Evapotranspiration is the most important part of the hydrological cycle, which plays a key role in water resource management, crop yield simulation, and irrigation scheduling. The purpose of this research was to estimate the Reference Evapotranspiration using ‘ panel-data’ models. Panel-data multivariate analysis endows regression analysis with both spatial and temporal dimensions. This study was carried out using weather data of 9 synoptic stations of Khorasan Razavi during 1971-2000. Data were divided randomly into two sub-sets, 75% for model development and 25% for model evaluation. The panel-data models were developed using the monthly mean air temperature and monthly mean wind speed as inputs in order to estimate monthly Reference Evapotranspiration. The results indicated that the two-way fixed effects models were superior. The statistical index (RMSE = 9. 85, MAE = 7. 38 and R2 = 0. 99) revealed the effectiveness of this model. In addition, these results were compared with the results of ordinary least squares regression and Hargreaves-Samani equation which showed the superiority of the panel-data models.

Introduction: Subdaily estimates of Reference Evapotranspiration (ETo) are needed in many applications such as dynamic agro-hydrological modeling. However, in many regions, the lack of subdaily weather data availability has hampered the efforts to quantify the subdaily ETo. In the first presented paper, a physically based framework was developed to desegregate daily weather data needed for estimation of subdaily Reference ETo, including air temperature, wind speed, dew point, actual vapour pressure, relative humidity, and solar radiation. The main purpose of this study was to estimate the subdaily ETo using disaggregated daily data derived from developed disaggregation framework in the first presented paper.Materials and Methods: Subdaily ETo estimates were made, using ASCE and FAO-56 Penman–Monteith models (ASCE-PM and FAO56-PM, respectively) and subdaily weather data derived from the developed dailyto-subdaily weather data disaggregation framework. To this end, long-term daily weather data got from Abadan (59 years) and Ahvaz (50 years) synoptic weather stations were collected. Sensitivity analysis of Penman–Monteith model to the different meteorological variables (including, daily air temperature, wind speed at 2 m height, actual vapor pressure, and solar radiation) was carried out, using partial derivatives of Penman–Monteith equation. The capability of the two models for retrieving the daily ETo was evaluated, using root mean square error RMSE (mm), the mean error ME (mm), the mean absolute error ME (mm), Pearson correlation coefficient r (-), and Nash–Sutcliffe model efficiency coefficient EF (-). Different contributions to the overall error were decomposed using a regression-based method.Results and Discussion: The results of the sensitivity analysis showed that the daily air temperature and the actual vapor pressure are the most significant meteorological variables, which affect the ETo estimates. In contrast, low sensitivity coefficients got for wind speed and the solar radiation. The similar patterns of ETo sensitivity coefficient to the air temperature (SETdo/TA) and the air temperature (TA) showed that the extent of the seasonal variation of SETdo/TA was mainly determined by the TA. Results showed a good agreement between daily and 24h sum of subdaily ETo derived from ASCE-PM (with an EF of 0.990 to 0.994) and FAO56-PM (with an EF of 0.992 to 0.995) models. The results showed a good generalization capability of the disaggregation models to estimate the subdaily ETo for the validation data set (Ahvaz). The 24h sum of subdaily ETo derived from both models underestimated and overestimated the daily ETo in calibration (Abadan) and validation (Ahvaz) data sets, respectively. In case of both models, the daily values of aerodynamic component of ETo were reproduced more efficiently, compared to radiation part. In case of the FAO56-PM model, the goodness of agreement between 24h sum of subdaily and daily values of aerodynamic part of the ETo showed a low sensitivity to variation of the time scale of weather data. With the increase of the time scale of the subdaily weather data, the ability of both models in retrieving the radiation component of the daily ETo was improved. Generally, there was no apparent relationship between the efficiency of the ASCE-PM and FAO56-PM models for retrieving the daily ETo and the time scale of weather data. Results showed that adoption of a smaller time step does not always leads to an improvement in the agreement between 24h sum of subdaily and daily values of ETo. For most of the studied subdaily time scales (1 to 360 min), the FAO56-PM model had better performance in retrieving the daily ETo, compared to the ASCE-PM model.Conclusion: The results of this study showed that the developed disagregation framework was able to estimate the subdaily ETo. In this study, the promising results got in retrieving the daily ETo can be attributed mainly to the high sensitivity of ETo to the air temperature and actual vapor pressure (which were desegregated with a reasonable accuracy) and low sensitivity to the wind speed (which were desegregated with a low accuracy) and the solar radiation (which were disaggregated with a reasonable accuracy). The main reason for the absence of an apparent relationship apparent relationship between the efficiency of the ASCE-PM and FAO56-PM models for retrieving the daily ETo and the time scale of weather data can be attributed to adopted nighttime and daytime criteria in both models which is highly affected by time-scale of weather data and the estimated net long wave radiation.

