Articles | Volume 21, issue 8
https://doi.org/10.5194/nhess-21-2523-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-21-2523-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 Aug 2021
Research article |
| 25 Aug 2021
| 25 Aug 2021
Towards an efficient storm surge and inundation forecasting system over the Bengal delta: chasing the Supercyclone Amphan
Md. Jamal Uddin Khan
CORRESPONDING AUTHOR
LEGOS UMR5566, CNRS/CNES/IRD/UPS, 31400 Toulouse, France
Fabien Durand
LEGOS UMR5566, CNRS/CNES/IRD/UPS, 31400 Toulouse, France
Laboratório de Geoquímica, Instituto de Geociencias, Universidade de Brasilia, Brasília, Brazil
Xavier Bertin
UMR 7266 LIENSs, CNRS – La Rochelle University, 17000 La Rochelle, France
Laurent Testut
LEGOS UMR5566, CNRS/CNES/IRD/UPS, 31400 Toulouse, France
UMR 7266 LIENSs, CNRS – La Rochelle University, 17000 La Rochelle, France
Yann Krien
UMR 7266 LIENSs, CNRS – La Rochelle University, 17000 La Rochelle, France
currently at: SHOM (DOPS/STM/REC), Toulouse, France
A. K. M. Saiful Islam
Institute of Water and Flood Management (IWFM), Bangladesh University of Engineering and Technology (BUET),
Dhaka-1000, Bangladesh
Marc Pezerat
UMR 7266 LIENSs, CNRS – La Rochelle University, 17000 La Rochelle, France
Sazzad Hossain
Flood Forecasting and Warning Centre, BWDB, Dhaka, Bangladesh
Department of Geography and Environmental Science, University of Reading, Reading, UK
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Cited
16 citations as recorded by crossref.
- Influence of wave setup and tide-surge interaction on storm surges in the Bay of Bengal V. Adithyan et al. 10.1007/s11069-024-07038-6
- A novel deep learning approach for typhoon-induced storm surge modeling through efficient emulation of wind and pressure fields I. Mulia et al. 10.1038/s41598-023-35093-9
- Effects of tropical cyclone Amphan on the copepods of the Ganges estuary S. Paul et al. 10.1080/17451000.2023.2235591
- Development of Storm Surge Inundation Model and Database for Enhanced Climate Services in Bangladesh A. Rezaie & A. Haque 10.3389/frwa.2022.887631
- Wave-induced mean currents and setup over barred and steep sandy beaches K. Martins et al. 10.1016/j.ocemod.2022.102110
- Research on and application of the operational storm surge ensemble forecast model in the Bay of Bengal Q. Liu et al. 10.1016/j.apor.2022.103413
- Observed oceanic response to Tropical Cyclone Amphan (2020) from a subsurface mooring in the Bay of Bengal Y. Peng et al. 10.1016/j.pocean.2023.103148
- Bangladesh's vulnerability to cyclonic coastal flooding A. Bernard et al. 10.5194/nhess-22-729-2022
- Storm surge hazard over Bengal delta: a probabilistic–deterministic modelling approach M. Khan et al. 10.5194/nhess-22-2359-2022
- Understanding hazards: Probabilistic cyclone modelling for disaster risk to the Eastern Coast in Bangladesh B. Fakhruddin et al. 10.1016/j.pdisas.2022.100216
- Increased population exposure to Amphan‐scale cyclones under future climates D. Mitchell et al. 10.1002/cli2.36
- Novel optimized deep learning algorithms and explainable artificial intelligence for storm surge susceptibility modeling and management in a flood-prone island M. Alshayeb et al. 10.1007/s11069-024-06414-6
- Impact of Cyclone Yaas 2021 Aggravated by COVID-19 Pandemic in the Southwest Coastal Zone of Bangladesh R. Subhani et al. 10.3390/su132313324
- Modeling of impact assessment of super cyclone Amphan with machine learning algorithms in Sundarban Biosphere Reserve, India T. Nasrin et al. 10.1007/s11069-023-05935-w
- Influence of Grid Resolution and Assimilation Window Size on Simulating Storm Surge Levels X. Bi et al. 10.3390/jmse12071233
- A new tropical cyclone surge index incorporating the effects of coastal geometry, bathymetry and storm information M. Islam et al. 10.1038/s41598-021-95825-7
15 citations as recorded by crossref.
- Influence of wave setup and tide-surge interaction on storm surges in the Bay of Bengal V. Adithyan et al. 10.1007/s11069-024-07038-6
- A novel deep learning approach for typhoon-induced storm surge modeling through efficient emulation of wind and pressure fields I. Mulia et al. 10.1038/s41598-023-35093-9
- Effects of tropical cyclone Amphan on the copepods of the Ganges estuary S. Paul et al. 10.1080/17451000.2023.2235591
- Development of Storm Surge Inundation Model and Database for Enhanced Climate Services in Bangladesh A. Rezaie & A. Haque 10.3389/frwa.2022.887631
- Wave-induced mean currents and setup over barred and steep sandy beaches K. Martins et al. 10.1016/j.ocemod.2022.102110
- Research on and application of the operational storm surge ensemble forecast model in the Bay of Bengal Q. Liu et al. 10.1016/j.apor.2022.103413
- Observed oceanic response to Tropical Cyclone Amphan (2020) from a subsurface mooring in the Bay of Bengal Y. Peng et al. 10.1016/j.pocean.2023.103148
- Bangladesh's vulnerability to cyclonic coastal flooding A. Bernard et al. 10.5194/nhess-22-729-2022
- Storm surge hazard over Bengal delta: a probabilistic–deterministic modelling approach M. Khan et al. 10.5194/nhess-22-2359-2022
- Understanding hazards: Probabilistic cyclone modelling for disaster risk to the Eastern Coast in Bangladesh B. Fakhruddin et al. 10.1016/j.pdisas.2022.100216
- Increased population exposure to Amphan‐scale cyclones under future climates D. Mitchell et al. 10.1002/cli2.36
- Novel optimized deep learning algorithms and explainable artificial intelligence for storm surge susceptibility modeling and management in a flood-prone island M. Alshayeb et al. 10.1007/s11069-024-06414-6
- Impact of Cyclone Yaas 2021 Aggravated by COVID-19 Pandemic in the Southwest Coastal Zone of Bangladesh R. Subhani et al. 10.3390/su132313324
- Modeling of impact assessment of super cyclone Amphan with machine learning algorithms in Sundarban Biosphere Reserve, India T. Nasrin et al. 10.1007/s11069-023-05935-w
- Influence of Grid Resolution and Assimilation Window Size on Simulating Storm Surge Levels X. Bi et al. 10.3390/jmse12071233
Latest update: 13 Dec 2024
Short summary
The Bay of Bengal is well known for some of the deadliest cyclones in history. At the same time, storm surge forecasting in this region is physically involved and computationally costly. Here we show a proof of concept of a real-time, computationally efficient, and physically consistent forecasting system with an application to the recent Supercyclone Amphan. While challenges remain, our study paves the path forward to the improvement of the quality of localized forecast and disaster management.
The Bay of Bengal is well known for some of the deadliest cyclones in history. At the same time,...
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