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Renewable energy technologies convert renewable resources into forms of energy that can complement or replace conventional energy sources such as fossil fuels. Wind, solar, earth energy systems, small-scale hydro systems and biomass (eg. Straw, wood, corn) are all forms of renewable energy. Wind, solar and small-scale hydro systems have zero Greenhouse Gas Emissions. For example, for every kilowatt-hour generated by a wind turbine instead of by burning fossil fuels, about one kilogram of co2 is not emitted into the atmosphere (Alberta Environmentally Sustainable Agriculture Council, 2001). Wind turbines capture wind energy and convert it to electricity. Wind energy systems can either be small, stand-alone “off-grid” systems, or connected to the provincial power grid. Because wind is an intermittent resource, a back-up system is needed. Wind systems require an average annual wind speed greater then 15 kilometers per hour may only be feasible in some part Earth. Electricity generating coasts are reported to have dropped from $0.25 per kilowatt-hour (kWh) in the 1980’s to below $0.10 per kWh in 2001. One opportunity for farmers is the potential to lease land to wind energy producers. Every ten days, the earth receives solar energy of an amount equal to the world’s entire fossil fuel reserves, and approximately one precent of this is converted to wind energy (Freris, 1990). This solar radiation is converted to wind energy as a result of the unequal heating of the equator as compared to the poles, and of the oceans as compared to the continents. This unequal heating leads to motion within the atmosphere as it tries to equalize its pressure- resulting in what we know as wind. A second cause wind is the motion of the earth. Many meteorological quantities are transported via air currents. In fact, because of the winds role in the transmission of physical and meteorological parameters atmospheric, are very important. Further movement of wind as a source of new and inexhaustible energy is considered. In recent years, the kinetic energy of wind as a source of new and inexhaustible energy is considered by many countries. Recently use of wind energy as one of the most popular renewable power resources for producing electrical energy, is growing up. The purpose of this study is evaluating the amount of wind energy production and finding the windward areas in the Ilam province. In this study, the wind speed and wind direction daily data from 7 weather stations in the state (from established year to 2013) and 15 weather stations from outside of the state boundary are collected. At First, the days with incomplete data are eliminated, then for unifying dimensions of each data base, the average of long-term daily data are calculated. For averaging the long-term daily data related to wind speed and wind direction, two data bases with the 366*22 dimensions are established separately. By utilizing the two data bases, the orbital and meridional components are calculated. Based on the orbital and meridional components, and using krigingchr('39')s geostatistics method, the orbital and meridional components of wind speed of the area study, are estimated. Finally, tow data bases with the 896*366 dimensions are created for the Ilam province that 366 of data are belonged to the orbital and meridional components of wind speed average and 896 of data are belonged to estimated cells in the Ilam province. The dimensions of each cell were 4.7*4.7 square kilometers. With cluster analysis of the two data bases, the windward areas of Ilam province are specified. For better understanding of wind speed characteristics, the monthly maps of windward areas of Ilam province and the annual coefficient of variability of wind speed are plotted. Based on wind speed, wind density, and the size of utilized wind turbine (rotatory radius 5, 10, 15, 20 meters), the amount of wind power generations (from 896 cells), are estimated. The annually and monthly equipotential maps of wind power generation are plotted. The results show that Mehran is the most windward area in the Ilam province. Also the western areas of Ilam province have more wind speed availability compared to the eastern areas of the Ilam province. Darehshahr has minimum average wind speed in all months of the year. The variability coefficient of wind speed in the Ilam province is between 17.2 and 40.6. The northern areas of the state have less variability coefficient compared to other areas of the state. The evaluation findings of the four mentioned wind turbines show that the wind turbines can produce maximum wind power generation at the west areas of the state (Mehran). Among all of the months in a year, the July has the most wind power generation, as the time viewpoints. By utilizing the wind turbines with five meters blades, the amount of wind production threshold is about 1 to 11 million watts per squared meters. Also by utilizing the wind turbines with 10 and 15 meters blades, the amount of minimum and maximum annual wind energy generation can be about 5 to 45, and 12 to 101, million watts per squared meters, respectively.

