Modeling and Trend Analysis of Reference Evapotranspiration in Western Maharashtra, India
DOI:
https://doi.org/10.52151/jae2026632.2015Keywords:
artificial neural network, climate-based model, linear regression, Mann-Kendall test, Penman-Monteith equationAbstract
In this study, reference evapotranspiration (ETo) was estimated over nine stations of western Maharashtra, India, using nine climate-based models, four linear regression (LR) models and four artificial neural network (ANN) models. None of the climate-based models replicated the spatial patterns of ETo as depicted by the FAO-56 Penman-Monteith (P-M) method. The P-M method based ETo values indicated an increase in ETo values from the central region toward the southeast part of the study area, with comparatively higher values also observed in the northeast area. The LR and ANN model outputs showed spatial patterns closely aligning with the P-M method. Overall, all LR and ANN models provided reliable ETo predictions. Hargreaves-Samani (H-S) and Pan Evaporation (PAN) models showed close ETo patterns, with ETo values of H-S model being higher (4.79-5.40 mm day-1) than that of PAN (3.22-5.11 mm day-1). Likewise, Jensen-Haise (J-H) and Turc models showed almost similar patterns, but ETo values of J-H model were higher (3.02-4.01 mm day-1) than that of Turc (1.68-1.87 mm day-1). Also, Priestly-Taylor (P-T) and Radiation (RAD) models showed spatial patterns differing from other climate-based methods, with ETo ranging from 3.92-4.39 mm day-1 and 5.72-6.02 mm day-1, respectively. Trend results using Mann-Kendall test indicated a decrease in ETo, estimated using the P-M model, at all stations. The slopes obtained from the P-M method estimated ETo indicated decreasing ETo trends at all stations. The climate-based models, SCS-BC, THOR, and Hargreaves-Samani (H-S) exhibited positive slope values across most stations, indicating a gradual increase in ETo. These findings highlighted the importance of developing region-specific ETo estimation tools to support effective irrigation planning, drought mitigation, and sustainable water resource management under conditions of climatic variability and limited facilities available to record meteorological observations. The LR and ANN approaches demonstrated reliable capability for ETo predictions and strong agreement with that of the P-M method, even under limited inputs.
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Abtew, W., Obeysekera, J., & Iricanin, N. (2011). Pan evaporation and potential evapotranspiration trends in South Florida. Hydrological Processes, 25, 958–969. https://doi.org/10.1002/hyp.7887 DOI: https://doi.org/10.1002/hyp.7887
Ahmadi, S. H., & Fooladmand, H. R. (2008). Spatially distributed monthly reference evapotranspiration derived from the calibration of Thornthwaite equation: A case study. Irrigation Science, 26, 303–312. https://doi.org/10.1007/s00271-007-0094-8 DOI: https://doi.org/10.1007/s00271-007-0094-8
Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop evapotranspiration: Guidelines for computing crop water requirements, FAO Irrigation and Drainage Paper No. 56. Food and Agriculture Organization of the United Nations, Rome.
Anonymous. (2016). Economic survey of Maharashtra 2015–16. Directorate of Economics and Statistics, Planning Department, Government of Maharashtra. Available at: https://mahades.maharashtra.gov.in/files/publication/ESM_1516_Eng.pdf (Last accessed on 26 May 2026).
