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Adopting methodologies utilizing exogenous data from ancillary stations for determining crop water requirement is a suitable approach to exempt local shortcomings due to the lack of meteorological data/stations. Meanwhile, soft computing techniques might be suitable tools to be used with such data management scenarios. The present paper aimed at evaluating the generalizability of the gene expression programming (GEP) technique for estimating reference evapotranspiration (ET 0 ) through cross-station assessment and exogenous data supply, using data from Turkey and Iran. The GEP-based models were established and learnt using data from 10 stations in Turkey, and then the developed models were tested (validated) in 18 stations of Iran with considerable latitude differences. Different time periods (beginning and the end of time series) were selected for the training and testing stations so that there was no overlap among the dates of the events in both the groups. A comparison was also performed between the GEP models and the corresponding commonly used empirical equations. The obtained results revealed that the generalized GEP models presented promising outcomes in simulating daily ET 0 values when they were trained and tested in quite distant stations with different chronological periods of the applied parameters. The performance accuracy of the empirical equations calibrated using exogenous data was reduced in comparison with their original (non-calibrated) versions. Further, although the generalization ability of the GEP models was reduced when the climatic context of the training-testing stations was different, the overall performance accuracy of those models was higher than those of the commonly used classic empirical equations.

Evaporation, as the main source of water loss from closed lakes, makes a significant contribution to the water balance equation of the lake and can lead to changes in the chemical composition thereof. The objective of the study was to develop an equation for estimation of evaporation from the water surface with different depths and concentrations. To that end, 48 barrels were used to model evaporation at 6 different depths and 8 different concentrations of salinity. The experiments have been conducted in the same meteorological condition for all the barrels near the Urmia Lake. Data were collected in March 1, 2019, to Aug 31, 2019. Different equations fitted to data for each concentrations of salinity separately with different depths, and the equations with the least errors were selected. A model was then developed for the estimation of evaporation, considering the effect of salinity and depth, and the results were compared with daily measurements. The results were evaluated using the root mean square error (RMSE), correlation coefficient (CC), and Nash-Sutcliffe efficiency coefficient (NS). The results indicated that evaporation (Horizontal row) from water surface with high concentrations of salinity to low concentrations of salinity in different depths had an incremental trend. However, it can be seen in the vertical row that evaporation increased from low depth to high depth, and then decreased at a certain depth (120 cm) while the maximum evaporation rate belonged to 90-cm barrels for each concentration of salinity (in the vertical and horizontal row). At the end, the comparison of evaporation computed from the model and measured data showed that the model estimated evaporation at different depths and concentrations of salinity satisfactorily.