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Abstract: Importance of soil moisture temporal scale, depth, and antecedent conditions for machine-learning based predictions of dryland carbon and water fluxes (AGU25)

Importance of soil moisture temporal scale, depth, and antecedent conditions for machine-learning based predictions of dryland carbon and water fluxes

Rubaya Pervin1, Mallory Barnes2, Scott Robeson2, Steve Kannenberg3, Jie Hu4 and Natasha MacBean5, (1)Indiana University Bloomington, Bloomington, IN, United States, (2)Indiana University Bloomington, Bloomington, United States, (3)West Virginia University, Morgantown, United States, (4)West Virginia University, Biology, Morgantown, United States, (5)Western University, Department of Geography & Environment and Department of Biology, London, Canada
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