Antonopoulos, V. Z., & Antonopoulos, A. V. (2017). Daily reference evapotranspiration estimates by artificial neural networks technique and empirical equations using limited input climate variables. Computers and Electronics in Agriculture, 132, 86-96, ISSN 0168-1699. https://doi.org/10.1016/j.compag.2016.11.011 DOI: https://doi.org/10.1016/j.compag.2016.11.011
Aschale, T. M., Peres, D. J., Gullotta, A., Sciuto, G., & Cancelliere, A. (2023). Trend analysis and identification of the meteorological factors influencing reference evapotranspiration. Water, 15(3), 470. https://doi.org/10.3390/w15030470 DOI: https://doi.org/10.3390/w15030470
Bandyopadhyay, A., Bhadra, A., Raghuwanshi, N. S., & Singh, R. (2009). Temporal trends in estimates of reference evapotranspiration over India. Journal of Hydrologic Engineering, 14(5), 508–515. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000006 DOI: https://doi.org/10.1061/(ASCE)HE.1943-5584.0000006
Caloiero, T., Coscarelli, R., Ferrara, E., & Mancini, M. (2011). Trend detection of annual and seasonal rainfall in Calabria (Southern Italy). International Journal of Climatology, 31, 44–56. https://doi.org/10.1002/joc.2055 DOI: https://doi.org/10.1002/joc.2055
Chen, D., Gao, G., Xu, C. Y., Guo, J., & Ren, G. (2005). Comparison of the Thornthwaite method and pan data with the standard Penman–Monteith estimates of reference evapotranspiration in China. Climate Research, 28, 123–132. https://doi.org/10.3354/cr028123 DOI: https://doi.org/10.3354/cr028123
Chitale, M. (1999). Maharashtra water & irrigation report. Government of Maharashtra. Available at: https://wrd.maharashtra.gov.in/Site/Upload/PDF/1999_1.pdf (Last accessed on 26 May 2026).
Dai, X., Shi, H., Li, Y., Ouyang, Z., & Huo, Z. (2009). Artificial neural network models for estimating regional reference evapotranspiration based on climate factors. Hydrological Processes, 23(3), 442–450. https://doi.org/10.1002/hyp.7153 DOI: https://doi.org/10.1002/hyp.7153
Dawson, C. W., & Wilby, R. (1998). An artificial neural network approach to rainfall-runoff modeling. Hydrological Sciences Journal, 43(1), 47–66. https://doi.org/10.1080/02626669809492102 DOI: https://doi.org/10.1080/02626669809492102
Dinpashoh, Y., Jhajharia, D., Fakheri-Fard, A., Singh, V. P., & Kahya, E. (2011). Trends in reference crop evapotranspiration over Iran. Journal of Hydrology, 399(3–4), 422–433. https://doi.org/10.1016/j.jhydrol.2011.01.021 DOI: https://doi.org/10.1016/j.jhydrol.2011.01.021
Doorenbos, J., & Pruitt, W. O. (1977). Guidelines for prediction of crop water requirements, FAO Irrigation and Drainage Paper No. 24. Food and Agriculture Organization of the United Nations, Rome.
Draper, N. R., & Smith, H. (1998). Applied Regression Analysis (3rd ed.). John Wiley & Sons, Inc. https://doi.org/10.1002/9781118625590 DOI: https://doi.org/10.1002/9781118625590
Elsayed, A., Levison, J., Binns, A., Larocque, M., & Goel, P. (2026). A review of machine learning applications in the prediction of selected groundwater quality parameters: Key lessons, knowledge gaps, and future directions. Science of The Total Environment, 1027, 181693. https://doi.org/10.1016/j.scitotenv.2026.181693 DOI: https://doi.org/10.1016/j.scitotenv.2026.181693
Eshete, D. G., Sinshaw, B. G., & Legese, K. G. (2020). Critical review on improving irrigation water use efficiency: Advances, challenges, and opportunities in the Ethiopia context. Water-Energy Nexus, 3, 143–154. https://doi.org/10.1016/j.wen.2020.09.001 DOI: https://doi.org/10.1016/j.wen.2020.09.001
Fisher, J. B., Whittaker, R. J. & Malhi, Y. (2011), ET come home: Potential evapotranspiration in geographical ecology. Global Ecology and Biogeography, 20, 1-18. https://doi.org/10.1111/j.1466-8238.2010.00578.x DOI: https://doi.org/10.1111/j.1466-8238.2010.00578.x
Gong, L., Halldin, S., & Xu, C.-Y. (2011). Large-scale runoff generation: Parsimonious parameterisation using high-resolution topography. Hydrology and Earth System Sciences, 15(8), 2481–2494. https://doi.org/10.5194/hess-15-2481-2011 DOI: https://doi.org/10.5194/hess-15-2481-2011
Hagan, M. T., Demuth, H. B., Beale, M. H., & De Jesús, O. (2014). Neural Network Design (2nd ed.). Available at: https://hagan.okstate.edu/NNDesign.pdf (Last accessed on 26 May 2026).
