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An evaluation of gridded weather data sets for the purpose of estimating reference evapotranspiration in the United States

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  • Blankenau, Philip A.
  • Kilic, Ayse
  • Allen, Richard

Abstract

This study assessed the quality of gridded weather data for calculating reference evapotranspiration (ETref), which, by definition, represents a near maximum ET occurring in a well-watered agricultural environment. Six gridded weather data sets – GLDAS-1, NLDAS-2, the CFSv2 operational analysis, gridMET, RTMA, and NDFD – were compared to weather data collected from 103 weather stations located in well-watered settings across the conterminous United States. ETref along with the weather variables used to compute it – near-surface air temperature, vapor pressure, wind speed, and shortwave solar radiation – were compared.

Suggested Citation

  • Blankenau, Philip A. & Kilic, Ayse & Allen, Richard, 2020. "An evaluation of gridded weather data sets for the purpose of estimating reference evapotranspiration in the United States," Agricultural Water Management, Elsevier, vol. 242(C).
    • Handle: RePEc:eee:agiwat:v:242:y:2020:i:c:s0378377420304753
    DOI: 10.1016/j.agwat.2020.106376
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    References listed on IDEAS

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    1. Paredes, Paula & Martins, Diogo S. & Pereira, Luis Santos & Cadima, Jorge & Pires, Carlos, 2018. "Accuracy of daily estimation of grass reference evapotranspiration using ERA-Interim reanalysis products with assessment of alternative bias correction schemes," Agricultural Water Management, Elsevier, vol. 210(C), pages 340-353.
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    Cited by:

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    2. Cécile Couharde & Rémi Generoso, 2023. "The financial cost of stabilizing US farm income under climate change," EconomiX Working Papers 2023-18, University of Paris Nanterre, EconomiX.
    3. Paredes, P. & Pereira, L.S. & Almorox, J. & Darouich, H., 2020. "Reference grass evapotranspiration with reduced data sets: Parameterization of the FAO Penman-Monteith temperature approach and the Hargeaves-Samani equation using local climatic variables," Agricultural Water Management, Elsevier, vol. 240(C).
    4. Long Qian & Lifeng Wu & Xiaogang Liu & Yaokui Cui & Yongwen Wang, 2022. "Comparison of CLDAS and Machine Learning Models for Reference Evapotranspiration Estimation under Limited Meteorological Data," Sustainability, MDPI, vol. 14(21), pages 1-24, November.
    5. Yajie Wu & Yuan Chen & Yong Tian, 2022. "Incorporating Empirical Orthogonal Function Analysis into Machine Learning Models for Streamflow Prediction," Sustainability, MDPI, vol. 14(11), pages 1-19, May.
    6. Hu, K.X. & Awange, J.L. & Kuhn, M. & Zerihun, A., 2022. "Irrigated agriculture potential of Australia’s northern territory inferred from spatial assessment of groundwater availability and crop evapotranspiration," Agricultural Water Management, Elsevier, vol. 264(C).
    7. Kelley, Jason & Olson, Bailey, 2022. "Interannual variability of water productivity on the Eastern Snake Plain in Idaho, United States," Agricultural Water Management, Elsevier, vol. 265(C).
    8. Kayatz, Benjamin & Baroni, Gabriele & Hillier, Jon & Lüdtke, Stefan & Freese, Dirk & Wattenbach, Martin, 2024. "Supporting decision-making in agricultural water management under data scarcity using global datasets – chances, limits and potential improvements," Agricultural Water Management, Elsevier, vol. 296(C).
    9. Allen, Richard G. & Dhungel, Ramesh & Dhungana, Bibha & Huntington, Justin & Kilic, Ayse & Morton, Charles, 2021. "Conditioning point and gridded weather data under aridity conditions for calculation of reference evapotranspiration," Agricultural Water Management, Elsevier, vol. 245(C).

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