نشریه پژوهش های اقلیم شناسی
پیاپی 58 (تابستان 1403)

Evidence for the negative impacts of climate change on biodiversity is mounting, showing that climate change is one of the greatest threats to global biodiversity (Chen et al., 2011; Dubey and Shine,2011; Hannah, 2015; Terribile et al., 2018; Archis et al., 2018). Climatechange can affect the diversity and composition of species within bothterrestrial and aquatic ecosystems (Diaz et al., 2003; Dijkstra et al., 2011; Ruiz-Labourdette et al., 2013;Hannah, 2015; Liu et al., 2018). It is predicted that some species will become more dominant while others will decline or disappear (Hannah, 2015). While the negative impact of climate change is well documented on some taxonomic groups like vertebrates and plants species (Hannah, 2015) little is known about the negative impacts of climate change on fungi and macro fungi(Shrestha et al., 2014; Burgess et al., 2017; Gao et al., 2019; Hao et al.,2020).Macro fungi are important components of ecosystems and play important roles in nutrient cycling, decomposition, symbiosis, and food sources for many animals and humans. However, climate change can have negative impacts on macro fungi, such as altering their distribution and diversity thus, it is necessary to investigate potential impacts of climate change on them (Cao et al., 2021). Recent studies have shown that climate change will have negative impacts on Iran’s biodiversity (Yousefi et al., 2019).  However, little is known about the potential impacts of climate change on the diverse taxonomic groups in the country particularly macro fungi (Yousefi et al., 2019). Thus, the aim of this study is to predict the impact of climate change on distribution of the genus Trametes in north of Iran. 

Distribution records of the genus Trametes were collected during our fieldwork from 2000 to 2022 in Iran. Current and future climate data (Annual Mean Temperature (Bio1), Mean Diurnal Range (Mean of monthly (max temp - min temp)) (Bio2), Isothermality (Bio3), Temperature Seasonality (Standard Deviation) (Bio4), Annual Precipitation (Bio12), Precipitation of Driest Month (Bio14), Precipitation Seasonality (Coefficient of Variation) (Bio15)) were obtained from CHELSA high resolution climatologies version 2.1 (Kargeret al., 2017). For future climate we considered following five CMIP6 (the Coupled Model Intercomparison Project Phase 6) Global Circulation Models (GCMs): GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MR-ESM2-0, UKESM1-0-LL. We applied an ensemble approach, using four distribution modeling methods (Generalized Linear Models (McCullagh & Nelder 1989), Generalized Additive Models (Hastie & Tibshirani 1990), Maximum Entropy Modelling (Phillips et al. 2006) and Random Forest (Breiman 2001)) to predict the impacts of climate change on distribution pattern of the genus. We also identified the most important climatic predictor of the genus distribution (Phillips et al. 2006). In this study AUC and TSS were used to assess the performance of the model.

According to the AUC and TSS the model performed well (AUC=0.942 and TSS=0.811). The model showed that under current climate the genus has 36,456km2 suitable habitats but the genus suitable habitats will decrease to 14,749 km2. We found that the genus will lose 60% of its suitable habitat under the climate change (2070 SSP585). Our results are in line with previous studies that have shown that fungi species will lose their suitable habitats under future climate change. For instance, Gua et al. (2017) have shown that Tricholoma matsutake will lose considerable proportions of its suitable habitat due to climate change. However, it is predicted that some species like Ophiocordyceps sinensis will expand their range under the changing climate (Shrestha and Bawa, 2014). Results also showed that precipitation of driest month and precipitation seasonality are the most important predictors of the genus distribution. These findings are also in line with previous studies that have identified temperature and precipitation of driest and warmest months as the most important determinants of fungi species distribution (Yuan et al.,2015; Yuan et al., 2019).

Iran is a biodiversity rich country in Asia hosting high diversity of macro fungi (Ghobad-Nejhad et al., 2020). In this study for the first time, we predicted the negative impacts of climate change on macro fungi in Iran. We believe that the stable suitable habitats identified in this study for the genus Trametes have high priority for conservation of the genus in Iran under the changing climate.

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.

