FAIR data infrastructure and tools for AI-assisted streamflow prediction

Abstract

Focal Area(s) Areas: We discuss how the integration of AI into Earth Science models can impact streamflow predictions at both the science and data levels. Doing so, we address cross-cutting needs related to the goal of making data FAIR (Findable, Accessible, Interoperable, and Re-usable [1]) for seamless use with Artificial Intelligence/Machine Learning (AI/ML) in Earth System Science at DOE. A novel idea is that AI/ML itself can help with the FAIR data goal and address issues in targeted areas e.g. missing data, data quality and reduction. In addition, the interpretability of results obtained with new AI methods is poised to impact broader scientific challenges in hydrology..