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Estimation of Sediment Transport Parameters From Measured Suspended Concentration Time Series Under Waves and Currents With a New Conceptual Model
Estimation of Sediment Transport Parameters From Measured Suspended Concentration Time Series Under Waves and Currents With a New Conceptual Model
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Estimation of Sediment Transport Parameters From Measured Suspended Concentration Time Series Under Waves and Currents With a New Conceptual Model
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Estimation of Sediment Transport Parameters From Measured Suspended Concentration Time Series Under Waves and Currents With a New Conceptual Model
Estimation of Sediment Transport Parameters From Measured Suspended Concentration Time Series Under Waves and Currents With a New Conceptual Model

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Estimation of Sediment Transport Parameters From Measured Suspended Concentration Time Series Under Waves and Currents With a New Conceptual Model
Estimation of Sediment Transport Parameters From Measured Suspended Concentration Time Series Under Waves and Currents With a New Conceptual Model
Journal Article

Estimation of Sediment Transport Parameters From Measured Suspended Concentration Time Series Under Waves and Currents With a New Conceptual Model

2024
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Overview
In‐situ observations of hydrodynamics and suspended sediment concentrations (SSCs) were conducted on an abandoned lobe in the northern part of the modern Yellow River Delta, China. The SSC record at the site is found to be the superposition of a general trend (fast increase and slow decrease cycle) caused by storm waves (SubSSC1) and relatively smaller fluctuations caused by tidal currents (SubSSC2). Physically, this indicates that storm waves eroded the bottom sediments while tidal currents then re‐suspended and advected the suspended sediments in the study area. To further obtain the suspended sediment transport parameters, first, SubSSC1 is modeled with significant wave height which incorporates a “memory curve” to consider the remaining impacts of historical waves. It is detected that waves in the past 75 hr still influence the present SSC which is reasonable because 75 hr is roughly the typical duration of a normal storm. Second, SubSSC2 is modeled with tidal excursion and trigonometric functions with measured periodicities. Finally, some sediment transport parameters, for example, the background SSC, the horizontal SSC gradient, the tidal constituents that advect it, and their relative time lags are optimized from the best fits of the measured and modeled SSC time series. The proposed framework for model construction and parameter optimization can be extended to other sea areas for inferring sediment transport parameters from field SSC time series at a specific station. Plain Language Summary The evolution of our coastal zone fundamentally depends on the transport volume and direction of sediment, and understanding them is very beneficial for our coastal engineering construction and long‐term planning. Conducting in situ observations on‐site is one of the most reliable methods, but its observation cost is expensive. Therefore, we hope to extract as much information as possible from as few observation points as possible. This article successfully extracted the above information from the observation data of a station. First, we analyzed the data to preliminarily clarify that the suspended sediment in the area mainly comes from local erosion resuspension and advection transport from other regions. Furthermore, we conducted data modeling (i.e., constructed mathematical expressions of suspended sediment components from different sources, but with undetermined coefficients). Finally, we adjusted the model parameters to approximate the measured results, determined the undetermined coefficients, and explained the practical significance of the undetermined coefficients in physics. The analysis method we proposed can be extended to other sea areas. Key Points Storm wave‐induced suspended sediment concentration (SSC) variation is modeled with a “memory curve” of wave height Tidally‐induced SSC variation is modeled with tidal excursion and trigonometric functions Sediment transport parameters are estimated from the optimal matching of measured and modeled SSC time series