تبخیر و تعرق پتانسیل

Climatic changes and human activities are among the key factors influencing river flow. Determining the contributions of climate change and human activities is essential for the sustainable management of water resources. Climate change is associated with variations in temperature and precipitation, leading to changes in the spatial and temporal distribution patterns of rainfall. Human activities, both directly and indirectly, affect water resources. The rational use of water resources, including runoff, is therefore critical. This study aims to quantify the contributions of climate change and human activities to runoff variations in the Qazvin Plain salt marsh.

The Qazvin Plain, covering approximately 450,000 hectares, is located between longitudes 49°25′ to 50°35′ E and latitudes 35°25′ to 36°25′ N in Iran. In this research, the Mann-Kendall test was applied to analyze the trends in annual precipitation, runoff, air temperature, and potential evapotranspiration during the period 1990–2020. The Pettitt test and the double mass curve method for precipitation-runoff analysis were used to identify the change point in runoff values. Finally, hydrological sensitivity analysis based on the Budyko-Zhang hypothesis was employed to determine the respective contributions of human activities and climate change to runoff variations.

The results of the Mann-Kendall test revealed a significant decreasing trend in runoff at a 0.01 significance level. Conversely, the average annual temperature and potential evapotranspiration exhibited significant increasing trends at the same significance level. Despite a reduction in average annual precipitation at the basin level, no statistically significant trend was observed for rainfall. The results of the Pettitt test and the cumulative precipitation-runoff curve identified 1996 (1375 in the Persian calendar) as the change point in the annual runoff series. Using hydrological sensitivity analysis based on the Budyko-Zhang hypothesis, the contributions of climate change and human activities to runoff variations were quantified as -0.21 mm (-161.2%) and 0.08 mm (61.2%), respectively.

Trend analysis in the study area demonstrated a decreasing trend in runoff and an increasing trend in average annual temperature and potential evapotranspiration. Runoff values in Shorezar identified 1996 as the change point in the time series. According to the Budyko-Zhang method, climate change has contributed to a decrease in runoff, while human activities have increased the water level in the salt marsh.

For the optimal management of the surface water, it is necessary to identify the effect of meteorological drought on the river flows in terms of time. In this study, the relationship of meteorological and hydrological droughts in Sufi Chai River basin of Maragheh (East Azarbaijan province) was evaluated using SPI, RDI and SDI. The daily weather data of the Maragheh synoptic station and the daily data of Tazehkand hydrometric station (upstream of Alavian dam) were used. The statistical period of 34 years were determined (1983-2017) and 3, 6, 12, 24 and 48 month time series were created. In RDI, ETO was calculated using FAO-Penman-Monteith and Hargreaves-Samani methods. The correlation of hydrological and meteorological indices was performed for 3 to 48 month time series without delay and with delay of one-five month. The results in without delay condition showed the highest correlation between the indices is belonged to the series with similar period. The correlation between the similar RDIFPM and SDI series were less than the ones corresponding to SPI and SDI, or RDIHS and SDI. It is concluded considering the other weather parameters, in addition to rainfall, for calculating the indices, results in reduction of the correlation between the meteorological and hydrological indices. Among RDIHS and SDI values with delay condition, in the 3, 6 and 12 time series, the greatest effect of the meteorological drought on river flow was observed with the 3 month delay and in the 24 and 48 month time series, this effect was simultaneous.

Evapotranspiration (ET) is one of the most important components of the hydrological cycle and an important factor in determining water demand, its spatial distribution and accurate estimation for many studies such as water balance, crop production simulation, program Irrigation planning and water resources management are very important. Also, using of geostatistical methods is one of the ways to estimate evapotranspiration in places without stations. In this study, SWAT hydrological model was used to simulate evapotranspiration of Samalghan watershed . To evaluate the simulation results, indices R2 , bR2 , NS was used. In the calibration stage, the coefficients of R2, bR2 and NS were equal to 0.74, 0.71 and 0.68, respectively, and in the validation stage, they was equal to 0.69, 0.62 and 0.59. .Then, using the results of spatial analysis of data in this study, the exponantial model was selected as the most appropriate model for zoning due to the lower RMSe value and higher correlation.

