مجله مهندسی و مدیریت آبخیز
سال هفدهم شماره 3 (پاییز 1404)

One of the most significant impacts of climate change has been the alteration in the average value of meteorological variables, which has led to noticeable changes in the characteristics of meteorological extremes. The warming and ongoing increase in global temperatures primarily exert profound impacts worldwide on human societies, ecosystems, and the environment through the increased occurrence of climatic extremes. Compared to climatic averages, extreme climatic events cause more significant changes in the natural and human environment, and due to their disastrous environmental and socio-economic consequences, they are of great concern to the general public, governments, and academic communities. Based on studies conducted by universities and other research institutions, the most significant consequence of climate change in Iran is the increased occurrence of extreme climatic events, particularly extreme warm temperature events. The increase in extreme events affects various sectors of the environment, socio-economy, agriculture, and others at national, regional, and local levels. The decrease in the number of rainy days and the lengthening of the dry season, along with the increase in extreme warm temperature events and the decrease in extreme cold temperature events, lead to reduced water availability for irrigation and increased crop water demand, this also promotes the spread of pests and diseases in agricultural crops, causes delays in planting and harvesting, results in poor plant growth, reduces cultivation efficiency, alters cropping patterns and crop types, and ultimately leads to a decline in agricultural yields. Therefore, investigating extreme climate events and analyzing their spatio-temporal changes in the past and future of Iran is of great importance for making necessary decisions to confront and mitigate their consequences, which is the primary focus of this research.

To investigate the spatio-temporal change of extreme temperature events in Iran during the observational period as well as the future periods, daily maximum and minimum temperature data from 123 synoptic stations across the country were used, along with data from the CNRM-CM6-1 model for the period 2020–2060 under two scenarios: the optimistic SSP126 and the pessimistic SSP585. The quantitatively and qualitatively control of the receive data was carried out using the Climpact2 software. To calculate cold and warm extreme temperature indices over different periods, 14 temperature indices recommended by the CCL/CLIVAR expert group were used. The calculation of the indices and their final outputs were performed in the MATLAB software.

Overall, the calculation of warm extreme temperature indices during the observational period showed that for most stations, the frequency and trend of warm indices—such as warm nights, warm days, the number of summer days, and the number of tropical nights—have exhibited an upward and increasing trend. In contrast, the temporal changes in cold indices showed that for most stations, the trends of cold indicators such as cold days, cold nights, and the number of frost days have generally been decreasing and declining. An analysis of the frequency of the SU25 index as one of the most common extreme warm indices revealed that the highest occurrence of this index was recorded at Konarak (Airport) station with 363 days, while the lowest value occurred at Ardabil station with 21 days. The maximum value of the frost days (FD) index—one of the most common extreme cold indices in the country—was recorded at Sarab station with 179 days, while the lower limit, indicating the absence of frost, was observed at some stations in the southwestern part of the country. An analysis of future temperature indices for the period 2020–2060 indicates that the behavior of extreme cold and warm temperature indices does not depend on the geographical location and topographical conditions of different regions of the country. This is because, according to the outputs of the CNRM-CM6-1 model for the two scenarios, SSP126 and SSP585 during the period 2020–2060, the trend of extreme warm indices in regions with higher-elevation and higher-latitude regions did not differ significantly compared to lower-elevation and lower-latitude. The trend behavior across areas with different geographical characteristics is quite similar, with only minor variations.

In summary, based on the findings of this study and similar research, it can be concluded that warm temperature extremes in Iran have increased in the past and are projected to continue increasing in the future, while cold extreme have decreased, and the rate of these changes varies considerably across different regions of the country. This condition is particularly evident when comparing indices of the lowest minimum and maximum temperatures and the highest minimum and maximum temperatures. This situation indicates a warming climate in Iran, a shift in the climate types of various regions, and a trend toward increased aridity. One of the main reasons for this situation, in addition to local characteristics, is the change in the Earth’s energy balance and the higher rate of warming at the poles compared to the equator, this has altered the Earth's energy equilibrium, ultimately leading to changes in atmospheric conditions and shifts in climate patterns. Therefore, it is necessary for decision-makers at various management levels to adopt appropriate measures to confront or mitigate the potential consequences of rising temperatures and extreme warm events in different regions of Iran.

