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The treatment of input data uncertainty in hydrologic models is of crucial\udimportance in the analysis, diagnosis and detection of model structural\uderrors. Data reduction techniques decrease the dimensionality of input data,\udthus allowing modern parameter estimation algorithms to more efficiently\udestimate errors associated with input uncertainty and model structure. The\uddiscrete cosine transform (DCT) and discrete wavelet transform (DWT) are used\udto reduce the dimensionality of observed rainfall time series for the\ud438 catchments in the Model Parameter Estimation\udExperiment (MOPEX) data\udset. The rainfall time signals are then reconstructed and compared to the\udobserved hyetographs using standard simulation performance summary metrics\udand descriptive statistics. The results convincingly demonstrate that the DWT\udis superior to the DCT in preserving and characterizing the observed rainfall\uddata records. It is recommended that the DWT be used for model input data\udreduction in hydrology in preference over the DCT

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