Abstract: To improve the treatment of uncertainty in hydrological modeling, a hybrid framework of simultaneous optimization and data assimilation (SODA) was adopt to assess the uncertainty of HyMOD model in this paper, and then was compared with the classical method of the Shuffled Complex Evolution Metropolis-UA (SCEM-UA). The strengths of the SODA can be described as follows (1) high parameter search efficiency and explorative capabilities; (2) explicit treatment of the various important sources of uncertainty (i.e., input, output, parameter and model structure uncertainties) associated with the application of hydrological models. The results of the SODA applied in the Weihe River Basin demonstrate that in comparison to the performances of SCEM-UA, the SODA could notably improve the streamflow prediction efficiency, and also could derive more accurate prediction interval. The successful application of the SODA is helpful to improving model concepts and understanding of the functioning of hydrological systems.

Key words: simultaneous optimization and data assimilation (SODA), HyMOD, hydrological modeling uncertainty, Shuffled Complex Evolution Metropolis-University of Arizona(SCEM-UA), Ensemble Kalman Filter(EnKF)