دما

By examining the trend of air temperature changes, it is possible to search for traces of climatic changes in the area of Iran. Temperature is one of the most important meteorological parameters that is used in many studies. This parameter is of special importance in climate change studies, as the increase in temperature is considered one of the most important human environmental issues. In this research, the purpose of the research is to look at the average temperature changes in the base and future period of Khuzestan province. The evaluation of the model and the reproduction of climatic variables and the perspective of the future climatic conditions are examined, and this question is raised: Is the Sdsm model in Khuzestan province highly accurate?

The area studied in the current research is the synoptic stations of Khuzestan province. In this study, meteorological data including the values of minimum temperature, maximum temperature and average temperature for the studied period have been used. In this research, we used SDSM statistical micro-scale exponential model for temperature prediction and 6 synoptic stations of Khuzestan province, which had 45-year (1961-2005) and 40-year (1966-2005) climatic statistics, were selected. The outputs of the CanESM2 climate model have been used under RCP2.6 and RCP8.5 scenarios. The data of the base period (1961-2005) were used for the first 30 years of data (1961-1990) for calibration and the second 15 years (1991-2005) for the syntactic evaluation of the model performance. Error and accuracy measures are evaluated.

MAE, NRMSE, RMSE, MSE and R2 were calculated based on the average values of the variables in each month. These values were obtained according to the daily temperature produced by the model and the observed values for calibration and validation data. The results showed that according to the NRMSE, the error rate in temperature estimation is acceptable (less than 10%) and is almost the same in all stations. The results showed that according to the high correlation coefficient of 87%, the performance of the model is confirmed. Finally, it indicates that the model has relatively good accuracy in estimating the climatic variable of temperature. In most stations, they overlap the most in the first months of the year, which is the reason for the accuracy of the model in the first months of the year. In the stations of Ahvaz, Bandar Mahshahr, Omidiye Aghajari and Bagh Malek in the first seven months of the year, the highest overlap and accuracy are included, and in the last five months of the year, the average retrospective temperature in these stations is 2.4, 2.4, 2.6 respectively. and 2.7 degrees Celsius shows the difference with the observational data. Dezful, Abadan and Shushtar stations have the highest overlap and accuracy in the first three months of the year and July. In the rest of the months, the average retrospective temperature in these stations is 2.6, 2 and 2 degrees Celsius, respectively, the difference with the data Shows observations. The temperature has increased in all periods and for the RCP2.6 scenario, it increases more than the RCP8.5 scenario. The highest RCP2.6 average temperature increase in Ahvaz is predicted to be 1.9 degrees and the lowest RCP8.5 increase is 0.2 degrees in Dezful and Shushtar stations. The average temperature in the forecast period with RCP2.6 and RCP8.5 scenarios is 26 and 25.7 degrees Celsius respectively, which shows an increase of 0.7 and 0.4 degrees compared to the previous period, and also the highest average temperature in the period Predicted with RCP2.6 and RCP8.5 scenarios and the observation period is approximately 28.2, 27.5 and 27.3 degrees Celsius corresponding to Shushtar station and the lowest average temperature is approximately 22.7, 22.6 and 22.2 degrees Celsius corresponding to Bagh Malek station respectively. In most of the studied stations, the increasing and decreasing trends of the observation and forecast period are similar. Aghajari station shows the most overlap. Shushtar, Abadan and Omidiye Aghajari stations have the highest temperature with an average temperature of 27.3, 26.5 and 26.4 degrees Celsius, respectively, and Bagh Malek station, which is located in the east of the province, has the lowest temperature with 20.9 degrees Celsius.

The most important results obtained from the performance evaluation of the SDSM model using statistical tests and various error measurement indicators showed that this model has been investigated in Khuzestan province and has the appropriate accuracy to simulate climate variables at the level of the studied region. It is absolutely necessary to evaluate the effects of global warming on the occurrence of climatic extremes. An increase in temperature has occurred in all studied stations in the coming period. In two scenarios, RCP2.6 (commitment of countries to reduce greenhouse gases) and RCP8.5 (in case of non-compliance to reduce greenhouse gases) were measured in the studied periods. Meanwhile, during the annual study period, the areas adjacent to the southern coasts of Iran will have the lowest temperature increase, so that the temperature increase in the stations located in the land is more than the stations in the coastal areas. The highest RCP2.6 average temperature increase in Ahvaz is predicted to be 1.9 degrees and the lowest RCP8.5 increase is 0.2 degrees in Dezful and Shushtar stations. In this research, the trends and types of seasonal changes have been investigated. The results obtained from the data analysis show that in all stations, in Khuzestan province in general, the average temperature parameter shows an increasing trend. Research has shown that the maximum temperature trend is an increasing trend in the base period of 1961-2005 and this trend will continue in the future periods. In the future periods, the temperature trend in the last few decades, the increase in the earth's temperature has upset the climate balance of the earth and has caused extensive climate changes in most areas of the earth, which is referred to as climate change. The minimum temperature is increasing, and as a result, it reduces the coldness of the air and moderates it.

