بارش

Precipitation is the most important factor in the hydrological cycle, which has many temporal and spatial changes. The use of satellite precipitation data can be of great use in climatic and hydrological research, especially in areas without rainfall statistics. Over the past three decades, a large number of satellite-derived global precipitation datasets have been developed and used. The purpose of the current research is to investigate the accuracy of the satellite precipitation data of the PERSIANN family products, which includes: PERSIANN, PERSIANN-CCS, PERSIANN-CDR and PERSIANN-PDIR. For this purpose, the daily, monthly and annual data of these products were compared with the observational data of 129 meteorological stations in Iran and in the period of 2008-2022. The evaluation of the accuracy of satellite precipitation data was done using CC and RMSE statistical indices and POD, FAR, CSI, HSS and KSS Categorical metrics. The results on a daily scale indicate the better efficiency of the PERSIANN-PDIR source with correlation and RMSE is 0.30 and 3.4 mm respectively and better performance in POD, CSI and KSS indices. In the monthly and annual scale, PERSIANN-CDR has the best estimation in monthly and annual precipitation with correlation values of 0.81, 0.78 and RMSE 23.4 and 152.7 mm. The PERSIANN source has an underestimation of annual precipitation and the PERSIANN-CCS source also has an overestimation of precipitation. However, in the high rainfall areas located in the northern belt and the Zagros mountain range, PERSIANN family products reported the annual rainfall lower than the actual amount and perform better on a monthly scale than on a daily and annual scale.

Due to the significance of forests in both the natural and human environment, this study aims to investigate the impact of meteorological drought on oak forest dieback in Ilam province. Specifically, the study seeks to determine the relationship between Zagros Forest drought and droughts in this particular region. The analysis utilizes the Standard Precipitation Index (SPI) to identify the frequency of droughts during different time periods. The results indicate that the years 2007, 2008, 2011, 2015, and 2016 experienced the highest occurrence of droughts. Additionally, remote sensing data from MODIS images were employed to examine the trend in tree greenness (NDVI) from 2000 to 2016. The analysis reveals a significant correlation (R2 = 0.9999) between the greenness trend and the drought index (SPI). Moreover, a land survey of oak drying points and simulation using Landsat satellite images, with a 15×15 pixel output from GIS software, indicate that approximately 17,894 hectares of forests in the region experienced drying and destruction between 2000 and 2016. By combining the oak forest drying layer with the output layers derived from drought zoning, visual indicators were created, and statistical analysis was conducted for three 5-year time series. The results demonstrate a correlation coefficient of 96.6% and an explanation coefficient of R2 = 0.985 for the 2002-2006 time series, a correlation coefficient of 95.4% and an explanation coefficient of R2 = 0.980 for the 2007-2011 time series, and a correlation coefficient of 98.8% and an explanation coefficient of R2 = 0.995 for the 2012-2016 time series. These findings illustrate the influence of drought and its variations in terms of intensity and duration on oak forests in the Zagros region of Ilam. Based on the study results, it is predicted that if the drought persists with the same trend, approximately 1,118.4 hectares of oak forests in Ilam province will dry up and be destroyed annually.

To conduct this research, data on monthly synoptic and hydrometric precipitation observations from the National Meteorological Organization and the Ministry of Energy were obtained for a 30-year period (1976-2005). To assess future changes in rainfall, historical data from the period (1976-2005) and simulated climate data from the period (2021-2050) using two models (CM3 and CSIRO-Mk3.6) from the CMIP5 series were used. These simulations were based on four scenarios (RCP2.6, RCP4.5, RCP6, and RCP8.5) with a spatial resolution of 0.5 x 0.5 using the BCSD method. A mean-based (MB) strategy was employed to correct any bias in the model outputs.  The results of the AOGCM models indicated that the CSIRO-Mk3.6 model had a lower error coefficient than the GFDL-CM3 model when simulating precipitation in the Large Karoun case. The average future rainfall (2021-2050) across the entire basin, compared to the average observed rainfall during the statistical period of 1976-2005, exhibited a significant decrease in both the amount and extent of precipitation in both basins for all models and scenarios. In the Great Karoun Basin, heavy rains were consistently concentrated east of the basin across all scenarios and models, with the central foothills experiencing the highest rainfall and the southwest and southeast regions receiving the lowest amounts.  The findings of this study estimate rainfall to range between 83-116 mm, with the highest rainfall expected in the Greater Karoun Basin under the rcp4.5 and rcp2.6 scenarios for both models.

