Researchers from the University of Arizona and Princeton University will spearhead a $1 million project funded by the National Science Foundation (NSF) that will use artificial intelligence to simulate the nation’s natural groundwater system in an effort to improve water management and help people better prepare for flooding and drought.
The project was one of 29 nationwide that received $27 million for the first phase of the NSF’s Convergence Accelerator program. Now in its second year, the initiative is designed to advance team-based multidisciplinary research that address challenges of national importance.
The research team is led by Laura Condon, Ph.D., assistant professor of hydrology and atmospheric sciences at the University of Arizona, and includes Princeton coprincipal investigators Reed Maxwell, Ph.D., professor in civil and environmental engineering and the Princeton Environmental Institute, and Peter Melchior, assistant professor of astrophysical sciences, jointly appointed in Princeton’s Center for Statistics and Machine Learning.
Maxwell is The Groundwater Foundation’s 2020 Darcy Lecturer.
Project coleads also include Patrick O’Leary, Ph.D., assistant director of scientific computing at scientific software company Kitware, and Nirav Merchant, director of the University of Arizona’s Data Science Institute and cohead of CyVerse, a national computational infrastructure for the life sciences that is funded by the NSF.
The project will combine the researchers’ strengths in data science, machine learning, and hydrology — which examines the dynamics and management of Earth’s water cycle — to improve hydrologic forecasting or the prediction of how much groundwater is available, how it can be sustainably managed, and how it will influence the severity of extreme events.
Groundwater can prevent drought or exacerbate flooding but predicting how these events could be affected requires knowing how much groundwater there is, said Maxwell, who will head the Princeton end of the project. Currently, effective and comprehensive hydrologic forecasting is hindered by a patchwork of models and data sources that are maintained by various scientists and institutions, he said.
“We don’t know how much groundwater we have, so we don’t know how much we can rely on it during normal years — let alone drought years — nor the extent to which it could exacerbate flooding, especially in mountain systems,” Maxwell said.
“This project brings many data sets and model results together in a seamless framework. This is a complicated problem that bridges disciplinary data, complex numerical simulations, substantial software development, data science and machine learning, user engagement, and education and outreach. We are bringing all of those elements together in a coherent way, which I believe is very unusual, if not unique, for an NSF-funded research project.”
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