Climatic changes as a natural phenomenon are usually multivariate phenomena that are influenced by various factors and have a kind of heterogeneity. Examining these phenomena in a comprehensive, single and homogeneous manner, especially in multivariate mode, can lead to completely misleading results. In many practical problems, identifying the appropriate model for the possible distribution of climate changes is of particular importance. Because climate changes, as a natural phenomenon, are usually multi-variable phenomena that are influenced by various factors and have a kind of heterogeneity. Investigating these phenomena in a comprehensive, unified and homogeneous manner, especially in multivariate mode, can lead to completely misleading results. In general, the probability distribution of multivariate random data is more complicated compared to their univariate state due to the nonlinear dependence between random variables. One of the ways to solve this problem is the use of detailed functions, which has been the focus of researchers in recent years. Due to its high flexibility, the application and use of detailed function is a very useful tool in most scientific fields, including medicine, agriculture, meteorology, marketing, management, etc. The theory of detailed functions as the basis of this science was presented by Sklar (1956). Detailed functions are a powerful tool for constructing multivariate distribution functions based on one-dimensional marginal distribution functions. In fact, detailed functions describe the type and how the variables are related. show. Detailed functions express the non-parametric and dependence features of distribution functions of random variables well. Detailed functions can be used in risk measurement problems. Because, in quantitative risk problems, the role dependence structure It plays an important role and with the knowledge of the dependence structure, a measure of risk can be obtained with the help of the detailed function.Therefore, the probability distribution of multivariate random data is more complicated compared to their univariate state due to the nonlinear dependence between random variables. One of the ways to solve this problem is the use of detailed functions. In this article, using detailed functions, a combined analytical model between temperature and precipitation was presented in the prediction of climate changes in Ilam city. The results showed that the performance of the joint functions were close to each other and among the examined joint functions, the Legamble-Barnett joint was more suitable for modeling the dependence of rainfall and temperature at Ilam station. These results have been analyzed based on the comparison of the dependence sizes between the original data and the simulated data for 1000 samples. The results showed that the performance of all five FGM, Clayton, GB, NC and AMH joint functions are close to each other, but considering that among the five joint functions examined, only Gumbel Barnett (GB) joint has the ability to model negative dependencies. , so it was chosen as a suitable detailed function to model the dependence of rainfall and temperature in Ilam city station. The simulated data also showed that there is a consistency between the original temperature and precipitation data of Ilam city by detailed functions with Sperman's correlation coefficient. Sani Khani et al. (2013) used Frank's joint to model their climate data. In the only study conducted regarding the simultaneous modeling of climate variables using detailed functions, we can refer to the studies of Scholzel and Friedrich (2008), who investigated the relationship between precipitation and wind speed on a daily scale from a simple model based on detailed functions. . In their studies, Scholzel and Friedrich (2008) used a wide range of joint functions, including Archimedesian, semi-elliptical and normal joint functions, to model precipitation and wind speed in two stations, Postdam and Berlin, in Germany. The results indicated the acceptable performance of detailed functions in the investigated range and introduced detailed functions as practical and useful tools in climatology studies.By using the combined distribution of temperature and precipitation of Ilam city, important information about the data can be obtained. This possibility is very useful in critical conditions of global warming and how to manage the effects of global warming and be safe from this phenomenon. By using the detailed function and marginal distribution functions, it is easy to obtain the probabilities and other information about the temperature and precipitation of Ilam city and the relationship between the two factors. Conditional distribution functions can also be determined by using Gamble-Barnett detail and based on them, the probability of how one factor changes against the controlled changes of another factor can be discussed.

Barberry is one of the healthy and organic products of the country. This product can make an important contribution to the export of the country's agricultural sector with systematic planning and infrastructure creation. Barberry is one of the crops that require little water and is recommended for optimal use of water and soil resources and replacement with plants with high water consumption. Seedless barberry is one of the valuable native plants that is grown only in Iran as a garden product. Due to its high resistance in the conditions of water shortage and desert weather, it is a strategic product for many people in desert areas and especially in South Khorasan. This agricultural product is one of the strategic agricultural products in the province and Iran and plays a high role in creating wealth for the livelihood of villagers. Therefore, investigating the characteristics and growth conditions of this product is of great importance. One of the things that should be studied for this product is the effect of climatic parameters and especially temperature changes on the growth and yield of this type of plant.

To conduct this research, the monthly data of minimum temperature, maximum temperature, average temperature, absolute maximum temperature and absolute minimum temperature of Qain and Gonabad stations during the period of 1989-2021 have been used. Also, the data of 16 teleconnection patterns at the same time as the mentioned period were used to measure the relationship between the studied parameters and teleconnection patterns. These patterns were extracted and used from the Noa site. Correlation and linear regression tests will be used in order to investigate the relationship between teleconnection patterns and studied temperature parameters and the amount of barberry production and yield. In this way, the existence of correlation and connection between the studied parameters will be identified.

The results of the correlation between the average temperature of the studied basin and teleconnection patterns showed that in Qain station, the average temperature parameter is more correlated with TNA, AMO, and AMOS patterns. The correlations occurred mostly in the second half of the year during the months of June to December, and in the first half of the year, almost all indicators were without correlation. In the minimum temperature parameter, TNA, AMO, NTA, AMOS have shown more correlation than other indices. In this parameter, more correlations have been observed in the second half of the year. In terms of time, in the two months of September and November, more indicators have been correlated with the minimum temperature. In the maximum temperature parameter, EA.WR, NTA and AMOS indicators have shown more correlation with the maximum temperature of Qain than other indicators. The correlations occurred mostly during the months of March to December. The NAO, Nino1.2, and TNI indices did not show any correlation with the maximum temperature of Qain in any of the months. The absolute maximum temperature of Qain station in July has shown a correlation with the teleconnection indices. In the months of June, September to December, it has not shown any correlation with any of the link patterns. The absolute minimum temperature of Qain station in January has shown more correlation with the link patterns than other months. AMOS, AMO and NTA models have more correlation with this parameter than other models. Correlation between teleconnection indices and barberry production and yield showed that the Nino3 index had a 99% significant inverse correlation with the barberry production and the AO index had an inverse correlation with the barberry yield at a 95% significance level. The correlation between the studied temperature parameters and the amount of barberry production and yield during the studied period showed that there was no correlation between them.

In total, the results of the obtained correlations indicate that the correlations occurred mostly in the second half of the year. Also, respectively, AMOS, AMO and TNA indices have been correlated more than other indices with the studied temperature series. Nino indices were also uncorrelated in almost all temperature series in both Qain and Gonabad stations. In terms of time, average temperature, minimum temperature and maximum temperature in the months of July and October and the absolute maximum temperature in July more than other months have shown a correlation with the link indices. The absolute minimum temperature at Qain station in January and October and at Gonabad station in January and September were more correlated with the teleconnection indices. The results of the analysis of variance also showed that two patterns, AMO and AMOS, had more influence on the studied temperature series than other patterns. According to the results of the correlation between teleconnection patterns and barberry crop efficiency, the Nino3 index has shown an inverse correlation with the barberry production rate at a 99% significance level, and the AO index has shown an inverse correlation with the crop performance at a 95% significance level. According to these results, it can be stated that the indices related to the Atlantic Ocean have a direct correlation and a greater impact on the temperature series of the studied area. These patterns affect Iran through the influence on the Mediterranean and Azores subtropical high pressure systems. The indices related to the Pacific Ocean have also been inversely correlated with the yield of the crop and the tropical Pacific indices with the amount of barberry production.

Global warming process is one of the most important climate changes of the current century that researchers have addressed in regional and planetary scales. Global warming and climate change is one of the most important environmental issues in the world. The phenomenon of climate change, especially the increase in the minimum and maximum temperature of the studied area, will be overshadowed. Finding the future climate of each climate zone and examining the system of their changes and examining the consequences of climate change can open the way for planning. In this research, the author tries to show a perspective of the conditions of climate change in the next 50 years in Khuzestan province, emphasizing the element of temperature.

The area studied in the current research is the synoptic stations of Khuzestan province. In this study, meteorological data including the values of minimum temperature, maximum temperature and average temperature for the studied period have been used. In this research, we used SDSM statistical micro-scale exponential model for temperature prediction and 6 synoptic stations of Khuzestan province, which had 45-year (1961-2005) and 40-year (1966-2005) climatic statistics, were selected. The outputs of the CanESM2 climate model have been used under RCP2.6 and RCP8.5 scenarios. The data of the base period (1961-2005) were used for the first 30 years of data (1961-1990) for calibration and the second 15 years (1991-2005) for the syntactic evaluation of the model performance.

