Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
Viewed
Since the preprint corresponding to this journal article was posted outside of Copernicus Publications, the preprint-related metrics are limited to HTML views.
Total article views: 2,096 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
BibTeX
EndNote
1,807
229
60
2,096
39
50
HTML: 1,807
PDF: 229
XML: 60
Total: 2,096
BibTeX: 39
EndNote: 50
Views and downloads (calculated since 05 Feb 2026)
Cumulative views and downloads
(calculated since 05 Feb 2026)
Total article views: 881 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
BibTeX
EndNote
610
229
42
881
39
50
HTML: 610
PDF: 229
XML: 42
Total: 881
BibTeX: 39
EndNote: 50
Views and downloads (calculated since 29 Jun 2026)
Cumulative views and downloads
(calculated since 29 Jun 2026)
Total article views: 1,215 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
BibTeX
EndNote
1,197
0
18
1,215
0
0
HTML: 1,197
PDF: 0
XML: 18
Total: 1,215
BibTeX: 0
EndNote: 0
Views and downloads (calculated since 05 Feb 2026)
Cumulative views and downloads
(calculated since 05 Feb 2026)
Viewed (geographical distribution)
Since the preprint corresponding to this journal article was posted outside of Copernicus Publications, the preprint-related metrics are limited to HTML views.
Total article views: 2,096 (including HTML, PDF, and XML)
Thereof 1,966 with geography defined
and 130 with unknown origin.
Total article views: 881 (including HTML, PDF, and XML)
Thereof 732 with geography defined
and 149 with unknown origin.
Total article views: 1,215 (including HTML, PDF, and XML)
Thereof 1,215 with geography defined
and 0 with unknown origin.
Global floods pose serious risks, but existing models are too slow for large-scale prediction. We redesigned the Catchment-based Macro-scale Floodplain (CaMa-Flood) model for graphics processing units (GPUs), reformulating irregular river networks, flux updates, and floodplain dynamics into highly parallel algorithms. CaMa-Flood-GPU runs global simulations in hours instead of days with the same accuracy, enabling larger ensembles, better flood-risk analysis, and improved preparedness worldwide.
Global floods pose serious risks, but existing models are too slow for large-scale prediction....