جستجوی مقالات مرتبط با کلیدواژه « lars-wg » در نشریات گروه « آب و خاک »

Climate change has greatly challenged the management of water resources due to its impact on water requirements of crops. In this research, the effect of climate change on water requirement of Rice crop was simulated in the study area of ​​ the Tajan watershed and its changes until 2060 were studied. To investigate climate change, Lars-WG software and HadGEM2 model outputs under (RCP 2.6) and (RCP 8.5) climate scenarios have been used. To evaluate the impact of climate change in the next 40 years on water requirement, using CROPWAT 8.0 software, potential and actual evapotranspiration and effective rainfall were calculated for the future period and crop water requirement was determined and compared with the water requirement of the current period. The results showed that the mean water requirement of Rice crop in the RCP 2.6 scenario were predicted in 2040-2021 and 2041-2060 periods is equal to 658.27 and 658.46 mm, respectively, and in the RCP 8.5 scenario in 2040-2021 and 2041-2060 periods were predicted to be equal to 672.93 and 673 mm, respectively. Therefore, according to both climate scenarios, the water requirement has increased in all time periods compared to the current period, which is about 648 mm.Mazandaran is a province based on agricultural production and it has long been one of the centers of Rice cultivation as the second strategic product of the country. planting Rice is very vital and important for the economy of the whole region. Therefore, it is necessary to study the evaluation of changes in the water requirement of Products in response to climate change conditions.Various researchers investigated the effects of climate change on water requirement, some of which are mentioned below.Casolani et al. (2020) in a study investigated the effect of climate change on water requirement of crops. The results showed that the water requirement in the periods of 2011-2040, 2041-2070, and 2071-2100 will increase by 6, 19, and 24 percent, respectively, compared to the current period. Jia et al. (2021) investigated the changes in the water requirement of crops under climate change conditions. The results showed that the effective rainfall increases by 0.98 mm per year and the water requirement decreases by 2.27 mm per year.

Investigating the effects of climate change on improving agricultural productivity is of great importance. It is predicted that the rising temperature trend will continue in the 21st century, leading to changes in the climatic conditions of  regions. Changes in precipitation distribution ، temperature and water resources are considered as harmful consequences of climate change، which could have detrimental effects on agricultural production. . In this study، the impact of climate change and cultivation on different dates was examined in terms of the yield of the 'Parsi' spring wheat variety in the Qazvin Plain. This study covered the period from 2021to 2100, comparing two information sources, LARS-WG and DKRZ, for producing annual climate change data and using the Aquacrop model to simulate the plant’s response to the mentioned changes. During the periods 2021-2040، 2041-2060، 2061-2080، and 2081-2100، the best planting date (February 4, February 20, March 5, March 20, and April 4) for increasing wheat yield was evaluated.  According to the model results in both scenarios 4.5 and 8.5، in all four periods (2021-2040، 2041-2060، 2061-2080 and 2081-2100), the spring wheat yield will increase compared to the baseline.. The highest yield in all of these periods and models is predicted for the period 2081-2100 under the LARS-WG climate model of in scenario 8.5,  assuming planting on February 4, with an estimated yield of 12.43 tons per hectare and a standard deviation of 0.31 tons per hectare. the lowest yield is expected for the period 2021-2040 under LARS-WG climate conditions in  scenario4.5, with planting on April 4, with an estimated yield of 7.87 tons per hectare and a standard deviation of 0.36 tons per hectare. In all four periods (2021-2040، 2041-2060، 2061-2080 and 2081-2100), February 4 is recommended as the most suitable planting date to increase wheat yield in the Qazvin plain.

The average weather condition in a specific region is defined as climate. The diversity of climatic variables is effective in determining the climate of a region and causes the formation of diverse and different climates. One of the effects of climate change is that causes an increase or decrease in a climate zone and, as a result, a shift in climate zones. Climate classification is an attempt to identify and recognize the differences and similarities of climate in different regions and to discover the relationships between different components of the climate system. Climate classification indicators are used to visualize current climate and quantify future changes in climate types as predicted by climate models. The studies conducted on these methods show that climatic variables affecting experimental methods such as temperature and precipitation should be considered effective variables in determining climatic boundaries in a new way. The De Martonne aridity index is an empirical index for climate classification based on two components, precipitation and temperature. Due to its high accuracy, and the use of variables that are more accessible and can be measured at most meteorological stations, De Martonne’s index has received more attention from researchers and has been used in many studies of climate change. Therefore, the purpose of this research is to evaluate the effects of climate change on the climatic classification of Iran.