Flood, as one of the important natural crises, causes a lot of damage to the affected areas. In order to manage floods, first the factors of its production and creation must be identified, then the areas that have a great potential in generating floods should be identified. Floods are the most common natural disasters that affect urban areas around the world and their effects in developing countries are due to social and economic inequalities, poor development infrastructure, unplanned change in land use patterns, Climate conditions, insufficient flood mitigation systems and increasing urbanization rate are intensifying. Like many urban areas of the country, vulnerability to flooding in Shiraz is difficult due to low drainage standards, poor drainage capacity, rapid housing development, etc. The most important role of the dry river, which is flood control, has become a problem with the creation of a bypass across the river. On the other hand, the dumping of construction waste along the river has created the ground for more flood risk

The data required in this research is between 2010 and 2020 and using Landsat satellite images and SCS model, the maximum possible runoff in the basin has been calculated using the SCS method. To obtain the CN of the hydrological group of the soil, land use, type of agriculture and hydrological status, the maximum possible precipitation was calculated by using the meteorological data and the geostatistics method, and the maximum runoff was obtained by running the SCS model. By obtaining the maximum runoff, the maximum discharge was calculated with the physiographic data of the catchment area, and with the discharge and the shape of the riverbed, the simulation of floods and floodplains was carried out, and finally, with the information layer, the floodplains at risk were identified.

The time for the flood to reach its peak in this river was between 8 and 12 hours, but with the construction measures and the changes in use in the upper part of the basin, this time has been reduced and the response time for managing the flood crisis has been reduced. By covering the roads that end in the dry river, including the Koran gate road, which was turned into an entrance road by the Shiraz municipality, this problem has been further exacerbated. As in the flood of 1998, 21 people were injured and 119 people were injured. Due to this flood, the areas around the river were dry, including Haft Ton and Saadi areas, suffered damages due to this flood. In general, it can be said that unprincipled actions such as land use change, encroachment on the river course, unprincipled constructions and close to the river bed, pouring construction materials on the river course, creating bypasses on the river bed have created more flood risk. And the more these measures increase in the western part of the basin, i.e. in the Golestan, Khollar and Gardneh Shul parts, the more damage will be caused to the middle and eastern part of the basin, which is inside the city of Shiraz, because first of all, the slope of the basin is also from the west side. It is to the east, secondly, the amount of rainfall decreases from west to east. Also, by pouring construction materials, etc., the load of the river has increased along with the sediments that the river itself carries in the form of alluvium and uplift. This city will be very dangerous, including the flood that happened in 1965 and caused heavy damages. On the other hand, vegetation can be effective in this river when the width-to-depth ratio is less than 16, which is a potential factor in the western part of the basin. But unfortunately, not only this factor is not strengthened in the western part of the basin, but with the creation of constructions, the ground for it has been reduced. If the flow rate entering the river bed is between 100 and 250 cubic meters per second, according to the width of the river and Other factors can be very dangerous unless there is a ground to reduce the flow in the upstream of the basin, and one of the solutions is to create vegetation in the western part of the basin.

Based on the obtained results, in the upper reaches of the basin, which has a high slope, bare lands, impermeable soils, there is more dispersion, the soil retention is low, and as a result, the amount of runoff production is also higher. With the increase of runoff at the level of the upper basin, the maximum discharge has also increased, and with this increase, the floodplains are submerged with a greater extent. Through the surveys conducted on the dry river, it was found that the amount of erosion and destruction of the river banks in the routes Direct and meander are far more in places without vegetation than in places with vegetation. If the flow entering the river bed is between 100 and 250 cubic meters per second, considering the width of the river and other factors, it can be very dangerous. Also, the more land use changes in the western part of the basin, i.e. in the Golestan, Khollar and Gardane Shul parts, will cause more damage to the middle and eastern part of the basin, which is inside the city of Shiraz, because first of all, the slope of the basin is also from the west side. It is to the east, secondly, the amount of rainfall decreases from west to east

