E. Tavakoli, B. Ghahraman, K. Davari, H. Ansari,
Volume 17, Issue 65 (12-2013)
Abstract
Quantitative evaluation of evapotranspiration on a regional scale is necessary for water resources management, crop production and environmental assessments in irrigated lands. In this study, in order to estimate ETo and because of few synoptic stations and also little recorded meteorological data in North Khorasan Province, Iran, with arid and semi-arid climate, 7 stations from neighboring provinces were used. Reference evapotranspiration was calculated using 6 different methods which required a small amount of input data, including Class A pan, Hargreaves-Samani, Priestly-Tailor, Turc, Makkink and the method proposed by Allen et al (1998) to estimate ETo with missing climate data. Besides, the standard FAO-Penman-Monteith was used (because there was no Lysimetric data in the region) to evaluate the applied formulas. Since there was no agreement over the appropriate method to calculate ETo in the selected stations, by using significance test of regression lines, a linear regression equation was computed for each month, in order to convert the best calculating method to FAO-Penman-Monteith formula. Evaluations of these equations showed their acceptable accuracy, in comparison with the previous researches, specifically for cold months (MAE values ranged from 0.3 to 1.4 mm/day).
Y. Sabzevari, M. Saeidinia,
Volume 25, Issue 2 (9-2021)
Abstract
The FAO Penman-Monteith is a baseline method to estimate reference evapotranspiration. In many cases, it is difficult to access all data, so replacing simpler models with lower input data and appropriate accuracy is necessary. The purpose of this study is to investigate the capability of the experimental models, gene expression programming, stepwise regression, and Bayesian network in estimating reference evapotranspiration. In this research, daily information of the Boroujerd synoptic station in the period of 1996 -2017 was used as model inputs. Based on the correlation between input and output parameters, six input patterns were determined for modeling. The results showed that the Kimberly-Penman model has the best performance among the experimental models. Gene expression programming with fourth pattern and Default Model Operators (R2 = 0.98 and RMSE = 0.9), Bayesian Network with sixth pattern (R2=0.91 and RMSE = 1.01), and stepwise regression with sixth pattern have the most accurate patterns at R2 = 0.91 and RMSE = 0.9 in the training stage. Comparison of the performance of the three models showed that the gene expression programming model was superior to the other two models with the Average Absolute Relative Error (AARE) of 0.12 and the Mean Ratio (MR) of 0.94. The results showed that gene expression programming had an acceptable ability to estimate reference evapotranspiration under the weather conditions of Boroujerd and could be introduced as a suitable model.
