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Performance Evaluation of Reference Evapotranspiration Estimation Using Climate Based Methods and Artificial Neural Networks

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  • Seema Chauhan
  • R. Shrivastava

Abstract

This study is an attempt to find best alternative method to estimate reference evapotranspiration (ET o ) for the Mahanadi reservoir project (MRP) command area located at Raipur (Chhattisgarh) in India, when input climatic parameters are insufficient to apply standard Food and Agriculture Organization (FAO) of the United Nations Penman–Monteith (P–M) method. To identify the best alternative climatic based method that yield results closest to the P–M method, performances of four climate based methods namely Blaney–Criddle, Radiation, Modified Penman and Pan evaporation were compared with the FAO-56 Penman–Monteith method. Performances were evaluated using the statistical indices. The statistical indices used in the analysis were the standard error of estimate (SEE), raw standard error of estimate (RSEE) and the model efficiency. Study was extended to identify the ability of Artificial Neural Networks (ANNs) for estimation of ET o in comparison to climatic based methods. The networks, using varied input combinations of climatic variables have been trained using the backpropagation with variable learning rate training algorithm. ANN models were performed better than the climatic based methods in all performance indices. The analyses of results of ANN model suggest that the ET o can be estimated from maximum and minimum temperature using ANN approach in MPR area. Copyright Springer Science+Business Media B.V. 2009

Suggested Citation

  • Seema Chauhan & R. Shrivastava, 2009. "Performance Evaluation of Reference Evapotranspiration Estimation Using Climate Based Methods and Artificial Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(5), pages 825-837, March.
    • Handle: RePEc:spr:waterr:v:23:y:2009:i:5:p:825-837
    DOI: 10.1007/s11269-008-9301-5
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    References listed on IDEAS

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    1. C.-Y. Xu & V. Singh, 2002. "Cross Comparison of Empirical Equations for Calculating Potential Evapotranspiration with Data from Switzerland," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 16(3), pages 197-219, June.
    2. Kashyap, P. S. & Panda, R. K., 2001. "Evaluation of evapotranspiration estimation methods and development of crop-coefficients for potato crop in a sub-humid region," Agricultural Water Management, Elsevier, vol. 50(1), pages 9-25, August.
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    1. Wenjuan Liu & Yang Hong & Sadiq Khan & Mingbin Huang & Trevor Grout & Pradeep Adhikari, 2011. "Evaluation of Global Daily Reference ET Using Oklahoma’s Environmental Monitoring Network—MESONET," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(6), pages 1601-1613, April.
    2. M. Majidi & A. Alizadeh & M. Vazifedoust & A. Farid & T. Ahmadi, 2015. "Analysis of the Effect of Missing Weather Data on Estimating Daily Reference Evapotranspiration Under Different Climatic Conditions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(7), pages 2107-2124, May.
    3. Ozgur Kisi & Levent Latifoğlu & Fatma Latifoğlu, 2014. "Investigation of Empirical Mode Decomposition in Forecasting of Hydrological Time Series," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(12), pages 4045-4057, September.
    4. Mohamed K. Abdel-Fattah & Sameh Kotb Abd-Elmabod & Zhenhua Zhang & Abdel-Rhman M. A. Merwad, 2023. "Exploring the Applicability of Regression Models and Artificial Neural Networks for Calculating Reference Evapotranspiration in Arid Regions," Sustainability, MDPI, vol. 15(21), pages 1-15, October.
    5. Safar Marofi & Hossein Tabari & Hamid Abyaneh, 2011. "Predicting Spatial Distribution of Snow Water Equivalent Using Multivariate Non-linear Regression and Computational Intelligence Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(5), pages 1417-1435, March.
    6. Ali Rahimikhoob, 2014. "Comparison between M5 Model Tree and Neural Networks for Estimating Reference Evapotranspiration in an Arid Environment," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(3), pages 657-669, February.
    7. Vishwakarma, Dinesh Kumar & Pandey, Kusum & Kaur, Arshdeep & Kushwaha, N.L. & Kumar, Rohitashw & Ali, Rawshan & Elbeltagi, Ahmed & Kuriqi, Alban, 2022. "Methods to estimate evapotranspiration in humid and subtropical climate conditions," Agricultural Water Management, Elsevier, vol. 261(C).
    8. Ozgur Kisi & Mohammad Zounemat-Kermani, 2014. "Comparison of Two Different Adaptive Neuro-Fuzzy Inference Systems in Modelling Daily Reference Evapotranspiration," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(9), pages 2655-2675, July.
    9. Asnor Ishak & Renji Remesan & Prashant Srivastava & Tanvir Islam & Dawei Han, 2013. "Error Correction Modelling of Wind Speed Through Hydro-Meteorological Parameters and Mesoscale Model: A Hybrid Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(1), pages 1-23, January.
    10. Osama Mohawesh, 2010. "Spatio-temporal Calibration of Blaney–Criddle Equation in Arid and Semiarid Environment," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(10), pages 2187-2201, August.
    11. Matin Ahooghalandari & Mehdi Khiadani & Mina Esmi Jahromi, 2016. "Developing Equations for Estimating Reference Evapotranspiration in Australia," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(11), pages 3815-3828, September.
    12. Sungwon Kim & Jalal Shiri & Ozgur Kisi, 2012. "Pan Evaporation Modeling Using Neural Computing Approach for Different Climatic Zones," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(11), pages 3231-3249, September.
    13. Süleyman Özhan & Ferhat Gökbulak & Yusuf Serengil & Mehmet Özcan, 2010. "Evapotranspiration from a Mixed Deciduous Forest Ecosystem," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(10), pages 2353-2363, August.
    14. Ali Rahimikhoob & Maryam Asadi & Mahmood Mashal, 2013. "A Comparison Between Conventional and M5 Model Tree Methods for Converting Pan Evaporation to Reference Evapotranspiration for Semi-Arid Region," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(14), pages 4815-4826, November.

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