آمنه میان آبادی

Crop growth simulation models are extensively used for various agricultural studies, including optimal crop selection, irrigation management, and assessing climate change impacts. Among these models, the DSSAT (Decision Support System for Agrotechnology Transfer) is particularly prominent for its ability to simulate growth, yield, and other dynamics for 34 different crops. The DSSAT model integrates various components such as soil, weather, crop management, and genetic factors to provide comprehensive insights into crop performance (Jones et al., 2003). Accurate parameter calibration in this model is crucial for reliable simulations. However, the inherent variability and uncertainty in parameter values pose significant challenges. Uncertainty can arise from various sources, including measurement errors, spatial and temporal variability, and model structure. Addressing these uncertainties is essential to enhance the reliability and accuracy of the model predictions. The Generalized Likelihood Uncertainty Estimation (GLUE) algorithm offers a robust framework for quantifying and incorporating parameter uncertainty into model simulations (Beven & Binley, 1992).In this study, we focus on the application of the GLUE algorithm to the DSSAT model for cotton, aiming to improve the model's predictive accuracy by accounting for parameter uncertainty. We utilize observational data from different irrigation treatments to calibrate the model and evaluate the posterior probability distributions of the parameters.

The study used data from a 2009 experiment conducted at the Birjand University research farm. The DSSAT v4.5 model was employed, requiring inputs such as weather, soil properties, and crop management data. Four irrigation treatments (50%, 75%, 100%, and 125% of crop water requirement) were tested to evaluate the GLUE algorithm’s performance in estimating model parameters.

The results demonstrated that the GLUE algorithm effectively estimated the probability distributions of the DSSAT model parameters for cotton. The algorithm’s performance was compared with previous models lacking uncertainty assessments, showing significant improvements in simulation accuracy (Qasemi et al., 2019). The findings highlighted the importance of considering parameter uncertainty for better predictive accuracy and model reliability.

The GLUE algorithm, through Monte Carlo simulations, provides a robust method for assessing and incorporating parameter uncertainty in crop growth models like DSSAT. This approach enhances the model's reliability in predicting crop performance under varying conditions, which is crucial for agricultural planning and management.

In this study, the CMIP6 climate models were used to predict the change in the date of the last spring chilling and the number of chilling days during the pistachio growing season. For this purpose, 5 climate models including BCC-CSM2-MR, CNRM-CM6-1, CMCC-CM2-SR5, GFDL-ESM4 and MRI-ESM2-0 were selected and the minimum temperature of these models was compared to observed values in 6 stations of Anar, Kerman, Rafsanjan, Shahrbabak, Sirjan and Zarand in the pistachio growing areas of Kerman for the base period (1990-2014). The evaluation criteria showed that among the climate models, CNRM-CM6-1 in Anar (r=0.74, ME=-0.89), Kerman (r=0.73, ME=-1.19), Rafsanjan (r=0.81, ME=- 1.88), Sirjan (r=0.75, ME=-0.43) and Zarand (r=0.74, ME=-2.71) and the GFDL-ESM4 in Shahrbabak (r=0.75, ME=1.42) performed better than the others. After determining the best model for each station, the minimum temperature was used to determine the number of chilling days and the last spring chilling (based on the 4°C threshold) in the base and future periods (near (2050-2026), middle (2075-2051), and far (2100-2076)) and for 4 scenarios SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 and to evaluate the changes. The results showed that the last spring chilling will occur earlier in future than the base period and the number of chilling days will also decrease. Based on the compare means test, the change in the last spring chilling at all stations in the middle and far future and for the SSP3-7.0 and SSP5-8.5 scenarios is statistically significant (P_value<0.05). The change in the number of chilling days is also significant in all stations and all scenarios. Based on the results, the effects of these changes on pistachio phenological stages should be taken into consideration in providing management plans.