Introduction: Precipitation is one of the most important and sensitive parameters of the tropical climate that influence the catchments hydrological regime. The prediction of rainfall is vital for strategic planning and water resources management. Despite its importance, statistical rainfall forecasting, especially for long-term, has been proven to be a great challenge due to the dynamic nature of climate phenomena and random fluctuations involved in the process. Various methods, such as time series and artificial neural network models, have been proposed to predict the level of rainfall. But there is not enough attention to global warming and climate change issues. The main aim of this study is to investigate the conformity of artificial neural network and time series models with climate scenarios. Materials and Methods: For this study, 50 years of daily rainfall data (1961 to 2010) of the synoptic station of Urmia, Tabriz and Khoy was investigated. Data was obtained from Meteorological Organization of Iran. In the present study, the results of two Artificial Neural Network (ANN) and Time Seri (TS) methods were compared with the result of the Emission Scenarios (A2 & B1). HadCM3 model in LARS-WG software was used to generate rainfall for the next 18 years (2011-2029). The results of models were compared with climate scenarios over the next 18 years in the three synoptic stations located in the basin of the Lake Urmia. At the first stage, the best model of time series method was selected. The precipitation was estimated for the next 18 years using these models. For the same period, precipitation was forecast using artificial neural networks. Finally, the results of two models were compared with data generated under two scenarios (B1 and A2) in LARS-WG. Results and Discussion: Different order of AR, MA and ARMA was examined to select the best model of TS The results show that AR(1) was suitable for Tabriz and Khoy stations .In the Urmia station MA(1) was the best performance. Multiple Layer Perceptron with a 10 neurons in hidden layer and the output layer consists of five neurons had the lowest MSE and the highest correlation coefficient in modeling the values of annual precipitation. So MLP was determined as the best structure of neural network for rainfall prediction. According to results, precipitation predicted by the ANN model was very close to the results of A2 and B1 scenario, whereas TS has a significant difference with these scenarios. Average rainfall predicted by two A2 and B1 scenarios in Urmia station has more difference than other stations. Based on the B1 scenario, precipitation will increase 11 percent over the next two decades. It will decrease 10.7 percent according to A2 emissions scenario. According to ANN models and two A2 and B1 scenarios, the rates of rainfall will increase in Tabriz and Khoy stations. However, according to TS model, rainfall will decline 5.94 and 3.63 percent for these two stations, respectively. Conclusion: Global warming and climate change should have adverse effects on groundwater and surface water resources. Different models are used for simulating of thes effects. But, conformity of these models with the results of climate scenarios is an issue that has not been addressed. In the present research coincidence of TS model, ANN model and climate change scenarios was investigated. Results show under emissions scenarios, during the next two decades in Tabriz and Khoy stations, precipitation will increase. In Urmia station B1 and A2 scenario percent increase by 11 percent and 10.5 percent decline predicted, respectively. The results of Roshan and et al (4) and Golmohammad and et al, (7) investigations show increasing trend in the rainfall rate and confirming the results of this study According to results, the performance of ANN model is better than TS model for rainfall prediction and its result is similar to climate change scenarios. Similar results have been reported by Wang et al (29) and the Norani et al (20). Due to the significant difference between the TS and climate scenarios used in the study area, is not recommended, though it can be used as a plausible climate scenario to predict the precipitation of stations in the future studied. At the end, it is suggested that the similar studies carried out in a larger number of stations in the country with respect to global warming and climate change, to determine the validity of the methods used to the predicted rainfall.

