Articles | Volume 26, issue 21
https://doi.org/10.5194/hess-26-5473-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-5473-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
03 Nov 2022
Research article |
| 03 Nov 2022
| 03 Nov 2022
Investigating coastal backwater effects and flooding in the coastal zone using a global river transport model on an unstructured mesh
Dongyu Feng
Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
Darren Engwirda
T-3 Fluid Dynamics and Solid Mechanics Group, Los Alamos National
Laboratory, Los Alamos, NM, 87545, USA
Chang Liao
Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
Donghui Xu
Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
Gautam Bisht
Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
Tian Zhou
Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
Hong-Yi Li
Department of Civil and Environmental Engineering, University of
Houston, TX, 77204, USA
L. Ruby Leung
Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
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Cited
12 citations as recorded by crossref.
- Topological Relationship‐Based Flow Direction Modeling: Mesh‐Independent River Networks Representation C. Liao et al. 10.1029/2022MS003089
- Understanding the compound flood risk along the coast of the contiguous United States D. Feng et al. 10.5194/hess-27-3911-2023
- pyflowline: a mesh-independent river network generator for hydrologic models C. Liao & M. Cooper 10.21105/joss.05446
- Combining statistical and hydrodynamic models to assess compound flood hazards from rainfall and storm surge: a case study of Shanghai H. Xu et al. 10.5194/hess-28-3919-2024
- Nonlinear Flood Responses to Tide Level and Land Cover Changes in Small Watersheds H. Huang et al. 10.3390/land12091743
- Physics‐Informed Neural Networks of the Saint‐Venant Equations for Downscaling a Large‐Scale River Model D. Feng et al. 10.1029/2022WR033168
- Modified geomorphic flood index (GFI) method on backwater problem in tidal tropical river for rapid flood assessment A. Abusarif et al. 10.1088/1755-1315/1266/1/012064
- Thresholds for estuarine compound flooding using a combined hydrodynamic–statistical modelling approach C. Lyddon et al. 10.5194/nhess-24-973-2024
- Impact of Tides and Surges on Fluvial Floods in Coastal Regions H. Liang & X. Zhou 10.3390/rs14225779
- A multi-algorithm approach for modeling coastal wetland eco-geomorphology Z. Tan et al. 10.3389/feart.2024.1421265
- Topological Relationship‐Based Flow Direction Modeling: Stream Burning and Depression Filling C. Liao et al. 10.1029/2022MS003487
- Influence of data source and copula statistics on estimates of compound flood extremes in a river mouth environment K. Dubois et al. 10.5194/nhess-24-3245-2024
12 citations as recorded by crossref.
- Topological Relationship‐Based Flow Direction Modeling: Mesh‐Independent River Networks Representation C. Liao et al. 10.1029/2022MS003089
- Understanding the compound flood risk along the coast of the contiguous United States D. Feng et al. 10.5194/hess-27-3911-2023
- pyflowline: a mesh-independent river network generator for hydrologic models C. Liao & M. Cooper 10.21105/joss.05446
- Combining statistical and hydrodynamic models to assess compound flood hazards from rainfall and storm surge: a case study of Shanghai H. Xu et al. 10.5194/hess-28-3919-2024
- Nonlinear Flood Responses to Tide Level and Land Cover Changes in Small Watersheds H. Huang et al. 10.3390/land12091743
- Physics‐Informed Neural Networks of the Saint‐Venant Equations for Downscaling a Large‐Scale River Model D. Feng et al. 10.1029/2022WR033168
- Modified geomorphic flood index (GFI) method on backwater problem in tidal tropical river for rapid flood assessment A. Abusarif et al. 10.1088/1755-1315/1266/1/012064
- Thresholds for estuarine compound flooding using a combined hydrodynamic–statistical modelling approach C. Lyddon et al. 10.5194/nhess-24-973-2024
- Impact of Tides and Surges on Fluvial Floods in Coastal Regions H. Liang & X. Zhou 10.3390/rs14225779
- A multi-algorithm approach for modeling coastal wetland eco-geomorphology Z. Tan et al. 10.3389/feart.2024.1421265
- Topological Relationship‐Based Flow Direction Modeling: Stream Burning and Depression Filling C. Liao et al. 10.1029/2022MS003487
- Influence of data source and copula statistics on estimates of compound flood extremes in a river mouth environment K. Dubois et al. 10.5194/nhess-24-3245-2024
Latest update: 16 Nov 2024
Short summary
Sea level rise, storm surge and river discharge can cause coastal backwater effects in downstream sections of rivers, creating critical flood risks. This study simulates the backwater effects using a large-scale river model on a coastal-refined computational mesh. By decomposing the backwater drivers, we revealed their relative importance and long-term variations. Our analysis highlights the increasing strength of backwater effects due to sea level rise and more frequent storm surge.
Sea level rise, storm surge and river discharge can cause coastal backwater effects in...