Haykin, S. (2009). Neural Networks and Learning Machines. Third Edition, Pearson Education, Inc., New Jersey.
Huo, Z., Feng, S., Kang, S., & Dai, X. (2012). Artificial neural network models for reference evapotranspiration in an arid area of northwest China. Journal of Arid Environments, 82, 81–90. https://doi.org/10.1016/j.jaridenv.2012.01.016 DOI: https://doi.org/10.1016/j.jaridenv.2012.01.016
Ingle, P. M., Purohit, R. C., Bhakar, S. R., Mittal, H. K., Jain, H. K., & Singh, P. K. (2016). Trend analysis of reference evapotranspiration (ETo) using Mann–Kendall for South Konkan region. International Research Journal of Environment Sciences, 5(10), 35–39.
Jensen, M. E., Barman, R. D., & Allen, R. G. (eds.) (1990). Evapotranspiration and irrigation water requirements. A manual prepared by the Committee on Irrigation Water Requirements of the Irrigation and Drainage Division of the American Society of Civil Engineers, ASCE, New York.
Kartal, V. (2024). Prediction of monthly evapotranspiration by artificial neural network model development with Levenberg–Marquardt method in Elazig, Turkey. Environmental Science and Pollution Research, 31, 20953–20969. https://doi.org/10.1007/s11356-024-32464-1 DOI: https://doi.org/10.1007/s11356-024-32464-1
Karuppanan, S., Ramasamy, S., Lakshminarayanan, B., & Anuthaman, S. N. (2025). An effective machine learning model for the estimation of reference evapotranspiration under data-limited conditions. Research in Agricultural Engineering, 71(1), 22–37. https://doi.org/10.17221/101/2023-RAE DOI: https://doi.org/10.17221/101/2023-RAE
Katerji, N., & Rana, G. (2006). Modelling evapotranspiration of six irrigated crops under Mediterranean climate conditions. Agricultural and Forest Meteorology, 138(1–4), 142-155, https://doi.org/10.1016/j.agrformet.2006.04.006 DOI: https://doi.org/10.1016/j.agrformet.2006.04.006
Kendall, M. G. (1975). Rank correlation methods (4th ed.). Charles Griffin.
Khedkar, D. D., & Thorat, N. R. (2025). Regional estimation for water requirement of Rabi season crops. Journal of Agricultural Research and Technology, 50(1), 9 –14. https://doi.org/10.56228/JART.2025.50102 DOI: https://doi.org/10.56228/JART.2025.50102
Kim, S.-J., Bae, S.-J., & Jang, M.-W. (2022). Linear regression machine learning algorithms for estimating reference evapotranspiration using limited climate data. Sustainability, 14(18), 11674. https://doi.org/10.3390/su141811674 DOI: https://doi.org/10.3390/su141811674
Kousari, M. R., & Ahani, H. (2012). An investigation on reference crop evapotranspiration trend from 1975 to 2005 in Iran. International Journal of Climatology, 32(15), 2387–2402. https://doi.org/10.1002/joc.3404 DOI: https://doi.org/10.1002/joc.3404
Kumar, A., Prasad, V., & Baghel, S. (2023). Estimation and evaluation of trend analysis of the Penman-Monteith reference evapotranspiration of Raipur region, Chhattisgarh central India. MAUSAM, 74(1), 199–206. https://doi.org/10.54302/mausam.v74i1.6129 DOI: https://doi.org/10.54302/mausam.v74i1.6129