Evapotranspiration is one of the key components of water balance and irrigation planning. Thus, the accurate estimation of this component and the water consumption of plants can improve the management of water use and increase the efficiency of water consumption. Due to the limitation of tools for measuring evaporation-transpiration, remote sensing methods can be used for this purpose. There are several remote sensing algorithms for actual evaporation estimation including SEBAL, SEBS, Metric, etc. In this study we used the triangle method which only was used by Salimifard et al. (2022) in Mashhad Plain. They evaluated the results for the agricultural products, i.e., wheat and maize. The aim of this study is to evaluate the triangle method for a horticultural crop, i.e., pistachio in Kerman Plain. 

The study area is Kerman Plain in which pistachio is one of the most important agricultural products. Due to water scarcity in this plain, determining the water requirement of the crops is crucial for agricultural activities. Accordingly, it is important to have an appropriate estimation of actual evapotranspiration in the plain. In this paper, the triangular algorithm was used to estimate actual evapotranspiration in the Kerman Plain in the growing seasons of 2020 (1399) and 2021 (1400). For this purpose, the Landsat 8 satellite images with less than 10% cloudiness were used. The variables such as NDVI, LST, etc., were calculated by using the JAVA programming language in the Google Earth Engine code (GEE) system environment. The required meteorological data of Kerman station were acquired from IRIMO. The triangular algorithm is based on the two-dimensional spatial plot of normalized LST and normalized NDVI, which were calculated using bands 10, 5, and 4 of the Landsat 8 in the GEE. Estimation of the wet and dry edges was conducted by MATLAB code. the actual evapotranspiration obtained using the triangular method for a pistachio orchard, which was under irrigation management, was compared to the values obtained by the FAO-56 method. The results were evaluated by correlation coefficient (r), Root Mean Square Error (RMSE), and Mean Error (ME).

The results showed that the amount of evapotranspiration for pistachio was estimated with acceptable accuracy (r= 0.73 and RMSE=1.8, nRMSE=0.4, ME=-1.6). However, the NSE less than zero (-1.3) shows that the observed (FAO-56) mean is a better predictor than the Triangle algorithm. The values obtained from the triangular algorithm were lower than the values of FAO 56, which was in line with the results of the previous studies for both Agricultural and horticultural crops. This underestimation could be due to the uncertainty of the algorithm, uncertainty in the measured data, or due to the time difference between the date of the selected images and the date of irrigation. Moreover, inappropriate quality of water and soil in Kerman Plain and the uncertainty of plant coefficients used are among the factors that can underestimate evapotranspiration values by the algorithm. 

In this study, the triangular algorithm was used to estimate actual evapotranspiration in Kerman plain using remote sensing data. Actual evapotranspiration values obtained from the triangular algorithm were lower than FAO 56 values, which might be due to the uncertainty of the algorithm, uncertainty in the measured data, uncertainty of plant coefficients, or due to the time difference between the date of the selected images and the date of irrigation. To have a better evaluation of the remote sensing algorithms, it can be suggested to develop and apply a micro lysimeter in the farms and orchards, or to use the soil water balance of the farms and orchards. These may help to choose the more appropriate algorithm for the given study area, leading to providing the more proper and applicable advices for the farmers for managing the shortage of the water resources. Furthermore, it may help to update the crop coefficients which may lead to better estimation of evapotranspiration.

Intensity-Duration-Frequency (IDF) curves are crucial in the design of water and hydraulic infrastructures. These curves are estimated based on rainfall data collected by rain gauge stations. However, due to global climate change, the intensity and frequency of extreme events can be altered. Hence, the existing IDF curves may not be reliable for the design of infrastructures and should be revised based on more recent data. Climate models can be used to investigate the impact of climate change on IDF curves. Typically, after developing the IDF curves from each climate model, the final curve used is extracted based on the median of the data. This study explores the use of five CMIP6 climate models to estimate the intensity-frequency curves of 24-hour rainfall at 12 stations in Iran. In addition to evaluating each model individually, this study also assesses the combined output of the models and compares its results with the curve obtained from the median of the data. Evaluation metrics such as mean error (ME), root mean square error (RMSE) and relative error (RE) indicated that among the studied models, the CMCC-CM2-SR5 model provides a better estimate of the 24-hour rainfall intensity at most stations, likely due to its finer resolution. Furthermore, pooling the models and estimating the IDF curve with the pooled data yielded better results than the median method and most of the individual models, particularly for longer return periods. This suggests that using pooled data from multiple models could improve the accuracy of IDF curve estimates

