Optimizing Reference Evapotranspiration Estimation in Data-Scarce Regions Using ERA5 Reanalysis and Machine Learning

This study aims to optimize the estimation of reference evapotranspiration (ETo) in data-scarce regions by integrating ERA5-Land reanalysis data with machine learning (ML) models. Daily meteorological data from 33 stations across Turkey’s diverse climate zones (1981–2010) were utilized to train and validate three ML models: Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Extreme Learning Machine (ELM). The methodology involved rigorous quality control of ground-based observations, spatial correlation of ERA5-Land grids to station locations, and performance evaluation under various data-limited scenarios. Results indicate that while ERA5-Land provides highly accurate solar radiation (Rs) and temperature (T) data, variables like wind speed (U2) and relative humidity (RH) exhibit systematic biases. Among the used models, XGBoost demonstrated superior performance (R2 = 0.95, RMSE = 0.43 mm day−1, and MAE = 0.30 mm day−1) and computational efficiency. This study provides a robust, regionally calibrated framework that corrects reanalysis biases using ML, offering a reliable alternative for ETo estimation in areas where local measurements are insufficient for sustainable water management.