Nitrogen dioxide is a significant factor affecting air quality in various regions worldwide. The aim of this study is to examine the concentration and trends of nitrogen dioxide pollution between 2005 and 2018, and explore its association with precipitation levels in the region. Based on data derived from the OMI sensor in Iran, the average vertical column concentration of nitrogen dioxide during this period revealed that the highest concentration was observed in the troposphere. Megacities, particularly Tehran metropolis, exhibited elevated levels of nitrogen dioxide due to the high population density and extensive road transportation. Analyzing the annual changes in nitrogen dioxide concentration in the troposphere alongside the average annual precipitation in Iran, it was observed that the pollutant concentration increased from 2005 to 2016 and subsequently decreased from 2016 to 2018, primarily due to population growth. However, when considering the overall trend, there was an upward trend with a slope of 3.53× -2. In contrast, the time series analysis of average annual precipitation in Iran demonstrated a declining trend with a slope of (-0.159 mm × ). Comparing the trends of these two variables, it can be deduced that they exhibit a negative correlation.

Precipitation is a critical component of the hydrological cycle, significantly influencing water resource management across diverse regions. To optimize water utilization within basins, a comprehensive examination of precipitation patterns is essential. Global warming has induced climate change, affecting various aspects of human life and the environment. These alterations directly modify precipitation amounts and evapotranspiration rates while indirectly influencing the hydrological cycle through the occurrence of extreme weather events. Changes in precipitation patterns and water distribution can have profound consequences for agriculture, potable water supplies, and ecosystems. Therefore, understanding and rigorously analyzing these processes and their implications are crucial for the planning and sustainable management of water resources. Climate change represents one of the most pressing global challenges of recent centuries, resulting in severe and unforeseen phenomena that have significant implications for human societies and ecosystems. The consequential impacts include rising temperatures, altered precipitation patterns, and an increased frequency of natural events such as storms and floods. Addressing this crisis necessitates international collaboration and the development of sustainable solutions to mitigate its adverse effects and ensure a better future for generations to come.

This research investigates climate change and its ramifications across various sectors of East Azerbaijan Province. To achieve this, meteorological data and generative models are employed. The LARS-WG model is selected as a tool for assessing self-capabilities. For this analysis, observational data spanning 20 years from six synoptic stations within East Azerbaijan Province, collected from 1995 to 2014, was utilized. In order to project future conditions, data for the period 2021-2040 was generated using the CMIP6 model under three distinct scenarios: the optimistic scenario (SSP1-2-6), the intermediate scenario (SSP2-4-5), and the pessimistic scenario (SSP5-8-5). The stations considered in this study include Tabriz, Maragheh, Ahar, Mianeh, Jolfa, and Sarab. Daily precipitation data from the six synoptic stations for the period 1995 to 2014 was examined to compute extreme precipitation indices. These indices, which are designed to evaluate weather patterns and climate variability, conform to the global standards established by the Expert Team on Climate Change Detection and Indices (ETCCDI). The goal of analyzing this data is to enhance the understanding of precipitation patterns and their impact on regional weather conditions. The assessment of extreme indices may facilitate more accurate predictions and improved planning in response to climate change, ultimately contributing to a deeper understanding of climate change and its effects on various regions.

