جستجوی مقالات مرتبط با کلیدواژه "تغییر اقلیم" در نشریات گروه "زیست شناسی"

Environmental problems in the world are a manifestation of unsustainable and inconsistent development with the capacities of the earth. Climate change is among all the problems, due to the global impact on the scale of the earth and effects such as floods, storms, unusual rain showers, increasing intensity, duration and volume of droughts, heat waves, melting of polar ice, rising sea water levels and going under water, loss of coastal lands, increase in minimum and maximum temperature in most areas, increase in evaporation and transpiration and water requirement of crops and Baghi, more evaporation of surface water and reduction of available fresh water sources, reduction of precipitation, increase of erosion and dust storms have attracted the attention of countries more than before. Iran, as one of the countries influencing the increase of greenhouse gases, is trying to increase adaptation and reduce the risk of climate change risks in order to manage surface and underground water resources, agriculture and improve the level of food security, natural resources and biodiversity (biological resources) and health of the country. In this regard, it is necessary to establish the governance of climate change in the policies and executive measures of the country at different national, regional, provincial and local levels in such a way that adaptation to it is implemented as a part of the country's daily life and development environment and as a result of increasing the level of environmental resilience, social, economic and cultural, all classes will benefit from its results. Climate change is one of the important factors that will affect different parts of human life on the planet and will have harmful effects on environmental, economic, social and especially water resources. For a long time, the change in meteorological characteristics of different regions of the world has been proven for researchers. The reduction of precipitation, reduction of surface currents and changes in the production and performance of agricultural products can be felt in many regions of the world. Our country is also facing many issues in this regard. Climate change has a significant impact on water resources and environment, which in turn is reflected in agriculture, society and economy.The increase in the concentration of greenhouse gases in the last few decades and the resulting increased temperature have led to significant changes in meteorological elements. Global warming, climate change and changes in temperature and precipitation parameters have now become one of the most important environmental challenges in the world. The reason for this is that these changes will cause droughts or severe, short and long term floods in the future. Therefore, one of the ways to reduce the effects of climate change is to evaluate its effects on rainfall and temperature in each region.

In order to simulate the climatic data, at first, the observational data of Arak synoptic station including minimum temperature, maximum temperature, precipitation and daily sunshine hours in the statistical period of 1980-2005 were obtained from the country's meteorological organization. Also, CanESM2 global model data under RCP2.6, RCP4.5 and RCP8.5 scenarios along with observational data related to National Center for Environmental Variables Prediction (NCEP) were downloaded from this center's website. In order to scale the output data of CanESM2 and HadGEM2-ES models, LARS-WG model was used. LARS-WG model is also one of the statistical exponential microscale models. This model is one of the most famous meteorological data generation models, which is used to generate daily data of maximum and minimum temperature, precipitation and sunny hours in a station under current and future climate conditions (Alizadeh et al., 2013). In this research, the 6th version of this model was used to scale the output of the HadGEM2-ES model. In the LARS-WG model, after receiving the daily observation data (minimum temperature, maximum temperature, precipitation and sunny hours), the statistical characteristics of the data were extracted. Then, in order to ensure the ability of the model, a series of artificial data was produced in the base period (1980-2005) and compared with the observational data. In order to calibrate and ensure the accuracy of LARS-WG model performance, simulated variables and real data were evaluated. The results show that the LARS-WG model has a good accuracy (R2=0.99) in simulating the minimum and maximum temperature. Also, the accuracy of the model in simulating precipitation (R2=0.98) is acceptable.

Based on the results of the model under the RCP8.5 scenario, the LARS-WG model shows a decrease in rainfall in all three periods, which is consistent with the assumption that the continuation of global warming will lead to a decrease in rainfall. Based on the results, LARS-WG model has predicted minimum temperature increase in all three scenarios and all three periods. Regarding the annual maximum temperature, the results of the LARS-WG model have increased in all three scenarios and all three periods compared to the base period. The minimum temperature and the maximum temperature simulated by the LARS-WG model increase under all three scenarios to reach the maximum value in the RCP8.5 scenario, which indicates the effect of the intensity of carbon dioxide emission concentration in the atmosphere as well as other It is a greenhouse gas. Also, the results showed that the most changes in the maximum temperature related to the RCP 8.5 scenario (from 23.12 in the first period to 26.51 degrees Celsius in the third period), the most changes in the minimum temperature related to the RCP 8.5 scenario (from 8.71 in the first period to 60/11 in the third period) and the most precipitation changes are related to the RCP 8.5 scenario. The period studied in this research was 2021-2040 (first period), 2041-2060 (second period) and 2061-2080 (third period). The modeling results show a decrease in rainfall in all three periods, which is consistent with the assumption that the continuation of global warming will lead to a decrease in rainfall. The minimum and maximum temperature changes showed that in most cases, the minimum and maximum temperature will increase in the future. The maximum temperature increase was more than the minimum temperature, so that the highest temperature increase occurs in the month of February by 1.89 degrees Celsius compared to the base period. Examining the changes in the amount of rainfall also showed that the lowest amount of rainfall in the coming period will be observed in the month of June in the amount of 07.07 mm. A decrease in rainfall on the one hand and an increase in temperature on the other hand can indicate that the increase in evaporation and transpiration and the decrease in snow cover can move the water balance towards land and reduce water reserves.

Small changes in temperature and rainfall regime or changes in the frequency and limit of climatic events can reduce the composition and distribution of plant species as well as their production. For the effective protection and management of natural ecosystems, it is necessary to identify the climatic factors affecting the distribution of plants in the present and use them to predict the distribution of habitats in the future. Climate change has an effect on the biological characteristics of species and therefore it is considered as a major concern for the management and protection of biodiversity. One of the important effects of climate change is to create changes in the distribution range of species. The studies conducted in the field of the effect of climate change on the distribution of plant species and communities show that in the 2030s and 2080s decreases to a large amount of domain the spread of all plant species and communities. he results of research on the effect of climate change based on species prediction models show that in the coming decades, due to the increase in temperature, they will migrate to higher altitudes and their area will decrease.
Predictive habitat models determine the suitability of the habitat for the establishment of plant species and help natural resource managers to identify factors that threaten the population of species, determine important factors in conservation planning. Climate change scenarios on the geographical distribution of species and favorable habitats of plant species. he results of the modeling of the potential habitat of some plant species in the Alborz and Zagros mountains show that in 2030 and 2080, the area of some habitats will decrease and the level of expansion of the species will be extended to higher altitudes.
One of the important effects of climate change is to create changes in the distribution range of plants, so it is necessary to investigate the effect of climate change on the distribution of plant species. Therefore, determining the prediction of the habitat of plant species using modeling methods in this direction can help in the management and exploitation of ecosystems.
In this research, by preparing a forecast occurrence map of the current and future range of A. Aucheri species under two climate warning models (Rcp4.5 and Rcp8.5 scenarios, the displacement of this species in geographical latitudes was investigated in Alborz pasture ecosystems in Mazandaran province.

The studied area is Mazandaran province, the province with an area of 2375500 hectares, occupies about 1.46% of the total area of the country. The lowest point of the province with a height of -28 meters above sea level is located in the coastal areas of the province and the highest point is Damavand peak with a height of 5610 meters above sea level in the Alborz mountain range in the south of the province. The average annual rainfall in the coastal strip of the province is equal to 977 mm. Artemisia Aucheri species is one of the representative plants of Alborz rangelad habitats and has a relatively wide spread and plays an important role in soil protection and water storage, and for this reason, it has been studied.Occurrence points (presence and non-presence) of A. Aucheri species were initially prepared using the updated map of plant types and the initial map of the distribution areas of A. Aucheri species. Then, by visiting different areas of the habitat of the species, the minimum and maximum height of distribution was determined. Also, by using the land use map, uses other than pasture use were removed from the polygons, and in the ArcGIS ver10.5 environment, the maps were modified and the current species presence map was finalized. Using 15 synoptic stations, a database, including precipitation, night temperature, daily temperature, and the average temperature was formed, and 19 climatic variables were calculated. Also, using a digital height model with an accuracy of 30 meters, three physiographic variables, including slope, direction, and height were prepared. Then, the presence and absence points of A.Aucheri were identified using updated maps of ecological zones and field visits.
Logistic regression was used to predict the habitat distribution of A. Aucheri species. In this way, the environmental variables in the logistic regression model are entered as predictor variables (independent) and the presence and absence of the species as response variables (dependent) and the vegetative behavior of the studied species in the current conditions is calculated and the relevant relationship is determined. This relationship was used to predict the habitat in 2050 using the 0-ESM2-MRI general circulation model under RCP4.5 and RCP8.5 scenarios. This method was implemented in SPSS Ver24 software and its results were converted into a map using Arc GIS Ver10.5..

