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Prediction of annual reference evapotranspiration using climatic data

Li, Yi, Horton, Robert, Ren, Tusheng, Chen, Chunyan
Agricultural water management 2010 v.97 no.2 pp. 300-308
crop production, irrigated farming, plant growth, water requirement, irrigation requirement, prediction, estimation, hydrologic models, probabilistic models, evapotranspiration, weather, meteorological parameters, temporal variation, evaporation, evaporation rate, sampling, data analysis, equations, calibration, methodology, validity, China
It is important to determine how well ET o can be estimated from easily observed E pan (free water evaporation measured by a pan) measurements and the other climatic data. Our objectives are to predict annual ET o with E pan data (with a calibrated k p (=ET o/E pan)) and with a 4-variable regression function method. The significance of the trends of E pan, ET o and k p series were detected. The whole data series (ET o, E pan, mean temperature, sunlight hours, relative humidity and wind speed) were divided into the early (L-5) years for calibrating k p and coefficients of a 4-variable function and the last 5 years for predicting ET o. From the results, significance of series trends decreased when using the modified Mann-Kendall (MMK) test compared to the Mann-Kendall (MK) method. For ET o, five out of six sites showed significant trends according to the MK statistic Z, and two sites were significant in trend combining with the MMK statistic Z*(j). For E pan, two sites were significant in trends according to Z, and zero sites were significant in trends combining with Z*(j). For k p, two sites were significant in trends according to Z, and no sites were significant in trends combining with Z*(j). Thus the calibrated k p can be treated as a constant when using the E pan method. The predicted annual ET o using the E pan and the multi-variable methods showed generally good agreements with the estimated annual ET o (based on monthly PM equation) with low relative errors (RE). Mean ET o values were well predicted by both methods. When using E pan method, RE ranged from −14.7 to −3.3% for Urumqi, from 17.6 to 21.7% for Xning, from 1.8 to 10.7% for Lanzhou, from 4.7 to 17.0% for Huhehaote, from −7.4 to 9.1% for Beijing, and from −8.6 to 2.3% for Changchun. RE of predicting annual ET o with 4-variable regression function were even lower compared to E pan method. The main error source of the predictions came from the deviation between calibrated k p and the actual k p of the predicted years when using E pan method and from random fluctuations of climatic data when using the 4-varible regression function. In conclusion, the MMK test was a robust method for trend detection because it considered serial time dependence. Insignificant trend of the k p series supports the choice of a mean value as the calibrated k p and for ET o predictions. The E pan method is recommended for prediction of annual ET o.