The problem of drought and water crisis in the in the central Iran watershed has become serious in recent years due to consecutive droughts and increased water consumption in drinking, agriculture and industry sectors. Since rain is the only source of water supply in this catchment area, a decrease in the amount of rainfall with this intensity can cause a widespread drought and eventually large migrations from these areas. Therefore, the aim of this research is to analyze the changes in the aridity index in the vast area of the central Iran watershed in future.

In order to achieve the study's objective, the minimum and maximum air temperature, sunshine hours, and precipitation of 40 synoptic stations in the Central Iran watershed for a period of 20 years (1991-2010) were received from the Meteorological Organization.Then the values of potential evapotranspiration, aridity index, and Angot precipitation index were calculated for each station in the current conditions. In order to project changes in the aridity index in the future, the output of three MIROC5, HadGEM2-ES, and GFDL-CM3 models for the historical period (1991–2010) and the medium future (2041–2060) under the RCP4.5 scenario and based on the LARS- WG6 scaled down. After evaluating the accuracy of the models against the observational data, the index values were calculated in future conditions, and zoning maps were drawn for the study area.

Projection of changes in evapotranspiration values under the RCP4.5 scenario (2041–2060) showed that the most changes were in January, March, December, and November. Shahrekord showed the highest percentage of incremental changes, with 36.8% change in January compared to the base period. In terms of rainfall regime, the Central Iran watershed has a winter rainfall regime based on the Angot precipitation index in the current conditions. The HadGEM2-ES model is consistent with the base period, but based on the MIROC5 and GFDL-CM3 models, the winter precipitation in many stations will change to the all-season precipitation regime in the future. Climatic zoning of the UNEP aridity index in the studied area indicated that in current conditions, more than half of the stations have a dry climate. Only Kohrang has a humid climate with an aridity index value of 0.78. Shemiranat and Oligoders have semi-dry conditions with values ​​of 0.21 and 0.20, respectively. The evaluation of the future climate conditions according to the GFDL-CM3 model shows that the humid climate in central Iran will gradually disappear, while the semi-arid climate will expand in this region. The output of the MIROC5 model also shows the expansion of the ultra-arid climate zone in different parts of the catchment area of Central Iran.

The results showed that the aridity index values based on selected GCM models have decreased in the studied area compared to the observation period. Therefore, a large number of stations that have dry conditions in the base period will experience ultra-dry conditions in the future climate.The evapotranspiration potential in the Central Iran watershed is increasing during all months of the year, especially in wet stations in the west. This issue shows the dominance of the aridity index over this region in future conditions.

In order to carry out the appropriate program for agricultural operations and to know the appropriate planting time of dry wheat crops, the length of the growth period and its beginning and end are special. The studies carried out during the growing period of dry wheat in the country are based on the growth degree day method, which does not exist for dry or semi-arid regions. In this study, the length of the dry season wheat growth period for a statistical length of 30 years (1371 to 1400) for 38 sinopic stations of the country by taking meteorological temperatures and precipitation, with the help of the proposed method of the ecological zoning guidelines of the Food and Agriculture Organization of the United Nations (FAO) and By finding the intersection points of the graphs of precipitation and 50% potential evaporation and transpiration, the beginning and the end, the length of the growth period has been estimated, which corresponds to the results presented for the length of the growth period in the ecological classification publication of the FAO organization. Results of the obtained results, Astara station with 301 days of growth period and Isfahan, Saravan, Tabas, Semnan, Zahedan and Kerman stations with 0 days of growth period are respectively the most and the least. to give have given. Also, the results showed that the use of the FAO method to estimate the length of the growth period in arid regions obtains more realistic estimates than the growth degree day method.