Wind speed is one of the determining factors in dust production in the east and southeast of Iran, this issue increases with the location of Sistan basin in the east .Therefore, forspatio-temporal prediction, the number of annual dust days, wind speed of 15 meters per second and more and horizontal visibility of less than 1000 meters were considered as dust days, which by using the space-time layout algorithm, Data were defined in two classes of space-time, horizontal visibility and wind speed as SP Data array as a combination of matrix and vector in STFDFandSTF classes.For this purpose, in this study, with the help of the packages gstat(Pebesma et al. 2022), Space time(Pebesma et al., 2022) and SP (Pebesma et al, 2017), Raster (Robert J. 2017), spdep (Bivand et al , 2022) and R Google Maps (Climbarda, 2013 andLochter, 2016) of the software environment R, using Kriging Method, the spatio-temporal changes of the number of annual dust days and its prediction in the coming years were examined.

In this study using all synoptic stations in the study area, a completespatio-temporal network design was used. This design is most used in spatio-temporal analysis of data in which data with a special feature is collected in a regular network design were collected. then in long formats by specifying each record, the spatio-temporal composition of the data was obtained. The data were then adjusted to a complete n × m matrix. And was considered for records without NA statistics.After that, the empirical Spatial –Temporal variogram were calculated using the Kriging method (Mohammadzadeh, 2012) Used to predict the number of dusty days .Then 144 separable and non-separable models were fitted to the experimental data model The spatio-temporal metric model with Mattern marginal variable with the lowest mean square error was selected as the best model for predicting the number of annual dust days.

The results showed that the most important points that have more dust days in the eastern and southeastern regions of the country in the coming years, including: northern and central and southwestern regions of Sistan and Baluchestan province, where Zabol and surrounding areas are more intense. And limited points in the west and east of Yazd province. In South Khorasan province in 2022, the only Nehbandan station with 19 days, which will reach 41 days with 95% confidence, and other stations do not show much.In Sistan and Baluchestan province, Zabol, Zahak, Zahedan Mirjaveh, Nosratabad, Khash, Nikshahr, Konarak and Iranshahr stations with 19,10,14,38,26,41,46,59,85 days, respectively, the highest number of dusty days With 95% confidence, the forecast value at Zabul station will reach 106 days, which was a maximum of 116 days in 2018. This rate shows that by 2022, the number of dust days in this province will be reduced. However, it can still be said that this province has the highest number of dust days in the east and southeast of Iran And Zabol can be considered the center of dust in the east of the country, Saravan, Chabahar and Rusk stations are forecasted with 8, 3, 14 days with the lowest number of dust days in 2022, respectively. In Kerman province in 2022, Kerman, Zarand, Kahnooj and Shahdad stations with 10, 10, 23, 16 days, respectively and Bam and Sirjan stations with 4 and 6 days, the lowest number of dust days are forecasted.In Yazd province, Abarkooh, Meybod, Aghda, Bahabad, Herat and Bafgh stations have the highest number of dusty days with 43, 32, 42,60,63,64 days, respectively. And Robat Roof Station will have the lowest number of dusty days in 2022 with 25 days.Finally, the high and low limit of the number of annual dust days shows that in the Southeast Iran, the average number of dust days has increased significantly. In 2018, it will increase from 22 days to 24 days in 2022 Also in this region out of 43 stations, 13 stations are facing critical conditions, of which there are 7 stations in Sistan and Baluchestan province, 4 stations in Yazd province and one station in Kerman province. The only province that does not have a critical station is South Khorasan. In general, it can be said that South Khorasan province has the most standard air quality index in the study area.

An analysis of the number of dusty days in the Southeast Iran shows that this region is experiencing a significant increase in the number of dusty days, from an average of 11 days in 1987 to 16 days in 2016. Which will increase to 24 days in 2022 by using the space-time forecasting model. The results show that out of 43 stations in the study area, 13 stations are in good condition, 12 stations are in normal condition and 19 stations are in critical condition. Calculations of the maximum probability of occurrence of dust days show that the lowest number of dust days in the east of the country in 2022 is related to Birjand station with 23 days and the highest number of dust days is related to Zabol station 106 days.Spatio-temporal studies of the data showed that in Sistan and Baluchestan province, the number of dusty days is gradually decreasing. For example, at Zabol station, the upper limit of the values predicted in 2018 is 116 days, which will reach 106 days in 2022. However, the most critical province in terms of the number of dusty days is Sistan and Baluchestan province Out of 12 stations studied, 7 stations are in critical condition and one station is at the beginning of entering critical conditions and only Rusk and Chabahar stations are in optimal condition. However, the Sistan region can be considered the center of dust in the east and southeast of Iran. The results also showed that in 2022, Birjand city with one day a year and 95% confidence level with a maximum of 23 days has the most desirable and cleanest air in terms of the number of dusty days in the coming years.

