A Stream Power Based Sediment Entrainment Model Across Geophysical Flows Informed by Machine Learning
文献类型:期刊论文
| 作者 | Lu, Xueqiang6; Zhou, Gordon G. D.4,5,6; Turowski, Jens M.3; Cui, Kahlil F. E.6; Tang, Hui3; Cao, Bo2; Xie, Yunxu5,6; Pasuto, Alessandro1; Zhao, Yuting6 |
| 刊名 | WATER RESOURCES RESEARCH
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| 出版日期 | 2025-10-31 |
| 卷号 | 61期号:11页码:22 |
| 关键词 | entrainment stream power Shields number turbulent geophysical flows |
| ISSN号 | 0043-1397 |
| DOI | 10.1029/2025WR040190 |
| 英文摘要 | Sediment entrainment marks the initiation of particle motion on the bed surface and plays a crucial role in quantifying sediment transport. While the entrainment behavior might vary among different geophysical flows, the underlying mechanisms are often similar. To explore the shared dynamics, we compiled a global database of diverse geophysical flows: stream flow (SF), hyperconcentrated flow (HF), and debris flow (DF), obtained from field observations and laboratory experiments. We first validate existing Shield's number based entrainment frameworks but find them inadequate to account for entrainment fluxes of all flow types, particularly for the HF and DF, across a wide range of excess Shields number (theta/theta c) where theta c represents the critical value. Utilizing the random forest regression algorithm, we then proposed a new stream power (omega) dependent bursting area formula (A P) for all types of mass flows considered, resulting in a unified omega-based entrainment model. The revised model achieves an R 2 of 0.924, which is more than twice that of the theta-based model (R 2 = 0.427) when applied to the same compiled database. This work provides valuable insights for improving sediment transport modeling, which are essential for developing effective river management strategies and optimizing the design of related infrastructure systems. |
| WOS关键词 | BED-LOAD TRANSPORT ; SUSPENDED-LOAD ; BEDLOAD TRANSPORT ; SHEAR-STRESS ; TURBULENT ; CHANNEL ; MOTION ; GRAVEL ; RIVER ; VARIABILITY |
| 资助项目 | National Natural Science Foundation of China[U24A20618] ; National Key Research and Development Program of China[2024YFF0810700] ; Sichuan Science and Technology Program[2024NSFJQ0043] |
| WOS研究方向 | Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources |
| 语种 | 英语 |
| WOS记录号 | WOS:001605173100001 |
| 出版者 | AMER GEOPHYSICAL UNION |
| 资助机构 | National Natural Science Foundation of China ; National Key Research and Development Program of China ; Sichuan Science and Technology Program |
| 源URL | [http://ir.imde.ac.cn/handle/131551/59265] ![]() |
| 专题 | 成都山地灾害与环境研究所_山地灾害与地表过程重点实验室 |
| 通讯作者 | Zhou, Gordon G. D. |
| 作者单位 | 1.Natl Res Council Res Inst Geohydrol Protect CNR IR, Padua, Italy 2.Lanzhou Univ, Coll Earth & Environm Sci, Key Lab Western Chinas Environm Syst MOE, Lanzhou, Peoples R China 3.GFZ Helmholtz Ctr Geosci, Potsdam, Germany 4.CAS HEC, China Pakistan Joint Res Ctr Earth Sci, Islamabad, Pakistan 5.Univ Chinese Acad Sci, Beijing, Peoples R China 6.Chinese Acad Sci, Inst Mt Hazards & Environm, Key Lab Mt Hazards & Engn Resilience, Chengdu, Peoples R China |
| 推荐引用方式 GB/T 7714 | Lu, Xueqiang,Zhou, Gordon G. D.,Turowski, Jens M.,et al. A Stream Power Based Sediment Entrainment Model Across Geophysical Flows Informed by Machine Learning[J]. WATER RESOURCES RESEARCH,2025,61(11):22. |
| APA | Lu, Xueqiang.,Zhou, Gordon G. D..,Turowski, Jens M..,Cui, Kahlil F. E..,Tang, Hui.,...&Zhao, Yuting.(2025).A Stream Power Based Sediment Entrainment Model Across Geophysical Flows Informed by Machine Learning.WATER RESOURCES RESEARCH,61(11),22. |
| MLA | Lu, Xueqiang,et al."A Stream Power Based Sediment Entrainment Model Across Geophysical Flows Informed by Machine Learning".WATER RESOURCES RESEARCH 61.11(2025):22. |
入库方式: OAI收割
来源:成都山地灾害与环境研究所
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