globalchange  > 气候变化事实与影响
DOI: 10.5194/hess-22-757-2018
Scopus记录号: 2-s2.0-85041181721
论文题名:
A discrete wavelet spectrum approach for identifying non-monotonic trends in hydroclimate data
作者: Sang Y; -F; , Sun F; , Singh V; P; , Xie P; , Sun J
刊名: Hydrology and Earth System Sciences
ISSN: 10275606
出版年: 2018
卷: 22, 期:1
起始页码: 757
结束页码: 766
语种: 英语
Scopus关键词: Discrete wavelet transforms ; Evaporation ; Time series ; Wavelet analysis ; Annual temperatures ; Climate science ; Discrete wavelets ; Mann-Kendall test ; Monotonic trend ; Potential evaporation ; Spatiotemporal variability ; Statistical significance ; Wavelet transforms ; climate conditions ; data set ; evaporation ; hydrometeorology ; potential evapotranspiration ; spatial variation ; spectral analysis ; statistical analysis ; temperature profile ; temporal variation ; time series ; time series analysis ; transform ; trend analysis ; warming ; wavelet ; wavelet analysis ; China
英文摘要: The hydroclimatic process is changing nonmonotonically 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. © Author(s) 2018.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79410
Appears in Collections:气候变化事实与影响

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作者单位: Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China; Department of Atmospheric Sciences, University of Washington, Seattle, WA, United States; State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing, China; Department of Biological and Agricultural Engineering, Zachry Department of Civil Engineering, Texas A and M University, 321 Scoates Hall, 2117 TAMU, College Station, TX, United States; State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, China

Recommended Citation:
Sang Y,-F,, Sun F,et al. A discrete wavelet spectrum approach for identifying non-monotonic trends in hydroclimate data[J]. Hydrology and Earth System Sciences,2018-01-01,22(1)
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