T. Periods of climatic comfort, periods with hot and cold thermal stress, can be shifted in the climate change conditions and their length can also change during the year. Knowledge of these bioclimatic changes of climate comfort can provide a good basis for climate change adaptation planning. The city of Ahvaz is one of the main and major metropolises of the country, which faces significant challenges in terms of bioclimatic conditions. Thermal stresses in the hot period of the year and the frequency of heat waves along with dust events, especially in the last two decades, have severely affected the climatic comfort and bioclimatic conditions of the city.

Ahvaz city is the center and largest city of Khuzestan province. Ahvaz city is located between 48 degrees to 49 degrees and 29 minute’s east longitude from the Greenwich meridian and 30 degrees and 45 minutes to 32 degrees north latitude from the equator. In this research, daily temperature and relative humidity data were used during the statistical period of 1970-2020, for the synoptic station of Ahvaz city. The monthly average of minimum and maximum temperature as well as the monthly average of minimum and maximum relative humidity for the mentioned statistical period were obtained from the National Meteorological Organization. The data related to the near future climate, i.e. the statistical period of 2021-2040, was obtained from the output of the HADGM3 general circulation model based on the 6th CMIP6 climate change report under the SSP245 release trajectory, for the Ahvaz station location. The bioclimatic calendar of Ahvaz city was produced during three periods using the EVANZ model, which included the base period (1970-1990), the current period (2000-2020) and the near future period (2021-2040).

The average minimum and maximum temperature in the current period is generally 1.2 degrees Celsius and in the future climate period is 1.5 to 2 degrees Celsius more than the base period, and based on this, the bioclimatic calendar of Ahvaz city has undergone changes. In the metropolis of Ahuz, in the basic and present period in 6 months of the year, i.e. from May to October, during the day there is heat stress and a feeling of heat discomfort in the city, while the number of these months is associated with heat stress in the climate. The simulated future is extended to nearly 8 months of the year, considering that the use of electricity-based cooling energy during daytime hours to adjust the indoor temperature is one of the active strategies recommended in the EVANZ model in these months. Therefore, in the future climate period, due to the lengthening of the heat stress period from 6 months to 8 months, the amount of electricity consumption in Ahvaz city will increase by about 30%. On the other hand, in this research, it was seen that in the basic period during the 4 months of December to March, the conditions of bioclimatic comfort are available during the day, while in the current climate and the future climate, the length of the climatic comfort period is close to 3 months (November until February) has decreased. Climatic comfort at night in Ahvaz metropolis also saw significant changes in the future climate compared to the climate of the base period. The results of this research showed that in the climate of the current and base period, in 6 months of the year, from November to April, there is limited cold stress during the night during the hours of the night, while the simulated future climate of these cold stress conditions It exists only in 5 months of the year, from November to March, and the month of April has changed the bioclimatic situation in the future climate and entered the climatic comfort class without cold stress. Another significant change that has been made in the bioclimatic calendar of Ahvaz city is that in the climate of the basic period only in July and August there are nights with heat stress causing discomfort, which requires artificial cooling of the environment, while In the climate of the present period and the near future period, this period has been extended to 4 months of the year, i.e. June to September, and there is a need for artificial cooling of the home environment during the night hours, which is the problem of the electricity consumption of Ahvaz city in the field of cooling the home environment. It increases by about 50%.

