A Modified Particle Filter‐Based Data Assimilation Method for a High‐Precision 2‐D Hydrodynamic Model Considering Spatial‐temporal Variability of Roughness: Simulation of Dam‐Break Flood Inundation

The particle filter‐based data assimilation method is an effective tool to adjust model states based on observations. In this study, we proposed a modified particle filter‐based data assimilation method with a local weighting procedure (MPFDA‐LW) for a high‐precision two‐dimensional hydrodynamic model (HydroM2D) in dam‐break flood simulation. Moreover, a particle filter‐based data assimilation method with a global weighting procedure (PFDA‐GW) for the HydroM2D model was also investigated. The MPFDA‐LW and the PFDA‐GW for the HydroM2D model, respectively, adopted spatially nonuniform and uniform Manning's roughness coefficients. The MPFDA‐LW considering spatial‐temporal variability of Manning's roughness coefficient could significantly improve the performances of the HydroM2D model in simulating water stages at all gauges simultaneously, whereas the PFDA‐GW considering temporal variability of Manning's roughness coefficient could only slightly improve the performances of the HydroM2D model in simulating water stages at a few gauges. The MPFDA‐LW is more suitable for improving the performance of 2‐D hydrodynamic models in flood inundation simulation than the PFDA‐GW. Key Points We proposed a data assimilation framework for a high‐precision 2‐D hydrodynamic model (HydroM2D) based on a modified PF method The PF‐based data assimilation (PFDA) method for the HydroM2D model considered the spatial‐temporal variability of Manning's roughness coefficient The PF‐based data assimilation could simultaneously improve the performances of the HydroM2D in simulating water stages at all gauges