Improving the Confidence in Hydrologic Model Calibration and Prediction by Transformation of Model Residuals
Publication: Journal of Hydrologic Engineering
Volume 20, Issue 9
Abstract
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.
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Journal of Hydrologic Engineering
Volume 20 • Issue 9 • September 2015
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© 2015 American Society of Civil Engineers.
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Received: Jul 9, 2012
Accepted: Nov 5, 2014
Published online: Jan 5, 2015
Discussion open until: Jun 5, 2015
Published in print: Sep 1, 2015
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