https://www.irimo.ir/ Journal of Climate Research 2228-5040 1405 17 2026 05 22 Determining the start, end, and duration of climatic seasons at selected stations in Iran based on the temperature regime. Determining the start, end, and duration of climatic seasons at selected stations in Iran based on the temperature regime. 1 20 244300 10.22034/jcr.2026.528184.1702 FA Faramarz Khoshakhlagh physical Geography, Faculty of Geography, University of Tehran 0000-0003-4602-7084 Ghassem Azizi Department of Physical Geography, Faculty of Geography, University of Tehran, Tehran, Iran 0000-0002-9797-4834 Mostafa Karimi Department of Physical Geography, Faculty of Geography, University of Tehran, Tehran, Iran 0000-0001-7820-6728 Alireza Mohebalhojeha Institute of Geophysics, University of Tehran, Tehran, Iran 0000-0002-5906-8486 Hosien Koshky Department of Physical Geography, Faculty of Geography, University of Tehran, Tehran, Iran 0009-0005-2497-1500 Journal Article 2025 06 03 Introduction A climatic season is a part of the year that is distinguished from other parts due to the regular recurrence of astronomical or climatic phenomena. Seasons are generally defined using two main methods: a) the astronomical or calendar-based method, which is based on the apparent movement of the sun. According to this method, spring and autumn begin at the equinoxes (March 21 and September 21), summer at the summer solstice (June 22), and winter at the winter solstice (December 22). b) The climatic method, which defines seasons based on atmospheric conditions and temperature patterns in each region, is particularly applicable in subtropical areas such as Iran. This approach uses temperature values and their trends to determine the onset and end of the seasons. Materials and Methods In this study, temperature data from 35 synoptic stations across Iran from 1992 to 2022 were used. The identification of seasons was based on daily temperatures and the slope of their changes relative to the long-term averages. By reviewing the most current criteria for distinguishing the climatic seasons starting and ending, a new approach was designed for determining the season as follows: 1. Start of spring: daily temperature rises above the long-term minimum and average but remains below the maximum. 2. Start of summer: daily temperature rises above all long-term averages (minimum, average and maximum). 3. Start of autumn: daily temperature falls below the long-term maximum but remains above the minimum and average. 4. Start of winter: The daily temperature fell below all long-term averages and exhibited a downward trend. Results and Discussion In the south, southwest, southeast, and central regions of Iran, spring begins in mid-February, whereas in the north, northwest, Alborz foothills, and west, it starts from early to mid-April. The shortest springs (54–64 days) were observed in areas with rapid warming or persistent high-pressure systems. Spring in the south starts with temperatures between 14 and 21°C, and in the northwest and Alborz, it begins with temperatures between 4 and 10°C. Summer in the south, southeast, and central regions starts from early to late May, whereas in the northwest and west, it begins from early June to late July. The shortest summers (60–75 days) are observed in the mountainous areas of the west and northwest, and the longest summers (120–196 days) are observed in the south and east of the country. The starting temperature of summer ranged from 19 to 31°C. The warmest starts were recorded in the south and southwest, and the coolest in the northwest and east of the country (19–22°C). Autumn started earlier in the northwest, west, northeast, and mountains (mid to late September) and later in the south and southwest (until mid-November). The length of autumn in most regions is 74–97 d. The shortest autumns (45–62 days) were observed in Khuzestan, Hormozgan, and Mazandaran owing to the influence of the sea. Autumn in the south begins with temperatures between 25 and 31°C, and in the north and east, it begins with temperatures between 6 and 22°C. Winter in the south, southeast, and southwest starts later (January to mid-February), whereas in the northwest, west, Alborz, and northeast, it starts earlier (late November to mid-December). The shortest winters (35–45 days) are found in the southern and central regions, influenced by the subtropical high-pressure ridge, and the longest winters (84–121 days) occur in the north and west of the country. Winter in the northwest and northeast begins with lower temperatures (3–5°C), whereas in the southwest and south, it begins with higher temperatures (18–21°C). Conclusion Spring in Iran begins in mid-February with temperatures ranging from 14 to 21°C in the southern and southwestern regions and eventually starts in mid-April with temperatures between 4 and 10°C in the northwestern and mountainous regions. Summer begins in early May in the southern and southwestern parts of the country with temperatures between 27 and 31°C, and then from mid-July, it starts in the northwest and western mountainous areas of Iran with temperatures ranging from 19 to 22°C. Autumn begins in mid-September in the northwestern, western, northeastern, and southwestern highlands of the country, with an average temperature of 16°C, and in the southern and southwestern regions, it begins in mid-November with temperatures between 25 and 31°C. Winter starts in late November to mid-December in the