Rapid evaluation of reference evapotranspiration in Northern China

Due to the lack of adequate measurement of meteorological factors, the estimation of reference evapotranspiration (ET0) over a long period of time is difficult to estimate in many areas. Meteorological factors such as wind speed, sunshine hours, and relative humidity variously influence ET0-a key element for the quantification of agriculture water requirement. Due to less meteorological input requirement, the Thornthwaite and Hargreaves equations are the most common empirical methods used to estimate ET0. Thus, here in this study, the two simple empirical methods are compared with the standard FAO-56 Penman-Monteith (FAO-56 PM) equation for the period 1966-2002 using 267 meteorological stations in Northern China. The Northern China study area is divided into three climatic regions, consisting of North China, Northeast China, and Northwest China. Linear regression analysis shows highly significant correlations (P < 0.001) between the empirical and FAO-56 PM models in the three climatic regions. The analysis shows that the Thornthwaite method generally underestimates ET0. To more accurately estimate ET0, improved empirical models (based on the analyzed regression equations) are proposed and validated using 17 representative meteorological stations in the three climatic regions. The results suggest that of errors of both methods are small and therefore negligible. The mean absolute errors (MAEs) for the improved Thornthwaite method are 0.45, 0.39, and 0.41 mm day(-1) with corresponding root mean square errors (RMSEs) of 0.57, 0.50, and 0.53 mm day(-1) for the North China, Northeast China, and Northwest China climatic regions, respectively. Similarly, MAEs for the improved Hargreaves method in the three climatic regions are 0.27, 0.21, and 0.36 mm day(-1) with RMSEs of 0.34, 0.30, and 0.44 mm day(-1), respectively. The adjustments enhanced ET0 estimation accuracy significantly, which is the prediction performance of the user interface of the two methods. The better prediction enhances a more reliable discussion of the trends in ET0, precipitation, and wetness index. The apparent increase in drought after the mid-1980s is a major concern for food security in the study area.