Perspectives on Artificial Intelligence for Predictions in Ecohydrology

Journal Article · 30 September 2023 · Artificial Intelligence for the Earth Systems

DOI: https://doi.org/10.1175/AIES-D-23-0005.1 · OSTI ID:2008135

Massoud, Elias C.  ^[1]; Hoffman, Forrest ^[1]; Shi, Zheng ^[2]; Tang, Jinyun ^[3]; Alhajjar, Elie ^[4]; Barnes, Mallory ^[5]; Braghiere, Renato K. ^[6]; Cardon, Zoe ^[7]; Collier, Nathan ^[1]; Crompton, Octavia ^[8]; Dennedy-Frank, P. James ^[9]; Gautam, Sagar ^[10]; Gonzalez-Meler, Miquel A. ^[11]; Green, Julia K. ^[12]; Koven, Charles ^[3]; Levine, Paul ^[13]; MacBean, Natasha ^[14]; Mao, Jiafu ^[15]; Mills, Richard Tran ^[16]; Mishra, Umakant ^[10] ; Mudunuru, Maruti ^[17]; Renchon, Alexandre A. ^[18]; Scott, Sarah ^[19]; Siirila-Woodburn, Erica R. ^[9]; Sprenger, Matthias ^[9]; Tague, Christina ^[20]; Wang, Yaoping ^[15]; Xu, Chonggang ^[21]; Zarakas, Claire ^[22]

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1. a Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee
2. b Department of Microbiology and Plant Biology, University of Oklahoma, Norman, Oklahoma
3. c Climate Sciences Department, Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California
4. d Engineering and Applied Sciences Division, RAND Corporation, Arlington, Virginia
5. e O’Neill School of Public and Environmental Affairs, Indiana University Bloomington, Bloomington, Indiana
6. f Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, g Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, California
7. h Marine Biological Laboratory, Ecosystems Center, Woods Hole, Massachusetts
8. i USDA–ARS Hydrology and Remote Sensing Laboratory, Beltsville, Maryland
9. j Earth and Environmental Sciences Area, Lawrence Berkeley National Laboratory, Berkeley, California
10. k Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Berkeley, California, l Bioscience Division, Sandia National Laboratories, Livermore, California
11. m Department of Biological Sciences and Earth and Environmental Sciences, University of Illinois at Chicago, Chicago, Illinois
12. n Department of Environmental Science, The University of Arizona, Tucson, Arizona
13. f Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California
14. o Department of Geography, Indiana University Bloomington, Bloomington, Indiana
15. p Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, Tennessee
16. q Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, Illinois
17. r Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington
18. s Environmental Science Division, Argonne National Laboratory, Lemont, Illinois, g Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, California
19. t Thermal/Fluid Science and Engineering, Sandia National Laboratories, Livermore, California
20. u Bren School of Environmental Science and Management, University of California, Santa Barbara, California
21. v Earth and Environmental Sciences Division, Los Alamos National Laboratory, New Mexico
22. w Department of Atmospheric Sciences, University of Washington, Seattle, Washington

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.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Biological and Environmental Research (BER)
• Grant/Contract Number: AC05-00OR22725
• OSTI ID: 2008135
• Journal Information: Artificial Intelligence for the Earth Systems, Journal Name: Artificial Intelligence for the Earth Systems Journal Issue: 4 Vol. 2; ISSN 2769-7525
• Publisher: American Meteorological SocietyCopyright Statement
• Country of Publication: United States
• Language: English