Evaluation and error attribution of evapotranspiration products over the Haihe River Basin: Implications for irrigation scheduling and agricultural water management

Accurate evapotranspiration (ET) estimates are vital in the water-stressed Haihe River Basin (HRB); however, the suitability and error modes of existing ET products remain unclear. This study develops an end-to-end diagnostic framework to reveal error mechanisms, define applicability limits, and link site-level error processes to basin-scale performance patterns. The framework's novelty lies in its multi-pronged attribution strategy designed to disentangle complex error sources. It leverages high-precision lysimeters and an eddy covariance (EC) network to separate and validate evaporation versus transpiration errors. It then integrates a physics-based scenario matrix to diagnose error patterns under distinct hydrothermal stresses, and employs a generalized additive model (GAM) to quantify nonlinear relationships between these errors and environmental drivers. The framework reveals systematic, condition-dependent error patterns: at site scale, products overestimate bare-soil evaporation but underestimate cropland transpiration; at basin scale, land-surface models generally show higher skill in near-natural mountain regions, whereas their skill is reduced in intensively managed, irrigation-dominated plains. Failure to capture the spring irrigation-driven ET peak is one factor contributing to this contrast. These irrigation-induced biases directly affect irrigation scheduling, agricultural water budget, and crop water requirement estimates. Product choice should be context-specific: GLEAM demonstrates a superior ability to capture irrigation signals in managed plains, whereas reanalysis products are more reliable in near-natural mountains. These investigations can help optimize water resource utilization and allocation in agricultural fields. • Site-scale evapotranspiration biases propagate to basin-scale errors. • Model errors vary by surface type with bare soil high and irrigated cropland low. • Missing spring irrigation peaks is a factor for lower model skill in irrigated fields. • Plains management need corrected satellite evapotranspiration to schedule irrigation. • Land surface models suit annual water accounting in near-natural mountains.