An improved dynamic bidirectional coupled hydrologic-hydrodynamic model for efficient flood inundation prediction

Shen, Yanxia; Zhu, Zhenduo; Zhou, Qi; Jiang, Chunbo

To improve computational efficiency while maintaining numerical accuracy, coupled hydrologic-hydrodynamic models based on non-uniform grids are used for flood inundation prediction. In those models, a hydrodynamic model using a fine grid can be applied for flood-prone areas, and a hydrologic model using a coarse grid can be used for the rest of the areas. However, it is challenging to deal with the separation and interface between the two types of areas because the boundaries of the flood-prone areas are time-dependent. We present an improved Multigrid Dynamical Bidirectional Coupled hydrologic-hydrodynamic Model (IM-DBCM) with two major improvements: 1) automated non-uniform mesh generation based on the D∞ algorithm was implemented to identify the flood-prone areas where high-resolution inundation conditions are needed; 2) ghost cells and bilinear interpolation were implemented to improve numerical accuracy in interpolating variables between the coarse and fine grids. A hydrologic model, two-dimensional (2D) nonlinear reservoir (NLR) model was bidirectionally coupled with a 2D hydrodynamic model that solves the shallow water equations. Three cases were considered to demonstrate the effectiveness of the improvements. In all cases, the mesh generation algorithm was shown to efficiently and successfully generate high-resolution grids only in those flood-prone areas. Compared with the original M-DBCM (OM-DBCM), the new model had lower RMSEs and higher NSEs, indicating that the proposed mesh generation and interpolation were reliable and stable. It can be adapted adequately to the real-life real flood evolution process in watersheds and provide practical and reliable solutions for rapid flood prediction.

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Shen, Yanxia / Zhu, Zhenduo / Zhou, Qi / et al: An improved dynamic bidirectional coupled hydrologic-hydrodynamic model for efficient flood inundation prediction. 2023. Copernicus Publications.

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