EAGER: CHIRRP: Responding to Groundwater Depletion and Building Resilience by Examining Emerging Social-Environmental-Technical Structures

Southern Illinois University At Carbondale, Carbondale IL

Investigators

Abstract

In recent decades, groundwater resources in major U.S. agricultural regions have reached critical levels of depletion. Such significant groundwater depletion causes a multitude of hazards, including agricultural and drinking water shortages, land subsidence, and reduced ecosystem health. Groundwater sustainability is essential for improving the viability of the agricultural sector and for supporting the resilience of farming communities. This project is focused on enhancing the resilience of groundwater resources through a participatory planning approach. Conventional water resources management practices based on Earth system models alone are often insufficient to address these sustainability challenges, partially due to the lack of appropriate tools for engaging stakeholders. To cope with these challenges, the project team will develop an artificial intelligence (AI) based modeling system that empowers farming communities to address groundwater depletion and assess resilience outcomes under different climate and management scenarios. Key research activities include community engagement and AI-based integrated model development to replicate social, hydrological, and technical dynamics in an adaptive groundwater management system. It will directly benefit farming community members by providing new collaborative planning tools for developing groundwater sustainability strategies. The overarching goal of this project is to create a new paradigm of integrated social-Earth science models that place the community at the center of the design process. The project will develop a semi-structured digital twin (DT) that empowers farming communities to collaboratively tackle groundwater depletion and examine resilience strategies and outcomes under existing and emerging social-environmental-technical structures. The team will develop a graph network to represent the complex topological structures of multifaceted social, hydrological, and technical subsystems, and employ machine learning and deep learning models to simulate intricate hydrological processes and support community planning initiatives. The project will address critical research gaps, including (1) how changes in dynamic interactions among hydrological, climatic, technology use, and irrigation processes affect the equilibrium of groundwater storage, (2) how farming communities effectively participate in collaborative planning and co-design groundwater management strategies, and (3) how research endeavors and community knowledge and priorities can be bridged for actionable solutions to confront groundwater depletion. The project will advance the understanding of groundwater susceptibility and irrigation system resilience to various exogenous and endogenous challenges through the lens of social-environmental-technical systems. By equipping communities with participatory planning tools, the project will facilitate effective groundwater sustainability planning and solution development, enhance scientific communication among stakeholders, and promote responsible groundwater usage for a sustainable and productive future. 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.