The sustainability of crucial earth resources, such as groundwater, is a critical issue. Physically sustainable groundwater use is an unavoidable necessity for sustainable development in Iran. In many watersheds, overexploitation and neglecting the reasonable criteria for sustainable use of groundwater have led to the destruction of groundwater-dependent ecosystems as well as many other inevitable losses. Due to the necessity of applying these basic concepts in groundwater management and determining maximum well deepening, in this paper, we assessed the most cited of these concepts during the last century. After examining the historical change in these fundamental concepts, these concepts have been categorized for the use of water resources experts. We found three basic concepts (sustainable yield, safe yield, and Mining Yield) are the most commonly used and the others are defined according to these three concepts. To answer the question of how much water can be extracted from the aquifer, it is needed to explain reasonable and desirable criteria based on these concepts. At the end of the article, in the meta-analysis and summary section, the current approach (executive and scientific) in Iran with the evolution of groundwater management in the world (reviewed in this article) is compared. This comparison indicates that there is a profound gap between groundwater sustainability and the current groundwater management conditions in Iran.

Global warming in recent decades has caused significant changes in precipitation and temperature, including changes in the mean and standard deviation of these variables and changes in the intensity and frequency of climatic extremes (floods and droughts). Given the importance of these changes in water resources management, it is crucial to study the trends in these variables. In this study, in 12 selected stations in different climatic regions in Iran, the changes in monthly and annual precipitation and mean temperature during 1961-1990 and 1991-2020 were examined. The results of Mann-Kendall test showed in most stations precipitation had an increasing trend in the first period, and a decreasing trend in the second period; although in both periods the trend was not significant (Z<1.645). The mean temperature has increased in both periods, which in the second period has increased with a higher level of confidence (Z>2,576) and greater slope than in the first period. The average annual rainfall has decreased in most stations, and the average annual temperature has increased in all stations. The distribution of precipitation and temperature showed that in some stations, the probability of occurrence of extreme events and hot and cold periods in the second period has increased compared to the first period. In some other stations, droughts/floods are more/less likely to occur. This indicates that the activity of air masses affecting each station can be intensified or weakened due to climate change.

In this research, the effects of climate change on the amount and distribution of precipitation and temperature in the Kerman synoptic station were investigated. For this purpose, the output of global climate models of CMIP6 for the historical period (1965 to 2014) for precipitation and temperature were evaluated in comparison with the station data. To evaluate the models, evaluation metrics including correlation coefficient (r), root mean square error (RMSE), mean error (ME) and KGE index were used. Then the best-performed models were selected to predict these two variables in the future period (2051 to 2100) based on different climate scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5). Finally, changes in the distribution of precipitation and temperature in the future period compared to the base period were investigated. The results showed that after bias correction, the ACCESS-CM2 and MRI-ESM2-0 models performed more accurately for temperature (ME=0 °C, RMSE=1.87 °C, r=1, KGE=0.998) and precipitation (ME=-0.002 mm/month, RMSE=17 mm/month, r=0.484, KGE=0.468) estimation, respectively. The results of trend analysis indicated the trends in the amount of precipitation were not significant (P-value>0.1). Comparison between the average and variance of precipitation also was not significant (P-value<0.05), and therefore the possibility of increasing the occurrence of extreme precipitation cannot be statistically expected. However, trends in temperature and its average and variance are statistically significant and the possibility of heat stress will increase in the future. A significant increase in temperature in the future can affect the quantity and quality of Kerman's water resources, requiring more attention to the proper management of water resources.

