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Medium Range Daily Reference Evapotranspiration Forecasting by Using ANN and Public Weather Forecasts

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Abstract

Medium range daily reference evapotranspiration (ETo) forecasts are very helpful for farmers or irrigation system operators for improving their irrigation scheduling. We tested four artificial neural networks (ANNs) for ETo forecasting using forecasted temperatures data retrieved from public weather forecasts. Daily meteorological data were collected to train and validate the ANNs against the Penman–Monteith (PM) model. And then, the temperature forecasts for 7-day ahead were entered into the validated ANNs to produce ETo forecast outputs. The forecasting performances of models were evaluated through comparisons between the ETo forecasted by ANNs and ETo calculated by PM from the observed meteorological data. The correlation coefficients between observed and forecasted temperatures for all stations were all greater than 0.91, and the accuracy of the minimum temperature forecast (error within ± 2 °C) ranged from 68.34 to 91.61 %, whereas for the maximum temperature it ranged from 51.78 to 57.44 %. The accuracy of the ETo forecast (error within ± 1.5 mm day−1) ranged from 75.53 to 78.14 %, the average values of the mean absolute error ranged from 0.99 to 1.09 mm day−1, the average values of the root mean square error ranged from 0.87 to 1.36 mm day−1, and the average values of the correlation coefficient ranged from 0.70 to 0.75. The results suggested that ANNs can be considered as a promising ETo forecasting tool. The forecasting performance can be improved by promoting temperature forecast accuracy.

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Acknowledgments

This work was financially supported by the National Natural Science Foundation of China (NSFC 51179048), the Ministry of Water Resource under the Public Welfare Scientific Research Project (201301014-02) and Advanced Science and Technology Innovation Team in Colleges and Universities in Jiangsu Province. The observed meteorological data obtained from the China Meteorological Data Sharing Service System (http://cdc.cma.gov.cn) and weather forecast data from Weather China (http://www.weather.com.cn) are gratefully acknowledged. The authors would like to thank the anonymous reviewers for their precious and insightful comments and suggestions that greatly improved the quality of this manuscript.

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Authors and Affiliations

  1. State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, Hubei, 430072, China

    Yufeng Luo

  2. Department of Biological and Agricultural Engineering, Texas A&M University, College Station, TX, 77843, USA

    Yufeng Luo, Seydou Traore & Guy Fipps

  3. State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing, Jiangsu, 210098, China

    Yufeng Luo, Weiguang Wang, Yongyu Xie & Xiyun Jiao

  4. School of Software Engineering, University of Science and Technology of China, Hefei, Anhui, 230026, China

    Xinwei Lyu

  5. Department of Water Conservancy, Yunnan Agricultural University, Kunming, Yunan, 650201, China

    Ying Wang

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  1. Yufeng Luo
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  2. Seydou Traore
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  3. Xinwei Lyu
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  4. Weiguang Wang
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  8. Guy Fipps
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Correspondence to Yufeng Luo.

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Luo, Y., Traore, S., Lyu, X. et al. Medium Range Daily Reference Evapotranspiration Forecasting by Using ANN and Public Weather Forecasts. Water Resour Manage 29, 3863–3876 (2015). https://doi.org/10.1007/s11269-015-1033-8

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