Kumar, M., Bandyopadhyay, A., Raghuwanshi, N. S., & Singh, R. (2008). Comparative study of conventional and artificial neural network-based ETo estimation models. Irrigation Science, 26, 531–545. https://doi.org/10.1007/s00271-008-0114-3 DOI: https://doi.org/10.1007/s00271-008-0114-3
Kumar, M., Raghuwanshi, N. S., Singh, R., Wallender, W. W., & Pruitt, W. O. (2002). Estimating evapotranspiration using artificial neural network. Journal of Irrigation and Drainage Engineering, 128(4), 224–233. https://doi.org/10.1061/(ASCE)0733-9437(2002)128:4(224) DOI: https://doi.org/10.1061/(ASCE)0733-9437(2002)128:4(224)
Lakhiar, I. A., Yan, H., Zhang, C., Wang, G., He, B., Hao, B., Han, Y., Wang, B., Bao, R., Syed, T. N., Chauhdary, J. N., & Rakibuzzaman, M. (2024). A review of precision irrigation water-saving technology under changing climate for enhancing water use efficiency, crop yield, and environmental footprints. Agriculture, 14(7), 1141. https://doi.org/10.3390/agriculture14071141 DOI: https://doi.org/10.3390/agriculture14071141
Landeras, G., Ortiz-Barredo, A., & López, J. J. (2008). Comparison of artificial neural network models and empirical and semi-empirical equations for daily reference evapotranspiration estimation in the Basque Country (Northern Spain). Agricultural Water Management, 95(5), 553–565. https://doi.org/10.1016/j.agwat.2007.12.011 DOI: https://doi.org/10.1016/j.agwat.2007.12.011
Li, D., & Liu, Z. (2024). Artificial neural networks (ANNs) and machine learning (ML) modeling employee behavior with management towards the economic advancement of workers. Sustainability, 16(21), 9516. https://doi.org/10.3390/su16219516 DOI: https://doi.org/10.3390/su16219516
Li, Z. L., Xu, X. Z., Li, J.Y., & Li, Z. J. (2008). Shift trend and step changes for runoff time series in the Shiyang River Basin, northwest China. Hydrological Processes, 22, 4639–4646. https://doi.org/10.1002/hyp.7127 DOI: https://doi.org/10.1002/hyp.7127
Liyew, C. M., Di Nardo, E., Ferraris, S., & Meo, R. (2025). Hyperparameter optimization of machine learning models for predicting actual evapotranspiration. Machine Learning with Applications, 20, 100661. https://doi.org/10.1016/j.mlwa.2025.100661 DOI: https://doi.org/10.1016/j.mlwa.2025.100661
Ma, X., Zhang, M., Li, Y., Wang, S., Ma, Q., & Liu, W. (2012). Decreasing potential evapotranspiration in the Huanghe River watershed in climate warming during 1960–2010. Journal of Geographical Sciences, 22, 977–988. https://doi.org/10.1007/s11442-012-0977-3 DOI: https://doi.org/10.1007/s11442-012-0977-3
Mahida, H. R., & Patel, V. N. (2015). Impact of climatological parameters on reference crop evapotranspiration using multiple linear regression analysis. International Journal of Civil Engineering, 2(1), 22–25. https://doi.org/10.14445/23488352/IJCE-V2I1P103 DOI: https://doi.org/10.14445/23488352/IJCE-V2I1P103
Mann, H. B. (1945). Nonparametric tests against trend. Econometrica, 13, 245–259. https://doi.org/10.2307/1907187 DOI: https://doi.org/10.2307/1907187
MathWorks (2011). MATLAB Neural Network Toolbox™ User’s Guide, Version 7.0 (R2011a). Natick, Massachusetts, USA: The MathWorks, Inc.
Montgomery, D. C., Peck, E. A., & Vining, G. G. (2012). Introduction to linear regression analysis (5th ed.). John Wiley & Sons, Inc.