The current distribution map of A. Aucheri species shows that in the western, central and to some extent eastern parts of the province, this species is observed in 75-100 percent. The lowest species presence is in the 0-25 percent class. The produced map, in four classes, showed that in 64% of the entire province, the probability of this type of occurrence is 75-100%, which is equal to 1527354 hectares. In this connection, the value of Kappa coefficient was 0.85, which according to the classification of Kappa coefficients (Ilunga Nguy and Shebitz, 2019), the model has good and acceptable accuracy. The maps obtained from the prediction of the logistic regression model show that under the RCP 4.5 scenario, the presence of the species increases in high altitude areas (2500 to 3000 meters), which includes all areas in the central, western and eastern parts of the province. The percentage of presence in the 75-100% class was observed in 60.6% of the area of the province and the highest percentage is the 75-100% class. The percentage of the 0-25% class will decrease. The RCP 8.5 scenario shows that the presence of the species in the 75-100% class will decrease in the central, western and eastern parts of the province. There is a large decrease in 0-25% class and a very small increase in 50-75% class and a large increase in 25-50% class.

The ongoing climate change will change all aspects of biological systems, from genetics to ecosystems (Scheffers et al. 2016, 724). By 2100, climate change will make extinct one sixth of animal and plant species and will change the abundance and distribution of many remaining species, which will result in new communities (Urban, 2015, 573). Based on this, it will be very important to investigate the annual and decade changes of average temperature and precipitation at the regional level. It is also important to understand how temperature changes and related indicators such as heat stress in different time scales in order to make informed decisions regarding economic development and climate action plans (CAP).
One of the main sources of data for the study of climate change are general circulation models (GCM), which are widely used to monitor and predict past and future climate change (Khan et al. 2020). GCMs have a remarkable ability to simulate temperature and precipitation. However, they also have limitations. Among these limitations are systematic errors in reproducing average temperature and precipitation, especially in areas with complex topography, such as Iran (IPCC, 2013). In this context, IPCC, as the most important reference for researches and forecasts related to climate change, has so far presented several generations of emission surveys and based on the results of different climate change modeling, six climate change assessment reports. has published. In the recent IPCC report, the latest climate change models are called CMIP6 series, which simulate the future climate under ssp emission scenarios (IPCC, 2021). The final version of the CMIP6 model design was confirmed by the two CMIP working groups and the WGCM coupled model work group in October 2014, and the complete results and various models are expected to be published before the end of 2022. The scenarios of the sixth report are a combination of socio-economic trajectories (ssp) (sustainable development sp1, development based on intermediate policies sp2, regional competition sp3, inequality sp4, and fossil fuel development sp5) and greenhouse gas concentration trajectory different levels of coercion) are produced; So that they provide the possibility of feedback analysis between changes and socio-economic factors such as global population growth, economic development and technological progress.

In this report, eight scenarios are presented in two rows. The first row includes the new scenarios SSP1-2.6, SSP2-4.5 and SSP5-8.5, which are respectively the updated scenarios of the forcing levels RCP2.6, RCP4.5 and RCP8.5 of the fifth report. The SSP1-2.6 scenario shows the lowest level of radiative emissions; SSP2-4.5 considers the average forcing level and SSP5-8.5 presents the upper limit of radiative forcing (Stock et al. 2020, Rogelj et al. 2018). In addition to these three scenarios, the non-decreasing forcing scenario (SSP3-7.0) with high emission of suspended particles and land use change has been added in this group. In the second row, two mitigation scenarios have been added to achieve a relatively low forcing output and one scenario considering limiting the average global temperature to below 1.5 degrees Celsius compared to forcing levels over industrialization and a scenario with a very high trajectory.
GCM models are the best tools for investigating the effects of climate change on weather parameters. These models are three-dimensional and are able to model and produce atmospheric and oceanic parameters for a long-term period on a global or continental scale, taking into account the approved IPCC scenarios (Chen et al., 2019). Considering the importance of uncertainty analysis, evaluation and selection of GCMs based on their performance in simulating climate variables is a method that can be used to select the best models and reduce uncertainties (Zamani et al. , 2020). The importance of water in Razavi Khorasan Province led the current research to reveal the trend of spatial and temporal changes of precipitation and temperature in the region in current conditions and also to evaluate the performance of CMIP6 models in reproducing annual and seasonal precipitation under the combined scenarios of common socio-economic paths (SSPS). ) to be concentrated in the future climate. Forecasting climate change plays an essential role in improving the understanding of the climate system and also identifying its social risks in the future. cmip6 activities focused on scenarios were formed in 2013 among climate communities. The Scientific Steering Committee of ScenarioMIP for this project was formed from the 17th meeting of the World Climate Research Working Group (WCRP) in October 2013 in Victoria, Canada. The main activity in Phase 6 of the Coupled Model Cross Project (CMIP6) is the Scenario Cross Project (ScenarioMIP). that the prediction of these climate models is a combination of a new set of release and land use scenarios produced by IAMs models based on the common socio-economic trajectories (SSP) of the future (which includes elements such as population, economic growth, urbanization, age, education and…) and is related to RCPs greenhouse gas concentration scenarios. This new structure provides two important elements in the designed space of scenarios, first: it standardizes all the socio-economic assumptions in each scenario, and secondly, it allows for a more detailed examination of various trajectories that can be It achieves the future climate results. The climate forecast of the CMIP6 project is different from the CMIP5 projects due to the production of updated versions of climate models and the use of SSP-based scenarios based on the updated data in the process of publication. The scenarios of joint socio-economic trajectories (SSPs) are the new group of scenarios of non-climate emissions resulting from coupled models of the sixth phase of climate change (CMIP6) in line with the sixth assessment report of climate change (AR6). These scenarios are presented with the aim of providing forecasts in the common socio-economic path. These scenarios include possible alternative changes in social aspects such as demographic, economic, technological, social, governance and environmental factors based on integrated analyzes of climate impacts, vulnerability, policies related to adaptation and They describe adjustment.
To determine the accuracy of each CMIP6 model, the simulation results of rainfall and temperature of each basin in the historical period were compared with observational statistics. In this step, the Kling-Gupta statistical test (KGE) was used to determine the accuracy of each model (correlations). This measure, while being simple, includes the mean, standard deviation, and correlation coefficient of the series of observation and simulation data obtained from the model, and weighting based on this measure can be of great help in increasing the accuracy of the modeling results. And finally, for micro-scale, Oribi method based on various approaches such as probability distribution mapping, empirical cumulative distribution function mapping, quantile mapping and quadrature density distribution mapping is presented, which is used in many studies to evaluate networked and recorded rainfalls. to be This method works by correcting the mean, standard deviation and quantiles by equalizing the distribution functions of model outputs and observational data. In the bias method, it is assumed that the simulated and observed precipitation follow the same proposed distribution, which in turn may cause bias. Based on this, gamma distribution in the form of α and β scale is often used for the distribution of precipitation events.