According to the degree of correlation between the average temperature data and the predictor variable data, it has the highest correlation with the ncep_temp predictor variable, which is shown in graph (3) of the annual correlation between the ncep_temp predictor and the average temperature data. shows that the annual temperature in Ahvaz station is 96%, Abadan station is 95%, Dezful station is 94%, Shushtar station is 95%, Bagh Malek station is 88%, Bandar Mahshahr station is 94% and Omidiye Aghajari station is 95%. . The average temperature changes in the province is 25.7 with two optimistic and pessimistic scenarios with an increase of 0.4 and 0.7 respectively. Therefore, the trend of the temperature climatic element regarding the studied area and the future period is changing and increasing. In Khuzestan province, with optimistic and pessimistic scenarios, the frequency of heat waves increases by 2 and 1 days respectively. In the past period, in the northern regions of the province, Dezful station, Shushtar and the southeastern regions of Omidiyeh Aghajari province had the highest frequency of heat waves on average, 22, 19 and 18 days per year respectively.

.The most important results of its implementation are as follows: The results obtained from the data analysis show that in all stations, in Khuzestan province in general, the average temperature parameter shows an increasing trend. An increase in temperature has occurred in all studied stations in the coming period. In two scenarios, RCP2.6 (commitment of countries to reduce greenhouse gases) and RCP8.5 (in case of non-compliance to reduce greenhouse gases) were measured in the studied periods. Meanwhile, during the annual study period, the areas adjacent to the southern coasts of Iran will have the lowest temperature increase, so that the temperature increase in the stations located in the land is more than the stations in the coastal areas. The studies of temperature increase in Khuzestan province are aligned and consistent with the studies and researches of researchers such as Abbasnia (2016) and Ansari (2016). Because climate change can have an important effect on maximum and average temperature.The results of evaluating the performance of SDSM model using statistical tests and different error measurement indicators showed that this model is investigated in Khuzestan province and has a suitable accuracy for simulating climatic variables in the studied area.As a result, according to the monthly forecast for future periods, according to the existing scenarios, the results obtained are as follows:_ July is the hottest and January is the coldest month of the year in all studied stations of Khuzestan province during the forecast period.Research has shown that the maximum temperature trend is an increasing trend in the base period of 1961-2005 and this trend will continue in the future periods. Based on this, the consequences of climate change in the southwest of the country (Khuzestan province) have been calculated. it will reduce the coldness of the air and moderate it, and severe frosts will be reduced. The study of hot days zoning shows an increase in the number of hot days in the future climate period, among other results of data analysis in the future period. The number of hot days has increased and it is consistent with Pudina's studies (2014). Investigating the spatial behavior of heat waves in Khuzestan province. The results showed that there are high occurrences of heat waves in the east and northwest of the province. They are significant in terms of location, in Dezful and Shushtar stations located in the north of the province and Omidiye Aghajari located in the south of the province, they decreased by almost 2 days and in the rest of the studied stations, they increased by an average of 4 days, which according to the research of Esmailnejad (2004) approximately It is aligned and in recent years, this consequence has been frequent. In the last few decades, the increase in the earth's temperature has upset the climate balance of the earth and has caused extensive climate changes in most areas of the earth, which is referred to as climate change. The number of dry days in the future will increase in all stations. Currently, finding out about the amount of climate changes and the behavior of climate variables in order to apply the necessary measures against the effects of climate change has been discussed and the focus of attention of many researchers, especially climatologists. Recognizing and evaluating climate changes in the coming decades with the aim of proper environmental planning in order to adapt to future climate conditions and reduce its effects is an effective matter.

Equilibrium moisture content definition (EMC) is the moisture level where the wood neither gains nor loses moisture since it is at equilibrium with the temperature and relative humidity of the surrounding environment. Climate change has been one of the challenges in recent century and has affected the sectors related to it. One of the most important and influential parameters affected by these changes is the Equilibrium moisture content, which is of special importance in the sciences related to wood industries and their products. EMC is the moisture level where the wood neither gains nor loses moisture since it is at equilibrium with the temperature and relative humidity of the surrounding environment. The EMC of wood is strongly influenced not only by the physical properties of wood but also by temperature and relative humidity. The relative humidity in air varies with the temperature and the amount of water damp present. The hotter the air the more water damp it can hold. The EMC will follow the RH in air, through a direct relationship if the wood and air left to reach equilibrium. Therefore, changes in these two climate variables will affect the EMC of wood and consequently alter the physical properties of wood products. The aim of this study is to investigate the trends of temperature, relative humidity, and the EMC during the period of 1961-2015 at 27 selected stations in Iran.

Calculating the EMC based on the relative humidity and temperatureAlthough the relationship between RH and EMC of wood is not linear, an increase in RH or a decrease in temperature will increase the predicted moisture content of wood, after its equilibration with the air. The non-linear nature of the RH-EMC relationship is a typical sorption isotherm and has been described by sorption theory. An adsorption model employed by Simpson (1973) uses the theory developed by Hailwood and Horrobin (1946) to predict the EMC based on the combination of temperature and RH. The form of the equation is as seen in Eq. 1,In this equations T is the dry-bulb temperature (°F).Thus, given two pieces of information, dry-bulb (or ambient) temperature and the RH, the EMC can be readily calculated.The Mann-Kendall TestThe purpose of the Mann-Kendall (MK) test (Mann 1945, Kendall 1975, Gilbert 1987) is to statistically assess if there is a monotonic upward or downward trend of the variable of interest over time. It does not require that the data be normally distributed or linear. It does require that there is no autocorrelation.List the data in the order in which they were collected over time, x_1, x_2, …, x_n,which denote the measurements obtained at times 1,2,…,n, respectively, the MK test statistics is calculated based the sign of the difference between two consecutive observations, 𝑠𝑔𝑛(𝑥𝑗 − 𝑥𝑖).

The results of this study, conducted using the non-parametric Mann-Kendall method (monthly, seasonal, and annual time scales), showed an increasing trend in annual temperature at 25 stations and a decreasing trend in annual relative humidity at 18 stations. The annual trend in EMC also had a significant decreasing trend at 20 stations. The result indicated that the EMC over a 55-year period is influenced by changes in temperature and relative humidity, such that the increasing trend in temperature and decreasing trend in relative humidity have led to a decrease in the EMC percentage.

Over the past decades, there has been a significant change in climate which has increased the attention towards the potential effects of the changes in different sectors including timber structures. From various research on wood and its behavior and characteristics, it is known that wood is very sensitive to changes in climate, shown especially by shrinkage and swelling, but also by properties like stiffness and strength (Lanata, 2014). According to these results, it was determined that the trend of the EMC in the 55-year period were affected by the trends of temperature and relative humidity. These results should be considered as a warning to the timber industry and similar industries that are affected by the climate variability and climate change.