To investigate the effects of climate change on the climatic classification of Iran, the De Martonne aridity index has been used. To show the effects of climate change in the past and the future on Iran's climate, data from 120 meteorological stations of Iran, which are distributed in different locations with different climates, were collected and analyzed in the statistical period of 1933-2022. The climatic condition of Iran in the base period was determined according to the De Martonne aridity index. In addition, to investigate the effects of climate change in the coming periods on the climatic classification of Iran, the data related to the output of the CanESM2 model, which is one of the CMIP5 models that is hybridized by the Canadian Center for Climate Modeling and Analysis (CCCMA) by combining CanCM4 and CTEM models, were used. To examine the changes in climatic classes of Iran under different scenarios and conditions, the output of two release scenarios, RCP2.6 and RCP8.5, were utilized. Due to the large-scale output of General Circulation Models (GCM), the output of this model was downscaled using the LARS-WG model. The LARS-WG model, which is considered one of the most famous and widely used models for downscaling weather data, was used to generate precipitation values, minimum and maximum temperatures, as well as daily radiation, under base and future climate conditions.

According to the results, the majority of Iran (90.49%) has an arid and semi-arid climate. The percentage of arid climate is 68.82%, while that of semi-arid climate is 21.97%. Therefore, Iran should be called an arid and semi-arid country in terms of climate. By analysis of the effects of climate change indicates that in future periods, the precipitation and average temperature will increase. This increase will be greater under the RCP8.5 scenario than the RCP2.6 scenario. The study of the climatic classification of Iran in the coming periods indicates that the majority of the country will continue to experience arid and semi-arid climates. The sum of arid and semi-arid climates will reach its lowest level in the period of 2020-2041. This is following the RCP2.6 scenario, after which these climates are expected to expand once more. According to the RCP8.5 scenario, during the periods of 2021-2040, 2041-2060, and 2061-2080, the total area of arid and semi-arid climates will decrease. However, from 2081 to 2100, this trend will be reversed, increasing in these climates. According to the results of this research and according to the forecast, although according to different release scenarios, the difference in the area of different classes can be seen, in the future, arid and semi-arid climatic zones will still form the majority of Iran.

In this research, by using the latest available data, Iran's climate is classified by the De Martonne aridity index, and then the changes in Iran's climate classes under the effects of climate change in the future periods, according to the output of the CanESM2 model from the CMIP5 modes, which is downscaled using the LARS-WG model. It has been investigated according to two emission scenarios, RCP2.6 and RCP8.5. The results indicated that the arid climate with 68.82% and the semi-arid climate with 21.97% constitute the largest area of Iran. The remaining climatic classes collectively comprise less than 10% of Iran's area. Therefore, Iran should be called an arid and semi-arid country in terms of climate. Investigating the effects of climate change on precipitation and temperature showed that both precipitation and average temperature will increase in future periods. However, the increase in both variables will be greater under the RCP8.5 scenario. The study of the climatic classification of Iran in the coming periods indicates that the majority of the country will continue to experience arid and semi-arid climates. The findings of this study indicate the necessity of addressing the issue of climate change and the importance of involving experts and macro planners in the analysis of the effects of climate change. It is suggested to use the output of other GCM models in future research due to the uncertainty of climate scenarios. Also, the use of diverse climate classification methods that incorporate other variables is suggested for more precise identification of climate characteristics.

