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Perspectives on Artificial Intelligence for Predictions in Ecohydrology
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. ; © 2023 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. ; 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 ...
Perspectives on Artificial Intelligence for Predictions in Ecohydrology
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. ; © 2023 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. ; 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 ...
Perspectives on Artificial Intelligence for Predictions in Ecohydrology
Massoud, Elias C. (author) / Hoffman, Forrest (author) / Shi, Zheng (author) / Tang, Jinyun (author) / Alhajjar, Elie (author) / Barnes, Mallory (author) / Braghiere, Renato K. (author) / Cardon, Zoe (author) / Collier, Nathan (author) / Crompton, Octavia (author)
2023-10-01
oai:authors.library.caltech.edu:exdkz-yam09
Artificial Intelligence for the Earth Systems, 2(4), e230005, (2023-10)
Article (Journal)
Electronic Resource
English
DDC:
690
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