In recent decades, with the trend of warmer weather and heavy rains caused by climate change, events such as floods or droughts have clearly increased in many parts of the world. Therefore, it seems necessary to predict climate change by models to know the future conditions and manage water resources in line with adaptation.

The aim of the current research was investigation and evaluation of climate change forecasting on Hashemabad Gorgan synoptic station which located in the Qarasu basin of Golestan province. Precipitation data, maximum temperature and minimum daily temperature of the studied station were obtained from the Meteorological Department of Golestan province and the data of 1990-2014 were considered as the base period. The homogeneity of the observational data checked by using four tests of Standardized Normality Homogeneity (SNH), Bishand Range (BHR), Pettit (PET), Van-Neumann Ratio (VON) provided in the trend package in RStudio environment. Further, by examining the statistical tests of correlation coefficient (R), Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Kling Gupta (KGE) among the three models MIROC-ES2L, EC-Earth3-Veg-LR and EC- Earth3-CC From the set of CMIP6 models, the EC-Earth3-CC model was selected as the best model at this station. Straw scaling was performed using the linear scaling method (LS) by Climate Model data for Hydrologic modeling (CMHyd) software. Projection was made for three statistical periods of the near future (2026-2050), medium (2051-2075) and long-term (2076-2100) according to two intermediate (SSP2-4.5) and very pessimistic (SSP5-8.5) scenarios. Also, the trend of observational data and future periods was determined by the non-parametric Mann-Kendall test and age slope.

The results of the statistics parameter’s error showed that the models do not have a good ability to estimate precipitation and have high uncertainty, but they will have good results for temperature. The results of the investigation of the average monthly changes of the precipitation variable in all three future periods, except for the SSP2-4.5 scenario in the middle future, have a decreasing trend compared to the observation period, and this decrease in the SSP2-4.5 scenario is more in the near future than in the SSP5-8.5 scenario and less in the distant future. The average changes of the maximum temperature and the minimum temperature in all three future periods have an increase compared to the observation period, and this increase is more evident in the two middle and far future periods in the SSP5-8.5 scenario than in the SSP2-4.5 scenario. It can also be said that the increase in the maximum temperature variable for both scenarios will be higher than the minimum temperature in all three future periods. The highest increase in maximum temperature according to the SSP2-4.5 scenario compared to the observation period in all three future periods in July and the lowest increase respectively in the near, middle and far future period in Jan, Dec and Jan and according to the SSP5-8.5 scenario the highest increase respectively in The months of March, July and August and the lowest increase in all three periods was observed in January. The highest minimum temperature increase according to the SSP2-4.5 scenario compared to the observation period in the near, middle and far future respectively in July, Feb and Aug and the lowest increase respectively in April, Dec and Jan and according to the SSP5-8.5 scenario the highest and lowest increase. The close period is observed in Feb and Jan, and in the other two periods in August, and the highest and lowest increases of both middle and far periods are observed in August and April, respectively. The results of examining the situation of SSP2-4.5 scenario, the monthly values of precipitation in all the months of the future period except April, the monthly values of the maximum temperature of all the months of the three future periods and the minimum temperature of all the months of the three future periods except of May and June of the near period and The distant future Jan has no significant trend at the 95% and 99% confidence levels. The significant trend status of the climatic parameters of the SSP5-8.5 scenario has increased in all three future periods compared to the SSP2-4.5 scenario.

Finally, the results of this research showed that the air temperature in the desired station is getting warmer and it can affect the quality and quantity of water resources, so it is suggested to investigate the effect of climate change on runoff in future researches.

Changes in temperature and precipitation are one of the most important debates in the field of environmental sciences. This phenomenon is very important because of its scientific and practical dimensions, because human systems dependent on climate elements such as water, agriculture, industries and the like are designed and operate on the basis of climate stability. Therefore, predicting and knowing about temperature and precipitation changes in the coming years can be a solution to problems such as drought, sudden floods, high evaporation and environmental destruction. The civilization of several thousand years of Sistan has been completely dependent on the flow of water in the Sistan River. Currently, the only source of water supply in the region is the flow of water in the Hirmand River and the water storage sources of Chahnimeh. Considering the dry climate of the region and the rainfall of about 50 mm per year and the presence of 120-day dry winds in Sistan and the lack of suitable underground water resources in the region, the Hirmand River and the Chahnimeh are the only source of water supply in the region. As a result, investigating climate changes and predicting changes in temperature and precipitation within the limits of these devices is very important to save Sistan. In this research, 8 GCM models from CMIP6 were evaluated according to their high resolution and available meteorological data, and after correcting the bias using the LSBC method, the performance of these models was evaluated in simulating the relevant parameters. The study was conducted in the basic period of these models (1990-2014). After receiving the data, the observational and historical parameter values for each of the studied stations were extracted by preparing a in the MATLAB environment using the closest GCMs grid in the base period (1995-2014). Then the bias correction method was used to correct the data. In the following, the difference between the values of observational and historical parameters was evaluated using different indices. After validating and evaluating the accuracy of different GCM models, using the best model, temperature and precipitation changes in the future periods were predicted under three different scenarios and its changes in the future period compared to the historical period (1995-2014) Was investigated. In order to reveal the trend of changes in temperature and precipitation, non-parametric Mann-Kendall test and Sen,s slope estimator were also investigated. The results showed that the rainfall in Zabul and Zahak stations had a decreasing trend and this decreasing trend was not significant in any of the mentioned stations. Based on this, the decreasing trend of precipitation in the studied area is the type of short-term fluctuations of water and It is aerial. The highest decrease in precipitation is related to Zabul station with a gradient of -2.4. The trend of temperature also shows an increasing trend in both meteorological stations and this increasing trend is significant at the level of 5% in Zabul station. Examining the slope of temperature changes also shows that the highest slope of temperature changes is related to Zabul station with a slope of 0.07. After bias correction, the performance and accuracy of the models were evaluated in the simulation of the studied parameters using various indicators at the Zabul syndication station as the selected station of the region. The results showed that based on the RMSE index, BCC_CSM2_MR model and then MPI-ESM1-2-LR have higher accuracy than other models for simulating precipitation. The RMSE of the mentioned models with the observed rainfall data in the studied station on a monthly scale is equal to 0.94 and 0.95, respectively. FGOALS-g3 model also has the weakest performance among the investigated models for simulating precipitation in the study area with RMSE equal to 5.6. In general, based on various indicators of accuracy, the BCC_CSM2_MR model is suitable for simulating rainfall in the study area, so that its coefficient of determination is equal to 0.97, and its MSE and MAE values are lower than other models. The results of projection the rainfall in the future period (2021-2050) based on different scenarios show that based on each scenario, the amount of rainfall will decrease in both stations under study, and its amount is on average For Zabul station it is equal to 4.1% and for Zahak station it is equal to 4.2%. Overall, based on the average scenarios studied, the amount of precipitation in the study area will decrease by 1.4% compared to the observation period. The highest and lowest precipitation changes are estimated based on SSP5-8.5 and SSP1-2.6 scenarios, respectively. According to the average scenarios studied in the future period, the temperature in the study area will increase by 1.4 degrees Celsius compared to the observation period. The most changes in terms of stations are related to the drainage station with 5.9 percent. Also, scenarios SSP5-8.5 and SSP1-2.6 show the highest and lowest temperature changes in the studied area with 1.6 and 1.2 degrees Celsius, respectively. In general, according to the results of the CMIP6 model and the straw scale of the LSBC method, the amount of precipitation in the study area will decrease and the temperature will increase. to follow The totality of these conditions can cause a decrease in the storage and supply of water resources in the Chahnimeh, as a result of which the climatic conditions of the region will also change.