Today, the importance of water in maintaining life, especially in arid and semi-arid areas, is not hidden from anyone,.. There are various techniques to determine evaporation and transpiration, which include the use of empirical formulas, lysimeters, remote sensing and satellite methods. It is micro-climatic technologies and methods of using plant transpiration coefficient. This research started in April 2014 and was completed by the middle of September 2014. Studies were conducted on three species of bay leaf (Laurus nobilis L.), rosemary (Rosmarinus officinalis L.) and oleander (Nerium oleander L.) located in the parks and boulevards of all 9 districts of Shiraz municipality. To calculate the rate of evaporation and transpiration of the target plant, first the rate of evaporation and transpiration of the reference plant was calculated using the data of the Shiraz synoptic station and the standard Penman-Mantis-FAO method, and then the coefficient (KL) was calculated based on the California method. This coefficient is the key factor in estimating the water requirement of green space. The highest rate of reference evaporation and transpiration is related to the months of August and July, respectively, and the lowest is related to the months of Bahman and January. . The highest water requirement is between June and October. In order to solve a large part of the lack of water resources mentioned, methods such as recycling and reuse of returned water, using non-conventional water sources (sewage, sewage, gray water, etc.) and surface water and runoff are suggested as solutions.

Drought monitoring using appropriate drought indices is of importance in water resources management, especially in arid and semi-arid regions. Therefore, choosing the suitable drought index and calculating the desired drought index appropriately is of considerable importance in the study of drought. This study is aimed to determine the appropriate statistical distribution to calculate RDI drought index in arid and semi-arid regions of Central Iran. For this purpose, 16 synoptic stations in Central Iran were selected. To calculate RDI, precipitation and potential evapotranspiration values are required. The FAO-Penman-Monteith method was used to calculate potential evapotranspiration. To select the most appropriate statistical distribution, 17 statistical distributions were used. RDI for each synoptic station was calculated annually by fitting to each of the 17 distributions, separately. Then, based on the AIC and BIC criteria, the best statistical distribution was selected to calculate RDI for each station. While based on the literature, it is recommended to calculate RDI by fitting the data to one of the Gamma or log Normal distributions, the results showed that in most of the studied stations, the log Normal and Gamma distributions are not the most appropriate distribution. However, according to the results, Gamma distribution was one of the top 6 distributions in all the studied stations. The results also showed that the difference of RDI values ​​calculated based on different distributions in dry and wet years are relatively significant, which shows the importance of choosing the appropriate statistical distribution. The fit of the studied distributions to the precipitation data at different stations showed that the Nakagami distribution presents the best performance. In case of potential evapotranspiration, different distributions provided the best fit at different stations.

Drought forecasting is of particular importance in water resources management. Drought forecasting allows planners to schedule for reducing the negative impacts of drought as well as to adapt to it. Drought prediction is more important in arid regions. Because these areas are inherently water scares and the consequences of drought in these areas are more severe. Due to the high variabilities of the temporal and spatial distribution of precipitation in these areas, the frequency of drought is higher and results in more difficulty to model and predict drought. In this study, since drought time series is nonlinear and cyclic, nonlinear autoregressive neural networks (NARs) were used to predict short-term and long-term drought in Yazd synoptic station from 2006 to 2018. Reconnaissance Drought Index (RDI) which in addition to precipitation, considers potential evapotranspiration to monitor droughts, for one, three, and six months timescales was calculated. Potential evapotranspiration was calculated using the FAO-Penman-Monteith method. The results of short-term (one month) drought prediction presented that the model provides high performance in predicting three and six months RDI values. The results of long-term (13-years) drought forecasting (without access to real drought data from 2006 to 2018) indicated that RDI values in dry months show best fit to real values ​​ in ​​ three months’ timescale. To improve the efficiency of the model in the long-term drought forecasting, long-term precipitation and potential evapotranspiration (without model access to real data from 2006 to 2018) were predicted. RDI values ​​were then calculated based on the predicted precipitation and potential evapotranspiration data. The results showed that the prediction accuracy increased in one and three months scales. Also, on six months’ timescale, RDI data were more accurately predicted in dry months.

