Exact estimation of EVAPOTRANSPIRATION is an important parameter in water cycle, study, design and management of irrigation systems. In this study, the efficiency of intelligent models such as fuzzy rule base, fuzzy regression and Artificial Neural Networks for estimating daily EVAPOTRANSPIRATION has been examined and the results are compared to real data measured by lysimeter on the basis of a grass reference crop. Using daily climatic data from Ekbatan station in Hamadan in western Iran, including maximum and minimum temperatures, maximum and minimum relative humidities, wind speed and sunny hours, EVAPOTRANSPIRATION was estimated by the aforementioned intelligent models. The predicted EVAPOTRANSPIRATION values from fuzzy rule base, fuzzy linear regression and artificial neural network provided root mean square error (RMSE) of 0.72, 0.86 and 0.74 mm/day and determination coefficient (R2) of 0.88, 0.86 and 0.84, respectively. The fuzzy rule base was hence found to be the most appropriate method employed for estimating EVAPOTRANSPIRATION.

Monitoring the spatial and temporal variations of EVAPOTRANSPIRATION is crucial for irrigation management and the crop water requirement, especially in arid and semi-arid areas. The purpose of the present study is to estimate the actual EVAPOTRANSPIRATION using the SEBAL algorithm and compare it with the FAO 56 standard EVAPOTRANSPIRATION to determine pistachio orchards under drought stress in Yazd province. To do so, Landsat 8 satellite images time series with 15 images in 2015 were used. At first, actual EVAPOTRANSPIRATION was calculated in 15 days of pistachio phenology and then by summation of EVAPOTRANSPIRATION in 15 days, total EVAPOTRANSPIRATION was determined in four main stages of pistachio phenology covering the whole period of annual growth. The FAO 56 standard EVAPOTRANSPIRATION was also obtained by using the KC-NDVI relationship as the standard for comparing with actual EVAPOTRANSPIRATION. Based on the results, SEBAL algorithm has an acceptable capability to determine the EVAPOTRANSPIRATION rate in the study area. However, due to lack of valid Lysimeter data in the study area, It was not possible to validate the results of the SEBAL algorithm. But comparing the results with the FAO 56 standard method showed that the two methods are in good agreement with each other. In average, the coefficient determination, RMSE and MAE between the results of SEBAL algorithm and FAO KC-NDVI approach were 0.8, 16.7 mm and 14.5 mm, respectively, for the 15-day of pistachio phenology stages. The average of actual and standard EVAPOTRANSPIRATION rates during a pistachio growing season at the study area were 950 and 1086 mm, respectively. Comparison of actual and standard EVAPOTRANSPIRATION shows that in most of the study area actual EVAPOTRANSPIRATION is lower than standard conditions.

This paper compares the evaluation of three geostatistical interpolation methods including ordinary kriging, residual kriging and cokriging for the interpolation of long-term monthly and yearly reference crop potential EVAPOTRANSPIRATION (ETo). This study has been conducted in a region including Fars, Booshehr, Hormozgan, and Kohgilooye-Boyrahmad provinces. Long-term mean values of monthly and yearly ETo were computed from recorded meteorological variables at 119 weather stations using the Hargreaves-Samani method. ETo estimates and estimation errors were evaluated at 19 validation stations. In general, estimates were in good agreement with observed values for residual kriging and cokriging methods. Based on mean absolute error (MAE), mean square error (MSE), mean error percent (MEP) and root mean square interpolation error (RMSIE), the best method for Farvardin (April) is kriging and for Khordad (June), Tir (July), Aban (November), and Azar (December) is cokriging. For other months and for mean annual ETo the best method is residual kriging. It should also be noted that MAE, MSE, and MEP for Mordad (August), Mehr (October), Dey (January), and Bahman (February) are very similar for cokriging and residual kriging. With the exception of Farvardin (April), Ordibehesht (May), and Shahrivar (September), for the other months and for annual ETo, the deviation of cokriging estimations from a 1:1 line is less than kriging and residual kriging. In other words, the points from these methods are more spread out around the 1:1 line, but the band of the deviation in cokriging is less than the two other methods. Therefore, the best method for estimation of monthly and yearly ETo is cokriging, except in Farvardin (April), Ordibehesht (May) and Shahrivar (September).

METRIC (Mapping EVAPOTRANSPIRATION at High Resolution with Internalized Calibration) is known as an appropriate surface energy balance model for the estimation of the spatial distribution of EVAPOTRANSPIRATION (ET) in semi-arid regions. Based on lysimeter measurements, METRIC has shown ET estimates of 10% on a sub-field scale on a daily basis. There is a need to identify how the model is sensitive to the input parameters. Therefore, the most influential parameters in the algorithm can be identified and the model can be further improved. Sensitivity analysis at three levels of vegetation cover shows that METRIC is highly sensitive to dT, surface temperature, net radiation, sensible heat flux, surface albedo, soil heat flux, and air temperature. It is also moderately sensitive to friction velocity, aerodynamic resistance to heat transfer, surface emissivity and less sensitive to leaf area index, soil adjusted vegetation index, wind speed (except wind speed at low level of vegetation cover), and roughness length for momentum (except Zom<0.1). A two-factor analysis of the algorithm’s primary inputs showed that the pair albedo-surface temperature is the most and the normalized vegetation index-soil adjusted vegetation index or normalized vegetation index-leaf area index is the least effective pair in this model. In order to improve the accuracy of METRIC, this study suggests upgrading the equations for the above-mentioned effective variables.

