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Hydrological model performance and parameter estimation in the wavelet-domain

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Academic year: 2017

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Fig. 2. Estimated wavelet-power for the Rhone case study: (a) wavelet periodogram of the observed discharge (zoom on the 2nd half of the available time series, black line: cone of influence), (b) wavelet periodogram of the simulated discharge, (c) average
Table 1. Exact parameter values of the ARMAX process, inter- inter-vals delimiting the search space for parameter estimation and the identified best parameter sets under R W , R F and R N (columns 5–
Fig. 3. Parameter sensitivity around the optimum value identified under the different calibration criteria; top: experiment 2, the most sensitive parameter a snow (a), and the least sensitive parameter β (b), the other parameters are kept constant to the v
Fig. 6. Real-world case study: scatter-plot of observed discharge (during calibration period) against best simulation under each of the two calibration criteria.
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