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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Cited articles
Adam, J. C. and Lettenmaier, D. P.: Application of new precipitation and
reconstructed streamflow products to streamflow trend attribution in northern
Eurasia, J. Climate, 21, 1807–1828, 2008.
Allen, M. R. and Ingram, W. J.: Constraints on future changes in climate and
the hydrologic cycle, Nature, 419, 224–232, 2002.
Almasri, A., Locking, H., and Shukar, G.: Testing for climate warming in
Sweden during 1850–1999 using wavelet analysis, J. Appl. Stat., 35,
431–443, 2008.
Arguez, A. and Vose, R. S.: The definition of the standard WMO climate
normal: The key to deriving alternative climate normal, B. Am. Meteorol.
Soc., 92, 699–704, 2011.
Brutsaert, W. and Parlange, M. B.: Hydrologic cycle explains the evaporation
paradox, Nature, 396, 30, https://doi.org/10.1038/23845, 1998.
Burn, D. H. and Hag Elnur, M. A.: Detection of hydrologic trends and
variability, J. Hydrol., 255, 107–122, 2002.
Chen, D., Gao, G., Xu, C. Y., Guo, J., and Ren G.: Comparison of the
Thornthwaite method and pan data with the standard Penman-Monteith estimates
of reference evapotranspiration in China, Clim. Res., 28, 123–132, 2005.
Cohn, T. A. and Lins, H. F.: Nature's style: naturally trendy, Geophys. Res.
Lett., 32, L23402, https://doi.org/10.1029/2005GL024476, 2005.
de Artigas, M., Elias, A., and de Campra, P.: Discrete wavelet analysis to
assess long-term trends in geomagnetic activity, Phys. Chem. Earth, 31,
77–80, 2006.
Diffenbaugh, N. S., Giorgi, F., and Pal, J. S.: Climate change hotspots in
the United States, Geophys. Res. Lett., 35, L16709,
https://doi.org/10.1029/2008GL035075, 2008.
Dixon, H., Lawler, D. M., and Shamseldin, A. Y.: Streamflow trends in western
Britain, Geophys. Res. Lett., 32, L19406, https://doi.org/10.1029/2006GL027325, 2006.
Foufoula-Georgiou, E. and Kumar, P. (Eds.): Wavelets in geophysics, vol. 4,
Academic Press, San Diego, USA, 2014.
Gaucherel, C.: Use of wavelet transform for temporal characteristics of
remote watersheds, J. Hydrol., 269, 101–121, 2002.
Gong, S. L., Zhao, T. L., Sharma, S., Toom-Sauntry, D., Lavoue, D., Zhang, X.
B., Leaitch, W. R., and Barrie, A.: Identification of trends and interannual
variability of sulfate and black carbon in the Canadian High Arctic:
1981–2007, J. Geophys. Res.-Atmos., 115, D07305, https://doi.org/10.1029/2009JD012943,
2010.
Jiang, T., Chen, Y. D., Xu, C. Y., Chen, X., Chen, X., and Singh, V. P.:
Comparison of hydrological impacts of climate change simulated by six
hydrological models in the Dongjiang Basin, South China, J. Hydrol., 336,
316–333, 2007.
Kallache, M., Rust, H. W., and Kropp, J.: Trend assessment: applications for
hydrology and climate research, Nonlin. Processes Geophys., 12, 201–210,
https://doi.org/10.5194/npg-12-201-2005, 2005.
Kosaka, Y. and Xie, S. P.: Recent global-warming hiatus tied to equatorial
Pacific surface cooling, Nature, 501, 403–407, 2013.
Labat, D.: Recent advances in wavelet analyses: Part 1. A review of concepts,
J. Hydrol., 314, 275–288, 2005.
Labat, D., Ababou, R., and Mangin, A.: Rainfall-runoff relations for karstic
springs. Part II: continuous wavelet and discrete orthogonal multiresolution
analyses, J. Hydrol., 238, 149–178, 2000.
Medhaug, I., Stolpe, M. B., Fischer, E. M., and Knutti, R.: Reconciling
controversies about the “global warming hiatus”, Nature, 545, 41–47, 2017.
