Articles | Volume 26, issue 3
https://doi.org/10.5194/hess-26-665-2022
© 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-26-665-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
10 Feb 2022
Research article |
| 10 Feb 2022
| 10 Feb 2022
A new large-scale suspended sediment model and its application over the United States
Department of Civil and Environmental Engineering, University of
Houston, Houston, Texas, USA
Pacific Northwest National Laboratory, Richland, Washington, USA
Hongbo Ma
Department of Civil and Environmental Engineering, University of
California Irvine, Irvine, California, USA
Zhenduo Zhu
Department of Civil, Structural and Environmental Engineering,
University at Buffalo, State University of New York, Buffalo, New York, USA
Guta Wakbulcho Abeshu
Department of Civil and Environmental Engineering, University of
Houston, Houston, Texas, USA
Senlin Zhu
Department of Civil and Environmental Engineering, University of
Houston, Houston, Texas, USA
currently at: College of Hydraulic Science and Engineering, Yangzhou University, Yangzhou, China
Sagy Cohen
Department of Geography, University of Alabama, Tuscaloosa, Alabama, USA
Tian Zhou
Pacific Northwest National Laboratory, Richland, Washington, USA
Donghui Xu
Pacific Northwest National Laboratory, Richland, Washington, USA
L. Ruby Leung
Pacific Northwest National Laboratory, Richland, Washington, USA
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Cited
14 citations as recorded by crossref.
- Where rivers jump course S. Brooke et al. 10.1126/science.abm1215
- Suspended sediment load modeling using Hydro-Climate variables and Machine learning S. Aldin Shojaeezadeh et al. 10.1016/j.jhydrol.2024.130948
- Pay-for-practice or Pay-for-performance? A coupled agent-based evaluation tool for assessing sediment management incentive policies C. Lin et al. 10.1016/j.jhydrol.2023.129959
- Investigating coastal backwater effects and flooding in the coastal zone using a global river transport model on an unstructured mesh D. Feng et al. 10.5194/hess-26-5473-2022
- Development of a machine learning model for river bed load H. Hosseiny et al. 10.5194/esurf-11-681-2023
- Modeling and Analysis of Sediment Trapping Efficiency of Large Dams Using Remote Sensing N. Moragoda et al. 10.1029/2022WR033296
- Median bed-material sediment particle size across rivers in the contiguous US G. Abeshu et al. 10.5194/essd-14-929-2022
- Improving River Routing Using a Differentiable Muskingum‐Cunge Model and Physics‐Informed Machine Learning T. Bindas et al. 10.1029/2023WR035337
- Retrieval of suspended sediment concentration (SSC) in the Arabian Gulf water of arid region by Sentinel-2 data R. Sankaran et al. 10.1016/j.scitotenv.2023.166875
- Recent intensified erosion and massive sediment deposition in Tibetan Plateau rivers J. Li et al. 10.1038/s41467-024-44982-0
- Assessing the impact of climate change on sediment discharge using a large ensemble rainfall dataset in Pekerebetsu River basin, Hokkaido R. Kido et al. 10.1186/s40645-023-00580-0
- Spatial Trends and Drivers of Bedload and Suspended Sediment Fluxes in Global Rivers S. Cohen et al. 10.1029/2021WR031583
- A Physics‐Informed Bayesian Storyline Approach to Assess Sediment Transport in the Mekong B. Xu & X. He 10.1029/2022WR032681
- Understanding the compound flood risk along the coast of the contiguous United States D. Feng et al. 10.5194/hess-27-3911-2023
14 citations as recorded by crossref.
- Where rivers jump course S. Brooke et al. 10.1126/science.abm1215
- Suspended sediment load modeling using Hydro-Climate variables and Machine learning S. Aldin Shojaeezadeh et al. 10.1016/j.jhydrol.2024.130948
- Pay-for-practice or Pay-for-performance? A coupled agent-based evaluation tool for assessing sediment management incentive policies C. Lin et al. 10.1016/j.jhydrol.2023.129959
- Investigating coastal backwater effects and flooding in the coastal zone using a global river transport model on an unstructured mesh D. Feng et al. 10.5194/hess-26-5473-2022
- Development of a machine learning model for river bed load H. Hosseiny et al. 10.5194/esurf-11-681-2023
- Modeling and Analysis of Sediment Trapping Efficiency of Large Dams Using Remote Sensing N. Moragoda et al. 10.1029/2022WR033296
- Median bed-material sediment particle size across rivers in the contiguous US G. Abeshu et al. 10.5194/essd-14-929-2022
- Improving River Routing Using a Differentiable Muskingum‐Cunge Model and Physics‐Informed Machine Learning T. Bindas et al. 10.1029/2023WR035337
- Retrieval of suspended sediment concentration (SSC) in the Arabian Gulf water of arid region by Sentinel-2 data R. Sankaran et al. 10.1016/j.scitotenv.2023.166875
- Recent intensified erosion and massive sediment deposition in Tibetan Plateau rivers J. Li et al. 10.1038/s41467-024-44982-0
- Assessing the impact of climate change on sediment discharge using a large ensemble rainfall dataset in Pekerebetsu River basin, Hokkaido R. Kido et al. 10.1186/s40645-023-00580-0
- Spatial Trends and Drivers of Bedload and Suspended Sediment Fluxes in Global Rivers S. Cohen et al. 10.1029/2021WR031583
- A Physics‐Informed Bayesian Storyline Approach to Assess Sediment Transport in the Mekong B. Xu & X. He 10.1029/2022WR032681
- Understanding the compound flood risk along the coast of the contiguous United States D. Feng et al. 10.5194/hess-27-3911-2023
Latest update: 26 Apr 2024
Short summary
We introduce a new multi-process river sediment module for Earth system models. Application and validation over the contiguous US indicate a satisfactory model performance over large river systems, including those heavily regulated by reservoirs. This new sediment module enables future modeling of the transportation and transformation of carbon and nutrients carried by the fine sediment along the river–ocean continuum to close the global carbon and nutrient cycles.
We introduce a new multi-process river sediment module for Earth system models. Application and...
