Potential evapotranspiration (PET) is one of important hydrological cycle parameters which its prediction is useful for water resources planning in the future, changes in the crop water requirements, and drought forecasting. In the case of PET long term prediction, global climate models (GCMs) based on the emission scenarios and then downscaling the outputs are used. For short term forecasting, statistical models are suggested. PET is a nonlinear phenomenon which is affected by temperature, humidity, sunshine and wind speed. In this study, PET was calculated by FAO-Penman-Monteith model for Yazd synoptic satiation during 1965-2010. Recently, NARX has widely used to predict hydrological and climatic parameters. In this research, performance of NARX for PET forecasting was analyzed. NARX is a nonlinear model which in addition to the target parameter, takes exogenous variables that affect target variable into account. Different nonlinear functions can be selected. In this research, a feedforward neural network because of its high ability in nonlinear processes modeling was chosen. The network was trained by GDX algorithm. Then, PET was predicted using nonlinear autoregressive model (NAR) which its input is just PET. Next, the results of NARX and NAR were compared. The results showed adding exogenous variables increases the accuracy of PET prediction, remarkably. R2 correlation coefficient between one month observed and predicted PET by NAR and NARX for 2002 to 2010 was 0.72 and 0.92, respectively.

Solar energy is the most important effective factor in evapotranspiration that can be change with the amount of any changes in the energy that reached to the ground surface.
The aim of this study is to show the impact of altitude, slope and direction on the amount of estimated ETP in the Sabzevar township.
Results showed that there is 23.34% more ETP in the south compared to the north.
Then, in a GIS environment, the map of DEM was created by using topographic map, and then the ETP map (according to dip direction). This map showed that the most area was occupied by the north face of slopes.
Finally, the main ETP map (according to altitude and dip direction) was made, based on the obtained coefficient at the first step, multiplied to this ETP map. In the last step, by using tables in the GIS environment, the amount of ETP of each pixel on the ETP map multiplied by occupied area, the average of ETP for Sabzevar township was calculated.
This study showed that the amount of ETP by this method is 1164.8 mm.

The evapotranspiration is the transfer of energy between the Earth's surface and the atmosphere and it is the most productive mechanism of communication between the hydrosphere, lithosphere and biosphere. This study focuses on predicting potential evapotranspiration in Golpayegan basin as a response to climate change. For this purpose, six algorithms including Hargreaves-Samani, Thornthwaite, Romanenko, Oudin, Kharrufa and Blaney-Criddle and also, Penman- Monteith- FAO as a standard algorithm, were used for estimating the potential evapotranspiration. The results showed that the Hargreaves-Samani algorithm performed closer to the Penman-Monteith-FAO standard algorithm compared to other algorithms. Therefore, this algorithm was used to evaluate the potential impact of climate change in future periods on the rate of potential evapotranspiration. After that, the amount of potential evapotranspiration using general circulation models (GCM) was estimated under RCP scenarios 2.6,4.5,8.5 for near, middle and far periods of 2021-2040, 2041-2060 and 2061-2080 by the LARS-WG6 model using the HadGEM2-ES climatic model. Finally, the predicted evapotranspiration values in future periods were compared with the evapotranspiration results in the baseline period of 1992-2017 to investigate the impact of climate change on potential evapotranspiration. The results showed an increase in potential evapotranspiration under all RCP scenarios in future periods. Increase under scenarios of RCP2.6, RCP4.5 and RCP8.5 in the near future were obtained 6.31, 7.5 and 7.10 percent, In the middle future period, 9.69, 9.84 and 11.82 percent and in the distant future period, 8.17, 13.79 and 18.15 percent, respectively.

In water systems, precipitation is considered as input and evaporation as the output of the system. Water availability can be estemated from the relationship between these two factors. Therefore, evapotranspiration is the most important factor after precipitation in hydrological cycle. Evapotranspiration is influenced by climatic parameters such as temperature, wind, humidity and sunshine hours. In this research, changes in PET and effective climatic parameters, influencing on PET changes including temperature, wind, humidity, and solar radiation were investigated. For this purpose, PET in 14 weather stations of Yazd province were calculated using the FAO-Penman-Monteith method. Due to the lack of sunshine hours data in some stations, regeneration of the incomplete data was done by using regression method. Due to the lack of wind speed data at some stations, their reconstruction by using data from other stations was done by applying three methods of Inverse Distance Weighted, Kriging and Cokriging. After calculating potential evapotranspiration, PET data were zoned and their monthly and annual trends tested by Mann-Kendall test. Despite occurrence of climate change and increasing of temperature in 13 stations out of the 14 stations, it is expected an increase in potential evapotranspiration in past few decades, while, there is a decreasing trend in PET. Investigating on the effective parameters in potential evapotranspiration showed that wind speed has declined in the last few decades, and despite of an increase in temperature, potential evapotranspiration rate reduces in 64.3% of the stations. General trend of evapotranspiration was -0.86 in this period, which indicates a decrease in evapotranspiration in the Yazd province.

