A coupled ensemble filtering and probabilistic collocation approach for uncertainty quantification of hydrological models

In this study, a coupled ensemble filtering and probabilistic collocation (EFPC) approach is proposed for uncertainty quantification of hydrologic models. This approach combines the capabilities of the ensemble Kalman filter (EnKF) and the probabilistic collocation method (PCM) to provide a better treatment of uncertainties in hydrologic models. The EnKF method would be employed to approximate the posterior probabilities of model parameters and improve the forecasting accuracy based on the observed measurements; the PCM approach is proposed to construct a model response surface in terms of the posterior probabilities of model parameters to reveal uncertainty propagation from model parameters to model outputs. The proposed method is applied to the Xiangxi River, located in the Three Gorges Reservoir area of China. The results indicate that the proposed EFPC approach can effectively quantify the uncertainty of hydrologic models. Even for a simple conceptual hydrological model, the efficiency of EFPC approach is about 10 times faster than traditional Monte Carlo method without obvious decrease in prediction accuracy. Finally, the results can explicitly reveal the contributions of model parameters to the total variance of model predictions during the simulation period.