Every year, the occurrence of heavy rains in the flood-prone basins of the country leads to the occurrence of floods and the resulting damages. Therefore, accurate prediction of rainfall is of great importance to take preventive measures. Therefore, rainfall forecasting is one of the most important issues in the field of water resources management. So far, various methods have been used to predict rainfall. Advanced hydrological research in short-term weather forecasting is uncertain and its impact is still being investigated and understood. The concept of connecting the hydrological model (WRF-Hydro) with the atmospheric model (WRF) is expected to reduce the uncertainties related to the spatial and temporal distribution of storm events, especially for areas with complex characteristics.In this study, the WRF/WRF-Hydro model was evaluated in order to predict 4 rainfall events that resulted in floods. In precipitation simulations, this model was underestimated and the model provided better results in coupled mode. ERA5 data was used to run the model, and these data had a good performance for the model in the mentioned field. Also, Lin, Thompson, and WSM6 were used to configure the model, and according to the error evaluation criteria of RMSE and NSE, all three of these schemes performed similarly.

The significant reduction in water volume of the two important wetlands of Hamoun and Jazmourian in the arid region of the southeast of the country has had several economic, social, and environmental consequences. Although the reduction of water in these two wetlands is influenced by climatic and human factors, often climate change, especially the decrease in rainfall is considered as the main factor. Therefore, in this study, the trend of precipitation changes in the southeastern region of the country is investigated using the CHIRPS satellite product. To evaluate the accuracy of CHIRPS compared to station precipitation data, the Categorical Statistical Indices including Probability of Detection (POD), False Alarm ratio (FAR) and Critical Success Index (CSI) and statistical criteria including coefficient of determination (R2), Pearson correlation coefficient (PCC), root mean square error (RMSE) and mean error (ME) were used in three time scales: daily, monthly and annual. The results showed that this product does not have good accuracy in detecting the number of rainy and non-rainy days (POD<0.2, CSI<0.2, FAR>0.6). This product does not provide an accurate estimate of daily precipitation (R2<0.2 and PCC<0.4), but has relatively good accuracy on a monthly and annual scale (R2>0.6 and PCC>0.8). The trend analysis of precipitation in the southeastern region of the country showed that in general, the amount of precipitation on a monthly, seasonal and annual scale has been increasing, or in some cases insignificant decreasing, during the last 40 years. Accordingly, the decrease in the water level of wetlands cannot be due only to changes in rainfall, and the role of other factors, especially human activities should be considered.

Therefore, this study aimed to evaluate the revised Gash model in estimating interception by a Robinia pseudoacacia (L.) stand during the leafed and leafless periods in Chitgar Forest Park.

A circular plot with an area of 0.5 ha in Chitgar Forest Park was selected and rainfall, throughfall, and stemflow were measured for two years (from 22 December 2013 to 21 December 2015). Then, the amounts of canopy and trunks ecohydrological parameters were calculated, and finally, the efficiency of the revised Gash model for estimating interception was evaluated.

In this study, the mean amount of rainfall interception in the leafed period (12.7%) was significantly higher than the leafless period (9.7%). The determination coefficient (R2) value between the estimated interception values and the measured in the leafless period was higher than in the leafed period. Based on all model evaluation metrics, the performance of the revised Gash model in estimating interception in the leafless period was better than in the leafed period.

Based on the findings of this study, the Revised Gash model showed good ability in estimating interception during the leafless period, and a probable reason for the high estimation error of the model in the leafed period is the lack of direct measurement of the canopy percentage parameter. Accurately determining the amount of interception, as a canopy water loss, contributes significantly to the planning and decision-making process of forest managers and water resources managers for selecting the appropriate species for plantations.

Adaptation to water scarcity is a topic that has received more attention recently by the Ministry of Energy as well as water experts. Providing appropriate solutions and long-term and efficient plans for this issue requires discussion and consensus on the subject. One of the issues that causes consensus to fail in various scientific disciplines is misunderstanding of concepts and la ack of common technical reference. To come up with appropriate solutions to water scarcity, it is necessary to understand both “adaptation” and “Scarcity”. There are various meanings associated with these two terms that need to be examined in relation to each other and avoid the use of them interchangeably by providing the correct definition of these concepts. This article discusses some of these concepts.