The output of general atmospheric circulation models in terms of temporal and spatial resolution is about tens of kilometers on a daily and monthly scale, which are large scale compared to climate processes. In addition, GCM simulations in both temporal and spatial scales have uncertainty in the parameterization of processes, so the output of these models cannot be directly used in climate change studies. Therefore, exponential scaling and skew correction of GCM simulations are necessary to obtain information at a suitable scale. Therefore, the present study investigated and evaluated the accuracy of CMIP6 models, which were recently published by IPCC, for the simulation of temperature and precipitation in Razavi Khorasan Province. For this purpose, the output of GCM models during the period of 1988-2018 was extracted and their accuracy was based on the observational data of seven synoptim stations (Mashhad, Gonabad, Qochan, Sarkhes, Sabzevar, Tarbiat Heydarieh and Torbat Jam) using the index King-Kupta (KGE) was investigated. The results showed that the GESM2 model has the highest accuracy for temperature estimation and the HadGEM3-GC model for the precipitation variable, and also based on this person, it was determined that in CMIP6 models, temperature is more accurate than precipitation. The results of rainfall variable simulation by CMIP6 climate models under three scenarios SSP1-2.6, SSP2-4.5 and SSP5-8.5 during the upcoming period (2020 to 2100) for Razavi Khorasan province showed that in all stations of this province We will witness an increase in rainfall from 0.40% to 6.8%, the most increase in rainfall will occur in the east of the province and the least increase in rainfall will be in the center of the province. The average temperature in all parts of the province under the CMIP6 scenarios will be from 0.29 degrees Celsius to 2.75 degrees Celsius, and the largest increase will be related to Mashhad and Sabzevar stations.

The impact of climate change and global warming on Earth's climate and other systems such as water sources, agriculture, environment, industry, health and the economy as a known the threats to sustainable development. So the effects of climate change on different systems and adaptive solutions to deal with the negative consequences of this phenomenon in future periods is essential. So according to importance of the issue, the purpose of this study was to evaluate the effects of climate change on temperature, precipitation, runoff and finally hydrological drought in the “Roude Zard” watershed in the future. For this purpose, data from the years 2000-1971 as a base data to small-scale model of LARS-WG introduction and calibration data for the years 1997-1990 and 1989-1974 years data were used to validate models of rainfall-runoff IHACRES. In order to evaluate hydrologic drought index SDI was used. Results show that increase in mean annual temperature in the study area 1.02 degrees Celsius as well as the expected changes in mean annual precipitation to the study area 11/91 percent and 73/78 millimeters. Results IHACRES model to simulate rainfall-runoff study area show that this model is suitable. The simulation results runoff also indicated an increase in the average runoff affected by climate change in some of the months of January, February and March and a decrease in average monthly runoff out of the area in September, October. Also results of the SDI index show increase in intensity of drought in the future compared to the base period. The results of this study will be to adapt to climate change studies in order to provide suitable management strategies used in the study area.

This study was carried out in the Gowatr mangrove forests in Gulf of Oman, on September 2017 and May 2018 during high tide with the aim of quantifying production, biomass carbon stocks of Avicennia marina and introduce of PnET-CN model. The results were showed that the mean of aboveground biomass was 28.09 ± 4.52 and 28.51 ± 4.49 t/ha, moreover, the mean of aboveground carbon stock was 11.22 ± 1.83 and 11.34 ± 1.7 t/ha, and the mean of primary production was 219.251 and 238.171 gC/m2.mo in September 2017 and May 2018, respectively. The estimated of the production and biomass carbon stocks using PnET-CN model was showed that the mean of production was 289.051 and 291.487 gC/m2.mo and the mean of aboveground biomass carbon was 12.29 and 12.76 t/ha in September 2017 and May 2018, respectively. The PnET-CN model could predict the effects of simultaneous changes in several environmental variables on the interactions among several ecosystem processes and it could estimate the amount of tree carbon stock and primary production with proper validation. PnET-CN model shown ecosystem models extended our understanding of the forest carbon cycle spatially and temporally and generated additional information about carbon stocks and fluxes.

The purpose of this study was to investigate the metabolism and role of gamma-aminobutyric acid as an effective compound in inducing stress tolerance in plants, in cultivars of strawberry ʻ Camarosaʼ (heat-sensitive) and ʻKurdistanʼ (heat-tolerant). For this purpose, the strawberry plants were transferred to Isfahan University of Technology on 15 November 2021 and with a fully expanded 5–6 leaf stage, they were transferred to growth chambers with temperatures of 25, 30, 35 and 40 ºC, relative humidity of 70% and 1200 lux of light. After 10 hours of temperature stress, the plants were taken out of the growth chamber and some physiological, biochemical and molecular traits were measured. In order to investigate the expression of the Succinic semialdehyde dehydrogenase gene, sampling was done at 0, 2, 5 and 10 hours after the start of a temperature stress of 40 ºC. This research was conducted as a completely randomized mixed-analysis experiment. The results of the experiment showed that high temperature (40 ºC) in cultivar ʻCamarosaʼ significantly 20.43% decrease the content of gamma-aminobutyric acid and 12.19% the amount of soluble carbohydrates, as well as 43.81% increased the percentage of ion leakage, 20.66% proline, and 200% in injury rating value compared to the control (temperature of 25 ºC). In contrast, High temperature stress (40 ºC) in the cultivar ʻKurdistanʼ caused a 17.78% increase in gamma-aminobutyric acid compared to the control treatment. Injury rating value was statistically non-significant in the cultivar ʻKurdistanʼ at a temperature of 40 ºC compared to the control. The expression level of the SSADH gene (the key gene in the entry of GABA into the tricarboxylic acid pathway) was constant in cultivar ʻCamarosaʼ, but in cultivar ʻKurdistanʼ, the gene expression level increased significantly 2 hours after heat stress.

Climate change has a direct impact on hydrological components and water resources and plays an important role in aggravating possible risks such as drought and floods. Therefore, it is necessary to investigate the effects of climate change on water components such as runoff. This study evaluates the effect of climate change on climatic parameters (temperature and precipitation) and the amount of runoff in Kiwi Chai basin from an environmental point of view. The hydrological model of the Soil and Water Assessment Tool (SWAT) was used to analyze the effects of climate change on the water resources of the Kiwi Chai basin, which is one of the Sefidroud sub-basins. Runoff simulation by applying climate change conditions for models (EC-EARTH, HadGEM2-ES, MIROC5, MPI-ESM) under two scenarios (RCP 4.5 and RCP 8.5) in three periods (2040-2021), (2041-2060), ( 2061-2080) and statistical analysis was performed to identify which climate model is more consistent with the changes in the mean and standard deviation of the historical series. An increasing trend in precipitation and a significant increase in average annual temperature were predicted in the early, middle, and late 21st century. The results of simulation of basin runoff with SWAT model also showed a significant decrease in basin runoff in future periods compared to the base period. These findings provide local water management authorities with useful information to aid decision-making in the face of climate change.

Examining long-term changes in the integrity or fragmentation trends of mangroves due to climate change can lead to a more precise understanding of structural changes in mangroves and their connection to the impacts of climate change. This study investigates the changes in the integrity status of the Hara Biosphere Reserve mangroves in Qeshm in response to variations in rainfall and drought over a 31-year period (1986-2017). For this purpose, a 31-year time series of satellite images and rainfall data were used, and values for the Number of Patches (NP) and Largest Patch Index (LPI) metrics, as well as SPI values, were prepared over the period. The results of changes in the number of patches and the largest patch index of mangroves during periods of wet years (before 1998) and drought years (after 1998) showed that with the increase in SPI values (positive values) before 1998, the number of patches and the largest patch index decreased, while in the period after 1998, with negative SPI values, the number of patches and the largest patch index increased. In fact, the results indicated an increase in the extent of core areas and large vegetative patches (increased structural integrity) of the reserve during the wet period and the reverse trend during the drought period. Based on landscape ecology principles, the increase in the number of patches and the largest patch index (due to the reduction in the total area of vegetation) in the period after 1998 indicates the degradation of this habitat in recent years. The results of this study can be used to assess the vulnerability of these habitats to the impacts of climate change.

Forests are regarded as an economically viable solution to reduce greenhouse gas emissions and mitigate climate change. Carbon sequestration in forests is accomplished through surface and underground biomass as well as soil, which are interconnected and important sources of carbon storage. using direct calculations based on stem analysis and plant biomass. Four poplar plantations were selected, and a one-hectare plot was sampled in each plantation. DBH and the total height of all trees were measured in each plot, and two poplar trees were randomly selected and cut for analysis. Stem analysis, wood biomass determination, and carbon measurement were conducted. In each plot, soil profiles were dug, and samples were taken to measure the physicochemical properties. The results showed that Shaft had the highest density (N/ha) (326), followed by Siahkal (216), Langrud (129), and Talesh (190), respectively. Stand tree stem carbon content (tons per hectare) was the highest in Talesh with 52 tons per hectare, followed by Shaft (38.7), Siahkal (28.3), and Langrood (24.16). Allometric equations were established based on the highest correlation coefficient (r2) and the lowest standard error value (SE) between age as the independent variable and carbon as the dependent variable. Calculation of carbon sequestration in plantation stand stock and soil can provide insights into species function and their responses. Furthermore, comparing carbon sequestration in different sites can aid in the restoration of degraded lands and the conversion of such lands into high-yield plantations, which can be an effective measure in managing carbon sequestration using fast-growing species.