Simulated results were compared with observed data to evaluate the model's capacity to replicate precipitation patterns. Future precipitation was subsequently simulated for the period 2021-2040 under three distinct climate change scenarios. At the studied stations, the highest levels of precipitation were both observed and simulated in April and May, whereas the lowest levels occurred in August. Although the LARS-WG7 model demonstrated commendable performance in simulating general precipitation patterns, discrepancies were noted in the amounts and timing of precipitation between the observed and simulated datasets. Overall, peak precipitation in East Azerbaijan was associated with April and May, while minimum levels were recorded in August. Results from the R10mm index in the ACCESS-ESM1-5 model for the SSP1-2-6, SSP2-4-5, and SSP5-8-5 scenarios during the 2021-2040 period indicate that precipitation exceeding 10 mm is projected to increase in the mountainous and northern regions of East Azerbaijan, while a decrease is anticipated in the eastern regions. The Ahar and Tabriz stations exhibited the highest frequency of extreme precipitation events. Furthermore, the R95P index suggests an increasing frequency of such events in the southwest and southeast, while a decline is observed in the northwest. The Maragheh, Sarab, and Mianeh stations recorded the highest frequency for the R99P index, whereas Jolfa exhibited the lowest frequency. The results of the trend and p-value analysis of the R10mm, R95p, and R99p indices for the SSP1-2-6, SSP2-4-5, and SSP5-8-5 scenarios in East Azerbaijan from 2040 to 2021, compared to observed data from 1995 to 2014, indicate a decreasing trend in heavy precipitation. The Tabriz, Ahar, and Jolfa stations displayed the most significant trends, while Maragheh and Sarab showed the least. P-values exceeding 0.05 at certain stations suggest insignificant changes. Moreover, these analyses underscore the necessity for further investigations to accurately comprehend variations related to climate change.

The findings indicate that the intensity and frequency of extreme precipitation events are projected to increase significantly in certain regions while decreasing in others. These changes pose severe risks, including an increased likelihood of flooding, soil erosion, infrastructure damage, and detrimental effects on agriculture and water resources. This underscores the necessity for policymakers and planners to integrate these findings into future water resource management and disaster risk reduction strategies. By doing so, they can effectively mitigate the impacts of climate change and adapt to the evolving climatic conditions that threaten the region's sustainability and resilience. Addressing these challenges will be crucial for ensuring a stable future for communities affected by climate variability.

Historically, global producers have chosen maize, wheat, and rice as the main crops for their remarkable adaptability in different regions and climates, their wide acceptance and consumption by the population, and their nutritional status (FAO, 1995). Together, these three products provide 60% of global food energy consumption (FAO, 1995) because more than half of the world's population uses them as their main food (FAO, 2021; Haile et al., 2017). The process of climate change has been raised as a major challenge of the 21st century, which has great effects on the agricultural sector, water resources, the environment, and ultimately food security. Meanwhile, wheat is considered the most important rainfed crop in arid and semi-arid regions of the world, especially in Iran. The phenological stages and yield of rainfed wheat will shift in response to climate change. According to a study of research, changes in the climate would also affect agricultural and climatic processes, resulting in changes to yield, phenological periods, and climatic variables (Helali et al., 2022). The goal of this study was to examine how the climate and agricultural factors have changed over time using the AquaCrop and CMIP6 models in the context of the SSP scenario in the western Iranian province of Hamedan.

The study area is Hamedan province, located in the west of Iran, which mainly has a semi-arid climate. This province is considered one of the leading provinces in the production of rainfed wheat, so that the average yield of wheat in the studied locations fluctuates, which is mainly due to changes in climatic variables, droughts, cultivated varieties, and management strategies. In this study, the data of Hamedan, Malayer, Nahavand and Nowhzheh stations were used. The climatic data included minimum and maximum temperature, precipitation, relative humidity and wind speed on a daily scale. In this research, the fluctuation of climatic and agroclimatical variables in different stages of rainfed wheat phenology was investigated using the CMIP6 climate model under the SSP scenario and the AquaCrop model. Then the values of evapotranspiration, heat demand and length of phenological period were calculated for rainfed wheat variety Azar 2, which is the dominant variety of the studied area. The phenological periods included germination, vegetative, flowering and ripening periods and the entire growth period. The Socio-Economic Pathway (SSP) scenarios for the years 2021–2050 and the base period from 1990 to 2014 were used. Additionally, the AquaCrop model was used to compute the modeling of phenological stages, evapotranspiration, and heat demands (growing degree days). Based on the crop year, six planting dates were taken into consideration, starting on September 22 and spaced out by ten days.

