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Review and improvement of conventional models for reservoir sediment trapping efficiency

Sediment accumulation has been the most important factor influencing the comprehensive benefit of reservoirs. A quantitative investigation of the sediment trapping efficiencies of reservoirs is a key to understanding the impact of sedimentation on reservoirs. Generally, the simplest method to assess...

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Detalles Bibliográficos
Autores principales: Tan, Guangming, Chen, Peng, Deng, Jinyun, Xu, Quanxi, Tang, Rouxin, Feng, Zhiyong, Yi, Ran
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6819860/
https://www.ncbi.nlm.nih.gov/pubmed/31687559
http://dx.doi.org/10.1016/j.heliyon.2019.e02458
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author Tan, Guangming
Chen, Peng
Deng, Jinyun
Xu, Quanxi
Tang, Rouxin
Feng, Zhiyong
Yi, Ran
author_facet Tan, Guangming
Chen, Peng
Deng, Jinyun
Xu, Quanxi
Tang, Rouxin
Feng, Zhiyong
Yi, Ran
author_sort Tan, Guangming
collection PubMed
description Sediment accumulation has been the most important factor influencing the comprehensive benefit of reservoirs. A quantitative investigation of the sediment trapping efficiencies of reservoirs is a key to understanding the impact of sedimentation on reservoirs. Generally, the simplest method to assess sediment accumulation ratio is adopting sediment concentration curve with trap efficiency (TE) of the reservoir. Many empirical and semi-empirical models have been proposed to determine this term related to the average annual inflow, features and characteristics of the reservoir watershed area. In this article four different empirical models decided by capacity to inflow ratio (C/I), capacity to watershed ratio (C/W) were used. These different models were summarized and utilized to determine TE of large reservoirs on the Upper Yangtze River for recent decades. Based on these conventional models, an improved model to estimate sediment trapping efficiency is proposed, and experimental data from other 18 reservoirs come from different basins were used to validate the model. The results indicate that the sediment trapping efficiencies that were estimated by the four empirical model were similar to the measured efficiencies for reservoirs on the Upper Yangtze River. Among the the Brune and Siyam empirical models were the most reliable and can be applied to estimate sediment trapping efficiency for reservoirs on the Upper Yangtze River. The improved model takes the capacity/annual inflow ratio and capacity/watershed area ratio into account comprehensively, the effect of particle size and settling velocity of the sediment are also considered, it is more applicable and accuracy to predict large reservoir sediment trapping efficiency. The results of this study provide a valuable reference for predicting large reservoir sedimentation and sediment regulation.
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spelling pubmed-68198602019-11-04 Review and improvement of conventional models for reservoir sediment trapping efficiency Tan, Guangming Chen, Peng Deng, Jinyun Xu, Quanxi Tang, Rouxin Feng, Zhiyong Yi, Ran Heliyon Article Sediment accumulation has been the most important factor influencing the comprehensive benefit of reservoirs. A quantitative investigation of the sediment trapping efficiencies of reservoirs is a key to understanding the impact of sedimentation on reservoirs. Generally, the simplest method to assess sediment accumulation ratio is adopting sediment concentration curve with trap efficiency (TE) of the reservoir. Many empirical and semi-empirical models have been proposed to determine this term related to the average annual inflow, features and characteristics of the reservoir watershed area. In this article four different empirical models decided by capacity to inflow ratio (C/I), capacity to watershed ratio (C/W) were used. These different models were summarized and utilized to determine TE of large reservoirs on the Upper Yangtze River for recent decades. Based on these conventional models, an improved model to estimate sediment trapping efficiency is proposed, and experimental data from other 18 reservoirs come from different basins were used to validate the model. The results indicate that the sediment trapping efficiencies that were estimated by the four empirical model were similar to the measured efficiencies for reservoirs on the Upper Yangtze River. Among the the Brune and Siyam empirical models were the most reliable and can be applied to estimate sediment trapping efficiency for reservoirs on the Upper Yangtze River. The improved model takes the capacity/annual inflow ratio and capacity/watershed area ratio into account comprehensively, the effect of particle size and settling velocity of the sediment are also considered, it is more applicable and accuracy to predict large reservoir sediment trapping efficiency. The results of this study provide a valuable reference for predicting large reservoir sedimentation and sediment regulation. Elsevier 2019-09-16 /pmc/articles/PMC6819860/ /pubmed/31687559 http://dx.doi.org/10.1016/j.heliyon.2019.e02458 Text en © 2019 Published by Elsevier Ltd. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Article
Tan, Guangming
Chen, Peng
Deng, Jinyun
Xu, Quanxi
Tang, Rouxin
Feng, Zhiyong
Yi, Ran
Review and improvement of conventional models for reservoir sediment trapping efficiency
title Review and improvement of conventional models for reservoir sediment trapping efficiency
title_full Review and improvement of conventional models for reservoir sediment trapping efficiency
title_fullStr Review and improvement of conventional models for reservoir sediment trapping efficiency
title_full_unstemmed Review and improvement of conventional models for reservoir sediment trapping efficiency
title_short Review and improvement of conventional models for reservoir sediment trapping efficiency
title_sort review and improvement of conventional models for reservoir sediment trapping efficiency
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6819860/
https://www.ncbi.nlm.nih.gov/pubmed/31687559
http://dx.doi.org/10.1016/j.heliyon.2019.e02458
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