Articles | Volume 21, issue 3
https://doi.org/10.5194/nhess-21-1071-2021
© Author(s) 2021. 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-21-1071-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
23 Mar 2021
Research article |
| 23 Mar 2021
| 23 Mar 2021
The uncertainty of flood frequency analyses in hydrodynamic model simulations
Xudong Zhou
CORRESPONDING AUTHOR
Institute of Industrial Science, The University of Tokyo, 4-6-1, Komaba, Meguro-ku, Tokyo 153-8505, Japan
Wenchao Ma
Institute of Industrial Science, The University of Tokyo, 4-6-1, Komaba, Meguro-ku, Tokyo 153-8505, Japan
Wataru Echizenya
Corporate Planning Department, MS&AD InterRisk Research & Consulting, Inc., 2-105, Kanda Awajicho, Chiyoda-ku, Tokyo 101-0063, Japan
Dai Yamazaki
Institute of Industrial Science, The University of Tokyo, 4-6-1, Komaba, Meguro-ku, Tokyo 153-8505, Japan
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- An integrated modeling approach to evaluate the impacts of nature-based solutions of flood mitigation across a small watershed in the southeast United States B. Guido et al. 10.5194/nhess-23-2663-2023
- Effect of streamflow measurement error on flood frequency estimation N. Velásquez & W. Krajewski 10.1007/s00477-024-02707-1
- Evaluation of river flood extent simulated with multiple global hydrological models and climate forcings B. Mester et al. 10.1088/1748-9326/ac188d
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- Inundation prediction in tropical wetlands from JULES-CaMa-Flood global land surface simulations T. Marthews et al. 10.5194/hess-26-3151-2022
- Assessment of the structural uncertainty of hydrological models and its impact on flood inundation mapping I. Dasari & V. Vema 10.1080/02626667.2023.2271456
- A first continuous and distributed satellite-based mapping of river discharge over the Amazon V. Pellet et al. 10.1016/j.jhydrol.2022.128481
- Substantial increase in future fluvial flood risk projected in China’s major urban agglomerations R. Jiang et al. 10.1038/s43247-023-01049-0
- Global flood exposure from different sized rivers M. Bernhofen et al. 10.5194/nhess-21-2829-2021
- Emulating the estuarine morphology evolution using a deep convolutional neural network emulator based on hydrodynamic results of a numerical model W. Weber de Melo et al. 10.2166/hydro.2022.068
- Quantitative evaluation of flood damage methodologies under a portfolio of adaptation scenarios J. Boulange et al. 10.1007/s11069-023-06017-7
- Using global datasets to estimate flood exposure at the city scale: an evaluation in Addis Ababa A. Carr et al. 10.3389/fenvs.2024.1330295
- Learning Global Evapotranspiration Dataset Corrections from a Water Cycle Closure Supervision T. Hascoet et al. 10.3390/rs16010170
- Comparative Evaluation of Global Flood Hazard Maps and Recommendations for Corporate Practice Y. HIRABAYASHI et al. 10.3178/jjshwr.35.175
- Uncertainty of internal climate variability in probabilistic flood simulations using d4PDF Y. Kita & D. Yamazaki 10.3178/hrl.17.15
- A non-uniform grid approach for high-resolution flood inundation simulation based on GPUs J. Wang et al. 10.1007/s42241-021-0060-6
- Robust Flood Risk Management Strategies Through Bayesian Estimation and Multi-objective Optimization Z. Sobhaniyeh et al. 10.1007/s41742-021-00370-w
2 citations as recorded by crossref.
Latest update: 24 Apr 2024
Short summary
This article assesses different uncertainties in the analysis of flood risk and found the runoff generated before the river routing is the primary uncertainty source. This calls for attention to be focused on selecting an appropriate runoff for the flood analysis. The uncertainties are reflected in the flood water depth, inundation area and the exposure of the population and economy to the floods.
This article assesses different uncertainties in the analysis of flood risk and found the runoff...
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