northwestern, western, Alborz foothills, and northeastern regions with temperatures ranging from 3 to 5°C, and from early January to mid-February, it starts in the southern, southwestern, and southeastern regions with temperatures between 18 and 21°C. Overall, in most parts of Iran, spring and summer begin earlier, autumn starts later, and the beginning of winter remains unchanged. Additionally, the duration of the transitional seasons has decreased and the length of summer has increased. Introduction A climatic season is a part of the year that is distinguished from other parts due to the regular recurrence of astronomical or climatic phenomena. Seasons are generally defined using two main methods: a) the astronomical or calendar-based method, which is based on the apparent movement of the sun. According to this method, spring and autumn begin at the equinoxes (March 21 and September 21), summer at the summer solstice (June 22), and winter at the winter solstice (December 22). b) The climatic method, which defines seasons based on atmospheric conditions and temperature patterns in each region, is particularly applicable in subtropical areas such as Iran. This approach uses temperature values and their trends to determine the onset and end of the seasons. Materials and Methods In this study, temperature data from 35 synoptic stations across Iran from 1992 to 2022 were used. The identification of seasons was based on daily temperatures and the slope of their changes relative to the long-term averages. By reviewing the most current criteria for distinguishing the climatic seasons starting and ending, a new approach was designed for determining the season as follows: 1. Start of spring: daily temperature rises above the long-term minimum and average but remains below the maximum. 2. Start of summer: daily temperature rises above all long-term averages (minimum, average and maximum). 3. Start of autumn: daily temperature falls below the long-term maximum but remains above the minimum and average. 4. Start of winter: The daily temperature fell below all long-term averages and exhibited a downward trend. Results and Discussion In the south, southwest, southeast, and central regions of Iran, spring begins in mid-February, whereas in the north, northwest, Alborz foothills, and west, it starts from early to mid-April. The shortest springs (54–64 days) were observed in areas with rapid warming or persistent high-pressure systems. Spring in the south starts with temperatures between 14 and 21°C, and in the northwest and Alborz, it begins with temperatures between 4 and 10°C. Summer in the south, southeast, and central regions starts from early to late May, whereas in the northwest and west, it begins from early June to late July. The shortest summers (60–75 days) are observed in the mountainous areas of the west and northwest, and the longest summers (120–196 days) are observed in the south and east of the country. The starting temperature of summer ranged from 19 to 31°C. The warmest starts were recorded in the south and southwest, and the coolest in the northwest and east of the country (19–22°C). Autumn started earlier in the northwest, west, northeast, and mountains (mid to late September) and later in the south and southwest (until mid-November). The length of autumn in most regions is 74–97 d. The shortest autumns (45–62 days) were observed in Khuzestan, Hormozgan, and Mazandaran owing to the influence of the sea. Autumn in the south begins with temperatures between 25 and 31°C, and in the north and east, it begins with temperatures between 6 and 22°C. Winter in the south, southeast, and southwest starts later (January to mid-February), whereas in the northwest, west, Alborz, and northeast, it starts earlier (late November to mid-December). The shortest winters (35–45 days) are found in the southern and central regions, influenced by the subtropical high-pressure ridge, and the longest winters (84–121 days) occur in the north and west of the country. Winter in the northwest and northeast begins with lower temperatures (3–5°C), whereas in the southwest and south, it begins with higher temperatures (18–21°C). Conclusion Spring in Iran begins in mid-February with temperatures ranging from 14 to 21°C in the southern and southwestern regions and eventually starts in mid-April with temperatures between 4 and 10°C in the northwestern and mountainous regions. Summer begins in early May in the southern and southwestern parts of the country with temperatures between 27 and 31°C, and then from mid-July, it starts in the northwest and western mountainous areas of Iran with temperatures ranging from 19 to 22°C. Autumn begins in mid-September in the northwestern, western, northeastern, and southwestern highlands of the country, with an average temperature of 16°C, and in the southern and southwestern regions, it begins in mid-November with temperatures between 25 and 31°C. Winter starts in late November to mid-December in the northwestern, western, Alborz foothills, and northeastern regions with temperatures ranging from 3 to 5°C, and from early January to mid-February, it starts in the southern, southwestern, and southeastern regions with temperatures between 18 and 21°C. Overall, in most parts of Iran, spring and summer begin earlier, autumn starts later, and the beginning of winter remains unchanged. Additionally, the duration of the transitional seasons has decreased and the length of summer has increased.

