This session highlights advances in the development and applications of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in hydrological, 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, 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.

Submitters: Guiling Wang, Univ. of Connecticut, Storrs, CT; Sujay V. Kumar, Code 617 (HSL), GSFC, Greenbelt, MD; Forrest M. Hoffman, PhD, Earth System Science, Computational Earth Sciences Group, ORNL, Oak Ridge, TN and Heather Grams, Element 84, Alexandria, VA