Climate change is a global issue that has attracted the attention of many researchers in recent years. Climate change causes changes in temperature and precipitation patterns, and these changes affect plant performance. As far as can be said, the agricultural sector is the most affected by the climate and its changes. Climate changes in the long term lead to changes in the pattern of cultivation and distribution of plants. Knowledge of how agricultural and horticultural plants react to global warming and climate change and predicting its effects on the performance and area of cultivation of agricultural and horticultural plants in the future requires knowledge of the effects of climate change on plant phenology. The effects of climate change on agricultural production have attracted the attention of many researchers. Severe droughts and temperature increase affect the development of plants and this can be one of the reasons for the reduction of agricultural products (Ridsma, 2007 and Saklaskin, 2008).

To conduct this research, the monthly average temperature data of Ghaen, Gonabad, Ferdous, Birjand, Khorbirjand, Kashmar and Torbat Heydarieh stations during the period of 1989-2021 have been used. In order to check the trends of the studied data, first, the normality of the data was measured using the Anderson-Darling test. Then, parametric t test was used for normal data and non-parametric Mann-Kendall test was used for non-normal data. Next, temperature zoning maps of the basin were drawn using the topographic layer in Arc Gis software.

The average temperature trend of the studied stations showed that in April, only Kashmer station had no trend and other stations had an increasing trend. In May, Birjand and Khorbirjand were without, and other stations showed an increasing trend. In June Khorbirjand and in July Khorbirjand and Birjand were without trend. Other stations have shown an increasing trend. In August, Birjand and Khorbirjand had no trends. In September, only Ghaen station had an increasing trend, and other stations had no trend. In October, Ferdous, Birjand, Ghaen and Torbat Heydarieh stations have shown an increasing trend. Other stations have also been without trends. In February, Birjand and Khorbirjand stations had no trend and other stations had an increasing trend. According to the altitude factor, isothermal maps showed that in April, the south to southwest and the northern half of the basin from the northwest to the east of the basin have the appropriate temperature, height and slope for barberry germination. In May, areas of the basin in the form of a strip from the northwest to the east of the basin and the southern half of the basin have a temperature of 19 to 23 degrees Celsius and are suitable for barberry flowering. During the months of May to October, temperatures between 19 and 23 degrees Celsius have been observed in areas of the Qaenat basin. In identifying suitable areas for barberry growth, in addition to temperature, the factor of height and slope should also be considered. Because there are areas that have a temperature of 19 to 23 degrees Celsius, but they are in low altitude areas or areas with high slopes and are not suitable for barberry cultivation. Examining the topography, slope and isothermal maps of the basin shows that in Khordad month, the areas from the west to the southeast of the basin, which are suitable for barberry growth in terms of height and slope, also have favorable conditions for barberry in terms of temperature. In the months of July and August, the size of suitable areas for barberry growth, which was observed in June, has decreased. In September, areas in the center and south of the basin have a temperature of 19 to 23 degrees, but in terms of altitude, the areas in the center of the basin have more suitable conditions for barberry growth than the southern areas of the basin. In October, areas in the north, south, and northwest of the basin have a temperature of 19 to 23 degrees Celsius, but the northern areas of the basin do not have suitable conditions for growing barberry in terms of altitude, and the areas to the south of the basin are suitable for growing barberry. In the months of Shahrivar and Mehr, the most suitable area is the busiest area of the basin. From the middle of November, barberry growth ends. In the months of November, December, January, February and March, the temperature of the entire area of the Qaenat basin is below 12 degrees Celsius and the period of winter stagnation is barren.

In this research, the trend of average temperature changes in the Qaenat basin was investigated and its effects on barberry yield were investigated. Examining the temperature trend in the studied basin showed that the temperature is increasing and in the months of April to August and also in the month of February, the temperature inside the basin has been increasing. Examining the isothermal maps of the basin indicates that the areas of the basin that have a height of over 1000 meters and are stretched from the west to the southeast of the basin have suitable temperature conditions for germination, flowering and fruiting of barberry. Correspondence of the time series of May temperature with the temperature range of 19 to 23 °C showed that since 1999, when the average temperature of May in the basin was between 19 and 23 °C, the production and yield of barberry has also increased. Considering that the temperature in the study basin has an increasing trend and the growing period of barberry has different temperature conditions in each stage, the increase in the temperature of the basin can lead to the displacement of the months of the growing period of barberry. Barberry is a product that grows better in cold and mountainous areas, if the temperature increases in the study area, it affects the yield of the product and causes a quantitative and qualitative decrease of this product.

This research study aims to investigate the effect of climatic variables, specifically precipitation, temperature, and humidity, on changes in vegetation indices of orange orchards in Hassan Abad, Darab County, using satellite data. Consequently, observational data, including orange tree phenology data and meteorological data from the agricultural weather station, were collected over a period of more than 10 years (2006 to 2016). MODIS images from 2006 to 2016 were referenced based on territorial data and 1:25000 maps from the Iran National Cartographic Center. These images were used to calculate remote sensing vegetation indices, namely the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). The results demonstrated that the variables of maximum humidity, minimum temperature, and precipitation have a significant positive effect on the NDVI variable. Additionally, the variables of maximum temperature and minimum humidity have a significant negative effect on both the NDVI and EVI. To determine the significance of each independent variable in predicting the dependent variables, the artificial neural network method was employed. The findings showed that the climatic elements of precipitation, minimum temperature, maximum temperature, minimum humidity, and maximum humidity had the greatest effect on EVI, with values of 0.39, 0.3, 0.13, 0.1, and 0.06 respectively. Moreover, the effect of these variables on the NDVI index is equal to their coefficients, which are 0.2, 0.28, 0.22, 0.11, and 0.17 respectively. Finally, the ARMAX regression method was used to improve the explanatory power of the model. The results indicated that this method enhanced the explanatory power of the model and reduced the forecasting error.

