A hybrid Penman-Monteith and machine learning model for simulating evapotranspiration and its components

Chen, Han; Good, Stephen P.; Caylor, Kelly; Fiorella, Richard Pascal; Wang, Lixin

doi:10.1016/j.jhydrol.2026.134985

A hybrid Penman-Monteith and machine learning model for simulating evapotranspiration and its components

Journal Article · Wed Jan 21 00:00:00 EST 2026 · Journal of Hydrology

DOI:https://doi.org/10.1016/j.jhydrol.2026.134985· OSTI ID:3016114

^[1]; ^[2]; ^[3]; ^[4]; Wang, Lixin ^[5]

Oregon State Univ., Corvallis, OR (United States); Tianjin Univ. (China)
Oregon State Univ., Corvallis, OR (United States)
Univ. of California, Santa Barbara, CA (United States)
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Indiana Univ.-Purdue Univ. Indianapolis (IUPUI), Indianapolis, IN (United States)

Integrating physical processes with machine learning has advanced evapotranspiration (ET) simulation, yet most hybrid models fail to partition total ET into its components: soil evaporation (E) and vegetation transpiration (T). This study introduces Residual Neural Network–Penman–Monteith (RNN-PM), a novel hybrid dual-source ET model designed to overcome this limitation. The model synergizes the physically-based Penman–Monteith framework with three specialized residual neural networks trained to estimate key conductance parameters (canopy conductance, soil surface conductance, and aerodynamic conductance). Furthermore this explicit parameterization allows for the direct partitioning of total ET. Validation at National Ecological Observatory Network (NEON) flux sites using high-frequency partitioned E and T shows that RNN-PM reliably reproduces ET and the transpiration fraction (T/ET). For ET, the model achieves an average Kling–Gupta efficiency (KGE) of 0.89 and a root-mean-square error (RMSE) of 0.55 mm/day; for T/ET, the KGE is 0.87 with an RMSE of 0.06. Furthermore, RNN-PM demonstrates robust generalization, accurately simulating ET and its components well beyond the initial training dataset, even under extreme climatic conditions. This study extended the analysis by comparing the RNN-PM model with seven established dual-source ET models. The results indicate that RNN-PM outperforms both conventional machine learning models and purely physical process-based models in simulating ET components in most cases. Among the purely physical process-based dual-source models, those based on surface temperature decomposition showed improved performance as the leaf area index (LAI) decreased when evaluated against high-frequency ET component datasets. In contrast, the performance of conductance-based dual-source models declined with decreasing LAI. Although purely machine learning-based models can produce relatively accurate simulations of ET components, they often exhibit limited generalization capability, an issue that the RNN-PM model effectively overcomes. Ultimately, the RNN-PM model represents a significant advance in simulating ET components, offering a novel and scalable approach for improving the representation of land–atmosphere interactions in Earth system models.

View Accepted Manuscript (DOE)

Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: National Science Foundation (NSF); USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC), Biological and Environmental Research (BER). Earth & Environmental Systems Science (EESS); Zegar Family Foundation

Grant/Contract Number:: 89233218CNA000001; SC0024297

OSTI ID:: 3016114

Report Number(s):: LA-UR--25-30325; 10.1016/j.jhydrol.2026.134985

Journal Information:: Journal of Hydrology, Journal Name: Journal of Hydrology Vol. 668; ISSN 0022-1694

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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