This session highlights advances in the development and applications of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in hydrological, ecohydrological, hydrometeorological, and hydroclimatological analysis, modeling, and predictions across a wide range of scales in space (local-to-global) and time (hours-to-decades). This includes theoretical developments in physics-informed machine learning, knowledge guided deep learning, hydroinformatics, and their applications in areas related to hydrology and water resources.  We also encourage papers on the application of AI/ML/DL in improving process-based hydrological and land surface models, uncertainty quantification, ecosystem feedbacks, and hydrologically relevant climate downscaling. Papers describing current challenges for model developers and users are also welcome, with a special interest in highlighting needs and opportunities to address national and societal challenges for living in a changing environment on a hopeful and thriving planet.

Submitters:  Forrest M. Hoffman, ORNL, Oak Ridge, TN; Sujay V. Kumar, Code 617 (HSL), GSFC, Greenbelt, MD and Elias Massoud, Computational Sciences and Engineering Division, ORNL, Oak Ridge, TN