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Published October 2023 | Version Published
Journal Article Open

Perspectives on Artificial Intelligence for Predictions in Ecohydrology

Creators

  • 1. ROR icon California Institute of Technology
  • 2. ROR icon Jet Propulsion Lab

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.

Copyright and License

© 2023 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license.

Acknowledgement

The authors acknowledge all of the efforts made as part of the Artificial Intelligence for Earth System Predictability (AI4ESP) workshop. This research was partially supported by the RUBISCO Science Focus Area (RUBISCO SFA KP1703), which is sponsored by the Regional and Global Model Analysis (RGMA) activity of the Earth and Environmental Systems Modeling (EESM) Program in the Earth and Environmental Systems Sciences Division (EESSD) of the Office of Biological and Environmental Research (BER) in the U.S. Department of Energy Office of Science. This paper has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the U.S. Department of Energy (DOE). The U.S. government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this paper, or allow others to do so, for U.S. government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan. Part of this research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. Government sponsorship is acknowledged. The authors declare no conflicts of interest. All authors contributed to the discussions that inspired this paper and were responsible for writing parts of the paper in the early stages of preparation. Author Massoud wrote the first complete draft, and all authors contributed to writing and editing the current version of the paper.

Data Availability

There were no specific datasets used in this study.

 

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Additional details

Funding

United States Department of Energy
KP1703
United States Department of Energy
DE-AC05-00OR22725