Simulating suspended sediment in hydrological systems has always been challenging due to inherent complexities and uncertainties. This issue has led to the use of intelligent models such as Artificial Neural Networks (ANNs) as a suitable approach for predicting suspended sediment load. Therefore, the use of  intelligent models like ANNs has expanded in this field. However, determining the optimal network structure (including the number of neurons, layers, weights, and biases) is usually done through trial and error, which is both time-consuming and inefficient. In this study, a multilayer perceptron neural network was used to simulate the daily suspended sediment load in the Qarasu Sarab watershed (Quri Chay and Hir Chai rivers) located in Ardabil province, Iran.

In this research, an Artificial Neural Network (ANN) of the Multilayer Perceptron (MLP) type was utilized to simulate the daily suspended sediment load in the Sarab Qareh Su watershed (including the Quri Chay and Hir Chay rivers) in Ardabil province. The neural network models were trained not only whit the conventional backpropagation algorithm but also using the Particle Swarm Optimization (PSO) algorithm to optimize the weights and biases of the neurons. Furthermore, to increase the models' generalization capability, a Self-Organizing Map (SOM) clustering was employed. In addition to the backpropagation algorithm, the Particle Swarm Optimization (PSO) algorithm was also employed to optimize the network weights and biases. Furthermore, to enhance the model's generalization power, SOM clustering was used. The use of evolutionary algorithms such as PSO in training neural networks is an effective approach to improve the accuracy of intelligent models, especially in simulating river suspended sediment and applications related to water resources and watershed management structures.

Using SOM clustering and the Davies-Bouldin index, the optimal number of clusters was determined as 12 for Koozeh Toupraqi station and 15 for Hir Chai station. Statistical analysis and the Kolmogorov-Smirnov (KS) test showed that data distributions across training, validation, and testing sets were similar, which enhances the generalization capability of the models. Training the neural network models with PSO yielded better performance and lower prediction errors compared to backpropagation. The ANN-PSO-Sig and ANN-PSO-Tan models achieved the best results at Koozeh Toupraqi and Hir Chai stations, respectively. Bias analysis further confirmed that PSO-trained models had lower errors in total sediment load estimation. Overall, results showed that PSO-based training outperforms pure backpropagation training. At Koozeh Toupraqi station, the hybrid ANN-PSO model with sigmoid activation function (ANN-PSO-Sig), and at Hir-Hirchai Topraghi station, the hybrid model with hyperbolic tangent activation function (ANN-PSO-Tan) were selected as optimal models, showing biases of +5.25% and -19.2% and RMSE values of 86.28 and 89.2 tons per day, respectively. These findings demonstrate the improvement in suspended sediment load prediction accuracy by using PSO in neural network training.

The use of the PSO metaheuristic algorithm in training neural network models improved their performance in simulating suspended sediment load. This method outperformed gradient-based error algorithms and provided more accurate weight optimization. The improved bias accuracy in PSO-trained models is crucial for designing hydraulic structures and water resource management. Furthermore, SOM clustering helped select homogeneous and representative datasets for model training, enhancing model generalizability. Overall, considering the complexities and uncertainties in hydrological systems, employing intelligent models combined with evolutionary optimization algorithms like PSO is an effective approach for simulating and monitoring suspended sediment loads. The obtained results can be applied in planning and implementing watershed engineering measures and water resource management.

In recent decades, the increasing trend of natural hazards and the destruction of natural resources under the influence of natural and human factors have become increasingly intense. One of the important methods in controlling and reducing surface runoff to mitigate flooding and peak discharge is the implementation of watershed management measures, including management and protection measures, biological and mechanical interventions. Although valuable watershed measures have been implemented in the country in recent years, unfortunately, the scientific documentation and studies regarding their performance, weaknesses, strengths and effectiveness are very little. By studying and evaluation the watershed management measures effects on the important factors of a watershed, such as discharge and water flow, we can take steps to strengthen the strengths and reduce the weaknesses. Therefore, the present study aims to evaluate the performance and effects of biological and mechanical watershed management measures on the peak discharge of treatment and control watersheds in Zidasht Taleghan, Alborz province.

