A HPC Theory-Guided Machine Learning Cyberinfrastructure for Communicating Hydrometeorological Data Across Scales

Abstract

Focal Area(s)(2) Predictive modeling through the use of AI techniques and AI-derived model components. In this white paper, we propose the design and development of a high-performance computing (HPC) -powered cyberinfrastructure to provide a computation-efficient downscaling solution that can interpolate high-resolution predictions of hydrometeorological variables (e.g., precipitation and flood depth grid) using low-resolution raster-based simulation outputs. The cyberinfrastructure adopts a theory-guided machine learning approach to enable timely and high-resolution hydrometeorological predictions for supporting decisions in hydropower operations and mitigation of multiple water-related hazards (e.g., flood, sedimentation, stream bank instability).