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: 2,978 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
BibTeX
EndNote
2,410
432
136
2,978
91
120
HTML: 2,410
PDF: 432
XML: 136
Total: 2,978
BibTeX: 91
EndNote: 120
Views and downloads (calculated since 05 Feb 2026)
Cumulative views and downloads
(calculated since 05 Feb 2026)
Total article views: 1,621 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
BibTeX
EndNote
1,072
432
117
1,621
91
120
HTML: 1,072
PDF: 432
XML: 117
Total: 1,621
BibTeX: 91
EndNote: 120
Views and downloads (calculated since 29 Jun 2026)
Cumulative views and downloads
(calculated since 29 Jun 2026)
Total article views: 1,357 (including HTML, PDF, and XML)
HTML
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XML
Total
BibTeX
EndNote
1,338
0
19
1,357
0
0
HTML: 1,338
PDF: 0
XML: 19
Total: 1,357
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: 2,978 (including HTML, PDF, and XML)
Thereof 2,848 with geography defined
and 130 with unknown origin.
Total article views: 1,621 (including HTML, PDF, and XML)
Thereof 1,472 with geography defined
and 149 with unknown origin.
Total article views: 1,357 (including HTML, PDF, and XML)
Thereof 1,357 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....