https://clima.irimo.ir/article_244300_e17da9c71d0ff2ba71efa43ec33eab18.pdf

https://www.irimo.ir/ Journal of Climate Research 2228-5040 1405 17 2026 05 22 Analysis of Caspian Sea Coastal Precipitation Using Metaheuristic Algorithms Analysis of Caspian Sea Coastal Precipitation Using Metaheuristic Algorithms 21 31 217212 10.22.34/jcr.2025.501246.1681 FA Hamidreza Babaali Associate Professor, Department of Civil Engineering, Islamic Azad University, Khorramabad branch, Khorramabad, Iran Ebrahim Nohani Assistant Professor, Department of Civil Engineering, Materials and Energy Research Center, Dezful Branch, Islamic Azad University, Dezful, Iran. Reza Dehghani PhD in Water Sciences and Engineering, Department of Soil Conservation and Watershed Management, Lorestan Province Agriculture and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization, Khorramab Journal Article 2025 01 21 Abstract Introduction: Precipitation, as an essential process in the hydrological cycle, plays a vital role in maintaining the balance between fresh and saline water resources globally. It is one of the most important components in hydrology and climatology, directly influencing the hydrological cycle. Accurate precipitation forecasting is essential for properly estimating the cost of water, planning, managing, maintaining, and storing water under adverse weather conditions and droughts, as well as designing flood warning systems. In recent years, researchers have employed models based on artificial intelligence approaches due to the nonlinear and complex nature of hydrological issues. These models are inspired by the characteristics of living organisms and are capable of solving problems that are highly complex and extensive.According to conducted studies, the Support Vector Regression (SVR) model is an efficient tool for estimating precipitation and hydrological issues. Today, to enhance the efficiency and performance of the Support Vector Regression model, combining this model with metaheuristic algorithms is considered a suitable solution for precipitation forecasting. In this research, hybrid models of Support Vector Regression-Particle Swarm Optimization and Support Vector Regression-Whale Optimization were used to estimate precipitation in the coastal areas of the Caspian Sea. Methodology: The temperate and humid climate along the southern shores of the Caspian Sea, which is situated as a strip between the Alborz mountain range and the Caspian Sea, is predominantly comprised of low-lying plains. Generally, this area constitutes the smallest climatic zone in Iran and is divided into two regions: the lowland and mountainous areas. The Caspian Sea is located between the longitudes of 38 degrees 46 minutes west and 34 degrees 54 minutes east and the latitudes of 34 degrees 36 minutes south and 33 degrees 47 minutes north, in northern Iran. The Babolsar synoptic station, located on the shores of the Caspian Sea, is one of the most important meteorological stations in northern Iran. This area, designated as a special coastal region, affects economic investment, aquaculture production, significant commercial ports, shipping, fishing, and its unique geographical location impacts tourism, provincial land use planning, and even national considerations. Therefore, analyzing and examining daily precipitation is essential and necessary. On the other hand, although Support Vector Regression (SVR) models are widely used for estimating precipitation, research comparing Particle Swarm Optimization and Whale Optimization algorithms in this coastal region has not yet been conducted. Consequently, this study employed optimization algorithms in conjunction with the Support Vector Regression model to estimate precipitation in the coastal areas of the Caspian Sea. Results and Discussion: In this study, Support Vector Regression (SVR), combined with wavelet algorithms, Particle Swarm Optimization, and Whale Optimization, was used to model daily precipitation in the coastal areas of the Caspian Sea. The input parameters included relative humidity (RH), maximum temperature (T.max), minimum temperature (T.min), wind speed (WV), and sunshine hours (SSH), while the output parameter was precipitation §. This analysis was conducted over a daily time period from 2013 to 2024 for the Babolsar synoptic station. The results indicated that hybrid models in the combined scenario, which included all the input parameters, had lower error rates compared to other scenarios. Therefore, increasing the number of effective parameters in hybrid models based on Support Vector Regression led to improved model performance. Additionally, all models that utilized the radial basis kernel function exhibited better accuracy. Furthermore, the SVR-wavelet model demonstrated superior performance compared to the other models examined. Conclusions: Estimation of precipitation using hybrid models based on Support Vector Regression (SVR) serves as an efficient tool in the design of climatological and meteorological systems. In the present study, a case study was conducted to evaluate the performance of the hybrid optimization model of SVR for estimating precipitation in the coastal areas of the Caspian Sea, specifically at the Babolsar synoptic station located in Mazandaran Province. The results of the research, based on the evaluation of scenarios consisting of input parameters, demonstrated that in all examined models, increasing the number of effective parameters leads to better performance in precipitation estimation. Furthermore, the outcomes from the evaluation criteria revealed that the SVR-wavelet model exhibited high accuracy with minimal error. Additionally, according to the examined graphs, the SVR-wavelet model provided precipitation estimates close to their actual values, as evidenced by the Taylor diagram. In summary, the findings of this research indicate that the use of artificial intelligence models based on the Support Vector Regression approach can be beneficial for estimating precipitation in other regions of the country and could serve as a step towards making appropriate management decisions. Abstract Introduction: Precipitation, as an essential process in the hydrological cycle, plays a vital role in maintaining the balance between fresh and saline water resources globally. It is one of the most important components in hydrology and climatology, directly influencing the hydrological cycle. Accurate precipitation forecasting is essential for properly estimating the cost of water, planning, managing, maintaining, and storing water under adverse weather conditions and droughts, as well as designing flood warning systems. In recent years, researchers have employed models based on artificial intelligence approaches due to the nonlinear and complex nature of hydrological issues. These models are inspired by the characteristics of living organisms and are capable