Validation of Empirical and Semi-empirical Net Radiation Models versus Observed Data for Cold Semi-arid Climate Condition

Document Type : Research Article


Bu-Ali Sina University, Hamedan


Introduction: Solar Net Radiation (Rn) is one of the most important component which influences soil heat flux, evapotranspiration rate and hydrological cycle. This parameter (Rn) is measured based on the difference between downward and upward shortwave (SW) and longwave (LW) irradiances reaching the Earth’s surface. Field measurements of Rn are scarce, expensive and difficult due to the instrumental maintenance. As a result, in most research cases, Rn is estimated by the empirical, semi-empirical and physical radiation models. Almorox et al. (2008) suggested a net radiation model based on a linear regression model by using global solar radiation (Rs) and sunshine hours. Alados et al. (2003) evaluated the relation between Rn and Rs for Spain. They showed that the models based on shortwave radiation works perfect in estimating solar net radiation. In another work, Irmak et al. (2003) presented two empirical Rn models, which worked with the minimum numbers of weather parameters. They evaluated their models for humid, dry, inland and coastal regions of the United States. They concluded that both Rn models work better than FAO-56 Penman-Monteith model. Sabziparvar et al. (2016) estimated the daily Rn for four climate types in Iran. They examined various net radiation models namely: Wright, Basic Regression Model (BRM), Linacre, Berliand, Irmak, and Monteith. Their results highlighted that on regional averages, the linear BRM model has the superior performance in generating the most accurate daily ET0. They also showed that for 70% of the study sites, the linear Rn models can be reliable candidates instead of sophisticated nonlinear Rn models. Having considered the importance of Rn in determining crop water requirement, the aim of this study is to obtain the best performance Rn model for cold semi-arid climate of Hamedan.
Materials and Methods: We employed hourly and daily weather data and Rn data, which were measured during December 2011 to June 2013 in climatology station of Bu-Ali Sina University. This experiment was performed for the cold semi-arid site of Hamedan (Iran). The study site (Hamedan) is a mountainous research station (1860 meters above sea-level) which is located at the eastern side of central Zagros Mountain Range. The net radiation fluxes were measured by four SW (300-2800 nm) and LW (4500-42000 nm).Hukseflux Thermal Sensors mounted on an automatic logger system. The logger reported four upward and downward solar components in every 8-minute intervals. In this study, total daily net radiation was estimated by 12 empirical and semi-empirical Rn models including: Basic Regression Models (BRM), Extended Regression Models (ERM), Linacre, Berliand, Wright and FAO-56 Penman-Monteith. The model performances were evaluated by R2, RMSE, MBE and MPE criteria and the best model was selected accordingly.
Results and Discussion: In this research, the model calculations were done for seasonal and annual time scales. The results indicate that Basic Regression Model Rn(BRM-4) performs the best estimates in spring time. Further, for summer and autumn seasons, Rn (BRM-3) was superior for the cold semi-arid climate of Hamedan. Therefore, with the exception of winter, the BRM models performed the best estimates. Unlike the other seasons, for winter, Irmak presented the most accurate results. This is due to the fact that net radiation as estimates by Irmak Model is mainly dependent on daily maximum (Tmax) and minimum temperatures. For Irmak model, as the Tmax is increased, Rn will be reduced proportionally. For this reason, Irmak does not perform good estimates for warm months. In annual time scale, the Basic Regression Model of Rn (BRM-3) presented the most accurate estimates of net radiation. The study of Monteith and Szeicz (1961)and MirgaloyBayat (2011) also showed that Rn (BRM-3) model can generated the best Rn estimate in annual scale for mountain regions.
Conclusion: Unlike the recommendation of FAO for using Penman-Monteith and Wright approaches in evapotranspiration models, it was found that the aforesaid Rn models are not suitable for cold semi-arid regions such as Hamedan. This result is in good agreement with the findings of Izoimon et al. (2000) and MirgaloyBayat (2011). In general, for cold climate condition of Hamedan, the Basic Regression Models are more reliable than the other Rn models. This study was performed based on 18-month field data and 12-Rn models. To achieve more accurate results, using a longer term experimental data and examining more Rn models are suggested as the future works. To achieve a regional Rn zoning, inclusion of satellite-based dataset is also recommended.


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