Comparison of computed sugar beet evapotranspiration by the Penman-Monteith equation using measured climatological parameters and predicted products of GFS, ECMWF and GFS meteorological forecasting models in the Jovein region

Irrigation should be applied in accordance with an accurate estimate of the crop water requirement and the crop growth stages (Jensen and Allen, 2014). In recent years, several meteorological forecasting models (MFM) have been developed which are capable of forecasting weather data. Such data could be used to calculate and predict crop water requirements during the next few days. The performance of irrigation canals and water delivery systems can be significantly improved if future short-term demands based on the predicted crop water requirement are available. The appropriate performance of these models in the agricultural sector depends on the quality of their predictions of various weather variables. The aim of this study is to evaluate the accuracy of the predicted spring sugar beet water requirement in the Jovien region when 5-day forecasted meteorological variables by ECMWF, GFS, and MeteoBlue MFM were used as the climatological parameters in the Penman-Monteith equation. The prediction accuracy of these models was evaluated under four categories, 1. Inclusion of the three main meteorological variables involved in calculating reference evapotranspiration (ET_0), including maximum air temperature (T_max) and minimum air temperature (T_min), and maximum wind speed, 2. Inclusion of radiation term (ET_0^Rad) and advection term (ET_0^Adv) of the Penman-Monteith equation, 3. Inclusion of ET_0 and water requirement of spring sugar beet.

Meteorological forecasts for ECMWF, GFS, and MeteoBlue databases were obtained from https://www.windy.com. The meteorological variables presented by these models and used in this research were temperature, wind speed, and the degree of cloudiness. The study period was selected from May to December of 2020.ET0 was calculated using the FAO-Penman-Monteith method presented in the FAO 56(Allen et al., 1998). The first part of the FAO-Penman-Monteith (0.408∆(R_n-G)/(∆+γ(1+0.34 u_2 ) )) represents the contribution of the energy terms to the process of evapotranspiration and the second term ((γ 900/(T+273) u_2 (e_s-e_a ))/(∆+γ(1+0.34 u_2 ) )) signifies the importance of advective forces. Meteorological data obtained from the Jovein weather station was checked to take into consideration the possibility of non-standard surface cover surrounding the weather station in accordance with the method recommended in Annex 6 of FAO 56. Crop coefficients in early, developing, middle, and ending growth periods were also calculated based on the method recommended by the FAO 56.

The GFS model had the best performance in estimating T_maxwith the lowest Bias and RMSE errors of 1.4 and 2.3 degrees Celsius, respectively. Also, MeteoBlue with Bias and RMSE values of -2.2 and 1/3, respectively, had the best performance in estimating T_min. All three models underestimated the air temperature. The bias error of GFS, ECMWF, and MeteoBlue models in predicting the maximum daily wind speed in the Jovein region were 0.1, 1.3, and 1.8 m/s, respectively, and their RMSE Respectively 3.3, 3.3, and 3.4 m/s.Since the GFS model estimated Tmean and consequently vapor pressure deficit (VPD) with a higher degree of accuracy as compared with the other model, the advective term (ET_0^Adv) computed using data estimated by the GFS model wasmore accurate than that of the other models. The bias and RMSE errors of this model in estimating the mentioned variable were 0.4 and 0.7 mm per day, respectively. All the other three models underestimated ET_0^Adv.The bias error of GFS, ECMWF, and MeteoBlue models in estimating ET0Rad were -0.4, -0.5, and -0.2 mm/day, respectively, and their RMSE were 0.61, 0.67, and 0.65 mm per day, respectively.When the overestimated ET0Rad and underestimated ET_0^Adv terms were summed up to calculate ET_0 according to the FAO-Penman-Monteith method, the bias error of the outcome (ET_0) was significantly reduced for all the models. The bias error of the ET_0 for the GFS, ECMWF, and Meteoblue models were 0.03, -0.03, and 0.47 mm/day, respectively. Their RMSE values were 0.62, 0.58 and 0.94 mm/day, respectively.K_(c_ini ) of sugar beet for Jovein region with 4-day wetting intervals and light soil texture with an average ET_0 of 5.9 mm/day was 0.56, and K_(c_mid ) and K_(c_end ) were also calculated as 1.176 and 0.676, respectively. The seasonal water requirement of spring sugar beet for the Jovein region was 866 mm. The seasonal water requirements calculated using the outputs of GFS, ECMWF, and Meteoblue were 860, 866, and 788, respectively. However, the cumulative error of these models were 66, 65, and 101 mm, respectively, during the growing season.

A notable result in this study is the interaction of the ET_0^Adv and ET_0^Rad terms, which canceled out each other over and under estimations—all the three models underestimated and overestimated ET_0^Adv and ET_0^Rad terms, respectively. Thus the absolute value of the oblique error of the computed ET_0 was less than that of the ET_0^Adv and ET_0^Rad terms. However, the RMSE of ET_0, which indicates the noise and random behavior of the error sources, was not reduced.