Preprints
https://doi.org/10.5194/gmd-2024-12
https://doi.org/10.5194/gmd-2024-12
Submitted as: development and technical paper
 | 
05 Feb 2024
Submitted as: development and technical paper |  | 05 Feb 2024
Status: this preprint is currently under review for the journal GMD.

Development of a Novel Storm Surge Inundation Model Framework for Efficient Prediction

Xuanxuan Gao, Shuiqing Li, Dongxue Mo, Yahao Liu, and Po Hu

Abstract. Storm surge is a natural process that generates flood disasters in coastal zone and causes massive casualties and property losses. Therefore, the 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 on 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 simplified shallow water momentum equation. The wetting and drying algorithm was applied to rectangle grid that iterates through 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 that from a widely used hydrodynamic numerical model, respectively. The comparisons demonstrated that HCA‐FM can reproduce the observed inundation distributions, and predict consistent results with the numerical simulation in terms of the inundation extent and submerged depth, with much improved computational efficiency (predict 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 space scales.

Xuanxuan Gao, Shuiqing Li, Dongxue Mo, Yahao Liu, and Po Hu

Status: open (until 01 Apr 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2024-12', Anonymous Referee #1, 26 Feb 2024 reply
Xuanxuan Gao, Shuiqing Li, Dongxue Mo, Yahao Liu, and Po Hu

Data sets

Data for HCA-FM Manuscript Submitted to GMD X. Gao, S. Li, D. Mo, Y. Liu, and P. Hu https://doi.org/10.5281/zenodo.10596631

Model code and software

HCA-FM code and instruction X. Gao https://doi.org/10.5281/zenodo.10596826

Xuanxuan Gao, Shuiqing Li, Dongxue Mo, Yahao Liu, and Po Hu

Viewed

Total article views: 149 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
124 20 5 149 14 5 2
  • HTML: 124
  • PDF: 20
  • XML: 5
  • Total: 149
  • Supplement: 14
  • BibTeX: 5
  • EndNote: 2
Views and downloads (calculated since 05 Feb 2024)
Cumulative views and downloads (calculated since 05 Feb 2024)

Viewed (geographical distribution)

Total article views: 147 (including HTML, PDF, and XML) Thereof 147 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 02 Mar 2024
Download
Short summary
Storm surges generate coastal inundation and expose populations and properties in danger. We developed a novel storm surge inundation model for efficient prediction. Estimates compare well with in-situ measurements and results from a numerical model. The new model significantly improves over the existing numerical models with much higher computational efficiency and stability, which allows timely disaster prevention and mitigation.