When rainfall, maximum temperature, minimum temperature, relative humidity, and wind speed were simulated using various CMIP6 models, it was shown that the BCC-ESM2-MR, MRI-ESM2-0, and Can-ESM5 models performed better than the other models. While other climate variables, particularly in the SSP5-8.5 scenario, have had incremental changes compared to the base period, the projection results using the best model in the base period showed that the amount of precipitation in the future period will decrease under all scenarios in all stations, with the largest decrease occurring in the SSP5-8.5 scenario. The summary of the obtained results shows that the amounts of precipitation, evapotranspiration, thermal demand, and the length of the phenological period will increase or decrease with the change of the cultivation date, which shows that the change of the cultivation date can be used as a solution to adapt to climate change. The results showed that in the future climate, under CMIP6 models and SSP scenarios, precipitation amounts will decrease, while the variables of growing degree day, evapotranspiration, and relative humidity will experience incremental changes depending on the phenological stage. Meanwhile, the variable length of phenological periods will experience both decreasing and increasing trends. Based on these findings, it is anticipated that climate effects on the various phenological stages of rainfed wheat in Hamedan province would intensify in the future. In light of these changes, it is imperative to investigate the impact of climate on rainfed wheat production. It is recommended that the researchers use a variety of agricultural models and other management techniques to investigate the impact of these occurrences on the yield of rainfed wheat.

Time series analysis is a suitable tool that is used in mathematical modeling, predicting future events, revealing trends, investigating diffraction in climate data, as well as reconstructing incomplete data, and expanding information. Climatic changes are mainly caused by fluctuations, fluctuations, or changes in climatic elements, especially temperature and precipitation. These developments leave undeniable effects on local phenomena, hence the evidence of the past climate can be traced in all wet and dry, hot and cold environments, and biological areas (Ghayour, 2006:85). The temperature of the earth's surface is an important parameter for evaluating the energy budget of the earth's surface (Trigo et al, 2008:1). With the change in climate (temperature and rainfall), many changes are made on the surface of the earth, including vegetation. In fact, with the increase in temperature and decrease in rainfall, vegetation in the region decreases. Considering the importance of the issue and the relationship between climatic indicators and vegetation, by determining the relationship between them, one can predict the changes based on the other, which leads to an increase in the speed and accuracy of the work. Therefore, it seems important to use satellite images and extract and investigate the relationship between temperature and rainfall factors as well as vegetation in different areas, especially watersheds (Zhu et al, 2016:792). With the expansion of satellite technology, satellite images have widely provided access to information on land resources, and remote sensing tools have taken an important role in obtaining information about climate phenomena, because multi-spectral satellite images have important advantages, including They have the availability and ability of digital interpretation (Lillesand and Kiefer, 1994:750).

Materials & Methods:

 In this research, using monthly rainfall data from a CHIRPS sensor with a spatial resolution of five kilometers, NDVI vegetation index from a MODIS sensor for 16 days, with a resolution of 250 meters, and day and night surface temperature of 8 days from a MODIS sensor with a resolution of one kilometer, to analyze the changes in surface temperature and its relationship with climatic factors in Kerman province during a statistical period of 22 years (2001-2022) were studied. In the investigation of the annual precipitation fluctuations of Kerman province, standardized values of Z have been used, and these values have varied between -1.5 and +1.5. After receiving the data, first the CHIRPS images, then the NDVI and LST images were processed in the ArcGIS software environment and the values were extracted for Kerman province and then analyzed in the Excel software environment.

According to SPI results, drought is observed in 2010, 2016, 2018, and 2021, and drought in 2004, 2009, 2017, 2019 and 2020. In the rest of the years, the SPI index has been normal. Also, the seasonal rainfall showed that the highest rainfall was in the winters of 2005, 2017, and 2019 with an amount of 90 mm and more and the lowest rainfall was in the summer of 2019 with an amount of less than 1.04 mm. The value of the vegetation cover index (NDVI) is also in the spring season with a value of 1.05, which has an increasing trend, and the lowest value of the vegetation cover index (NDVI) in the autumn and winter seasons, whose lowest value is 0.35 and 0.42 on December 19 and November 17 with a trend A decrease is shown. The seasonal vegetation also shows that as we move from the west of the region to the east, the amount of vegetation decreases. The seasonal changes in the temperature of the surface of the earth during the day in Kerman province show that the hottest seasons are summer and spring and the coldest season is winter. The seasonal changes in the earth's surface temperature at night also show that the highest surface temperature is related to summer and spring, and the lowest is in autumn and winter.

