A comparison of the discrete cosine and wavelet transforms for hydrologic model input data reduction
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2017-07-01
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| Series: | Hydrology and Earth System Sciences |
| Online Access: | https://www.hydrol-earth-syst-sci.net/21/3827/2017/hess-21-3827-2017.pdf |
| Summary: | 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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| ISSN: | 1027-5606 1607-7938 |