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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/hess-2017-26
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
06 Feb 2017
Review status
A revision of this discussion paper is under review for the journal Hydrology and Earth System Sciences (HESS).
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 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. Model input 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 rainfall time series observations from the 438 catchments in the MOdel Parameter Estimation eXperiment (MOPEX) data set. The rainfall time signals are then reconstructed and compared to the measured hyetographs using standard simulation performance summary metrics and descriptive statistics as well as peak discharge errors. The results convincingly demonstrate that the DWT is superior to the DCT and best preserves and characterizes 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.

Citation: Wright, A., Walker, J. P., Robertson, D. E., and Pauwels, V. R. N.: A Comparison of the Discrete Cosine and Wavelet Transforms for Hydrologic Model Input Data Reduction, Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-26, in review, 2017.
Ashley Wright et al.
Ashley Wright et al.
Ashley Wright et al.

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Short summary
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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