Nam, W. H., Hong, E. M., & Choi, J. Y. (2015). Has climate change already affected the spatial distribution and temporal trends of reference evapotranspiration in South Korea? Agricultural Water Management, 150, 129–138. https://doi.org/10.1016/j.agwat.2014.11.019 DOI: https://doi.org/10.1016/j.agwat.2014.11.019
Ogunbo, J. N., Alagbe, O. A., Oladapo, M. I., & Shin, C. (2020). N-hidden layer artificial neural network architecture computer code: Geophysical application example. Heliyon, 6(6), e04108. https://doi.org/10.1016/j.heliyon.2020.e04108 DOI: https://doi.org/10.1016/j.heliyon.2020.e04108
Patel, A., Ali, S. T., & Pandey, M. K. (2025). Estimation of reference evapotranspiration using ensemble machine learning models based on regional scenario. Applied Water Science, 15, 307. https://doi.org/10.1007/s13201-025-02654-4 DOI: https://doi.org/10.1007/s13201-025-02654-4
Patle, G. T., Mandal, B. P., Kumar, M., & Jhajharia, D. (2023). Modelling of reference evapotranspiration using neural network and regression approaches for semi-humid region of Sikkim. Journal of Agricultural Engineering (India), 60(2), 205-217. https://doi.org/10.52151/jae2023602.1808 DOI: https://doi.org/10.52151/jae2023602.1808
Piticar, A., Mihaila, D., Lazurca, L. G., Bistricean, P. I., Putuntica, A., & Briciu, A. E. (2016). Spatiotemporal distribution of reference evapotranspiration in the Republic of Moldova. Theoretical and Applied Climatology, 124, 1133–1144. https://doi.org/10.1007/s00704-015-1490-2 DOI: https://doi.org/10.1007/s00704-015-1490-2
Raimondi, G., Bonato, J., & Maucieri, C. (2026). Machine learning models for reference evapotranspiration estimation in new locations under data-limited conditions in Northeastern Italy. European Journal of Agronomy, 178, 128132. https://doi.org/10.1016/j.eja.2026.128132 DOI: https://doi.org/10.1016/j.eja.2026.128132
Sabziparvar, A. A., & Tabari, H. (2010). Regional estimation of reference evapotranspiration in arid and semiarid regions. Journal of Irrigation and Drainage Engineering, 136(10), 724–731. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000242 DOI: https://doi.org/10.1061/(ASCE)IR.1943-4774.0000242
Salmi, T., Maatta, A., Anttila, P., Ruoho-Airola, T., & Amnell, T. (2002). Detecting trends of annual values of atmospheric pollutants by the Mann–Kendall test and Sen’s slope estimates: The Excel template application MAKESENS (Version 1.0). Publications on Air Quality No. 31. Finnish Meteorological Institute, Helsinki, Finland.
Sentelhas, P. C., Gillespie, T. J., & Santos, E. A. (2010). Evaluation of FAO Penman–Monteith and alternative methods for estimating reference evapotranspiration with missing data in Southern Ontario, Canada. Agricultural Water Management, 97(5), 635–644. http://dx.doi.org/10.1016/j.agwat.2009.12.001 DOI: https://doi.org/10.1016/j.agwat.2009.12.001
Sibale, D., Mane, M. S., Patil, S. T., & Ingle, P. M. (2016). Evaluation of three methods for estimating reference evapotranspiration at Dapoli, Maharashtra. Journal of Agrometeorology, 18(1), 157–158. https://doi.org/10.54386/jam.v18i1.925 DOI: https://doi.org/10.54386/jam.v18i1.925
Silva, H. J. F., Santos, M. S., Cabral Junior, J. B., & Spyrides, M. H. C. (2016). Modeling of reference evapotranspiration by multiple linear regression. Journal of Hyperspectral Remote Sensing, 6(1), 44–58. https://doi.org/10.5935/2237-2202.20160005 DOI: https://doi.org/10.5935/2237-2202.20160005
Singh, M., Niwas, R., Khichar, M. L., & Rajeev. (2016). Trend and variability in potential evapotranspiration over north-west India. Journal of Agrometeorology, 18(2), 335–338. https://doi.org/10.54386/jam.v18i2.965 DOI: https://doi.org/10.54386/jam.v18i2.965
