Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 30, issue 16
Articles | Volume 30, issue 16
https://doi.org/10.5194/hess-30-5373-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-30-5373-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 30, issue 16
Research article
 | Highlight paper
 | 
25 Aug 2026
Research article | Highlight paper |  | 25 Aug 2026

Learning evaporative fraction with memory

Wenli Zhao, Alexander J. Winkler, Markus Reichstein, Rene Orth, and Pierre Gentine

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Editorial statement
This study proposes a novel framework for predicting evaporative fraction (EF) by explicitly incorporating meteorological memory into machine learning models. By demonstrating that long short-term memory (LSTM) networks substantially outperform conventional approaches across a range of ecosystems, the study reveals that vegetation responses to water availability are fundamentally governed by temporal dependencies that are often neglected in current modeling frameworks. This work provides new process-level understanding of land–atmosphere interactions.
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Short summary
We used explainable machine learning that incorporates memory effects to study how plants respond to weather and drought. Using data from 90 sites worldwide, we show that memory plays a key role in regulating plant water stress. Forests and savannas rely on longer past conditions than grasslands, reflecting differences in rooting depth and water use. These insights improve our ability to anticipate ecosystem vulnerability as droughts intensify.
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