Fugeshi River flooding: the effects of the 2024 Noto Peninsula earthquake and climate change

Abstract This study investigates compound flooding on the Fugeshi River (Noto Peninsula, Japan) in the aftermath of the January 2024 Noto Peninsula earthquake (M7.6). The authors aimed to quantify the impacts of ground deformation on flooding, while integrating the historical evolution of the floodplain and potential climate change effects by simulating sea-level rise. The method is based on the analysis of aerial photographs (1947–2024), pre/post-earthquake LiDAR data, rainfall records (1944–2024), and 2D hydraulic modeling to identify flood vulnerability points from the LiDAR-derived DEMs pre- and post-earthquake. The main findings are that: (1) Rainfall intensity increased by 0.132 mm/year since 1944, with events > 14 mm/h rising by 1.4% over 70 years; (2) earthquake-induced ground deformation (1–3 m uplift) created new flooding patterns at the transition between artificially straightened downstream reaches and meandering upstream sections; (3) river modifications (1947–1965) established uneven flood protection, with smaller stopbanks upstream creating vulnerability points. In conclusion, the convergence of climate change–induced rainfall intensification, seismic ground deformation, and river engineering legacy effects amplifies flood hazards in coastal regions, highlighting the need for integrated multi-hazard flood management approaches.

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Publication Details

Journal
Journal of Disaster Science and Management
Published
2025-05-15
DOI
https://doi.org/10.1007/s44367-025-00009-z
Citations
1
Primary Topic
Flood Risk Assessment and Management
Type
article
Field-Weighted Citation Impact
1.11
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article

Fugeshi River flooding: the effects of the 2024 Noto Peninsula earthquake and climate change

Yutaka Gonda, Daiichirou Hori, Liu Caihong, Takashi Koi et al.
1 citations
Journal of Disaster Science and Management
Flood Risk Assessment and Management
1.11
article

Fugeshi River flooding: the effects of the 2024 Noto Peninsula earthquake and climate change

Yutaka Gonda, Daiichirou Hori, Liu Caihong, Takashi Koi, Christopher Gomez, Balázs Bradák, Rikuto Daikai, Daizo Tsutsumi, Norifumi Hotta, M. Zhang, Z. Huazhu
article en
1 citations

Abstract

Abstract This study investigates compound flooding on the Fugeshi River (Noto Peninsula, Japan) in the aftermath of the January 2024 Noto Peninsula earthquake (M7.6). The authors aimed to quantify the impacts of ground deformation on flooding, while integrating the historical evolution of the floodplain and potential climate change effects by simulating sea-level rise. The method is based on the analysis of aerial photographs (1947–2024), pre/post-earthquake LiDAR data, rainfall records (1944–2024), and 2D hydraulic modeling to identify flood vulnerability points from the LiDAR-derived DEMs pre- and post-earthquake. The main findings are that: (1) Rainfall intensity increased by 0.132 mm/year since 1944, with events > 14 mm/h rising by 1.4% over 70 years; (2) earthquake-induced ground deformation (1–3 m uplift) created new flooding patterns at the transition between artificially straightened downstream reaches and meandering upstream sections; (3) river modifications (1947–1965) established uneven flood protection, with smaller stopbanks upstream creating vulnerability points. In conclusion, the convergence of climate change–induced rainfall intensification, seismic ground deformation, and river engineering legacy effects amplifies flood hazards in coastal regions, highlighting the need for integrated multi-hazard flood management approaches.

Journal of Disaster Science and ManagementVol. 1(1)
Kobe Design University (JP), Kobe City University of Foreign Studies (JP), Shinshu University (JP), Universitas Gadjah Mada (ID), Hokkaido University (JP), Kokusai Junior College (JP), China University of Geosciences (Beijing) (CN), Kobe University (JP), East China Normal University (CN), Niigata University (JP), The University of Tokyo (JP)
Openalex Percentile: Top 24%
Flood Risk Assessment and Management
1.11
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