One of the most important effects of climate change is on plants, especially on agricultural products. Therefore, it will be necessary to investigate this phenomenon and its effects on agriculture. In this study, the effect of climate change and cultivation in different dates; The amount of green footprint was investigated during the cultivation period of Parsi variety of spring wheat in Qazvin plain. This study was conducted in the period of 2021-2100 and by comparing two information sources LARS-WG and DKRZ in producing annual climate change data and using the AquaCrop model in simulating plant response to the mentioned changes. In the periods of 2021-2040, 2041-2060, 2061-2080, and 2080-2100, different planting dates (Bahman 15, Esfand 1, Esfand 15, April 1, and April 15) were investigated in order to increase wheat yield and reduce green water footprint. According to the results of the climatic conditions of the LARS-WG model under scenarios 4.5 and 8.5, in all the next 4 periods, the average footprint of green water will decrease compared to its value in the base period, except for three cases in all dates. Meanwhile, under the climatic conditions of the DKRZ database in scenarios 4.5 and 8.5, in most dates and periods, the average footprint of green water will decrease compared to its value in the base period. According to the results, the largest amount of green water footprint in all these periods and models in the period of 2021-2040 under the climatic conditions of the DKRZ database in scenario 4.5, if the planting date is 15th of Bahman, it is estimated that the amount of water consumption It is predicted to be 273 cubic meters per ton with a standard deviation of 32.65 cubic meters per ton.

Climate change is one of the most important issues in the world in the 21st century which affects various sectors of agriculture, forestry, water and financial markets, and has serious economic consequences (Reidsma et al., 2009). In recent years, the management of agricultural water consumption has always been considered as one of the important issues in water resources management. Koochaki and colleagues (Koochaki and Kamali, 2006) by evaluating the climatic indicators of Iran's agriculture showed that during the next 20 years, the average monthly temperature will increase in almost all regions of the country, and the increase in evaporation and transpiration is one of the most important consequences of this warming. Simulated climate parameters can be obtained through different general GCM atmospheric models. Due to the low spatial resolution of these models, its output should be downscaled using dynamic or statistical methods.

The LARS-WG model predicts meteorological variables for a period of time in the future by using a series of basic and fine-scale meteorological data, output of one of the GCM models. Research has shown that the LARS-WG model has the necessary accuracy for this task. Calculating the amount of evapotranspiration and yield of very complex plants are time-consuming and dependent on spending a lot of money and limited to the tests performed, the shortness of the test time and also the limitation in the number of scenarios that are checked by the test. Therefore, plant models are considered and evaluated by researchers. The AquaCrop model has demonstrated commendable accuracy in various regions of Iran and globally for forecasting plant growth, water consumption efficiency, and evapotranspiration requirements. These predictions hold significant potential for optimizing irrigation strategies across different agricultural settings. AquaCrop is one of the applied agricultural models that was obtained from the modification and revision of FAO publication No. 33 by prominent experts from all over the world. In this study, the values of green water footprint of winter wheat plant (Pishgam) were estimated in climatic conditions obtained from LARS-WG model and DKRZ database under scenarios 4.5 and 8.5 and at different planting dates (15 October, 1 November, 15 November, 30 November and 15 December), in the next 4 periods (2021-2040, 2041-2060, 2061-2080 and 2081-2100) and by Aquacrop model.

The results showed that if planting date is on October 15, in the climatic conditions obtained from the LARS-WG model and under scenarios 4.5 and 8.5, in all future periods, the footprint of green water will increase compared to its value in the base period, and if planting is the rest of the dates, in each of the next 4 periods, the average green water footprint will decrease compared to its value in the base period. The results obtained for the DKRZ database show that the green water footprint attained for the dates of cultivation and periods investigated in scenarios 4.5 and 8.5 does not have a particular trend. On the planting dates of October 15 and November 1 for the periods of 2061-2080 and 2081-2100, the green water footprint will decrease and on the other three dates (15 November, 30 November, and 1 November) for these periods, there will be an increasing trend. On 15 December, for the DKRZ database, in both scenarios defined for all periods, an increase in green water footprint compared to the base period is reported. However, in the period of 2081-2100 in scenario 8.5, a decrease compared to the base period will be observed. The highest amount of green water footprint in all these periods and models for the period 2041-2060 under the climatic conditions of the DKRZ database in scenario 4.5, if the planting date is 15 October, it is estimated that the amount of water consumed is equal to 4272 cubic meters per ton with a standard deviation of 5018 cubic meters per ton is predicted. The lowest footprint of green water for the period 2081-2100 under the climatic conditions obtained from the LARS-WG model in scenario 8.5, if the planting date is on 15 December, is reported to be 232 tons per hectare with a standard deviation of 52.3 tons per hectare.

