EAR-Climate: Towards Better Understanding of Global Low Flow Dynamics Under Climate Change With Next-Generation, Differentiable Global Hydrologic Models

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Freshwater resources are critically important to many competing human and ecosystem needs around the world. Global hydrologic models are needed to assess climate change impacts on water resources, but their accuracy is often limited. Model equations are sometimes arbitrary and we do not fully leverage the value of available data. In particular, traditional models have trouble describing low flow periods when rivers run dry, and sometimes predict trends that are opposite to observational records. These errors could lead to inadequate climate mitigation strategies, under-preparation for drought, or misallocation of disaster relief resources. Machine learning models tend to be accurate, but they remain challenging for humans to decipher, are not well suited to ask precise questions, and do not necessarily respect physical laws we know to be true such as the conservation of mass. This work will seek to build a new genre of hydrologic modeling currently termed differentiable modeling in hydrology¸ or, simply, differentiable hydrology. Not only will the next-generation global hydrologic models developed from this project improve our ability to estimate future low flows, but this work will also establish a new avenue in hydrology that combines the best aspects of machine learning and processes. The new avenue will provide the flexibly to ask new scientific questions and learn the answers from big data. As a result, hydrologists will no longer be limited by the generalist model design in artificial intelligence where interpretability is traded for model genericity. To achieve the project goals, the layers of artificial intelligence will be peeled off to harness one of its core technologies, namely, differentiable programming, to build learnable process-based models. Next-generation hydrologic models will evolve based on global hydrologic data, reduce structural deficiencies, build global parameterization schemes for groundwater, and address scale issues. The project will characterize errors attributable to structural deficiencies, improve reliability of predictions in data-sparse regions, and improve model physical significance. Outcomes will be disseminated to the climate change impact assessment community. Low flow predictions will also be shared through continuing collaboration with non-profit organizations with footprints in Africa. The research effort will be incorporated into educational activities for graduate students, symposium and workshop attendants, and high-schoolers via their teachers.This project is funded by the Hydrologic Sciences program, as well as a collaboration between the Directorate for Geosciences and Office of Advanced Cyberinfrastructure to support AI/ML and open science activities in the geosciences.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.