Temporal-Spatial Variations And Predictions Of Reference Crop Evapotranspiration In Shaanxi Province

It is the foundation of crop water requirement and production management to conduct accurate estimation of reference crop evapotranspiration (simplified as ET₀), particularly, it is also key in agricultural water-saving irrigation planning.In this research, based on daily and monthly climatic data of 22 meteorological stations in Shaanxi province from 1951 to 2007, daily and monthly ET₀ were calculated using FAO56 PM equation, the temporal and spatial variation characteristics of ET₀ and its meteorological factors were analyzed. Besides, the daily ET₀ was computed by 5 methods with the meteorological data from six representative stations in Shaanxi. Taking the PM method as the standard, the comparisons for other methods were made. Based on PM equation, sensitivity coefficients of ET₀ to climatic elements and theire spatial variations were analyzed. The predictive powers of sensitive coefficient method were discussed when single or multi- meteorological factors were changed. Taking the observed meteorological factors as independent variables and ET₀ estimated by PM method as dependent variables, multi-variable linear regression analysis was conducted. Four groups of regressive models were obtained for each station. Based on monthly pan evaporation data and the calculated ET₀ data from 1978 to 1997, evaporation pan coefficient (Kp) were calculated, and monthly ET₀ from 1998 to 2001 were forecasted. The main results are as follows:(1) The annual average air temperature and wind speed fluctuantly increased, the average precipitation, the relative humidity, the sunshine hours and the ET₀ decreased fluctuantly in Shaanxi province over rencent 50 years. From spatial distributions, maximum air temperature, minimum air temperature, average air temperature, relative humidity and precipitation showed increased gradually from north to south. Average wind speed, sunshine hours and E_pan decreased from north to south. The annual and four seasons'(except winter) ET₀ decreased gradually from north to south.(2) For the selected six stations in Shaanxi, the difference of the computed ET₀ results using Harg method was small, with a relative good adaptablity. The values estimated by 1948Pen and Prs-Tylr were closer to PM method for each climatic area than the values estimated using FAO-24 Pen. If climatic data are lack, Prs-Tylr method may obtain reliable ET₀ estimations and be more suitable to wet regions. Results obtained using FAO-24 Pen method was lower than Prs-Tylr method and should not be used generally. There wer good linear correlations between the observed E_pan and the ET₀ values calculated by PM method, their slopes decreased with the increase of drought degree.(3) The sensitivity coefficients of ET₀ to perturbation of a climatic variable in Shaanxi province varied from region and time scale. At yearly scale, ET₀ was most sensitive to relative humidity, followed by solar radiation, mean aire temperature and wind speed at 2 meter high. At seasonal scale, solar radiation was the most sensitive variable and wind speed at 2 meter high were the least sensitive variables in spring and summer, relative humidity was the most sensitive variable in autumn and winter, wind speed at 2 meter was the least sensitive variable in autumn, and mean air temperature was the least sensitive variable in winter. Spatially speaking, responses of ET₀ to variations of air temperature (STmean) and solar radiation (SRs) were more sensitive in south Shaanxi. Responses of ET₀ to the variations of relative humidity (SRH) were more sensitive in Hengshan and Wuqi but less sensitive in Huashan. Generally, values of sensitive coefficients of relative humidity changed with an order of north Shaanxi, south Shaanxi and Guanzhong, changing from large values to small values, respectively. Sensitivity of ET₀ to the variations of wind speed (SU2) increased from south to north, and were larger in Dingbian but smaller in Hanzhong. Trend analysis of sensitivity coefficients showed that, from 1951 to 2007, trends of STmean, SU2 and SRH increased, and the increased trend of SRH was most obvious, while trend of SRS decreased. Sensitivity coefficients had high predictive powers for ET₀ under the perturbation of single or multi-meteorological factors.(4) The more observed meteorological factors used for multi-variable regression analysis, the better correlation and higher accuracy of the models. At daily and monthly time scales, the deviations between the forecasted ET₀ using 4 groups of multi-variable regression models and calculated values by PM method were relatively small. These models can be used to estimate ET₀ with preferable accuracy for the selected sites in Shaanxi province.(5) Pan evaporation coefficient, Kp at each sites increased from January to April with a peak value during April to September, and decreased after September. Kp changed periodically. Kp was large in autumn followed by winter and was small in spring. Spatially speaking, monthly Kp from March to December increased from north to south in Shaanxi province. The seasonal (except winter) Kp increased gradually from north to south. The difference between ET₀ estimated from E_pan data and ET₀ calculated by PM method was small with good correlations. Kp can be used to estimate ET₀ with preferable accuracy in Shaanxi province.