2000
DOI: 10.1016/s0022-1694(00)00322-x
|View full text |Cite
|
Sign up to set email alerts
|

Rainfall–runoff relations for karstic springs. Part II: continuous wavelet and discrete orthogonal multiresolution analyses

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

2
196
1
10

Year Published

2008
2008
2017
2017

Publication Types

Select...
8
1

Relationship

0
9

Authors

Journals

citations
Cited by 361 publications
(209 citation statements)
references
References 29 publications
2
196
1
10
Order By: Relevance
“…It has been used for numerous studies in geosciences (e.g., [13,58,59]). In particular, applications of wavelets to discharge time series allow describing signals of climatic oscillation [14,16,60,61].…”
Section: Methodsmentioning
confidence: 99%
“…It has been used for numerous studies in geosciences (e.g., [13,58,59]). In particular, applications of wavelets to discharge time series allow describing signals of climatic oscillation [14,16,60,61].…”
Section: Methodsmentioning
confidence: 99%
“…The key and difficulty of the wavelet analysis is to select appropriate wavelet bases [28]. This paper selects the Morlet complex wavelet [29] that can faithfully reflect the time sequence various cycles and time domain distribution as the wavelet base. The real part and module of the Morlet complex wavelet exchange are two important variables in the wavelet analysis.…”
Section: The Wavelet Analysis Methodsmentioning
confidence: 99%
“…The CWT is a mathematical tool which allows the decomposition of the signal f (t) in terms of elementary contributions called wavelets, which can be thought of as a packet of sine waves of varying amplitude and wavelength (Sadowskey, 1996;Torrence and Compo, 1998;Labat et al, 2000). For time series f (t) ∈ L 2 (R), R is the domain of real numbers, or finite energy signal, the CWT of signal f (t) with the analyzing wavelet φ is the convolution of f (t) with a set of dilated and translated wavelets:…”
Section: Continuous Wavelet Transform (Cwt) and Wavelet Power Spectrumentioning
confidence: 99%
“…Since the hydrometeorological signals are highly nonstationary and their physical processes often operate under a large range of scales varying from 1 day to several decades, the continuous wavelet transform (CWT) has been introduced and developed to reveal the multiple time scales characteristics of these nonstationary signals (Mandelbrot and Wallis, 1968;Grossmann and Morlet, 1984;Mallat, 1989;Daubechies, 1994;Torrence and Compo, 1998;Labat et al, 2000). Because the CWT can provide information about both time and frequency simultaneously, and enables a separation to be made between features associated with different characteristic length scales, it has some advantages over the traditional Fourier transform and has been widely used in revealing the periodic characteristics of hydrometeorological signals at multiple time scales (Walker, 1997;Nener et al, 1999;Coulibaly and Bum, 2004;Labat et al, 2005;Shark and Yu, 2006;Liu et al, 2009;Yu et al, 2013).…”
Section: Introductionmentioning
confidence: 99%