Articles | Volume 26, issue 7
https://doi.org/10.5194/nhess-26-3129-2026
https://doi.org/10.5194/nhess-26-3129-2026
Research article
 | 
07 Jul 2026
Research article |  | 07 Jul 2026

Evaluation of AI-based seasonal weather ensembles as input for fluvial flood risk estimation: a case study over the Elbe basin

John Ashcroft, Alison Poulston, Marius Koch, Georg Ertl, Kirsty Brown, James Butler, Anthony Hammond, Owen Jordan, Sarah Warren, Rob Lamb, Paul J. Young, and David Wood

Model code and software

NVIDIA Earth2Studio PhysicsNeMo Contributors https://github.com/NVIDIA/earth2studio

Rainfall-Runoff modelling playground Ondřej Čertík et al. https://github.com/kratzert/RRMPG

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Short summary
Floods cause major social and economic losses, but estimating risk is difficult because extreme events are rare. We used artificial intelligence to generate over a thousand realistic winter weather seasons and river flows for the Elbe basin. The approach reproduced observed patterns and produced a wider range of extreme storms, showing that artificial intelligence can expand plausible flood scenarios for improved risk assessment.
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