Advancing sedimentation modeling in large reservoir systems: Insights from multi-scale process coupling and machine learning
Study region: The lower Jinsha River, China, one of the largest cascade reservoir systems in the world. Study focus: Sedimentation directly affects reservoir operation and management. The study area is a typical mountainous basin with complex sediment transport dynamics, which challenge both the acc...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-08-01
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| Series: | Journal of Hydrology: Regional Studies |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214581825003982 |
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| author | Yuning Tan Huaixiang Liu Yongjun Lu Zhili Wang Wenjie Li |
| author_facet | Yuning Tan Huaixiang Liu Yongjun Lu Zhili Wang Wenjie Li |
| author_sort | Yuning Tan |
| collection | DOAJ |
| description | Study region: The lower Jinsha River, China, one of the largest cascade reservoir systems in the world. Study focus: Sedimentation directly affects reservoir operation and management. The study area is a typical mountainous basin with complex sediment transport dynamics, which challenge both the accuracy and efficiency of modeling efforts. To address this issue, this study proposes a novel modeling framework that integrates multi-scale physical processes (sediment sources, inflow, and deposition) with machine learning (ML) techniques. New hydrological insights for the region: The proposed framework firstly reduced the total sedimentation error from 53.42 % to 3.44 % and the maximum group-wise error from 90.88 % to 13.46 %, highlighting the dominant influence of tributary sediment inputs and flocculation factor on reservoir sedimentation. The subsequently developed ML models effectively captured short-term sedimentation rate variations (R2 > 0.83, KGE > 0.85 in testing). Random Forest outperformed XGBoost, Support Vector Regression, and Artificial Neural Network, demonstrating the most robust performance and the clearest feature attribution. Our climate scenario simulations revealed uneven sedimentation patterns and projected declining sedimentation rates over the next 30 years. This study can offer valuable insights for modeling sediment dynamics in mountainous basins with large reservoir systems. |
| format | Article |
| id | doaj-art-2e89e09db3e84789b5ef2f24997b9519 |
| institution | Kabale University |
| issn | 2214-5818 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Hydrology: Regional Studies |
| spelling | doaj-art-2e89e09db3e84789b5ef2f24997b95192025-08-20T03:51:35ZengElsevierJournal of Hydrology: Regional Studies2214-58182025-08-016010257310.1016/j.ejrh.2025.102573Advancing sedimentation modeling in large reservoir systems: Insights from multi-scale process coupling and machine learningYuning Tan0Huaixiang Liu1Yongjun Lu2Zhili Wang3Wenjie Li4National Inland Waterway Regulation Engineering Research Center, Chongqing Jiaotong University, Chongqing 400074, ChinaNational Key Laboratory of Water Disaster Prevention, Nanjing Hydraulic Research Institute, Nanjing 210029, China; Corresponding author.National Key Laboratory of Water Disaster Prevention, Nanjing Hydraulic Research Institute, Nanjing 210029, ChinaNational Key Laboratory of Water Disaster Prevention, Nanjing Hydraulic Research Institute, Nanjing 210029, ChinaNational Inland Waterway Regulation Engineering Research Center, Chongqing Jiaotong University, Chongqing 400074, ChinaStudy region: The lower Jinsha River, China, one of the largest cascade reservoir systems in the world. Study focus: Sedimentation directly affects reservoir operation and management. The study area is a typical mountainous basin with complex sediment transport dynamics, which challenge both the accuracy and efficiency of modeling efforts. To address this issue, this study proposes a novel modeling framework that integrates multi-scale physical processes (sediment sources, inflow, and deposition) with machine learning (ML) techniques. New hydrological insights for the region: The proposed framework firstly reduced the total sedimentation error from 53.42 % to 3.44 % and the maximum group-wise error from 90.88 % to 13.46 %, highlighting the dominant influence of tributary sediment inputs and flocculation factor on reservoir sedimentation. The subsequently developed ML models effectively captured short-term sedimentation rate variations (R2 > 0.83, KGE > 0.85 in testing). Random Forest outperformed XGBoost, Support Vector Regression, and Artificial Neural Network, demonstrating the most robust performance and the clearest feature attribution. Our climate scenario simulations revealed uneven sedimentation patterns and projected declining sedimentation rates over the next 30 years. This study can offer valuable insights for modeling sediment dynamics in mountainous basins with large reservoir systems.http://www.sciencedirect.com/science/article/pii/S2214581825003982Sediment transportCascade reservoirFactorsMountainous basinJinsha River |
| spellingShingle | Yuning Tan Huaixiang Liu Yongjun Lu Zhili Wang Wenjie Li Advancing sedimentation modeling in large reservoir systems: Insights from multi-scale process coupling and machine learning Journal of Hydrology: Regional Studies Sediment transport Cascade reservoir Factors Mountainous basin Jinsha River |
| title | Advancing sedimentation modeling in large reservoir systems: Insights from multi-scale process coupling and machine learning |
| title_full | Advancing sedimentation modeling in large reservoir systems: Insights from multi-scale process coupling and machine learning |
| title_fullStr | Advancing sedimentation modeling in large reservoir systems: Insights from multi-scale process coupling and machine learning |
| title_full_unstemmed | Advancing sedimentation modeling in large reservoir systems: Insights from multi-scale process coupling and machine learning |
| title_short | Advancing sedimentation modeling in large reservoir systems: Insights from multi-scale process coupling and machine learning |
| title_sort | advancing sedimentation modeling in large reservoir systems insights from multi scale process coupling and machine learning |
| topic | Sediment transport Cascade reservoir Factors Mountainous basin Jinsha River |
| url | http://www.sciencedirect.com/science/article/pii/S2214581825003982 |
| work_keys_str_mv | AT yuningtan advancingsedimentationmodelinginlargereservoirsystemsinsightsfrommultiscaleprocesscouplingandmachinelearning AT huaixiangliu advancingsedimentationmodelinginlargereservoirsystemsinsightsfrommultiscaleprocesscouplingandmachinelearning AT yongjunlu advancingsedimentationmodelinginlargereservoirsystemsinsightsfrommultiscaleprocesscouplingandmachinelearning AT zhiliwang advancingsedimentationmodelinginlargereservoirsystemsinsightsfrommultiscaleprocesscouplingandmachinelearning AT wenjieli advancingsedimentationmodelinginlargereservoirsystemsinsightsfrommultiscaleprocesscouplingandmachinelearning |