Introduction: Due to the effects of climate change on water resources and hydrology, Changes in low flow as an important part of the water cycle, is of interest to researchers, water managers and users in various fields. Changes in characteristics of low flows affected by climate change may have important effects on various aspects of socioeconomic , environmental, water resources and governmental planning. There are several indices to assess the low flows. The used low flow indices in this research for assessing climate change impacts, is include the extracted indices from flow duration curve (Q70, Q90 and Q95), due to the importance of these indices in understanding and assessing the status of river flow in dry seasons that was investigated in Tang Panj Sezar basin in the west of Iran. Materials and methods: In this paper, the Tang Panj Sezar basin with an area of 9410 km2 was divided into 6 smaller sub catchments and the changes of low flow indices were studied in each of the sub catchments. In order to consider the effects of climate change on low flow, scenarios of temperature and precipitation using 10 atmospheric general circulation models (to investigate the uncertainty of GCMs) for both the baseline (1971-2000) and future (2011-2040) under A2 emission scenario was prepared. These scenarios, due to large spatial scale need to downscaling. Therefore, LARS-WG stochastic weather generator model was used. In order to consider the effects of climate change on low flows in the future, a hydrologic model is required to simulate daily flow for 2011-2040. The IHACRES rainfall-runoff model was used for this purpose . After simulation of daily flow using IHACRES, with two time series of daily flow for the observation and future period in each of the sub catchment, the low flow indices were compared. Results Discussion: According to results, across the whole year, the monthly temperature in the future period has increased while rainfall scenarios show different variations for different months, also within a month for different GCMs. Based on the results of low flow indices, in most cases, the three indices of Q70, Q90, and Q95 will show incremental changes in the future compared to the past. Also, the domain simulation by 10 GCMs for all three indices is maximum in Tang Panj Sezar and less for other sub catchments, which is related to better performance of IHACRES model in smaller sub catchments. In order to investigate the uncertainty of type changes in different indices in every sub catchment, changes in any of the indices were considered based on the median of GCMs. To achieve the correct type of changes in low flow indices, the amount of error in a simulation of the indices of IHACRES rainfall-runoff model should also be taken into consideration. Therefore, considering the error, the three indices Q70, Q90 and Q95 in all sub catchments (except for Tang Panj Sezar) will have the relative increase in the future period. The improvement of low flow state in the future period is related to the changes occurred in the state of climate scenarios. As the results indicated, most often, there is an increase in rainfall in dry seasons. Also, in different months of the wet season wet season, if the result of changes in quantity of rainfall is incremental, it can lead to an increase in river flow through groundwater recharge. On the other hand due to the limestone and karst forms in most of the basin area, water storage ability and increase the amount of river flow during low water season in this area is expected. The study on rainfall quantity in Tang Panj Sezar sub catchment also indicated that, there will be no significant increase or decrease in the quantity of rainfall in the dry season. Thus, it is expected that there will not be significant changes in low flow indices. In this sub catchment, changes in various low flow indices do not match perfectly, so more difficult to obtain reliable results. With regard to incremental changes of Q95, low flow index with less uncertainty, as well as improving indices of low flow in other sub-basins, it is possible to predict a relatively better state for low flow indices of Tang Panj Sezar in the future period. Conclusion: Using temperature and rainfall scenarios to simulate river flow in the future, a relative increase of all three low flow indices Q70, Q90 and Q95 was predicted compared with the past period. Although all three of mentioned indices show the amount of low flow in the dry season, it is recommended that only two indices of Q90 and Q95 to assess the effects of climate change be considered. Q90 and Q95 indices are more suitable indices than Q70 for studying the effects of climate change on low flow state. These two indices indicate less quantity of flow in dry seasons; therefore, the changes of the two indices are more important in identifying the low flow state. However, there is less uncertainty in the estimation of the two Q90 and Q95 indices than Q70.

