气候变化领域集成服务门户(CCPortal): Identifying the optimal spatial distribution of tracers for optical sensing of stream surface flow

CCPortal

DOI	10.5194/hess-24-5173-2020
	Identifying the optimal spatial distribution of tracers for optical sensing of stream surface flow
	Pizarro A.; Dal Sasso S.F.; Perks M.T.; Manfreda S.
发表日期	2020
ISSN	1027-5606
起始页码	5173
结束页码	5185
卷号	24 期号:11
英文摘要	River monitoring is of particular interest as a society that faces increasingly complex water management issues. Emerging technologies have contributed to opening new avenues for improving our monitoring capabilities but have also generated new challenges for the harmonised use of devices and algorithms. In this context, optical-sensing techniques for stream surface flow velocities are strongly influenced by tracer characteristics such as seeding density and their spatial distribution. Therefore, a principal research goal is the identification of how these properties affect the accuracy of such methods. To this aim, numerical simulations were performed to consider different levels of tracer clustering, particle colour (in terms of greyscale intensity), seeding density, and background noise. Two widely used imagevelocimetry algorithms were adopted: (i) particle-tracking velocimetry (PTV) and (ii) particle image velocimetry (PIV). A descriptor of the seeding characteristics (based on seeding density and tracer clustering) was introduced based on a newly developed metric called the Seeding Distribution Index (SDI). This index can be approximated and used in practice as SDI D=v0.1/(ρ/ρcv1) where v, ρ, and ρcv1 are the spatial-clustering level, the seeding density, and the reference seeding density at v= 1, respectively. A reduction in image-velocimetry errors was systematically observed for lower values of the SDI; therefore, the optimal frame window (i.e. a subset of the video image sequence) was defined as the one that minimises the SDI. In addition to numerical analyses, a field case study on the Basento river (located in southern Italy) was considered as a proof of concept of the proposed framework. Field results corroborated numerical findings, and error reductions of about 15.9% and 16.1% were calculated - using PTV and PIV, respectively - by employing the optimal frame window. © Author(s) 2020.
语种	英语
scopus关键词	Flow measurement; Reduction; Rivers; Spatial distribution; Turbulent flow; Velocimeters; Velocity measurement; Water management; Distribution index; Emerging technologies; Image velocimetry; Monitoring capabilities; Particle image velocimetries; Particle tracking velocimetry; Spatial clustering; Video image sequences; Tracers; identification method; spatial distribution; subsurface flow; tracer
来源期刊	Hydrology and Earth System Sciences
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/159270
作者单位	Pizarro, A., Department of European and Mediterranean Cultures, University of Basilicata, Matera, 75100, Italy; Dal Sasso, S.F., Department of European and Mediterranean Cultures, University of Basilicata, Matera, 75100, Italy; Perks, M.T., School of Geography, Politics and Sociology, Newcastle University, Newcastle-upon-Tyne, NE1g 7RU, United Kingdom; Manfreda, S., Department of Civil, Architectural and Environmental Engineering, University of Naples Federico II, Naples, 80125, Italy
推荐引用方式 GB/T 7714	Pizarro A.,Dal Sasso S.F.,Perks M.T.,et al. Identifying the optimal spatial distribution of tracers for optical sensing of stream surface flow[J],2020,24(11).
APA	Pizarro A.,Dal Sasso S.F.,Perks M.T.,&Manfreda S..(2020).Identifying the optimal spatial distribution of tracers for optical sensing of stream surface flow.Hydrology and Earth System Sciences,24(11).
MLA	Pizarro A.,et al."Identifying the optimal spatial distribution of tracers for optical sensing of stream surface flow".Hydrology and Earth System Sciences 24.11(2020).