The purpose of this study is to predict climate change and investigate the effect of climate change on Growth Day-days and total days of wheat growth in Fars province. For this purpose, the climate data of three synoptic stations of Shiraz, Lar and Abadeh in Fars province and the data of two models HADGEM2-ES and EC-EART were scaled under RCP45 and RCP85 scenarios. LARS-WG statistical model was used for Downscaling. The results showed that the LARS-WG model has appropriate accuracy in Downscaling of Fars province climate, minimum and maximum temperature parameters. The minimum and maximum temperatures of all three stations of Shiraz, Lar and Abadeh increased under both RCP45 and RCP85 scenarios. The increase in the minimum temperature of the base period (1980– 2015) in Fars province relative to the period (2021– 2040) for the RCP45 and RCP85 scenarios is 1. 43 and 1. 65 ° C, respectively. The maximum increase in base temperature over this period for the RCP45 and RCP85 scenarios is 1. 51 and 1. 66 ° C, respectively. The amount of changes in the rainfall of the baseline period increased by 2. 93% and 1. 95% for the RCP45 and RCP85 scenarios, respectively. Then, using ADP equation, the number of day-index under two temperature thresholds of 4 and 25 ° C for the basic and future time interval (2021-2040) was calculated and the results showed the degree of days of wheat growth period (GDD) and total days’ number. The growth period (DAP) had an increasing and decreasing trend compared to the baseline period.

Due to climate changes, precipitation forecasting and precipitation estimation, one of the most important climatic parameters in the field of water resources management, is of particular importance. Therefore, in this research, the application of SDSM model in precipitation estimation was investigated. In this research, the data related to Ahvaz, Abadan and Dezful synoptic stations were used. The forecast time frame for the future period (climate scenarios) is also 30 years between 2031 and 2060. The outputs of the HadCM3 model, under the A2 and B2 scenarios and the CanECM2 model, under the RCP26, RCP45 and RCP85 scenarios, were micro scaled by applying the SDSM statistical exponential micro scale model in the prediction of the precipitation parameter, also using statistical and graphical methods. Micro scaled and basic data were analyzed and then calibrated. Base period modeling was done with higher accuracy in CanESM2 data compared to HadCM3 data. The results indicated that in the coming years, the total rainfall will increase in all three stations and the maximum amount of rainfall will decrease in all three stations. According to the modeling results, it seems that the climate of Khuzestan will have a wetted winter climate and drier summers in the near future.

Due to climate changes, precipitation forecasting and precipitation estimation, one of the most important climatic parameters in the field of water resources management, is of particular importance. Therefore, in this research, the application of SDSM model in precipitation estimation was investigated. In this research, the data related to Ahvaz, Abadan and Dezful synoptic stations were used. The forecast time frame for the future period (climate scenarios) is also 30 years between 2031 and 2060. The outputs of the HadCM3 model, under the A2 and B2 scenarios and the CanECM2 model, under the RCP26, RCP45 and RCP85 scenarios, were micro scaled by applying the SDSM statistical exponential micro scale model in the prediction of the precipitation parameter, also using statistical and graphical methods. Micro scaled and basic data were analyzed and then calibrated. Base period modeling was done with higher accuracy in CanESM2 data compared to HadCM3 data. The results indicated that in the coming years, the total rainfall will increase in all three stations and the maximum amount of rainfall will decrease in all three stations. According to the modeling results, it seems that the climate of Khuzestan will have a wetted winter climate and drier summers in the near future.

The main objective of this research is to compare the performance of artificial neural network (ANN) and deep neural network (DNN) models in rainfall-runoff modeling of Kashafrood and to predict the effects of climate change on meteorological parameters and river discharge.

