poster - International Conference of Agricultural Engineering

location of the study, López-Urrea et al. (2006) indicated that FAO56 P-M equation is the
most accurate method to estimate ETo in semiarids conditions. For these reasons, this
equation has been chosen to calibrate the Hargreaves equation.
This paper aims to calibrate the Hargreaves equation for estimating ETo, comparing it with
daily average estimations obtained through the FAO56 P-M equation in a zone of the
Southeast of Spain.
2. Materials and Methods
Weather data sets were obtained from agrometeorological stations in the provinces of Murcia
and Albacete (Spain). These stations are integrated in the irrigation advisory service in the
Spanish autonomous regions of Murcia and Castilla-La Mancha. Data were daily registered
during period 2005-2009.
From these climatic data, and by using the Penman-Monteith FAO56 equation, ETo was
calculated (Allen et al., 1998). The used Penman Monteith equation is the next:
ET
0
900
0,408· ∆(Rn
− G) + γ u2(e
=
T + 273
∆ + γ(1+
0.34·u )
Where:
ET o = reference evapotranspiration (mm·day -1 )
Rn = crop surface net radiation (MJ·m -2 day -1 )
Ra = extraterrestrial radiation (mm·day -1 )
G = soil heat flux (MJ·m -2 día -1 )
T = average air temperature at 2 m height (ºC)
u2 = wind speed at 2 m height (m·s -1 )
e s = saturated vapor pressure (kPa)
e a = real vapor pressure (kPa)
e s - e a = vapor pressure deficit (kPa)
∆ = slope of the vapor pressure curve (kPa·ºC -1 )
γ = psicrometric constant (kPa·ºC -1 ).
2
s
− e
a
)
Moreover, and by using the same climatic data, estimations of ETo values by means of
Hargreaves equation were obtained (Hargreaves and Samani, 1985).
ET
0.5
0
= 0.0135·(t
ave
+ 17.78)·R
0·KT·(t
max
− tmin)
ET o = referente evapotranspiration (mm·day -1 )
t ave = daily average temperature (ºC)
R o = extraterrestrial radiation (MJ·m -2 day -1 )
KT = coefficient (0.162 for inner regions and 0.19 for coastal regions)
In order to transform MJ·m -2 day -1 to mm·day -1 , the value has to be multiplied by 0.408.
Several comparisons were obtained through the simple linear regression analysis technique
and a set of statistics. These statistics are:
Root mean square error (RMSE):
⎡
⎢
RMSE = ⎢
⎢
⎢⎣
n
∑
i=
1
2 ⎤
(yi
− xi)
⎥
⎥
n0
⎥
⎥⎦
0,5