Considering the undeniable impact of agricultural plants on climatic and regional changes, it seems necessary to conduct regional research to understand the reaction of each agricultural plant in different stages of growth in relation to weather elements. If the temperature of the air along with the warm cloud is lower or higher than a certain threshold, its development will stop. Between the two limits, there is an optimal temperature where the plant grows the fastest. Temperature and clouds are both the most important climatic elements in agriculture. Both climatic parameters together cause stress in wheat and lower the productivity of the product. Considering the strategic nature of wheat, in order to increase the level of production, in the present research, while taking advantage of the experiences and methods and models used in foreign and domestic researches, it was practical in Kermanshah province due to the extent of the land under wheat cultivation and The significant amount of production, which has a special place in this field at the level of the country, the determination of the statistical threshold and the synoptic analysis of warem cloud temperatures on the performance of the wheat crop are investigated. According to the investigations and consultations with agricultural engineers, the maximum temperature along with cloudy days causes the phenomenon of greenhouse and excessive heat, which causes the fall of flowers, rot, sterility of pollen grains, fruit reduction, Premature aging and poverty become seeds, and this phenomenon occurs mostly in the months of May and June.

In order to comprehend the water flow characteristics and variations of the Karun River, we examined the Zaz, Bazoft, and Beshar sub-basins from its main branches. The reason for choosing these basins was the proximity to the catchment centers of the Middle Zagros and their location upstream of the dams.
 Iran Water Resources Management Company provided all the required data (from the water year
1356-57 to 1395-96), and we analyzed them using Kolmogorov-Smirnov tests, data skewness, skewness, and Pearson correlation. Then, we performed the linear regression test to determine the effect of temperature and precipitation on river discharge, and they conducted the Mann-Kendall test to identify the trend and jump points. The results of the data analysis showed that all of them are in normal conditions, although they have some elongation and skewness. The Pearson correlation test revealed a correlation between meteorological and hydrometric data.
 The regression model used shows the changes in precipitation and discharge (unlike temperature and discharge) well. The significance number of all stations in the model is less than 0.05, which shows that the changes that occurred between predictor and dependent variables are significant. We see the high performance of the model in explaining the changes in discharge compared to precipitation. According to the regression charts, the decreasing trend of precipitation and discharge and increasing temperature are clear in all three basins.
The Mann-Kendall test reveals a significant trend of increasing temperature in Bazeft and Bashar basins, a decreasing trend of discharge in Bazeft and Bashar basins, and a decreasing trend of precipitation in Zaz and Bazoft basins.except for the temperature of the Zaz basin, all variables show mutations in mutation basins.
 

Climate change is a significant threat to water resources, potentially altering precipitation patterns and increasing the likelihood of droughts in certain regions. This study aims to project future drought conditions in the Aras Downstream Watershed for the period 2025-2050, employing CMIP6 climate models (NorESM2-LM, CanESM5, and MPI-ESM1-2-HR) and the CMhyd software. Historical daily precipitation data from the selected models were compared with data from five stations (Parsabad, Aslan Duz, Jafarabad, Dasht, and Shorgol) within the study watershed. The comparison was conducted using statistical metrics (R2, MAE, MSE, and RMSE), and the results indicated the superior performance of the MPI-ESM1-2-HR model in precipitation estimation. This model was bias-corrected using the LS method in the CMhyd software, and future precipitation was projected based on the outputs of three scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5). The Standardized Precipitation Index (SPI) was employed on a three-month timescale to assess drought conditions. The findings revealed that the overall region will experience normal conditions based on SPI classifications. However, there will be a relatively higher potential for drought in the southern part of the watershed under the SSP2-4.5 and SSP5-8.5 scenarios compared to SSP1-2.6. The analysis of the network station averages indicated that the optimistic scenario suggests favorable conditions for the watershed, while the intermediate and pessimistic scenarios suggest‎ a contrasting picture, with drought becoming prevalent across the entire region in the coming decades.

