Abstract
Accurate forecasting of daily evapotranspiration (ET) is essential for enhancing real-time irrigation scheduling and informed decision-making in water resources allocation. This study investigates the intricate relationships between meteorological variables and evapotranspiration (ET) to enhance the accuracy of ET estimation models. Robust correlations were identified, emphasizing the significance of net radiation (Rn) in predicting ET. The study explores three distinct scenarios, incorporating different combinations of weather variables as input. The first scenario incorporates all weather variables, including date and time, as inputs for model development. The second scenario utilizes only Rn as input to predict ET values. In the third and final scenario, all weather variables, along with date and time, are employed as inputs for comprehensive model development. The multivariate linear regression (MLR) model demonstrated exceptional performance when exclusively using Rn, achieving an impressive R2 value of 0.99 in both calibration and validation phases. However, limitations were observed when Rn was excluded, highlighting the necessity of a comprehensive set of input data. Penalized regression models, including ridge regression, LASSO, and ELNET, exhibited improved performance with the inclusion of Rn, supporting the importance of this variable in refining ET estimates. Machine learning models displayed remarkable performance, with most achieving R2 values exceeding 0.95 in scenarios involving extensive input data. The Support Vector Regression (SVR) model faced challenges, indicating potential overfitting in certain scenarios. In scenarios with limited input data, machine learning models exhibited varying performance, with the Random Forest (RF) model emerging as the most robust model with R2 value of 0.99 and 0.84 during the calibration and validation, respectively.
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Data availability
The datasets analysed during the current study are available from the corresponding author on reasonable request.
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The code are available from the corresponding author on reasonable request.
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Acknowledgements
The authors extend their appreciation to the Deanship of Scientific Research, King Saud University for funding through the Vice Deanship of Scientific Research Chairs; Research Chair of Prince Sultan Bin Abdulaziz International Prize for Water. Authors also thank the Department of Agrometeorology, G.B. Pant University of Agriculture and Technology for providing the required facilities to conduct the study.
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This research was funded by the Deanship of Scientific Research, King Saud University through the Vice Deanship of Scientific Research Chairs; Research Chair of Prince Sultan Bin Abdulaziz International Prize for Water.
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Anurag Satpathi and Abhishek Danodia conducted the formal analysis, investigation and wrote the manuscript. Ajeet Singh Nain edited and supervised the work. Makrand Dhyani, Dinesh Kumar Vishwakarma, Ahmed Z. Dewidar, and Mohamed A. Mattar prepared the necessary figures and revised the manuscript. All authors have reviewed the results and approved the final version of the manuscript.
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Satpathi, A., Danodia, A., Nain, A.S. et al. Estimation of crop evapotranspiration using statistical and machine learning techniques with limited meteorological data: a case study in Udham Singh Nagar, India. Theor Appl Climatol (2024). https://doi.org/10.1007/s00704-024-04953-3
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DOI: https://doi.org/10.1007/s00704-024-04953-3