@Article{Massoud_AIES_20231001,
 author		= {Elias C. Massoud and Forrest M. Hoffman and Zheng Shi and Jinyun Tang and Elie Alhajjar and Mallory Barnes and Renato K. Braghiere and Zoe Cardon and Nathan Collier and Octavia Crompton and P. James Dennedy-Frank and Sagar Gautam and Miquel A. Gonzalez-Meler and Julia K. Green and Charles Koven and Paul Levine and Natasha MacBean and Jiafu Mao and Richard Tran Mills and Umakant Mishra and Maruti Mudunuru and Alexandre A. Renchon and Sarah Scott and Erica R. Siirila-Woodburn and Matthias Sprenger and Christina Tague and Yaoping Wang and Chonggang Xu and Claire Zarakas},
 title		= {Perspectives on Artificial Intelligence for Predictions in Ecohydrology},
 journal	= AIES,
 volume		= 2,
 number		= 4,
 pages		= {e230005},
 doi		= {10.1175/AIES-D-23-0005.1},
 year		= 2023,
 month		= oct,
 day		= 1,
 abstract	= {In November 2021, the Artificial Intelligence for Earth System Predictability (AI4ESP) workshop was held, which involved hundreds of researchers from dozens of institutions (Hickmon et al., 2022). There were 17 sessions held at the workshop, including one on Ecohydrology. The Ecohydrology session included various break-out rooms that addressed specific topics, including: 1) Soils \& Belowground, 2) Watersheds, 3) Hydrology, 4) Ecophysiology \& Plant Hydraulics, 5) Ecology, 6) Extremes, Disturbance \& Fire, and Land Use \& Land Cover Change, and 7) Uncertainty Quantification Methods \& Techniques. In this paper, we investigate and report on the potential application of Artificial Intelligence and Machine Learning (AI/ML) in Ecohydrology, highlight outcomes of the Ecohydrology session at the AI4ESP workshop, and provide visionary perspectives for future research in this area.}
}