## 1 Introduction  
Evapotranspiration (_ET_) is the main consumer of agricultural water, so it will be worthwhile to clarify its change pattern and driving factors during the growth period [1]. Previous studies have typically included environmental and physiological factors as variable indicators for studying the driving factors of _ET_[2, 3, 4]; however, for each agroecosystem, the main driving factors of _ET_ are not the same. [PERSON] [5] pointed out, based on structural equation modeling, the radiation was the main driver of _ET_ changes in the rainfed maize agroecosystem. [PERSON] [6] obtained similar results as [PERSON] [5] and concluded that a significant response was found between the leaf area and _ET_. [PERSON] [7] identified radiation, temperature, vapor pressure deficit, and wind speed as the main drivers. In summary, both environmental and physiological factors, such as leaf area, affect the change of _ET_ during the growth period. However, most of the studies have focused on grain crops such as wheat and maize. In addition, most analyses of water fluxes have investigated the effects of environmental and physiological factors on changes in water use efficiency [8, 9, 10]. At present, there are two main ways to obtain _ET_ on the field agroecosystem scale. One is direct observation, such as based on the eddy correlation system (EC system), large aperture scintillator [11], or a large weighing lysimeter [12]. To be more precise, the EC system can observe the changes in water fluxes at the field agroecosystem scale more continuously and accurately, and the application of this technology [13; 14; 15] effectively promotes the study of \(ET\) in fields. The other method is to simulate the \(ET\) during the growth period of crops through physical models and crop models. For instance, the Root Zone Water Quality Model Version 2.0 (RZWQM2 model) is a typical one that can accurately simulate the crop growth pattern and external environmental factors under different conditions. The RZWQM2 model couples the RZWQM model and the DSSAT model [16; 17]. It has been proved by previous studies [18; 19] to be able to accurately simulate plant height, leaf area, yield, \(ET\), soil water content, and other factors during the growth period [20]. [PERSON] [21] found that the \(ET\) of silage maize simulated by the RZWQM2 model was close to the accuracy of the observed data from a large weighing lysimeter. [PERSON] [22] simulated the \(ET\) of urban turf with different irrigation schedules based on the RZWQM2 model and developed a more reasonable irrigation schedule for urban turf in the North China Plain. [PERSON] [23] simulated the \(ET\) of maize, soybean, and cotton agroecosystems in a humid climate based on the RZWQM2 model and found that the simulation accuracy was better at weekly scales than an EC system. In conclusion, the RZWQM2 model has been widely used to simulate the \(ET\) of crops with a clear mechanism and higher accuracy. As a cash crop widely planted in the arid region of Northwest China [1], cabbage has high yield and economic benefits. However, based on the literature research, there is little research on the driving factors of \(ET\) changes in cabbage agroecosystems in this area. We integrate the knowledge and methods of meteorology, agromomy, ecology, and other disciplines to fill the gap in the research on the driving factors of \(ET\) of typical cash crops in the arid region of Northwest China. Understanding the changing patterns and drivers of \(ET\) is important for predicting the water use of agroecosystems under changing environments and ensuring sustainable use of water resources in SYRB [24; 25]. SYRB is located in the east of the Hexi Corridor, at the intersection of the Qinghai Tibet Plateau, the Inner Mongolian Plateau and the Loess Plateau, and is an important sub basin of Yellow River. SYRB is an important agricultural production base and economic center in Gansu Province, China.