2025 CSDMS meeting-063

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Sediment Infilling Rate of Lowermost Mississippi River Borrow Pits and Impacts on Downstream Dredging

Francesca Messina, The Water Institute Denver Colorado, United States. fmessina@thewaterinstitute.org

Recent analytical and modeling studies on the Lowermost Mississippi River (LMR) have enhanced our understanding of sand flux at key locations, providing a framework for evaluating sand as a renewable resource for coastal restoration projects. This research is part of the Louisiana Sediment Management Program (LASMP) and aims to evaluate and quantify recharge rates of borrow areas within the LMR. It also seeks to determine whether channel maintenance dredging requirements are reduced downstream of borrow areas after they are dredged.

To characterize pit evolution and calculate infill rates, time-series bathymetric surveys from two recent projects with borrow areas - Spanish Pass Ridge and Marsh Creation (borrow area at Venice Anchorage), and Upper Barataria Marsh Creation (borrow areas at Alliance Bar) - were analyzed. Existing Delft3D numerical models were updated, refined, and calibrated for each reach using these time-series bathymetric data and recent sediment data.

Observations of infilling rates within a month of dredging were approximately 225,000 m3/month, declining rapidly to approximately 74,000 m3/month in February 2022 and declining further to 40,000 m3/month through March 24, 2022. During low flow conditions, infilling was minimal, but it increased to 230,000 m³/month in January 2023. For another area, initial infilling rates in the first month were around 125,000 m³/month, rapidly decreasing to approximately 75,000 m³/month by May 23, 2022. These rates fluctuated between 1,000 and 10,000 m³/month until December 27, 2022, before rising to 110,000 m³/month in January 2023.

This analysis provides a framework to forecast sediment recharge rates in LMR borrow pits, which can be incorporated into LASMP sediment resource availability estimates. It also suggests that sand extracted from the LMR for restoration can help reduce navigation channel maintenance costs. Future research opportunities include leveraging local model results and additional observed infilling rates from various locations along the Mississippi River. This research could lead to a better understanding of the relationship between the location of borrow pits, hydrograph characteristics, and corresponding infilling rates.

Furthermore, machine learning tools could be utilized to develop this correlation. Such a relationship would provide engineers and planners with an easy-to-use tool to evaluate first-order infilling rates and the time required for infilling, which is crucial for restoration project planning, including dredging operations and determining locations for borrow pits. Ultimately, this would support and promote sustainable sand extraction for restoration projects.