One of the most fundamental processes, which influence climate and weather, both global and local scales that a fact which gives it the status of agriculture, is Evapotranspiration (ET). In irrigation and water resources management, ecosystem modelers, environmental assessment and solar energy system, accurate assessment of evapotranspiration is essential. Potential evapotranspiration (ET) has commonly applied to calculate the actual evapotranspiration, which was difficult to estimate by lysimeter measurement and water balance approach under field conditions. Until now, many methods have reported to estimating ET, however, due to the availability of the observed data, it is difficult to choose the optimal method. In this study, to determination of the best potential evapotranspiration method for the Khorasan Razavi Province, three temperature-based methods, Hargreaves–Samani (HS), Hamon (HAM) and Linacre (LIN) and five radiation-based methods, Jensen-Haise (JH), Makkink (MAK), McGuinness and Bordne (MB), Abtew (ABT), and Priestley–Taylor (PT), were compared with PM at yearly scale, using long-term (11-67 years) data from 13 meteorology stations. Indicators, viz. The correlation coefficient (CC), Relative bias (BIAS), normalized root mean squared error (NRMSE) and Relative error(Re) were used to evaluate the performance of ET estimations by the above-mentioned eight methods. The results showed that the performance of the methods in ET estimation varied among regions; ETLIN overestimated ET, while others underestimated. In Dargaz and Golmakan, ETHS yielded similar estimations to that of ETPM, while, in Torbate-jam and Khaf, ETLIN showed better performances. Also in Mashhad and Gonabad, ETHAM, in Neyshabour, ETJH and in Torbat-e Heydarieh, ETABT showed better performances. But in Fariman, Kashmar, Sabzevar, Sarakhs and Quchan, all methods showed poor performance. It indicated that ETPM is acceptable for ET simulation for the yearly timescale in these areas.    ​

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 multivariate models, the modeling and predicting various parameters can improve by involving other factors. Also Since nonlinear models with conditional variance, the remaining portion of the linear models to adequately model, we expect that the combination of linear and nonlinear models, partly to increase the accuracy of modeling and predictions. In this study, were used the potential evapotranspiration values of stations in the provinces (Birjand, Mashhad, Zahedan and Zabol stations) during the statistical period of 1973-2010 at monthly scale. Since the goal model is multivariable, in addition to potential evapotranspiration data, relative humidity data, wind speed and sunshineare used to modeling the monthly evapotranspiration values. The models studied in this study are MPAR and MPAR-EGARCH models. The results of the verification and validation of the model data showed that both models are highly accurate. In this study, in all cases, the multivariate compilation model with conditional variance was more accurate than the multivariate periodic ARMA model. The results also showed that the MPAR-EGARCH compilation model fitted the minimum and maximum points of the studied data. The average error rate for estimating potential evapotranspiration values by MPAR model at stations of Birjand, Mashhad, Zabol and Zahedan was 0.4, 0.43, 1.05 and 3.04, respectively, and in the MPAR-EGARCH compilation models Respectively is equal to 0.16, 0.19, 0.55 and 0.59 respectively.

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.

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.