Agricultural production vulnerable to damage due to climate changes. Effect of these changes on the annual rainfull, temperature and consequently in agricultural production isn't negligible. It is necessary to estimate climate change in the future and management plantation and irrigation in agriculture in line with it. In this study, the effects of climate change and plantation on different dates, Yield, cultivation period autumn wheat types in Qazvin plain were studied. This study was conducted in the period 2021-2100 by comparing the two information sources LARS-WG and DKRZ in the production of annual climate change data and using the AquaCrop model in simulating the plant's response to the mentioned changes. In the periods 2021-2040, 2041-2060, 2061-2080 and 2081-2100, the most suitable dates were studied to increase the wheat yield and reduce other variables. Accoding to the results obtainded from the climate conditions LARs-WG model and DKRZ database under release scenarios 4.5 and 8.5 in each 4 future period the average yield will increase compared to the base period. The highest performance in all these periods and models is for period 2081-20100 under climate conditions DKRZ database in scenario 8/5, in the event that the planting date is Novmber 6 , it is prediction that its amount is equal to 11.71 tons per hectare with standard deviation of 0.64 tons per hectare. The lowest performance is also for the period of 2041-2060 under climate condition DKRZ database in scenario 8.5, in the event that the planting data was October 7, It is reported that its amount is equal to 1.34 tons per hectare with standard deviation of 1.15. In the periods of 2021-2040, 2041-2060and 2061-2080 of February 4 and in the period 2081-2100 of October 23 is recommended as the most appropriate date in these periods.

It is necessary to predict the effects of climate change on agricultural production in the coming periods in order to ensure the food security of the strategic plant wheat, which plays an essential role in international treaties. Models that generate artificial climate data, such as valid GCM models, are used to investigate the effects of climate change on various systems and are able to model climate parameters for a long period of time using scenarios approved by the Intergovernmental Panel on Climate Change. In the current research, two information sources, LARS-WG and DKRZ, were used to generate climate change data of the Qazvin plain in the period of 2021-2100, then the actual evapotranspiration values of Parsi spring wheat were calculated by the Aquacrop model in different planting dates and the amount of their changes Compared to the base course.

In this research, from the data obtained from the DKRZ web database and the LARS-WG model, to calculate the three variables of minimum temperature, maximum temperature and precipitation, related to Qazvin observation station and five atmospheric general circulation models of the fifth IPCC report (EC-EARTH , GFDL-CM3, HadGEM2-ES, MIROC5, MPI-ESM-MR) were used under two emission scenarios of 4.5 and 8.5 in future rounds (2021-2040, 2041-2060, 2061-2080, 2081-2100). Using the obtained data and applying the Aquacrop model, the amount of actual evaporation-transpiration of spring wheat on 5 different planting dates (15 Bahman, 1 Esfand, 15 Esfand, 1 April and 15 April) was calculated and the amount of their changes compared to the period the base was checked.

Observations show that with cultivation on 15th of Bahman and 1st of March under the climatic conditions obtained from the LARS-WG model in scenario 4/5; In the future period (2040-2021), transpiration will increase compared to its value in the base period, but in the periods (2041-2060, 2061-2080 and 2081-2100) and in the scenario 4.5 and 5.8 from the LARS model - WG average real evapotranspiration will decrease compared to its value in the base period. DKRZ database under scenarios 4.5 and 8.5 predicts a decrease in the average actual evapotranspiration compared to its value in the base period for these two dates in each of the next 4 periods. by planting on March 15, April 1 and April 15, according to the results of the climate conditions of the LARS-WG model and the DKRZ database under scenarios 4/5 and 8/5, in each of the next 4 periods; The average actual evapotranspiration will decrease compared to its value in the base period.