Climate change and its consequences have become a threat to the planet, the natural and man-made environment, and it leads to an increase in greenhouse gas emissions, global warming. In recent decades, the amount of carbon dioxide in the atmosphere has increased, and this factor of climate change, such as temperature, has decreased. One of the effective factors in the management of agricultural and industrial projects is that changing the pattern of distribution and its amount, especially its decreasing trend, can play important role in determining the time of crop cultivation and water resources planning. Temperature as one of the important climatic factors, causes changes in the temporal and mechanical pattern of climatic phenomena and droughts, floods, storms and heat waves. Destructive climate change is due to a great increase or intensity of extreme weather phenomena and climatic events. In fact, extreme events rarely occur, but they have a direct impact on communities and vulnerable areas. Studies have proved that climate change is driven by simultaneous increases in evapotranspiration and changes, contributing to water cycle processes. Social and environmental impacts of extreme events are locally significant as a result and can severely affect specific sectors and locations. From a statistical point of view, climatic events, changes in the average limit and deviation of climatic indicators alters the occurrence probability of number of frost days, cold and hot days and nights, and the length of the growing season. Climate change intensify the hydrological cycle and change the amount of evaporation and transpiration and the pattern of precipitation. Climate change and global warming increase droughts and their continuation, and this change also causes uneven distribution and affects water resources. The purpose of this study was to evaluate the temporal and spatial variations of climatic rainfall indices in the central part of Ardabil province.

In this study, 11 climatic extreme indices were selected and evaluated in a 40-year period (1973-2014) to evaluate changes. At first, the required data were prepared and after processing, the values of the indices were calculated using Excel software, then the temporal trend of the indices was analyzed by ProUCL software. Evaluating the existence of a trend in the time series data is one of the important aspects of time series. In this regard, non-parametric methods are usually used due to the lack of statistical assumptions. In this research, the Man-Kendal method was used to investigate the changing process of climatic extreme indices. The non-parametric Mann-Kendall test has the ability to adopt with non-normal time series that does not follow a specific distribution. Also, this test has a small influence of the outlier values in the time series. Regarding the spatial interpolations, there are several methods to estimate the spatial changes of variables. The difference between these methods is the way of calculating the weight that is given to the observed points around the unknown point. In this research, the inverse distance weighting method was used, which estimates the values of unknown points through the weighted average of observational data with identical points.

In general, the changes in rainfall extreme indices have a decreasing trend in all stations, but it is significant in Hir station. Based on the results of interpolation maps, the indicators of maximum 1-day monthly rainfall, maximum 5-day monthly rainfall and total annual rainfall on rainy days in the central and western parts show the least and most values, respectively. Raw rainfall intensity indicators, total annual rainfall in 95% of rainy days in the northern, southern and western parts of Ardabil province show the most values and in the eastern part of the region the least values. The indicators of the number of days with rainfall of 10 mm or more, the number of days with rainfall of 20 mm or more and the total annual rainfall in 99 percent of rainy days showed the least and most changes in the east and north-west of the region, respectively. The indicators of wet period length and Rnnmm indices show the most values in the western and eastern parts and the least values in the central part of the province. The high values of number of rainy days index with more than 20 mm precipitation are related to Sablan slopes. It should be noted that the maximum length of the dry season is longer in the middle parts than in the areas of Musharraf and the north, as well as in the slopes of Sablan. The spatial distribution pattern of the maximum wet period index is also such that the maximum length of the wet period is in the stations located on the path of rainfall caused by the incoming moisture from the Caspian Sea and Gilan province, and this effect of the increase of the wet period is also clearly visible in the stations of Sablan slopes.

According to the findings of the study, the least changes in precipitation extreme indices occurred in central stations and the highest values in precipitation extreme indices occurred in stations located in western part of the study area. Overall, it can be said that assessing changes in extreme indices can be a guide in assessing climate change and its negative effects on soil and water conditions. It should be noted that the changes in extreme indicators in different regions will be different based on climatic conditions, but in most areas, the changes in extreme indicators are such that extreme events are intensified. One of the important effects of climate change is changing the rainfall regime and the pattern in the future. In many cases, changes in precipitation characteristics and extreme events may not follow the general trend of precipitation reduction. Spatial evaluations of changes in the occurrence and trend of extreme climatic indicators can help in identifying the signs of climate change. The dominant trend of increasing changes in rainfall extremes can have a significant contribution in intensifying the flood occurrence. The extreme rainfall events have undergone many changes in recent years under the influence of many factors. Considering the occurrence of severe floods under the effect of extreme rainfall events, examining the changes in the trend of these events in the past years and predicting them in the future will be effective in planning and managing water resources and natural disasters caused by these events. Forecasting changes in climatic extreme events is necessary to take effective considerations to deal with the harmful effects of climate change. In general, it can be said that the analysis of indicators related to extreme events can complement the results of evaluating the annual average values of climatic variables.

Today, climate issues threaten the security of the world, security which is considered necessary and vital in all fields and for all people. The phenomenon of climate change can affect the water requirement of crops by changing the amount of evaporation and transpiration of plants and the duration, intensity and time of rainfall. The studies related to climate change that have been conducted in Iran in recent years have focused more on climatic indicators and the effects of these changes on agricultural production have been given less attention. Therefore, assessing the effects of climate change on agriculture is an essential need. Due to the fact that Ardabil province is one of the poles of agriculture and animal husbandry and any change in the climate will endanger the lives of most of the residents of this region and will cause a change in the use of farms, pastures and the loss of agricultural production.kkkT Therefore, assessing the effects of climate

The study area is located on the slopes of Sabalan Mountain in Ardabil and East Azerbaijan provinces. Its geographical location is located at latitudes of 37 degrees and 44 minutes to 38 degrees 25 minutes and longitude 46 degrees and 22 minutes to 48 degrees 41 minutes. The minimum height of the area is 371 meters and its maximum is 4811 meters above sea level (Fig, 1). This study was conducted to influence climate change in wheat cultivation on slopes of Sabalan mountain. The important part of this research is based on statistics and information about meshkinshahr and sarab synoptic stations. In order to investigate the climate change conditions, a basic statistical period and a period as climate change should be determined. Therefore, statistical periods in this study from 1995 to 2015 and climate change period from 2016 to 2045 and 2046 were selected. Statistics about the studied stations were obtained from the Statistics and Information Bank of the National Meteorological Organization. The data taken from these statistics include: maximum and minimum temperature,daily rainfall and sunshine. to work with the LARS-WG model; First, the studied data should be sorted as Julius days. After collecting data in the Excel environment, it should be noted that there is no missing data, in case of missing, it must be encoded with -99. Aggregated data must be stored in a folder in nodpad with st extension.The address of folders stored in the N environment should be provided to the model from the option series, and the output data address should be given to the model. How to choose a scenario. First, in Excel environment, statistical data is placed in a column and in the next step, the generated data for each scenario is placed in the columns in front of each column of observational statistics, after preparing this step, the data is taken to the spss environment and correlation is taken between observational and production data. Production scenarios that are highly correlated with observational statistics are accepted as the studied scenario. CROPWAT software was used to estimate water requirement and effective precipitation. By entering minimum temperature, maximum temperature, relative humidity, wind speed and number of sunshine hours related to the plant, as well as the environment and region and its cultivation time, you can calculate the water requirement of the plant at different growth stages. Figure 2 shows the steps of data analysis in CROPWAT software.

