Flood exposure and structure damage under deterministic design-storm and stochastic storm transposition rainfall scenarios: a case study in South Louisiana, USA

ElSaadani, Mohamed; Habib, Emad; Morsy, Mohamed M.

doi:10.5194/nhess-26-5201-2026

Articles | Volume 26, issue 10

https://doi.org/10.5194/nhess-26-5201-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/nhess-26-5201-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 26, issue 10

Research article

| Highlight paper

|

23 Oct 2026

Research article | Highlight paper |

| 23 Oct 2026

Flood exposure and structure damage under deterministic design-storm and stochastic storm transposition rainfall scenarios: a case study in South Louisiana, USA

Mohamed ElSaadani, Emad Habib, and Mohamed M. Morsy

Supplement

https://doi.org/10.5194/nhess-26-5201-2026-supplement

Data sets

Vermilion design storms: analysis code and SST rainfall realizations M. ElSaadani et al. https://doi.org/10.5281/zenodo.22988703

Model code and software

Vermilion design storms: analysis code and SST rainfall realizations M. ElSaadani et al. https://doi.org/10.5281/zenodo.22988703

Go-consequences USACE https://github.com/USACE/go-consequences

Video supplement

Vermilion design storms: two-minute NHESS highlight video M. ElSaadani et al. https://doi.org/10.5281/zenodo.23086954

Video abstract

Vermilion design storms: two-minute NHESS highlight video M. ElSaadani et al. https://doi.org/10.5281/zenodo.23086954

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Editorial statement

The paper makes an important methodological contribution by demonstrating that conventional spatially uniform design storms can substantially underestimate localized flood exposure and structural damage, whereas stochastic storm transposition (SST) captures the spatial organization and variability of real storms and reveals previously overlooked risks. The paper can be considered a highlight paper because it provides compelling evidence that representing spatial rainfall variability can fundamentally alter flood-risk estimates, with direct implications for floodplain management, infrastructure design, and climate-resilient planning.