Perspectives on Artificial Intelligence for Predictions in Ecohydrology
Elias Massoud, Forrest M. Hoffman, Zheng Shi, Jinyun Tang, Elie Alhajjar, Mallory L. Barnes, Renato K. Braghiere, Zoë G. Cardon, Nathan Collier, Octavia Crompton, P. James Dennedy‐Frank, Sagar Gautam, Miquel A. Gonzàlez‐Meler, Julia K. Green, Charles D. Koven, Paul A. Levine, Natasha MacBean, Jiafu Mao, Richard T. Mills, Umakant Mishra, Maruti Kumar Mudunuru, Alexandre A. Renchon, Sarah Scott, Erica R. Siirila‐Woodburn, Matthias Sprenger, C. Tague, Yaoping Wang, Chonggang Xu, Claire M. Zarakas
Abstract
Abstract In November 2021, the Artificial Intelligence for Earth System Predictability (AI4ESP) workshop was held, which involved hundreds of researchers from dozens of institutions. There were 17 sessions held at the workshop, including one on ecohydrology. The ecohydrology session included various breakout rooms that addressed specific topics, including 1) soils and belowground areas; 2) watersheds; 3) hydrology; 4) ecophysiology and plant hydraulics; 5) ecology; 6) extremes, disturbance and fire, and land-use and land-cover change; and 7) uncertainty quantification methods and techniques. In this paper, we investigate and report on the potential application of artificial intelligence and machine learning in ecohydrology, highlight outcomes of the ecohydrology session at the AI4ESP workshop, and provide visionary perspectives for future research in this area.