of solving problems that are highly complex and extensive.According to conducted studies, the Support Vector Regression (SVR) model is an efficient tool for estimating precipitation and hydrological issues. Today, to enhance the efficiency and performance of the Support Vector Regression model, combining this model with metaheuristic algorithms is considered a suitable solution for precipitation forecasting. In this research, hybrid models of Support Vector Regression-Particle Swarm Optimization and Support Vector Regression-Whale Optimization were used to estimate precipitation in the coastal areas of the Caspian Sea. Methodology: The temperate and humid climate along the southern shores of the Caspian Sea, which is situated as a strip between the Alborz mountain range and the Caspian Sea, is predominantly comprised of low-lying plains. Generally, this area constitutes the smallest climatic zone in Iran and is divided into two regions: the lowland and mountainous areas. The Caspian Sea is located between the longitudes of 38 degrees 46 minutes west and 34 degrees 54 minutes east and the latitudes of 34 degrees 36 minutes south and 33 degrees 47 minutes north, in northern Iran. The Babolsar synoptic station, located on the shores of the Caspian Sea, is one of the most important meteorological stations in northern Iran. This area, designated as a special coastal region, affects economic investment, aquaculture production, significant commercial ports, shipping, fishing, and its unique geographical location impacts tourism, provincial land use planning, and even national considerations. Therefore, analyzing and examining daily precipitation is essential and necessary. On the other hand, although Support Vector Regression (SVR) models are widely used for estimating precipitation, research comparing Particle Swarm Optimization and Whale Optimization algorithms in this coastal region has not yet been conducted. Consequently, this study employed optimization algorithms in conjunction with the Support Vector Regression model to estimate precipitation in the coastal areas of the Caspian Sea. Results and Discussion: In this study, Support Vector Regression (SVR), combined with wavelet algorithms, Particle Swarm Optimization, and Whale Optimization, was used to model daily precipitation in the coastal areas of the Caspian Sea. The input parameters included relative humidity (RH), maximum temperature (T.max), minimum temperature (T.min), wind speed (WV), and sunshine hours (SSH), while the output parameter was precipitation §. This analysis was conducted over a daily time period from 2013 to 2024 for the Babolsar synoptic station. The results indicated that hybrid models in the combined scenario, which included all the input parameters, had lower error rates compared to other scenarios. Therefore, increasing the number of effective parameters in hybrid models based on Support Vector Regression led to improved model performance. Additionally, all models that utilized the radial basis kernel function exhibited better accuracy. Furthermore, the SVR-wavelet model demonstrated superior performance compared to the other models examined. Conclusions: Estimation of precipitation using hybrid models based on Support Vector Regression (SVR) serves as an efficient tool in the design of climatological and meteorological systems. In the present study, a case study was conducted to evaluate the performance of the hybrid optimization model of SVR for estimating precipitation in the coastal areas of the Caspian Sea, specifically at the Babolsar synoptic station located in Mazandaran Province. The results of the research, based on the evaluation of scenarios consisting of input parameters, demonstrated that in all examined models, increasing the number of effective parameters leads to better performance in precipitation estimation. Furthermore, the outcomes from the evaluation criteria revealed that the SVR-wavelet model exhibited high accuracy with minimal error. Additionally, according to the examined graphs, the SVR-wavelet model provided precipitation estimates close to their actual values, as evidenced by the Taylor diagram. In summary, the findings of this research indicate that the use of artificial intelligence models based on the Support Vector Regression approach can be beneficial for estimating precipitation in other regions of the country and could serve as a step towards making appropriate management decisions.

https://clima.irimo.ir/article_217212_276004f2c93dfa42b9c55302e206386f.pdf

https://www.irimo.ir/ Journal of Climate Research 2228-5040 1405 17 2026 05 22 Analysis of climate capacity building in the exploitation of moisture in the southern strip of Iran Analysis of climate capacity building in the exploitation of moisture in the southern strip of Iran 33 51 232570 10.22034/jcr.2025.512514.1692 FA Ghasem Azizi Faculty 0000-0002-9797-4834 Mostafa Ghavidel student Masoumeh Moghbel Faculty 0000-0001-9393-9954 Saeid Bazgir Faculty Aliakbar Shamsipour Faculty Journal Article 2025 03 15 Analysis of climate capacity building in the exploitation of moisture in the southern strip of Iran Introduction In today's world, climate change and water resource shortage have become two of the biggest challenges for humanity. In arid and semi-arid regions, water supply is of great importance. One of the new solutions to deal with this challenge is to exploit air humidity as an unconventional and sustainable resource (Sadeghi et al, 2012). In the southern strip of Iran, which faces a shortage of water resources due to its specific climatic conditions, this method can be used as an effective and sustainable solution. For example, in Qazvin province, research has shown that using different methods, water can be extracted from air humidity and used for various purposes, including irrigation of green spaces and farms. This research shows that by applying correction factors to the estimation of temperature and wind speed, accurate results can be achieved in extracting water from air humidity (Kuhy et al, 2012). In the arid regions of southern Iran, water scarcity is a constant challenge, so effective management of water resources is important for meeting needs and sustainable development. Since climate change exacerbates these challenges, building climate capacity in the water resource sector is essential. Recent studies show significant gaps in climate change-related statistics and the need for increased capacity-building efforts to address these gaps (Yuan DS, 2023). Climate change in Iran has led to an increase in extreme phenomena such as drought. Building climate capacity to deal with the consequences of climate change, especially in sensitive areas such as the southern strip, is a scientific and administrative necessity (Akbari and Sayyad, 2021). Climate change will significantly affect water access in southeastern Iran. Climate