The paired Zidasht Taleghan watershed, comprising a treatment watershed (104 hectares) and a control watershed (92 hectares), is located inside within a representative watershed with an area of ​​2750 hectares. In the treatment watershed, along with the protection of the entire area, banqueting along with seeding has been implemented on the area of about 12.5 hectares. In the mechanical measures section, 943 and 72.5 m2 of gabion structure and dry stone structure were implemented in the treatment watershed, respectively. The total reservoir created by the gabion and dry stone structures was 13,550 and 125 m2, respectively. In this research, to consider the importance of vegetation cover effect on runoff and peak discharge through curve number in modeling, study and field measurement of vegetation cover changes during the last 5 years (2018 to 2019) in two treatment and control watersheds are presented. In order to investigate the impact of watershed management measures on the peak flow of the control and treatment watersheds, meteorological and discharge data were used from the meteorological station and outlet flumes of the paired watersheds. In addition to comparing the observational data of the peak flow at the outlet of both control and treatment watersheds, rainfall-runoff modeling with the HEC-HMS model was used.

The evaluation of changes in vegetation under the effects of watershed measures showed that the percentage of vegetation and plant production is about 8% and 265 kg on average in the treatment watershed (on average, 51% vegetation and 777 kg/hectare production) more than the control watershed (on average, 43% vegetation percentage and 512 kg/hectare production). In the discussion of reducing the peak discharge of the treatment and control watersheds, the results of the evaluation of observational data showed that the peak discharge of the treatment watershed under the effects of watershed management measures was 65% lower than that of the control area (without the implementation of watershed management measures). The results of flood modeling showed that in the treatment watershed, the peak flow has decreased about 40% with the implementation of biological measures and 66% with the implementation of biological and mechanical measures compared to the control watershed. The flood volume has also decreased by 30% on average under the effects of biological and mechanical watershed management measures in the treatment watershed compared to the control watershed.

As a result of the implementation of biological and mechanical measures, in addition to reducing the peak flow, the time to peak flow in the treatment area has also increased (discussion of increasing concentration time). And the peak flow occurred with a longer time delay than the hydrograph of the flood in the case of no watershed management measures in the control watershed. As a result, it can be reported that under the influence of watershed management measures, in addition to reducing flood damage by reducing the peak discharge, with a delay in the time of runoff and flood, more water is infiltrated and stored in the area. The highest amount of peak discharge in these areas occurred in the state without implementation of watershed measures (without structures). Also, considering that the base flow in the treatment watershed was higher than the control watershed in most of the year (on the contrary to the peak flood discharge), the richness of the vegetation cover in the treatment watershed can be considered an important factor in the higher base water flow.

Studies conducted in the field of climate change in the world indicate that even a small change in temperature causes a change in the occurrence of extreme phenomena such as drought, heavy rainfall, and storms. Severe changes in the behavior of atmospheric indicators, especially during the 21st century, indicate signs of climate change occurrence. Therefore, climate change and climate warming can directly affect the extreme values ​​of climate and the temporal and spatial changes of these events; thus, this study analyzes the trend of occurrence of extreme climate events shortly in Fars Province. 

