Study region: Using weather data from 340 meteorological stations in Iran spanning 2011-2021, we conducted a comprehensive mapping of reference evapotranspiration (ET0) across the country. This extensive network of stations spans diverse climatic and environmental conditions, enabling us to evaluate the influence of meteorological and geographic drivers on the precision of ET0 estimates across various regions of Iran.
Study focus: Reference evapotranspiration (ET0) is a crucial variable in hydrologic, environmental, ecological, and meteorological modeling, and its large-scale mapping is essential for agricultural optimization and water resources management. Although most studies map ET0 at large scales based on point-scale measurements, this study integrates regression-kriging (RK) methods with meteorological and coordinate data to produce more accurate ET0 maps. Three RK variants are developed, including generalized linear model-ordinary kriging (GLM+OK), generalized additive model-ordinary kriging (GAM+OK), and random forest-ordinary kriging (RF+OK) for the first time for large-scale ET0 mapping. The ReliefF algorithm is used to identify influential input variables, and different strategies are defined based on geographical coordinates, meteorological variables, and point-scale ET0 measurements. The proposed models are then compared with standalone GLM, GAM, RF, and OK ones. New hydrological insights for the region: The GAM+OK hybrid model achieved the highest accuracy (testing R2 = 0.932) by explicitly capturing nonlinear spatial patterns and residual autocorrelation across Iran's hyper-arid interior, semi-arid plateaus, and humid coastal zones. ReliefF-guided feature selection demonstrated that omitting wind speed markedly degrades ET0 estimates in coastal and island regions, where sea-breeze-driven advection dominates evaporative demand, whereas coordinate information (latitude and elevation) is indispensable for inland orographic gradients. These findings provided a high-resolution, uncertainty-quantified framework for refining regional water-balance assessments, optimizing irrigation scheduling in water-scarce agricultural basins, and enhancing drought-early-warning systems under climate variability, offering hydrological insights for sustainable water-resources management in arid regions.