This article was written with the aim of predicting the process of climate change in Tehran. In this article, the status of rainfall and temperature (minimum and maximum) of Tehran was reviewed at geophysical, Shemiranat and Mehrabad synoptic stations during the 1992 to 2018 period and was used to identify the existence of leaps and revelations of the years of mutation. It was also used to predict climate elements in Tehran using CMIP5 data and Lars model for three scenarios of RCP2.6, RCP4.5, RCP8.5. Results of the Time Time and Rainfall Series Trends of the Three Stations Investigated in Tehran, using my Kendall process analysis test, showed that in the case of none of the climatic factors in the three stations under investigation, a significant change in P_Value = confidence level = 0.05 did not occur. But many annual mutations and fluctuations were observed in the temperature and rainfall series. Also, the results of the climatic parameters of the minimum, maximum and precipitation temperature parameters showed that the minimum temperature and maximum by 2050 compared to the base period (1992 to 2018) in all three carbon dioxide emission lines RCP2.6 , RCP4.5, RCP8.5, will be significantly increased in all three studies. But rainfall will be significantly reduced by 1.7 mm per month compared to the base period of 1992 to 2018 in all three carbon dioxide emissions in all three stations

Temperature changes are an important issue in the present age and it is a new topic for climatologists. The purpose of this research is to monitoring the changes in the biomass of trees during the growth periods of pole, beam, vigorous and old by using temperature characteristics. In this research, 307 parcels were studied. In this research, 30 sample plots were implemented in Parcel 307 using regular random method. Then, using a drone, the temperature of the centers of the samples was measured. In this research, using the mode of the tree mass was measured during the growing period of the trees, the trees, the trees, the stout, and the old trees. In these models, temperature was considered as the main variable and biomass was considered as the dependent variable. The preliminary results of this research showed that F. orientalis trees have a correlation coefficient of 0.96, 0.97, 0.96, and 0.94. The obtained results showed that Q. Castanifolia trees have correlation coefficients of 0.79, 0.81, 0.96 and 0.64. The results of A. glutinosha trees showed that the correlation coefficient of T.subcordata trees was 0.75, 0.99, 0.99 and 0.41. The results obtained from this show that deciduous trees have a correlation coefficient of 0.82, 0.19, 0.19 and 0.19. The results of this research showed that the use of the temperature characteristics of the sample centers has high accuracy in determining the biomass of trees in various vegetative stages............................................................................................................................................................................................................................................... ..................................................... ........................................................................................................................................................................................

Temperature is one of the most important meteorological variables and any change in temperature variable causes changes in the occurrence of extreme phenomena such as drought, heavy rainfall, and storms that will cause irreparable damage in various social, economic, and agricultural sectors. Therefore, it is important to study the trend of these climatic variables in order to achieve methods for controlling and managing damages. Methods based on the mean or median of the data are generally used in studies related to trend investigation, since mean is a measure of central tendency, if studied alone may not provide information about trend variation in different parts of meteorological and hydrological data distribution, especially distribution tails. While extreme weather events often result from extreme values of climatic parameters. For this purpose, to study trend variation in the different data ranges, the quantile regression method was proposed, which has no limitations of previous parametric and nonparametric methods and has the ability to study trend variation and Show changes in different quantiles or different values of a climatic parameter. Therefore, the purpose of this study is to investigate the trend of temporal and spatial changes of minimum and maximum temperature on an annual scale using the quantile regression method in the geographical area of Iran.

The study area in the present study is the geographical area of Iran, which due to its location in the middle latitudes of 30 degrees, most of its area is covered by arid and semi-arid climates. In order to analyze a trend, maximum and minimum daily temperature data of 102 meteorological stations with a statistical period of 30 years (1988-2017) were obtained from the Meteorological Organization. After preparing the data, the annual time series was formed from the minimum and maximum temperature for this period of 30 years. Then the quantile regression method was used to analyze the trend variation in different quantiles of minimum and maximum temperature and the estimated slopes for the whole country were zoned using different interpolation methods in the GIS environment after that the Bayesian kriging interpolation method was selected for interpolation and the results were analyzed.

The results showed that the quantile regression method showed different trends for the minimum and maximum temperature variables in different quantiles and for different parts of Iran during the year. In general, both temperature variables had an increasing trend in all studied quantiles for all parts of Iran; Lower quantiles of the minimum temperature have an increasing trend in most parts of Iran and the most increasing trend slopes have been observed in the western half of the country, and about 63% of the area of Iran had a positive slope of 5-10%. While in the median quantile, the trend variation is more severe and all regions of Iran have a significant increasing trend that has been significant in most regions. in general, about 73% of the regions have a slope of 5-10%, which is visible in the western half, northeast, and southeastern parts and about 24% of the areas have a slope of 10-15% which is seen in eastern Iran. However, upper quantiles of minimum temperature that indicate high-temperature values also have a positive and significant trend in most parts of Iran, which in general 69% of the regions have a trend slope of 2-5%, which is located in the eastern half, north and south of the country, while 29% of Iran's area has a slope of 5-10%, which is mainly located in the western half and parts of the east and center of the country. However, in the study of the lower quantiles of the maximum temperature, the trend variation was more than the minimum temperature and there were significant increasing trends in most parts of Iran that 47% of the area had a slope of 2-5% which is located in the eastern half of Iran, and also 43% and 10% of the area of Iran had a slope of 5-10 and 10-15 %, respectively, which were observed in the western half of the country, but the number of increasing slopes was higher in the west. The median quantiles of the maximum temperature have a slope of 5-10% in 73% of the area, and 24% of the areas have a slope of 10-15%, which was significant in all cases. However, for the upper quantiles of the maximum temperature, trend variation was not significant, so that 64% of the area had a slope of 2-5% in the southern half and 36% of the areas had a slope of 5-10% in the northern half of Iran.

The most increasing trends for low values of minimum temperature were in the western half, median values in the eastern half, and high values in the western half, east and central part of Iran. In contrast, the highest upward trends for low values of maximum temperature are obtained in the northwest, median values in the eastern, western, and central half, and high values in the northern half of Iran. trend slopes for both minimum and maximum temperature have been higher in the median quantile and in general, it can be inferred that the temperature in Iran has increased due to climate change and the quantile regression method is useful to study and control very high and very low temperatures that are more important than the average temperature in climate risk studies.

