Morphological Evolution of an Energetic Tidal Channel: Quantifying Frictional Feedbacks Across Multiple Scales Using High Resolution Observations and Modeling

GSTDTAP

项目编号	1634481
	Morphological Evolution of an Energetic Tidal Channel: Quantifying Frictional Feedbacks Across Multiple Scales Using High Resolution Observations and Modeling
	Peter Traykovski
主持机构	Woods Hole Oceanographic Institution
项目开始年	2016
	2016-10-01
项目结束日期	2019-09-30
资助机构	US-NSF
项目类别	Standard Grant
项目经费	901878(USD)
国家	美国
语种	英语
英文摘要	This project will combine advanced observational techniques with high resolution hydrodynamic modeling to quantify the morphological evolution of bedforms in a tidal channel and their feedbacks with hydrodynamic roughness. In complex coastal environments, our ability to understand interactions across multiple scales is often limited by gaps in the observational data or modeling framework. This study will span those gaps in order to address fundamental questions on morphological evolution and frictional effects. Morphodynamic modeling has made great progress at simulating basin-scale evolution over centuries to millennia, and yet quantitative skill and process-based assessments remain limited by observations. Similarly, hydrodynamic models have incorporated various processes that can contribute to effective roughness and alter predicted velocities and bed stress, and yet quantitative assessment of friction at the scale of the model parametrizations makes the merits of the inclusion of complex roughness formulations ambiguous. This research will directly address these limitations in spatial and temporal resolution, and the results will be applicable more broadly to larger scale and more slowly evolving coastal settings. This would significantly improve our abilities to characterize and manage a wide range of coastal processes. Inundation, eutrophication, harmful algal blooms, and long-term morphological response to sea level rise in back-barrier and shallow estuarine environments all depend on the small-scale hydrodynamic and bedform coupling studied here. This project will support the training of both a graduate student and postdoc. The multidisciplinary topic and a research approach that integrates advanced observational and modeling tools will prepare them to make contributions either in basic research or engineering and management of coastal systems. In the nearshore ocean, bedforms interact across a cascade of spatial and temporal scales. Ripples respond to tidal velocities, and convergences and divergences in ripple migration lead to the growth and movement of mega-ripples. Similarly, mega-ripple evolution at spring-neap to event time scales contributes to the formation of dunes and larger scale features. Using autonomous surface and aerial vehicles the team will map out the evolution of a dynamic tidal channel across this range of forcing conditions, explicitly resolving the spectral evolution from wavelengths of 10s of cm to 100s of m. Shipboard and moored observations will also measure the frictional effects of the bedforms on the overlying flow across multiple scales, providing a direct comparison between local turbulent stresses and the barotropic pressure gradient. A spatially limited and dynamically evolving domain permits high resolution modeling of the hydrodynamics and sediment transport in the system, including unprecedented quantitative evaluation of the morphological response. Small scale, process-based modeling will be paired with larger scale morphodynamic simulations to link hydrodynamic features at the bedform scale with the integrated effect on effective roughness. Parameterization of subgrid-scale roughness will be compared with observations to assess how multiple scales of bedforms affect the frictional response with changing tidal forcing.
来源学科分类	Geosciences - Ocean Sciences
文献类型	项目
条目标识符	http://119.78.100.173/C666/handle/2XK7JSWQ/70531
专题	环境与发展全球科技态势
推荐引用方式 GB/T 7714	Peter Traykovski.Morphological Evolution of an Energetic Tidal Channel: Quantifying Frictional Feedbacks Across Multiple Scales Using High Resolution Observations and Modeling.2016.