In general, the results show that according to temperature fluctuations, there is a positive and significant relationship between the temperature of the earth's surface and vegetation (P-value at the 0.01 level). And there is a negative and significant relationship between the temperature of the earth's surface and precipitation. So precipitation has the greatest effect on the variability of the earth's surface temperature and vegetation has the least effect on the surface temperature changes. The increase in day and night temperatures in the spring and summer seasons causes an increase in evaporation and a subsequent decrease in water resources throughout the province and pressure on underground water. On the other hand, with the increase in temperature, the amount of evaporation and transpiration (plants' water needs) will also increase and will lead to a potential decrease in water resources, especially in the eastern regions of the province, but the presence of vegetation can almost reduce the temperature of the earth's surface. In the autumn and winter seasons, during the last decades, with the increase in temperature, the amount of precipitation and vegetation has decreased. Also, an increase in temperature can increase the water demand, which in turn leads to more extraction of surface and underground water resources. This means that the surface temperature has increased significantly in the mentioned statistical period. Also, the different conditions of each region are important factors in determining the type of relationship between temperature, vegetation, and precipitation. The results of this research on the relationship between the earth's surface temperature and climatic factors with the research of Mianabadi et al (2023), and Mazidi et al (2023) based on the method of the experimental relationship between surface temperature and other factors are consistent. According to the findings, the temperature trend in Kerman province is significant and the possibility of heat stress will increase in the future.

The climatic conditions of a region are determined by the frequency and cumulative effects of weather systems passing through that area. The recurrence, change or continuity of weather systems in any given location is of paramount importance in determining and identifying climate. The continuity and change of systems are determined by the process of classifying or determining atmospheric circulation patterns and meteorological types, and for this reason the classification of weather systems is one of the main objectives of synoptic climatology. Ocean-atmosphere interactions are one of the most important discussions in marine meteorology, which has found a wide place in scientific research in the field of meteorology and oceanography. Teleconnection patterns are defined as the simultaneous relationships between fluctuations in climatic elements of a location and changes in atmospheric pressure and sea surface temperature in distant geographical areas. Recent research has focused on explaining climate behavior through the mechanisms of teleconnection patterns. As a global climate feature, teleconnection patterns represent large-scale shifts in atmospheric wave patterns and jet streams, influencing temperature, precipitation, storm tracks, and jet stream positions and intensities across vast regions. Given the significance of teleconnection patterns and their connection to climatic parameters, particularly temperature and precipitation in Iran, this study investigated the monthly temperature and precipitation data from four stations within the AbarkOuh-Sirjan basin, analyzing their relationships with six indices.

In this study, monthly precipitation and average temperature data were used for four stations in the Abarkouh-Sirjan basin (Sirjan, Shahrbabak, Abarkouh and Marvast) during the statistical period of 2003-2022. Data from six teleconnection patterns (IOD, NINO 3+4, NINO1+2, NINO4, NAO and AO) were also used to investigate the effects of teleconnection patterns on changes in the climate data of the study area. The Pearson correlation test was used to evaluate the relationship between the investigated parameters. After determining the correlation of the variables, a multivariate regression was performed to gain a better insight into the effects of the teleconnection patterns on the temperature and precipitation of the stations.

When examining the correlation coefficient between the monthly temperature of the stations and each of the teleconnection patterns, in January the NINO4 pattern, in March the NINO4, NAO and AO patterns, in April the NINO3.4 index in April, NINO4 in May, NINO3.4, NINO1.2 and NINO4 in June, NINO1.2, NINO3.4, NINO4 in July, NAO in October, IOD in November and NINO3.4 in December showed significant correlations. There was no significant correlation in February, August and September. When examining the correlation coefficient between the monthly temperature of the stations and each of the teleconnection patterns with a time lag of three months, the index NINO1.2 in January, NINO4 in March, NINO3.4 and NINO1.2 in April and May, IOD, NINO3.4 and NINO4 in July, NINO4 in August, IOD and NINO1.2 in October showed significant correlations. In February, June, September and November, there was no significant correlation between the index and the temperature. The correlation coefficient between the monthly temperature of the stations and each of the teleconnection patterns with a 6-month time lag showed significant correlations in different months: NINO1.2 in January, NAO in February, NINO1.2 in March and April, NINO3+4 and NINO4 in April, NINO3+4 and NINO4 in May, NINO1.2, NINO3+4, NINO4 in June, IOD in July, NINO1.2 in August, IOD and NINO4 in October and November, and NINO3+4 in December.