Unsustainable use of water resources including inefficient irrigation, overexploitation of groundwater resources, damming, evaporating, river piping and pumping, lakes and wetlands shrinking, has become a global problem. Scientists have been studying hydrology without considering the role and impacts of human interventions and the coupling of human-nature effects. But it has been shown later that without it, the investigation of a hydrological cycle would be accompanied by error. For this reason, a novel concept of socio-hydrology was proposed to investigate the two-way coupling of human-water systems. Based on the results of studies and considering the importance of the human role in causing environmental changes, particularly in hydrological responses of basins, it seems necessary to study river basins in the form of socio-hydrology. Therefore, water experts in hydrology, water resources management, sociology, and economics need to become familiar with new interdisciplinary concepts, including socio-hydrology in order to tackle water challenges in river basins. In this paper, in addition to introducing the concept of socio-hydrology, the differences between socio-hydrology, integrated water resources management, and phytosociology are addressed. Furthermore, the different kinds of coupling human-water systems investigation including historical socio-hydrology, comparative socio-hydrology, and process socio-hydrology are introduced. Finally, the challenges that socio-hydrology encounters with; were argued.

Due to increasing human activities and its impacts on the environment and water resources, a paradigm shift in hydrological modeling is inevitable. For better modeling of basins’ hydrological behavior, especially with considering the human influences, hydrologist should have an appropriate understanding of the hydrology, models and modeling. By definition, hydrology, which describes the behavior of water above, over and through the Earth, is a multi-disciplinary science which investigates the relationship between the earth and life sciences. There are two approaches for hydrological modeling, Newtonian and Darwinian, based on which the bottom-up and top-down approaches were developed. Considering the pros and cons of each approach, combination of the bottom-up and top-down approaches can help to a better understanding and modeling of the hydrological system of the basins. In this paper, we discuss the novel approach and its challenges in hydrological modeling. Based on the previous studies, we must change our approach to model hydrological systems for a better understanding of the complex coupled human-environmental systems. Emerging the new sciences of socio hydrology and sustainability science indicates the undeniable role of humans in the environment and the importance of sustainable development. To reach a sustainable development, it is crucial to empower the young hydrologists to cope with the new water resources challenges in a changing world by reframing hydrology education.

The SEBAL algorithm is used to estimate spatial distribution of actual evapotranspiration using remote sensing imageries of MODIS or Landsat. Despite having a better spatial resolution than MODIS imageries (30 m instead of 1000 m), Landsat imageries do not have an appropriate temporal resolution (every 16 days instead of daily). On the other hand, the daily imageries of MODIS can be difficult to use under cloudy condition. Additionally, it is also a time-consuming process to interpret all the imageries. In this research, we chose the appropriate imageries from MODIS to be able to monitor the sudden weather changes as well as rainfall events and to reduce the interpretation time, while keeping the important information of the daily MODIS imageries in order to obtain the better actual evapotranspiration estimates. Due to the importance of hot and cold pixel selection, whose selection needs time and proficiency, we applied the automated method of hot and cold pixel selection (without user-intervention) using Landsat imageries. To integrate evapotranspiration over time, we used linear-logarithmic interpolation method in addition to linear interpolation method. The estimated actual evapotranspiration by SEBAL was compared to the estimated actual evapotranspiration from water balance equation and SWAT model for 3 years including a wet year (2004-2005), a normal year (2005-2006) and a dry year (2007-2008) in the Neishaboor-Rokh watershed. Furthermore, we used the Budyko framework to validate the evapotranspiration estimated by SEBAL and SWAT. The results showed that in comparison to SWAT, the linear-logarithmic interpolation method performed better than the linear method to estimate evapotranspiration. For linear-logarithmic method, RMSE, MBE, and MAE were 20.4, 0.09, and 18.4 mm year-1, respectively; and for the linear method, they were 21.8, -2.4, and 20.8 mm year-1, respectively. The results also demonstrated that the SEBAL algorithm with automated cold and hot pixel selection is able to have a good estimate of actual evapotranspiration at annual time scale and watershed scale. But, the algorithm does not perform well at HRUs and monthly scale in comparison to SWAT model. Results showed that by taking irrigation into account, evaporation estimated by SEBAL and SWAT follows the Budyko framework.

