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Volume 22, issue 1 | Copyright
Hydrol. Earth Syst. Sci., 22, 757-766, 2018
https://doi.org/10.5194/hess-22-757-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 26 Jan 2018

Research article | 26 Jan 2018

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

Yan-Fang Sang1,2,3, Fubao Sun1, Vijay P. Singh4, Ping Xie5, and Jian Sun1 Yan-Fang Sang et al.
  • 1Key Laboratory of Water Cycle & Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
  • 2Department of Atmospheric Sciences, University of Washington, Seattle 98195, Washington, USA
  • 3State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China
  • 4Department of Biological and Agricultural Engineering and Zachry Department of Civil Engineering, Texas A&M University, 321 Scoates Hall, 2117 TAMU, College Station, Texas 77843-2117, USA
  • 5State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China

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 1961–2013 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 30 years or so (i.e. the widely defined climate timescale). The significance of trends in potential evaporation measured at 150 stations in China, with an obvious non-monotonic trend, was underestimated and was not detected by the Mann–Kendall test. Comparatively, the DWS approach overcame the problem and detected those significant non-monotonic trends at 380 stations, 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.

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