Sediment Transport near Ship Shoal for Coastal Restoration in the Louisiana Shelf: A Model Estimate of the Year 2017–2018

Haoran Liu,Z George Xue,Zhengchen Zang,Robert Bales,Yanda Ou,Kehui Xu

doi:10.3390/w12082212

Abstract

Ship Shoal has been a high-priority target sand resource for dredging activities to restore the eroding barrier islands in LA, USA. The Caminada and Raccoon Island pits were dredged on and near Ship Shoal, which resulted in a mixed texture environment with the redistribution of cohesive mud and noncohesive sand. However, there is very limited knowledge about the source and transport process of suspended muddy sediments near Ship Shoal. The objective of this study is to apply the Regional Ocean Modeling System (ROMS) model to quantify the sediment sources and relative contribution of fluvial sediments with the estuary and shelf sediments delivered to Ship Shoal. The model results showed that suspended mud from the Atchafalaya River can transport and bypass Ship Shoal. Only a minimal amount of suspended mud from the Atchafalaya River can be delivered to Ship Shoal in a one-year time scale. Additionally, suspended mud from the inner shelf could be transported cross Ship Shoal and generate a thin mud layer, which is also considered as the primary sediment source infilling the dredge pits near Ship Shoal. Two hurricanes and one tropical storm during the year 2017–2018 changed the direction of the sediment transport flux near Ship Shoal and contributed to the pit infilling (less than 10% for this specific period). Our model also captured that the bottom sediment concentration in the Raccoon Island pit was relatively higher than the one in Caminada in the same period. Suspended mud sediment from the river, inner shelf, and bay can bypass or transport and deposit in the Caminada pit and Raccoon Island pit, which showed that the Caminada pit and Raccoon Island pits would not be considered as a renewable borrow area for future sand dredging activities for coastal restoration.

Highlights

Multi-resolution numerical models have been used in studies of coastal sediment modeling and morphological evolution in the Gulf of Mexico (GoM) in recent decades [1,2,3]
For the period from 1 July 2017 to 1 October 2018, the time-averaged surface salinity from the Regional Ocean Modeling System (ROMS) model was low near the mouth of Atchafalaya River, which increased southwestward from the river mouth (Figure 7a)
The wave orbital velocity was high near Ship Shoal and south of the barrier island near Terrebonne Bay (Figure 7b)

Summary

Introduction

Multi-resolution numerical models have been used in studies of coastal sediment modeling and morphological evolution in the Gulf of Mexico (GoM) in recent decades [1,2,3]. The sediment module in Delft3D can be used to simulate the effect of sediment properties. Caldwell and Edmonds [5] used DELFT3D to simulate the effect of sediment properties (the median, standard deviation, skewness, and percent of cohesive sediment) on the deltaic processes and morphology of the Mississippi River. Statistical modeling, including machine learning and deep learning, is becoming popular in sediment modeling studies, especially in satellite image analysis and seafloor morphology identification [7,8,9,10]. Diesing et al [7] tested the accuracy of different approaches, including geostatistics, image analysis, and machine-learning methods for acoustic data interpretation off the Scottish coast of the United Kingdom and found that the model performances were similar among the approaches. Liu et al [9] tested multiple machine learning classifiers to identify the sediment types of the Caminada dredge pit in the eastern part of the submarine sandy Ship Shoal of the Louisiana inner shelf of the USA

Objectives

Results

Discussion

Conclusion