Location: Sustainable Water Management Research
Project Number: 6066-13000-006-041-A
Project Type: Cooperative Agreement
Start Date: Jul 1, 2026
End Date: Jun 30, 2028
Objective:
The overall objective of this agreement is to conduct research in support of congressionally mandated obligations on innovative water systems. Accomplishing the objectives of this agreement will result in increased profitability for farmers and long-term productivity of the land in keeping with the Secretary's directives on departmental research and development priorities. Specific objectives are:
1. Artificial Intelligence (AI)-Assisted MODFLOW framework for screening, capacity estimation, and preliminary design of aquifer systems in the Southeast region to account for contaminant flow, aquifer recharge, and thermal storage modeling.
2. Determine the impact of native rivercane (Arundinaria gigantea) in a riparian buffer for improvements in runoff control and soil moisture retention in the Lower Mississippi River Basin.
Approach:
This project addresses a pressing groundwater management challenge in both West Alabama and the broader Southeastern region, including the Lower Mississippi Valley (LMV), by focusing on the coupled problems of contaminant flow and aquifer recharge in complex aquifer systems.
The approach for Objective 1 is to develop an artificial intelligence (AI)-assisted groundwater modeling framework to characterize recharge dynamics, preferential flow pathways, and contaminant transport using widely available hydrogeologic, climatic, and land-use data. Building from regional aquifer screening to site-specific assessment, the work will integrate advanced numerical modeling approaches representing both diffuse groundwater flow and rapid movement through fractures, conduits, or other high-permeability pathways. Standard groundwater models fail to capture the complexity of subsurface connections among recharge zones, shallow aquifers, and deeper groundwater systems. Accounting for these dynamics will improve estimates of aquifer vulnerability, contaminant migration, recharge potential, capture zones, and the impacts of seasonal and extreme hydrologic conditions on groundwater quality and resilience. This objective will Integrate geospatial AI, monitoring networks, well data, remote sensing, and predictive groundwater models into a digital twin–enabled decision-support platform that will provide a regionally tailored tool for groundwater protection and sustainable aquifer management. In doing so, it will establish a scalable model for addressing groundwater vulnerability and recharge across some of the most hydrologically and economically important landscapes in the South.
Objective 2 will evaluate ground-penetrating radar as a non-invasive tool for mapping and modeling rivercane rhizomes in Alabama in order to better understand subsurface vegetation structure, shallow soil conditions, and water movement processes relevant to agricultural water management. This geophysical component will develop a field-tested protocol for detecting rhizome networks, generating high-resolution georeferenced subsurface imagery, and assessing the feasibility of three-dimensional modeling of below ground cane structure. Rather than framing these outcomes primarily in terms of restoring rivercane, this approach uses rivercane as a means of improving understanding of how perennial riparian vegetation interacts with soil moisture, shallow stratigraphy, infiltration zones, and erosion-sensitive margins in working agricultural watersheds. Additionally, a geophysical survey will be conducted using a high-frequency ground-penetrating radar (GPR) system designed for shallow, fine-scale targets such as rivercane rhizomes. The survey will be carried out within a precisely georeferenced grid, allowing subsurface data to be spatially aligned with aboveground vegetation, topography, and other environmental variables. The resulting radar profiles, depth-slice maps, and three-dimensional models will provide new information on rhizome distribution, soil layering, and near-surface structural conditions that influence how water behaves in areas adjacent to agricultural lands.