According to Limited water resources in the country, Management of water resources as a strategy, it is essential for the crisis. For correct and scientific management of water resources that are needed A better understanding and knowledge of the complex collection of interactions associated with water in a water balance catchment. EVAPOTRANSPIRATION is one of the most important components of the water balance that it is difficult to measure the actual rate And have limited methods. In this study tried to achieve accurate estimates of actual EVAPOTRANSPIRATION in catchment-year scale by using remote sensing method (SEBAL). To accomplish this, firstly using meteorological data and the SPI Index Years 84-83, 85-84 and 87-86 Were determined As the wet, normal and dry years Respectively. Then it was calculated actual EVAPOTRANSPIRATION for Neishabour plain in catchment-year scale by using MODIS satellite images and SEBAL method. The results were compared with the results of the SWAT model that is showed good accuracy. Due to the purpose of this study is to provide an accurate, simple and inexpensive estimate for actual EVAPOTRANSPIRATION in catchment-year scale, from the calibration relationship, Young's equation for the entire basin with RMSE=28.3 mm and R2=0.90 and abcd equation for the plain with RMSE=16.24 mm and R2=0.90, and Young's equation for the mountain with RMSE=19.37 mm and R2=0.90 estimate The best response. Of course The results of Zhang’s and Fu’s equations were similar to Young's equation and there are a few differences.

EVAPOTRANSPIRATION (ET), a major component of the hydrologic cycle, is important in water resources management and irrigation scheduling. Nowadays, due to the lack of the lysimetric data in weather stations, the ET values calculated by the standard FAO Penman-Monteith model (𝐸 𝑇 0) are used as benchmark values of grass reference crop. Also, the Penman-Kimberly model is widely applied for computing the alfalfa-reference crop ET (𝐸 𝑇 𝑟 ). In the present study, the meteorological data from 6 weather stations located in the Sistan-Va-Baluchestan Province covering a period of 10 years were used to calculate the 𝐸 𝑇 0 and 𝐸 𝑇 𝑟 values. Then, the 𝐸 𝑇 𝑟 to 𝐸 𝑇 0 ratios were computed for all six stations during the studied period. The Penman-Kimberly model at Mirjavah station had the worst result compared to other stations. The NS coefficient values for this station are the lowest (0. 07) and the SI and RMSE values for this station are 0. 43 and 2. 48, respectively, which is the highest value among the study stations. Finally, the contributions of the energy balance and aerodynamic components on the final ET values were determined using the Penman-Kimberly model, which showed the important influence of both components on the ET process. Consequently, the use of radiation-based models e. g. Priestly-Taylor model in these stations should be carried out by special care.

BACKGROUND AND OBJECTIVES: EVAPOTRANSPIRATION is an important component of water balance associated with the hydrological cycle and biological processes. Accurately estimating the rate of EVAPOTRANSPIRATION is crucial for understanding fluctuations in water availability and effectively managing water resources in a sustainable manner. The study aims to examine the correlation between actual EVAPOTRANSPIRATION and potential EVAPOTRANSPIRATION by assessing the linkages with vegetation and snow cover in an ecologically fragile located in the northwestern Himalaya.MATERIALS AND METHODS: The present study uses remote sensing Landsat satellite data series to map vegetation cover and snow cover in the area. Remote sensing data accessed from Moderate Resolution Imaging Radiometer EVAPOTRANSPIRATION project data was used for calculating EVAPOTRANSPIRATION and potential evaporation. The data from the Climatic Research Unit (2000–2022) was additionally utilized for the computation of potential EVAPOTRANSPIRATION. The study investigates variances in EVAPOTRANSPIRATION and explores correlations between normalized difference vegetation index and normalized difference snow index. It further examines the correlation between potential EVAPOTRANSPIRATION and actual EVAPOTRANSPIRATION.FINDINGS: The study conducted from 1991 to 2021 demonstrates a notable rise in vegetation cover by 20.18 percent, showcasing spatial variations across the region. Conversely, there has been a significant decline in the extent of snow cover throughout this period. A positive correlation was identified between vegetation cover and EVAPOTRANSPIRATION, whereas a negative correlation was observed between snow cover and EVAPOTRANSPIRATION. Actual EVAPOTRANSPIRATION is on the rise while potential EVAPOTRANSPIRATION is declining throughout the region.CONCLUSION: Hydrological cycle of a region is governed by many factors such as climate (precipitation, temperature), geohydrology, land use and land cover, socio-economic condition of habitants and institutions. Vegetation cover, snow cover, actual EVAPOTRANSPIRATION and potential EVAPOTRANSPIRATION and their relationship indicates changes in local and regional climate. An incremental rise in plant growth across the study site, coupled with spatial variability and a reduction in snow cover in the elevated mountainous zone, is influencing both actual EVAPOTRANSPIRATION and potential EVAPOTRANSPIRATION. Increase in actual EVAPOTRANSPIRATION in the High Himalayan area of Himachal Pradesh attribute to substantial increase in vegetation cover in the dry cold desert region. The findings of the study will contribute to the comprehension of essential elements of water cycles and water budgets, facilitating improved resource allocation for climate-resilient sustainable initiatives.

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.