Improved continuous wavelet analysis of variation in the dominant period of hydrological time series;Analyse par ondelettes continues améliorée de la variation de la période principale de séries hydrologiques

Hydrological Sciences Journal

Volumn 58, Issue 1, 2013, Pages 118-132

  Sang, Yan Fang ^a,b   Wang, Dong ^b   Wu, Ji Chun ^b   Zhu, Qing Ping ^c   Wang, Ling ^d  

^a INSTITUTE OF GEOGRAPHIC SCIENCES AND NATURAL RESOURCES RESEARCH   (China)

^b NANJING UNIVERSITY   (China)

^c China Water International Engineering Consulting Co Ltd   (China)

^d YELLOW RIVER INSTITUTE OF HYDRAULIC RESEARCH   (China)

Author keywords

change points; continuous wavelet transform; de noising; hydrological series analysis; maximum entropy spectral analysis; multi temporal scale; period; trend; wavelet basis function; wavelet spectrum

Indexed keywords

CHANGE-POINTS; CONTINUOUS WAVELET TRANSFORM; DE-NOISING; HYDROLOGICAL SERIES; MAXIMUM ENTROPY; MULTI-TEMPORAL; PERIOD; TREND; WAVELET BASIS FUNCTIONS; WAVELET SPECTRUM;

SPECTRUM ANALYSIS; TIME SERIES; WAVELET ANALYSIS; WAVELET TRANSFORMS;

MAXIMUM ENTROPY METHODS;

HYDROLOGICAL MODELING; MAXIMUM ENTROPY ANALYSIS; PRECIPITATION (CLIMATOLOGY); RUNOFF; TIME SERIES; WAVELET ANALYSIS;

EID: 84873283964     PISSN: 02626667     EISSN: 21503435     Source Type: Journal    
DOI: 10.1080/02626667.2012.742194     Document Type: Article

References (44)

