Author details

method. This result can be explained by the low propagation of small variations or errors in the calculation of ET using the scintillometer method. However, in the FAO-PM56 method with few input variables, small variations in the parameters for the calculation of ET lead to

Two methods to acquire ET from a crop field (Maize) are presented. One of them is based on atmospheric turbulence data acquired with a laser beam scintillometric method and another one is based on the calculation with limited meteorological data using the FAO-PM56 method. The comparison between the two ET measurements shows a difference in the final result. The measurements with micro-meteorological parameters are lower than with scintillometric parameters leading to an underestimation of the real ET. This is an important result because farmers must accommodate in advance their crop water demand to irrigation requirements. An overestimation of ET can lead to a deficit of irrigation water, and on the contrary, a low estimation of ET can lead to water waste. The measurements have shown a lower measured value of ET with the FAO-PM56 method. Moreover, the FAO-PM56 method for obtaining ET is more sensible to a small error in the acquisition of the temperature. Then, while the scintillometer measurements are representative of the turbulent fluctuations along the whole beam path, the FAO-PM56 measurements are typically representative measurements of localized areas

Finally, using artificial neural network, evapotranspiration forecast for short-term near-future is obtained. In addition, it is presented results for two different input training data, and it is showed that evapotranspiration data based on scintillometric data acquisition are more reliable for forecasting. An optimum value was found in the number of days of training data to obtain the best forecast. In this case also, evapotranspiration with scintillometric data increases

near the respective different meteorological acquisition instruments.

ETP solution from a set of variable values with a Mathematica script:

�ð1:35�ðMin½RS=ðð0:75 þ 0:00002�HÞ

et½TA\_, WD\_, HD\_, RS\_, H\_, DAY\_, Y\_, LAT\_� :

¼ ð0:408�ð4098�ð 0:611�Exp½17:27�TA=ðTA þ 237:3Þ�Þ=ðTA þ 237:3Þ^2Þ�ðð0:77�RSÞ � ðð4:903�10^ð�9Þ�ðTA þ 273Þ^4

�ð24�60=Pi�ð0:0820Þ�ð1 þ 0:033�Cos½2�Pi � DAY=Y�Þ

�DAY=Y � 1:39�Þ�Þ=ðMax½1 � Tan½ð3:14=180�LATÞ�^2�

�ð0:34 � 0:14�Sqrt½ð 0:611�Exp½17:27�TA=ðTA þ 237:3Þ�Þ�HD�ÞÞ

�ðð\½Pi�=2 � ArcTan½ð�Tan½ð3:14=180�LAT��Tan½ð0:409�Sin½2�\½Pi�

the number of predicting reliable days.

Appendix A. Supplementary data

larger uncertainties.

42 Current Perspective to Predict Actual Evapotranspiration

4. Conclusion

Antonin Poisson<sup>1</sup> , Angel Fernandez1,3, Dario G. Gomez2 , Régis Barillé<sup>1</sup> \* and Benoit Chorro<sup>4</sup>

\*Address all correspondence to: regis.barille@univ-angers.fr

