River flow forecasting using wavelet and cross‐wavelet transform models

In this study, short‐term river flood forecasting models based on wavelet and cross‐wavelet constituent components were developed and evaluated for forecasting daily stream flows with lead times equal to 1, 3, and 7 days. These wavelet and cross‐wavelet models were compared with artificial neural network models and simple perseverance models. This was done using data from the Skrwa Prawa River watershed in Poland. Numerical analysis was performed on daily maximum stream flow data from the Parzen station and on meteorological data from the Plock weather station in Poland. Data from 1951 to 1979 was used to train the models while data from 1980 to 1983 was used to test the models. The study showed that forecasting models based on wavelet and cross‐wavelet constituent components can be used with great accuracy as a stand‐alone forecasting method for 1 and 3 days lead time river flood forecasting, assuming that there are no significant trends in the amplitude for the same Julian day year‐to‐year, and that there is a relatively stable phase shift between the flow and meteorological time series. It was also shown that forecasting models based on wavelet and cross‐wavelet constituent components for forecasting river floods are not accurate for longer lead time forecasting such as 7 days, with the artificial neural network models providing more accurate results. Copyright © 2008 John Wiley & Sons, Ltd.

[1]  P. E. O'connell,et al.  River flow forecasting through conceptual models part III - The Ray catchment at Grendon Underwood , 1970 .

[2]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .

[3]  J. Morlet,et al.  Wave propagation and sampling theory—Part I: Complex signal and scattering in multilayered media , 1982 .

[4]  Lonnie H. Hudgins,et al.  Wavelet transforms and atmopsheric turbulence. , 1993, Physical review letters.

[5]  Kuolin Hsu,et al.  Artificial Neural Network Modeling of the Rainfall‐Runoff Process , 1995 .

[6]  Franz Barthelmes,et al.  Detection of nonstationarities in geological time series: wavelet transform of chaotic and cyclic sequences , 1996 .

[7]  Laurence C. Smith,et al.  Stream flow characterization and feature detection using a discrete wavelet transform , 1998 .

[8]  C. Torrence,et al.  A Practical Guide to Wavelet Analysis. , 1998 .

[9]  T. Stocker,et al.  North atlantic oscillation dynamics recorded in greenland ice cores , 1998, Science.

[10]  P. Webster,et al.  Interdecadal changes in the ENSO-monsoon system , 1999 .

[11]  A. Mangin,et al.  Analyse en ondelettes en hydrologie karstique. 2e partie : analyse en ondelettes croisée pluie-débit , 1999 .

[12]  Praveen Kumar,et al.  Coherent modes in multiscale variability of streamflow over the United States , 2000 .

[13]  Bernard Bobée,et al.  Daily reservoir inflow forecasting using artificial neural networks with stopped training approach , 2000 .

[14]  Ana P. Barros,et al.  Quantitative flood forecasting using multisensor data and neural networks , 2001 .

[15]  Z. Gedalof,et al.  Interdecadal climate variability and regime‐scale shifts in Pacific North America , 2001 .

[16]  D. Enfield,et al.  Tropical monsoons around Africa: Stability of El Niño–Southern Oscillation associations and links with continental climate , 2002 .

[17]  Juan B. Valdés,et al.  NONLINEAR MODEL FOR DROUGHT FORECASTING BASED ON A CONJUNCTION OF WAVELET TRANSFORMS AND NEURAL NETWORKS , 2003 .

[18]  Maha Tawfik.  Linearity versus non-linearity in forecasting Nile river flows , 2003 .

[19]  J. Lundquist,et al.  Snow‐fed streamflow timing at different basin scales: Case study of the Tuolumne River above Hetch Hetchy, Yosemite, California , 2005 .

[20]  Taikan Oki,et al.  Coupled wavelet-autoregressive model for annual rainfall prediction , 2005 .

[21]  K. P. Sudheer,et al.  Short‐term flood forecasting with a neurofuzzy model , 2005 .

[22]  Alessandra Fanni,et al.  River flow forecasting using neural networks and wavelet analysis , 2005 .

[23]  Adam P. Piotrowski,et al.  Flash-flood forecasting by means of neural networks and nearest neighbour approach – a comparative study , 2006 .

[24]  Stuart N. Lane,et al.  Assessment of rainfall‐runoff models based upon wavelet analysis , 2007 .