Goods and currency smuggling is a widespread issue in Iran, disrupting economic, political, and social planning. While not limited to border regions, these areas are particularly vulnerable due to their unique geographical conditions. This research focuses on the natural factors influencing smuggling in border areas, examining the roles of topography, vegetation, climate, and water resources.

The study aims to determine the extent and mechanisms by which each of these natural factors affects the control of goods and currency smuggling in the country's border regions. This is a fundamental-applied study using descriptive-analytical and quantitative methods. Data were collected through surveys of military and border guard personnel stationed in border regions and analyzed using Pearson correlation coefficient, regression analysis, and path analysis modeling.

The results indicate that topography, vegetation, and climate have the most significant impact on hindering the operations of military and border guard forces in combating smuggling. The Pearson correlation coefficient (0.620) demonstrates a strong relationship between natural geography and the military geography of anti-smuggling efforts. In regression analysis, the "climate" variable shows the highest impact with a beta coefficient of 0.350, while "water resources" has the lowest impact with a beta coefficient of 0.124. Furthermore, the path analysis model shows that vegetation, topography, and water resources, influenced by climatic conditions, affect the quality of anti-smuggling efforts in the country's border regions, thereby creating challenges for military and border guard forces in controlling, monitoring, and combating smuggling.

“The global water crisis is exacerbated by significant spatial data gaps in key water stress indicators such as Baseline Water Stress (BWS), posing serious challenges for policy-making and water resource management. This study leverages the XGBoost algorithm one of the most efficient machine learning methods for regression modeling to estimate missing BWS values at the global scale. Key predictor variables include soil moisture, climatic factors (precipitation, temperature, and evapotranspiration), land use/land cover, and elevation, derived from the Aqueduct Water Risk Atlas 4.0 and remote sensing datasets. Through comprehensive data preprocessing and hyperparameter optimization, the model explains approximately 71% of the variance in observed BWS values (R² = 0.711), achieving a Mean Absolute Error (MAE) of 0.727 and a Root Mean Square Error (RMSE) of 1.143 on the standardized 0–5 BWS scale—demonstrating competitive performance compared to prior studies (R² range: 0.60–0.75). Feature importance analysis reveals soil moisture as the dominant hydrological integrator (35.41%), followed by climate variables as the primary driver of the hydrological cycle (24.58%), land use/land cover (18.30%) and population density as key anthropogenic factors (16.04%), and elevation as a topographic modulator (5.67%). The reconstructed global BWS map highlights pronounced spatial heterogeneity in water stress: critical hotspots emerge across the Middle East, North Africa, and South Asia, while higher water security is observed in tropical and temperate regions. This model provides a practical tool for policymakers to identify high-risk areas, develop early-warning systems, and support sustainable water planning. The unexplained variance (29%) underscores the need to integrate socioeconomic data and implement local-scale calibration representing a pivotal step toward addressing the global water crisis.

The Siberian High-pressure (HP) has various effects on Iran’s climate. Climate warming, especially in the last century, has raised the possibility of changes in the Outbreaks of the Siberian High-pressure extending toward Iran (OSH). In this study, to test the mentioned hypothesis, daily sea level pressure maps for 50 years (1972 to 2021) during the month in which the SH appeared more strongly in the monthly composite maps were downloaded from the NCEP/NCAR atmospheric data reanalysis database. The selection of this month was based on the highest central pressure intensity of the high (intensity index) and its greatest spatial extent (spatial index) compared to the other six months. The axes of the OSHs extending toward Iran were classified into four categories: “continuous and reaching,” “continuous and non-reaching,” “discontinuous and reaching,” and “discontinuous and non-reaching.” The design of their synoptic patterns showed a clear decrease in the frequency of “continuous and reaching” OSHs. Continuity refers to the directness of the OSH, and a reaching OSH is one that extends to the borders of Iran or penetrates into the country. Using two criteria— “the integrated advance of cold and dry air from the high-pressure center toward Iran” and “verification of the OSH entering or reaching Iran’s borders”—it was found that cold and dry air travels from Central Asia toward Iran through three main pathways: 1. The pathway of cold and dry air transfer from the western slopes of the Altai Mountains, then across the Turan Plain, and finally into central Iran. 2. The pathway of air transfer from the Dzungarian Plain in western China, then along the Tian Shan Mountains, through Afghanistan, and finally into the north of Sistan-va-Baloochestan Province. 3. The pathway of air transfer from the Kazakh steppes, through the Caucasus corridor (west of the Caspian Sea), reaching Azerbaijan, and extending along the Zagros Mountains. The frequency of OSHs has indicated a decline in five separated decades along all three pathways during the past half century. This decrease is more evident in the third pathway, namely the Caucasus corridor, which already had a lower frequency from the beginning.

