Dispersion model is an important tool for decision makers to accurately assess risks and effectively plan countermeasures during river pollution accidents, but their applications suffer from significant modeling uncertainties, primarily due to the scarce information of the source. A fully sequential inverse estimation method is proposed to reconstruct the temporal release for accidental river pollution. The method is based on a one-dimensional advection-dispersion model in conjunction with the augmented ensemble Kalman filter method. Detailed analysis of the ensemble background error covariance (BEC) matrix is conducted to elucidate the "flow-dependent" mechanism, which enables the inversion method to simultaneously take into account the uncertainties in the hydrological parameters (mean flow velocity and longitudinal dispersion coefficient). The method is evaluated with six field tracer experiments with various mean flow velocities, ranging from 0.085 to 0.889 m s(-1), and also compared with the commonly used Tikhonov regularization inverse estimation method to demonstrate its performance improvement. The results indicate that it successfully reconstructs the temporal release and reduces the relative errors of the total release estimation by about 12.4% on average compared with the Tikhonov method, since the errors caused by the uncertainties in mean flow velocity and longitudinal dispersion coefficient are effectively alleviated. (C) 2017 Elsevier B.V. All rights reserved.