Sensitivity study of reference crop evapotranspiration during growing season in the West Liao River basin, China

Abstract

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.

Assessment of Drought Impact on Main Cereal Crops Using a Standardized Precipitation Evapotranspiration Index in Liaoning Province, China

“…However, in the context of current global warming, 91% of stations had ET 0 declines during the main crop growth stages, with 61% showing a decreasing trend (p < 0.05). Our assessment agreed with the finding reported by Gao et al [22], who indicated a decrease in ET 0 over the Liao River basin (part of which was in Liaoning province), despite province-wide temperature increases. This is because the decreases in wind velocity and sunshine duration over Liaoning have a higher negative impact on ET 0 than the positive impact of increased temperature [45].…”

Quantifying Climatic Impact on Reference Evapotranspiration Trends in the Huai River Basin of Eastern China

“…Ye et al [32] found SD was the dominating factor, with RH, WS, and TA following in the Poyang Lake catchment. Gao et al [30] studied the sensitivities of ET ref to the climate factors in the crop growth period in West Liao River Basin, then found SR was the most sensitive factor, with RH following. Moreover, Gao et al [35] also have demonstrated SR was the most dominant factor for ET ref in the same study area at the annual time scale, but varied at the seasonal time scale.…”

“…Prior to applying the M-K test in this study, a pre-whitening method should be employed to eliminate the effect of a serial correlation on the trend value [57]. Yue et al [58] improved this method and proposed an alternative approach, namely TFPW (trend-free pre-whitening), which is widely applied in many related study areas [17,30,59]. …”

Evaluation and Parameter Optimization of Monthly Net Long-Wave Radiation Climatology Methods in China

“…Furthermore, the empirical or semi-empirical f studied because of the lack of measured values, and the FAO56-PM f studies over China [25][26][27][28][29][30][31][32][33] without calibration. However, the param ; (d) FAO24; (e) FAO56-PM; (f) Deng Genyun; (g) Tong Hongliang) and CERES data (h) at the nineteen radiation stations.…”