In this study, first, the power of LARS-WG model for the basic statistical period of the years (1995-2015) was measured. The purpose of this assessment is whether the model has the ability to simulate for future periods. To do this, the tst file containing the results of comparing the statistical characteristics of the observed data with the simulated data was plotted as a diagram (Shapes, 2 and 3). The results showed that in Meshkinshahr station, the model has better efficiency for simulating maximum temperature and minimum temperature. The observed temperature and simulated temperature for 1995-2015 are similar and the diagrams are overlapping. Also, the deviation of production criteria is in the range of number one. This model is not effective in simulating the sunshine hour because it simulates the amount of sunshine hours in the first half of the year less than the actual size. In the case of rainfall, the model is better than April and May in other months. In Sarab station, the data were performed based on LARS-WG model by comparing the statistical period data and the produced data. To ensure the ability of computational data model, they were compared by model and observational data in the studied stations. Comparative results show the data of minimum temperature, maximum temperature, precipitation and sunshine in mirage synoptic stations for the base period. The capability of LARS-WG model in modeling minimum temperature, maximum temperature and radiation in these stations is completely in accordance with the observed data. The standard deviation rate is between 0.5 and 1. The results of this study show that in Meshkinshahr station, precipitation and temperature in the period 2016 to 2045 are -3.9 and +1.73, respectively, and these two variables in the period 2046 to 2065 are -6.67 and +1.80, respectively. In Sarab station, precipitation and temperature in the period 2016 to 2045 were +6.17 and -0.69, respectively, and in the statistical period of 2046 to 2065 were -12.91 and +0.37, respectively. As a result, rainfall in the coming periods is associated with a decrease in Sarab and Meshkinshahr stations, respectively. Temperatures are also rising at about 2 °C in Meshkinshahr station and the temperature increase is low in Sarab station. In comparison, Sarab plain has a wonderful state because by the period 2046 to 2065 the rainfall in this plain will decrease about 13% compared to the base period, in the same time period, wheat evapotranspiration will reach from 530 mm in the base period to 887 mm in the period 2046 to 2065. Wheat water requirement also increases from 422 mm in the base period to 810 mm, i.e. about 92%. Also, modeling shows that the average minimum temperature of this region decreases from -3 in the base period to -9.67 °C in January and from -4.3 to -8.23 in February. According to the modeling and with the decrease in rainfall in this region, wheat cultivation in this plain will face limitations in the future. However, the results of the models indicate that meshkinshahr plain is better in future periods than Sarab plain.

The purpose of this research is to estimate and evaluate the future climate change of Iran using climatic elements (minimum temperature, maximum temperature and precipitation) until the year 2100 in Iran. For this purpose, in the current research, the innovative method and tools of the algorithm and coding and NASA data to evaluate and predict the aforementioned climate elements based on two intermediate scenario (RCP 4.5) and worst-case scenario (RCP 8.5) of the Canadian CanESM2 model from the system Google Earth Engine was used under the web. In order to better analyze, investigate and compare Iran's future climate changes, the studied period of 80 years, was divided into the first 40-year period (2021-2060) and the second 40-year period (2061-2100). The results of the current research based on the intermediate scenario (RCP 4.5) indicate that the minimum and maximum; minimum temperature of the second 40-year period compared to the first 40-year period is 2.69 and 0.62 degrees Celsius, respectively, and the minimum and maximum; maximum temperature The second 40-year period compared to the first 40-year period was 3.37 and 0.91 degrees Celsius respectively; Also, based on the worst-case scenario (RCP 8.5), the minimum and maximum; minimum temperature of the second 40-year period compared to the first 40-year period is 0.54 and 3.32 degrees Celsius, respectively, and the minimum and maximum; maximum temperature of the second 40-year period compared to the period An increasing trend was predicted for the first 40 years of 2.47 and 3.46 degrees Celsius respectively. Based on the results obtained from the present research, in the studied area, the frequency of rainfall decreases in the first 40-year period and increases in the second 40-year period.

Although climate change is expected to affect many parts of the environment, water is the most critical factor affected by climate change. Iran is always facing limited water resources, and due to the fact that climate change can be considered an aggravating factor in the water crisis, projeting the effect of climate change on the future drought is very important for managing water resources. In this study, the effects of climate change on the meteorological drought of Karkheh basin in the future have been investigated by dynamical model (PECIS) and under A2 and B2 emission scenarios.

In this study, PRECIS dynamic downscaling model was used to estimate precipitation and temperature in the base period (1960 to 2000) and future period from 2070 to 2100 and under two scenarios A2 and B2. For this purpose, the PRECIS model was implemented with a horizontal separation of 0.44 degrees in grids. To use PRECIS model results, it is necessary to evaluate the model first. For this purpose, the model was first run for the 1960 to 1990 years and the projected precipitation and temperature values were compared with the observed values of the stations. The study of climate changes in Karkheh basin showed that under scenario A2, the amount of precipitation will increase by about 11% and the average minimum and maximum temperature will increase by about 5 degrees. For scenario B2, the amount of precipitation will increase by about 7% and the average minimum and maximum temperature will increase by about 3 degrees.Then, using SPI index drought analysis was performed. The variability of SPI index for 1, 3 and even 6 months is very high compared to the average because any amount of rainfall can change the value of the index quickly in this time scale. This is while the 12-month index can better determine the number of drought events and evaluate their duration and intensity. Therefore, to investigate the meteorological drought in the study area, the 12-month SPI value was used as an annual drought index.In order to investigate the characteristics of droughts, two selected stations in the region, Khorramabad and Kermanshah stations, which are the closest points to the output grid of the climate model, have been used. The choice of these two stations is due to the fact that in order to compare the drought characteristics of the base period with the drought of the future period, the rainfall data simulated by the climate model which is in the form of a grid (raster) should be used and in order to be able to compare It is necessary that the output grid points of the model match the stations as much as possible in terms of location. Therefore, by adapting the stations on the network of output points of the climate model, two stations, Khorramabad and Kermanshah, which are the closest points to the output grid of the model, were selected. The study of meteorological drought using the SPI index shows that several major droughts occurred in 1966-67, 1970-71, 1973, 1977-78, 1984, 1991, 1995, 1998-2000. The study of drought during the period 2070 to 2100 in Kermanshah and Khoramabad stations shows that under both scenarios, the years 2077, 2081 to 2083, 2087 to 2089, and 2095 to 2096 will experience drought. However, in both scenarios, the intensity and magnitude of the drought will decrease compared to the base period, and this decrease is greater under scenario B2 than A2. The results also show that the number of dry months and the duration of drought will increase in both scenarios. Examining the probability of drought occurrence under different scenarios shows that the probability of drought occurrence for Kermanshah station is equal to 9% under scenario A2 and 15% under scenario B2, which is a decrease compared to the probability of occurrence of drought in the base period of the same station. The probability of drought for Khorramabad station under scenarios A2 and B2 are 12 and 19 percent, respectively, and the same probability is 15 percent for the base period of Khorramabad station. Comparing the probability of occurrence for different scenarios shows that the occurrence of drought under scenario B2 will be associated with a higher probability than scenario A2.

Investigating the effects of climate change on the meteorological drought of the Karkheh basin by dynamic climate model during the period from 2070 to 2100 shows the following results in brief:The evaluation of scenario A2, during the period from 2070 to 2100, shows that the average rainfall will increase by about 11%. On the other hand, the average minimum and maximum temperature under scenario A2 shows an average increase of 5 degrees in all months. Meanwhile, the projected rainfall under the B2 scenario has shown an average increase of about 7%. Under scenario B2, the average minimum and maximum temperature has increased by about 3 degrees Celsius.Study of climate change on the meteorological drought of Karkheh Basin shows that the most number of droughts occurred in the stations are 1 to 3 month droughts; Events of 4 to 7 months also show a significant number in stations. The study of meteorological drought during the period from 2070 to 2100 shows that under both scenarios A2 and B2, the severity of droughts will decrease compared to the base period, although the duration of drought and the number of dry months will increase. Examining the probability of drought occurrence under different scenarios shows that the probability of drought occurrence under scenario A2 and B2 will decrease in the region. Comparing the probability of occurrence for different scenarios shows that the occurrence of drought under scenario B2 will be associated with a higher probability than scenario A2.The results show that to evaluate the drought, the SPI index three and six months shows large variability in most of the stations. Therefore, according to these variabilities and the fact that in order to study drought changes in future periods, the output data of climate models, which are usually less accurate for short-term periods, should be used, to study the meteorological drought from the SPI index 12 months was used.

