Integrating machine learning with analytical surface energy balance model improved terrestrial evaporation through biophysical regulation [* Cross-Reference *]

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Yun Bai; Kanishka Mallick; Tian Hu; Sha Zhang; Shanshan Yang; Arman Ahmadi, 2026, "Integrating machine learning with analytical surface energy balance model improved terrestrial evaporation through biophysical regulation [* Cross-Reference *]", https://lore.list.lu/citation?persistentId=perma:LIST.VOG7FK, LIST Open Repository, V1

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Description	Global evaporation modeling faces challenges in understanding the combined biophysical controls imposed by aerodynamic and canopy-surface conductance, particularly in water-scarce environments. We addressed this by integrating a machine learning (ML) model estimating surface relative humidity (RH0) into an analytical model (Surface Temperature Initiated Closure - STIC), creating a hybrid model called HSTIC. This approach significantly enhanced the accuracy of modeling water stress and conductance regulation. Our results, based on the FLUXNET2015 dataset, showed that ML-RH0 markedly improved the precision of surface water stress variations. HSTIC performed well in reproducing latent and sensible heat fluxes on both half-hourly/hourly and daily scales. Notably, HSTIC surpassed the analytical STIC model, particularly in dry conditions, owing to its more precise simulation of canopy-surface conductance (gSurf) response to water stress. Our findings suggest that HSTIC gSurf can effectively capture physiological trait variations across ecosystems, reflecting the eco-evolutionary optimality of plants. This provides a fresh perspective for process-based models in simulating terrestrial evaporation. (2024-03-08) *This entry has been automatically imported via OpenAlex by LIST harvest scripts. Please refer to https://doi.org/10.5194/egusphere-egu24-5661 for the original and latest version of the publication* (2026-07-01)
Subject	Astronomy and Astrophysics; Earth and Environmental Sciences; Physics
Keyword	Energy balance, Balance (ability), Evaporation, Environmental science, Energy (signal processing), Surface (topology), Computer science, Physics, Meteorology, Biology, Thermodynamics, Neuroscience, Mathematics
License/Data Use Agreement	CC BY 4.0


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Citation Metadata

Persistent Identifier	perma:LIST.VOG7FK
Publication Date	2026-07-06
Title	Integrating machine learning with analytical surface energy balance model improved terrestrial evaporation through biophysical regulation [* Cross-Reference *]
Other Identifier	https://doi.org/10.5194/egusphere-egu24-5661 OpenAlex ID: https://openalex.org/W4392600488
Author	Yun Bai (Hebei Normal University) - ORCID: https://orcid.org/0000-0002-3477-7884 Kanishka Mallick (Luxembourg Institute of Science and Technology) Tian Hu (Luxembourg Institute of Science and Technology) - ORCID: https://orcid.org/0000-0001-7280-1921 Sha Zhang (Qingdao University) - ORCID: https://orcid.org/0000-0002-9047-4247 Shanshan Yang (Qingdao University) - ORCID: https://orcid.org/0009-0005-4642-2422 Arman Ahmadi (Zimmer Biomet (United States), University of California, Berkeley) - ORCID: https://orcid.org/0000-0001-7962-1990
Point of Contact	Use email button above to contact. LIST QDKM (LIST)
Description	Global evaporation modeling faces challenges in understanding the combined biophysical controls imposed by aerodynamic and canopy-surface conductance, particularly in water-scarce environments. We addressed this by integrating a machine learning (ML) model estimating surface relative humidity (RH0) into an analytical model (Surface Temperature Initiated Closure - STIC), creating a hybrid model called HSTIC. This approach significantly enhanced the accuracy of modeling water stress and conductance regulation. Our results, based on the FLUXNET2015 dataset, showed that ML-RH0 markedly improved the precision of surface water stress variations. HSTIC performed well in reproducing latent and sensible heat fluxes on both half-hourly/hourly and daily scales. Notably, HSTIC surpassed the analytical STIC model, particularly in dry conditions, owing to its more precise simulation of canopy-surface conductance (gSurf) response to water stress. Our findings suggest that HSTIC gSurf can effectively capture physiological trait variations across ecosystems, reflecting the eco-evolutionary optimality of plants. This provides a fresh perspective for process-based models in simulating terrestrial evaporation. (2024-03-08) *This entry has been automatically imported via OpenAlex by LIST harvest scripts. Please refer to https://doi.org/10.5194/egusphere-egu24-5661 for the original and latest version of the publication* (2026-07-01)
Subject	Astronomy and Astrophysics; Earth and Environmental Sciences; Physics
Keyword	Energy balance Balance (ability) Evaporation Environmental science Energy (signal processing) Surface (topology) Computer science Physics Meteorology Biology Thermodynamics Neuroscience Mathematics
Topic Classification	Meteorological Phenomena and Simulations
Deposit Date	2024-03-08
Data Type	Preprint

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