Preprints
https://doi.org/10.5194/nhess-2024-50
https://doi.org/10.5194/nhess-2024-50
29 Apr 2024
 | 29 Apr 2024
Status: this preprint is currently under review for the journal NHESS.

Simulating the Ahr valley 2021 flood event: a comparative assessment of 2D shallow water solvers for early flood warning

Shahin Khosh Bin Ghomash, Heiko Apel, and Daniel Caviedes-Voullième

Abstract. Flash floods pose a distinct challenge compared to traditional fluvial flooding, with infrastructure-based solutions proving less effective. Effective responses hinge on advanced early warning systems providing actionable information, emphasising the necessity for computational flood forecasting models. However, hydrodynamic models, renowned for accuracy and completeness, face limitations due to computational intensity.

This study explores two 2D flood forecasting models, RIM2D and SERGHEI, both with GPU implementations which allow to maximise the forecast lead time. While RIM2D is less computationally intensive, suitable for operational use, SERGHEI, with higher computational costs, targets large-scale High-Performance Computing (HPC) systems.

The assessment of applicability and trade-offs is carried out on the 2021 Eifel flood event, particularly in the lower Ahr valley. A set of simulations were performed at various resolutions from 1 m to 10 m, which reveal similar accuracy among both models at coarser resolutions, yet discrepancies arise at finer resolutions due to the distinct formulations. Both models exhibit a rapid computational cost escalation, but at resolutions equal to or coarser than 5m, forecasts are remarkably faster than real-time—ideal for operational use, paving the way for their use in early warning systems. However, higher resolutions necessitate multi-GPU and HPC capabilities, underlining the importance of embracing such technology in addressing broader flood domains.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Shahin Khosh Bin Ghomash, Heiko Apel, and Daniel Caviedes-Voullième

Status: open (until 10 Jun 2024)

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  • RC1: 'Comment on nhess-2024-50', Anonymous Referee #1, 19 May 2024 reply
Shahin Khosh Bin Ghomash, Heiko Apel, and Daniel Caviedes-Voullième

Model code and software

SERGHEI D. Caviedes-Voullième et al. https://gitlab.com/serghei-model/serghei

Shahin Khosh Bin Ghomash, Heiko Apel, and Daniel Caviedes-Voullième

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Short summary
Early warning is essential to minimise the impact of flash floods. We explore the use of highly detailed flood models to simulate the 2021 flood event in the lower Ahr valley (Germany). Using very high resolution models resolving individual streets and buildings in, we produce detailed, quantitative and actionable information for early flood warning systems. Using state-of-the-art computational technology, these models can guarantee very fast forecasts which allow for sufficient time to respond.
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