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We have analyzed the trends of reference crop evapotranspiration (ET 0 ) through the Penman-Monteith model and climate factors in the West Liao River basin using the Mann-Kendall test after removing the effect of significant lag-1 serial correlation from the time series of the data by trendfree pre-whitening. The changing characteristics of the sensitivity coefficients and the spatial distribution during growing season are investigated, and the correlation between the sensitivity coefficients with elevation and the key climate factors by relative contribution and stepwise regression methods are evaluated. A significant overall increase in air temperature, and a significant decrease in wind speed, solar radiation, sunshine duration, relative humidity, and a slight decrease in ET 0 are observed. Sensitivity analysis shows that ET 0 is most sensitive to solar radiation, followed by relative humidity. In contrast, ET 0 is least sensitive to the average air temperature. The sensitivity coefficients for the maximum and minimum air temperature and relative humidity have a significant negative correlation with elevation, while the coefficients for other variables are not strongly correlated with elevation. The spatial distribution of the sensitivity coefficients for wind speed and solar radiation is opposite, i.e., in regions where the sensitivity coefficients for wind speed are high; the sensitivity coefficients for solar radiation are low and vice versa. The sensitivity for relative humidity and average air temperature is region specific in the plain area. However, ET 0 is most sensitive to the climate change in regions of high elevation. Wind speed is the most dominant contributor followed by solar radiation. Average air temperature contributes the least. The stepwise regression analysis indicates that wind speed is the foremost dominant variable influencing ET 0 . Relative contribution and stepwise regression analysis can be used to determine the main variables affecting ET 0 , and it also strongly supports that the aerodynamic component is the dominant factor.

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With the wide application of drip irrigation under mulch in semi-arid agricultural region in China, it not only improves agricultural water efficiency, but also affects formation of groundwater and the mechanism of water infiltration to a certain extent. This paper takes the typical semi-arid agricultural region in China as the research object. The movement of soil water under the three types of underlying surface was simulated by the Hydrus-2D model for the quantitative analysis of groundwater recharge. The influence of drip irrigation under mulch on groundwater infiltration depth and cumulative infiltration amount under different level years was simulated. Taking normal flow year as an example, the simulated results showed that the maximum infiltration depth of drip irrigation under mulch reached 250 cm, which was greater than that of border irrigation (138 cm) and bare area (158 cm). The cumulative infiltration amount of drip irrigation under mulch at 80 cm, 120, 140 and 200 cm was respectively 1,484.8 m3/hm2, 686.3 m3/hm2, 554.1 m3/hm2 and 238.1 m3/hm2, which were greater than that of border irrigation and bare land at the same depth. The results proved that drip irrigation under mulch could increase the infiltration depth and cumulative infiltration amount, which was beneficial to groundwater recharge in semi-arid agricultural region of China.

Stemflow (SF) is an important source of water for maize (Zea mays L.) growing in arid areas, and is very important for agricultural water management. However, the results of previous studies in different regions are quite different. There is also a lack of in‐depth research on the SF process of maize at each growth stage. The purpose of this study was to analyze the effects of rainfall characteristics and maize morphology on SF during the entire maize‐growing period, quantify the stemflow rate (SR) in western Liaoning, analyze the differences in the maize SF process during different growing periods, and establish a multiple regression model for the different growth stages to predict SF. The results showed that the SR in the study area was 12.62%–43.44% during the 28 rainfall events from 2015 to 2016, and the mean SR was 32.80%. The mean SR during the jointing, tasseling, and mature stages were 24.10%, 42.72%, and 35.37%, respectively. SF generally increases with increased precipitation, and with an increase in the leaf area index, but the effects of various factors on SF differed between the early (jointing) and later stages (tasseling and maturity) of maize growth. A multiple regression model of the different growth stages was established to better predict SF in the research region. This study is significant as it improves local farmland water cycle theory and improves the efficiency of irrigation water. In the next step, the effects of leaf angle and canopy coverage warrant further investigation.