Introduction: The final energy consumption per capita in Iran in the agricultural sector is 3.4, as well for household sector is 2, besides the commercial and public sectors are 1.6, and transportation and industry are 1.4 times the global average. This is due to low efficiency in operation, high energy consumption, as well as the use of energy goods and services. The use of renewable energy in the agricultural sector, while increasing the security of energy supply, will reduce global warming, stimulate economic growth, create jobs, and increase per capita income and social justice and environmental protection in all areas. The purpose of this study is to investigate farmers' preferences for using solar energy in Sari.Materials and Methods: The Choice Experiment methods allow researchers to focus on valuing final changes as multidimensional features rather than discrete changes. Choosing between options encourages respondents to examine their preferences in detail related to different management programs. The Choice Experiment test approach consists of several steps, which include designing the Choice Experiment test, determining the sample size and method of data collection, estimation process, and modeling the Choice Experiment test. Designing a Choice Experiment test consists of five important steps which are defining attributes, determining the relevant levels, conducting an experimental design, constructing Choice sets, and measuring preferences. After determining the criteria affecting the prioritization of renewable energy, liketechnical, environmental, economic, social, and political criteria, in order to investigate the willingness to Pay of Sari farmers, a test questionnaire was designed. The criteria obtained from the review of prioritization of renewable energy were considered as the attributes of the Choice Experiment and the price attribute was added to the above criteria. A total of six technical, economic, social, political, environmental, and price attributes were considered to investigate farmers' willingness to pay. In the review of the studies and the current situation, the levels of each of the attributes were determined. To determine the levels of price attribute, these points were considered; the price of agricultural electricity per kilowatt-hour is 383 Rials, which was approximately 400 Rials for the current situation.Results and Discussion: To investigate the farmers' preferences for using solar energy, 98 questionnaires of farmers in Sari were completed in September 2019. Each questionnaire included 8 choice set cards and each card included three options, based on which, the number of observations in Sari is equal to 2352 observations. The purpose of this study is to investigate the preferences of farmers in Sari for the use of solar energy. For this purpose, the Multinomial logit, the Random parameter logit, the latent class, and the Random parameter logit latent class are used. Based on the results of the Multinomial logit method, environmental and price attributes at the level of one percent and economic attribute at the level of five percent are statistically significant, but political, social, and technical attributes are not statistically significant. The Alternative-specific Constants (ASC) in the first and second options are not statistically significant. Based on the results of the Random Parameter Logit estimation method, environmental, economic and price attributes are statistically significant at the level of one percent. Technical, political, and social attributes are not statistically significant, which shows that farmers do not make a significant difference between these two attributes. The Alternative-specific Constants (ASC) are significant in the first option at the level of five percent and the second option at the level of one percent. The results of latent class estimation show that in the first class, environmental, economic, political, social, and price attributes are statistically significant at the level of one percent and technical attribute at the level of ten percent. The Alternative-specific Constants (ASC) are statistically significant at the level of one percent in the first class. In the second class, technical attribute at the level of five percent and environmental attribute at the level of ten percent are significant, besides other attributes in the second class are not statistically significant. The most sensitive class is the first class and farmers of the second class are considered the base class. The results obtained from the Bayesian and Akaike criteria of different classes showed that the two classes have the lowest values of BIC and AIC criteria and the class is appropriate. After determining the appropriate class, the model was estimated. The results of model estimation were calculated by the Latent Class Random Parameter logit method. In the first class, environmental attributes and price are significant at the level of one percent and economical attributes at the level of five percent. Also, the Alternative-specific Constants (ASC) is significant at the level of one percent, but, in the second class, the attributes are not statistically significant. Technical, environmental, economic, political, social, and price attributes, as well as the option of status quo or the Alternative-specific Constants (ASC) in the second class, do not affect farmers' utility due to the lack of statistical significance.Conclusion: A comparison of the results obtained from the four methods shows that the highest value of the estimated coefficient for environmental attributes was in the latent class method and the lowest value was in the multinomial logit method; Comparison of fitted methods shows that the highest Log-likelihood is related to the latent class random parameter logit method and the lowest value is related to the multinomial logit method. Accordingly, the highest value of Akaike and Bayesian criteria is related to the multinomial logit method and the lowest value is related to the latent class random parameter logit method which is better than other methods according to the good fit criterion.

