In order to reduce the uncertainty of the numerical model, the particle filter method is introduced into the calculation of one-dimensional unsteady flow to establish a multivariable correction method for water quantity simulation. The optimal estimation of the state variables is realized by statistically estimating the water state particles. Taking the flood process of a single channel as an example, the performance of the proposed method was discussed and the influence of particle number, state variable disturbance and uncertainty of the boundary condition on the filtering effect was analyzed. The performance of particle filter and ensemble Kalman filter was compared in terms of accuracy, stability and efficiency. The results show that 100 particles can guarantee both the filtering effect and computational efficiency. The best filtering effect can be obtained when the water level disturbance variance and the flow disturbance variance are both 10 percent of the actual value. When the relative error of the boundary condition is within 20%, the simulated precision can be over 94%. The particle filter is slightly less accurate and stable than the ensemble Kalman filter, but its computational efficiency is about 2. 3 times higher than that of the Kalman filter. Application to the river network water quantity correction in the Taihu Basin shows that particle filter can significantly improve the simulation accuracy.