JWSS - Journal of Water and Soil Science

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Global solar radiation (Rs) has wide applications in several disciplines. The data of measured or predicted Rs are widely applied by solar engineers, architects, agriculturists and hydrologists. Due to the importance of Rs, several empirical models have been developed to predict its values all over the world. In this study, Angstrom model was calibrated based on the ratio of actual and possible sunshine hours n/N by using measured daily data of Rs at Bajghah meteorological station in Fars province during 2003-2004. The model was modified by using air temperature for considering the effect of cloudy conditions as well as n/N ratios. The results showed that using both the air temperatures and the ratios of n/N led to a higher accuracy. In regard to estimation of the Rs values, the results showed that mean air temperatures have a higher accuracy compared with differences between maximum and minimum air temperatures. Also, a new local model with higher accuracy was developed based on a number of daily meteorological parameters such as deficit vapor pressure, relative humidity, precipitation, mean air temperature, maximum and minimum air temperatures difference and n/N. This new local model that used different meteorological parameters had the highest accuracy in comparison with the other models. Also, a number of models developed by other investigators for estimation of Rs were calibrated for the study area. Finally, different selected models were validated by using the measured data of Rs in 2005. The results showed that the developed local multi-variable model provided higher accuracy results in comparison with the other radiation models.

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Daily solar radiation intercepted at the earth’s surface is an input required for water resources, environmental and agricultural studies. However, the measurement of this parameter can only be done in a few places. This has led researchers to develop a number of methods for estimating solar radiation based on frequently available meteorological records such as hours of sunshine or air temperature. In this study two empirical Angestrom and Hargreaves- Samani models, which are respectively based on air temperature and sunshine duration were calibrated and evaluated for estimating solar radiation in southeast of Tehran, Iran. Also, two neural networks models were presented using similar inputs and above-mentioned empirical models. The results showed that the both empirical and neural network models provided closer agreement with the measured values, but the models based on sunshine hours gave better estimates than the models based on air temperature. The neural network model based on sunshine hours with a R2 of 0.97 and a RMSE of 1.34 MJ m-2 d-1 provided the best results

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In this study, the remote sensing statistical approach was used to determine the global solar radiation from NOAA-AVHRR satellite data in southeast of Tehran. This approach is based on the linear correlation between a satellite derived cloud index and the atmospheric transmission measured by the clearness index on the ground. A multiple linear regression model was also used to convert the five AVHRR data channels and extraterrestrial radiation to global solar radiation. The results of this study showed that multiple linear regression model estimated the solar radiation with an R2 of 0.93 and a root mean square error (RMSE) of 5.8 percent, which was better than the statistical approach.

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Global solar radiation (Rs( on a horizontal surface in the estimation of evapotranspiration of plants and hydrology studies is an important factor. Average daily global solar radiation on a horizontal surface was estimated by artificial neural networks (ANNs) and five empirical models including FAO (No.56), Hargreaves-Samani, Mahmood-Hubard, Bahel and Annandale. The weather data was selected from Karaj, Shiraz, and Ramsar weather stations, which have arid, semi arid and very humid climates (based on De Martonne classification). Daily solar radiation was measured at the three sites selected. The ANN, with actual duration of sunshine and maximum possible duration of sunshine as input parameters, generated daily solar radiation estimates with highest level of accuracy among all models tested. Rs estimates by ANNs with only temperature indices as input and by Hargreaves-Samani, Annandale and Mahmood-Hubard, which are all temperature oriented models, had lower accuracy at all three sites. In contrast, ANNs with actual duration of sunshine and maximum possible sunshine hours as inputs in Karaj, Shiraz and Ramsar station with root mean square error (RMSE) of 2.08, 1.85 and 2.05 Mj m-2 day-1 respectively were the best models. After ANNs, FAO-56 model which is based on sunshine hours produced results closer to the measured values. Rs estimates by ANNs with only temperature indices as input and by Hargreaves-Samani, Annandale and Mahmood-Hubard which are all temperature oriented models, had lower accuracy at all the three sites. These models are not appropriate for estimating daily global solar radiation.

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Concerning the drying problem of the Lake Urmia in Iran, so far the relevant scientific research has not been conducted based on watershed management principles. The surface solar radiation (Rs) is one of the key input parameters in most of reference evapotranspiration (ET0) prediction models. In the present research, four solar radiation models were evaluated to predict the monthly-mean values of daily ET0 at seven synoptic stations located in the Lake Urmia basin during the 1985-2005 period. For the ET0 prediction, we applied the Penman-Monteith-FAO 56 model (PMF56). At first, we evaluated four radiation models consisting of Hybrid: H, Ångström-Prescott: AP, Modified Daneshyar: MD, and Modified Sabbagh: MS. Four statistical criteria used included the mean error (ME), the mean absolute error (MAE), the root mean square error (RMSE), and the mean percentage error (MPE). The mean RMSE value of hybrid model was 1.7 MJ/m2/day while the RMSEs for the AP, the MD and the MS models were 2.9, 2.3, and 2.9 MJ /m2/day, respectively. The results revealed a higher performance of hybrid model to predict the monthly radiation. In addition, the Rs models used in the original PMF56 model were compared with a case in which the measured daily Rs data was used. Finally, by integrating the hybrid model and the PMF56, we developed a coupled model as PMF56-Hybrid. The application of the Hybrid and the MD models resulted in a decrease in the RMSEs. The AP model used in the PMF56 showed about 19% overestimation.