In recent years, repeated droughts have had negative impacts on agricultural products. Farmers can reduce the effects of droughts by using adaptive strategies. The aim of this study was to investigate farmers' strategies in response to drought consequences and to examine the factors affecting the use of these strategies. In terms of purpose, the study is an applied one, and the method is descriptive-analytical. The data gathering tool was a questionnaire and interviews. The statistical population of the research consisted of the rural households in Roshtkhar County (N = 13716). Using the Cochran formula, 373 farmers were selected as the sample. For data analysis, the unlimited exploratory uncertainty test and the binary logistic model (LM-Newton-Marcard-Raphson method) were used. The results showed that decreased water resources (0.882), reduced crop area (0.735) and increased living expenses (0.698) were the most important consequences of drought. Drought-tolerant plants such as saffron, irrigation, and non-agricultural jobs were the most important strategies adopted by the farmers in response to drought consequences. Variables such as age, sex, indigenous knowledge, labor force, income, farmers’ experience, and farm size were significantly associated with the application of strategies. The results of the research can be used for more adaptation of farmers to drought consequences in the villages of the county.

Introduction: Particulate matter refers to the combination of atmospheric pollutants that a portion of this particulate is bioaerosol. The aim of this study was the evaluation of bacteria, fungi and biomass in particulate matter in ambient air of Khorramabad during summer and autumn 2012. Materials and Methods: This study was a cross sectional study that conducted in Khorramabad city during summer and fall 2012. Sampling has been done via high-volume sampler. The special cultures were used for cultivation and determination of fungal and Heterotrophic Plate Count (HPC (and Bradford method were used to determine bacteria and protein as biomass indicator, respectively. Relationship between these variables with metrological parameters was evaluated too. Results: The highest PM10 in July (257.18 µg/m3) and lowest in September (92.45 µg/m3) had been recorded. The highest amount of bacteria and fungi were measured as monthly in November (605 No/m3) and December (120 No/m3), respectively. The highest of protein concentration was measured in August, September and December (27-30 µg/m3). With the increase in PM10, biomass concentration in the air showed a meaningful increase. Conclusion: Biomass concentration in the air increased with increasing PM10 but it had no significant effect on the concentration of bacteria and fungi in the air. Meteorological factors such as temperature, humidity, wind speed, solar radiation and the amount of exposure time had a significant impact on bioaerosol concentrations in the air.

Introduction: The rapid population growth in Iran and the corresponding increases in water demands, including drinking water, industry, agriculture and urban development and existing constraints necessitate optimal scheduling necessity in use of this crucial source. Furthermore, the phenomenon of climate change as a major challenge for humanity can be considered in future periods. Climate change is caused by human activity have also been identified as significant causes of recent climate change, referred to as "global warming". Climate change indicates an unusual change in the Earth's atmosphere and climate consequences of the different parts of planet Earth. Climate change may refer to a change in average weather conditions, or in the time variation of weather around longer-term average conditions. A Warmer climate exacerbates the hydrologic cycle, altering precipitation, magnitude and timing of runoff. The purpose of this study was to evaluate the effect of climate change on water consumption and demand in Bar river basin of Neighbor. Climate change affects precipitation and temperature patterns and hence, may alter on water requirements and demand at three sectors; agriculture, industry and urban water. Materials and Methods: At present, Global coupled atmosphere-ocean general circulation models (AOGCMs) are the most frequently used models for projection of different climatic change scenarios. AOGCMs models represent the pinnacle of complexity in climate models and internalize as many processes as possible. These models are based on physical laws that are provided by mathematical relations. AOGCMs models used for climate studies and climate forecast are run at coarse spatial resolution and are unable to resolve important sub-grid scale features such as clouds and topography. As a result AOGCMs output cannot be used for local impact studies. Therefore, downscaling methods were developed to obtain local-scale weather and climate, particularly at the surface level, from regional-scale atmospheric variables that are provided by AOGCMs. Four different downscaling methods exist: regression methods, weather pattern-based approaches, stochastic weather generators, which are all statistical downscaling methods, and limited-area modeling. For this research, HadCM3 and statistical downscaling model (SDSM), precipitation and temperature variations were simulated under A2 scenario. Then the impacts of these variations on Bar River discharge were analyzed, i.e. water resources at three sectors of agriculture, industrial and potable water under