Articles | Volume 21, issue 10
https://doi.org/10.5194/hess-21-5143-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-21-5143-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
12 Oct 2017
Research article |
| 12 Oct 2017
Impacts of spatial resolution and representation of flow connectivity on large-scale simulation of floods
Cherry May R. Mateo
CORRESPONDING AUTHOR
CSIRO Land and Water, ACT, 2601, Australia
Institute of
Industrial Science, The University of Tokyo, Tokyo, 153-8505, Japan
Dai Yamazaki
Institute of
Industrial Science, The University of Tokyo, Tokyo, 153-8505, Japan
Department of Integrated Climate Change Projection Research, Japan
Agency for Marine-Earth Science and Technology, Yokohama, 236-0001, Japan
Hyungjun Kim
Institute of
Industrial Science, The University of Tokyo, Tokyo, 153-8505, Japan
Adisorn Champathong
Royal Irrigation Department, Bangkok, 10300, Thailand
Jai Vaze
CSIRO Land and Water, ACT, 2601, Australia
Taikan Oki
Institute of
Industrial Science, The University of Tokyo, Tokyo, 153-8505, Japan
United Nations University, 5 Chome-53-70 Jingumae, Shibuya, Tokyo,
150-8925, Japan
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- Geometric Accuracy Assesment Of Orthorectification Method Based On Sensor Model Refinement In Open Source System Environment (Case Study : Sangir Subdistrict, South Solok District, West Sumatra Province) A. Suprayogi & L. Golok Jaya 10.1088/1757-899X/797/1/012015
- Observation‐Constrained Projection of Global Flood Magnitudes With Anthropogenic Warming W. Liu et al. 10.1029/2020WR028830
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- Changes in floodplain regimes over Canada due to climate change impacts: Observations from CMIP6 models M. Mohanty & S. Simonovic 10.1016/j.scitotenv.2021.148323
- Flood propagation modeling with the Local Inertia Approximation: Theoretical and numerical analysis of its physical limitations L. Cozzolino et al. 10.1016/j.advwatres.2019.103422
- Reconstructing the pristine flow of highly developed rivers − a case study on the Chao Phraya River A. Champathong et al. 10.3178/hrl.14.89
- Mapping the Sensitivity of Population Exposure to Changes in Flood Magnitude: Prospective Application From Local to Global Scale A. Zischg & M. Bermúdez 10.3389/feart.2020.534735
- An application of the automatic domain updating to the Tonle Sap Lake, Cambodia T. Tanaka & H. Yoshioka 10.3178/hrl.14.68
- Can regional to continental river hydrodynamic models be locally relevant? A cross-scale comparison A. Fleischmann et al. 10.1016/j.hydroa.2019.100027
- Multiple Kernel Learning with Maximum Inundation Extent from MODIS Imagery for Spatial Prediction of Flood Susceptibility Q. Hu et al. 10.1007/s11269-021-03010-2
- Estimating River Channel Bathymetry in Large Scale Flood Inundation Models J. Neal et al. 10.1029/2020WR028301
- Trade‐Offs Between 1‐D and 2‐D Regional River Hydrodynamic Models A. Fleischmann et al. 10.1029/2019WR026812
- Compound simulation of fluvial floods and storm surges in a global coupled river‐coast flood model: Model development and its application to 2007 Cyclone Sidr in Bangladesh H. Ikeuchi et al. 10.1002/2017MS000943
- Quantifying flood model accuracy under varying surface complexities W. Addison-Atkinson et al. 10.1016/j.jhydrol.2023.129511
25 citations as recorded by crossref.
