Physics-Based Target Emulation of Reference Evapotranspiration Using Machine Learning Surrogates: A Conceptual Digital Twin Perspective

Reference evapotranspiration (ET0) is a standardized climatic index of atmospheric evaporative demand defined and calculated within the FAO-56 Penman–Monteith (PM) framework. When the required quality-controlled meteorological inputs are available, the standardized FAO-56 PM formulation remains the appropriate method for calculating ET0. This study does not seek to replace that formulation. Instead, it presents a preliminary, site-specific comparative investigation of how six machine learning architectures emulate an existing FAO-56 PM-derived ET0 target. Linear Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), Multilayer Perceptron (MLP), and Long Short-Term Memory (LSTM) were evaluated using 1827 daily records from ERA5-Land/Open-Meteo for Western Macedonia, Greece. The evaluation combined chronological data partitioning, time-series-aware cross-validation, hydrological performance metrics, Diebold–Mariano comparisons, and SHAP-based interpretation. Radiation- and temperature-related variables were the strongest statistical predictors. SVR achieved the lowest test-set RMSE (0.0862 mm day−1; R2 = 0.9967; PBIAS = 0.96%), although its predictive accuracy was not statistically distinguishable from XGBoost (DM = −1.8884, p = 0.0600). LSTM achieved substantially lower accuracy (R2 = 0.7636) under the present experimental configuration. These findings characterize the comparative statistical performance of the emulators within the sampled domain; they do not demonstrate physical equivalence, superior operational performance, or transferability beyond it. The Digital Twin perspective is conceptual: no operational Digital Twin or integrated hydrological system was developed or validated. The study provides preliminary methodological evidence for subsequent investigations into the roles and limitations of data-driven components in hydrological modelling, particularly meteorological data reconstruction and quality control in support of standardized FAO-56 PM calculations.