A discrete wavelet spectrum approach to identifying non-monotonic trend pattern of hydroclimate data
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Abstract. Hydroclimate system is changing non-monotonically and identifying its trend pattern is a great challenge. Building on the discrete wavelet transform theory, we develop a discrete wavelet spectrum (DWS) approach for identifying non-monotonic trend patterns in hydroclimate time series and evaluating their statistical significance. After validating the DWS approach using two typical synthetic time series, we examined the 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. Interestingly, the identified trend patterns showed stable significance when the time series was longer than 30 years or so (i.e., the widely defined climate timescale). Our results suggest that non-monotonic trend patterns of hydroclimate time series and their significance should be carefully identified, and the DWS approach has the potential for wide use in hydrological and climate sciences.