Prediction of the water balance components¡ including actual evaporation is important for water resources management and climate change adaptation. Physically-based or distributed hydrological models are too complicated and they need lots of data as inputs to estimate the water balance components. Also the accuracy and the quality of the inputs can affect the accuracy of the models’ prediction. In some cases¡ it is not needed to have an accurate estimation of the water balance components and a primary estimation of annual evaporation and runoff is adequate for the management of the catchments. For these purposes¡ the conceptual lumped models were developed for the primary studies of hydrology. One of the most applied lumped models is developed based on Budyko hypothesis. Budyko curves were applied for the steady-state catchments in which the soil water storage is negligible. In the Budyko framework¡ a new equation (Greve et al.¡ 2016)was developed to estimate evaporation in non-steady-state situations where the soil water storage cannot be negligible. In this study¡ the new proposed model was calibrated for the non-steady-state catchment of Neishaboor Rokh using the precipitation¡ potential evaporation and actual evaporation and then using the calibrated equation¡ actual evaporation was estimated for the historical (1971-2005) and future (2016-2050) period of CORDEXproject. Based on the actual evaporation from SWAT model¡ Greve et al. model parameters (y0 and k) were 0.24 and 1.54¡ respectively. The results showed that¡ under climate change¡ the precipitation rate would decrease by 0.48% and mean temperature¡ potential evaporation and actual evaporation would increase by 11.25%¡ 4.66% and 2.11%¡ respectively.

Precipitation is a key input to different crop and hydrological models. Usually، the rain gauge precipitation data is applied for the most management and researching projects. But because of non-appropriate spatial distribution of rain gauge network، this data does not have a desirable accurate. So estimation of daily areal rainfall can be obtained by spatial interpolation of rain gauges data. However، direct application of these techniques may produce inaccurate results. In the last years، applying the remote sensing for estimation of rainfall have got so popular all around the word and many techniques have been developed based on the satellite data with high temporal and spatial resolution. In this paper، CMORPH model was validated for precipitation estimation over the northeast of Iran. Results showed that this model could not estimate precipitation accurately in daily scale، but in monthly and seasonal scale the estimation was more accurate. Farooj and Namanloo station had the highest correlation equal to 0. 31 in daily scale. The highest correlation in monthly scale was equal to 0. 62 for Barzoo، Namanloo and Se yekAb station. In Seasonal scale Gholaman station had the highest correlation which was equal to 0. 63. Also، the probability of detection has been estimated accurately by CMORPH. But this technique did not have an accurate estimation for wet and dry days، mean annual precipitation and the number of non-rainy days.

