Blab AI
University of Hawaii Secures $500,000 NSF Grant to Use AI for Coastal Water Protection
The University of Hawaiʻi at Mānoa (UH Mānoa) has been awarded a $500,000 grant from the National Science Foundation (NSF) under its Collaborations in Artificial Intelligence and Geosciences (CAIG) program. The funding will support a three‑year, nearly $1 million joint effort with the University of Texas at Austin to develop artificial‑intelligence (AI) tools that predict how rising seas and stronger storms threaten coastal freshwater supplies and ecosystems.
Sea levels are climbing, storms are getting fiercer, and the saltwater that follows is already intruding into coastal aquifers—threatening drinking water, agriculture, infrastructure, and local economies worldwide. In Hawaiʻi, those pressures are set to intensify as climate‑change‑driven sea‑level rise accelerates. The new project aims to provide faster, more accurate predictions of how groundwater and ocean waters interact, empowering communities to safeguard freshwater resources and coastal habitats.
Researchers will build AI models that work alongside established physics‑based groundwater simulations. By creating high‑speed surrogate models, the system can run complex aquifer–ocean exchange simulations at a fraction of the computational cost while still respecting the essential physical laws that govern the land‑sea boundary. The approach is designed to handle both long‑term ocean patterns and short‑term events such as storm surges.
Key features of the project include:
A flexible AI architecture that integrates multiple machine‑learning techniques to analyze diverse data streams. Real‑world testing of the models on benchmark coastal systems in Hawaiʻi and Texas. Near‑real‑time forecasting capabilities that can feed into digital‑twin models of coastal ecosystems. Practical decision‑support tools that translate model outputs into actionable insights for water‑management authorities, infrastructure planners, and resilience strategists. * Open‑source software, interactive visualization tools, and interdisciplinary training modules for students and early‑career researchers.
According to the project description, the AI system will help identify critical coastal risks such as seawater intrusion into freshwater aquifers and the discharge of freshwater into the ocean. These predictions are intended to inform both short‑term emergency responses and long‑term planning for coastal communities.
The collaboration places UH Mānoa as the lead institution, with the University of Texas at Austin serving as a partner. The combined effort will span three years, with the NSF grant covering a portion of the total budget. The project aligns with the CAIG program’s goal of advancing AI methods that enable significant breakthroughs in addressing geoscience research questions.
The initiative also emphasizes education and workforce development. Students and postdoctoral researchers working on the project will receive hands‑on experience at the intersection of groundwater hydrology, coastal science, computational modeling, and AI. The training is expected to prepare a new generation of scientists who can tackle Hawaiʻi’s water and environmental challenges.
While the project has secured initial funding and a clear research agenda, several aspects remain to be developed. The team will need to gather and curate high‑resolution data sets for training and validation, establish robust evaluation metrics for the surrogate models, and integrate the AI outputs with existing digital‑twin platforms used by local authorities. The timeline for the first operational prototype and public release of the open‑source tools has not yet been announced.
In summary, the NSF grant will enable UH Mānoa and its partner to create AI‑enhanced tools that improve the understanding and prediction of coastal groundwater–ocean interactions. The work promises to deliver faster, more accessible models that support decision makers in safeguarding freshwater supplies and coastal ecosystems amid a changing climate.
Sea levels are climbing, storms are getting fiercer, and the saltwater that follows is already intruding into coastal aquifers—threatening drinking water, agriculture, infrastructure, and local economies worldwide. In Hawaiʻi, those pressures are set to intensify as climate‑change‑driven sea‑level rise accelerates. The new project aims to provide faster, more accurate predictions of how groundwater and ocean waters interact, empowering communities to safeguard freshwater resources and coastal habitats.
Researchers will build AI models that work alongside established physics‑based groundwater simulations. By creating high‑speed surrogate models, the system can run complex aquifer–ocean exchange simulations at a fraction of the computational cost while still respecting the essential physical laws that govern the land‑sea boundary. The approach is designed to handle both long‑term ocean patterns and short‑term events such as storm surges.
Key features of the project include:
A flexible AI architecture that integrates multiple machine‑learning techniques to analyze diverse data streams. Real‑world testing of the models on benchmark coastal systems in Hawaiʻi and Texas. Near‑real‑time forecasting capabilities that can feed into digital‑twin models of coastal ecosystems. Practical decision‑support tools that translate model outputs into actionable insights for water‑management authorities, infrastructure planners, and resilience strategists. * Open‑source software, interactive visualization tools, and interdisciplinary training modules for students and early‑career researchers.
According to the project description, the AI system will help identify critical coastal risks such as seawater intrusion into freshwater aquifers and the discharge of freshwater into the ocean. These predictions are intended to inform both short‑term emergency responses and long‑term planning for coastal communities.
The collaboration places UH Mānoa as the lead institution, with the University of Texas at Austin serving as a partner. The combined effort will span three years, with the NSF grant covering a portion of the total budget. The project aligns with the CAIG program’s goal of advancing AI methods that enable significant breakthroughs in addressing geoscience research questions.
The initiative also emphasizes education and workforce development. Students and postdoctoral researchers working on the project will receive hands‑on experience at the intersection of groundwater hydrology, coastal science, computational modeling, and AI. The training is expected to prepare a new generation of scientists who can tackle Hawaiʻi’s water and environmental challenges.
While the project has secured initial funding and a clear research agenda, several aspects remain to be developed. The team will need to gather and curate high‑resolution data sets for training and validation, establish robust evaluation metrics for the surrogate models, and integrate the AI outputs with existing digital‑twin platforms used by local authorities. The timeline for the first operational prototype and public release of the open‑source tools has not yet been announced.
In summary, the NSF grant will enable UH Mānoa and its partner to create AI‑enhanced tools that improve the understanding and prediction of coastal groundwater–ocean interactions. The work promises to deliver faster, more accessible models that support decision makers in safeguarding freshwater supplies and coastal ecosystems amid a changing climate.