In Ahuz metropolis, during the base period from May to Mehr, during the day there is heat stress and a feeling of heat discomfort in the city, while the number of these months with heat stress in the simulated future climate has reached 8 months. It means that the two months of April and November have also been transferred to this floor along with the daily heat stress. Therefore, in the future climate period, due to the lengthening of the heat stress period from 6 months to 8 months, the amount of electricity consumption in Ahvaz city will increase by about 30%. Also, the results showed that in the climate of the current and base period, there is limited cold stress during the night for 6 months of the year, that is, from November to April, while the simulated climate of the future has this cold stress condition for 5 months. Also, in the climate of the basic period, there is a need for artificial cooling of the environment only in two months, while in the climate of the current period and the near future period, this period has increased to 4 months, and there is a need for artificial cooling of the home environment during the night hours, which The problem of electricity consumption in Ahvaz city increases in the field of environmental cooling.

Drought as a creeping natural disaster is one of the main hazards that affects agriculture, food security, and the general livelihood of the population worldwide. Temporal-spatial variations of drought in a watershed can have numerous effects on the engineering, management, and planning of water resources. Drought is a complex phenomenon with different effects. Drought is a chronic, potential natural disaster characterized by a prolonged, abnormal water shortage. However, determination of drought onset, duration, and recovery is often difficult due to the differences in hydro-meteorological variables, socioeconomic issues, and the complex nature of water demands in different areas over the world. Drought occurrences cause serious problems to different parts of society, such as agriculture, energy generation, recreation, and ecosystems Therefore, drought indices are used to determine the severity and extent of drought. Most of these indices are based on meteorological criteria and take into account variables such as such as soil moisture, temperature, and precipitation. Precipitation is one of the most important parameters used in the calculation of drought indices. Accordingly, drought occurs when rainfall falls below normal for a period of time. In 1993, McKay et al. proposed a new definition of drought called the Standardized Precipitation Index (SPI), which believed that it was dimensionless and applicable at any time and place. Because rainfall measuring stations are scattered and access to rainfall measurement is often do with a time delay, other methods for drought monitoring are essential. In this regard, satellite information and remote sensing data can be used. Remote sensing is the acquisition of information about an object or phenomenon without making physical contact with the object and thus in contrast to on-site observation, especially the Earth. Remote sensing is used in numerous fields, including geography, land surveying, and most Earth science disciplines (for example, hydrology, ecology, meteorology, oceanography, glaciology, geology); it also has military, intelligence, commercial, economic, planning, and humanitarian applications. In current usage, the term "remote sensing" generally refers to the use of satellite or aircraft-based sensor technologies to detect and classify objects on Earth. It includes the surface and the atmosphere and oceans, based on propagated signals (e.g. electromagnetic radiation). With the provision of different satellite data and the widespread use of them, it is possible to study drought using this technology. The advantages of satellite images include increasing the sampling points, wider coverage area, higher resolution and lower cost. The purpose of this study is the spatial and temporal drought monitoring in East Azerbaijan province using ground stations and satellite data. Therefore, indices (VCI and SPI) were used during the years 1993-2016. Rainfall and vegetation conditions were two variables used. These two variables were evaluated in two periods of dry and wet time. The SPI and VCI indexes were calculated using ground stations and satellite data. The SPI index has statistical consistency and is capable of showing both short and long-term drought effects in variable timescales of precipitation anomalies. The use of different time scales allows the effects of a precipitation deficit on different water resource components (groundwater, reservoir storage, soil moisture, streamflow) to be investigated. Due to SPI's probabilistic nature, its comparison in various regions is possible. The Vegetation Condition Index (VCI) compares the current NDVI to the range of values observed in the same period in previous years. The VCI is expressed in percent and gives an idea of where the observed value is situated between the extreme values (minimum and maximum) in the previous years. These two variables were evaluated in both dry and wet years. Also, the trend of drought variations in the selected period and the efficiency of the mentioned indices were investigated.The results showed that during the statistical period, the study area was faced with different classes of drought, but most stations had slight to moderate drought conditions. Dry and wet years were obtained based on SPI and VCI indices and it was observed that 2007 is the driest year and 2009 is the wettest year. The results of regional zoning showed that drought is one of the climatic characteristics of the region and the southern parts of the province often had more critical conditions. It was observed that the frequency of mild drought in the northern regions of the province and the occurrence of severe drought in the southern regions of the province were more likely. Also, the trend of variations in the SPI index in terms of Shen line slope and Menkendal values was positive. The results showed that the VCI index was a very suitable method for estimating drought through remote sensing techniques. There was a good agreement between the results obtained from ground stations and satellite data. Therefore, in areas where meteorological stations are scattered or non-existent, satellite data can be used to estimate drought.