Mangroves are the most efficient coastal environments for carbon sequestration, which can play an effective role in modification of increasing greenhouse gases. In this study, for the first time, the carbon storage in soils and mangrove trees in mangrove forest of Govatr Bay were evaluated. The amount of sediment carbon in sediment cores was determined as well as the diameter on breast height and height of trees were measured in 12 stations in Bahu and Govatr Estuaries. Satellite imagery showed that the total area of the Govatr Bay mangrove forest is 450 ha, 67% of which is high density forest. The amount of soil carbon in the region fluctuates between 1.9 to 3.8%, which is in the range of the global average (2.2%). The average carbon content of the top 1m of Govatr Bay is 393 tons per hectare (t ha-1), which is 30% higher than the global average. The total amount of carbon in top 1m of Govatr mangrove soil was estimated at 177,000 tons, 70% of which is sequestered in Bahu Estuary mangroves. The total amount of soil carbon in Govatr and Bahu Estuaries is 121600 and 210500 tons, respectively. A total of 64,100 tons of carbon is stored in the mangrove trees of Govatr Bay, 64% of which are related to the Bahu Estuary. A total of 376,000 tonnes of carbon (equivalent to 1,378,000 tonnes of carbon dioxide) has been stabilized in the Govatr forest, only 11% of which is stored in trees. Estimates show that if the Govatr are completely deforested, more than 279,000 tons of carbon dioxide will re-mineralize into the atmosphere.

Nowadays, climate change causes abundant worries in different spots in the world. For this reason, a study was conducted to find the trace of this global phenomenon in Khuzestan province. Therefore, we used meteorology data including temperature parameters, rainfall, evapotranspiration and relative humidity for 15 weather stations in a period of 21 years (1996-2016) and Mann Kendal graph to determine the trend. The result illustrates that there is a meaningful downward trend for rainfall parameter in SafiAbad, Hendijan, Masjedsoleiman and Bandar-E-Mahshar with confidence level of 95% and Omidiyeh with confidence level of 99%. Moreover, there is a meaningful upward trend with confidence level of 95% in tempe-rature parameters in most stations, while this level is 99% in Bostan, Hendijan and Izeh. In addition, a meaningful upward trend in evapotranspiration in two station of Bostan and SafiAbad with confidence level of 95% and 99% and a meaningful downward trend with confidence level of 99% for Shushtar, Izeh, Abadan, Masjedsoleiman and Ahwaz as well, is visible. There is a meaningful downward trend in relative humidity in Bostan, SafiAbad, Izeh, Omidiyeh and Masjedsoleiman, which all present the climate change effect in region.Climate change is one of the most important challenges in the 21st century. Global warming has caused very unstable changes in climate parameters such as: changes in rainfall patterns and frequency and intensity of climate changes. The issue of climate change has become one of the main concerns of scientists related to atmospheric sciences due to its impact on various dimensions of human life and the dependence of human activities on it, and many researchers are trying to understand the various dimensions of this important phenomenon. The change in climatic elements, especially temperature and precipitation, is one of the most important signs of this phenomenon. Changes in temperature and precipitation in different parts of the earth do not follow the same trend, and climate change does not necessarily mean simultaneous changes in precipitation and temperature. The amount, distribution and temporal and spatial changes of precipitation and temperature are essential factors for decision-making, management, planning and design, especially in areas such as Iran, which is geographically located in the 28 to 48 degree north latitude and It has a dry and semi-arid climate. In order to detect the trend in the time series of water and meteorological variables, various tests are used, and these tests can be divided into two categories, parametric and non-parametric. Parametric tests are more powerful in detecting trends than non-parametric tests, and when using them, the data should be random and have a normal distribution. On the other hand, non-parametric tests can be used if the data is random and are not sensitive to the normality of the data. Mann-Kendall's test is a non-parametric test that is used in researches to investigate the trends of water and meteorological variables. To carry out this research, climatic data (average rainfall, average evaporation and transpiration, average humidity and temperature parameters) prepared by the meteorological organization of Tehran province for a statistical period of 21 years from 1996 to 2016 have been used. Mann-Kendall test was used to analyze the trend of climatic parameters, which will be explained in detail below.• Mann-Kendall non-parametric testThis test was first presented in 1945 by "Mann" and then developed by "Kendall" in 1966. This test does not require a normal frequency distribution or linear behavior of the data, and it works very strongly compared to the data that deviates from the linear behavior and is used to evaluate the trend (Darabi et al., 2015). In this test, the null hypothesis (H0) and the opposite hypothesis (H1) are respectively equivalent to no trend and the presence of a trend in the time series of observational data. Mann-Kendall diagram test:The steps of the test are briefly as follows:We rank the data and calculate the ti statistic, which is defined as the ratio of rank I to its previous ranks, and then we obtain the cumulative frequency of the statistic ( ). Mathematical expectation, variance and Mann-Kendall index are calculated based on the following formulas (Zahedi et al., 2016):In these relationships, ni is the time order of the data. To check the changes, the index Ui' should also be calculated: rank the data and determine the statistic t'i, which is defined as the ratio of rank I to its next ranks, and then calculate the cumulative frequency of t'i we do The mathematical expectation, variance and index Ui' are as follows:In the above relationships, N is the statistical sample size under study. The intersection of the index Ui and Ui' is a sign of a sudden change in the time behavior of a statistical series. The non-intersection of the curve or their placement within the 95% confidence range does not indicate significant changes in the data, but if the mentioned lines intersect within the critical range of 1.96 and 2.58 and then leave the critical range, it is a sign of a sudden change and a significant trend at the 95% and 99% confidence levels, respectively. If the U curve moves to the positive side, it has a positive trend, otherwise it has a negative trend. Crossings outside the critical range indicate a sudden change in the behavior of the series (Alijani et al., 2009).In general, the results showed that during the studied statistical period, rainfall in all stations had a decreasing trend. Also, the minimum and maximum temperature parameters have been increasing in most of the stations. The parameters of evaporation and transpiration and relative humidity in most of the stations had a non-significant decreasing trend. Based on the results of Mann-Kandall charts, it was found that Safi Abad, Omidiyeh and Handijan stations were more affected by these changes than other stations. It is obvious that dealing with the effects of climate change and its consequences, such as the increase in the temperature of the earth's surface or the decrease in rainfall, requires a detailed investigation of the causes of this phenomenon and the provision of appropriate solutions in order to prevent endangering food security, increasing soil erosion, desertification and other problems.

Temperature is one of the fundamental elements of the climate of an area, whose transformation can transform the climate structure of any place, for this reason, the study of the temperature trend in different temporal and spatial rules occupies a large part of climatology researches. Most of what is referred to as global warming or climate change includes mostly warming changes and the upward trend of the three components (average, minimum and maximum) of air temperature. Currently, climate threshold phenomena are in the center of researchers' attention because the risk of increasing the frequency, duration and sensitivity of climate thresholds has increased due to the increase of greenhouse gases and aerosols in the atmosphere. The LARS-WG model is a model that downscales the output of GCM models.Researches that focus on the temperature parameter; It is increasing day by day, among them is the research related to the increase in frequency, intensity, recorded global warm periods and the continuity of heat waves, which was done by Perkins et al. (2012). Yu et al. (2019), in their study investigated the temperature changes in the world and the results showed that the temperature anomaly is higher in the oceans and southern latitudes compared to the land and northern latitudes. Hu and Huang (2020) investigated the high temperature anomaly and its relationship with the general circulation of the atmosphere, and the results of their research showed that the highest temperature anomaly occurred in the Arabian Peninsula. Rohbakhsh Sigaroudi et al. (2017) investigated the anomaly of the average minimum and maximum temperature of the warm period of Iran in the period (1951-2010) and concluded that the western half of the country had the largest decrease in the average minimum and maximum temperature. Karmi-Mirazizi et al. (2018) investigated the synoptic patterns that lead to temperature anomalies and thermal changes in the western and northwestern regions during the statistical period (1989-2018). Rababi Sabzevari et al. (1401), in the west and northwest of Iran, analyzed the synoptic patterns that lead to temperature anomalies for 1989 to 2018, and the results indicate the existence of the mid-latitude meridional current as the main cause of temperature anomalies.