Water scarcity, climate change / global warming, and desertification are three big challenges which human is facing them. These problems have mutual impacts on each other as well. For example, in some regions, climate change causes frequent drought occurring which results in water shortage and consequently, if drought continues, results in desertification. Arid regions because of their sensitive ecosystem are more susceptible to climate change effects and Yazd province, located in central Iran, is one of the driest parts of the country. In this research, to understand how climate change affects droughts in the region, drought trend in Yazd synoptic station during 1966-2009 is surveyedusing nonparametric Mann-Kendall test. Drought, estimated by Reconnaissance Drought Index (RDI), is considered as a result of a decrease in input humidity (rainfall), an increase in output moisture (Potential Evapotranspiration (PET)), or occurring both of them (deficiency in rainfall and higher PET). The 3, 6, 9, 12, 18, and 24 month time series are applied and the results showed a rising trend of drought occurring which caused by precipitation decreasing and PET (calculated by FAO-Penman-Monteith) increasing. In spite of the general drought rising trend, the 1975-1995 period was remarkably humid rather than the rest of the period (i.e.1966-2009) which resulted by PET decrease. Analysis of the major parameters affecting PET revealed that in spite of temperature and sunshine hours increasing in period 1975-1995, wind speed and relative humidity showed a clear decrease and increase, respectively. This caused PET decreasing and consequently humidity increasing. In drought analysis under climate change, therefore, in addition to temperature and rainfall, some other parameters also may be effective and even overcome temperature and rainfall effects.

Agriculture is one of the most sensitive sectors in terms of climate change, due to its high dependence on meteorological parameters. By increasing air temperature evaporative power of the atmosphere will be increased and crop growth period will be decreased. Under climate change situation crop yield and crop water requirement will be changed. Changes in these parameters under climate change scenarios can be estimated using simultaneous application of climate change and crop growth simulation models. In this study the impact of climate change on potential evapotranspiration of an autumn crop (barley) and a spring crop (maize) in three stations in different climatic regions of Kermanshah Province for the upcoming period of 2046-2064 were examined.
The study was carried out in three phases. In the first phase meteorological parameters in the future were estimated based on scenarios of A1B, A2 and B1 of HADCM3 climate change model and downscaled using LARS-WG package software. In the second phase reference crop evapotranspiration under current and the future situations was calculated using the FAO Penman-Monteith formula and compared together. In the third phase growth of studied crops were simulated by AquaCrop software using calibrated crop parameters in the region and based on current and future weather data sets. According to the model outputs seasonal evapotranspiration, maximum daily evapotranspiration and length of growth period under current and future climates were compared.
Results of first phase indicated the increasing in the minimum and maximum temperatures and fluctuations in rainfall and sunshine hours compared to the base period (1992-2010). Meanwhile, in the future potential reference crop evapotranspiration in all three stations will be increased. According to the results of this study seasonal evapotranspiration of maize in the future will be increased by 25-27% in Songhor, 16-18% in Kermanshah and 5-7% in Sarpol_e_Zahab. The estimated maximum daily evapotranspiration of maize in the future will be increased by 37-38% in Songhor, 19-20% in Kermanshah and 19-21% in Sarpol_e_Zahab. On the effect of climate change growth period of maize will be decreased by 20, 14 and 10 days respectively in Songhor, Kermanshah and Sarpol_e_Zahab. The trend of changes in the studied parameters for barley was the same as maize but the rate of changes was estimated less.
Results of simulation indicated the increasing of seasonal potential evapotranspiration and maximum daily evapotranspiration and decreasing of crop growth period in all three stations under climate change scenarios. The effect of climate change on studied parameters for both crops in Songhor station (cold region) will be higher than Kermanshah (mild) and much more than Sarpol_e_Zahab (tropical). In addition, the percentage of changes in evapotranspiration of spring crop (maize) under climate change will be more than winter crop (barley).

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.

In recent years human activities induced increases in atmospheric carbon dioxide (CO2) which caused global warming and climate change. Temperature increasing resulting from climate change lead to increase the evapotranspiration which play key role in determination of crop water requirement. Potential evapotranspiration (ETo) was calculated using Ref-ET model and FAO56 formula to assess the climate change impacts on ETo for the baseline (1971-2000) and future (2016-2045 and 2070-2099) periods in Shahrood city. Hence, climate change scenarios were generated from 13 coupled atmosphere-ocean general circulation models (AOGCM) under two greenhouse gases emission (GHG) scenarios (A2 and B1) and then was downscaled using LARS-WG model. The calculated ETo for different climate change scenarios were compared with those of baseline period. Result showed that in first future period (2016-2045), mean of ETo is increased about 4.5 percent for both A2 and B1 scenarios. In 2070-2099 period, mean of ETo is increased about 15 and 8 percent for A2 and B1 scenarios, respectively. It was also found that ETo is increased more in warmer months in comparison with cold months.

