جستجوی مقالات مرتبط با کلیدواژه « دما » در نشریات گروه « جغرافیا »

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.

In this research, the climatic variables of temperature and precipitation of Urmia Lake catchment area were evaluated and projected using CMIP6 models under two scenarios SSP1-2.6 and SSP5-8.5 in the future periods (2031-2055 and 2071-2095). First, the accuracy of the models for the base period (1990-2014) was evaluated using Taylor diagram and RMSE and NRMSE indices after downscaling with different methods of quantile mapping, and among the models, the MRI-ESM2-0 for temperature and INM-CM5-0 for precipitation using the SSPLIN downscaling method were selected to project the future climate, then the future temperature and precipitation data were produced. The results of comparing the temperature and precipitation of the future periods with the base period showed that the average annual temperature of the basin will increase under all scenarios. The average annual temperature increase of the basin in the near future in the optimistic and pessimistic scenarios will be 1.5 and 1.8 degrees Celsius, respectively, and in the far future, 1.4 and 4 degrees Celsius respectively. The average annual precipitation will decrease in all scenarios, in the near future it will decrease by 19.9 and 21.6 percent in the optimistic and pessimistic scenarios, respectively, and by 12 and 28.6 percent in the far future, respectively. Based on the spatial distribution of changes in temperature and precipitation in the future periods, the greatest increase in temperature and the greatest decrease in precipitation will occur in the northern areas of the basin.

The impact of climate change on hydrology and water cycle in nature is very serious and a little knowledge of these effects to deal with its consequences. In order to model the two watersheds of Telangu and Hosseinabad in Kerman province,. Also, by using the LARS-WG model, variables were predicted for the period from 2021 to 2060. In the other part of this study, to run from the SWAT model, sensitivity analysis was performed using the global sensitivity analysis method using 29 parameters. The results showed that in general, the temperature has increased in the next period compared to the previous period in the whole month, in all three stations this increase was less in the warm months of the year than in the cold months. Precipitation has generally increased and the highest monthly precipitation increase for all three cases is April, March and March, respectively Sutcliffe (ENS) is greater than 0.65. The results of evaluating the effect of climate change on the discharge of the basin show that in general the discharge of the Telango Bam watershed has been much lower than the Hosseinabad Rain watershed in the current period and in the future. In the next 60 years, the discharge of the Telango Bam watershed will increase compared to the current period, the discharges of January, May and July will increase and will decrease in the other months except September, which is unchanged. The general results of the present research indicate that in the near future in dry ecosystems similar to the study area, following the change in the rainfall regime resulting from global warming, the hydrological behavior of the basins will change, especially in the winter and spring seasons, and the occurrence of extreme rainfall and flood events in These seasons are more likely than before.

Earth's surface temperature is an important factor in global warming studies, and today it is the main challenge for many researchers worldwide. With remote sensing technology, it is possible to evaluate the temperature of the earth's surface and land use changes during different years with the help of satellite images, thermal infrared radiation, and physical models. In environmental studies, due to the location and location of the observations in the sample space, traditional statistics cannot be used due to the continuous structure of time and space. For this purpose, spatial statistics (spatial autocorrelation) is a suitable and new method for analyzing these data.

In this research, satellite data related to Landsat 5 and 8 images for the years 1990 and 2020 were obtained from the American Geological Survey. After correcting the images, the land use maps of Jiroft city were prepared and using the combination of visible and infrared bands, land use maps were prepared. The transformation of different land use classes and their changes during these years were analyzed in IDRISI software. Also, 150 control points from Google Earth were exploited to evaluate the accuracy of classified maps. In order to obtain the temperature of the earth's surface, the thermal bands of the received Landsat images were used. In two steps, the spectral radiance was converted to the temperature of the black body, and the surface temperature of the earth's surface was calculated. Finally, to reveal the spatial pattern of local differences, the local Moran's spatial autocorrelation statistic has been exerted.

The results showed that from 1990 to 2020, part of barren and flood channel lands was converted into water areas, which increased with the dam's construction after 1990. The average temperature of the earth's surface increased by 11.1 degrees in 30 years, which can be seen in all uses. The reason for this increase can be seen as the increase in air temperature. Another reason for the increase in the temperature of the earth's surface is the increase in construction in the region. The classification of the earth's surface temperature classes showed that the very hot and warm classes in the southern parts increased in 2020, and the average (most change) and cold classes decreased. The local spatial correlation statistics analysis results showed that hot clusters are gradually concentrated in southern regions and cool clusters in northern and northeastern regions.

The findings of this research showed that despite the increase in agricultural and garden use and the decrease in barren areas, the earth's surface temperature would increase to a large extent in all uses. Although manufactured areas are also increasing, the main reason for the temperature of the earth's surface can be considered the increase in air temperature and climate change.