Land Surface Temperature (LST) is one of the most important parameters of environmental and climatic conditions. Therefore, this study is to investigate the trend change of day and nighttime LST in ​​Iran at a monthly and annual scale. To achieve this goal, the monthly products of MODIS sensor, Aqua satellite for day and nighttime with a spatial resolution of 5 km were obtained from USGS from 2020 through 2003. Then a three-dimensional matrix with dimensions of 216 × 297 × 388, in which 297 × 388, the number of cells and 216 months, was created. Finally, the monthly and annual changes of these two parameters were performed using Mann-Kendall test. Although the average annual trend of day and nighttime LST is increasing, on a monthly basis, different patterns are observed. The LST at nighttime increased significantly in May and September and during the daytime in December, but in June, July and January both day and nighttime temperatures increased. In contrast, both in March and October had a decreased trend. There was also a decreasing trend in nighttime LST in February and daytime in April and November. The results showed that the nighttime LST increased more than the daytime temperature, which led to a decrease in the 24h temperature range. In the spatial dimension, the highest increasing trend was observed in the western and southwestern regions and the highest decreasing trend was observed in the central plateaus and southeastern. Another point is that the water areas undergoing change, such as Lake Urmia and the lakes of Fars province, have experienced a significant increase (decrease) in LST during the day (night). In addition to decreasing the range of 24h temperatures, due to the fact that in the northern and western half of the country the upward trend was more and in the center, east and southeast the decreasing trend of temperature was predominant, the amplitude of spatial temperature gradient also decreased.

Modeling of water flowing into dam reservoir is one of the most important steps in watershed management, exploitation of dams, flood warning systems, and priority areas for erosion and sedimentation. In fact, optimal management of water resource systems, such as dams requires the accurate prediction of inflow into the dam reservoir. Therefore, it is essential to estimate this parameter more accurately. Several methods have been developed to predict the dam reservoir inflow. In the current study, the intelligent Support Vector Machine (SVM) and Nero-Fuzzy Adaptive Inference System (ANFIS) methods are used to estimate the inflow rate of the Sattarkhan dam and the effect of different input parameters such as monthly precipitation, discharge and temperature on improving the models accuracy is investigated. The results showed the desired efficiency of the Meta model approaches in estimating the monthly inflow into the Sattarkhan dam reservoir. The best results for the test data, in the state of modeling based on monthly discharge and precipitation was obtained the values of R= 0.878 DC= 0.782, RMSE= 0.063 and in the state of modeling based on monthly temperature, precipitation and discharge was obtained the values of R= 0.805, DC= 0.708 and RMSE= 0.108 were obtained. According to the results, the model with the parameters of the monthly discharge and precipitation leads to more accurate results.

The creation and expansion of rural settlements is influenced by various factors, these factors are divided into two categories of natural and human factors, whose impact on the construction and pattern of these settlements is undeniable. Changes in the pattern and architectural style of rural housing have occurred in many different ways. The present study has examined all the factors affecting the rural housing model in Khalkhal county. The main purpose of this study is to investigate the role of natural and human factors in the rural housing model and determine the degree of correlation between these factors and rural housing. The research method in this study is applied in terms of purpose and descriptive -analytical in terms of method. In the first part, the results of studies show that natural factors have played a very effective role in the pattern of rural housing in this region. Therefore, the role of factors such as water resources, climate and, most importantly, the specific topography of the region have played a more colorful role than other environmental factors. In the second part, the effects of human factors in rural areas on the rural housing model are examined. The statistical population of the study is 5 villages of Khalkhal city with a population of 10,650 people in the form of 1940 households. The sample size is 320 rural households, which were interviewed by random sampling. For data analysis, Pearson and Spearman and multivariate regression methods were used using SPSS software. The results show that there is a significant and relatively strong relationship with a coefficient of 0.675 between human factors and the rural housing model in the study area, so that the socio -cultural index explains about 58.8% of the housing model .

Climate change and global warming may have direct effects on climate extreme values and temporal as well as spatial variations of these events. Alterations in natural and human communities caused by meteorological extreme events are more significant than those caused by climatic averages. These extreme events widely draw public attention, and are particularly foregrounded by governments and academic communities (An et al. 2019). Given the noticeable consequences of climate extremes, the Intergovernmental Panel on Climate Change (IPCC) organized a team of experts to investigate the challenges caused by extreme events and measure extreme indices (Houghton et al. 2001, Peterson et al. 2002). This team suggested 27 indices to investigate and measure climate extremes. These indices have globally drawn the attention of atmospheric sciences researchers, and many studies have been conducted based on these indices to investigate both past and future events. The literature review indicated that spatial and temporal variations of extreme climate events related to both the past and future have been sufficiently investigated by certain researchers abroad. However, extreme climate events in Iran have been rarely examined. The few studies to investigate the events related to temperature and precipitation extremes in the selected region using temperature and precipitation data and synoptic stations located in Western and Southwestern Iran.

The regions that have been investigated in the present study are Ilam, Lorestan, Khuzestan, Chaharmahal and Bakhtiari, Kohgiluyeh and Boyer-Ahmad, Bushehr, and Fars provinces in Western and Southwestern Iran. These provinces cover 28/9924 square kilometers comprising 17/6% of the whole country area. The data used in this study include average precipitation, and maximum as well as minimum daily temperatures at 28 synoptic stations located in Western and Southwestern Iran in a common statistical period of 30 years from 1988 to 2017. After selecting the stations, the Run Test was used for all the stations and precipitation parameters, and minimum were measured. Subsequently, a matrix of the daily precipitation data and minimum. as well as maximum daily temperatures for was prepared. Finally, extreme indices (26 precipitation and temperature indices as suggested by the expert group CCL/CLIVAR) were measured using programming in the context of the MMATLAB software environment, and the variation process in every index was examined using the Mann-Kendall test. Then, the required maps and diagrams were prepared.

The investigation of temporal variations in the occurrence of warm indices from 1988 to 2017 in Western and Southwestern Iran using the Mann-Kendall test (with a reliability level of 95%) indicated that the total trend of warm indices such as warm nights, warm days, the number of summer days, and the number of tropical nights for most of the stations has been increasing. However, the examination of temporal variations in the occurrence of cold indices using the Mann-Kendall test (with the reliability level of 95%) showed that the total trend of cold indices such as cold days, cold nights, and the number of frost days was declining.The significant finding revealed by the general investigation of the total warm and cold extreme indices in the regions covered in the present study is the prevalence of the warming trend over the examined statistical era. The results of the maximum of one-day precipitation amount and the maximum of five-day precipitation amount indices were also indicative of remarkable precipitation rates in the regions based on these indices. The average was 177 mm (in Kuhrang station). The average was 347 mm (in Kuhrang station). The trend of temporal variations in these two indices was also insignificant in the majority of stations in the regions corresponding to the R99p, R95p, R20mm, PRCPTOT, and CWD indices.

