Abstract
Wavelet transform is a new method of mathematical analysis. It can be used to decompose a signal into independent contributions of both time and frequency without losing information in the signal and is regarded as a mathematical microscope. In this paper, a method to simulate periodic hydrological time series (phts) is developed, which is called main periodic random combinatorial reconstruction (MPRCR) method. The MPRCR method is applied to generate monthly, ten-day, five-day and daily streamflow series for five hydrological gage stations in the Wei River basin, the largest tributary of the Yellow River. Comparing to measured series, the simulated series preserves main statistic features. Decomposition and reconstruction of the streamflow series are completed before the generation to analyze the applicability of wavelet transform to phts. It is shown through this study that the MPRCR method has the property of non-parametrization and may be capable of generating either stationary or non-stationary phts. The method may be applicable to simulate other periodic time series.
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References
Box, G.E.P. and Jenkins, G.M. (1976). Time Series Analysis: Forecasting and Control, Holden-Day, San Francisco.
Valencia, D. and Schaake, J.C. (1973). Disaggregation processes in stochastic hydrology, Water Resources Research 9, 580–585.
Yevjevich, V. (1972). Stochastic Processes in Hydrology, Water Resources Publications, Colorado.
Bras, R.L. and Rodriguez-Iturbe, I. (1985). Random Functions and Hydrology, Addison Wesley PublishingCompany, US.
Hipel, K.W. and Mcleod, A.I. (1994). Time Series Modeling of Water Resources and Environmental Systems, ElsevierScience, The Netherlands.
Feng, Guozhang. (1998). A methodof wavelettransform for generation of hydrological time series, in Dedun Song (ed.) The Selected Papers ofSymposium on Advances and Prospects for Hydrologic Computation ofChina. HehaiUniversity Press, Nanjing (in Chinese).
Chui, C.K. (1992). An Introduction to Wavelet, Academic Press Inc. Boston.
Liu, Guizhong and Shuangliang Di. (1995). Wavelet Analysisand Its Application, Press of Xi’an University of Electronic Scienceand Technology, Xi’an (in Chinese).
Grossmann, A. and Morlet, J. (1984). Decomposition of Hardy functions into square integrable wavelets of constantshape, SIAM J. Math. Anal. 15, 723–736.
Qin, Qianqing and Zongkai Yang. (1994). Applied Wavelet Analysis, Press of Xi’an University of Electronic Scienceand Technology, Xi’an (in Chinese).
Xu, Peixia and Gongliang Sun. (1996). Wavelet Analysis and Examples ofIts Applications, Press of the University of Scienceand Technology of China, Hefei (in Chinese).
Mallat, S. (1989). A theory for multi-resolution signal decomposition: the wavelet representation, IEEE Trans on Pattern. Anal and Math. Intel. 11, 674–693.
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© 2002 Springer Science+Business Media Dordrecht
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Feng, G. (2002). A Method for Simulation of Periodic Hydrological Time Series Using Wavelet Transform. In: Bolgov, M.V., Gottschalk, L., Krasovskaia, I., Moore, R.J. (eds) Hydrological Models for Environmental Management. NATO Science Series, vol 79. Springer, Dordrecht. https://doi.org/10.1007/978-94-010-0470-1_7
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DOI: https://doi.org/10.1007/978-94-010-0470-1_7
Publisher Name: Springer, Dordrecht
Print ISBN: 978-1-4020-0911-2
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