Development of a novel storm surge inundation model framework for efficient prediction

Abstract Storm surge is a natural process that causes flood disasters in coastal zones and results in massive casualties and property losses. Therefore, storm surge inundation is of major concern in formulating appropriate strategies for disaster prevention and mitigation. However, traditional storm surge hydrodynamic models have large limits with respect to computational efficiency and stability in practical applications. In this study, a novel storm surge inundation model was developed based on a wetting and drying algorithm established from a simplified shallow-water momentum equation. The wetting and drying algorithm was applied to a rectangular grid that iterates through a cellular automata algorithm to improve computational efficiency. The model, referred to as the Hydrodynamical Cellular Automata Flood Model (HCA-FM), was evaluated by comparing the simulations to regional field observations and to a widely used hydrodynamic numerical model. The comparisons demonstrated that HCA-FM can reproduce the observed inundation distributions and predict results that are consistent with the numerical simulation in terms of the inundation extent and submerged depth with much improved computational efficiency (predicting inundation within a few minutes) and high stability. The results reflect significant advancement of HCA-FM toward efficient predictions of storm surge inundation and applications at large spatial scales.

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch

References and Relationships

• Bibliographic citation: Development of a novel storm surge inundation model framework for efficient prediction ; volume:17 ; number:14 ; year:2024 ; pages:5497-5509 ; extent:13 ; Geoscientific model development ; 17, Heft 14 (2024), 5497-5509 (gesamt 13)

Contributors, Places and Time

• Creator: Gao, Xuanxuan ; Li, Shuiqing ; Mo, Dongxue ; Liu, Yahao ; Hu, Po

Further information

• DOI: 10.5194/gmd-17-5497-2024
• URN: urn:nbn:de:101:1-2407250419034.940509966091
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 14.08.2025, 10:49 AM CEST