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It is difficult - though important - to assess the necessary allocation of increasingly scarce water resources and yield estimation, by determining the exact amount of evapotranspiration (ET) and the partitioning of ET into transpiration (T) through the stomata of plants and evaporation (E) from the soil. An accurate and effective method for ET partitioning and estimation is, therefore, desirable. This paper aims to evaluate the performance of two main models for ET partitioning and estimation in terms of feasibility and accuracy: the AquaCrop (Version 4.0) model, and the SIMDualKc model. Field experiments were carried out between 2011 and 2015 in an arid region of Northwest China on two sites using full and deficit irrigation under plastic film-mulch, With the ET of maize for seed production and its partitioning components (T and E) being strictly measured by using the eddy covariance (EC) system or the sap flow system and micro-lysimeter cylinders. Subsequently, part of the measured data was used to calibrate the two models, so that the calibrated models could then be used to assess whether the agreement between simulation and measurement had proved successful, thus validating the models, or not. The results showed that the two models performed well with regard to their simulation of ET and T under full irrigation conditions. Under deficit irrigation conditions, the ET and T values simulated by the AquaCrop model were much closer to the actual measurement when compared with the results simulated by the SIMDualKc model. This was particularly the case when the soil was re-watered after a period of long-term water stress. For the simulated E, however, both models generated data that were distant from the actual measurements taken under full or deficit irrigation conditions using plastic film-mulch, although when the SIMDualKc model simulated E data, it came closer to the measurement than did the AquaCrop model. The comparison of two models in terms of their accuracy and feasibility based on the data analysis is discussed. (C) 2016 Elsevier Ltd. All rights reserved.