“…The PF is a sequential data assimilation method utilizing Monte Carlo ensemble filter methods (Moradkhani, Hsu, et al, ). Compared to the widely used EnKF, the PF can relax the assumption of the EnKF that the prior distributions of model states are Gaussian distributions (K. Kim et al, ; J. C. Neal et al, ) and is suitable for nonlinear and non‐Gaussian hydrodynamic models and water environment models (Mattern et al, ; Xu et al, ). For the nonlinear and non‐Gaussian HydroM2D model, its recursive formulation of model states can be expressed as where f is the HydroM2D model; x t +1 and x t are, respectively, the model states (water stages and discharges) at t + 1 and t time steps; θ t + 1 and u t +1 are, respectively, the model parameter (Manning's roughness coefficient) and forcing data (inflow boundary conditions) at t + 1 time step; and v t +1 is the random white noise associated with the uncertainty of the HydroM2D model structure at t + 1 time step with a mean of zero and standard deviation σ m .…”