Authors :
Manikumari Nagappan; Aarthika Kudiyarasumani; Swetha Jayamurugan
Volume/Issue :
Volume 10 - 2025, Issue 3 - March
Google Scholar :
https://tinyurl.com/3jzs6twd
Scribd :
https://tinyurl.com/2medfhbj
DOI :
https://doi.org/10.38124/ijisrt/25mar129
Google Scholar
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Note : Google Scholar may take 15 to 20 days to display the article.
Abstract :
This research paper presents a comparative analysis of reference evapotranspiration (ET0) estimation using the
Hargreaves and Penman-Monteith equations for Annamalai Nagar, a region characterized by a humid climate. Accurate
estimation of ET0 is vital for effective water resource management and agricultural planning. The study highlights the
limitations of the Hargreaves equation, which relies solely on temperature and extraterrestrial radiation, resulting in an
overestimation of ET0 at 12.08 mm/day. In contrast, the Penman-Monteith equation, incorporating temperature, wind speed,
relative humidity, and net radiation, estimated ET0 at 4.7 mm/day. The substantial difference arises from the Hargreaves
model's insensitivity to humidity and wind, making it less suitable for humid regions. The Penman-Monteith method,
recommended by FAO-56, provides a more comprehensive and accurate approach to ET0 estimation. This study emphasizes
the importance of selecting appropriate models based on local climatic conditions and suggests further research on
integrating real-time meteorological data and machine learning techniques to enhance ET0 predictions.
Keywords :
Reference Evapotranspiration (ET0), Hargreaves Equation, Penman-Monteith Equation, Humid Climate, Annamalai Nagar, Water Resource Management, Agricultural Planning, Model Comparison, FAO-56, Meteorological Data, Wind Speed, Relative Humidity, Temperature and Radiation, Machine Learning Integration, Climate-Specific Models.
References :
- 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). FAO.
- Hargreaves, G. H., & Samani, Z. A. (1985). Reference crop evapotranspiration from temperature. Applied Engineering in Agriculture, 1(2), 96–99.
- Monteith, J. L. (1965). Evaporation and environment. Symposia of the Society for Experimental Biology, 19, 205–234.
- Smith, M. (1992). CROPWAT: A computer program for irrigation planning and management (FAO Irrigation and Drainage Paper No. 46). FAO.
- 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.
- Shahidian, S., et al. (2013). Comparative study of different methods for estimating reference evapotranspiration in Portugal. Water Resources Management, 27(10), 3115–3131.
- Jensen, M. E., Burman, R. D., & Allen, R. G. (1990). Evapotranspiration and irrigation water requirements (ASCE Manuals and Reports on Engineering Practice No. 70). American Society of Civil Engineers.
- Tabari, H. (2010). Evaluation of reference crop evapotranspiration equations in various climates. Water Resources Management, 24(10), 2311–2337.
- Irmak, S., et al. (2003). Reference evapotranspiration with hourly and daily data: III. Model comparisons. Journal of Irrigation and Drainage Engineering, 129(6), 442–457.
- Trajkovic, S. (2007). Hargreaves versus Penman-Monteith under humid conditions. Journal of Irrigation and Drainage Engineering, 133(1), 38–42.
- Kumar, M., & Reddy, M. J. (2018). Reference evapotranspiration modeling using soft computing techniques. Theoretical and Applied Climatology, 131(3-4), 1149–1162.
- Lu, J., Sun, G., McNulty, S. G., & Amatya, D. M. (2005). A comparison of six potential evapotranspiration methods for regional use in the southeastern United States. Journal of the American Water Resources Association, 41(3), 621–633.
- López-Urrea, R., et al. (2006). Testing evapotranspiration equations using lysimeter observations in a semiarid climate. Agricultural Water Management, 85(1-2), 15–26.
- Donohue, R. J., Roderick, M. L., & McVicar, T. R. (2010). Can dynamic vegetation information improve the accuracy of Budyko’s hydrological model? Journal of Hydrology, 390(1-2), 23–34.
- Yoder, R. E., Odhiambo, L. O., & Wright, W. C. (2005). Evaluation of methods for estimating daily reference crop evapotranspiration at a site in the humid southeast United States. Applied Engineering in Agriculture, 21(2), 197–202.
This research paper presents a comparative analysis of reference evapotranspiration (ET0) estimation using the
Hargreaves and Penman-Monteith equations for Annamalai Nagar, a region characterized by a humid climate. Accurate
estimation of ET0 is vital for effective water resource management and agricultural planning. The study highlights the
limitations of the Hargreaves equation, which relies solely on temperature and extraterrestrial radiation, resulting in an
overestimation of ET0 at 12.08 mm/day. In contrast, the Penman-Monteith equation, incorporating temperature, wind speed,
relative humidity, and net radiation, estimated ET0 at 4.7 mm/day. The substantial difference arises from the Hargreaves
model's insensitivity to humidity and wind, making it less suitable for humid regions. The Penman-Monteith method,
recommended by FAO-56, provides a more comprehensive and accurate approach to ET0 estimation. This study emphasizes
the importance of selecting appropriate models based on local climatic conditions and suggests further research on
integrating real-time meteorological data and machine learning techniques to enhance ET0 predictions.
Keywords :
Reference Evapotranspiration (ET0), Hargreaves Equation, Penman-Monteith Equation, Humid Climate, Annamalai Nagar, Water Resource Management, Agricultural Planning, Model Comparison, FAO-56, Meteorological Data, Wind Speed, Relative Humidity, Temperature and Radiation, Machine Learning Integration, Climate-Specific Models.