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.