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ARS Home » Midwest Area » Ames, Iowa » National Laboratory for Agriculture and The Environment » Agroecosystems Management Research » Research » Publications at this Location » Publication #398368

Research Project: Sustainable Intensification in Agricultural Watersheds through Optimized Management and Technology

Location: Agroecosystems Management Research

Title: Semi-theoretical model for mean sediment resting time of spherical particles: the role of hydrodynamic impulses and sphere size nonuniformity

Author
item WYSSMANN, MICAH - Texas A&M University
item Papanicolaou, Athanasios - Thanos
item KYRIAKOPOULOS, THEODORE - Nuglobal Solutions (NGS)

Submitted to: Acta Geophysica
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/22/2022
Publication Date: 1/21/2023
Citation: Wyssmann, M.A., Papanicolaou, A.N., Kyriakopoulos, T. 2023. Semi-theoretical model for mean sediment resting time of spherical particles: the role of hydrodynamic impulses and sphere size nonuniformity. Acta Geophysica. https://doi.org/10.1007/s11600-022-01010-3.
DOI: https://doi.org/10.1007/s11600-022-01010-3

Interpretive Summary: Understanding the way grains move and when they rest has implications in the design and effectiveness of Best Management Practices such as sediment basins and check dams in headwater watersheds and streams. Here, we provide some ways to quantify the resting times of grains and identify flow resistance due to cover, vegetation patchiness, and terrain roughness as key factors on grain resting time. Grains move in steps along flow pathways. The larger the resistance to the flow is, the larger the resting time becomes. The work is valuable for agroecosystem managers for understanding fate and transport of particulate phosphorus and other pollutants. This research provides knowledge to develop science-based, optimal design procedures and is a step forward on soil/sediment transport predictions. The information about resting time of grains will be valuable to scientists and natural resources managers to understand the complex interactions between cover, vegetation patchiness, and terrain roughness with runoff in agricultural fields/watersheds. It is a step-forward in quantifying lag times of the particulate phase under different magnitude events.

Technical Abstract: Prediction of grain transport rates at low to moderate flow conditions, where grain movement is intermittent, remains a challenging problem. While the virtual velocity concept provides a useful approach to bedload rate estimation in the intermittent movement regime, virtual velocity estimation remains hindered by a lack of tools for predicting mean sediment resting time. As a first step toward sediment resting time estimation in gravel beds, the present study develops a semi-theoretical resting time model applicable to nonuniform gravel-sized spherical particles. The model is based on the consideration that interactions of near-bed flow with bed material leads to mobilization of individual resting particles during hydrodynamic momentum transfer events (i.e., impulses). Thus, resting time is affected by impulse magnitude and timing. The primary premise underpinning model development is that an instantaneous velocity time series generation approach based on the velocity spectrum can be used to mimic turbulent impulses and simulate resting times. Based on past findings, two model parameters are considered important to advancing resting time predictions in overland flow. First, the relative roughness allows size-fractional resting time predictions for a nonuniform sediment mixture. Second, the hindrance coefficient accounts for hiding effects and enables resting time predictions for different bed structure types. To provide calibration and verification data, laboratory experiments documenting impulse statistics and mean resting times for a range of flow and relative roughness conditions were also performed. The verified model exhibits mean resting times with similar magnitude and trends with increasing stress compared with experimental verification data.