Singh, M., Singh, J. P., & Singh, K. G. (2018). Development of mathematical models for predicting vapour pressure deficit inside a greenhouse from internal and external climate. Journal of Agrometeorology, 20(3), 238–241. https://doi.org/10.54386/jam.v20i3.552 DOI: https://doi.org/10.54386/jam.v20i3.552
Skhiri, A., Ferhi, A., Bousselmi, A., Khlifi, S., & Mattar, M. A. (2024). Artificial neural network for forecasting reference evapotranspiration in semi-arid bioclimatic regions. Water, 16(4), 602. https://doi.org/10.3390/w16040602 DOI: https://doi.org/10.3390/w16040602
Tabari, H., Grismer, M. E., & Trajkovic, S. (2011a). Comparative analysis of 31 reference evapotranspiration methods under humid conditions. Irrigation Science, 31(2), 107–117. https://doi.org/10.1007/s00271-011-0295-z DOI: https://doi.org/10.1007/s00271-011-0295-z
Tabari, H., Marofi, S., Aeini, A., Talaee, P. H., & Mohammadi, K. (2011b). Trend analysis of reference evapotranspiration in the western half of Iran. Agricultural and Forest Meteorology, 151(2), 128–136. https://doi.org/10.1016/j.agrformet.2010.09.009 DOI: https://doi.org/10.1016/j.agrformet.2010.09.009
Tabari, H., Nikbakht, J., & Talaee, P. H. (2012). Identification of trend in reference evapotranspiration series with serial dependence in Iran. Water Resources Management, 26, 2219–2232. https://doi.org/10.1007/s11269-012-0011-7 DOI: https://doi.org/10.1007/s11269-012-0011-7
Taheri, M., Bigdeli, M., Imanian, H., & Mohammadian, A. (2025). An overview of evapotranspiration estimation models utilizing artificial intelligence. Water, 17(9), 1384. https://doi.org/10.3390/w17091384 DOI: https://doi.org/10.3390/w17091384
Tikhamarine, Y., Malik, A., Kumar, A., Souag-Gamane, D., & Kisi, O. (2019). Estimation of monthly reference evapotranspiration using novel hybrid machine learning approaches. Hydrological Sciences Journal, 64(15), 1824–1842. https://doi.org/10.1080/02626667.2019.1678750 DOI: https://doi.org/10.1080/02626667.2019.1678750
Vicente-Serrano, S. M., Azorin-Molina, C., Sanchez-Lorenzo, A., Revuelto, J. López-Moreno, J. I., González-Hidalgo, J. C., Moran-Tejeda, E., & Espejo, F. (2014). Reference evapotranspiration variability and trends in Spain, 1961–2011. Global and Planetary Change, 121, 26-40. https://doi.org/10.1016/j.gloplacha.2014.06.005 DOI: https://doi.org/10.1016/j.gloplacha.2014.06.005
Xu, C.Y., Lebing, G., Tong, J., Deliang, C., & Singh, V. P. (2006). Analysis of spatial distribution and temporal trend of reference evapotranspiration and pan evaporation in Changjiang (Yangtze River) catchment. Journal of Hydrology, 327, 81–93. https://doi.org/10.1016/j.jhydrol.2005.11.029 DOI: https://doi.org/10.1016/j.jhydrol.2005.11.029
Yassin, M. A., Alazba, A. A., & Mattar, M. A. (2016). Artificial neural networks versus gene expression programming for estimating reference evapotranspiration in arid climate. Agricultural Water Management, 163, 110–124. https://doi.org/10.1016/j.agwat.2015.09.009 DOI: https://doi.org/10.1016/j.agwat.2015.09.009
Zhang, Y., Liu, C., Tang, Y., & Yang, Y. (2007). Trends in pan evaporation and reference and actual evapotranspiration across the Tibetan Plateau. Journal of Geophysical Research: Atmospheres, 112, D12110. https://doi.org/10.1029/2006JD008161 DOI: https://doi.org/10.1029/2006JD008161
Zuo, D., Xu, Z., Yang, H., & Liu, X. (2012). Spatiotemporal variations and abrupt changes of potential evapotranspiration and its sensitivity to key meteorological variables in the Wei River basin, China. Hydrological Processes, 26(8), 1149–1160. https://doi.org/10.1002/hyp.8206 DOI: https://doi.org/10.1002/hyp.8206