capacity analysis is essential for optimal exploitation of moisture and prevention of water crises in southern regions of Iran (Iranmanesh et al, 2021). Climate modeling and exploitation of moisture are very important for long-term resilience in southern regions of Iran (Abbaspour et al, 2009). Water resources in southern regions of Iran are under pressure from both the changing climate and the increasing population and may not be able to cope with future climate crises, therefore it is necessary and essential to conduct studies to address water challenges from now on in order to be able to deal with more severe future climate crises in this region. Materials and methods In this study, daily data on wind speed, pressure, relative humidity, and average temperature from 76 stations of the Southern Iran Meteorological Organization were used. The period of use of meteorological data was 2015-2024. To prepare the daily average of the aforementioned data for each of the 366 days of the year, the average was obtained from the period 2015-2024. For example, to average the air pressure on January 1, the air pressure data for January 1 of all years of the period 2015-2024 were used. This procedure was carried out for all days of the year in the mentioned period for the data of all relevant stations. In this study, the capacity building of air humidity exploitation in the southern strip of Iran was investigated. In order to measure the capacity of humidity exploitation in the southern strip of Iran, common methods of humidity exploitation were studied. The methods of cooling condensation, moisture absorption with absorbent materials and fog traps or fog collection nets were selected for investigation and analysis. After extracting the results of the utilization rate of heat using the aforementioned methods, these results, along with the average temperature, pressure, humidity, and wind speed data for the period 2015-2024 of various stations in the study area, provided the input matrix for the hierarchical analysis of the stations, and the hierarchical analysis was performed. Results and discussion By examining the map, it was found that the largest area of desirable areas for capacity building for moisture exploitation is located in the southeast of Sistan and Baluchestan Province, the center of Fars Province, the center of Hormozgan Province, and the northwest of Bushehr Province. Small parts of northern Khuzestan Province are also within the desirable areas. The largest areas that are undesirable for capacity building for moisture exploitation are located in the northern and eastern half of Fars Province, the northern half of the southern half of Sistan and Baluchestan Province, and the northwest of Hormozgan Province. Conclusion After extracting the results, it was found that the central regions of Fars Province and the southeast of Sistan and Baluchestan and Hormozgan provinces have achieved the highest possible scores compared to other regions and are introduced as desirable regions in building the capacity to exploit the moisture of the southern strip of Iran. Also, the eastern and northern regions of Fars Province, the northwest of Hormozgan Province, and the northern half of the southern part of Sistan and Baluchestan Province have achieved the lowest scores compared to other regions and are introduced as undesirable regions in building the capacity to exploit the moisture of the southern strip of Iran. By station analysis, Chabahar station, with a score of 18.9 percent in the southern half of Sistan and Baluchestan Province, was the most desirable. Mehrestan, with a score of 9 percent in the northern half of Sistan and Baluchestan Province, was the most undesirable station in this study for building climatic capacity to exploit moisture. The most important factors affecting the climatic capacity to exploit the moisture were also identified as air pressure, relative humidity, and the rate of water absorption from the methods of air moisture absorption. Analysis of climate capacity building in the exploitation of moisture in the southern strip of Iran Introduction In today's world, climate change and water resource shortage have become two of the biggest challenges for humanity. In arid and semi-arid regions, water supply is of great importance. One of the new solutions to deal with this challenge is to exploit air humidity as an unconventional and sustainable resource (Sadeghi et al, 2012). In the southern strip of Iran, which faces a shortage of water resources due to its specific climatic conditions, this method can be used as an effective and sustainable solution. For example, in Qazvin province, research has shown that using different methods, water can be extracted from air humidity and used for various purposes, including irrigation of green spaces and farms. This research shows that by applying correction factors to the estimation of temperature and wind speed, accurate results can be achieved in extracting water from air humidity (Kuhy et al, 2012). In the arid regions of southern Iran, water scarcity is a constant challenge, so effective management of water resources is important for meeting needs and sustainable development. Since climate change exacerbates these challenges, building climate capacity in the water resource sector is essential. Recent studies show significant gaps in climate change-related statistics and the need for increased capacity-building efforts to address these gaps (Yuan DS, 2023). Climate change in Iran has led to an increase in extreme phenomena such as drought. Building climate capacity to deal with the consequences of climate change, especially in sensitive areas such as the southern strip, is a scientific and administrative necessity (Akbari and Sayyad, 2021). Climate change will significantly affect water access in southeastern Iran. Climate capacity analysis is essential for optimal exploitation of moisture and prevention of water crises in southern regions of Iran (Iranmanesh et al, 2021). Climate modeling and exploitation of moisture are very important for long-term resilience in southern regions of Iran (Abbaspour et al, 2009). Water resources in southern regions of Iran are under pressure from both the changing climate and the increasing population and may not be able to cope with future climate crises, therefore it is necessary and essential to conduct studies to address water challenges from now on in order to be able to deal with more severe future climate crises in this region. Materials and methods In this study, daily data on wind speed, pressure, relative humidity, and average temperature from 76 stations of the Southern Iran Meteorological Organization were used. The period of use of meteorological data was 2015-2024. To prepare the daily average of the aforementioned data for each of the 366 days of the year, the average was obtained from the period 