中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
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
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
DOI10.1029/2025WR040190
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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