The investigation and analysis of the extreme indices trend revealed that the occurrence of warm extreme events was increasing, while the occurrence of cold extreme events was decreasing in the areas covered in this study. One of the main reasons behind these phenomena has been progressive global warming, particularly since the late 1990s. The results of the present study concerning temperature extreme events confirm previous findings (by Zhang et al. 2005, Alexander et al. 2006, Zhao et al. 2012, Varshavian et al. 2011, Miri and Rahimi 2015, Karimi et al. 2018) stated in some studies that investigated temperature extreme indices. The above-mentioned researchers have also highlighted the increasing trend of warm extreme indices and the declining trend of cold extreme indices in their studies. According to the results obtained by the investigation of the frequency of the occurrence and trend of extreme precipitation indices in the areas covered in this study, it can be asserted that the total precipitation rate has been declining corresponding to the majority of Iranian provinces. However, maximum precipitation rates have been declining from 1988 to 2017 despite exhibiting noticeable extreme amounts. Hence, it could be stated that extreme precipitation events increase, whereas the duration of the wet season shortens. Moreover, the declining trend of the wet days' index and the increasing trend of successive dry days' index in Western and Southwestern Iran could be indicative of the gradual intensification of water scarcity. The results of the present study concerning precipitation extreme events largely confirm the findings reported in corresponding studies (such as Klein Tank et al. 2006, I'm et al. 2010, Jones et al. 2012, Koozegaran and Mousavi Baigi 2015) that emphasized the increased extreme precipitation rate and the decreased amount of total precipitation rate.

In this research, the effects of climate change on the amount and distribution of precipitation and temperature in the Kerman synoptic station were investigated. For this purpose, the output of global climate models of CMIP6 for the historical period (1965 to 2014) for precipitation and temperature were evaluated in comparison with the station data. To evaluate the models, evaluation metrics including correlation coefficient (r), root mean square error (RMSE), mean error (ME) and KGE index were used. Then the best-performed models were selected to predict these two variables in the future period (2051 to 2100) based on different climate scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5). Finally, changes in the distribution of precipitation and temperature in the future period compared to the base period were investigated. The results showed that after bias correction, the ACCESS-CM2 and MRI-ESM2-0 models performed more accurately for temperature (ME=0 °C, RMSE=1.87 °C, r=1, KGE=0.998) and precipitation (ME=-0.002 mm/month, RMSE=17 mm/month, r=0.484, KGE=0.468) estimation, respectively. The results of trend analysis indicated the trends in the amount of precipitation were not significant (P-value>0.1). Comparison between the average and variance of precipitation also was not significant (P-value<0.05), and therefore the possibility of increasing the occurrence of extreme precipitation cannot be statistically expected. However, trends in temperature and its average and variance are statistically significant and the possibility of heat stress will increase in the future. A significant increase in temperature in the future can affect the quantity and quality of Kerman's water resources, requiring more attention to the proper management of water resources.

No study has so far evaluated the NEX-GDDP and CORDEX-WAS downscaling methods to validate the output of CMIP5 models in Iran for temperature and precipitation parameters. Therefore, this study is the first in Iran to compare the MPI-ESM-LR model performance from the CMIP5 model series for temperature and precipitation variables with the combined approach of dynamic and statistical downscaling methods for the historical period of 1980-2005. The verification was performed using the MBE, RMSE, and R statistics. The slope of the data trend in the time series is estimated using Sen’s non-parametric method. The findings revealed that degrees of bias equal to -0.34 and -0.46 C were recorded in CORDEX and NEX-GDDP projects, respectively, indicating the better performance of the MPI-ESM-LR model under the CORDEX dynamic downscaling project than the NEX-GDDP statistical project in temperature simulation. In both projects, the maximum and minimum temperatures were simulated in Iran's southern coasts and north-western heights. The MBE index shows a decreased bias in the NEX-GDDP project (-2.60 mm) compared to the CORDEX project (-8.21 mm), suggesting the better performance of the MPI-ESM-LR model in the NEX-GDDP project than the CORDEX project in precipitation simulation. Both projects' maximum and minimum precipitations were simulated in the Zagros highlands and the southeast of Iran, respectively.

Climate change is one of the most pressing problems on Earth today, so predicting these changes in the future and their effects on water resources, the natural environment, agriculture, and environmental, economic and social impacts is of particular importance. Therefore, in the present study, the effects of global climate change on different climatic regions of the country were studied in 12 climatic regions. In this study, NCEP data and climatic elements of precipitation, maximum and minimum temperature were used for statistical downscaling with SDSM model. And using the CanEMS2 model output under RCP scenarios for the three statistical periods of 2011-2040, 2041-2070, and 2071-2099 annual climate change data were obtained. Correlation coefficient, determination coefficient and error indexes of RMSE, MSE and MAD were used to evaluate the performance of the model. However, the results showed that the accuracy of the model was different at different stations. In this way, each model performs better than rainfall in simulating minimum and maximum temperatures. The annual long-run results also show that precipitation will decrease in all climates studied in the coming decades, with the largest decrease occurring in semi-warm (35%) and very humid and temperate (32%) desert areas. But minimum and maximum temperature variations will be different in different climatic regions so that under RCP scenarios during all statistical periods at Sabzevar and Tabas stations minimum temperature changes will decrease but in other climatic regions the trend of minimum and maximum temperatures will be incremental. The highest minimum and maximum temperature increases based on RCP scenarios under RCP8.5 scenario during the period 2071-2099 in the cold mountain climatic region will be 3.03, 4.27 ° C, respectively.

Teleconnection is one of the features of the climate on a global scale. Teleconnection patterns represent large changes that occur in the pattern of atmospheric waves and tornadoes and affect the pattern of temperature, precipitation, the direction of showers and the position and intensity of tornadoes in large areas.   

The aim of this study was to investigate the effect of these patterns on temperature series in Zahedan.

In this regard, minimum temperature, maximum and average temperature statistics of Zahedan station during the period of 1987-2019 on a monthly scale as well as standardized data of teleconnection patterns during the mentioned period, were used. Pearson correlation and multivariate regression tests were used in this regard.

Geographical Context: 

The geographical territory studied in this research is the city of Zahedan.

The results indicate that there is a significant relationship between teleconnection patterns and the average temperature of Zahedan. Among these, NTA, AMO and TNA patterns had the greatest effect on the average temperature of Zahedan. The correlations were direct, and only the NAO pattern was inversely correlated. The maximum and minimum temperatures of Zahedan also showed the highest correlation with the patterns located in the North Atlas. The maximum temperatures in March and October and the minimum temperatures in March and August had the highest correlation with the North Atlantic patterns.  

In general, it can be said that the patterns located in the North Atlas, more than other patterns, have affected the temperature series of Zahedan and especially its average temperature.