Modeling Earth Systems and Environment

, Volume 3, Issue 4, pp 1273–1283 | Cite as

A comparative evaluation of four evapotranspiration models based on Eddy Covariance measurement over a grass covered surface in Ile-Ife, Southwestern Nigeria

  • O. A. Babatunde
  • O. E. Abiye
  • L. A. Sunmonu
  • A. P. Olufemi
  • M. A. Ayoola
  • O. E. Akinola
  • E. O. Ogolo
Original Article


An Eddy Covariance (EC) system was set up to measure vertical transport of water vapour fluxes over a grass covered surface at a site located within Obafemi Awolowo university campus (7°33N, 4°35E) southwestern Nigeria between 31st of May and 14th of June, 2013. The EC measurement was used as a benchmark to evaluate the performances of four evapotranspiration models (the standardized FAO-56 Penman–Monteith (PM), Priestly-Taylor (PT), Makkink (MK) and Turc) which were employed to estimate evapotranspiration in the study area. The ET estimates from the models showed similar diurnal variation with the direct measurement from EC technique with daytime mean (mm/day) ranging between 0.79 and 2.37 for EC, 1.02–3.75 for PM, 1.58–5.46 for PT, 1.13–4.02 for MK and 1.21–4.27 for Turc. Based on regression analysis and standard error of estimates (SEE), the performances of the models ranked from PM (R = 0.96, slope, b = 0.687, SEE = 0.049), MK (R = 0.97, b = 0.569, SEE = 0.395), Turc (R = 0.97, b = 0.539, SEE = 0.553) to PT (R = 0.97, b = 0.386, SEE = 1.32). Recalibration of models coefficients using least square method showed significant improvement in their estimates and thus, the models were found very reliable for predicting ET which is a relevant parameter for irrigation scheduling.


Evapotranspiration Eddy covariance FAO-56 Penman–Monteith Irrigation 



The authors acknowledge and appreciate the support in terms of meteorological sensors and guidance provided by the Atmospheric Physics Research Group (APRG, headed by Prof. O.O. Jegede), Physics and Engineering Physics Department, Obafemi Awolowo University, Ile-Ife, Nigeria.