Predicting the quantity and quality of climate change is one of the complex issues that has occupied the minds of climatologists. Now, with the help of access to new technologies and having multiple series of necessary data from climate variables and with the help of knowledge of understanding the relationships between these variables, basic steps in understanding and predicting climate trends have emerged. As now, computer models all respond to climate prediction issues and factors affecting climate change to the best of their ability. Completely accurate predictions with zero error, regardless of the field and subject matter, are very difficult and almost impossible, especially since the prediction process is in a very complex environment and in a dense cloud of uncertainties and actors and drivers. Numerous and effective effects on the environment and the data and information used in forecasting also have vague and gray features . Among climatic elements, temperature and precipitation are of special importance due to their wide effect on other factors and especially the effects they have on human activities. because human systems are dependent on climatic elements. Like agriculture, industries and the like are designed and operate on the basis of climate stability.Time series models are experimental models and a powerful tool for simulating and predicting the behavior of climatic and hydrological systems such as temperature and precipitation. The classical approach in terms of time series modeling depends on the static and non-static time series according to the application of the Jenkins box approach. If such series show long-term memory properties, the prediction value based on the moving average self-regression (ARMA) and stacked moving average (ARIMA) models will not be valid. If there is long-term memory in time series, there will be a significant correlation between series observations at very long distances, apart and far from each other, which indicates that observations are not independent of each other, there is a correlation between them and past observations. They will help to predict the data. With these descriptions, the existence of long-term memory in atmospheric parameters weakly violates market efficiency, and then changes in the capital market will not be accidental and will be predictable. In the early 1980s, Granger and Jokes proposed an alternative approach to long-term memory modeling by creating their own model of partial stack moving average (ARFIMA), since the ARFIMA model between the short-term memory process and the long-term memory process in series. When differentiated, it creates a distinct advantage over classical S / R analysis, which has a very high tendency to accept the assumption of zero long-term memory despite the short-term memory process .The data required in this article include the average monthly temperature and precipitation in the last half century (1969-2018), which was prepared by the Meteorological Organization. In time series data analysis, time series models including ARFIMA model have been used for modeling and simulation of the mentioned parameters.Changes in temperature and precipitation time series are among the most important climatic parameters in studying hydrological, agricultural, environmental, health, industrial, and economic processes. Assessing and forecasting temperature and precipitation will be of great help to managers and planners of agriculture and water resources. One way to examine time-series data is to use statistical models. Due to the importance of the subject, in this article, the amount of temperature and monthly rainfall in the last half-century (1969-2018) of Tabriz Synoptic Meteorological Station is examined using the ARFIMA model, and to fit the model, R / S and GPH models have been used. To investigate the statics of the model, autocorrelation (ADF), partial autocorrelation (PACF) functions, and differentiation methods have been used. However, since the synoptic meteorological station data is evaluated for the first time, the evaluation is based on the ARFIMA model, the Bayesian Information Criteria (BIC), root-mean-square error (RMSE), and the Akaike information criterion (AIC). The results of R/S and GPH tests show that the model with long-term memory of Tabriz temperature and precipitation time series is approved at a 95% level. The only difference is that in the case of precipitation, this act seems fragile. In addition, the analysis of different structures showed that the temperature and precipitation data have the best fit or performance using the ARFIMA (3,0.2,1) and the ARFIMA (1,0.0004,4), respectively. It should be noted that the RMSE value of the fitting models between the observed values and the simulation of temperature and precipitation was equal to 2.2 and 38.4, respectively, indicating the appropriate accuracy of the model and its applicability for forecasting.