Ardabil province is located in the northwest of the Iranian plateau with an area of 17,953 square kilometers and has the coordinates of 37 degrees 7 minutes to 39 degrees 43 minutes north latitude and 47 degrees 19 minutes to 48 degrees 55 minutes east longitude. In this research, in order to achieve the goal of the research, the long-term statistics of the minimum, maximum and daily temperature averages of the selected synoptic stations of Ardabil province (Ardabil, Pars Abad and Meshginshahr) were analyzed. For this purpose, first, the different intensities of the temperature anomalies of the stations were calculated based on the data of 1980-2020 using the Z index. Then, to generate the data of each station under the conditions of climate change, after the preparation and quality control of the data, the variables of minimum and maximum temperature, precipitation and sunny hours were entered into the LARS-WG model on a daily basis and following the evaluation of the ability of the LARS-WG model in simulating the data observed in these stations, the data of the future period (2021-2021) of these stations was produced, and the ability of the LARS-WG model in simulating the data observed in the synoptic stations of Ardabil province was evaluated. This process is divided into three stages, which include spatial analysis, model validation, and generation of synthetic weather data. The model used is CanESM2 under RCP scenarios. The daily minimum, maximum and average temperature data of synoptic stations during the past statistical period (from 1980, 1985 and 1995 to 2020, respectively) were used to evaluate temperature changes and anomalies in the coming decades (2021-2100) And the frequency of each of the temperature anomaly intensity ranges of the three studied variables was counted and their percentage was calculated. Temperature anomalies were calculated using Z index.

The comparative graphs between minimum and maximum temperature observation data values and their values produced by LARS-WG model for selected stations of Ardabil province confirm the existence of a small difference between these two data and show the high efficiency of this model in simulating Creating the studied variables and producing synthetic air data. The evaluation of the frequency of anomalies of the three components of temperature in this province under three RCP scenarios showed that in the hot months of the coming period, warm anomalies are predominant (more than 50%) and normal conditions are second (30 percent) and cold anomalies have the lowest percentage.The predicted frequency of temperature anomalies using the CanESM2 model fine-tuning under the average scenario (RCP4.5) is higher than the optimistic scenario (RCP2.6) and under the pessimistic scenario (RCP8.5) is higher than the average scenario. The difference between the estimates of the RCP8.5 and RCP2.6 scenarios and the maximum difference between the two hot and cold anomalies reaches its maximum in August. The highest and the lowest percentage of average warm anomaly frequency belong to Pars-Abad and Meshkin-Shahr, respectively. The highest percentage of cold anomaly was calculated in Ardabil and the lowest in Pars-Abad.The order of the different intensities of the temperature anomalies of the three studied components in Ardabil province under the RCP4.5 scenario, from the highest to the lowest percentage, is: normal conditions (31%), moderate heat (29.4%), weak heat (21.4 percent), very hot (6.5 percent), slightly cold (6.1 percent), moderately cold (4.2 percent), very cold, extremely hot and extremely cold (about 1.5 percent)). It can be observed that among the types of temperature anomaly intensities, there is a predominance of moderate heat and weak heat, and extremely hot and extremely cold anomalies are rare and include about 1%. In the other two scenarios, the percentage of warm anomaly prevails over normal conditions and cold anomaly.In the RCP2.6 scenario, the warm anomaly has the highest frequency in August and the lowest frequency in October, and the cold anomaly has the lowest frequency in August and the highest frequency in September. In the RCP8.5 scenario, the warm anomaly is more frequent in August and July and the cold anomaly is more frequent in May and June.

Impact of climate change is one of the major human challenges in the third millennium, the main source of which is the increase in greenhouse gases in the Earth's atmosphere. But how much of this gas will enter the Earth's atmosphere by human societies in the future, and consequently what will happen to the Earth's climate system, is not certain. Therefore, a comprehensive and systematic review of climate change in the coming years with the aim of studying regional behaviors and predicting their effects is necessary to provide appropriate and appropriate solutions for decision makers to carry out reliable and coherent planning. Therefore, in order to predict the minimum and maximum temperatures and precipitation of Tajan watersheds in the period 2040-2014, first, the efficiency of 14 different models of general atmospheric circulation under two scenarios of 2.5 RCP and 8.5 RCP in 20 meteorological stations was evaluated and then the statistical downscaling of each model was performed by LARS model.

Data set of AOGCM models is accessible through the Data Distribution Center established by the IPCC in 1998. To access data related to the region in the base and future periods, by entering the spatial coordinates of the desired location as well as the required statistical length in the base period (1985-2005) and Future period (2014-2040) were obtained. The Characterize of these models are given in Table 1. Then, to evaluate the efficiency of the models in simulating the temperature and precipitation of the region, the monthly average of the base period from the output of the models was compared with the observed values and the future period. To create a climate change scenario in each AOGCM model, the values of "difference" for temperature and "ratio" for rainfall between the average year in future periods (2040-2014) and the base period (1985-2005) for each cell were obtained from the computational network. Then climate change scenarios in the future period (compared to the base period, separately for different AOGCM models were created under two emission scenarios RCP2.5 and RCP8.5. In this study, the WG-LARS model was used for Downscaling, which is able to produce a series of meteorological data with statistical characteristics similar to the climatic period, which consists of three main parts: calibration, evaluation and meteorological data.

In this research, out of 14 mentioned models, 6 models EC-EARTH, GISS-E2-R, MIROC-ESM, MIROC-ESM-CHEM, MPI-ESM-MR for temperature and 3 models EC-EARTH, GISS-E2 -R, MIROC-ESM was detected for suitable precipitation. The reason for the rejected models was due to the large changes in the monthly temperature of the future period compared to the base period. In all stations except Sari station, Pesharat and Dasht-e Naz, the minimum and maximum annual temperatures will increase in all models under both scenarios. The highest temperature increase is predicted for MIROC-ESM-CHEM model and the lowest temperature increase is predicted by GISS-E2 and EC-EARTH models. The highest annual minimum temperature increase of AOGCM models under scenario 2 was predicted to be 1.4 ° C and under scenario 8 was predicted to be 1.6 ° C at Burma and Sefidchah stations, respectively. The maximum maximum temperature increase under scenarios 8 and 2 will be in Peshart station (2.2 ° C) and Burma station (1.9 ° C), respectively. It is noteworthy that the temperature pattern changes under scenario 8 will be more intense in all stations (Figure 2).Also, due to the fact that the meteorological stations were located at different altitudes, the relationship between the rate of temperature changes and altitude difference was investigated, but no significant correlation was found. As the results show, among the meteorological stations under study, the minimum and maximum temperature changes under both scenarios will be more noticeable in Burma and Sefid Chah stations, which may be due to the geographical location of the stations. These two stations are located in the highland lands of watershed, in the easternmost part of Mazandaran province.The monthly comparison of the average temperature of the AOGCM models and the observational data, in all months, under scenario 2, except September (predicted by the GISS-E2 model) shows the temperature increase. In May, July, then in August and January, the highest temperature increase was observed, and the lowest temperature increase was observed in March, followed by February and September. Also, these comparisons under scenario 8 showed the highest temperature increase in May (2.6 ° C with MIROC-ESM model) and September (2.9 ° C with MIROC-ESM model) and the lowest The increase (0.1 ° C with the EC-EARTH model) will be in March (Figure 3).The results of seasonal precipitation of AOGCM models under both scenarios show that in spring, autumn and winter there will be a decrease in precipitation and in summer an increase in precipitation. Of course, the amount of change varies under each scenario. The highest percentage of rainfall decrease is 28% under scenario 2 in spring and 65% under scenario 8. On the other hand, the highest increase in rainfall in summer under scenarios 2 and 8 is projected at 50 and 7%, respectively. Also, the average annual rainfall under Scenario 8 in the three models EC-EARTH, GISS-E2 and MIROC-ESM for the next time period shows a decreasing trend. It is noteworthy that the precipitation pattern will also change under the effects of climate change, so that the maximum amount of precipitation has changed from winter and autumn to summer under scenarios 2 and 8 (Figure 4).

