Articles | Volume 24, issue 8
https://doi.org/10.5194/nhess-24-2647-2024
© Author(s) 2024. 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-24-2647-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
02 Aug 2024
Research article |
| 02 Aug 2024
| 02 Aug 2024
Probabilistic flood inundation mapping through copula Bayesian multi-modeling of precipitation products
Francisco Javier Gomez
CORRESPONDING AUTHOR
Department of Civil, Construction and Environmental Engineering, Center of Complex Hydrosystems Research, The University of Alabama, 35487 Tuscaloosa, USA
Keighobad Jafarzadegan
Department of Civil, Construction and Environmental Engineering, Center of Complex Hydrosystems Research, The University of Alabama, 35487 Tuscaloosa, USA
Hamed Moftakhari
Department of Civil, Construction and Environmental Engineering, Center of Complex Hydrosystems Research, The University of Alabama, 35487 Tuscaloosa, USA
Department of Civil, Construction and Environmental Engineering, Center of Complex Hydrosystems Research, The University of Alabama, 35487 Tuscaloosa, USA
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Cited
15 citations as recorded by crossref.
- Regional Copula Modeling of Rainfall Duration and Intensity: Derivation and Validation of IDF Curves in the Kastoria Basin E. Leivadiotis et al. https://doi.org/10.3390/hydrology13040117
- A probabilistic machine learning framework for daily extreme events forecasting A. Sattari et al. https://doi.org/10.1016/j.eswa.2024.126004
- Accounting for the uncertainty of precipitation forecasts and its impacts on probabilistic flood inundation mapping skill F. Gomez et al. https://doi.org/10.1016/j.jhydrol.2026.135429
- Enhancing streamflow predictions with machine learning and Copula-Embedded Bayesian model averaging A. Sattari et al. https://doi.org/10.1016/j.jhydrol.2024.131986
- Unraveling uncertainty in compound flood modeling: sensitivity of simulations to forcings and model parameters C. Wang et al. https://doi.org/10.1016/j.jhydrol.2026.135424
- Generating Boundary Conditions for Compound Flood Modeling in a Probabilistic Framework P. Maduwantha et al. https://doi.org/10.5194/hess-30-401-2026
- A cluster-based temporal attention approach for predicting cyclone-induced compound flood dynamics S. Daramola et al. https://doi.org/10.1016/j.envsoft.2025.106499
- Physics-Informed Bayesian Markov random field approach for road flooding inference from sparse post-disaster observations S. Cao et al. https://doi.org/10.1016/j.aei.2026.104432
- FLDSensing: Remote Sensing Flood Inundation Mapping with FLDPLN J. Edwards et al. https://doi.org/10.3390/rs17193362
- Performance evaluation and comparison of Delft3DFM and TUFLOW FV for urban flood simulation along Tapi River, India S. Rajendiran et al. https://doi.org/10.1007/s40808-025-02453-5
- A Transformer-Based Probabilistic Deep Learning Framework for Uncertainty-Aware Long-Range Flood Forecasting Using Satellite-Derived Mesoscale Inputs F. Ghobadi et al. https://doi.org/10.1109/JSTARS.2026.3681213
- DEM-based pluvial flood inundation modeling at a metropolitan scale A. Samadi et al. https://doi.org/10.1016/j.envsoft.2024.106226
- Spatial delineation of the compound flood transition zone using deep learning F. Yarveysi et al. https://doi.org/10.1016/j.advwatres.2025.105131
- Enhancing compound flood simulation accuracy and efficiency in urbanized coastal areas using hybrid meshes and modified digital elevation model E. Hamidi et al. https://doi.org/10.1016/j.scs.2025.106184
- Coping with data scarcity in extreme flood forecasting: A deep generative modeling approach A. Sattari & H. Moradkhani https://doi.org/10.1016/j.advwatres.2025.105063
15 citations as recorded by crossref.
- Regional Copula Modeling of Rainfall Duration and Intensity: Derivation and Validation of IDF Curves in the Kastoria Basin E. Leivadiotis et al. https://doi.org/10.3390/hydrology13040117
- A probabilistic machine learning framework for daily extreme events forecasting A. Sattari et al. https://doi.org/10.1016/j.eswa.2024.126004
- Accounting for the uncertainty of precipitation forecasts and its impacts on probabilistic flood inundation mapping skill F. Gomez et al. https://doi.org/10.1016/j.jhydrol.2026.135429
- Enhancing streamflow predictions with machine learning and Copula-Embedded Bayesian model averaging A. Sattari et al. https://doi.org/10.1016/j.jhydrol.2024.131986
- Unraveling uncertainty in compound flood modeling: sensitivity of simulations to forcings and model parameters C. Wang et al. https://doi.org/10.1016/j.jhydrol.2026.135424
- Generating Boundary Conditions for Compound Flood Modeling in a Probabilistic Framework P. Maduwantha et al. https://doi.org/10.5194/hess-30-401-2026
- A cluster-based temporal attention approach for predicting cyclone-induced compound flood dynamics S. Daramola et al. https://doi.org/10.1016/j.envsoft.2025.106499
- Physics-Informed Bayesian Markov random field approach for road flooding inference from sparse post-disaster observations S. Cao et al. https://doi.org/10.1016/j.aei.2026.104432
- FLDSensing: Remote Sensing Flood Inundation Mapping with FLDPLN J. Edwards et al. https://doi.org/10.3390/rs17193362
- Performance evaluation and comparison of Delft3DFM and TUFLOW FV for urban flood simulation along Tapi River, India S. Rajendiran et al. https://doi.org/10.1007/s40808-025-02453-5
- A Transformer-Based Probabilistic Deep Learning Framework for Uncertainty-Aware Long-Range Flood Forecasting Using Satellite-Derived Mesoscale Inputs F. Ghobadi et al. https://doi.org/10.1109/JSTARS.2026.3681213
- DEM-based pluvial flood inundation modeling at a metropolitan scale A. Samadi et al. https://doi.org/10.1016/j.envsoft.2024.106226
- Spatial delineation of the compound flood transition zone using deep learning F. Yarveysi et al. https://doi.org/10.1016/j.advwatres.2025.105131
- Enhancing compound flood simulation accuracy and efficiency in urbanized coastal areas using hybrid meshes and modified digital elevation model E. Hamidi et al. https://doi.org/10.1016/j.scs.2025.106184
- Coping with data scarcity in extreme flood forecasting: A deep generative modeling approach A. Sattari & H. Moradkhani https://doi.org/10.1016/j.advwatres.2025.105063
Saved (final revised paper)
Latest update: 09 Jun 2026
Short summary
This study utilizes the global copula Bayesian model averaging technique for accurate and reliable flood modeling, especially in coastal regions. By integrating multiple precipitation datasets within this framework, we can effectively address sources of error in each dataset, leading to the generation of probabilistic flood maps. The creation of these probabilistic maps is essential for disaster preparedness and mitigation in densely populated areas susceptible to extreme weather events.
This study utilizes the global copula Bayesian model averaging technique for accurate and...
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