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Completed STANDARD GRANT National Science Foundation (US)

I-Corps: Translation Potential of an Interface Designed for Real-time Monitoring of Groundwater Levels and Assessment of Land Subsidence

$500K USD

Funder National Science Foundation (US)
Recipient Organization Stanford University
Country United States
Start Date Nov 15, 2024
End Date Oct 31, 2025
Duration 350 days
Number of Grantees 1
Roles Principal Investigator
Data Source National Science Foundation (US)
Grant ID 2440615
Grant Description

The broader impact of this I-Corps project is the development of a software solution that enhances the understanding of groundwater flow dynamics and land settlement. This software aligns with the sustainability goals mandated for local irrigation districts and groundwater sustainability agencies. By preventing the need for land repurposing, the software will safeguard the agricultural economy and preserve vital farmland.

The solution will also improve scenario development tools used by engineering consulting companies, leading to more informed decision-making in groundwater sustainability planning. Additionally, the technology will assess future flood risk areas resulting from extensive groundwater extraction, providing valuable insights for insurers and reinsurers.

This solution will help mitigate the negative impacts of groundwater drilling on critical civil infrastructure such as highways, bridges, and canals, potentially saving billions of dollars for counties and taxpayers.

This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of a product with a user-friendly interface designed for real-time monitoring of groundwater levels and the assessment of land subsidence caused by excessive groundwater withdrawal.

The software integrates real-time observations from in-situ extensometers and remote airborne and satellite imaging. The technology employs a comprehensive workflow and methodology for reduced-order and machine learning-based surrogates. These surrogates are trained on data derived from physics-based, high-fidelity, numerical simulations.

This approach enables accurate and efficient tracking of managed aquifer recharge and provides essential insights for groundwater sustainability and land subsidence prevention.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

All Grantees

Stanford University

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