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.

The purpose of this paper is to identify the patterns used by of spatial distribution of moisture flux in rainy days in the southwestern stations of Iran. For this purpose, the codes of the current weather conditions transmitted by the stations located in seven southwestern provinces of Iran during the period of 1986-2016 and the most important cases of daily rainfall were examined in the thirty-year period in the region and were obtained a classification based on Geographies distribution from the maximum daily precipitation. In this study, ten case maps, which are based on gridded data of temperature, sea level pressure, geopotential height (HGT), component result Orbital (U) and meridional (V) winds and arrangement of vertical velocity patterns (Omega) prepared at different atmospheric levels from the United States National Oceanic and Atmospheric Sciences Base (NCEP/NCAR) databases (1986–2016) has been analyzed. The results indicate that in patterns with south to southwesterly currents, the low-pressure surface center extends from the south of the Red Sea to southern Turkey and is associated with the mid-level trough, where the moisture fluxes converge in the south of the Red Sea, southwest/south of Iran, and east of the Mediterranean Sea. On the other hand, the spatial distribution patterns of heavy rains are directly related to the topographical factors in the atmospheric conditions of the humid westerlies. In this study for investigating moisture flux patterns, meteorological data at 45 synoptic stations were studied during recent 30 years. «First of all, occurrence of daily rain is classified and then genesis and severity conditions of them were considered from the synoptical view point. Then prediction of time and spatial occurrences of daily rain is the major object of this study. Then using circulation under environment approach, the most important of patterns which play key role over climate of southwest provinces of Iran were classified. In the first step the "mean daily sea level pressure data were selected from the NCEP reanalysis data encompassing the region from 20°-60°N latitude by 20°-80°E longitude, with a 2.5 spatial resolution and for the 30 years period 1986-2016 in moisture flux. Selected territory is located in a geographical situation that covers all the effecting systems on Iran. For classification and delineate synoptical patterns Principal Component Analysis (PCA) and clustering method were used"(Kianipour et al., 2022).Results and Discussion Study of 10 selected moisture flux and rainfall samples show that extracted information presents general synoptical patterns. Any type pattern was studied at sea level pressure and 850 hPa level as follows. Results showed that in this region, "the moisture flux have three general circulation patterns. Analyzing these patterns show that there is a trough which restricted to 30-45°E longitude in 850 hPa chart, and at least one relatively strong low pressure over Arabian Peninsula in sea level pressure extending to the west, south west and the study region. This study shows that above aspects are the major specifications of moisture flux (Kianipour, 2022)." Based on the results, the presence of low pressure over the western Kazakhstan and high pressure over the Arabian Sea and low pressure in eastern Arabian Peninsula, play key roles in moisture penetration into Iran and increasing atmospheric moisture"(Golkar et al., 2016). This study has classified the synoptic patterns affecting Iran and the Middle East from 1986 to 2016, for all seasons months and days and with a higher accuracy using data with 0.5° resolution on a daily time scale. Then, composite maps of the mean sea level pressure (MSLP), and 500 hPa geopotential height (GPH) were prepared and analyzed. Previous studies have focused mainly on 500 hPa and MSLP parameters. «However, in the present study, the 850-hPa moisture flux, which was considered an important quantity for the precipitation occurrence, was calculated and analyzed in each of the patterns»(Raziei, 2007). One of the conditions in forming moisture flux is existence of a trough in 850 hPa charts, between 30 to 45°E longitudes that extends at least to 25°N latitude and lower. Because of cold advection in behind of the trough from mid-latitudes to the south, and warm advection in ahead of it from south latitudes to the north, increasing of pressure gradient and wind velocity at ground surface is observed. Existing at least one relatively strong low pressure on the north of Saudi Arabian Peninsula at sea surface charts, associated with a trough over red sea or west of Arabian Peninsula at 850 hPa level, and extending to the west, south west and the region under study in Iran. This situation is one of the most important specifications of moisture flux. This low pressure cause to increasing pressure gradient on the north of Saudi Arabian Peninsula and south west of Iran. Position of troughs at 850 hPa level are in a form that they can lead and send the air masses from deserts in north of Saudi Arabian Peninsula, Iraq, Syria and Jordan to Iran. Because these areas are at the front of the trough which is very unstable (Kianipour, 2022).