In this study, in order to analyze the trend of extreme climate events shortly in Fars Province, the ACCESS-ESM1-5 model related to the IPCC Sixth Assessment Report and the latest series of climate scenario releases (SSP) was used after exponential downscaling using the LARS-WG statistical model at the station level. Daily precipitation, maximum and minimum temperature data from three synoptic stations of Abadeh, Shiraz, and Lar in Fars Province were used. After exponential downscaling of the ACCESS-ESM1-5 model, precipitation, and minimum and maximum temperatures were predicted for the near future period (2026-2055). Then, using the RClimdex package in R software, fourteen extreme climate indices (7 temperature indices and 7 precipitation indices) were extracted for the base period and the near future. After calculating 14 climate extreme indices for the 30 years] of the near future period (2026-2055) and the base period (1991-2020) on an annual basis, the trend for each index was determined using the Mann-Kendall test and the slope of the age line. Then, the climate extreme indices were placed in two groups of the near future period and the base period, and the type of data distribution was determined. In order to reveal the existence of a difference in the means of the two groups (the first group: values ​​of the extreme indices of the base period, the second group: values ​​of the extreme indices of the near future period), an independent t-test was used. 

The results of the exponential downscaling of the ACCESS-ESM1-5 model indicate an increase in the minimum and maximum temperatures of the near future period (2026-2055) compared to the base period (1991-2020) in all three stations and all three scenarios. The average precipitation values ​​are also predicted to decrease at the Shiraz station and to increase at the Abadeh and Lar stations. The results of determining the trend in climate indices showed that the trend in temperature extreme indices is more noticeable than precipitation extreme indices. The number of frost days (FD) has a significant downward trend and the number of summer days (SU25), the monthly maximum daily maximum temperature (TXx) and the monthly maximum daily minimum temperature (TNx) have an upward trend in all three stations and three scenarios compared to the base period and a significant difference at the 95% confidence level. The maximum one-day precipitation index (RX1day) is also significant and has an upward trend in all three stations and three scenarios, with an average increase of 16.59 mm in the maximum one-day precipitation in the province compared to the base period. The daily precipitation intensity index (SDII) is also a significant upward trend in all three stations and three scenarios, with an average increase of 3.83 mm/day for the province in the near future compared to the base period. 

Severe climate changes and global warming in recent years have led to changes in weather patterns and the emergence of climate anomalies in most parts of the world. The present study shows a significant increase in extreme climate events in Fars Province. The trend in extreme temperature indices is more noticeable than extreme precipitation indices. An increasing trend in extreme hot indices and a decreasing trend in extreme cold indices will occur in the near future (2026-2055) in Fars Province; therefore, it is necessary to adopt and implement preventive decisions and plans at different management levels to deal with the possible consequences of increasing temperatures and extreme hot events. Based on the results obtained from examining the frequency of extreme precipitation events, it also shows an increase in the daily precipitation intensity index, an increase in the maximum one-day precipitation in three stations of Abadeh, Shiraz, and Lar, and an increase in the number of days with heavy and very heavy precipitation in two stations of Abadeh and Lar. These conditions could indicate an increase in intense and short-term rainfall, as well as a shortening of the region's rainy season. Consequently, given the damaging consequences of extreme rainfall events such as drought and flood, more attention should be paid to monitoring and observing these weather disasters in order to minimize the damage caused by them, so that active and intelligent management can be applied to move more quickly towards risk management and risk reduction.

Tillage is the most significant agricultural practice worldwide, playing a crucial role in soil quality, the environment, and crop yield. In the majority of Iran's rainfed farmlands, tillage is performed using moldboard plows and along the slope gradient. Due to its role in organic carbon depletion, soil erosion, and reduced crop yield, this method is considered the most detrimental form of tillage. Based on numerous studies worldwide, conventional tillage using a moldboard plow severely damages key soil properties, reduces moisture retention, increases costs, leads to nutrient loss, decreases crop yield, and causes numerous off-site consequences such as sedimentation, flooding, water pollution, and organic carbon depletion, these impacts are exacerbated under climate change, making it a distinct and critical issue in soil erosion, specifically termed tillage erosion. The aim of the study was to determine the effects of tillage methods on bulk density, aggregate stability, and soil erodibility, conducted at the Sararoud Dryland Agricultural Research sub-institute over a five-year period (2017–2022). 

