Integration of a Machine-Learning-Derived Parameter into the PML Model for Simulating and Attributing Actual Evapotranspiration and Its Components

Actual evapotranspiration (ETa) is a key component of the hydrological cycle, and its partitioning into soil evaporation (Es) and vegetation transpiration (Ec) is essential for understanding hydrological processes. Focusing on the Yiluo River Basin during 1960–2020, this study developed a hybrid framework combining the physically based Penman–Monteith–Leuning (PML) model with machine learning to dynamically parameterize the soil evaporation coefficient f. ERA5-Land reanalysis data were used to drive the model, while the Pettitt change-point test and ridge regression were applied to identify potential change points and quantify driving factors. The results show that the framework improved the agreement of Es simulations with the GLEAM-derived reference product (R2 and NSE > 0.8) and reduced the difference in ETa estimates by approximately 10% within the product-constrained modeling framework. ETa exhibited a significant upward trend (0.28 mm·yr−1) with a potential change point around 2004, while its components responded earlier, with Ec and Es changing in 1994 and 2002. Ec dominated ETa, accounting for about 70% of the total. Net radiation, temperature and leaf area index were primary controls, while increasing vapor pressure deficit, together with changes in relative humidity and precipitation, jointly regulated the identified shifts. These findings provide a process-based understanding of ETa dynamics and improve the representation of ETa components in hydrological modeling.