Articles | Volume 24, issue 8
https://doi.org/10.5194/nhess-24-2647-2024
https://doi.org/10.5194/nhess-24-2647-2024
Research article
 | 
02 Aug 2024
Research article |  | 02 Aug 2024

Probabilistic flood inundation mapping through copula Bayesian multi-modeling of precipitation products

Francisco Javier Gomez, Keighobad Jafarzadegan, Hamed Moftakhari, and Hamid Moradkhani

Viewed

Total article views: 1,549 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,193 302 54 1,549 20 35 29
  • HTML: 1,193
  • PDF: 302
  • XML: 54
  • Total: 1,549
  • Supplement: 20
  • BibTeX: 35
  • EndNote: 29
Views and downloads (calculated since 21 Feb 2024)
Cumulative views and downloads (calculated since 21 Feb 2024)

Viewed (geographical distribution)

Total article views: 1,549 (including HTML, PDF, and XML) Thereof 1,512 with geography defined and 37 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 14 Aug 2024
Download
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.
Altmetrics
Final-revised paper
Preprint