Climate change impacts on climate parameters such as rainfall, temperature, evapotranspiration and water resources. The aim of this research is the evaluation of the effect of climate change on climate parameters and evapotranspiration by the use of HADCM3 model in an arid (BandarAbbas) and semiarid station (ShahraKord). In order to simulate daily data of rainfall, sun shine, max temperature and min temperature based on a stochastic method, LARS-WG downscaling tool was used. This stochastic weather generator downscaled the climate of two synoptic stations by the use of HADCM3 model and A2, A1B, B2 emission scenarios during 2011-2030. The largest increase in the amount of max and min temperature was observed under A1B scenario in July in ShahreKord station. Winter precipitation increases in ShahreKord station base on all of the scenarios that it can be referred to the strengthening of the rainfall flows systems such as Mediterranean and Sudan Ian. Increased winter rainfall inBandarAbbas station can be attributed to the strengthening of the monsoon rain system. In both of stations, evapotranspiration increased in all of months and warm seasons. This increase is more considerable in warm months. Totally impact of climate change on rainfall behavior is different in both stations, while the other parameters of both stations have similar behavior.

Iran was located in the arid belt and desert region of the world. This climatic situation was presented in several parts of Iran such as Qazvin. Evapotranspiration is one the most important parameter which was effect on agricultural development. There are different methods for estimating this item which selected by climatic conditions. This research concentrates to evaluate 28 empirical and combined methods in Qazvin plain. Lysimeter data was selected as a base data and daily results of 28 methods was evaluated with lysimeter. The results showed that Hargreaves-Samani as empirical method and temperature group with r=0.87, RMSE=0.34 mm/day and MBE=-0.71 mm/day is best method in Qazvin. Orang from pan evaporative method was more appropriated daily results with lysimeter data. To estimate evaporate from free water surface, Penman 1948 is the best method which has highest correlation coefficient (0.7) and lowest standard error (1.65 mm/day) and also mean absolute error (8.63E-6 mm/day). Amount of average evaporating from water surface with Penman method was calculated 10.18 mm/day.

Reconnaissance Drought Index (RDI) is based on fitting a Log-normal distribution to the ratio of precipitation to evapotranspiration (ETo) values in selected periods. In this index value of ETo were calculated based on mean temperature by Thorenth-Waite (Th) method. Th method، may underestimated ETo values comparing to the actual in arid and semi arid regions. The log-normal distribution may not be fitted to the ratio of precipitation to ETo values of some regions. In order to investigate the effects of these two limitations on drought situations'' changes، meteorological parameters have been used during 50 years period at 8 Synoptic Stations of Iran. In the first step، the values of RDI (Th) for any stations during the mentioned time were calculated. Then، ETo values were calculated from best fitted empirical equation in any situation of lack of parameters. Subsequently RDI (select) index were established. The Kolmogorov–Smirnov (KS) test is used to assess the goodness of fitting most appropriate distribution function to the ratio of precipitation to ETo values. Then، according to equi-probability transformation the values of RDI (Th) were modified to *RDI (Th). The occurrence of different classes of drought according to RDI (select) and/or *RDI (Th) comparing to RDI (Th) showed the elimination of any mentioned limitations may leads to changing the amount of occurrence of any drought classes in RDI (Th). Hence، The RDI (Th) modified to *RDI (select) by estimating ETo values from selected method and applying appropriate distribution function to the ratio of precipitation to ETo values.