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

Authors :
Y. Shen
Z. Zhu
Q. Zhou
C. Jiang
Source :
Natural Hazards and Earth System Sciences, Vol 24, Pp 2315-2330 (2024)
Publication Year :
2024
Publisher :
Copernicus Publications, 2024.

Abstract

To improve computational efficiency while maintaining numerical accuracy, coupled hydrologic–hydrodynamic models based on non-uniform grids are used for flood inundation prediction. In these models, a hydrodynamic model using a fine grid can be applied to flood-prone areas, and a hydrologic model using a coarse grid can be used for the remaining 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-infinity algorithm was implemented to identify the flood-prone areas where high-resolution inundation conditions are needed and (2) ghost cells and bilinear interpolation were implemented to improve numerical accuracy in interpolating variables between the coarse and fine grids. A hydrologic model, the 2D nonlinear reservoir 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 in those flood-prone areas. Compared to the original M-DBCM (OM-DBCM), the new model had lower root-mean square errors (RMSEs) and higher Nash–Sutcliffe efficiencies (NSEs), indicating that the proposed mesh generation and interpolation were reliable and stable. It can be adequately adapted to the real-life flood evolution process in watersheds and provide practical and reliable solutions for rapid flood prediction.

Details

Language :
English
ISSN :
15618633 and 16849981
Volume :
24
Database :
Directory of Open Access Journals
Journal :
Natural Hazards and Earth System Sciences
Publication Type :
Academic Journal
Accession number :
edsdoj.57605e97cc254c528fead73a0dad92e6
Document Type :
article
Full Text :
https://doi.org/10.5194/nhess-24-2315-2024