This research was conducted at the Sararood research station (Dryland Agricultural Research sub-institute). The station is located 15 km east of Kermanshah city and 3 km from the main Kermanshah-Tehran road. This research was conducted as a split-split plot experiment based on a randomized complete block design with three replications. The experiment consisted of four main treatments (conventional tillage, combined tillage, chisel tillage, and no-tillage) and three sub-treatments (no plant residue, 33% plant residue, and 66% plant residue). To evaluate the long-term effects of the treatments, the locations of the main and sub-treatments remained fixed throughout the study period, with only the crop rotation being altered. Soil samples were collected from the 0-20 cm depth layer and analyzed for bulk density, organic carbon, and aggregate stability (using the wet sieving method). The erosion factor was calculated by determining the erodibility factor (K) in the Universal Soil Loss Equation, based on five parameters: organic matter content, percentage of silt + very fine sand (0.05–1 mm), percentage of coarse sand (1–2 mm), soil structure, and permeability. Statistical analysis of the data was performed using SAS software. 

The mean bulk density in the main treatments, including control (conventional tillage), combined tillage, chisel tillage, and no-tillage, was 1.62, 1.45, 1.40, and 1.37 g/cm³, respectively, with a significant decrease (p < 0.05) observed in the no-tillage treatment. The sub-treatments (crop residue levels) also had a significant effect on bulk density, with both 33% and 66% straw (crop residue) rates significantly reducing it. The effect of treatments on bulk density became significant from the third year onward. The statistical analysis also revealed the effects of tillage methods and crop residue levels on soil organic carbon. In the main treatments—control (conventional tillage), combined tillage, chisel tillage, and no-tillage—soil organic carbon content was 1.20%, 1.50%, 1.40%, and 1.70%, respectively, these values showed significant differences (p < 0.05), with no-tillage having the greatest effect on increasing soil organic carbon. The sub-treatments (crop residue levels) also had a significant effect on soil organic carbon, with both 33% and 66% straw (crop residue) rates significantly increasing it. The proportion of very fine aggregates was significantly lower in the no-tillage treatment with the highest crop residue level. The amount of medium-sized aggregates did not differ significantly among the treatments. The proportion of very large aggregates was significantly lower in conventional tillage and the sub-treatment without crop residue. The size of soil aggregates became significant in the third year and predominantly in the fourth year. In other words, an increase in large and very large aggregates and a corresponding decrease in fine and very fine aggregates were observed from the third year onward. The results indicate the effective role of various conservation tillage methods in reducing soil erodibility compared to conventional tillage. The results of this study showed that the three conservation tillage methods played an effective role in improving the evaluated soil properties. Certainly, the role of no-tillage (without plowing) was more prominent in this process. As indicated in the results, over time following the implementation of the study, organic carbon and the proportion of large (1–2 mm) and very large (2–4.6 mm) aggregates gradually increased in the conservation tillage treatments. 

Conservation tillage combined with retaining crop residues significantly improved the most important measured soil quality properties, including organic carbon, bulk density, and the proportion of large soil aggregates. No-tillage (direct seeding) with one-third crop residue (33%) is recommended as the most suitable tillage method under rainfed conditions and wheat-chickpea rotation, which is common in most semi-arid regions of the country, and is proposed as the optimal treatment in this study.

Drought, as an abnormal and dangerous phenomenon, seriously damages water resources, agriculture, economic sectors, and the environment. Within the framework of comprehensive watershed management, accurate and timely drought prediction is very important. This is a necessity, especially in sensitive and vulnerable areas such as Khuzestan Province, for the use of water resources, management of consumption and increasing the resilience of natural and human ecosystems. If a drought period coincides with a vegetation growth period, it disrupts the ecological balance, leading to changes such as reduced soil moisture, changes in ground surface temperature, and even impacts on evaporation and transpiration processes. AI metaheuristic algorithms are able to predict water demand by examining historical data and environmental factors. These predictions allow managers to make better plans for water supply and prevent waste of resources. Considering the uniqueness of Khuzestan Province in terms of its geographical location and water conflicts in recent years, examining the power and efficiency of artificial intelligence algorithms in predicting and identifying climate change can fill the research gap in this field and, through scientific innovation, have a significant impact on environmental protection and the balance of water resources in the face of drought conditions.

