Rethink hydrologic modeling framework with AI integrating multi-processes across scales

Abstract

The predictability of the current earth system modeling is hampered by some critical scientific gaps, including the difficulty of capturing processes and subgrid-processes across scales, mismatch of data and model resolutions, inconsistency of system and subsystem complexities, and lack of coupling with the human system. A proposed novel hydrologic modeling framework will identify a set of AI technologies to construct hybrid models to translate across spatiotemporal scales and complexities and address resolution mismatches, incorporate data-driven causal inference and learning to explore interactions and feedbacks among processes, and develop coupling with the human system by leveraging large amount of earth and human system data. We expect that the new modeling framework will significantly improve the predictability of coupled hydrologic, terrestrial, and biogeochemical processes and outcomes.