The results show that the average real evapotranspiration will increase compared to its value in the base period, on the two dates of February 15 and March 1 in the period of 2040-2021 in the climate conditions obtained from the LARS-WG model under scenario 4.5.If cultivation is carried out in the rest of the dates, according to the results of the climatic conditions of the LARS-WG model and the DKRZ database under scenarios 4.5 and 5.8, in each of the next 4 periods, the average real evapotranspiration will decrease compared to its value in the base period Will have. The highest evaporation-transpiration in the future periods will occur with cultivation on April 15, under the climate conditions obtained from the LARS-WG model under scenario 4/5 and in the period of 2040-2021. Its value is equal to 289.9 mm (with a standard deviation of 18.33 mm). The lowest evaporation-transpiration in the future periods with cultivation on 15th of Bahman, under the climatic conditions obtained from the DKRZ database under scenario 8.5 and in the period 2081-2100, which is equal to 166.6 mm (with a standard deviation of 82.5 mm).:

Awareness of water resources status is essential for the proper management of resources and planning for the future due to the occurrence of climate change in most parts of the world and its impact on different parts of the water cycle. Hence, many studies have been carried out in different regions to analyze the effects of climate change on the hydrological process in the coming periods. The present study examined the effects of climate change on surface runoff using the Atmosphere-Ocean General Circulation Model (AOGCM) in Khomeini Shahr City. The maximum and minimum temperatures and precipitation of the upcoming period (2020-2049) were simulated using a weighted average of three models for each of the minimum and maximum temperatures and precipitation parameters based on the scenario A2 and B1 (pessimistic and optimistic states, respectively) of the AOGCM-AR4 models. The LARS-WG model was also used to measure the downscaling. The HEC-HMS was used to predict runoff. The effects of climate change in the coming period (2020-2049) compared with the observation period (1971-2000), in the A2 scenario, the minimum and maximum temperatures would increase by 1.1 and 1.6 Degrees Celsius, respectively, and the precipitation would decrease 17.8 percent. In the B1 scenario, the minimum and maximum temperatures would increase by 1.1 and 1.4 degrees Celsius, respectively, and the precipitation would decrease by 13 percent. The results of runoff were different in the six scenarios in the way the most runoff reduction is related to the scenario of fixed land use and scenario A2 (22.2% reduction), and the most increase is related to the scenario of 45% urban growth and scenario B1 (5.8% increase). So, according to increase urban texture in the future and consequently enhance the volume of runoff, this volume of runoff can be used to feed groundwater, irrigate gardens, and green space in the city.

Today climate change is evident as a result of increasing greenhouse gases and its effects in different parts of the world, especially in arid and semi-arid regions. Since groundwater is the most important source to provide water, is essential future to analyzes of the impact of climate change on these resources and the estimate the measure of their nutritional in the future. In this research, the effects of climate change on the groundwater resources of Qorveh plain in the watershed of the Tolawar River have been investigated. For modeling the groundwater level of the study area, the MODFLOW model was used for 216 months of calibration and then validated using 163 months' data. Finally, the predicted precipitation was entered into the MODFLOW model as the feeding parameter and the effects of climate change in the three scenarios A1B, B1 and A2 at the static-water level with aquifer simulation were investigated. Also, with a 10% increase in harvest volume from wells and Expected rainfall prediction from scenario A2, the drop in static-water level has increased dramatically and every year we will have about 117 centimeters decrease in the static-water level. With a 10% reduction in the volume of wells in the coming years and projected rainfall from scenario B1, drop of static-water level has decreased from previous years and the measure of reduction in the static-water level was estimated to be about 33 centimeters annually.

