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
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
Viewed
Total article views: 3,145 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
Supplement
BibTeX
EndNote
2,454
615
76
3,145
76
87
116
HTML: 2,454
PDF: 615
XML: 76
Total: 3,145
Supplement: 76
BibTeX: 87
EndNote: 116
Views and downloads (calculated since 21 Feb 2024)
Cumulative views and downloads
(calculated since 21 Feb 2024)
Total article views: 2,095 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
Supplement
BibTeX
EndNote
1,771
293
31
2,095
76
46
76
HTML: 1,771
PDF: 293
XML: 31
Total: 2,095
Supplement: 76
BibTeX: 46
EndNote: 76
Views and downloads (calculated since 02 Aug 2024)
Cumulative views and downloads
(calculated since 02 Aug 2024)
Total article views: 1,050 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
BibTeX
EndNote
683
322
45
1,050
41
40
HTML: 683
PDF: 322
XML: 45
Total: 1,050
BibTeX: 41
EndNote: 40
Views and downloads (calculated since 21 Feb 2024)
Cumulative views and downloads
(calculated since 21 Feb 2024)
Viewed (geographical distribution)
Total article views: 3,145 (including HTML, PDF, and XML)
Thereof 3,064 with geography defined
and 81 with unknown origin.
Total article views: 2,095 (including HTML, PDF, and XML)
Thereof 2,043 with geography defined
and 52 with unknown origin.
Total article views: 1,050 (including HTML, PDF, and XML)
Thereof 1,021 with geography defined
and 29 with unknown origin.
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...