2015-2024. For example, to average the air pressure on January 1, the air pressure data for January 1 of all years of the period 2015-2024 were used. This procedure was carried out for all days of the year in the mentioned period for the data of all relevant stations. In this study, the capacity building of air humidity exploitation in the southern strip of Iran was investigated. In order to measure the capacity of humidity exploitation in the southern strip of Iran, common methods of humidity exploitation were studied. The methods of cooling condensation, moisture absorption with absorbent materials and fog traps or fog collection nets were selected for investigation and analysis. After extracting the results of the utilization rate of heat using the aforementioned methods, these results, along with the average temperature, pressure, humidity, and wind speed data for the period 2015-2024 of various stations in the study area, provided the input matrix for the hierarchical analysis of the stations, and the hierarchical analysis was performed. Results and discussion By examining the map, it was found that the largest area of desirable areas for capacity building for moisture exploitation is located in the southeast of Sistan and Baluchestan Province, the center of Fars Province, the center of Hormozgan Province, and the northwest of Bushehr Province. Small parts of northern Khuzestan Province are also within the desirable areas. The largest areas that are undesirable for capacity building for moisture exploitation are located in the northern and eastern half of Fars Province, the northern half of the southern half of Sistan and Baluchestan Province, and the northwest of Hormozgan Province. Conclusion After extracting the results, it was found that the central regions of Fars Province and the southeast of Sistan and Baluchestan and Hormozgan provinces have achieved the highest possible scores compared to other regions and are introduced as desirable regions in building the capacity to exploit the moisture of the southern strip of Iran. Also, the eastern and northern regions of Fars Province, the northwest of Hormozgan Province, and the northern half of the southern part of Sistan and Baluchestan Province have achieved the lowest scores compared to other regions and are introduced as undesirable regions in building the capacity to exploit the moisture of the southern strip of Iran. By station analysis, Chabahar station, with a score of 18.9 percent in the southern half of Sistan and Baluchestan Province, was the most desirable. Mehrestan, with a score of 9 percent in the northern half of Sistan and Baluchestan Province, was the most undesirable station in this study for building climatic capacity to exploit moisture. The most important factors affecting the climatic capacity to exploit the moisture were also identified as air pressure, relative humidity, and the rate of water absorption from the methods of air moisture absorption.

https://clima.irimo.ir/article_232570_be3d7b83ad9f4c80068a0a46629102f4.pdf

https://www.irimo.ir/ Journal of Climate Research 2228-5040 1405 17 2026 05 22 Comparison Between Quantile Mapping Downscaling Method and Random Forest Model Comparison Between Quantile Mapping Downscaling Method and Random Forest Model 53 65 241787 10.22034/jcr.2026.544319.1713 FA Elham Ghochanian Haghverdi PhD student in Water Resources, Department of Water Science and Engineering, Faculty of Agriculture, University of Birjand, Birjand, Iran & Expert of soil and water, Agriculture and Natural Resources Research Center of South Khorasan 0009-0005-7181-8234 Mahdi Amirabadizadeh Associate Professor, Department of Water Science and Engineering, Faculty of Agriculture, University of Birjand, Birjand, Iran Omid Khorashadizadeh MSc in Water Resources Engineering, South Khorasan Regional Water Company, Water Resources Management Company, Birjand, Iran 0009-0008-1937-5407 Journal Article 2025 08 31 Comparison Between Quantile Mapping Downscaling Method and Random Forest Model Extended abstract Introduction Accurate prediction of daily minimum air temperature in regions with complex topography, where elevation and terrain irregularities play a significant role in temperature variability, is of great importance due to its direct relationship with frost periods. However, Global Climate Models (GCMs), owing to their coarse spatial resolution and inherent systematic biases, are unable to accurately represent regional climatic processes and therefore require bias correction. In recent years, statistical models and machine learning approaches have attracted considerable attention for bias correction and downscaling. Nevertheless, to date, no study has been conducted comparing these models in predicting minimum temperature across Iran. Materials and Methods In this study, monthly data from the Birjand synoptic station for the period 1991–2020 were used as observational records to compare the quantile mapping and random forest methods. Additionally, output from the IPSL-CM6A-LR climate model of the CMIP6 project under the SSP2-4.5 scenario was employed for the historical period (1991–2020) and future projections (2030–2059). For the statistical quantile mapping approach in the R environment, the Qmap package with the asymmetric exponential transfer function (expasympt) was applied, while the machine learning model utilized the Random Forest package. The performance of both models was evaluated using statistical metrics including R², RMSE, MAE, NSE, and KGE, as well as through analysis of empirical cumulative distribution function (ECDF) plots and scatter diagrams. Results and Discussion In this study, bias correction of monthly minimum temperature at the Birjand synoptic station was examined using two different approaches. The statistical BCSD method employed the asymmetric exponential transfer function (expasympt) from the Qmap package, while the machine learning approach utilized the Random Forest algorithm. The data from 1991–2010 were used for calibration, and the period 2011–2020 was considered for validation. The evaluation of the two methods during the validation period (2011–2020) was carried out using statistical indices and analytical diagrams. The results indicated that the Random Forest model (R² = 0.94, NSE = 0.93, KGE = 0.91) showed a higher agreement with the observed data compared to the BCSD method (R² = 0.90, NSE = 0.90, KGE = 0.90). Furthermore, the error metrics of the Random Forest model (RMSE = 1.96 °C, MAE = 1.63 °C) indicated lower prediction errors compared to the BCSD method (RMSE = 2.39 °C, MAE = 1.93 °C). In the comparison of scatter plots, the Random Forest model was able to establish a nearly linear relationship close to the 1:1 line between predicted and observed values, whereas the BCSD results showed greater dispersion and deviation from perfect correlation. Similarly, in the empirical cumulative