1. Blanco, S. 1998. Time-frequency analysis of electroencephalogram series. III. Wavelet packets and information cost function. Physical Review, E, 57: 932-940.
2. Blazkova, S. and Beven, K. 1997. Flood frequency prediction for data limited catchments in the Czech Republic using a stochastic rainfall model and TOPMODEL. Journal of Hydrology, 195: 256-278.
3. Box, G.E.P., Ienkins, G.M. and Reinsel, G.C. 1994. Time series analysis: Forecasting and control. 3rd edition, Englewood Cliffs, NJ: Prentice Hall.
4. Bruni, V. and Vitulano, D. 2006. Wavelet-based signal de-noising via simple singularities approximation. Signal Processing, 86: 859-876.
5. Cahill, A.T. 2002. Determination of changes in streamflow variance by means of a wavelet-based test. Water Resources Research, 38: W01065
6. Chanerley, A.A. and Alexander, N.A. 2007. Correcting data from an unknown accelerometer using recursive least squares and wavelet de-noising. Computer Structure, 85: 1679-1692.
7. Chen, X.D. 1996. Yellow river hydrology (in Chinese), Zhengzhou: Water Conservancy of the Yellow River.
8. Chui, C.K. 1992. An introduction to wavelets, San Diego, CA: Academic Press.
9. Coulibaly, P. and Burn, D.H. 2004. Wavelet analysis of variability in annual Canadian streamflows. Water Resources Research, 40: W03105
10. Daubechies, I. 1990. The wavelet transform time-frequency localization and signal analysis. IEEE Transactions on Information Theory, 36: 961-1004.
11. Daubechies, I. 1992. Ten lectures on wavelets. Society for Industrial and Applied Mathematics,: 357
12. Donoho, D.H. 1995. De-noising by soft-thresholding. IEEE Transactions on Inform Theory, 41: 613-617.
13. Fleming, S.W. 2002. Practical applications of spectral analysis to hydrological time series. Hydrological Processes, 16: 565-574.
14. Gaucherel, C. 2002. Use of wavelet transform for temporal characterization of remote watersheds. Journal of Hydrology, 269: 101-121.
15. Hanson, R.T., Newhouse, M.W. and Dettinger, M.D. 2004. A methodology to assess relations between climatic variability and variations in hydrological time series in the southwestern United States. Journal of Hydrology, 287: 252-269.
16. Heil, C.E. and Walnut, D.F. 1989. Continuous and discrete wavelet transforms. SIAM Review, 31: 628-666.
17. Jansen, M. and Bultheel, A. 2001. Asymptotic behavior of the minimum mean squared error threshold for noisy wavelet coefficients of piecewise smooth signals. IEEE Transactions on Signal Processes, 49: 1113-1118.
18. Katz, R. 2002. Techniques for estimating uncertainty in climate change scenarios and impact studies. Climate Research, 20: 167-185.
19. Kayhan, A.S. 1999. Evolutionary maximum entropy spectral analysis of chirps in noise. Signal Processing, 78: 151-157.
20. Kottegoda, N.T., Natale, L. and Raiteri, E. 2007. Gibbs sampling of climatic trends and periodicities. Journal of Hydrology, 339: 54-64.
21. Kwon, H.H. 2006. Episodic inter-annual climate oscillations and their influence on seasonal rainfall in the Everglades National Park. Water Resources Research, 42: W11404
22. Labat, D. 2005. Recent advances in wavelet analyses: Part 1. A review of concepts. Journal of Hydrology, 314: 275-288.
23. Mallat, S.G. 1989. A theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11: 674-693.
24. Matalas, N.C. 1967. Mathematical assessment of synthetic hydrology. Water Resources Research, 3: 937-945.
25. Meyer, Y. 1992. Wavelet and applications, Berlin: Springer.
26. Neupauer, R.M. 2006. Characterization of permeability anisotropy using wavelet analysis. Water Resources Research, 42: W07419
27. Padmanabhan, G. and Rao, A.R. 1988. Maximum entropy spectral analysis of hydrological data. Water Resources Research, 24: 1519-1533.
28. Pelgrum, H. 2000. Length-scale analysis of surface albedo, temperature, and normalized difference vegetation index in desert grassland. Water Resources Research, 36: 1757-1765.
29. Percival, D.B. and Walden, A.T. 2000. Wavelet methods for time series analysis, Cambridge, UK: Cambridge University Press.
30. Ravines, R.R. 2008. A joint model for rainfall-runoff: the case of Rio Grande basin. Journal of Hydrology, 353: 189-200.
31. Sang, Y.F. and Wang, D. 2008. Wavelets selection method in hydrologic series wavelet analysis (in Chinese with English abstract). Journal of Hydraulic Engineering, 39: 295-300, 306.
32. Sang, Y.F., Wang, D. and Wu, J.C. 2010. Entropy-based method of choosing the decomposition level in wavelet threshold de-noising. Entropy, 12: 1499-1513.
33. Sang, Y.F. 2009a. Entropy-based wavelet de-noising method for time series analysis. Entropy, 11(4): 1123-1147.
34. Sang, Y.F. 2009b. The relation between periods' identification and noises in hydrologic series data. Journal of Hydrology, 368: 165-177.
35. Sang, Y.F., Wang, Z.G. and Liu, C.M. 2012a. Discrete wavelet-based trend identification in hydrologic time series. Hydrological Processes, doi:10.1002/hyp.9356
36. Sang, Y.F., Wang, Z.G. and Liu, C.M. 2012b. Period identification in hydrologic time series using empirical mode decomposition and maximum entropy spectral analysis. Journal of Hydrology, 424: 154-164.
37. Schaefli, B., Maraun, D. and Holschneider, M. 2007. What drives high flow events in the Swiss Alps? Recent developments in wavelet spectral analysis and their application to hydrology. Advance in Water Resources, 30: 2511-2525.
38. Seungho, H. 2005. Temporal analysis of the frequency and duration of low and high streamflow: years of record needed to characterize streamflow variability. Journal of Hydrology, 310: 78-94.
39. Smith, L.C., Turcotte, D.L. and Isacks, B.L. 1998. Stream flow characterization and feature detection using a discrete wavelet transform. Hydrological Processes, 12: 30-56.
40. Torrence, C. and Compo, G.P. 1998. A practical guide to wavelet analysis. Bulletin of the American Meteorological Society, 79: 61-78.
41. Walker, J.S. 1999. A primer on wavelets and their scientific applications, New York: Chapman and Hall, CRC.
42. Yevjevich, V. 1972. Stochastic processes in hydrology, Littleton, CO: Water Resources Publications.
43. Yevjevich, V. 1987. Stochastic models in hydrology. Stochastic Hydrology and Hydraulics, 1: 17-36.
44. Zuo, Q.T. and Gao, F. 2004. "Periodic overlapping prediction model and its three improved models of hydrological time series (in Chinese with English abstract)". In Journal of Zhengzhou University (Engineering Science) Vol. 25, 67-73.