Evapotranspiration estimation using a modified Priestley-Taylor model in a rice-wheat rotation system

The rice-wheat rotation system is one of the largest agricultural production systems worldwide. Accurate estimation of evapotranspiration (ET) in the rice-wheat rotation system is critical to enhance efficient irrigation management and water use. The variation of ET for a rice-wheat rotation system during 2015-2018 and its controlling meteorological factors was investigated using the Bowen ratio energy balance and path analysis methods. A modified Priestley-Taylor (PT) model considering soil water stress for soil evaporation (E) (f(sw)), and plant temperature constraint (deviation of air temperature from optimum for the crops used, f(t)), leaf senescence for transpiration was developed. The results showed that the diurnal variation of ET rate in different months exhibited a single peak curve with the maximum ET rates of 0.90 and 0.42 mm h(-1) for rice and winter wheat, respectively. The total ET of the rice-wheat rotation system over the whole growing season was 765-841 mm, of which 63-67% was consumed by the rice field. The average daily ET rate over the whole growing season was 3.27-4.13 and 1.50-1.65 mm d(-1) for rice and winter wheat, respectively. The results of ET partitioning showed that E accounted for 23-32% of the seasonal ET for rice and 48-51% for winter wheat. The ET partitioning of rice and winter wheat was closely linked to leaf area index (LAI). The ratio of E/ET reduced exponentially for rice with the increase of LAI, while it reduced linearly for winter wheat. The path analysis showed that the net radiation (R-n) was the dominant meteorological factor affecting short-term ET of the rice-wheat rotation system through the direct effect. The water vapour pressure deficit (VPD), another important factor influencing ET, showed mainly an indirect effect on ET through path of R-n and had a greater impact on ET for rice than that for wheat. The modified PT model could estimate ET for rice and winter wheat reasonably, with linear regression coefficient of 0.93-1.09 and coefficient of determination of 0.92 - 0.96. The model was sensitive to the f(sw) or f(t).