climate change during 2011-2040 using WEAP. Results at first part of simulation showed that temperature is increasing and precipitation is decreasing resulted in decreasing of Bar discharge. According to the decreasing on Bar discharge, water allocation was simulated under these conditions of agricultural and industrial development and increasing of population with WEAP. Simulation showed that watershed will face increasing of water demand for all three sectors; agriculture, industry and drinking water, so the highest water shortage would be in agricultural demand and then industry and drinking water respectively. IWRM is the basic managerial need to rest the demands especially for drought periods. Current allocation process is based on steady state conditions while allocation pattern would be done under climate change conditions so we need to be reinvestigat the last allocations for all three sectors. Another challenge for this watershed refers to the gardens and steel factory of Khorasan that they need to use new technologies for reduction of their water needs. Results Discussion: In this study, the outputs of General Circulation Models (HadCM3) and statistical downscaling model (SDSM) have been used to investigate the changes of rainfall and temperature under A2 scenario in Bar river basin of Neishaboor and assess the impacts of this changes on the Bar river’s discharge. Finally, using WEAP model under climate change conditions for the period of 2011-2040, the status of basin water resources was evaluated for the three sectors (agricultural, domestic and industrial). The results indicated increased temperature in the Arie station amounting to 16 percent and rainfall reduction in the Arie and Taghan stations amounting to 3.9 and 8.75 percent respectively. Under these conditions, according to the increasing water demands of agricultural and industrial sectors in the future, there will be a shortage of water supply resources in the region. So the agricultural sector with 12 percent will have the highest percentage of water shortage and water scarcity and of the industrial sector will be 2%. However, the drinking water or domestic demand will not face a shortage of supplies. Conclusion: Therefore given that the most part of agriculture sector’s share of basin is allocated to orchards and on the other hand the most shortages are related to agriculture, then while creating an integrated management of water resources, development and use of modern methods of irrigation during the period of 2011 - 2040 would seem to be necessary.

Background and aims: Inappropriate solid waste management industry in recent years has created a lot of risks. As a result, the health, safety and the humans environment is at risk for it. main objective of this study is solid waste management assessment of 10 large food industries in Tehran. Methods: This study is descriptive and sectional based on field visits, questionnaires, preparation of the database and the analysis of the results. The selected units were categorized based on ISWMI scale. The questionnaire consisted of 47 questions related with key elements of industrial solid waste management. This questions consist of 10 separate areas. 10 large industrial units of the food industry in Tehran were randomly selected. Results: The results showed that the index of selected industrial units based on ISWMI scale (based on100) is: 96 for J company (very good), 49 for A company (week), 85 for H and I company (very good), 72 for E, 77 for F and 80 for G (good), 57 for B, 63 for C and 64 for D company (moderate). Conclusion: According to the results of this study, industrial waste management situation in most of the selected units are above average. However it seems that factors such as a lack of senior management commitment and belief to implementation of the waste management system, the lack of requirement industrial units to employ an expert in environmental or environmental health and the lack of a comprehensive at the national level plan for waste management of the most effective factors to determine ISWMI values in industries.

Introduction and purpose: Groundwaters provide the main source of drinking water in Mazandaran province; however, the high concentration of soluble salts and hardness in these waters result in probable sedimentation and corrosion in water distribution systems of these areas. Therefore, the aim of this study was to determine the sedimentation and corrosion potential in the drinking water resources in Amol, Babol, Ghaemshahr, Sari, and Neka in the second and first halves of 2015 and 2016, respectively. Methods: A total of 192 water samples from selected cities of Mazandaran province were tested using determining parameters of sedimentation and corrosion indices by a standard method. The indices, such as Langelier saturation, Ryznar stability, Puckorius scaling, Aggressive, and Larson-Skold were determined using relevant data. Results: The results of the study showed that out of 192 analyzed samples in the surveyed cities, 76 and 81.3 % of them had the sedimentation propertiesand potentials considering Ryznar stability and Langelier saturation indices, respectively. With regard to Puckorius scaling index, sedimentation potential was observed in 68.8 % of water samples. Aggressive and Larson-Skold indices have also showed sedimentation properties of water. Conclusion: It can be concluded that the drinking water in the water distribution systems of these cities has relatively sedimentation properties.

