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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 21, issue 7
Hydrol. Earth Syst. Sci., 21, 3827–3838, 2017
https://doi.org/10.5194/hess-21-3827-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Hydrol. Earth Syst. Sci., 21, 3827–3838, 2017
https://doi.org/10.5194/hess-21-3827-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 27 Jul 2017

Research article | 27 Jul 2017

A comparison of the discrete cosine and wavelet transforms for hydrologic model input data reduction

Ashley Wright1, Jeffrey P. Walker1, David E. Robertson2, and Valentijn R. N. Pauwels1 Ashley Wright et al.
  • 1Department of Civil Engineering, Monash University, Clayton, Victoria, Australia
  • 2CSIRO, Land and Water, Clayton, Victoria, Australia

Abstract. The treatment of input data uncertainty in hydrologic models is of crucial importance in the analysis, diagnosis and detection of model structural errors. Data reduction techniques decrease the dimensionality of input data, thus allowing modern parameter estimation algorithms to more efficiently estimate errors associated with input uncertainty and model structure. The discrete cosine transform (DCT) and discrete wavelet transform (DWT) are used to reduce the dimensionality of observed rainfall time series for the 438 catchments in the Model Parameter Estimation Experiment (MOPEX) data set. The rainfall time signals are then reconstructed and compared to the observed hyetographs using standard simulation performance summary metrics and descriptive statistics. The results convincingly demonstrate that the DWT is superior to the DCT in preserving and characterizing the observed rainfall data records. It is recommended that the DWT be used for model input data reduction in hydrology in preference over the DCT.

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The accurate reduction of hydrologic model input data is an impediment towards understanding input uncertainty and model structural errors. This paper compares the ability of two transforms to reduce rainfall input data. The resultant transforms are compressed to varying extents and reconstructed before being evaluated with standard simulation performance summary metrics and descriptive statistics. It is concluded the discrete wavelet transform is most capable of preserving rainfall time series.
The accurate reduction of hydrologic model input data is an impediment towards understanding...
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