Abstract
Problem statement: Pan Evaporation has extensively been used for esti mating reference Evapotranspiration (ETo) due to its simplicity, low cost, ease of data interpretation and application and suitability for locations with limited availability of meteorological data. With this method, the pan coefficient (Kp) is a key element to be determined as well as the pan Evaporation (Ep) data. Approach: This study presents the development of new pan coe fficient (Kp) equations for Class A pan and Colorado sunken pan under green and dry fetch conditions by using M5 model tree based on soft computing technique. The Kp values were taken from FAO-24 Kp table for the development of Kp equations. Results: The results of the study indicate the usefulness a nd applicability of the M5 model tree in developing Kp equations. Those proposed equations based on the M5 model tree gave better performance in estimating Kp values than the previous Kp equations as well as the new Kp equations developed by indicator regression techniq ue. Conclusion: M5 model tree gave more accuracy in estimating Kp values. The new proposed Kp equations can be reliably used.
Highlights
Accurate and reliable reference Evapotranspiration (ETo) estimation is an essential hydrological parameter for optimum water resources planning and farm irrigation scheduling
Three Kp equations were developed apply M5 model tree for developing new pan based on the original data table (Allen and Pruitt, coefficient equations for class A and Colorado sunken 1991; Orang, 1998; Grismer et al, 2002)
Allen (1998) proposed two Kp equations for Colorado sunken pans surrounded by green and dry M5 model tree: M5 model tree was first introduced by fetch conditions
Summary
Accurate and reliable reference Evapotranspiration (ETo) estimation is an essential hydrological parameter for optimum water resources planning and farm irrigation scheduling. It is, interested to investigate the Wallender (1998) applied indicator regression performance of M5 model tree for pan coefficient technique, which is widely accepted approach for estimation. Three Kp equations were developed apply M5 model tree for developing new pan based on the original data table The. Pruitt (1991) used stepwise and multivariate general indicator regression were applied to linear regression procedures for FAO Class A pan determine pan coefficient equations for Class A pan placed in short green cropped area. Orang (1998) used placed in dry fallow area and Colorado sunken pan linear regression technique and interpolation between placed in short green cropped area and dry fallow area. Proposed Kp equation for the FAO Class A pan placed in dry fallow area
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