  1. Allen RG, Pereira LS, Reas D, Smith M (1998) Crop evapotranspiration guidelines for computing requirements. (FAO irrigation and Drainage paper 56. Food and Agric Organization, Rome, p 326)Google Scholar
  2. Allen RG, Pereira LS, Howell TA, Jensen ME (2011) Evapotranspiration information reporting 1: Factors governing measurement accuracy. Agric Water Manag 98(6):899–920CrossRefGoogle Scholar
  3. Ayoola MA, Sunmonu LA, Ajao IA, Jegede OO (2014) Measurement of net all-wave radiation at a tropical location, Ile-Ife, Nigeria. Atmosfera 27(3):305–315Google Scholar
  4. Baldocchi D, Falge E, Gu L, Olson R, Hollinger D, Running S, Anthoni P, Bernhofer C, Davis K, Evans R, Fuentes J, Goldstein A, Katul G, Law B, Lee X, Malhi Y, Meyers T, Munger W, Oechel W, Pilegaard K, Schmid P, Valentini R, Verma S, Vesala T, Wilson K, Wofsy S (2001) FLUXNET: a new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities. Bull Am MeteorolSoc 82:2415–2434CrossRefGoogle Scholar
  5. Carrillo-Rojas G, Silva B, Córdova M, Célleri R, Bendix J (2016) Dynamic mapping of evapotranspiration using an energy balance-based model over an Andean Páramo catchment of Southern Ecuador. Remote Sens 8:160. doi: 10.3390/rs8020160 CrossRefGoogle Scholar
  6. Daoo VJ, Panchal NS, Sunny F, Raj VV (2004) Scintillometric measurements of daytime atmospheric turbulent heat and momentum fluxes and their application to atmospheric stability evaluation. Exp Therm Fluid Sci 28(4):337–345CrossRefGoogle Scholar
  7. Dingman SL (1994) Physical hydrology. Prentice hall, Upper saddle RiverGoogle Scholar
  8. Doorenbos J, Pruitt WO (1977) Crop water requirements, irrigation and drainage paper No. 24. Food and Agriculture organization, RomeGoogle Scholar
  9. Fontenot R (2004) An evaluation of reference ET models in Louisiana. thesis, Baton Rouge, LA: Louisiana state University, Agricultural and Mechanical College, Dept. of Geography and Anthropology. Google Scholar
  10. Fox DG (1981) Judging air quality model performance:A summary of the AMS workshop on Dispersion Model Performance. Bull Am Meteorol soc 62:599–609CrossRefGoogle Scholar
  11. Hansen VE, Israelsen OW, Stringham GE (1980) Irrigation principles and practices 4th edn. Wiley, New YorkGoogle Scholar
  12. Hargreaves GH, Samani ZA (1985) Reference crop evapotranspiration from temperature. Appl Eng Agric 1(2):96–99CrossRefGoogle Scholar
  13. Houshang G, Vahid R, Erfan K, Hossein M (2012) Time and place calibration of the Hargreaves equation for estimating monthly reference evapotranspiration under different climatic conditions. J Agric Sci 4:3Google Scholar
  14. Howell TA, Schneider AD, Jensen ME (1991). History of Lysimeter design and use for evapotranspiration measurements. In: Howell RGTA, Pruitt WO, Walter IA, Jensen ME (eds.) Lysimeters for evapotranspiration and environmental measurements, pp 1–9. American Soc Civil Engineers, New YorkGoogle Scholar
  15. Irmak S, Haman DZ, Jones JW (2002) Evaluation of class A pan coefficients for estimating reference evapotranspiration in humid location. J Irrig Drain Eng 128(3):153–159CrossRefGoogle Scholar
  16. Jegede OO, Ogolo EO, Aregbesola TO (2006) Estimating net radiation using routine meteorological data at a tropical location in Nigeria. Int J Sustain Energy 25(2):107–115CrossRefGoogle Scholar
  17. Jensen ME, Burman RD, Allen RG (1990) Evapotranspiration and Irrigation water requirements. American Society of civil Engineers, Manuals and Report on Engineering practice No. 70, New YorkGoogle Scholar
  18. Krause P, Boyle DP, Base F (2005) Comparison of different efficiency for hydrological model assessment. Adv Geosci 5:89–97CrossRefGoogle Scholar
  19. Licciardello F, Zema DA, Zimbone SM, Bingner RL (2007) Runoff and soil erosion evaluation by the AnnAGNPS model in a small Mediterranean Watershed. Transac ASAE 50(5):1585–1585Google Scholar
  20. Loague K, Green RE, 1991. Statistical and Graphical methods for evaluating solute transport models: overview and application. J Contam Hydrol JCOHE6 7(112): P51–73CrossRefGoogle Scholar
  21. Makkink GF (1957) Testing the Penman Formula by means of lysimeters. J Inst Water Eng 11(3):277–288Google Scholar
  22. Martano P (2015) Evapotranspiration estimates over non-homogeneous mediterranean land cover by a calibrated “Critical Resistance”. Approach Atmos 6:255–272. doi: 10.3390/atmos6030255 Google Scholar
  23. Maselli F, Papale D, Chiesi M, Matteucci G, Angeli L, Raschi A, Seufert G (2014) Operational monitoring of daily evapotranspiration by the combination of modis ndvi and ground meteorological data: Application and evaluation in central italy. Remote Sens Environ 152:279–290CrossRefGoogle Scholar
  24. Odi-Lara M, Campos I, Neale CMU, Ortega-Farías S, Poblete-Echeverría C, Balbontín C, Calera A (2016) Estimating Evapotranspiration of an Apple Orchard Using a Remote Sensing-Based Soil Water Balance. Remote Sens 8:253. doi: 10.3390/rs8030253 CrossRefGoogle Scholar
  25. Panda RK, Kashyap PS (2001) Evaluation of evapotranspiration estimation methods and development of crop coefficients for potato crop in a sub-humid region. Elsevier Agric Water Manag 50(1):9–25 CrossRefGoogle Scholar
  26. Pereira LS, Perrier A, Allen RG, Alves I (1999) Evapotranspiration: concepts and future trends. J Irrig Drain Eng ASCE 131(1):37–58Google Scholar
  27. Priestly CHB, Taylor RJ (1972) On the assessment of surface heat flux and evaporation using large-scale parameters. Monthly Weather Rev 100(2):81–92 CrossRefGoogle Scholar
  28. Tabari H, Talaee PH (2011) Local calibration of the Hargreaves and Priestly-Taylor equations for estimating reference ET in Arid and cold climates of Iran based on the Penman-monteith. J Hydrol Eng 16(10):837–845CrossRefGoogle Scholar
  29. Thornthwaite CW (1948) An approach towards a rational classification of climate. Geogr Rev 38:55–94CrossRefGoogle Scholar
  30. Turc L (1961) Evaluation des besoinseneaud’irrigation evapotranspiration potentielle, formuleclimatiquesimplifee et mise a jour. (In French). Ann Agron 12(1):13–49Google Scholar
  31. Watson I, Burnett AD (1995) Hydrology, an environmental approach. CRC press, Boca RatonGoogle Scholar
  32. Willmott CJ (1982) Some comments on the evaluation of model performance. Bull Am Meteorol Soc 63(11):1309–1313CrossRefGoogle Scholar
  33. Willmott CJ (1981) On the Validation of Models. Phys Geogr 2:184–194Google Scholar
  34. Willmott CJ, Ankleson SJ, Davis RE, Feddema JJ, Klink KM, Legates DR (1985) Statistics for the evaluation and comparison of models. J Geophys Res 90(5):8995–9005CrossRefGoogle Scholar
  35. Zhang H, Gorelick SM, Avisse N, Tilmant A, Rajsekhar D, Yoon J (2016) A new temperature-vegetation triangle algorithm with variable edges (TAVE) for satellite-based actual evapotranspiration estimation. Remote Sens 8:735. doi: 10.3390/rs8090735 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • O. A. Babatunde
    • 1
  • O. E. Abiye
    • 2
  • L. A. Sunmonu
    • 1
  • A. P. Olufemi
    • 3
  • M. A. Ayoola
    • 1
  • O. E. Akinola
    • 1
  • E. O. Ogolo
    • 4
  1. 1.Department of Physics and Engineering PhysicsObafemi Awolowo UniversityIle-IfeNigeria
  2. 2.Centre for Energy Research and Development (CERD)Obafemi Awolowo UniversityIle-IfeNigeria
  3. 3.Department of PhysicsUniversity of Medical SciencesOndoNigeria
  4. 4.Department of PhysicsFederal University of TechnologyAkureNigeria

Personalised recommendations