Uncertainty analysis of coupling multiple hydrologic models and multiple objective functions in Han River, China

Three different hydrological models are chosen to simulate rainfall-runoff relationships under each of three objective functions including mean squared errors of squared transformed flows, squared root transformed flows and logarithmic transformed flows; thus nine individual models are constructed. By weighted averaging over these nine models, the method of Bayesian model averaging (BMA) was used to provide both the mean value and the uncertainty intervals of flow prediction. Three kinds of uncertainty information can be generated: the uncertainty of individual member model's predictions; the total uncertainty of BMA mean prediction; the between-model and within-model uncertainties in the BMA scheme. Based on the estimated results in this study, the coupling of multiple models with multiple objective functions in general offers better results for both the mean prediction and the uncertainty intervals for the runoffs in a selected basin in Han River, China, than the individual models.