Articles | Volume 26, issue 7
https://doi.org/10.5194/nhess-26-3129-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-26-3129-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Evaluation of AI-based seasonal weather ensembles as input for fluvial flood risk estimation: a case study over the Elbe basin
JBA Risk Management, Skipton, BD23 3FD, United Kingdom
Alison Poulston
JBA Risk Management, Skipton, BD23 3FD, United Kingdom
Marius Koch
Nvidia Corporation, 2788 San Tomas Expressway, Santa Clara, CA 95051, USA
Georg Ertl
Nvidia Corporation, 2788 San Tomas Expressway, Santa Clara, CA 95051, USA
Kirsty Brown
JBA Risk Management, Skipton, BD23 3FD, United Kingdom
James Butler
JBA Risk Management, Skipton, BD23 3FD, United Kingdom
Anthony Hammond
JBA Consulting, Skipton, BD23 3FD, United Kingdom
Owen Jordan
JBA Risk Management, Skipton, BD23 3FD, United Kingdom
Sarah Warren
JBA Consulting, Skipton, BD23 3FD, United Kingdom
JBA Trust, Skipton, BD23 3FD, United Kingdom
Lancaster Environment Centre, Lancaster University, Lancaster, LA1 4YQ, United Kingdom
Paul J. Young
JBA Risk Management, Skipton, BD23 3FD, United Kingdom
School of Engineering, Newcastle University, Newcastle, NE1 7RU, United Kingdom
David Wood
JBA Risk Management, Skipton, BD23 3FD, United Kingdom
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Lily Gouldsbrough, Ryan Hossaini, Emma Eastoe, Paul J. Young, and Massimo Vieno
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High-resolution spatial fields of surface ozone are used to understand spikes in ozone concentration and predict their impact on public health. Such fields are routinely output from complex mathematical models for atmospheric conditions. These outputs are on a coarse spatial resolution and the highest concentrations tend to be biased. Using a novel data-driven machine learning methodology, we show how such output can be corrected to produce fields with both lower bias and higher resolution.
Enrico Tubaldi, Christopher J. White, Edoardo Patelli, Stergios Aristoteles Mitoulis, Gustavo de Almeida, Jim Brown, Michael Cranston, Martin Hardman, Eftychia Koursari, Rob Lamb, Hazel McDonald, Richard Mathews, Richard Newell, Alonso Pizarro, Marta Roca, and Daniele Zonta
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Bridges are critical infrastructure components of transport networks. A large number of these critical assets cross or are adjacent to waterways and are therefore exposed to the potentially devastating impact of floods. This paper discusses a series of issues and areas where improvements in research and practice are required in the context of risk assessment and management of bridges exposed to flood hazard, with the ultimate goal of guiding future efforts in improving bridge flood resilience.
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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.
Floods cause major social and economic losses, but estimating risk is difficult because extreme...
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