Nowadays, global warming and climate change phenomena are considered as the most important threats to water resources and sustainable agricultural development. Due to the limitation of water resources in Iran, population growth and subsequent water demand increase, investigation of the effects of climate change on the water resources systems and agriculture is an important issue. In this study, the effect of climate change on inflow discharges to Jarreh Dam reservoir located in Zard River Basin was investigated. For this purpose, using the performance weighting method, among 20 atmospheric general circulation models (GCM), three models were chosen that were more compatible with historical data (CSIRO-Mk3.6.0, MIROC-ESM and GFDL-ESM2M). Then, precipitation and air temperature values at a height of two meters above the ground in future periods and under RCP4.5 and RCP8.5 climate scenarios were estimated. In order to rainfall- runoff simulation and estimating the inflow to the dam reservoir, the IHACRES model was applied and the daily precipitation, temperature and discharges series recorded at the Mashin Hydrometric Station in the period of 1976- 2005 were used to optimize the model parameters. After downscaling and generating the precipitation and temperature series and verifying the accuracy of the IHACRES model, the corresponding inflow discharge to dam reservoir for every climatic scenario was simulated. The results showed that the value of inflow to dam reservoir has decreased under both considered scenarios. The annual average of inflow into Jarreh Dam reservoir will decrease 27 and 40 percent under RCP 4.5 and RCP 8.5 scenarios, respectively.

Considering the effect of climatic factors on Canola production in Ahar region, north west of Iran, a study was conducted to prioritize climatic and environmental criteria including 21 effective sub-criteria involved in canola production decrease. The required meteorlogical and agronomic data and information were collected directly or via questionnaires prepared by local experts. The relative importance of each sub-criteria was determined by TOPSIS method. The significat climate variables were downscaled by LARS-WG model under A1B, A2 and B1 scenarios. The results showed that the inadequate water availability and air temperature variations were rankied as the first and second most signficat contributors in canola yield reduction. The baseline period (2000-2017) mean annual rainfall of Ahar region is 270.8 mm. The projected mean annual rainfall under A1B, A2 and B1 scenarios were 53.2, 50.4 and 52.8 mm, respectively. Selecting proper adaption measures for cope with these changes is recommended for sustainable production of this crop in Ahar region.

Climate change has many impacts on all environmental processes and society. In this study, three models selected from Coupled Model Intercomparison Project Phase 6 (CMIP6) including ACCESS-CM2, HadGEM3-GC31-LL, and NESM3 are validated. The best model (i.e. ACCESS-CM2) is selected to simulate the climatic parameters of the Sari Station using the latest emission scenarios called “shared socioeconomic pathways (SSP).” The LARS-WG is adopted for downscaling, and two emission scenarios SSP2-4.5 and SSP5-8.5 are used for two periods 2041-2060 and 2081-2100, respectively. Several statistical tests are conducted including F-test, T-student, Kolomogrov-Smirnov, coefficient of determination (R2), and root mean square error (RMSE) to validate the LARS-WG model. The verification results indicate the efficiency of the LARS-WG model. The Man-Kendal and Sen’s slope tests are adopted to determine the trend of climatic observational parameters. In general, the results show that the average temperature change increases in the range of 1.16-4.09 °C and also the average annual rainfall increases by 24-36 percent. The Sen’s slope results in terms of maximum and minimum temperatures show an ascending trend in this parameter, but it is descending in the rainfall. Since long-term climate change is one of the factors affecting groundwater and surface resources, it is necessary to develop proper management strategies for the future, preserving ecosystems, and adapting humans to these changes.

Changes in temperature and rainfall have a large impact on the quantity and quality of water resources, In this research, the effects of climate change on the development of special Qanats in Khusf city (Giw, Mazhan and Vahad Abad) were investigated. For this purpose, minimum and maximum temperature and precipitation were firstly estimated in the two near future (2030-2060) and far future (2070-2100) using the CANESM2 large scale model and two emission scenarios of RCP4.5 and RCP8.5. These data were then quantified by two downscaling models of LARS-WG and SDSM. Using rainfall data for the upcoming period, Discharge value for 3 Qanats were estimated for future periods. The results showed that the minimum and maximum temperatures in the RCP8.5 scenario would be higher than RCP4.5 in the results of both downscaling methods and will increase for both future periods. Rainfall also has slight variations for two scenarios of RCP8.5 and 4.5 for the upcoming period and the base period. Also, comparing two methods of downscaling, the LARS-WG model estimates the temperature changes less than the SDSM model and less changes in rainfall. The results of Qanat change in the upcoming period also showed that in all qanats, the discharge average for the coming period is less than the base period. Also, the RCP4.5 scenario compared to RCP8.5 in two methods of LARS-WG and SDSM is estimated to be greater. Given the changes in discharge in the future, management strategy can be adopted to deal with possible droughts.