Irrigated crop is the main component of Iranian agriculture and 93% of the total extractable water resources is used in this sector. In general, water requirements of field crops is estimated by FAO-Penman-Montieth method based on the data collected from well-irrigated stations (Reference stations). However, most of the climatological stations are not located in places where irrigation is practiced and therefore, the use of this data causes an overestimation of Reference evapo transpiration. The purpose of the present study was to survey these overestimations after correction according to temperature data nationwide. Data from 153 synoptic stations was used and amount of annual and seasonal overestimation was calculated. Results showed that overestimation was as large as 68% for Spring-Summer in central and west regions and 65% for Autumn-Winter season in central and southeastern regions. This value for annual evapo-transpiration was 41% in southeastern regions.

In recent years, automatic weather stations have been widely used for recording meteorological data in different time scales. Therefore the accurate estimation of ETo by combination equations can be evaluated using these set of short time scales data. Daily ETo can also be calculated by summation of hourly ETo values. The purpose of this study is to compare the ETo values estimated by hourly and daily data. Totally, 7270 hourly meteorological data obtained from the automated weather Reference station where placed in Shahid Bahonar University of Kerman, Iran during April to December 2005 and January to March 2006. The Penman-Monteith equations proposed by the Food and Agriculture Organization (FAO-56) and American Society of Civil Engineers (ASCE) were used for hourly and daily (24 hours) ETo estimation. The paired t- student test was used for comparison of estimated ETo values by two methods (daily and hourly summation) in each month. The results of this study showed significant differences between ETo values estimated by daily and hourly summation data in both equations at 5 percent level. The hourly summation method overestimated ETo values from 5.8 to 44.6 percent in different months using FAO-56 Penman-Monteith equation and from 7.4 to 47.6 percent using ASCE Penman-Monteith equation. The regression coefficients of correlation equations between the daily and hourly summation method in both combination models were strongly significant.

Estimation of Reference crop potential Evapotranspiration (ETo) is essential in many irrigation projects. Also, concerning the deficit of water reresources in Iran, ETo prediction through using time series has paramount role in future programming. Based on this, monthly ETo values were calculated using monthly weather data up to year 1388 of synoptic stations in Fars province containing Abadeh, Eghlid, Darab, Zarghan, Dorodzan Dam, Shiraz, Fasa and Lar. Then, in each station it was assumed that about 20 percent of last ETo values were not available, and consequently these values were predicted using time series model of SARIMA, and then the predicted and calculated values of ETo in each station were compared with line one: one, separately. The results showed that the same time series model were not appropriate for all stations. Moreover, the results indicated that there were not significant diferences between the predicted and calculated values of ETo in each stations. Therefore, ETo values were predicted up to year 1403 for each station, separately. The results generally indicated an increasing trend in ETo prediction in Fars province.

Evapotranspiration is one of the most important components in the optimization of water use in agriculture and water resources management. In recent years, artificial intelligence methods and wavelet based hybrid model have been used for forecasting of hydrological parameters. In the present study, applications of the adaptive network-based fuzzy inference system (ANFIS) and Wavelet-ANFIS models to forecast weekly Reference Evapotranspiration at the synoptic stations of Tabriz, Ahvaz, Bandar Abbas, and Ramsar were investigated. For this purpose, a 20-year statistical period (1990-2009) was considered: 15 years (1990-2004) for training and the final five years (2005-2009) for testing the various models. Various combinations of input data (various lag times) and different kinds of mother wavelets were evaluated. Results showed that, compared to the ANFIS model, the hybrid model Wavelet-ANFIS had greater ability and accuracy in forecasting weekly Evapotranspiration at all of the four synoptic stations. Moreover, the use of yearly lag times in the ANFIS model increased its accuracy. However, in the Wavelet-ANFIS, yearly lags not only did not increase the accuracy of the Wavelet-ANFIS model, but also reduced its accuracy in some cases. Investigation of various kinds of mother wavelets also indicated that the Meyer wavelet was the most suitable mother wavelet for forecasting weekly Reference Evapotranspiration. Results of this study can also be used for irrigation scheduling in the studied regions.