When looking at the correlation coefficient between monthly temperatures at different stations and various teleconnection patterns, the NINO3.4 index in the Abarkouh and Marvast stations and the NINO4 index in the Marvast station had the highest correlation at the 99% level. With a 3-month lag between temperature and teleconnection patterns in the Marvast station, the NINO3.4 index in May was significant at the 99% level. With a 6-month lag between temperature and teleconnection patterns, in Sirjan the IOD index, in Shahrbabak and Abarkuh the IOD and NINO1.2 indices had the highest correlation. In the Marvast station, the NAO, IOD, and NINO1.2 indices had the highest correlation with temperature. In the Shahrbabak station, the AO and NINO3.4 indices were significant at the 99% level. In the Abarkouh station, the IOD index had the highest correlation. In the Marvast station, the NAO and NINO4 indices were significant at the 99% level. In examining multivariate regression of temperature with teleconnection patterns, the Marvast station showed the highest correlation in June, with 80% of temperature changes in this station explained by teleconnection patterns. For the regression relationship of precipitation with teleconnection patterns, the highest correlation was identified in the Abarkouh station in July, with 71% of precipitation changes explained by teleconnection patterns. According to the step-by-step regression results, the NINO3.4 pattern in the three stations of Shahrbabak, Abarkouh, and Marvast was identified as the most effective pattern in explaining precipitation changes. In the Sirjan station, none of the indices were identified as an effective pattern for precipitation. In the Sirjan and Shahrbabak stations, the NINO3.4 pattern, and in the Abarkouh and Marvast stations, the NAO pattern were identified as the effective pattern for temperature.

The occurrence of climatic extremes such as heat waves, heavy rains and droughts cause significant damage to human societies. Analyzing the change in the frequency of these phenomena and examining the trend and intensity of the observed events and the predicted events can help significantly in planning to manage and adapt to these events. In the current research, the effects of climate change on the drought situation of Lorestan province have been evaluated using the Standardized Precipitation Drought Index (SPI) and then the trend of this index was investigated. Since the computational grids of the atmospheric general circulation model (GCM) must be downscaling in the dimensions of one station due to their large scale and low resolution for use, so at first the daily output data of the atmospheric general circulation model HADCM3 under the release scenario A2 and B1, downscaling by the LARS-WG version 5 statistical model and the ability of the LARS-WG5 model in simulating the past climate (1996-2011) in the synoptic stations of Aliguderz, Khorramabad and Borujerd has been verified, then the precipitation gauge for The period 2011-2050 has been simulated for the studied stations. The results obtained from the study of the drought trend indicate the intensification of droughts in the winter season during the coming decades in Aliguderz and Borujerd stations and the weakening of droughts in Khorramabad station. The spring season shows a weakening of the drought in all three studied stations except the INCM3 model, where Aliguderz and Khorramabad stations have significant trends under the HADCM3 model under scenario A2 and the INCM3 model under scenario A2 and B1, respectively. In order to adapt the agricultural sector to these changes, an adaptive capacity strategy should be adopted in the agricultural management and planning department of this province to plan the time and type of cultivation by considering these changes. Also, take action in the direction of better productivity.

The current research was carried out to analyze the changes in precipitation in northwest Iran during the coming decades based on GCM models. For this purpose, first, the precipitation of 1985-2014 was trended based on the Mann-Kendall test. Then, the daily precipitation data for each of the studied stations was simulated in SDSM6.1 software for 1985-2014. Then, under the scenarios (SSP2-4.5) and (SSP5-8.5) of CanEsm5 and MPI-ESMI-2HR models, the precipitation of 2015-2043 was predicted. To evaluate the performance of CMIP6 models and compare the basic and predicted values, MSE, RMSE, and MAE statistical measures were used. According to the results of the Man-Kendal test, the precipitation of the base period in the stations of Tabriz, Ardabil, Urmia, Takab, and Maragheh has a decreasing trend and in the stations of Meshginshahr, Sardasht, Mako, Khalkhal, Sarab, Jolfa, and Parsabad it has an increasing trend. Among the 12 investigated stations, only the Maragheh station had a significant decreasing trend. In other stations, precipitation trends were not significant. According to the predictions made based on the mentioned models, under the medium scenario (SSP 2-4.5), the precipitation will decrease in late winter and early spring. In other months, especially summer and autumn months, the percentage of precipitation will be higher. Based on the SSP5-8.5 scenario, the highest percentage of precipitation decrease in the MPI model was predicted by 33% in Jolfa, Sardasht, and Maragheh stations, and in the CanESM5 model, about 33-35% in Jolfa, Takab, and Urmia stations. According to the results, although both models predicted precipitation with a relatively high error, the MPI model had a lower error and more accuracy in predicting precipitation than the CanESM5 model.