Different models are provided for estimation of evapotranspiration that the main difference between them is the number of meteorological parameters needed,but due to the lake of the reliable long-term data in some stations,use of model with little data is needed and it will be necessary to help planners. The Simulation of EvapoTranspiration of Applied Water (SIMETAW) model is used for simulation of weather data from climatic records and estimation of reference and crop evapotranspiration with the simulation data. In this study, using the monthly values of four synoptic stations (Rasht: Very wet climate; Shahrekord: Semi arid climate; Ahvaz: Arid climate and Sanandaj: Moderate climate) in the period of 1961 to 2001, the ability of model for simulation of climate variables (maximum temperature, minimum temperature, dew point temperature, wind speed, solar radiation and precipitation) and potential evapotranspiration was evaluated and daily simulated using meteorological data, were compared with calculated values of potential evapotranspiration from observed meteorological data. To compare the results, were used from Coefficient of Determination ( ), Root Mean Square Error (RMSE) and index of agreement (d). The results showed that model simulations in all stations has acceptable accuracy. The highest accuracy in simulation maximum temperature( =0.9954)and precipitation( =0.3716) related to mediterranean climate, minimum temperature( =0.9947) and dew point temperature( =0.9942) Related to humid climate, wind speed( =0.8094) related to dry climate and solar radiation( =0.9902) related to semi-arid climate. The highest accuracy of SIMETAW model in simulated evapotranspiration related to Mediterranean climate( =0.9936), semi-arid climate( =0.9935), dry climate( =0.9903) and very wet climate( =0.9846) Respectively. Given the high correlation between simulated and observed values, SIMETAW model can be used for simulation of the climate data and estimate the potential evapotranspiration, net irrigation requirement and also filling the gaps in four climatic regions.

