Predicting Water and Sediment Partitioning in a Delta Channel Network Under Varying Discharge Conditions
Channel bifurcations control the distribution of water and sediment in deltas, and the routing of these materials facilitates land building in coastal regions. Yet few practical methods exist to provide accurate predictions of flow partitioning at multiple bifurcations within a distributary channel...
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ftriceuniv:oai:scholarship.rice.edu:1911/109751 2023-05-15T17:07:39+02:00 Predicting Water and Sediment Partitioning in a Delta Channel Network Under Varying Discharge Conditions Dong, Tian Y. Nittrouer, Jeffrey A. McElroy, Brandon Il'icheva, Elena Pavlov, Maksim Ma, Hongbo Moodie, Andrew J. Moreido, Vsevolod M. 2020 application/pdf https://hdl.handle.net/1911/109751 https://doi.org/10.1029/2020WR027199 eng eng Wiley Dong, Tian Y., Nittrouer, Jeffrey A., McElroy, Brandon, et al. "Predicting Water and Sediment Partitioning in a Delta Channel Network Under Varying Discharge Conditions." Water Resources Research, 56, no. 11 (2020) Wiley: https://doi.org/10.1029/2020WR027199. https://hdl.handle.net/1911/109751 https://doi.org/10.1029/2020WR027199 Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. Journal article Text publisher version 2020 ftriceuniv https://doi.org/10.1029/2020WR027199 2022-08-09T20:35:58Z Channel bifurcations control the distribution of water and sediment in deltas, and the routing of these materials facilitates land building in coastal regions. Yet few practical methods exist to provide accurate predictions of flow partitioning at multiple bifurcations within a distributary channel network. Herein, multiple nodal relations that predict flow partitioning at individual bifurcations, utilizing various hydraulic and channel planform parameters, are tested against field data collected from the Selenga River delta, Russia. The data set includes 2.5 months of time‐continuous, synoptic measurements of water and sediment discharge partitioning covering a flood hydrograph. Results show that width, sinuosity, and bifurcation angle are the best remotely sensed, while cross‐sectional area and flow depth are the best field measured nodal relation variables to predict flow partitioning. These nodal relations are incorporated into a graph model, thus developing a generalized framework that predicts partitioning of water discharge and total, suspended, and bedload sediment discharge in deltas. Results from the model tested well against field data produced for the Wax Lake, Selenga, and Lena River deltas. When solely using remotely sensed variables, the generalized framework is especially suitable for modeling applications in large‐scale delta systems, where data and field accessibility are limited. Article in Journal/Newspaper lena river Rice University: Digital Scholarship Archive Water Resources Research 56 11 |
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Rice University: Digital Scholarship Archive |
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language |
English |
description |
Channel bifurcations control the distribution of water and sediment in deltas, and the routing of these materials facilitates land building in coastal regions. Yet few practical methods exist to provide accurate predictions of flow partitioning at multiple bifurcations within a distributary channel network. Herein, multiple nodal relations that predict flow partitioning at individual bifurcations, utilizing various hydraulic and channel planform parameters, are tested against field data collected from the Selenga River delta, Russia. The data set includes 2.5 months of time‐continuous, synoptic measurements of water and sediment discharge partitioning covering a flood hydrograph. Results show that width, sinuosity, and bifurcation angle are the best remotely sensed, while cross‐sectional area and flow depth are the best field measured nodal relation variables to predict flow partitioning. These nodal relations are incorporated into a graph model, thus developing a generalized framework that predicts partitioning of water discharge and total, suspended, and bedload sediment discharge in deltas. Results from the model tested well against field data produced for the Wax Lake, Selenga, and Lena River deltas. When solely using remotely sensed variables, the generalized framework is especially suitable for modeling applications in large‐scale delta systems, where data and field accessibility are limited. |
format |
Article in Journal/Newspaper |
author |
Dong, Tian Y. Nittrouer, Jeffrey A. McElroy, Brandon Il'icheva, Elena Pavlov, Maksim Ma, Hongbo Moodie, Andrew J. Moreido, Vsevolod M. |
spellingShingle |
Dong, Tian Y. Nittrouer, Jeffrey A. McElroy, Brandon Il'icheva, Elena Pavlov, Maksim Ma, Hongbo Moodie, Andrew J. Moreido, Vsevolod M. Predicting Water and Sediment Partitioning in a Delta Channel Network Under Varying Discharge Conditions |
author_facet |
Dong, Tian Y. Nittrouer, Jeffrey A. McElroy, Brandon Il'icheva, Elena Pavlov, Maksim Ma, Hongbo Moodie, Andrew J. Moreido, Vsevolod M. |
author_sort |
Dong, Tian Y. |
title |
Predicting Water and Sediment Partitioning in a Delta Channel Network Under Varying Discharge Conditions |
title_short |
Predicting Water and Sediment Partitioning in a Delta Channel Network Under Varying Discharge Conditions |
title_full |
Predicting Water and Sediment Partitioning in a Delta Channel Network Under Varying Discharge Conditions |
title_fullStr |
Predicting Water and Sediment Partitioning in a Delta Channel Network Under Varying Discharge Conditions |
title_full_unstemmed |
Predicting Water and Sediment Partitioning in a Delta Channel Network Under Varying Discharge Conditions |
title_sort |
predicting water and sediment partitioning in a delta channel network under varying discharge conditions |
publisher |
Wiley |
publishDate |
2020 |
url |
https://hdl.handle.net/1911/109751 https://doi.org/10.1029/2020WR027199 |
genre |
lena river |
genre_facet |
lena river |
op_relation |
Dong, Tian Y., Nittrouer, Jeffrey A., McElroy, Brandon, et al. "Predicting Water and Sediment Partitioning in a Delta Channel Network Under Varying Discharge Conditions." Water Resources Research, 56, no. 11 (2020) Wiley: https://doi.org/10.1029/2020WR027199. https://hdl.handle.net/1911/109751 https://doi.org/10.1029/2020WR027199 |
op_rights |
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. |
op_doi |
https://doi.org/10.1029/2020WR027199 |
container_title |
Water Resources Research |
container_volume |
56 |
container_issue |
11 |
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1766063131830779904 |