Trade‐Offs Between Spatial and Temporal Accuracy of Complementary Relationship Models for Evaporation in an Ungauged Basin

The complementary relationship (CR) between actual and potential evaporation has undergone rapid development over the past decades. Commonly, the evaluation of CR models does not comprehensively cover spatial and temporal aspects of model performance; thus, the spatial and temporal accuracy and parameter sensitivity of different CR models remain relatively unknown, especially in ungauged basins. We examine the spatial and temporal performance and parameter sensitivity of four CR models with fixed parameters at the monthly scale over the source region of the Yellow River in China. Additionally, two CR models with distributed parameters were selected as comparisons. Because of the lack of evaporation spatiotemporal “true” values, the corrected water‐balance‐derived evaporation was used as the temporal reference, and the ensemble mean of multiple evaporation products was used as the spatial reference. We find that trade‐offs between the spatial and temporal accuracy of CR models with fixed parameters for basin evaporation should be considered in the application. Although four CR models with fixed parameters exhibited different spatial and temporal performances, their overall performance was generally similar. The parameters of CR models had consistent spatial and temporal sensitivities, and the sensitivity of parameter αe was stronger than that of other parameters. This study demonstrates that CR models with distributed parameters have a promising future in estimating basin or regional evaporation. An integrated spatial and temporal evaluation framework can better distinguish the performance of CR models. The methodologies can be used as a guideline for evaluating and calibrating CR models that match an application's needs. Key Points Trade‐offs between the spatial and temporal accuracy of complementary relationship (CR) models with fixed parameters for basin evaporation should be considered The parameters of CR models had consistent spatial and temporal sensitivities, and the sensitivity of αe is stronger CR models with distributed parameters can significantly improve time series and spatial distribution accuracy