According to the water crisis and the need for careful planning, prediction of potential evapotranspiration can be helpful to plan water resources and adopt appropriate management methods. Furthermore, knowing the exact amount of evapotranspiration is essential for estimation of water use and irrigation system design. Different models have been provided by researchers for estimation of evapotranspiration, which vary considerably from each other in terms of the number of meteorological parameters they take into account. But due to the lack of the reliable long-term data in some stations, use of model with little data is needed and will be necessary to help planners. In this study the Simulation of Evapotranspiration of Applied Water model (SIMETAW) was evaluated for generation of daily weather data from the long-term monthly values of four synoptic stations (Rasht: Very wet, Shahrekord: Semi-arid, Ahvaz: Arid and Sanandaj: Moderate), also estimation of potential evapotranspiration in the base period (1961-2000) and future period (2040–2080). In the first stage, SIMETAW model was run using long-term average monthly weather data in the base period and daily weather output data were obtained for 41 years. Then by using this data, long-term daily values were calculated and compared with observed data. To compare the results, Coefficient of Determination (R2), Root Mean Square Error (RMSE) and Index of Agreement (d) was used.
The results showed that the model simulations in all stations had acceptable accuracy and the highest accuracy of model in simulation of maximum temperature (R2=0.9954) and precipitation (R2=0.3716) related to Mediterranean climate, minimum temperature (R2=0.9947) and dew point temperature (R2=0.9942) related to very wet climate, wind speed (R2=0.8094) related to arid climate and solar radiation (R2=0.9902) related to semi-arid climate. The accuracy of the model for simulation of rainfall in four stations was low (R2=0.2457, R2=0.2435, R2=0.3553 and R2=0.3716 for arid, very wet, semi-arid and Mediterranean climate, respectively). The highest accuracy of SIMETAW model in simulation of evapotranspiration was belong to mediterranean climate (R2=0.9936), semi-arid climate (R2=0.9935), arid climate (R2=0.9903) and very wet climate (R2=0.9846), respectively. In the second stage, meteorological parameters were simulated in future period (2040-2080) under two emission scenarios (A2 and B2) by HadCM3 (Hadley Centre Coupled Model, version 3) outputs and using downscaling model of SDSM) Statistical Downscaling Model(. To verify the accuracy of SDSM model in downscaling outputs of HadCM3, long-term average of daily data in the base period 2001-1961 were compared with simulated data using the SDSM in the future period. The results showed the high accuracy in simulation of all parameters. Comparing data from downscaling in the future and observed data in the base period, showed that in four stations, in total, maximum temperature, minimum temperature, dew point temperature and wind speed will increase in the future period compared to the base period while rainfall will decrease. For example, in Ahvaz station, the average increase in the future maximum temperature, minimum temperature, wind speed and dew point temperature will be equal to 3.4, 8.0, 5.0 and 18.9 percent under the A2 scenario and 2.1, 5.7, 5.0 and 14.7 percent under the B2 scenario, respectively. Also, the average decrease in the future rainfall will be equal to 17.9 percent under the A2 scenario and 20.5 percent under the B2 scenario, respectively.
In the third stage, from SDSM model outputs in the future period, long-term monthly values were prepared and used as SIMETAW model input. Then from this data, long-term monthly values were produced and then compared with SIMETAW model outputs in the base period. It was found that in four stations, in total, the potential evapotranspiration will be increased under the A2 and B2 compared to the base period. The average increase in the future potential evapotranspiration will be equal to 10.4, 9.5, 10.7 and 4.3 percent under the A2 scenario and 10.3, 8.8, 9.4 and 2.9 percent under the B2 scenario, respectively, in Mediterranean, dry, very wet and semi-arid climate. Given the high correlation between simulated and recorded values, it can be concluded that SIMETAW model can be applied effectively and efficiently with high accuracy for simulation of weather data, potential evapotranspiration and also filling gaps in four stations. Also, this model can be used as an appropriate tool for irrigation engineers and decision makers to predict irrigation needs, using monthly records when daily data is not available.

Evaluating potential evapotranspiration plays an important role in planning, designing, and managing water resources, especially in arid and semi arid zones. The aim of the present research is considering spatio-temporal changes of different potential evapotranspiration methods such as Thornthwaite, Hargreaves, and Blaney-Criddle in the Maharlou Watershed, Fars province. So, yearly statistics of temperature in a course of 12 years (2002-2013) with 12 meteorology stations in Maharlou Watershed have been used. In order to zonation map of evapotranspiration and studing spatial changes, Inverse Distance Weighting (IDW) method has been used in GIS software. To choose the most suitable method for evaluating potential evapotranspiration using Blany- Criddle, Hargreaves- Samani, and Thornthwaite  in this present research, statistics indicators including Mean Absolute Error (MAE) (8.64, 12.06, 9.93), Mean Bias Error (MBE) (-0.42, -0.56, -1.36), and Root-Mean-Square error (RMSE) (2.49, 2.86, 3.48) is used. Results showed that spatio-temporal changes of potential evapotranspiration in Blaney- Criddle method indicates the most amount of evapotranspiration in Dobene Station in 2009 with the amount of 127.5 mm. Whereas the least that amount of attached is in Kaftar Station in 2006 with the amount of 85.1 mm. Also based on Hargreaves results, the most and least amount of evapotranspiration is in Jahan abad bakhtegan Stations in 2009 with the amount of 152.1 mm and Komhar station in 2003 with the amount of 96.5 mm, respectively. In contrast, based on Thornthwaite method, the most amount of evapotranspiration is included in Dobene Sstation in 2009 with the amount of 103.6 mm and the least that amount of attached is in Komhar Station in 2011 with the amount of 52.7 mm. Subsequently, the results of evaluating of different evapotranspiration methods according to RMSE and MAE showed that Blaney- Criddle method the least error amount (2.49, 8.64) and also according to the MAE and RMSE criteria, the most amount of error attached of Hargreaves- Samani method with the amount measured MAE 12.06 and RMSE 3.48. In general, the Blaney-Criddle method is the best potential evapotranspiration method. On the other hand, the low error rate is due to Hargreaves- Samani and Thornthwaite based methods.

