Articles | Volume 22, issue 1
https://doi.org/10.5194/hess-22-757-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-22-757-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.
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 Sang
CORRESPONDING AUTHOR
Key Laboratory of Water Cycle & 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 98195, Washington, USA
State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China
Fubao Sun
CORRESPONDING AUTHOR
Key Laboratory of Water Cycle & Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Vijay P. Singh
Department 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
State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China
Key Laboratory of Water Cycle & Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
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- An Enhanced Innovative Triangular Trend Analysis of Rainfall Based on a Spectral Approach B. Zerouali et al. https://doi.org/10.3390/w13050727
- Heterogeneity in Spatiotemporal Variability of High Mountain Asia's Runoff and Its Underlying Mechanisms Y. Zhu et al. https://doi.org/10.1029/2022WR032721
- A Novel Intelligent Method for the State of Charge Estimation of Lithium-Ion Batteries Using a Discrete Wavelet Transform-Based Wavelet Neural Network D. Cui et al. https://doi.org/10.3390/en11040995
- Heterogeneous spatiotemporal streamflow response to large-scale climate indexes in the Eastern Alps T. Pérez-Ciria et al. https://doi.org/10.1016/j.jhydrol.2022.128698
- Precipitation Complexity and its Spatial Difference in the Taihu Lake Basin, China J. Hu et al. https://doi.org/10.3390/e21010048
- Groundwater Drought and Cycles in Xuchang City, China J. Huang et al. https://doi.org/10.3389/feart.2021.736305
- Analysis of soil moisture trends in Europe using rank-based and empirical decomposition approaches L. Almendra-Martín et al. https://doi.org/10.1016/j.gloplacha.2022.103868
- Climate change and anthropological impacts on a karst aquifer: a multi-statistical assessment L. Mahdavi & N. Samani https://doi.org/10.1007/s00704-023-04707-7
- Detection of type of trends in surface air temperature in China X. Li et al. https://doi.org/10.1016/j.jhydrol.2021.126061
- Prewhitening-Aided Innovative Trend Analysis Method for Trend Detection in Hydrometeorological Time Series J. Huo & P. Xie https://doi.org/10.3390/w17050731
- Forecasting river water temperature time series using a wavelet–neural network hybrid modelling approach R. Graf et al. https://doi.org/10.1016/j.jhydrol.2019.124115
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- Uniform discrete wavelet spectrum for detection of hydrologic variability at multiple timescales Y. Sang et al. https://doi.org/10.1016/j.jher.2021.01.005
- Clarification of dominating drivers for streamflow changes in the upper reach of Mekong River Basin Z. Huang et al. https://doi.org/10.1016/j.ejrh.2023.101456
- Change-point detection in monsoon rainfall of Narmada River (central India) during 1901–2015 B. Zerouali et al. https://doi.org/10.1007/s12040-023-02140-y
- Is the relative thickness of ammonoid septa influenced by ocean acidification, phylogenetic relationships and palaeogeographic position? C. Weber et al. https://doi.org/10.1186/s13358-022-00246-2
- RetroSight and ForeSight ensemble model (ReForM) for improved time series prediction: A case study on river temperature prediction F. Bagheri et al. https://doi.org/10.1016/j.jhydrol.2026.135244
- Is there an underestimation of long-term variability of streamflow across the continental United States? Y. Sang et al. https://doi.org/10.1016/j.jhydrol.2019.124365
- Nonlinear response of runoff to atmospheric freezing level height variation based on hybrid prediction models Y. Qin et al. https://doi.org/10.1080/02626667.2019.1662023
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