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, 2024, "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
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