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Evaluating the Influence of Geophysical Data Integration for the Shellmound Inset Groundwater-Flow Model of the Mississippi Alluvial Plain
Proceedings of the 2020 Mississippi Water Resources Conference

Year: 2020 Authors: Guira M.N., Peterson S.M., Traylor J.P.

The U.S. Geological Survey (USGS) Mississippi Alluvial Plain project has been updating groundwater-flow models of the Mississippi Embayment and Mississippi River Valley Alluvial aquifers to provide stakeholders with tools that can be used to support water resources management decisions. Groundwater withdrawals from the Mississippi River Valley Alluvial aquifer have been vital to support agricultural production in the region near Shellmound, Mississippi, but substantial groundwater-level declines in the region have caused concerns for long term sustainability of the aquifer. Stakeholders are considering a number of actions to mitigate the groundwater-level declines, including managed aquifer recharge through riverbank filtration, whereby groundwater will be extracted near the Tallahatchie River and injected into the aquifer. High resolution airborne electromagnetic (AEM) survey data were collected to improve the understanding of the subsurface hydrostratigraphy in the study area, and for integration into a groundwater-flow model. A transient groundwater-flow model was constructed using MODFLOW6, the latest USGS modular three-dimensional finite-difference groundwater-flow model. The active model domain covers an area of about 1,000 square kilometers in northwestern Mississippi. The AEM data were processed at multiple vertical resolutions to build MODFLOW6 models with various layering configurations, including a single layer, a 5-meter constant layer thickness, and a 10-meter constant layer thickness. All three versions of the model use the same hydrologic input data and are calibrated against equivalent calibration targets. Simulated outputs along with calibration data will be compared to determine the influence of increased layer detail on model calibration, to the extent supported by the available observation data.

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