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Accuracy of daily estimation of grass reference evapotranspiration using ERA-Interim reanalysis products with assessment of alternative bias correction schemes

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  • Paredes, Paula
  • Martins, Diogo S.
  • Pereira, Luis Santos
  • Cadima, Jorge
  • Pires, Carlos

Abstract

This study aims at assessing the accuracy of estimating daily grass reference evapotranspiration (PM-ETo) computed with ERA-Interim reanalysis products, as well as to assess the quality of reanalysis products as predictors of daily maximum and minimum temperature, net radiation, dew point temperature and wind speed, which are used to compute PM-ETo. With this propose, ETo computed from local observations of weather variables in 24 weather stations distributed across Continental Portugal were compared with reanalysis-based values of ETo (ETo REAN). Three different versions of these reanalysis-based ETo were computed: (i) an (uncorrected) ETo based on the individual weather variables for the nearest grid point to the weather station; (ii) the previously calculated ETo corrected for bias with a simple bias-correction rule based only on the nearest grid point; and (iii) the ETo corrected for bias with a more complex rule involving all grid points in a 100 km radius of the weather station. Both bias correction approaches were tested aggregating data on a monthly, quarterly and a single overall basis. Cross-validation was used to allow evaluating the uncertainties that are modelled independently of any forcing; with this purpose, data sets were divided into two groups. Results show that ETo REAN without bias correction is strongly correlated with PM-ETo (R2>0.80) but tends to over-estimate PM-ETo, with the slope of the regression forced to the origin b0 ≥ 1.05, a mean RMSE of 0.79 mm day−1, and with EF generally above 0.70. Cross-validation results showed that using both bias correction methods improved the accuracy of estimations, in particular when a monthly aggregation was used. In addition, results showed that using the multiple regression correction method outperforms the additive bias correction leading to lower RMSE, with mean RMSE of 0.57 and 0.64 mm day−1 respectively. The selection of the bias correction approach to be adopted should balance the ease of use, the quality of results and the ability to capture the intra-annual seasonality of ETo. Thus, for irrigation scheduling operational purposes, we propose the use of the additive bias correction with a quarterly aggregation.

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  • 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.
    • Handle: RePEc:eee:agiwat:v:210:y:2018:i:c:p:340-353
    DOI: 10.1016/j.agwat.2018.08.003
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    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. 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).
    5. Paredes, P. & Pereira, L.S., 2019. "Computing FAO56 reference grass evapotranspiration PM-ETo from temperature with focus on solar radiation," Agricultural Water Management, Elsevier, vol. 215(C), pages 86-102.
    6. 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).
    7. Serra, J. & Paredes, P. & Cordovil, CMdS & Cruz, S. & Hutchings, NJ & Cameira, MR, 2023. "Is irrigation water an overlooked source of nitrogen in agriculture?," Agricultural Water Management, Elsevier, vol. 278(C).
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    9. Pereira, L.S. & Paredes, P. & Jovanovic, N., 2020. "Soil water balance models for determining crop water and irrigation requirements and irrigation scheduling focusing on the FAO56 method and the dual Kc approach," Agricultural Water Management, Elsevier, vol. 241(C).
    10. Qiu, Rangjian & Li, Longan & Liu, Chunwei & Wang, Zhenchang & Zhang, Baozhong & Liu, Zhandong, 2022. "Evapotranspiration estimation using a modified crop coefficient model in a rotated rice-winter wheat system," Agricultural Water Management, Elsevier, vol. 264(C).
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    12. Kang, Yan & Chen, Peiru & Cheng, Xiao & Zhang, Shuo & Song, Songbai, 2022. "Novel hybrid machine learning framework with decomposition–transformation and identification of key modes for estimating reference evapotranspiration," Agricultural Water Management, Elsevier, vol. 273(C).
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    14. 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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