JOURNAL OF NATURAL RESOURCES ›› 2019, Vol. 34 ›› Issue (1): 179-190.

• Orginal Article •

Comparison of different methods for estimating reference evapotranspiration with weather data from nearby stations

Xue-mei ZHANG(), Zi-kui WANG(), Yu-ying SHEN, Hui-min YANG

1. College of Pastoral Agriculture Science and Technology, Key Laboratory of Grassland Agro-ecosystem, National Demonstration Center for Experimental Grassland Science Education, Lanzhou University, Lanzhou 730020, China
• Received:2018-06-22 Revised:2018-09-26 Online:2019-01-20 Published:2019-01-20

Abstract:

Potential evapotranspiration (ET0) is one of the most critical parameters that are essential for evaluating regional vegetation water use and managing water and soil resources. However, accurate estimation of it is so difficult for many parts of China due to a limited number of weather stations. Weather data from nearby stations are available for most sites, but past relevant works mainly focused on ET0 predicting methods with local weather data and less work was done to investigate the approaches for estimating ET0 with data from other stations. Therefore, this study was conducted to test the reliability of estimating ET0 with weather information from nearby stations. Whether data of four weather stations located in Hetao Irrigation District of western Inner Mongolia were collected. The study area has an arid climate with annual rainfall and pan evaporation of 130-215 and 2100-2300 mm respectively. Three commonly used approaches, namely, FAO56 Penman-Monteith equation (PM56) with estimated weather data, the empirical formula corrected with meteorological data of nearby stations, and the artificial neural network model (ANN) developed using meteorological data from nearby stations, were compared for predicting ET0 when data are limited in this work. The results showed that: (1) When all of the necessary parameters were not measured at the study sites, weather data from nearby stations could be used directly, the average absolute error (MAE) of the ET0 calculation was 0.43-0.52 mm d-1, and the root mean square error (RMSE) was 0.56-0.63 mm d-1, and the error could be narrowed by correcting the radiation data using the latitude information of the stations; (2) When the maximum and minimum air temperature data were available, PM56 with estimated weather data performed the worst, and the performance of the ANN model is the best with the MAE and RMSE ranging between 0.14-0.22 mm d-1 and 0.17-0.29 mm d-1, respectively; and the results of the calibrated Hargreaves formula are intermediate with the MAE and RMSE values of 0.23-0.26 mm d-1 and 0.30-0.31 mm d-1, respectively; (3) When knowing the temperature and radiation data, the ANN model trained with meteorological data from nearby stations is still the best, with MAE and RMSE values of 0.13-0.19 mm d-1 and 0.17-0.25 mm d-1, respectively, and the other two methods have larger error and the performance is inconsistent among different stations. The results of this study demonstrated that we can estimated ET0 accurately using air temperature data in combination with weather data of nearby stations under arid conditions, and the reliability of the methods still need to be validated under other climatic conditions.