气候变化领域集成服务门户(CCPortal): A discrete wavelet spectrum approach for identifying non-monotonic trends in hydroclimate data

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DOI	10.5194/hess-22-757-2018
	A discrete wavelet spectrum approach for identifying non-monotonic trends in hydroclimate data
	Sang Y.-F.; Sun F.; Singh V.P.; Xie P.; Sun J.
发表日期	2018
ISSN	1027-5606
起始页码	757
结束页码	766
卷号	22 期号:1
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.
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
来源期刊	Hydrology and Earth System Sciences
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/160135
作者单位	Sang, Y.-F., Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China, Department of Atmospheric Sciences, University of Washington, Seattle, WA 98195, United States, State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing, 210029, China; Sun, F., Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China; Singh, V.P., Department of Biological and Agricultural Engineering, Zachry Department of Civil Engineering, Texas A and M University, 321 Scoates Hall, 2117 TAMU, College Station, TX 77843-2117, United States; Xie, P., State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, 430072, China; Sun, J., Key Laboratory of Water Cycle and Related Land Su...
推荐引用方式 GB/T 7714	Sang Y.-F.,Sun F.,Singh V.P.,et al. A discrete wavelet spectrum approach for identifying non-monotonic trends in hydroclimate data[J],2018,22(1).
APA	Sang Y.-F.,Sun F.,Singh V.P.,Xie P.,&Sun J..(2018).A discrete wavelet spectrum approach for identifying non-monotonic trends in hydroclimate data.Hydrology and Earth System Sciences,22(1).
MLA	Sang Y.-F.,et al."A discrete wavelet spectrum approach for identifying non-monotonic trends in hydroclimate data".Hydrology and Earth System Sciences 22.1(2018).