In this research, in order to monitor the drought areas of the stations located in the Khuzestan Province, the Precipitation data during the statistical period (1989-2020), and using the values Standardized Precipitation Index (SPI) were calculated to separate dry and wet years. In the following, the inverse distance weighted (IDW) method was used to interpolate the data obtained from SPI. The FCMR model was used to predict meteorological drought. The FCMR fuzzy regression model is a hybrid method that uses linear regression and fuzzy clustering to model data. The GOW and ACOR algorithms were used to build the hybrid model.

According to the results obtained from the goodness of fit assessment criteria at eight study stations, the 12-month and 6-month SPIs showed relatively better and more accurate results than the 3-month and 1-month SPIs. In the comparison of the 12-month and 6-month SPIs, the 12-month SPI also showed better performance. The RMSE, R, NS and MAE values ​​decreased, increased, and decreased after combining the GOW catalyst and the FCMR model compared to the individual FCMR model, respectively. The combination of the ACOR catalyst and the FCMR model also increased, decreased, decreased and increased in the RMSE, R, NS, and MAE values ​​compared to the individual FCMR model, respectively. Accordingly, it can be concluded that combining the gray wolf with the FCMR model has improved performance compared to using the individual FCMR model. Combining the ant colony catalyst with the FCMR model can also be used with reduced accuracy and lower performance compared to using the individual FCM model.

In the present study, the accuracy and performance of the individual FCMR model were compared and analyzed with the dual hybrid FCMR-GOW and FCMR-ACOR models at eight synoptic stations in Khuzestan province. According to Tables 2 to 4, the GOW catalyst improved the FCMR model and the ACOR catalyst reduced the accuracy of the FCMR model. At all eight stations, the dual hybrid FCMR-GOW model ranked first with the highest accuracy in predicting SPI. Also, the long-term SPI time windows had higher accuracy than the short-term time windows. Furthermore, there is no significant gap in terms of accuracy and precision between the individual FCMR model and the dual hybrid FCMR-GOW model. Therefore, it can be concluded that considering the increasing costs of the aforementioned dual hybrid models, using the individual FCMR model seems more logical. In general, it can be said that combining individual models with meta-heuristic algorithms does not necessarily mean increasing the accuracy of SPI index modeling.

Climate change and its consequences are among the most important concerns for agriculture and the environmental organizations worldwide. Water scarcity and decline soil moisture are the most significant consequences of this trend in arid and semiarid region. One of the consequences of climate change is the phenomenon of oak forest dieback in western Iran (Quercus persica). This issue can disrupt the balance of land resources in the Zagros region, leading to consequences such as degradation of land resources, increased dust storm, soil erosion, flooding, climate change, and impoverishment of local communities.

One of the fundamental solutions in this regard is to maintain soil moisture on slopes using rainwater harvesting systems, such as interrupted and crescent-shaped bunds. The key objective of this research is to determine the role of crescent-shaped bund systems in collecting surface runoff to control the phenomenon of oak forest dieback in western Iran. To achieve this goal, the Merek forest watershed in Kermanshah province where dieback of forest species has been observed, was selected as the study site. Subsequently, rainwater harvesting systems, including crescent-shaped bunds, were constructed in this forest area. Additionally, a nearby site with similar conditions was designated as the control site for comparison. After the construction of the bunds by the Department of Natural Resources of the province, data collection from the forest site was carried out over a three-year period (2022–2024), this included monthly monitoring of changes in soil aggregate size, organic matter, organic carbon, and soil pH, along with measurements of soil moisture percentage.

The results of this study explored that among the four soil moisture measurement points, including the bottom of bund, bund downwards, between the bund and the control (without bund), soil moisture storage in sub layer (15 to 30 cm) were about 20, 17, 14 and 13 percent, respectively, which was significantly higher in the bund bottom compeered with control plot. The results of this study also showed that in the third year, the soil organic carbon storage in the bund treatment and the control treatment was 4.01% and 1.6%, respectively, indicating a statistically significant difference between them. In addition, the bunds significant increase the proportion of large and very large soil aggregates. The combination of these conditions led to improvements in cation exchange capacity, essential nutrient elements, and some micronutrients within the area where the bunds were implemented. Finally, the increase in soil organic matter and the accumulation of runoff in the bunds reduced soil erodibility and stabilized it against erosive factors. As a result, the erodibility index in the bund treatment and the control treatment was approximately 0.19 and 0.32, respectively.