Panel reports on climate change suggest that climate change around the world is most likely due to human factors. Temperature and precipitation are two important parameters in the climate of a region whose variations and fluctuations affect different areas such as agriculture, energy, tourism and so on. Seymareh basin is one of the most significant sub-basins of Karkheh. The purpose of this study is to predict the impact of climate change on precipitation and temperature of the Seymareh Basin in 2021-2040 period. These effects were analyzed at selected stations with uncertainties related to atmospheric general circulation models (GCMs) of CMIP5 models under two scenarios of RCP45 and RCP85 through LARS-WG statistical model. Then the uncertainties of the models and scenarios were investigated by comparing the monthly outputs of the models by the coefficients of determination coefficient (R2) in the forthcoming period (2021-2040) with the base period (1980–2010). The root mean square error (RMSE) calculations presented the best model and scenarios for generating future temperature and precipitation data.              The Seymareh catchment is the largest and the main Karkheh sub-basin that covers parts of Kermanshah, Lorestan and Ilam provinces. The length of the largest river at the basin level to the site of the Seymareh Reservoir Dam is approximately 475 km, and the area of the basin is 26, 700 km2. Geographic coordinates of the basin are from 33° 16 ́ 03 ̋to 34°59 ́ 29 ̋north latitudes and 46°6 ́9 ̋to ̋ 5 ́ 0 ° 49 Eastern longitudes, minimum basin height 698 m at the dam outlet and its maximum height 3, 638 m. It is on the western highlands of Borujerd. The information used in this study was obtained from the Meteorological Organization of the country. For this study, three synoptic stations of Kermanshah, Hamadan and Khorramabad, which had the highest statistical records and had appropriate distribution at basin level, were used. These data included daily and monthly temperature and precipitation information, and sunshine hours. The LARS-WG fine-scale exponential model was proposed by Rasko et al., Semnoff and Barrow (1981). We used daily data at stations under current and future weather conditions. In order to select the best GCM model from the models mentioned above, minimum temperature, maximum temperature, precipitation and sunshine data were entered daily in the base period (1980–2010) and data were generated for five models under two scenarios of RCP45 and RCP85 for the period 2040–2021. The data were generated in 100 random series and the mean of required variables (minimum temperature, maximum temperature and rainfall) were extracted monthly in the period 2021-2040. Then, root mean square error (RMSE) and determination coefficient (R2) were used to evaluate the performance of the models and compare the results. To ensure the models' ability to generate data in the coming period, computational data from the model and observational data at the stations under study should have been compared. The capability of the LARS-WG model in modeling the minimum temperature, maximum temperature, and radiation at the stations under study was completely consistent with the observed data. The model's ability to exemplify rainfall was also acceptable, however the highest modeling error was related to March rainfall. By comparing the observed and produced data including monthly average precipitation, minimum and maximum temperatures through five mentioned models with their indices, the best model and scenario for future fabrication were determined. The results of this comparison showed that among the available models, HADGEM2-ES model under RCP 4. 5 scenario had the best result for precipitation and HADGEM2-ES under RCP 8. 5 scenario predicted the best result for maximum temperature. Determining the best model, precipitation data, minimum temperature and maximum temperature produced in the selected models and scenarios were analyzed to investigate the climate change temperature and precipitation for the future period. The results of this study indicated that due to the wide range of output variations of different models and scenarios, by not taking into account the uncertainties of the models and scenarios can have a great impact on the results of the studies. It was also found in this study that the LARS-WG exponential model was capable of modeling precipitation data and baseline temperature in the study area, so that the radiation data, minimum and maximum temperatures were completely consistent with the data. The observations are consistent and the models' ability to predict rainfall is very good and acceptable manner. In investigating the uncertainties caused by atmospheric general circulation models and existing scenarios, the best model to predict precipitation in the study area is HADGEM2-ES model under RCP 8. 5 scenario, the best model for temperature estimation model HADGEM2-ES under RCP scenario No. 4. 5. The overall results of this study revealed that the average precipitation in the basin will decrease by 4. 5% on average, while the minimum temperature will be 1. 5° C and the maximum temperature will be 2. 17° C. The highest increase will be due to the warmer months of the year. Notable are the disruptions of rainfall distribution and the high temperatures will have significantly negative consequences than rainfall reduction.