Milly, P. C. D., Betancourt, J., Falkenmark, M., Hirsch, R. M., Kundzewicz,
Z. W., Lettenmaier, D. P., and Stouffer, R. J.: Stationarity is dead: whither
water management?, Science, 319, 573–574, 2008.
Nalley, D., Adamowski, J., and Khalil, B.: Using discrete wavelet transforms
to analyze trends in streamflow and precipitation in Quebec and Ontario
(1954–2008), J. Hydrol., 475, 204–228, 2012.
Nourani, V., Baghanam, A. H., Adamowski, J., and Kisi, O.: Applications of
hybrid wavelet-artificial Intelligence models in hydrology: A review, J.
Hydrol., 514, 358–377, 2014.
Partal, T. and Cigizoglu, H. K.: Estimation and forecasting of daily
suspended sediment data using wavelet-neural networks, J. Hydrol., 358,
317–331, 2008.
Partal, T. and Kucuk, M.: Long term trend analysis using discrete wavelet
components of annual precipitation measurements in Marmara region (Turkey),
Phys. Chem. Earth, 31, 1189–1200, 2006.
Percival, D. B. and Walden, A. T.: Wavelet Methods for Time Series Analysis,
Cambridge University Press, Cambridge, UK, 2000.
Rajaram, H., Bahr, J. M., Blöschl, G., Cai, J. X., Scott Mackay, D.,
Michalak, A. M., Montanari, A., Sanchez-Villa, X., and Sander, G.: A
reflection on the first 50 years of Water Resources Research, Water Resour.
Res., 51, 7829–7837, 2015.
Roberts, C. D., Palmer, M. D., McNeall, D., and Collins, M.: Quantifying the
likelihood of a continued hiatus in global warming, Nat. Clim. Change, 5,
337–342, 2015.
Roderick, M. L. and Farquhar, G. D.: The cause of decreased pan evaporation
over the past 50 years, Science, 298, 1410–1411, 2002.
Sang, Y. F.: A practical guide to discrete wavelet decomposition of
hydrologic time series, Water Resour. Manag., 26, 3345–3365, 2012.
Sang, Y. F., Wang, Z., and Liu, C.: Period identification in hydrologic time
series using empirical mode decomposition and maximum entropy spectral
analysis, J. Hydrol., 424, 154–164, 2012.
Sang, Y. F., Wang, Z., and Liu, C.: Discrete wavelet-based trend
identification in hydrologic time series, Hydrol. Process., 27, 2021–2031,
2013.
Stocker, T. F., Qin, D., Plattner, G. K., Tignor, M., Allen, S. K., Boschung,
J., Nauels, A., Xia, Y., Bex, B., and Midgley, B. M.: IPCC: Climate Change
2013: The Physical Science Basis, Contribution of Working Group I to the
Fifth Assessment Report of the Intergovernmental Panel on Climate Change,
Cambridge Univ. Press, Cambridge, UK, 2013.
Torrence, C. and Compo, G. P.: A practical guide to wavelet analysis, B. Am.
Meteorol. Soc., 79, 61–78, 1998.
Trenberth, K. E., Dai, A., der Schrier, G., Jones, P. D., Barichivich, J.,
Briffa, K. R., and Sheffield, J.: Global warming and changes in drought, Nat.
Clim. Change, 4, 17–22, 2014.
Wild, M.: Global dimming and brightening: A review, J. Geophys. Res., 114,
D00D16, https://doi.org/10.1029/2008JD011470, 2009.
Yue, S., Pilon, P., and Cavadias, G.: Power of the Mann-Kendall and
Spearman's rho tests for detecting monotonic trends in hydrological series,
J. Hydrol., 259, 254–271, 2002.
Zhang, J., Sun, F., Xu, J., Chen, Y., Sang, Y. F., and Liu, C.: Dependence of
trends in and sensitivity of drought over China (1961–2013) on potential
evaporation model, Geophys. Res. Lett., 43, 206–213, 2016.
