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Estimating sunshine duration from other climatic data by artificial neural network for ET0 estimation in an arid environment

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Abstract

Sunshine duration data are desirable for calculating daily solar radiation (R s) and subsequent reference evapotranspiration (ET0) using the Penman–Monteith (PM) method. In the absence of measured R s data, the Ångström equation has been recommended by the Food and Agriculture Organization (FAO) of the United Nations. This equation requires actual sunshine duration that is not commonly observed at many weather stations. This paper examines the potential for the use of artificial neural networks (ANNs) to estimate sunshine duration based on air temperature and humidity data under arid environment. This is important because these data are commonly available parameters. The impact of the estimated sunshine duration on estimation of R s and ET0 was also conducted. The four weather stations selected for this study are located in Sistan and Baluchestan Province (southeast of Iran). The study demonstrated that modelling of sunshine duration through the use of ANN technique made acceptable estimates. Models were compared using the determination coefficient (R 2), the root mean square error (RMSE) and the mean bias error (MBE). Average R 2, RMSE and MBE for the comparison between measured and estimated sunshine duration were calculated resulting 0.81, 6.3 % and 0.1 %, respectively. Our analyses also demonstrate that the difference between the measured and estimated sunshine duration has less effect on the estimated R s and ET0 by using Ångström and FAO-PM equations, respectively.

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Acknowledgements

This study is the partial work under Project No. WR1-1389-631 supported by Sistan and Baluchestan Regional Water Corporation and was done in the Department of Irrigation and Drainage Engineering, Aburaihan Campus, University of Tehran.

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  1. Department of Irrigation and Drainage Engineering, University of Tehran, Aburaihan Campus, Tehran, Iran

    Ali Rahimikhoob

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  1. Ali Rahimikhoob
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Correspondence to Ali Rahimikhoob.

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Rahimikhoob, A. Estimating sunshine duration from other climatic data by artificial neural network for ET0 estimation in an arid environment. Theor Appl Climatol 118, 1–8 (2014). https://doi.org/10.1007/s00704-013-1047-1

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  • DOI: https://doi.org/10.1007/s00704-013-1047-1

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