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Quality assurance procedures for validating meteorological input variables of reference evapotranspiration in mendoza province (Argentina)

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  • Estévez, J.
  • García-Marín, A.P
  • Morábito, J.A
  • Cavagnaro, M.

Abstract

Validated meteorological data are required to make climate assessments, related decisions and to appropriately compute other important parameters such as reference evapotranspiration (ET0), vital to accurately estimate crops water requirements. In addition, quality meteorological datasets will increase the reliability of the results obtained by scientific or technical models that use them. In semiarid regions, with a structural water deficit as province of Mendoza (Argentina), the integrity and quality of these data are crucial to improve ET0 estimates, ensuring an adequate irrigation water management. In this work, several quality assurance procedures were applied to meteorological data—as a pre-requisite for ET0 computations—in order to detect erroneous and invalid data of each parameter from automated weather stations located in the three irrigated areas of province of Mendoza (Northern oasis, Western oasis and Southern oasis). Due to the lack and poor quality of solar radiation data, calibration of new based temperature solar radiation prediction models for each of the station are proposed. Results show the data flagged for each variable by range/limits, step, internal consistency and persistence tests, providing guidance of great value to end users. Finally, a simple comparison of ET0 estimations using original and validated meteorological datasets for each irrigated area in province of Mendoza is also reported.

Suggested Citation

  • Estévez, J. & García-Marín, A.P & Morábito, J.A & Cavagnaro, M., 2016. "Quality assurance procedures for validating meteorological input variables of reference evapotranspiration in mendoza province (Argentina)," Agricultural Water Management, Elsevier, vol. 172(C), pages 96-109.
    • Handle: RePEc:eee:agiwat:v:172:y:2016:i:c:p:96-109
    DOI: 10.1016/j.agwat.2016.04.019
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    References listed on IDEAS

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    1. Querner, E. P. & Morabito, J. A. & Manzanera, M. & Paws, J. A. & Ciancaglini, N. C. & Menenti, M., 1997. "The use of hydrological models in the irrigated areas of Mendoza, Argentina," Agricultural Water Management, Elsevier, vol. 35(1-2), pages 11-28, December.
    2. Martinez-Cob, A. & Tejero-Juste, M., 2004. "A wind-based qualitative calibration of the Hargreaves ET0 estimation equation in semiarid regions," Agricultural Water Management, Elsevier, vol. 64(3), pages 251-264, February.
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    1. Bellido-Jiménez, Juan A. & Estévez, Javier & García-Marín, Amanda P., 2022. "A regional machine learning method to outperform temperature-based reference evapotranspiration estimations in Southern Spain," Agricultural Water Management, Elsevier, vol. 274(C).
    2. Bellido-Jiménez, Juan Antonio & Estévez, Javier & García-Marín, Amanda Penélope, 2021. "New machine learning approaches to improve reference evapotranspiration estimates using intra-daily temperature-based variables in a semi-arid region of Spain," Agricultural Water Management, Elsevier, vol. 245(C).
    3. Bellido-Jiménez, Juan Antonio & Estévez Gualda, Javier & García-Marín, Amanda Penélope, 2021. "Assessing new intra-daily temperature-based machine learning models to outperform solar radiation predictions in different conditions," Applied Energy, Elsevier, vol. 298(C).
    4. Dilip Kumar Roy & Mohamed Anower Hossain & Mohamed Panjarul Haque & Abed Alataway & Ahmed Z. Dewidar & Mohamed A. Mattar, 2024. "Automated Model Selection Using Bayesian Optimization and the Asynchronous Successive Halving Algorithm for Predicting Daily Minimum and Maximum Temperatures," Agriculture, MDPI, vol. 14(2), pages 1-30, February.

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