- Considering the dynamics of water surface boundaries to measure the evolution of hydrological connectivity in the Yangtze River Delta, China Z. Li et al. 10.1177/03091333231213536
- Toward continental hydrologic–hydrodynamic modeling in South America V. Siqueira et al. 10.5194/hess-22-4815-2018
- On the discretization of river networks for large scale hydrologic-hydrodynamic models F. Fan et al. 10.1590/2318-0331.262120200070
- How far spatial resolution affects the ensemble machine learning based flood susceptibility prediction in data sparse region T. Saha et al. 10.1016/j.jenvman.2021.113344
- A globally applicable framework for compound flood hazard modeling D. Eilander et al. 10.5194/nhess-23-823-2023
- Downscaling climate projections over large and data sparse regions: Methodological application in the Zambezi River Basin N. Peleg et al. 10.1002/joc.6578
- Efficient analysis of hydrological connectivity using 1D and 2D Convolutional Neural Networks C. Nguyen et al. 10.1016/j.advwatres.2023.104583
- River network and hydro-geomorphological parameters at 1∕12° resolution for global hydrological and climate studies S. Munier & B. Decharme 10.5194/essd-14-2239-2022
- Global streamflow and flood response to stratospheric aerosol geoengineering L. Wei et al. 10.5194/acp-18-16033-2018
- Comparison of estimates of global flood models for flood hazard and exposed gross domestic product: a China case study J. Aerts et al. 10.5194/nhess-20-3245-2020
- Revealing the impacts of climate change on mountainous catchments through high-resolution modelling J. Moraga et al. 10.1016/j.jhydrol.2021.126806
- Geometric Accuracy Assesment Of Orthorectification Method Based On Sensor Model Refinement In Open Source System Environment (Case Study : Sangir Subdistrict, South Solok District, West Sumatra Province) A. Suprayogi & L. Golok Jaya 10.1088/1757-899X/797/1/012015
- Observation‐Constrained Projection of Global Flood Magnitudes With Anthropogenic Warming W. Liu et al. 10.1029/2020WR028830
- A dynamic connectivity metric for complex river wetlands C. Nguyen et al. 10.1016/j.jhydrol.2021.127163
- A first collective validation of global fluvial flood models for major floods in Nigeria and Mozambique M. Bernhofen et al. 10.1088/1748-9326/aae014
- Friction decoupling and loss of rotational invariance in 2D flooding models L. Cozzolino et al. 10.1016/j.advwatres.2021.103919
- Changes in floodplain regimes over Canada due to climate change impacts: Observations from CMIP6 models M. Mohanty & S. Simonovic 10.1016/j.scitotenv.2021.148323
- Flood propagation modeling with the Local Inertia Approximation: Theoretical and numerical analysis of its physical limitations L. Cozzolino et al. 10.1016/j.advwatres.2019.103422
- Reconstructing the pristine flow of highly developed rivers − a case study on the Chao Phraya River A. Champathong et al. 10.3178/hrl.14.89
- Mapping the Sensitivity of Population Exposure to Changes in Flood Magnitude: Prospective Application From Local to Global Scale A. Zischg & M. Bermúdez 10.3389/feart.2020.534735
- An application of the automatic domain updating to the Tonle Sap Lake, Cambodia T. Tanaka & H. Yoshioka 10.3178/hrl.14.68
- Can regional to continental river hydrodynamic models be locally relevant? A cross-scale comparison A. Fleischmann et al. 10.1016/j.hydroa.2019.100027
- Multiple Kernel Learning with Maximum Inundation Extent from MODIS Imagery for Spatial Prediction of Flood Susceptibility Q. Hu et al. 10.1007/s11269-021-03010-2
- Estimating River Channel Bathymetry in Large Scale Flood Inundation Models J. Neal et al. 10.1029/2020WR028301
- Trade‐Offs Between 1‐D and 2‐D Regional River Hydrodynamic Models A. Fleischmann et al. 10.1029/2019WR026812
2 citations as recorded by crossref.
- Compound simulation of fluvial floods and storm surges in a global coupled river‐coast flood model: Model development and its application to 2007 Cyclone Sidr in Bangladesh H. Ikeuchi et al. 10.1002/2017MS000943
- Quantifying flood model accuracy under varying surface complexities W. Addison-Atkinson et al. 10.1016/j.jhydrol.2023.129511
Latest update: 16 Jul 2024
Short summary
Providing large-scale (regional or global) simulation of floods at fine spatial resolution is difficult due to computational constraints but is necessary to provide consistent estimates of hazards, especially in data-scarce regions. We assessed the capability of an advanced global-scale river model to simulate an extreme flood at fine resolution. We found that when multiple flow connections in rivers are represented, the model can provide reliable fine-resolution predictions of flood inundation.
Providing large-scale (regional or global) simulation of floods at fine spatial resolution is...