Potential evapotranspiration (ETp) rates are needed for irrigation scheduling. ETp rates are commonly estimated from weather parameters. The Fao-Penman Monteith, is now accepted for computation of ETp. This study examined the suitability of fuzzy logic for estimating daily potential evapotranspiration with grass reference crop compared with artificial neural networks, Fao-Penman Monteith and evapotranspiration pan. The daily climatic data of the Borojen station in Chaharmahale-Bakhtiari was used. The estimated ETp values from a fuzzy logic model using six input parameters resulted in RMSE=0.74 mm/day, R2=0.88 which has the highest correlation and the lowest error in comparison with other methods.

As a hazardous complex climate condition, drought has affected many parts of the world, and in times when its duration is prolonged, its damage would be tremendous, affecting various sectors such as agriculture, environment, economic, social, etc. Due to the wide range of this phenomenonchr('39')s effects on all ecosystems, especially in arid and semiarid regions, continuous monitoring of drought is particularly important. Vegetation and its products are among the most significant parameters affected by this destructive phenomenon. Observation of such harmful effects can be done using remote sensing science. In recent years, this science has played a significant role in managing and monitoring drought, vegetation, and their interactions by increasing sampling points, widening the coverage area, improving temporal resolution, lowering costs, and applying regular and higher spatial resolution. Considering the importance and necessity of this issue, this study set out to investigate and monitor drought indices (SPEI, SPI), vegetation index (NDVI), and these indiceschr('39') interactions from 2000 to 2016 in Hormozgan province, Iran.

To study meteorological drought and potential evapotranspiration, the required data concerning the monthly rainfall and temperature of 164 rain-gauge stations in the region were collected. Then, the homogeneity test was applied to all stations. Statistical deficiencies were reconstructed for the data, and finally, the annual SPI values were calculated using MATLAB software. Kriging geostatistics was used to map the SPI drought. Due to the availability of satellite images for 2000-2016, a drought map was prepared for this period. The NDVI index obtained from the Modis satellite images (MOD13A2) was also used to study the vegetation. Finally, to investigate the effects of meteorological drought and potential evapotranspiration on vegetation, the average NDVI and drought index was calculated for each year, and the severe effects of drought on vegetation were examined. Moreover, the correlation between vegetation and drought was studied through the Pearson correlation coefficient.

According to the results of the study, the highest values of SPI and SPEI were found in the years 2009, 2014, 2012, and 2011, respectively. NDVI and annual rainfallchr('39')s changing trend indicated that these indiceschr('39') mean values were significant at 95 levels over the 17 years, showing an increasing trend. The trend of temperature, SPEI, and SPI changes was decreasing during this time interval. Also, the vegetation index peak was in accordance with the highest rainfall value in this time period. In other words, the highest rainfall occurred in 2005 in this region. The vegetation index showed the highest increase this year, suggesting that vegetation was affected by rainfall fluctuations in the region. Based on the results of the Pearson correlation coefficient between meteorological drought, evapotranspiration potential, NDVI vegetation index, precipitation parameter, and temperature at a 12-month time-scale, the highest correlation was reported between SPI index and precipitation, which was equal to 0.99, and the lowest correlation was found between temperature and SPEI, being 0.48. Moreover, this correlation coefficient analysis showed a high correlation between the meteorological drought index and NDVI vegetation index at 95% level in the study area. This correlation was reported as 0.792. The correlation between SPEI and NDVI was also reported as 0.797, indicating that with an increase in precipitation rate, the NDVI increased.

It could be concluded that, depending on the prevailing conditions of the area, the extent, time, and type of rainfall affect the amount of vegetation. Therefore, studying and identifying vegetation in each area could lead to the discovery of each regionchr('39')s conditions and the vulnerability status of the area. Any reduction in the effects of drought on these fragile and sensitive ecosystems requires careful planning according to each regionchr('39')s conditions. Planners and relevant authorities should take appropriate measures to improve the irrigation system, reduce evaporation, enhance the crop system, mitigate the damages caused by drought, and get the ecosystem adapted to climate conditions. Moreover, this study used Modis images with a kilometerchr('39')s pixel size. For a closer look at the vegetation index, satellite images with smaller pixel sizes, such as Landsat ones, could also be used to increase spatial accuracy. Finally, a more detailed study is recommended to investigate the effects of drought on vegetation in different land-uses.