distribution function (ECDF) plots, the Random Forest model closely matched the observational curve and more accurately reproduced the statistical distribution of minimum temperature particularly in the middle and upper portions of the distribution (moderate to warmer temperatures) compared to the BCSD model. In contrast, the BCSD approach performed less effectively in the lower tail of the distribution (colder temperatures).The superior performance of the Random Forest model can be attributed to its inherent structure, which leverages an ensemble of decision trees and aggregates their outputs, enabling it to identify and generalize hidden, non-linear relationships between input and output variables. This capability is particularly advantageous in climate datasets influenced by complex and variable factors. In contrast, the BCSD method, relying on predefined transfer functions and lacking the ability to learn data-driven relationships, cannot provide the same level of adaptability and precision.Based on the statistical evidence obtained in this research, the Random Forest model demonstrates strong potential for bias correction of climate data, particularly minimum temperature, and offers greater accuracy and stability compared to traditional statistical methods. Conclusion The findings of this study demonstrate that the Random Forest (RF) model possesses the capability to capture complex and nonlinear relationships, leading to superior performance in bias correction of minimum temperature values compared to the Quantile Mapping (QM) model. Therefore, employing machine learning algorithms such as RF can be an effective approach for improving climate predictions in arid regions like Iran. It is recommended that future research explore more advanced models, including XGBoost, deep neural networks, and hybrid methods. Keywords: Bias correction, downscaling, Coupled Model Intercomparison Project Phase 6 (CMIP6), quantile mapping, machine learning. Comparison Between Quantile Mapping Downscaling Method and Random Forest Model Extended abstract Introduction Accurate prediction of daily minimum air temperature in regions with complex topography, where elevation and terrain irregularities play a significant role in temperature variability, is of great importance due to its direct relationship with frost periods. However, Global Climate Models (GCMs), owing to their coarse spatial resolution and inherent systematic biases, are unable to accurately represent regional climatic processes and therefore require bias correction. In recent years, statistical models and machine learning approaches have attracted considerable attention for bias correction and downscaling. Nevertheless, to date, no study has been conducted comparing these models in predicting minimum temperature across Iran. Materials and Methods In this study, monthly data from the Birjand synoptic station for the period 1991–2020 were used as observational records to compare the quantile mapping and random forest methods. Additionally, output from the IPSL-CM6A-LR climate model of the CMIP6 project under the SSP2-4.5 scenario was employed for the historical period (1991–2020) and future projections (2030–2059). For the statistical quantile mapping approach in the R environment, the Qmap package with the asymmetric exponential transfer function (expasympt) was applied, while the machine learning model utilized the Random Forest package. The performance of both models was evaluated using statistical metrics including R², RMSE, MAE, NSE, and KGE, as well as through analysis of empirical cumulative distribution function (ECDF) plots and scatter diagrams. Results and Discussion In this study, bias correction of monthly minimum temperature at the Birjand synoptic station was examined using two different approaches. The statistical BCSD method employed the asymmetric exponential transfer function (expasympt) from the Qmap package, while the machine learning approach utilized the Random Forest algorithm. The data from 1991–2010 were used for calibration, and the period 2011–2020 was considered for validation. The evaluation of the two methods during the validation period (2011–2020) was carried out using statistical indices and analytical diagrams. The results indicated that the Random Forest model (R² = 0.94, NSE = 0.93, KGE = 0.91) showed a higher agreement with the observed data compared to the BCSD method (R² = 0.90, NSE = 0.90, KGE = 0.90). Furthermore, the error metrics of the Random Forest model (RMSE = 1.96 °C, MAE = 1.63 °C) indicated lower prediction errors compared to the BCSD method (RMSE = 2.39 °C, MAE = 1.93 °C). In the comparison of scatter plots, the Random Forest model was able to establish a nearly linear relationship close to the 1:1 line between predicted and observed values, whereas the BCSD results showed greater dispersion and deviation from perfect correlation. Similarly, in the empirical cumulative distribution function (ECDF) plots, the Random Forest model closely matched the observational curve and more accurately reproduced the statistical distribution of minimum temperature particularly in the middle and upper portions of the distribution (moderate to warmer temperatures) compared to the BCSD model. In contrast, the BCSD approach performed less effectively in the lower tail of the distribution (colder temperatures).The superior performance of the Random Forest model can be attributed to its inherent structure, which leverages an ensemble of decision trees and aggregates their outputs, enabling it to identify and generalize hidden, non-linear relationships between input and output variables. This capability is particularly advantageous in climate datasets influenced by complex and variable factors. In contrast, the BCSD method, relying on predefined transfer functions and lacking the ability to learn data-driven relationships, cannot provide the same level of adaptability and precision.Based on the statistical evidence obtained in this research, the Random Forest model demonstrates strong potential for bias correction of climate data, particularly minimum temperature, and offers greater accuracy and stability compared to traditional statistical methods. Conclusion The findings of this study demonstrate that the Random Forest (RF) model possesses the capability to capture complex and nonlinear relationships, leading to superior performance in bias correction of minimum temperature values compared to the Quantile Mapping (QM) model. Therefore, employing machine learning algorithms such as RF can be an effective approach for improving climate predictions in arid regions like Iran. It is recommended that future research explore more advanced models, including XGBoost, deep neural networks, and hybrid methods. Keywords: Bias correction, downscaling, Coupled Model Intercomparison Project Phase 6 (CMIP6), quantile mapping, machine learning.

