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Solar radiation and relative humidity based, empirical method, to estimate hourly reference evapotranspiration

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  • Chatzithomas, C.D.
  • Alexandris, S.G.

Abstract

A new empirical method which estimates hourly reference evapotranspiration is proposed, that utilizes two meteorological variables, namely incoming solar radiation and relative humidity. It also utilizes a term that combines both variables. The inverse of the natural logarithm of relative humidity and the vapor pressure deficit of the atmosphere were investigated and were found to correlate quite well. The equation was calibrated in a semi arid environment, using data from Davis station (year 2000), of the CIMIS network. The estimations of both, the empirical method and the ASCE PM method, were compared. Validation of the method was performed with hourly data from the same station for 8 years, using various statistical indices. The hourly empirical equation was investigated for the whole period, for each year separately and for the summer period. It was found that it performed satisfactorily in all cases. Yearly RMSE ranged from 0.036 to 0.045mm/h with an average for the whole period 0.042mm/h. For the summer period RMSE ranged from 0.040mm/h to 0.055mm/h with an average for all the values of the summer period 0.047mm/h. It was also validated with data from the grass reference meteorological station in the experimental field of the Agricultural University of Athens in Copais, Greece and was found to perform satisfactorily with RMSE equal to 0.043mm/h. The deviations of the new empirical method from the ASCE PM method were investigated for various ranges of wind speed and vapor pressure deficit data values. It was found that the empirical method estimates were acceptable for practically all cases when 0.073mm/h was considered as the threshold RMSE value. The proposed hourly empirical equation is recommended for use in semi arid climates.

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  • Chatzithomas, C.D. & Alexandris, S.G., 2015. "Solar radiation and relative humidity based, empirical method, to estimate hourly reference evapotranspiration," Agricultural Water Management, Elsevier, vol. 152(C), pages 188-197.
    • Handle: RePEc:eee:agiwat:v:152:y:2015:i:c:p:188-197
    DOI: 10.1016/j.agwat.2015.01.019
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    References listed on IDEAS

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    1. Lopez-Urrea, R. & Martin de Santa Olalla, F. & Fabeiro, C. & Moratalla, A., 2006. "Testing evapotranspiration equations using lysimeter observations in a semiarid climate," Agricultural Water Management, Elsevier, vol. 85(1-2), pages 15-26, September.
    2. C.-Y. Xu & V. Singh, 2002. "Cross Comparison of Empirical Equations for Calculating Potential Evapotranspiration with Data from Switzerland," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 16(3), pages 197-219, June.
    3. Allen, Richard G. & Pereira, Luis S. & Howell, Terry A. & Jensen, Marvin E., 2011. "Evapotranspiration information reporting: I. Factors governing measurement accuracy," Agricultural Water Management, Elsevier, vol. 98(6), pages 899-920, April.
    4. Alexandris, S. & Kerkides, P., 2003. "New empirical formula for hourly estimations of reference evapotranspiration," Agricultural Water Management, Elsevier, vol. 60(3), pages 157-180, May.
    5. Pereira, Antonio Roberto & Pruitt, William Oregon, 2004. "Adaptation of the Thornthwaite scheme for estimating daily reference evapotranspiration," Agricultural Water Management, Elsevier, vol. 66(3), pages 251-257, May.
    6. Slavisa Trajkovic & Srdjan Kolakovic, 2009. "Evaluation of Reference Evapotranspiration Equations Under Humid Conditions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(14), pages 3057-3067, November.
    7. Alexandris, S. & Kerkides, P. & Liakatas, A., 2006. "Daily reference evapotranspiration estimates by the "Copais" approach," Agricultural Water Management, Elsevier, vol. 82(3), pages 371-386, April.
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    Cited by:

    1. Fan, Junliang & Ma, Xin & Wu, Lifeng & Zhang, Fucang & Yu, Xiang & Zeng, Wenzhi, 2019. "Light Gradient Boosting Machine: An efficient soft computing model for estimating daily reference evapotranspiration with local and external meteorological data," Agricultural Water Management, Elsevier, vol. 225(C).
    2. Ferreira, Lucas Borges & da Cunha, Fernando França, 2020. "New approach to estimate daily reference evapotranspiration based on hourly temperature and relative humidity using machine learning and deep learning," Agricultural Water Management, Elsevier, vol. 234(C).

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