The estimation and measurement of precipitation in situ presents considerable challenges due to factors such as exorbitant costs, a scarcity of monitoring stations, point sampling limitations, and its lack of generalizability to broader surface areas. Consequently, it is imperative to evaluate the accuracy of satellite-derived precipitation products as viable alternatives to conventional field measurements. Given that precipitation is influenced by the climatic conditions and physiographic characteristics inherent to specific regions, this study aims to not only validate and verify satellite precipitation products but also to examine the impact of temperature and elevation on the efficacy of MERRA, TRMM, and CHIRPS satellite precipitation products over a monthly scale from 2005 to 2019, utilizing data from 222 synoptic stations located throughout Iran. The findings indicated that the root mean square error for the TRMM, MERRA, and CHIRPS satellites was recorded at 23.8 mm, 30.6 mm, and 35 mm respectively, suggesting a superior performance of the TRMM satellite in comparison to the other two products. Moreover, the results demonstrated that the TRMM satellite consistently outperformed the other two satellites across all temperature and elevation classifications. At elevations below 500 m and above 1500 m, as well as at temperatures less than 18 °C, MERRA exhibited superior performance relative to CHIRPS, offering more accurate estimations of actual precipitation. Overall, the results indicate that TRMM satellite products may serve as a reliable substitute for observational data, as this satellite not only demonstrates commendable performance in the assessment of satellite products but also excels across varying elevation and temperature conditions.

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.

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.

Although the per capita average of greenhouse gas emissions in Islamic countries is lower than the global average, examining the effects of climate change on the economies of these countries seems crucial due to their significantly higher emission growth rates compared to the global average. Therefore, in the current study, the effects of climate change on the Food Production Index (FPI) as an indicator of food security in Islamic countries were examined. This was done using CMIP6 published by the IPCC under four scenarios: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 (ranging from optimistic to pessimistic). The investigation covered three groups of countries: those with middle income, less developed countries, and oil-exporting countries until the year 2100. Based on the results, FPI exhibited an increasing trend for all three groups, with the highest average growth observed in the less developed countries group. Furthermore, according to the results of the fixed-effects model and the data fusion technique, the cultivated area of grains, gross domestic production, value added in the agricultural sector, and cumulative precipitation had positive effects. In contrast, population growth, inflation, and average temperature had negative effects on food security. Additionally, climate change exerted a negative impact on food security in all scenarios and across all three groups. Moreover, the changes in food security resulting from climate change ranged from 1% to 11%. In the OE group, Iran, Iraq, and Saudi Arabia experienced the most significant reductions in food production, with 8%, 6.6%, and 6%, respectively.The results of this study indicate that MIC, due to having the lowest growth in food security without considering climate change (1.8 units) and the highest reduction in food security due to climate change (6.3%), will face a more concerning situation compared to the other two groups. Therefore, there is a need for a greater focus on improving the capacities of these countries to reduce vulnerability and adapt to climate change, ultimately addressing vulnerabilities in food production and access.

Aerosols have a significant impact on cloud formation and behavior, ultimately influencing precipitation patterns. Understanding this relationship is crucial for predicting and mitigating the effects of climate change. These particles play an important role in the complex interaction between atmospheric conditions and precipitation patterns. Therefore, the objective of this study is to investigate and analyze the relationship between different types of aerosols and precipitation in Southeast Iran. For this purpose, daily precipitation data from 43 meteorological stations over the statistical period from 2000 to 2022 were used, and rainy days with an AOD index value above 0.3 were selected for regression analysis. In this study, to reveal the spatial relationship between various types of aerosols and precipitation in Southeast Iran, a geographically weighted regression model was first applied, and the correlation between the AOD index and precipitation was examined through cell-by-cell analysis. The results showed that the correlation between aerosols and precipitation in Southeast Iran ranges from a minimum of 0.3 to a maximum of 0.96. Additionally, the comparison of precipitation values with the AOD index on rainy days indicates that the highest frequency of precipitation occurred at specific aerosol densities, with 84.61% of significant cell precipitation occurring when the AOD index ranged between 0.5 and 0.8. At higher values, the frequency of precipitation significantly decreased. This finding suggests that an increase in aerosol concentration beyond an AOD value of 0.8 leads to a reduction in precipitation frequency in Southeast Iran

