Perspectives on artificial intelligence for predictions in ecohydrology
Perspectives on artificial intelligence for predictions in ecohydrology
Date
2023-10-09
Authors
Massoud, Elias C.
Hoffman, Forrest M.
Shi, Zheng
Tang, Jinyun
Alhajjar, Elie
Barnes, Mallory
Braghiere, Renato K.
Cardon, Zoe G.
Collier, Nathan
Crompton, Octavia
Dennedy-Frank, P. James
Gautam, Sagar
Gonzalez-Meler, Miquel A.
Green, Julia K.
Koven, Charles
Levine, Paul
MacBean, Natasha
Mao, Jiafu
Mills, Richard Tran
Mishra, Umakant
Mudunuru, Maruti
Renchon, Alexandre A.
Scott, Sarah
Siirila-Woodburn, Erica R.
Sprenger, Matthias
Tague, Christina
Wang, Yaoping
Xu, Chonggang
Zarakas, Claire
Hoffman, Forrest M.
Shi, Zheng
Tang, Jinyun
Alhajjar, Elie
Barnes, Mallory
Braghiere, Renato K.
Cardon, Zoe G.
Collier, Nathan
Crompton, Octavia
Dennedy-Frank, P. James
Gautam, Sagar
Gonzalez-Meler, Miquel A.
Green, Julia K.
Koven, Charles
Levine, Paul
MacBean, Natasha
Mao, Jiafu
Mills, Richard Tran
Mishra, Umakant
Mudunuru, Maruti
Renchon, Alexandre A.
Scott, Sarah
Siirila-Woodburn, Erica R.
Sprenger, Matthias
Tague, Christina
Wang, Yaoping
Xu, Chonggang
Zarakas, Claire
Linked Authors
Person
Alternative Title
Citable URI
As Published
Date Created
Location
DOI
10.1175/aies-d-23-0005.1
Related Materials
Replaces
Replaced By
Keywords
Ecology
Carbon cycle
Hydrologic cycle
Artificial intelligence
Machine learning
Carbon cycle
Hydrologic cycle
Artificial intelligence
Machine learning
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.
Description
Author Posting. © American Meteorological Society, 2023. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Massoud, E., Hoffman, F., Shi, Z., Tang, J., Alhajjar, E., Barnes, M., Braghiere, R., Cardon, Z., Collier, N., Crompton, O., Dennedy-Frank, P., Gautam, S., Gonzalez-Meler, M., Green, J., Koven, C., Levine, P., MacBean, N., Mao, J., Mills, R. T., Mishra, U., Mudunuru, M., Renchon, A. A., Scott, S., Siirila-Woodburn, E. R., Matthias Sprenger, M., Tague, C., Wang, Y., Xu, C., & Zarakas, C. (2023). Perspectives on Artificial Intelligence for Predictions in Ecohydrology. Artificial Intelligence for the Earth Systems, https://doi.org/10.1175/aies-d-23-0005.1
Embargo Date
Citation
Massoud, E., Hoffman, F., Shi, Z., Tang, J., Alhajjar, E., Barnes, M., Braghiere, R., Cardon, Z., Collier, N., Crompton, O., Dennedy-Frank, P., Gautam, S., Gonzalez-Meler, M., Green, J., Koven, C., Levine, P., MacBean, N., Mao, J., Mills, R. T., Mishra, U., Mudunuru, M., Renchon, A. A., Scott, S., Siirila-Woodburn, E. R., Matthias Sprenger, M., Tague, C., Wang, Y., Xu, C., & Zarakas, C. (2023). Perspectives on Artificial Intelligence for Predictions in Ecohydrology. Artificial Intelligence for the Earth Systems.