 (a) and S2 (b) identified by the discrete wavelet spectrum (DWS) approach, andthe linear trends in the two series
Figure 4. Non-monotonic trends in the annual time series of the mean air temperature (TEM, a) and the potential evaporation (PET, b) overChina from 1961–2013 identified by the discrete wavelet spectrum (DWS) approach, and the linear trends in the two series.
Figure 5. Evaluation of statistical significance of non-monotonic trends in the annual time series of the mean air temperature (TEM,the potential evaporation (PET,with the thresholds ofby the Mann–Kendall (MK) testconfidence interval under each data length a
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