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Temperature-and humidity-based simplified Penman’s ET0 formulae. Comparisons with temperature-based Hargreaves-Samani and other methodologies

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  • Valiantzas, John D.

Abstract

This study introduces a new simplified Penman formulae for estimating reference evapotranspiration, ET0 (mm/d). The new formulae require maximum and minimum air temperatures and relative humidity or in addition local average wind speed. The new ET0 formulae is produced by simplifications to the previously developed by Valiantzas (2006, 2013a) simplified Penman expression that requires a complete set of meteorological data. A calibration based on measurements obtained from a global data base is used for the derivation of the formula. In addition, a relationship previously proposed by the author, connecting solar radiation to temperature and humidity is used. The new formula requiring maximum and minimum temperatures, Tmax (oC) and Tmin (oC) respectively, and humidity data, RH (%) is: ET0 = 0.0118(1-RH/100)0.2(Tmax-Tmin)0.3(Ra(T+10)0.5-40)+0.1(T+20)(1-RH/100)

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  • Valiantzas, John D., 2018. "Temperature-and humidity-based simplified Penman’s ET0 formulae. Comparisons with temperature-based Hargreaves-Samani and other methodologies," Agricultural Water Management, Elsevier, vol. 208(C), pages 326-334.
    • Handle: RePEc:eee:agiwat:v:208:y:2018:i:c:p:326-334
    DOI: 10.1016/j.agwat.2018.06.028
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    References listed on IDEAS

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    1. Kisi, Ozgur, 2016. "Modeling reference evapotranspiration using three different heuristic regression approaches," Agricultural Water Management, Elsevier, vol. 169(C), pages 162-172.
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    2. Xiang, Keyu & Li, Yi & Horton, Robert & Feng, Hao, 2020. "Similarity and difference of potential evapotranspiration and reference crop evapotranspiration – a review," Agricultural Water Management, Elsevier, vol. 232(C).
    3. Ferreira, Lucas Borges & da Cunha, Fernando França & Fernandes Filho, Elpídio Inácio, 2022. "Exploring machine learning and multi-task learning to estimate meteorological data and reference evapotranspiration across Brazil," Agricultural Water Management, Elsevier, vol. 259(C).
    4. Tianao Wu & Wei Zhang & Xiyun Jiao & Weihua Guo & Yousef Alhaj Hamoud, 2020. "Comparison of five Boosting-based models for estimating daily reference evapotranspiration with limited meteorological variables," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-28, June.
    5. 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).
    6. Rana Muhammad Adnan & Salim Heddam & Zaher Mundher Yaseen & Shamsuddin Shahid & Ozgur Kisi & Binquan Li, 2020. "Prediction of Potential Evapotranspiration Using Temperature-Based Heuristic Approaches," Sustainability, MDPI, vol. 13(1), pages 1-21, December.

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