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Journal of Hydrology and Hydromechanics

The Journal of Institute of Hydrology SAS Bratislava and Institute of Hydrodynamics CAS Prague

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IMPACT FACTOR 2016: 1.654

CiteScore 2016: 1.72

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Volume 62, Issue 1


Wavelet based deseasonalization for modelling and forecasting of daily discharge series considering long range dependence

Elena Szolgayová
  • Corresponding author
  • The Centre for Water Resource Systems, Vienna Universtity of Technology, Karlsplatz 13, 1040, Vienna, Austria.
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Josef Arlt
  • University of Economics, Faculty of Informatics and Statistics, nám. W. Churchilla 4, 130 67, Prague, Czech Republic.
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Günter Blöschl
  • Institute of Hydraulic Engineering and Water Resources Management, Vienna University of Technology, Karlsplatz 13, 1040, Vienna, Austria.
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Ján Szolgay
Published Online: 2014-02-13 | DOI: https://doi.org/10.2478/johh-2014-0011


Short term streamflow forecasting is important for operational control and risk management in hydrology. Despite a wide range of models available, the impact of long range dependence is often neglected when considering short term forecasting. In this paper, the forecasting performance of a new model combining a long range dependent autoregressive fractionally integrated moving average (ARFIMA) model with a wavelet transform used as a method of deseasonalization is examined. It is analysed, whether applying wavelets in order to model the seasonal component in a hydrological time series, is an alternative to moving average deseasonalization in combination with an ARFIMA model. The one-to-ten-steps-ahead forecasting performance of this model is compared with two other models, an ARFIMA model with moving average deseasonalization, and a multiresolution wavelet based model. All models are applied to a time series of mean daily discharge exhibiting long range dependence. For one and two day forecasting horizons, the combined wavelet - ARFIMA approach shows a similar performance as the other models tested. However, for longer forecasting horizons, the wavelet deseasonalization - ARFIMA combination outperforms the other two models. The results show that the wavelets provide an attractive alternative to the moving average deseasonalization.

Keywords : Daily streamflow; Wavelets; ARFIMA; Deseasonalization; Long range dependence; Forecasting


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About the article

Published Online: 2014-02-13

Published in Print: 2014-03-01

Citation Information: Journal of Hydrology and Hydromechanics, Volume 62, Issue 1, Pages 24–32, ISSN (Print) 0042-790X, DOI: https://doi.org/10.2478/johh-2014-0011.

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