With the above analyzes, in general, it can be said that one of the most important changes in the Tajan watersheds is the change in the seasonal rainfall pattern and the increase in temperature in the warm seasons. These natural changes will also affect the river flow regime and peak discharge time, intensification of water cycle, change in precipitation characteristics, change in runoff amount and time, occurrence of drought, major floods and change in evapotranspiration rate. Also, Tajan watersheds as the agricultural center of the country are of great importance in the development of the country. Due to the fact that the reduction of river flow coincides with the period of plant growth and water needs of crops in the basin, it can reduce production in the agricultural sector of the basin. Therefore, the findings of this study affect the macro-planning related to the climate of the region, to estimate changes in surface and groundwater resources of the basin, changes in the performance of agricultural and rangeland products and the status of extreme climatic phenomena such as the number And the severity of flood and drought events and timely awareness will be very effective, as in recent years, due to lack of proper forecasting, or a reduction in the severity of natural disasters, we have witnessed an increase in the aforementioned events in the northern provinces of the country. Also, since the thermal regime plays a decisive role in the distribution of plants, the impact of definite changes in temperature, especially the warming of forest ecosystems, will cause changes in the composition of plant communities and their individual distribution. Usually, with increasing altitude every 100 meters, the temperature decreases by one degree Celsius; In particular, environmentally sensitive areas such as Dudangeh, Chahardangeh and Bola National Park are located in the study area, which shows the importance of natural habitats in the study area. Therefore, this research is the basis for many studies in various fields, including the consequences of climate change on water resources, water efficiency, crop yield, impact on irrigation networks, impact on the distribution of plant species in Hyrcanian forests, etc., which can be Manage and reduce climatic consequences to be useful.

In the present study, the interaction effect of climate change accomplished by phenanthrene as a representative of contaminants was investigated on the expression of nacrein gene in the mantle tissue of Pinctada radiata. To determine the studied gene expression pattern changes, pearl oysters were exposed to isolated/ combined warming in two levels (24 and 28 °C), acidification in two levels (8.1 and 7.6) and phenanthrene concentration in two levels (0 and 8 ng/L) for 4 weeks. Experimental setup was arranged in eight exposure treatments with triplicate aquaria per treatment and each aquaria containing 15 animals in 20 L. Sampling was performed three times, i.e.,24 h 48 h and 28 days after exposure. After 28 days of exposure, although no mortalities were observed in any treatments, the studied parameters caused significant reduction of nacrein expression in isolated exposure except for temperature, double interactions except for temperature and phenanthrene combination, and triple interaction. The quadruple interaction showed no significant effect on nacrein gene expression. These results demonstrated that the decreased seawater pH and elevated temperature in presence of contaminants caused downregulation of the nacrein gene and might impact the quality of pearl and shell of pearl oyster, P. radiata.

Due to the space and time limitations, measuring the flow of rivers, this task will face problems that in recent years, researchers have turned to designing hydrological models to check and estimate the flow of rivers. The existence of a tool to estimate discharge can lead to the best possible management of surface water and its optimal use. In addition to these, climate change, water quality changes and ecological studies can be evaluated using runoff estimation hydrology models. Successful management of water resources requires a qualitative analysis of the effects of climate change and land use practices on water flow and quality. While expert knowledge can provide indications of such impacts, detailed analysis requires the use of mathematical models to dynamically disentangle the water balance (at the time scale at which important processes are involved). This includes separating precipitation into evapotranspiration losses, runoff to streams, recharge to groundwater systems, and changes in short-term watershed storage. Some of the processes to consider are: evapotranspiration. and feedback to Joe. vegetation dynamics; The level of underground water and its effect on waterlogging and soil salinization; Reliability of tank storage capacity; wetland dynamics; flood urban runoff; Erosion in agricultural and pasture lands as well as channel erosion and sedimentation and aquatic ecosystem functions. Arid and semi-arid regions are usually affected by heavy rainfall events with a high degree of spatial variability. This usually results in a fast response profile, and in areas without weather radar coverage, the poor density of rain gauges prevents accurate estimation of precipitation depth and spatial distribution for a particular event. Furthermore, if only daily precipitation data are available, precipitation model calibration -Runoff in a daily step means that most of the information in the hydrograph is not used (note that runoff here means total streamflow, not just surface runoff). ). Another important consideration for calibrating models for watersheds in arid and semi-arid regions is the frequency of events. Such watersheds have less flow than watersheds in wetter climates. This means that longer calibration periods are needed to reduce the uncertainty in the model parameters. Otherwise, the parameter values are more prone to errors in the data, with a significant decrease in performance in the simulation compared to the calibration.

In this research, IHCRAES model is used for rainfall-runoff simulation. Due to physical and time limitations, the measurement of river flow is facing problems, researchers have turned to designing hydrological models to investigate and estimate river flow. Due to the lack of hydrometric stations in small or upstream basins, the development of tests that can estimate the water flow on a daily time scale and in a desired location is one of the necessary things that leads to the improvement of the information needed for management purposes related to water resources. In order to evaluate the performance of the model parameters, the coefficient of determination of the model (D), the Nash-Sutcliffe coefficient, the average relative error of the parameter (ARPE) and the total error in the flow volume (Bias) which are calculated and used by the model itself. The higher the D value and the lower the ARPE parameter values, the more ideal the model results. Bias parameter values also indicate whether the simulated flow is more or less than the observed flow, and in other words, it specifies that the model simulates the flow more than the reality or less than the reality. After validating the IHACRES model and ensuring its effectiveness, exponential microscale results are entered into it and the runoff of the next decade is predicted and evaluated. In the figure, we can see the model of the IHACRES model and how to simulate rainfall and runoff.In general, this model is an integrated metric conceptual model for rainfall-runoff simulation, which was developed by Jackman in 1990. The IHACRES model has always been of interest due to the need for less data and high power in daily estimation. Due to physical and time limitations, the measurement of river flow is facing problems, researchers have turned to designing hydrological models to check and estimate river flow. The existence of a tool to estimate discharge can lead to the best possible management of surface water and its optimal use. In addition, climate change, water quality changes and ecological research can be evaluated using hydrological models for runoff estimation. Hydrological relationships between precipitation and runoff have always been investigated and tested by water researchers. The IHACRES model has always been of interest due to the need for low data and high power in daily estimation. This model has been used by Karenko et al. (2008) for purposes such as evaluating climate variables such as changes in precipitation, temperature, and runoff coefficient changes. Due to the lack of hydrometric stations in small or upstream basins, the development of tests that can estimate the water flow on a daily time scale and in a desired location is one of the necessary things that leads to the improvement of the information needed for management purposes related to water resources.

In the section of predicting the amount of changes in discharge and runoff in the future using IHACRES rainfall and runoff simulation software, the results show that this model has a high ability to estimate discharge for basins with low discharge and is less suitable for high discharges. The main and essential point in this study is that the main factor affecting the reduction of water resources in the coming periods is the increase in temperature and, as a result, the increase in evaporation and transpiration in the river basin and the lack of proper management of water resources

The human threats have had many negative effects on the population of Iranian ungulates in recent decades. However, climate change can increase the vulnerability of these species by doubling the current habitat conditions. The purpose of this study, investigate of the effect of climate change on the favorable habitats for the wild goat (Capra aegagrus), the current species displacement rate and, How the future distribution of this species under two climate scenarios, optimistic and pessimistic (RCP126 and RCP585) in time efficiency of 2061 to 2080 throughout Iran using the Maximum Entropy (MaxEnt) method. In this study, we use 32 habitat variables in four groups of topographic maps, climate, land use, and land cover. The results of this study show that the favorable habitats of this species in Iran now comprise 30% of the total habitats of the country. However, this amount of habitat will be reduced to 26% in the future, and in optimistic and pessimistic scenarios, 10.26% and 9.73% of the present habitats will be destroyed in the future, respectively, Also the results show that factors of altitude, slope, and latitude have a significant effect on the rate of reduction or conservation of desirable habitats of this species so that the results of modeling show the distribution of this species in the western and northwestern parts of Iran.