Introduction: Forecasting the inflow to the reservoir is important issues due to the limited water resources and the importance of optimal utilization of reservoirs to meet the need for drinking, industry and agriculture in future time periods. In the meantime, ignoring the effects of climate change on meteorological and hydrological parameters and water resources in long-term planning of water resources cause inaccuracy. It is essential to assess the impact of climate change on reservoir operation in arid regions. In this research, climate change impact on hydrological and meteorological variables of the Shahcheragh dam basin, in Semnan Province, was studied using an integrated model of climate change assessment. Materials and Methods: The case study area of this study was located in Damghan Township, Semnan Province, Iran. It is an arid zone. The case study area is a part of the Iran Central Desert. The basin is in 12 km north of the Damghan City and between 53° E to 54° 30’ E longitude and 36° N to 36° 30’ N latitude. The area of the basin is 1,373 km2 with average annual inflow around 17.9 MCM. Total actual evaporation and average annual rainfall are 1,986 mm and 137 mm, respectively. This case study is chosen to test proposed framework for assessment of climate change impact hydrological and meteorological variables of the basin. In the proposed model, LARS-WG and ANN sub-models (7 sub models with a combination of different inputs such as temperature, precipitation and also solar radiation) were used for downscaling daily outputs of CGCM3 model under 3 emission scenarios, A2, B1 and A1B and reservoir inflow simulation, respectively. LARS-WG was tested in 99% confidence level before using it as downscaling model and feed-forward neural network was used as raifall-runoff model. Moreover, the base period data (BPD), 1990-2008, were used for calibration. Finally, reservoir inflow was simulated for future period data (FPD) of 2015-2044 and compared to BPD. The best ANN sub-model has minimum Mean Absolute Relative Error (MARE) index (0.27 in test phases) and maximum correlation coefficient (ρ) (0.82 in test phases). Results and Discussion: The tested climate change scenarios revealed that climate change has more impact on rainfall and temperature than solar radiation. The utmost growth of monthly rainfall occurred in May under all the three tested climate change scenarios. But, rainfall under A1B scenario had the maximum growth (52%) whereas the most decrease occurred (–21.5%) during January under the A2 climate change scenario. Rainfall dropped over the period of June to October under the three tested climate change scenarios. Furthermore, in all three scenarios, the maximum temperature increased about 2.2 to 2.6°C in May but the lowest increase of temperature occurred in January under A2 and B1 scenarios as 0.3 and 0.5°C, respectively. The maximum temperature usually increased in all months compared to the baseline period. Minimum and maximum temperatures enlarged likewise in all months, with 2.05°C in September under A2 climate change scenario. Conversely, solar radiation change was comparatively low and the most decreases occurred in February under A1B and A2 climate change scenarios as –4.2% and –4.3% , respectively, and in August under the B1 scenario as –4.2%. The greatest increase of solar radiation occurs in April, November, and March by 3.1%, 3.2%, and 4.9% for A1B, A2, and B1 scenarios, respectively. The impact of climate change on rainfall and temperature can origin changes on reservoir inflow and need new strategies to adapt reservoir operation for change inflows. Therefore, first, reservoir inflow in future period (after climate change impact) should be anticipated for the adaptation of the reservoir. A Feed-Forward (FF) Multilayer-Perceptron (MLP) Artificial Neural Network (ANN) model was nominated for the seven tested ANN models based on minimization of error function. The selected model had 12 neurons in the hidden layer, and two delays. The comparison of forecasted flow hydrograph by selecting an ANN model and observed one proved that forecasted flow hydrograph can follow observed one closely. By comparison with the IHACRES model, this model displayed a 54% and 46% lower error functions for validation data. The selected model was used to forecast flow for the climate change scenarios of the future period. Conclusions: The results show a reduction of monthly flow in most months and annual flow in all studied scenarios. The following main points can be concluded: • By climate change, flow growths in dry years and it declines in wet and normal years. • The studied climate change scenarios showed that climate change has more impact on rainfall and temperature than solar radiation.