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Short-term forecasting of daily reference evapotranspiration using the reduced-set Penman-Monteith model and public weather forecasts

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  • Yang, Yang
  • Cui, Yuanlai
  • Bai, Kaihua
  • Luo, Tongyuan
  • Dai, Junfeng
  • Wang, Weiguang
  • Luo, Yufeng

Abstract

Farm irrigation management and real-time irrigation decisions are based on a series of parameters including irrigation water demand predictions which estimated by the short-term daily reference evapotranspiration (ETo). Based on the temperature data, a simplest empirical model called the temperature-based method, was developed to forecast near-future ETo. Although temperature is one of the most influential weather variables in ETo forecasting, wind velocity also has non-ignorable impact on the forecast performance of ETo. Therefore, we employed the Reduced-set Penman-Monteith (RPM) model for short-term ETo forecasting using temperature data derived from public weather forecasts and wind speed data. Four different types of wind speed data were adopted as model inputs, including wind speed assumed as a default value, i.e., u2 = 2.0 m s−1 (W1), forecasted wind speed (W2), long-term daily average wind speed (W3) and annual average wind speed (W4). Based on the above-mentioned wind speed inputs, we presented four types of RPM models (RC, RF, RD and RM models respectively corresponding to W1, W2, W3 and W4) to forecast daily ETo for lead times up to 7 days for eight weather stations in China. The results confirmed that the highest precision wind speed input was W3, followed by W4, W1 and W2, and the most accurate ETo forecasts of four RPM models was provided by RC, followed with RD, RM and RF. By comparing with one of the most common used temperature-based models, the Hargreaves-Samani (HS), it was found that the performance of RPM approaches were better than that of HS model for arid and sub-arid regions whereas opposite results were found in sub-tropical area, suggesting inclusion of wind speed parameter makes positive impact on ETo forecast for arid and semi-arid regions. Further, it was found that the greatest type of RPM model, the RC model, was superior to HS method for most climate regions, including arid, humid, sub-arid and sub-humid areas, thus, the W1 wind speed type could be highly recommended for ETo forecasting for most climate regions.

Suggested Citation

  • Yang, Yang & Cui, Yuanlai & Bai, Kaihua & Luo, Tongyuan & Dai, Junfeng & Wang, Weiguang & Luo, Yufeng, 2019. "Short-term forecasting of daily reference evapotranspiration using the reduced-set Penman-Monteith model and public weather forecasts," Agricultural Water Management, Elsevier, vol. 211(C), pages 70-80.
    • Handle: RePEc:eee:agiwat:v:211:y:2019:i:c:p:70-80
    DOI: 10.1016/j.agwat.2018.09.036
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    5. 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).
    6. Shih-Lun Fang & Yi-Shan Lin & Sheng-Chih Chang & Yi-Lung Chang & Bing-Yun Tsai & Bo-Jein Kuo, 2024. "Using Artificial Intelligence Algorithms to Estimate and Short-Term Forecast the Daily Reference Evapotranspiration with Limited Meteorological Variables," Agriculture, MDPI, vol. 14(4), pages 1-20, March.
    7. 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).
    8. Santos, Jannaylton Everton Oliveira & Cunha, Fernando França da & Filgueiras, Roberto & Silva, Gustavo Henrique da & Castro Teixeira, Antônio Heriberto de & Santos Silva, Francisco Charles dos & Sediy, 2020. "Performance of SAFER evapotranspiration using missing meteorological data," Agricultural Water Management, Elsevier, vol. 233(C).
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    10. Zhang, Lei & Zhao, Xin & Zhu, Ge & He, Jun & Chen, Jian & Chen, Zhicheng & Traore, Seydou & Liu, Junguo & Singh, Vijay P., 2023. "Short-term daily reference evapotranspiration forecasting using temperature-based deep learning models in different climate zones in China," Agricultural Water Management, Elsevier, vol. 289(C).
    11. Lai, Chengguang & Chen, Xiaohong & Zhong, Ruida & Wang, Zhaoli, 2022. "Implication of climate variable selections on the uncertainty of reference crop evapotranspiration projections propagated from climate variables projections under climate change," Agricultural Water Management, Elsevier, vol. 259(C).
    12. Ahmed A. Abdelmoneim & Roula Khadra & Angela Elkamouh & Bilal Derardja & Giovanna Dragonetti, 2023. "Towards Affordable Precision Irrigation: An Experimental Comparison of Weather-Based and Soil Water Potential-Based Irrigation Using Low-Cost IoT-Tensiometers on Drip Irrigated Lettuce," Sustainability, MDPI, vol. 16(1), pages 1-15, December.
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