Solar Radiation Estimation using Temperature-based, Stochastic and Artificial Neural Networks Approaches

Abstract

Radiation is a variable that governs many hydrological and phenological processes, but its measurements are not made routinely. To overcome this problem, continuous hydrological models that include evapotranspiration, snowmelt (using solar radiation data) and plant growth modules have applied different strategies to generate daily radiation data. In this paper, artificial neural networks (ANNs), temperature-based (TB) and stochastic (ST) approaches for estimation of solar radiation have been used and compared. These three approaches have been applied to the Ammameh Catchment, an alpine subcatchment of the Jadjroud River, in Iran. Results reveal better performance for ANNs than for TB and ST. However, the TB method because of its capability to generalize results and to be easily linked with hydrological models appears to be a good candidate to be applied in the catchments where the climatological data are limited.