poster - International Conference of Agricultural Engineering

temperature-based (e.g., Thornthwaite, 1948; Blaney and Criddle, 1950) equations. The
Penman-Monteith equation is the most frequently used and recommended model
(Doorenbos and Pruitt, 1977; Allen et al., 1989; Jensen et al.1990) among actual
evapotranspiration (ET) models taking into account both meteorological and physiological
crop variables. Besides variables expressing the thermodynamic state of the atmosphere, it
contains two kinds of parametric data: bulk canopy resistance, rc and aerodynamical
resistance ra. The first one is the resistance that the canopy opposites to the diffusion of
water vapour from inner leaves toward the atmosphere and is influenced by biological,
climatological and agronomical variables. The second one is the aerial boundary layer
resistance and describes the role of the interface between canopy and atmosphere in the
water vapour transfer. The success of the Penman-Monteith evapotranspiration estimate
depends on the modelling of these two parameters. A major disadvantage to apply the
Penman-Monteith method is its relatively high data demand. The method requires, apart
from site location, air temperature, wind speed, relative humidity, and shortwave radiation
data. The number of meteorological stations where all of these parameters are observed is
limited in many areas, especially in developing countries (Droogers and Allen, 2002). The
American Society of Civil Engineers (ASCE) established a Task Committee on
‘Standardization of Reference Evapotranspiration Calculation’ (Allen et al., 2000; Itenfisu et
al., 2003). This Committee recommended the use of the ASCE–Penman–Monteith method,
as simplified by FAO Paper No. 56 (Allen et al., 1998), to quantify the reference ET. Some of
the most important advantages of using a standardized ET o equation are establishing a
common methodology for using and evaluating ET o estimates (Allen et al., 2000) and the
possibility of enhancing the transferability of K c values under different conditions (ASCE-
EWRI, 2005). To understand the characteristics of this model, we must first understand each
variable, and then know its relative role in the model. However, to understand the relative
role of each variable requires a sensitivity analysis. To understand the relative role of each
climate variable associated with computing ET o , sensitivity analysis is required (Saxton,
1975). Results of these analyses make it possible to determine the accuracy required when
measuring climatic variables used to estimate ET o (Irmak, et al., 2006). By definition,
sensitivity analysis is the study of how the variation in the output of a model can be
apportioned, quantitatively or qualitatively, to variation in the model parameters (Saltelli et
al., 2004). A sensitivity analysis shows the effect of change of one factor on another (Mc
Cuen, 1973).
If the change of the dependent variable of an equation is studied with respect to change in
each of several independent variables, the sensitivity coefficient will show the relative
importance of each of the variables to the model solution. In the past, several papers of the
sensitivity of Penman-Monteith ET o model have been devoted using single weather stations
and different input and parametric data. Saxton (1975) derived sensitivity coefficients by
differentiating the combination terms for the Penman (1948) method with respect to each
variable. Results showed that the equation was most sensitive to net radiation. Beven (1979)
carried out the most complete sensitivity analysis of a Penman-Monteith model for reference
grass and forest in three sites of southern England, with respect to both climatic and
parametric data. His study was limited to a humid climate, where the crops were generally in
good water status conditions. He found that for dry canopy conditions, the sensitivity of
Penman-Monteith estimates of ET o to differ input data and parameters is very dependent on
the values of the aerodynamic and canopy resistance. The results of that analysis, in spite of
their fundamental importance to the following research, were not completely applicable to
Penman-Monteith estimation under different climates. Piper (1989) showed that errors in
measurement of sunshine hours, wind speed and wet bulb temperature had the same
relative effect on estimated ET o . In the same context, Ley et al. (1994) conducted sensitivity
analysis for the Penman-Wright ET o model (same as Penman-Kimberly) to errors in
parameters and weather data using a factor perturbation simulation approach for
Washington State. This model was most sensitive to the error in maximum and minimum air
temperatures. Rana and Katerji (1998) analyzed the sensitivity of the original Penman-
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