Search Results (3)

The Penman–Monteith formula (P-M) is a well-established indirect method for estimating reference evapotranspiration (ET₀). The key input for this equation is global solar radiation (H). When real data are unavailable, other weather parameters are used to estimate H. In this study, sixteen years’ worth daily registers of H, sunshine duration (S), and air temperature (t) from 10 sites across Poland were used to determine coefficients for the Angström–Prescott (A-P) and Hargreaves–Sammani (H-S) equations. The H values obtained with locally calibrated, general Polish and global A-P and H-S equations were applied to the P-M formula. The ET₀ results thus obtained were compared to those derived with the P-M method and measured solar radiation data. The method of determination of the radiation component had a significant but sometimes unexpected impact on the ET₀ values. The better predictive power of the solar radiation model usually resulted in better accuracy of the evapotranspiration estimation; however, there were exceptions to this rule. Full article

Solar radiation is the main source of energy on the Earth’s surface. It is very important for the environment and ecology, water cycle and crop growth. Therefore, it is very important to obtain accurate solar radiation data. In this study, we use the highest temperature T_max, lowest temperature T_min, average temperature T_avg, wind speed U, relative humidity RH, sunshine duration H and maximum sunshine duration H_max as input variables to construct a deep learning prediction model of solar radiation in the Yellow River Basin. It is compared with the recommended and corrected values of the widely used Å-P method. The results show that: (1) The correction results of the Å-P equation are better in the upstream and downstream of the Yellow River Basin but worse in the midstream. (2) The prediction result of the deep learning model in the Yellow River Basin is far better than that of the Å-P equation using the FAO-56 recommended value. It is the best in the downstream of the Yellow River Basin: R² increases from 0.894 to 0.934; MSE, RMSE and MAE decrease by 43.12%, 27.73% and 25.80%, respectively. The upstream prediction result comes in second: R² increases from 0.888 to 0.921; MSE, RMSE and MAE decrease by 33.27%, 20.02% and 19.04%, respectively. The midstream result is the worst: R² increases from 0.869 to 0.874; MSE, RMSE and MAE decrease by −0.50%, 0.07% and 3.82%, respectively. (3) The prediction results of the deep learning model in the upstream and downstream of the Yellow River Basin are far better than those of the Å-P equation using correction. The R² in the upstream of the Yellow River Basin increases from 0.889 to 0.921. MSE, RMSE and MAE decrease by 22.11%, 11.84% and 8.94%, respectively. R² in the downstream of the Yellow River Basin increases from 0.900 to 0.934, and MSE, RMSE and MAE decrease by 13.21%, 11.40% and 5.55%, respectively. In the midstream of the Yellow River Basin, the prediction results of the deep learning model are worse than those of the Å-P equation using correction: R² increases from 0.870 to 0.874, but MSE, RMSE and MAE decrease by −24.93%, −10.83% and −11.56%, respectively. Full article

The Food and Agriculture Organization (FAO) Penman–Monteith equation, recognized as the standard method for the estimation of reference crop evapotranspiration ( ${\mathrm{ET}}_0$ ), requires many meteorological inputs. The Ångström–Prescott (A-P) formula containing parameters (i.e., a and b) is recommended to determine global solar radiation, one of the essential meteorological inputs, but may result in a considerable difference in ${\mathrm{ET}}_0$ estimation. This study explored the effects of A-P coefficients not only on the estimation of ${\mathrm{ET}}_0$ , but also on the irrigation water requirement (IWR) and design water requirement (DWR) for paddy rice cultivation, which is the largest consumer of agricultural water in South Korea. We compared and analyzed the estimates of ${\mathrm{ET}}_0$ , IWR, and DWR using the recommended (a = 0.25 and b = 0.5) and locally calibrated A-P coefficients in 16 locations of South Korea. The estimation of ${\mathrm{ET}}_0$ using the recommended A-P coefficients produced significant overestimation. The overestimation ranged from 3.8% to 14.0% across the 16 locations as compared to the estimates using the locally calibrated A-P coefficients, and the average overestimation was 10.0%. The overestimation of ${\mathrm{ET}}_0$ corresponded to a variation of 1.7% to 7.2% in the overestimation of the mean annual IWR, and the average overestimation of the IWR was 5.1%. On average, the overestimation was slightly reduced to 4.8% in DWR estimation, since the effect of A-P coefficients on the IWR estimation decreased as the IWR increased. This study demonstrates how the use of A-P coefficients can alter the estimation of ${\mathrm{ET}}_0$ , IWR, and DWR in South Korea, which underscores the importance of their proper consideration in agricultural water management. Full article