Precipitation and temperature are important elements of climate. Changes in these elements can have a significant impact on the changes in other environmental components, including changes in land use. This study aimed to investigate the long-term changes in temperature and precipitation in the Karun basin. Then the probable relationship between these changes and land use changes was investigated. Therefore, the daily temperature and precipitation data from 177 synoptic stations and 230 rain gauge stations were prepared from 1972 to 2014. Daily maps were made up of 4 x 4 km, using the kriging interpolation method. Due to the climatic and spatial diversity of the Karun, the basin was divided into 12 smaller sub-basins. Using google earth engine (GEE) and using the digital data of Landsat 5, 7, and 8 series satellites, TM, and OLI/TIRS sensors, land use maps were extracted by calculating the mean of each use. For the classification of images, the algorithm of the minimum distance from the mean was used for several years (1987 - 1997 - 2007-2018). To monitor the temporal changes in land use, the dynamic model of land use was also used. The results of the research showed that in the high basins of Karun, the variation in the climatic elements played a significant role in land use changes. In the sub-basins of Karon, the decrease in rainfall over time has had a destructive effect on the degradation of forests, pastures, water levels, and water agriculture in the region. So that in recent years, the amount of destruction of forests and pastures has reached its peak and it is mostly dedicated to aquatic cultivation. The drilling of deep and semi-deep wells and the unprincipled exploitation of the underground water have also caused that over time, Karun faces surface and underground water crises and as a result many economic, agricultural, and social crises.Using google earth engine (GEE) and landsat 5,7,8 images, land use maps were produced. For classification of images, the algorithm minimum distance of mean was used for years of 1987 - 1997 - 2007. In order to monitoring changes of land use, land use dynamics model was used. The results showed that in the Karun basin, the variation of climate elements has a significant role in land use changes. In the Karun, precipitation reduction had negative effects on the degradation of forests, pastures, water levels and aquatic agriculture. So, the destruction of forests and pastures has been increased. The Welles and exploiting the groundwater led the Karun into the surface of the surface and underground water crisis. Extended Abstract Introduction The increasing world population, greenhouse gases emission, and land use changes through dam construction, deforestation, desertification, etc., have caused changes in the climate system. These changes can be effective along with positive feedback on the natural ecosystems and the activity of human societies. Precipitation and temperature are important elements of climate. Precipitation and air temperature are important elements of climate. The change of these two elements can have a significant impact on the changes of other environmental components, including changes in land use. In this study, the temperature and precipitation trend in the Karun basin has been investigated. Then, the possible relation among these changes, temperature, and precipitation was evaluated by examining the decade-long changes in land use.MethodologyTo achieve the objectives of the research, two types of data including ground data and satellite data were used. GEE system was used to monitor land use changes on the surface of the earth using Landsat satellite images. Also, Kendall's test was used to identify the trend of climatic elements. To detect the land use changes, the classification algorithm of minimum distance from the mean was used. Using the dynamic model, the changes in the time series of users were evaluated.Results and discussionThe fluctuations and multiple collisions of Mann-Kendall diagrams showed that precipitation has varying behavior. Changes can be seen in mountain basins such as sub-basins 2, 3, and 4. As the height decreases, the amount of fluctuations is significantly reduced. This shows that height has an influential role in the rainfall changes in the region. The results of classification showed that considerable variation has occurred in subbasins land use. Most changes have been related to changes in water levels, arid regions, and forests. The impact of human activity, including afforestation and the development of hydroponics, the conversion of pastures into agricultural lands, increases the risk of floods, fires, soil erosion, and the entry of polluting substances into water sources.ConclusionIn the Karun basin, the decrease in rainfall over time has had a destructive effect on the reduction of forests, pastures, water levels and water agriculture in the region. So that during the last decade, the amount of destruction of forests and pastures has reached its peak and more is devoted to dryland farming. Also, the occurrence of these changes has led to an increase in the extent of arid areas in the region over time and has reached the maximum possible extent in recent years. In the Karun basin, taking into account that the area under cultivation of irrigated crops has increased and the amount of water in the region has decreased over time, it can be expected that the huge amount of water demand for irrigated cultivation and the lack of water will lead to water stress in the future. The increase in cultivation areas has been in line with the destruction of forests to be used in the agricultural sector. Therefore, from year to year, the extent of forests in the Karun region has decreased.Funding There is no funding support.Authors’ Contribution The authors contribute equally to the conceptualization and writing of the article. All authors approved the content of the article submitted for review and agree on all aspects of the work.Conflict of Interest Authors declared no conflict of interest.Acknowledgments We are grateful to all the persons for scientific consulting in this paper.

Temperature changes are one of the most important issues of human life in recent years and it is known as the most important manifestation of climate change in the current century. The current study was conducted with the aim of investigating the minimum temperature changes in the eastern basins of Iran and its relationship with the sea surface temperature teleconnections patterns. In this regard, the monthly minimum temperature data of Mashhad, Sarakhs, Birjand, Zabol, Zahedan and Saravan stations and the data of 14 teleconnections patterns of sea surface temperature changes during the statistical period of 1987-2019 were used. At first, minimum temperature changes were investigated using new methods of spatial statistics and hot spot analysis. Then, the relationship between the studied variables was checked using Pearson's correlation test and linear regression. The results show that in all months, hot spots were observed in the southern and eastern areas of the basin and cold spots were observed in the northern and western areas of the basin. Hot and cold spots were observed in May more than other months and hot spots covered 31.87% of the study area and cold spots covered 32.58%. Correlation of the studied parameters also showed that in terms of time, the patterns of distant connection with the minimum temperature of January showed more correlation than other months. In contrast, there was no correlation in the months of June and November.