Evapotranspiration is the main component of hydrologic cycle and has an important role in crop water requirement estimations, water balances studies, and water resource management. There are a lot of direct and indirect methods to estimate Reference crop Evapotranspiration, but each has some limitations. For example, limitations that can be mentioned for direct measuring are the insufficient precision in measuring devices and the scale problems. An indirect method like Penman-Monteith on the other hand needs a lot of daily climatic parameters. This research tried to use selforganizing maps as an unsupervised artificial neural network method to predict Evapotranspiration by minimum meteorological data input. Based on fuzzy clustering indices, Evapotranspiration values in the study area, Mashhad plain, are divided into two clusters with low and high ETo coincided with the climate of the area. Also, in order to validate the model, statistical indices containing root mean square error, determination coefficient, and Nash–Sutcliffe model efficiency coefficient are used and the results are compared with the experimental models output. The results showed that even the simplest SOM model which employs mean temperature and maximum sunshine duration as input have less errors compared to the experimental equations.

Estimation of Reference Evapotranspiration (ET0) is essential to determine water requirements of crops. In other words, to regionalize ET0 to a large area, some interpolation methods should be used (Goovaerts, 1997). A key parameter which may influence the proper performance of interpolattion methods, is the sequence of ET0 estimation process (Mardikis et al., 2005; Vilanova et al., 2012). That is why using some auxillary variables cross correlated with the main variable, could significantly improve the accuracy of interpolation methods. Therefore, this study aims to analyze the estimation process sequences while investigating spatial variability of annual and monthly ET0 in Iran. A comprehensive comparison of spatial interpolators is performed. Elevation is also used as a secondary variable in multivariate methods.

Arid regions are very diverse in terms of their land forms, soils, fauna, flora, and water resources. However, aridity is the one binding element to all arid lands and is the best indicators for definiation of the climate state in each region. Management of natural resources in an ecologically sustainable manner, especially in arid lands, require to identify and which areas are experiencing drought conditions and how severe the conditions. Linear momentum and cluster analysis techniques are useful in the process of determining of climatic homogeneous regions. The main objects of this research is to determine climatic homogeneous regions using UNEP aridity index applying cluster analysis and linear momentum techniques in eastern Iran. First, by using the meteorological data of 20 synoptic stations in the eastern part of the country (Iran), Reference Evapo-Transpiration were calculated using the FAO-Penman Montith method for the period of 36 years. Figure 1 shows distribution map of the selected synoptic stations in Iran. Then, an useful "representation" of aridity i.e. UNEP aridity index were calculated in each stations. The UNEP aridity index employs a ratio of annual precipitation to potential Evapotranspiration (P/PET). This aridity index includes evaporation from the soil and transpiration from the vegetation from a specific region in a given time interval. Under the UNEP classification, drylands are characterized by a P/PET of between 0.05 and 0.65 as shown in Table 1. In the next step, drought homogeneous stations were selected using cluster analysis technique. Then, the most important parameters which has affects on UNEP aridity index, were determined using factor analysis techniques. Finally, statistical distribution and probability of occurrence and homogeneous of stations were characterized in R software. Results show that combining the linear momentum and cluster analysis techniques are efficiency to identify homogeneous regions. It can be concluded that it is appropriated to determine homogeneous regions using cluster analysis technique initially, and then, it can be approved and accepted by using linear momentum technique.

Appropriate estimation of Reference Evapotranspiration (ET0) has always been challengeas the fundamental parameter to estimate crop water requirement (ETc) in places with a data limitation forresearchers of soil and water resources.In this research, (ET0) had been estimated using the ERA5 reanalysis dataset and the by Penman–Monteith FAO-56 (PM). In the next step, the result was evaluated at the location of meteorological stations in the catchment of Karoon.Climatic data to compute the (ET0) from 26 synoptic meteorological stations of the basin and ERA5 dataset were obtained. Then, daily ET0 were calculated from 1390 to 1400.At first, variables including Tmax, Tmean, Tdew, Tmin, solar radiation, and wind speed were compared with the data of the meteorological station.The results showed that, the ERA5 dataset showed provide reliable temperature parameters and solar radiation estimates (i.e., normalized root mean square error (nRMSE) of < 30%)at the majority of cases. Although this dataset showed high nRMSE in all stations for wind speed values. These comparison between ET0 from observation and ERA5 dataset shown that at the 17 station from 26 station nRMSE less than 30 percent. Overall, the use of ERA5 dataset and estimation of ET0 in places that are faced with data scarcityfor decision- making in new planning can be a useful and reliable tool.