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.

Rapid fluctuations in the Caspian Sea's water level play an important role in determining the extent of erosion hazards, adverse environmental consequences, degradation and drying of coastal areas, wetlands and coastal bays, and the loss of economic resources and the destruction of maritime industries.The effects of climate change on the Caspian Sea have led many scientists to pursue research and scientific topics related to climate change in order to achieve their goals. These studies are performed to determine temperature, evaporation, salinity, pressure, density, wind direction, wind speed and other related phenomena. In this study, we intend to investigate the climatic factors affecting the water level of the Caspian Sea using ECMWF data.The data were received by the ECMWF and the Ports Authority. In this study, using ECMWF data from 1992 to 2022, which includes the amount of precipitation on the Caspian Sea, the rate of evaporation from it and the average temperature of the Caspian Sea, the factors affecting water level fluctuations in the Caspian Sea were investigated. Reliable library sources were used to determine the inflow of rivers into the Caspian Sea. Using ProUCL software, the data trends of precipitation, evaporation, temperature, and river water flow from 1992 to 2022 were drawn. Using the same software, the Mann-Kendall trend test was used to determine the trend of the data. Caspian Sea fluctuation data was received from Ports Organization.The graph of the precipitation trend in the whole area of the Caspian Sea showed that in the period from 1992 to 2022, the precipitation decreased by 90 cubic kilometers. According to the Mann-Kendall test and considering that the p-value of 0.0009 was obtained, it can be said that this decreasing trend is significant with a confidence level of 99%.The diagram of the evaporation trend in the entire area of the Caspian Sea showed that in the period from 1992 to 2022, evaporation increased by 103 cubic kilometers. According to the Mann-Kendall test and considering that the p-value of 0.0001 was obtained, it can be said that this decreasing trend is significant with a confidence level of 99%.As a general result, evaporation has an increasing trend while precipitation has a decreasing trend. In the next section, in order to investigate the main cause of the increase in evaporation, the sea level temperature trend chart is analyzed.Sea surface temperature as one of the main criteria in heat exchange and an indicator in assessing the potential for evaporation from the water surface, which is one of the main components of output in the Caspian water balance, in studying the trend of water level changes and assessing the causes of fluctuations in the Caspian Placed. The rising temperature of the Caspian Sea, especially in recent years, has been a factor in reducing the water level. The graph of sea surface temperature changes in the whole area of the Caspian Sea showed that the average temperature has increased by 1.1 degrees Celsius in the period from 1992 to 2022. According to the Mann-Kendall test and considering that the p-value of 0.0000 was obtained, it can be said that this increasing trend is significant with a confidence level of 99%.The average annual inflow of Volga water into the Caspian Sea is about 240 billion cubic meters, and the annual estimate of the total inflow of the rivers leading to the Caspian is 300 billion cubic meters. From other important rivers such as Kura, Ural, Etrak, Sefidroud, Haraz, a total of 34 billion cubic meters of water enters the Caspian Sea. The recent decrease in water level is while the amount of water entering the Volga River, as the supplier of most of the river water to this sea, has decreased by about 22% in 2019, which can be considered as one of the effective reasons for the decrease in the recent water level.The main reason for the increase in sea level temperature is the increase in greenhouse gases, which prevents the release of ground radiation. Therefore, the trend of increasing temperature, which is directly related to increasing water evaporation, in Nowshahr station and the entire Caspian Sea area is one of the effective factors in increasing the water level fluctuations of the Caspian Sea, which has reduced the water level of this sea.The water flow of the Volga River shows a decreasing trend due to the decreasing trend of precipitation. As a general result, climate change is reducing precipitation, increasing temperature and increasing evaporation, and ultimately lowering the surface of the Caspian Sea, a trend that will continue for years to come.