Surface solar radiation is an important parameter in hydrological, meteorological, climatological and crop yield models. Some parameters such as precipitation and temperature are widely available. By contrast, direct measurements of surface solar radiation are very sparse in most regions, especially in highland and mountainous regions. Lack of adequate observations on solar radiation has ever been a persistent problem in studies of land-surface processes. Hence, alternative techniques are required to estimate solar radiation. Apart from astronomical and geographical factors, incoming solar radiation is strongly modified by cloud cover, the underlying surface albedo, atmospheric turbidity, absorption and scattering. Empirical models which express global solar radiation as a function of these variables have been proposed by various investigators. Since most proposed empirical models are not flexible but rather restrictive in their application, their suitability for a particular location would largely depend on validation against actual measurements. Mashhad is located at latitude 36º 17ʹ 45ʺ -N, longitude59º 36ʹ 43ʺ -E and at 992 meters altitude. Because of its situation, the city experiences different air masses and has a specific changing climate. Considering the Average temperature and precipitation (14.1 °C and 255.2 mm, respectively), the city has a semi-arid climate based on Demartonne Method. The average sunshine hours and solar radiation intensity in the city are about 2892 hours/ year and 195 W/m2, respectively. In this study, we used temperature data, sunshine hours, relative humidity and precipitation to estimate the solar radiation (Rs). To compare the estimated and measured data, we used the measured solar radiation by Pyranometer that is available for 10 years (1994-2003). Extraterrestrial radiation (Ra) was calculated by the equation obtained by Allen and his colleagues (Allen et al., 1998). In this study we investigated two solar radiation models - Angstrom–Prescott and Garg and Garg models- and determined their coefficients for Mashhad. For this purpose, the least squares error method was applied. To calculate coefficients of both equations, we used the MATLAB programming language. We used the data sets of 1994-2000 for calibration and 2001- 2003 for validation. Some of the radiation models are based on the cloudiness. Therefore in this study we developed 2 new radiation models based on cloudiness data. These two models are generally presented as equation 11 and 12: In these equations N is fraction of cloudiness (octal) and A, B, C, K and M are constant coefficients. Based on available data from 1994-2000, coefficients of Angstrom–Prescott Model were determined as a=0.25 and b=0.42. High determination coefficient (R2of 0.87 and 0.89 for calibration and validation respectively, and low RMSE (2.46 and 5.15) and MBE (-0.14 and -4.63) confirm that the coefficients are acceptable. Also coefficients of Garg and Garg model were determined as X=0.27, Y=0.42 and Z=-0.0028.The amounts of R2 (0.87 and 0.89 for calibration and validation stages), RMSE (2.45 and 5.08) and MBE (-0.11 and -4.56) show that the coefficients of Garg and Garg model are acceptable too. As mentioned before, Cloudiness is one of the most important parameters for determination of solar radiation and many equations were developed based on this parameter. Therefore, in this study, two models were developed based on cloudiness. The first equation obtained using 1994-2000 data sets are as follow: For these equations, determination coefficient is equal to 0.84 and 0.82, respectively. Although water vapor is effective on solar radiation and Angstrum-Prescott model is better than Garg and Garg model, but there isnt much difference between these two models. So, because of requiring less meteorological data than Garg and Garg model, Angstrum-Prescott model is suggested. Of the two developed models in this study, equation 1 has more accuracy and validity than equation 2. Meanwhile, developed models have higher accuracy compared to Angstrom-Prescott and Garg & Garg models. Also, we should consider the fact that, Angstrom-Prescott model for calculating the radiation requires two parameters, which from these two parameters the N value can be obtained. Additionally, this value may not be accurate for the study area. Overall, using developed Model 1 cab is recommended for this research when only cloudiness parameter is known.

Evapotranspiration is one of the most important parts of water cycle in the nature but, the measurement of actual evapotranspiration (via Lysimeter as an exact measurement instrument) is so difficult and not practical. Therefore, equations that can estimate the value of evapotranspiration only by using meteorological data are necessary. In this study, four different reference crop evapotranspiration of Penman-Montith FAO56 (PMF56), Penman-Montith FAO with Irmak Radiation (PMFI), Jensen-Hais1 (JH1) and Jensen-Hais2 (JH2) have been assessed in a semi-arid climate of Iran to estimate the ETo. The investigations showed that, when the data of radiation, temperature and wind changed by 5% and 10%, the radiation was more effective than other parameters. Therefore to calculate the short wave radiation, radiation models of Angstrom, Sabbagh, Gelever-Mc Culoch and Black have been used. Calibration of obtained results with data of three weighting microlysimeter during the growing season (May to October) duration 95 days by using different statistical parameter such as coefficient of determination (R2), Root mean square error (RMSE), Mean bios error (MBE) and t-test criteria of 0.781, 0.625, 0.038 and 0.468 respectively, indicated that PMF56 model with radiation models of Angstrom is the best model for estimation of reference evapotranspiration and the models of JH1 and PMFI have low exactness for this climate. The finding of the present research reveal that in all investigated evapotranspiration models, the models that used Angstrom radiation have high accuracy and radiation models of Sabbagh, Gelever-Mc Culoch and Black are in next orders.