Wheat is the main human food that is consumed directly. Recognition of climatic parameters and study of climatic needs of crop plants is one of the most important factors in the production of rainfed wheat. This study is due to the importance of climatic parameters in rainfed wheat production and also due to the potential of rainfed rainforests in Tajikistan, including Ryan Panjkent and Qa in Wadi Zarafshan. The data used in this study were collected through the Tajik Meteorological Department and the Tajik Ministry of Agriculture and the Pentecostal Agricultural Office in the field and in libraries. In the first step, the data were checked for homogeneity and uniformity. In the next step, using Lars Wg software using HadGEM2-ES series models and three scenarios of RCP26, RCP45, RCP85 in the period 2011-2050, the Lars model's ability to predict the climatic variables of Panjkent station was evaluated and then the data. The prediction was evaluated with observational data and also through Anova correlation and test between climatic parameters and production of rainfed wheat per hectare by Toronto White Climate Method. Connection results between climatic parameters and rainfed wheat production Using the analysis of variance (F) test and comparison with the table of coefficients of F showed, There is a significant relationship between rainfall in May and maximum temperature in June with wheat production and also rainfall in October, maximum temperature in November with rainfed wheat production in Panjkent station, there is no significant relationship per hectare.

The prediction of climactic changes is of great importance due to their destructive effects on aquatic, environmental, economic, and social resources. Accordingly, the purpose of this study was to predict the climactic changes of Kermanshah city using micro-scale general atmospheric circulation models accessible in LARS-WG6 (GFDL-CM3, MPI-ESM-MR, MIROC5) model in scenarios RCP4. 5 and RCP8. 5 for the 2020 to 2100 period based on the benchmark period of 1980-2010. In order to evaluate the data forecasted in LARS-WG model, the error rate of the observed and predicted data was addressed using R2, RMSE, MSE, and MAD criteria. The results showed that LARS-WG model had the needed capability to predict climactic data in future. In the secondary models, the MPI-ESM-MR model in scenario RCP4. 5 showed a higher reliability rate compared to other secondary models evaluated in the study. Moreover, all models indicated increases in the average minimum and maximum temperature and forecasted changes in rainfall pattern in future periods in the studied area. Then, the SPI and De Martonne indices were calculated for all models. According to SPI index, all evaluated climactic models demonstrated that by the year 2100, the years with normal index would decrease while the years with dry conditions would increase. Moreover, based on De Martonne index, the GFDL model in RCP8. 5 scenario estimated the climactic changes level more than other models, and predicted that dry and semi-dry years will be more than wet years. Contrarily, the MIRO model in RCP45 scenario acted more optimistically and predicted less climactic changes.

Global warming leads to changes in precipitation patterns, temperature, and other climatic variables. These impacts affect the risk of thermal stresses in cropping systems. This study was carried out to evaluate the effect of climate change on the cold stress variation of potato plants in the tropical regions of Kerman province. To this end, changes in the occurrence of cold stress in the observation data of Jiroft, Kahnouj, and Manoajan stations were analyzed(from 1981 to 2005) and the future period (from 2011to2100). The future course data, using general circulation model CanESM2 with respect torcp26, rcp45 and RCP 85 scenarios were quantified by SDSM software. For this, the climactic indices were used to evaluate the changes in cold stress of potatoes by the tolerance thresholds of potatoes and based on the average of long-term climate data and the probability of its occurrence during the growth period. The results showed that in the Jiroft station, the probability of occurrence of early and late temperature below 5º decreased to 83% and 63% respectivelydintheKahnoj and Manoajan stations the probability of occurrence of early and late temperature below 5º in the future period decreased and increased, respectively. Also, the date of the beginning and end of cold stress was shifted to the beginning and end of the cold season. The statistical analysis of data indicated that the days of cold stress (5≤ T≤ 0) will increase from 2011to2040 in the Jiroft station, and from 2071to2100 in the Manojan station but it showed a decreasing in the Kahnouj station. The results show that the days with freezing stress will increase in the future in the Jiroft, Kahnoj, and Manojan stations, as a result, date of early and late cold stress will happen 10 days earlier and 14 days later compared to long term average of climate in the study area.