According to the results of this research, the proper and precise construction of these bunds without the use of heavy machinery is a simple, economic and effective measure for rainwater harvesting, In addition to soil moisture storage and enhancement of plant biological indicators, it improves key soil characteristics mainly soil organic matter, coarse soil aggregates proportion and soil nutrients, which ultimately leads to soil sustainable, carbon sequestration, curtailing flooding hazards on the hill-slope, and strengthening subsurface flow.

Clouds play a vital role in Earth’s energy balance, and changes in their properties, frequency, and distribution can have either positive or negative feedback effects on global warming. Cloud formation and its impact on the Earth’s climate remain key topics in environmental sciences. By influencing the global energy budget, clouds alter convection patterns and cloud distribution, significantly affecting the global water cycle. Total cloudiness refers to the extent of coverage by any type of cloud in the sky, and this concept is used to describe the proportion of the sky covered by clouds. Cloud cover refers to the fraction of the sky covered by any type of cloud, typically expressed in oktas, ranging from 0 to 9. The aim of this study is to conduct a spatial and temporal analysis of significant changes in TCC on a seasonal scale over six decades across Iran. This analysis is based on statistical and observational evaluations during the selected study period. Due to Iran’s climatic characteristics, summer season—which is generally associated with atmospheric stability, low precipitation, and minimal cloud cover—was excluded from the scope of the study, this allows the research to focus on the seasons that experience the most atmospheric activity and variations in cloud cover. 

The monthly total cloud cover data used in this study were extracted from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis dataset, with a spatial resolution of 0.25°. The study period spans 60 years (1961–2021). By averaging the monthly data (three months per season), a seasonal time series of total cloud cover was generated over a 60-year period. For each season the distribution of total cloud cover over the six decades of the study was compared across different grid points. The significance of differences in cloud cover for each decade compared to other decades at each grid point was determined using the Mann–Whitney U test, with randomness mitigated via Monte Carlo randomization technique with 10,000 iterations, at a 95% significance level. Statistically significant differences were expressed as percentages. 

Analysis of the 60-year climatological mean of TCC over Iran reveals a decreasing gradient in total cloud cover variability from the northwest to the southeast during spring, autumn, and winter. Comparison of the seasonal distribution of total cloud cover in Iran revealed that spring cloud cover variability has significantly increased in the southern and southwestern regions of the country, while a notable decrease has been observed in the northeast, east, and southeast. In autumn, significant changes are limited to small areas in the northern part of the country, while most regions show no notable variations. However, in winter a widespread significant declines in TCC, particularly across the Zagros Mountains and southwestern Iran. This decline was most pronounced during the sixth decade (2011–2021), affecting regions that historically experienced moderate to high cloud cover. 

Analysis of significant changes in total cloud cover revealed that during spring, significant decreasing and increasing cycles are observable across different decades, with these variations being most pronounced in the arid and semi-arid regions of the east and southeast of the country. Therefore, in future research, investigating potential changes in the formation and arrival of precipitation systems affecting the region could be considered. In autumn, significant change in cloud cover were rarely observed, highlighting the need to investigate monthly cloud cover variations to detect potential fluctuations in autumn cloudiness. For winter, the widespread and significant reduction in cloud cover, particularly in the sixth decade compared to the previous five decades, is of great importance. Notably, when comparing the fifth and sixth decades, the entire country experienced a significant decrease in cloud cover. These findings is significant for understanding energy balance and potential changes in winter precipitation, and could be a focus of future research. The periodic cloud cover variations observed in this study could have implications for water resource management, rainfed agriculture, and evapotranspiration, necessitating further in-depth research.