Daily maximum temperature data was extracted from ERA5 products in HDF5 file format and processed using the Python programming language. For data analysis, Principal Component Analysis (PCA) was used for dimensionality reduction, and Ward's clustering method was employed to determine homogeneous climatic regions based on the similarity of weather elements. The long-term mean maximum temperature for the country during this period was estimated at 24.2∘C, with a range of variation from −0.4∘C to 53.7∘C. The results from smoothing the data's time series showed a significant temperature jump around the year 1998, which led to an increase in the mean maximum temperature from 23∘C to 25∘C. In the spatial dimension, the maximum temperature was directly influenced by topography, altitude, and latitude. The southern and southeastern regions were identified as the warmest, while the high-altitude areas of the west, north, and northeast were the coolest. The results of the PCA showed that the first two components explained more than 78% of the spatial variance and 93% of the temporal variance, respectively. This indicates the existence of consistent and interpretable climatic patterns. Additionally, the Ward's clustering analysis, which divided the data into five distinct clusters, reflected the diversity of temporal behavior of the maximum temperature across the country.
 

This study was conducted to examine the relationship between specific climatic parameters, namely ambient temperature and air pollution, and behavioral abnormalities—including aggression and impulsive behaviors—among children diagnosed with Autism Spectrum Disorder (ASD). The research was carried out in Khorasan Razavi Province, Iran, and the statistical population comprised children with ASD aged 4 to 13 years who attended daily educational–rehabilitation centers dedicated to individuals with ASD. From this population, a total of 186 children were selected through convenience sampling. Data on behavioral variables were collected using three standardized instruments: the Shaheem Children’s Aggression Questionnaire (2006) to assess levels and dimensions of aggressive behavior; the Hirschfield Impulsivity Scale (1965) to measure impulsivity traits; and the Gilliam Autism Rating Scale (1994) for confirming ASD diagnosis and severity. Simultaneously, climatic and air quality information—covering daily ambient temperature and concentrations of key air pollutants—was obtained from reliable local meteorological and environmental monitoring stations. The results demonstrated statistically significant relationships between both temperature and air pollution levels with the severity of aggression and impulsive behaviors in children with ASD. Higher temperatures and increased air pollutant concentrations were associated with elevated behavioral abnormalities. These findings underscore the importance of incorporating environmental and climatic considerations into the clinical management, educational planning, and rehabilitation program design for children with autism. Furthermore, they highlight the need for interdisciplinary collaboration  to develop strategies aimed at mitigating the adverse behavioral impacts of climatic and environmental stressors on vulnerable pediatric populations.