It is necessary the (GCMs) act on the local behavior of the station data, to understand the status of a local station using these models. In this study using SDSM, LARS_WG models and artificial neural network method Based on observational data obtained from 6 synoptic stations in Isfahan province, temperature and precipitation forecasts of these stations from 2005 to 2050 have been investigated. Pearson's correlation coefficient (r) and coefficient of determination (R2) between observational data and scenario data at all stations are above 0.6.The results show that the SDSM model is more efficient for predicting temperature than the other two models. Based on the results, the temperature of Isfahan province increased from 2005 to 2050 and In 2005 it had the lowest value and in 2050 it had the highest value and the temperature difference of these years is about 2/3°C, Precipitation changes are almost similar to current changes and we will have the highest amount of precipitation in 2024 and 2028 which, in daran statian which, it has more rainfall than other stations, it will be about 2/5 mm, therefore, Isfahan province needs appropriate management strategies to reduce water consumption to prevent the possibility of dehydration and drought in the years ahead.

In the present study, the SWAT hydrological model was developed for the upstream of the Zayandehrood dam to evaluate the inflow to this dam. Accordingly, after entering the meteorological and hydrometric information of the region, the runoff simulation was performed. Due to the high volume of entrances to the Zayandehrood Dam, Shahrokh Castle hydrometric stations were selected as the base station for calibration and validation during the statistical period of 1990-2015. After hydrological simulation and accuracy of results, climate prediction was performed using the fifth model of the climate change for the RCP scenarios. According to the forecast, by using climate change models, the temperature could be assumed to increase in all models and the highest rate of increase would occur under the RCP 8.5 climate scenario. After evaluating climate change in different diffusion scenarios, the runoff of the basin was simulated in the SWAT model. The simulation results of runoff in the catchment area showed that although the amount of rainfall was increased in the region, increasing the temperature had a greater effect, reducing the amount of runoff in the basin. Based on the results of climate change, hydrological simulation was performed using the SWAT model. The results showed that the effect of diffusion scenarios in the region was different, causing an increase in temperature and precipitation. The highest increase was observed in the RCP8.5 scenario, which was consistent with the nature of this emission scenario, with the highest emission of greenhouse gases and carbon dioxide. Then, the evaluation of the hydrological model was done; the results showed that although the amount of rainfall in the region had been increased, the increase in temperature of this basin had a greater effect and efficiency in reducing the amount of runoff.

Considering the significant effect of chilling and frost phenomenon on agricultural production in Iran, the aim of this study is projection of minimum temperature in three provinces of Kurdistan, Kermanshah and Ilam, Western of Iran. For this purpose, the data of 17 meteorological stations during the baseline period of (1989-2018) were collected. Then, the HadGEM2 and CanESM2 climate models outputs were statistically downscaled using LARS-WG and SDSM weather generators under three climate change scenarios of RCP2.6, RCP4.5 and RCP8.5 during future period (2021–2050) and compared with observed data. The performance of the two weather generators, were compared using MSE, RMSE, MAE and R2 indices. The results indicated the good accuracy of both statistical models in simulating the minimum temperature in the study area; however the SDSM model performed better than the LARS-WG. The projected changes of minimum temperature compared to the baseline period revealed a significant increase varying between 0.6 – 1.5 oC in study stations. The most significant change was observed in northern parts of study region especially in the Saqhez and Zarineh stations. The findings of this study can be used in frost risk management and agroclimatic planning in the region.

