Improving the Confidence in Hydrologic Model Calibration and Prediction by Transformation of Model Residuals

Alireza Safari Myandareh, Florimond De Smedt

Onderzoeksoutput: Articlepeer review

3 Citaten (Scopus)


Calibration of a distributed hydrologic model (WetSpa) for modeling river flows is performed using automatic parameter optimization.
The main purpose of this research is to provide more confidence in the uncertainty analysis of the model parameters and predictions.
A Box-Cox transformation and an autoregressive integrated moving average (ARIMA) time series model are used to transform the correlated
and nonstationary model residuals to white noise disturbances, which can be minimized by ordinary least squares optimization. The WetSpa
model is applied to the Illinois River basin, with a spatial resolution of 30 m and 1 h time step for a 10-year simulation period (1996–2006).
The model is calibrated using river flow records (1996–2002) and validated using the remaining flow data (2002–2006). The results show that
simple calibration of the model is inaccurate, as the residuals exhibit heteroscedasticity, which results in inaccurate estimates of the model
parameters and large prediction uncertainty. The model calibration is improved when the calibration is combined with a Box-Cox transformation
of the discharge and ARIMA modeling of the residuals, which considerably enhances the confidence of the model parameter
estimates and of the model predictions
Originele taal-2English
Aantal pagina's12
TijdschriftJournal of Hydrologic Engineering
Nummer van het tijdschrift9
StatusPublished - 2015


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