The ENSO phenomenon is considered one of the most important interannual oscillations in the Earth–atmosphere system and plays a significant role in precipitation variability across different regions of the world. In this study, to identify the multiscale relationship between different ENSO phases and monthly precipitation variability in Iraq, the monthly Niño3.4 index, the Maximum Overlap Discrete Wavelet Transform (MODWT), and the Continuous Morlet Wavelet Transform were employed. First, using multiresolution decomposition of the monthly precipitation signal from 16 stations across Iraq (1990-2020) into six (6) frequency levels (from monthly to multi-year scales), it was revealed that the precipitation signals at all stations follow a relatively similar pattern, although with different oscillation amplitudes. The amplitude of precipitation fluctuations at monthly and seasonal scales was found to be stronger at northern and foothill stations (Kirkuk, Mosul, and Khanaqin) compared to other regions of Iraq, indicating a shorter transition between wet and dry months in northern Iraq. Furthermore, the overall trend of the A6 component at all stations exhibited a decreasing pattern during 1995–2010, with this downward trend being more pronounced in the central and southern regions than in the north. The results of correlation analysis and multiscale wavelet coherence demonstrated a positive and multiscale relationship between ENSO and monthly precipitation in Iraq. Surrogate significance testing indicated that this relationship is not significant at wavelet levels 1 to 3 but becomes significant at 1.5–3-year and 2.5–5.5-year scales (wavelet levels 4 and 5). Overall, precipitation in Iraq tends to increase during El Niño (warm ENSO phase) events and decrease during La Niña (cold ENSO phase) events.

studying and identifying the middle levels change affecting the formation of a circular pattern creation is inevitable. In this study, the annual rainfall data for selected stations Zab River Basin during the period 2015-1986 were the standard time. After indexing and spatial-temporal threshold, 184 days without rainfall were selected in the wet period of three severe drought in the region. Level 500 HP height data located in the range of 0 to 80 degrees northern latitude and eastern longitude on dry days as a matrix S_Mode were used and these data using principal component analysis were processed using modern statistical methods. Based on the correlation matrix, the main focus of the 500 HP topography of the basin dry days were identified and analyzed. The results show that, in the happening time of dry days, twelve atmospheric middle levels of height change focus have been effective. In this regard, the following two centers having the highest anomalies (R≥0.7) are detected according to the high levels of the atmosphere: 1) the center of Eurasia-Africa, 2) the center of West Africa, respectively, 48% and 10% of the total area of the study. Such changes in middle atmospheric levels cause to strengthen and deepen the traffic axis and on the ridges. In the meantime, the change of first center has the highest impact on creation of stability and domination of dryness in days of Zab River Basin.

The aim of this study is modeling spatiotemporal variations of albedo. This study was conducted using simultaneous effects of several components, such as wetness of surface layer of soil, cloudiness, topography and vegetation density (NDVI), using MEERA2 model with a resolution of 50 in 50 km during 2000-2010 in Iran. The results of spatial analysis of albedo values in Iran showed that the highest value is in 44 to 45 degrees of east longitude about 2.8 to 3.3 and the lowest value of albedo is also in 52 to 53 degrees of east longitude, that is, the eastern slopes of the Zagros Mountains, have been recorded at 1 to 1.5 units. In terms of provincial rank, the largest albedo is about 0.25 units in Ilam province and the Fars province is ranked next about 0.24 units. The lowest amount of albedo also in the Gilan provinces and in next Mazandaran province are about 0.19 and 0.18 respectively. In addition, the results of temporal analysis in seasonal scale showed that the highest albedo in Iran in winter was 0.26 and its lowest amount was recorded in spring with 0.23 units. In general, according to the factors used, it can be said that the western and central parts of the country have a highest albedo, and the north and northwest regions of the country have a lowest albedo.
 

Air pollution is one of the most important natural hazards in cities that is one of the priorities of climate research.  In this research, synoptic situation of days polluted by ozone in Tehran have been studied and by environmental to circulation approach and cluster analysis. At first, was formed a matrix in 2417*41. Rows are indicated days and columns represent the number of stations. by cluster analysis and Ward method eight different clusters were identified. The results showed that the frequency of the ozone days have a seasonal trend and more can be seen in the first half of year in these cases the establishment subtropical high pressure in Iran. Therefore, cause the persistency of pollution in Tehran.