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

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

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

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

The outputs of general circulation models (GCMs) usually have a bias compared to observational data, and some corrections must be made before using them to develop future climate scenarios. The bias correction methods are the standard statistical methods for processing the output of climate models. In this research, the effect of five bias correction methods on the projected precipitation of the GFDL-ESM4 model in the Lake Urmia basin has been evaluated. The methods used in this research include linear scaling (LS), local intensity scaling (LOCI), power transformation (PT), distribution mapping (DM) and delta change factor (DC). Statistical metrics such as the correlation coefficient, root mean square error (RMSE) and percentage bias (PBias) have been used to evaluate the accuracy of the corrected data in the period of 1990-2014 compared to the observational data and to choose the best method for correcting the data of future scenarios. research results showed that the delta change method significantly improved the raw estimates after correction; Therefore, this method was used to correct the data of scenarios SSP1-2.6, SSP2-4.5 and SSP5-8.5. In addition, the projection of the mean annual precipitation shows a decrease between 2 and 9 percent in SSP1-2.6, between 5 and 17 percent in SSP2-4.5, and between 8 and 26 percent in SSP2-8.5 compared to the observed data.

Extreme rainfall fluctuations are one of Iran's environmental challenges. The heavy rains of 2019 and the relative drought of 2018 and 2021 show the unevenness of rainfall.

This study utilized the daily rainfall data from 360 weather stations spanning the years 1990 to 2021, along with the synoptic indices of the winter seasons of 2019 and 2021. The geographical range of the data, spanning from latitude N 10-90 to longitude E º 10-90, was chosen to provide a comprehensive view of the Iranian climate.

The probability of drought is 42%, drought is 35.5%, and normal conditions are 22.5%. Markov chain indicated the highest probability of normal drought to drought at 57% and consecutive drought at 46.2%. ANOVA showed a significant difference between the climatic variables of wet and dry years. Observational conditions showed that in the wet year (2019) sample year, the Siberian high-pressure center operator had more pressure (1054 megabytes) in 2019 and 1032 mb in 2021. In the wet year, the frequency of cyclones and the temperature difference at the level of 500-1000 are higher, but the omega values are lower. These conditions are for the drought (2021), the amount of pressure in Siberia, the temperature difference between 500 and 1000 is less, and Omega is more (stable air).

The amount of rainfall in Iran fluctuates from year to year, and low rainfall years occur more often than high rainfall years.

Innovation: 

Planners should increase the risk of water supply and development and plan based on the minimum amount of precipitation. The physical capacity of the country's ecology should be reviewed, and population concentration in limited areas should be avoided to reduce the vulnerability of the country's geographical environment.

In many scientific researches, error measurement statistics are often used without taking notices into account when selecting a model or method for the spatial analysis of environmental hazards. In order to assess the accuracy of precipitation interpolation methods in Fars province, the performance of widely used error measurement statistics and some comments were implemented. Spatial interpolation of precipitation was accomplished using inverse distance weighting, kriging, co-kriging, and radial basis functions methods with 161 weather stations (22 synoptic and 139 rain gauge stations) for 2018 as a rainy year. The results of MBE statistic evaluation indicated that the researcher may have chosen the incorrect interpolation method in certain cases where the sum of the positive and negative values became zero. In addition, this statistic is limited to indicating overestimation or underestimation and should not be used for assessing accuracy or selecting interpolation techniques. Regarding the coefficient of determination (r2), the results revealed that due to the lack of compatibility in the magnitude of the range of this coefficient (0 to 1) with error values (100 to 400 mm for the interpolation of precipitation in Fars province), its use in evaluation of the accuracy of a method is not recommended. In terms of NRMSE, the results showed that samples with a small number of observations (n=3), its value increased excessively (NRMSE=0.35) when compared to samples with a bigger number of data (n=20, NRMSE=0.097). Therefore, it is not advised to use this statistic. In conclusion, since MAE and RMSE statistics provide a more realistic error value, it is advised to use them for assessing the accuracy of interpolation methods.