Drought is one of the natural destructive phenomena which cause a lot of damage to the affected area. In this study, outputs of Fifth climate change report were used to investigate changes in precipitation and drought intensity at Birjand Synoptic Station during the next two periods. Precipitation data for two near future periods (2015-2045) and far future period (2045-2075) against base period (1975-2005) were downscaled using two scenarios RCP4.5 and RCP 8.5 using the LARS-WG model. Then the SPI values were determined for six time scales 1,3,6,12,24 and 48 months. The results showed that in most models, precipitation changes for both the near and far future periods would not be much higher than the base period. However, precipitation will drop further in the near future (2045-2075) than in the near future (2015-2045). Among the GCM models, the CanESM2 model shows the highest change over the base period and the RCP8.5 scenario estimates a lower rainfall than the RCP4.5 scenario. Also, in comparing the GCM models, NorESM1-M estimates the number of years with more severe drought. The results also showed that in future periods, as the SPI time period increases from short to long intervals, SPI values will show more severe drought conditions. Models uncertainty analysis also showed that the highest uncertainty value among models for long-term and short-term SPIs was related to NorESM1-M and Mpi-esm-mr models, respectively. The overall results also indicate an increase in severity and duration of drought in the future (especially the distant future).

Climate change is one of the most important factors influencing the hydrological cycle.The present study investigate the impact of climate change on runoff and sedimentation of the Dehbar watershed for the next 30 years using statistical model, LARS-WG and conceptual hydrological model, SWAT and WEPP. First, LARS-WG model was calibrated to generate future meteorological data in the basin. Then, the HadGEM2 model with three scenarios; RCP8.5, RCP4.5 and RCP2.6 were used to downscale meteorological data for period of 2020 to 2050. The results indicate a decrease in rainfall and an increase in temperature on the horizon of 2050. Also, the results of SWAT model show the runoff variations for RCP8.5, RCP4.5 and RCP2.6 scenarios which are -22.16%, -17.93% and -17.34%, respectively, and for sedimentation variations which are -4.4%, +9.4% and +3.2%, respectively, relative to the base period. In the WEPP model by choosing the hillslop method and using downscaled results of RCP8.5, RCP4.5 and RCP2.6 scenarios, sediment variations were estimated -6.4%, +12.6%, and +4.8%, respectively, compared to the base period.

In the present research, the climate change effect on groundwater resources of Miandoab plain in West Azerbaijan province was investigated. In this direction, the scenarios including A1B, A2 and B1 via LARS-WG downscaling model and with applying the HadCM3 general circulation model and artificial neural network model in two different periods (2046-2065, 2080 -2099) were studied. For this purpose, monthly groundwater depths data of 25 piezometric wells in the Miandoab plain with a 10-year statistical period (2005-2014) and daily and monthly data of rainfall, minimum and maximum temperatures and sunshine hours of the Miandoab synoptic station in a 20-year statistical period (1995-2014) were used. The evaluation results of the observed and simulated data by the LARS-WG model, using different statistical indices indicates that there is no significant differences between simulated and observed values. The performance analysis of the artificial neural network model shows that the mentioned model has good and suitable accuracy in simulating the changes in groundwater depth in the studied plain. The results showed that the average depth of groundwater level in the first period (2046-2065) and the second period (2080-2099) increases 2.87% and 9.3%, respectively. In fact, considerable augmentation of temperature and consequently increasing the groundwater consumption cause to deeper depth of the groundwater.

Excessive use of fossil fuels, the increasing world population and the ever-increasing of industrial activities accordingly to provide welfare and the needs of humans, thereby has been increased the concentration of greenhouse gases, particularly carbon dioxide in recent decades. This increase in greenhouse gases cause a phenomenon called climate change. The purpose of this study was to investigate the effect of climate change on the output of three CANESM2 MPI-ESM-MR and CCSM4 models selected from the CMIP5 model series in the near future (2020-2020) and the future of the future (2070-2099). ) Under the RCP2.6, RCP4.5, RCP8.5 radiation induction scenarios on precipitation, minimum temperature and maximum temperature. To simulate the climatic parameters of the Zarrine River basin, the data of these three models were microscopically compared with the LARS-WG model. According to the evaluation statistics, the MPI-ESM-MR climatic model has better performance than other two models for simulating climatic parameters. Subsequently, by examining the results for all stations, the temperature trend with increasing and precipitation decreased. Saqez Station showed a more intense trend in simulation as the temperature in this station is increased by an average of 0.5 to 1 ° C higher than the Takab station and in the rainfall, with a decrease of 3 to 30 mm, the ratio Shows Takab. Also, the minimum temperature is higher than the maximum maximum temperature at all stations.