Abstract

Accurate modeling and partitioning of forest evapotranspiration (ET) are essential for understanding water cycle processes in forest ecosystems. This study develops an improved three-source ET model by integrating a physically based canopy interception evaporation (Ei) scheme into the Shuttleworth–Wallace–Hu (SWH) model. A Monte Carlo stochastic parameterization scheme was applied to optimize model parameters. The proposed framework disaggregates the total ET flux into three distinct components: vegetation transpiration, soil evaporation, and Ei, thereby reducing uncertainties associated with the original SWH model in humid forest regions. The new model’s performance was assessed using flux observations from two subtropical forest sites and compared to the SWH model. The verification results indicate that the three-source model provided reliable estimates of daily ET. At the QYZ station (2004–2007) and the DHS station (2005–2007), the fitting slopes for simulating daily ET were 0.97 and 1.01, respectively, with corresponding coefficients of determination of 0.92 and 0.81. The root mean square errors (RMSE) for the three-source model were 0.38 mm day−1 and 0.52 mm day−1, respectively, with a reduction of 4.33% and 3.10% in RMSE compared to the SWH model. Additionally, the new model simulated the annual T/ET ratio more accurately, with values closer to site-measured data than the SWH model’s estimates. At both sites, the T/ET ratios simulated by the new model were closer to the observed values than those simulated by the SWH model, indicating an improved representation of ecohydrological processes. Furthermore, environmental analysis revealed that vapor pressure deficit and precipitation primarily govern the T/ET ratio, exerting the strongest positive and negative effects, respectively. Importantly, it requires only one additional precipitation parameter compared to the SWH model, yet achieves higher simulation accuracy and a more realistic representation of hydrological processes. Overall, the three-source model provides an improved framework for estimating ET in humid forest ecosystems. Ultimately, these results offer deeper insights into the coupled water and energy fluxes within forest ecosystems, thereby facilitating more effective water management and guiding sustainable forestry under a shifting climate.

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