https://clima.irimo.ir/article_241787_0f6d159ded0f5dfc89016faaab616d0e.pdf

https://www.irimo.ir/ Journal of Climate Research 2228-5040 1405 17 2026 05 22 Probable Maximum Precipitation in the Baseline and Future Period (2030–2060) under Climate Change Conditions in Iran Probable Maximum Precipitation in the Baseline and Future Period (2030–2060) under Climate Change Conditions in Iran 67 86 244708 10.22034/jcr.2026.562814.1728 FA Ebrahim Fatahi Prof of Research Institute of Meteorology and Atmospheric Science (RIMAS), Tehran, Iran. 0009-0004-4871-2004 Hadis Sadeghi Research Institute of Meteorology and Atmospheric Science (RIMAS), Tehran, Iran. Journal Article 2025 11 28 Introduction In recent decades, climate change has substantially intensified environmental and hydrological challenges worldwide. Rising temperatures, increased frequency of extreme events, and altered precipitation patterns have a significant impact on the stability and performance of hydraulic structures. One of the most important parameters used in the design of dams, reservoirs, channels, spillways, and other critical hydraulic structures is the Probable Maximum Precipitation (PMP), which plays a key role in estimating the likelihood of extreme floods and in designing structures capable of withstanding such extreme conditions. In arid and semi-arid regions such as Iran, the impacts of climate change on Probable Maximum Precipitation (PMP) are complex. Therefore, evaluating changes in Probable Maximum Precipitation (PMP) during the baseline and future periods under various climate scenarios is crucial for flood risk management, the design of resilient hydraulic structures, and reducing vulnerability across different regions of the country. Materials and methods In this study, daily precipitation data from 96 Iranian synoptic stations for the baseline period 1990–2020 and outputs from CMIP6 models for the future period 2030–2060 were used under the SSP2.6, SSP4.5, and SSP8.5 scenarios. First, the performance of the GCMs at each station in simulating precipitation was evaluated using Taylor diagrams, and the optimal model for each station was identified. After selecting the optimal climate model for each station based on the Taylor diagram, daily precipitation data for the future period (2030–2060) were downscaled using the LARS-WG model under three emission scenarios. The modified Desa-Hirschfield method was used to estimate the Probable Maximum Precipitation (PMP). In this method, extreme values are excluded from the data series, after which the revised mean and standard deviation are calculated. This process improves the accuracy of the results and reduces estimation errors. Previous studies in Iran have shown that the classical Hershfield method overestimates PMP, whereas the Desa-Hershfield method produces more reasonable results that are better aligned with the characteristics of extreme precipitation. Therefore, in this study, the baseline and future PMP were estimated for each of the 96 stations using the modified Desa method. Then, the obtained values for both the baseline and future periods were mapped using the Kriging method to reveal the spatial patterns of PMP changes in Iran. Results and discussion The results indicated that the spatial distribution of PMP in Iran is highly heterogeneous, and factors such as topography, elevation, distance from moisture sources, proximity to the sea, and humid atmospheric flows play a key role in determining PMP values. The highest PMP values during the baseline period were observed in the humid regions along the Caspian Sea coast, the northwestern highlands, and parts of the western slopes of the Zagros Mountains. In contrast, the central, eastern, and southeastern regions of Iran exhibited the lowest PMP values due to their distance from moisture sources and relatively flat topography. The results of Probable Maximum Precipitation (PMP) for the future period indicated that the spatial distribution pattern of PMP is similar to that of the baseline period, with an increase in PMP values expected in most parts of Iran compared to the baseline. The largest increase occurs under the SSP4.5 scenario. According to this scenario, the greatest rise in PMP relative to the baseline period is expected in the humid temperate regions of northern Iran and the semi-humid areas of western and northwestern Iran. An increase in PMP was also observed in the arid and semi-arid central and southern regions, but it is less intense compared to the humid climates. According to the SSP2.6 scenario, the greatest increase in PMP relative to the baseline period is observed primarily in the southern and southeastern regions. This difference in the spatial pattern of PMP increase is likely due to the differing responses of climate models to changes in temperature, pressure, and synoptic circulations. The results also indicated that the Probable Maximum Precipitation (PMP) under the SSP8.5 scenario is lower than under the other two scenarios. Under the SSP8.5 scenario, a relative decline in PMP compared to the baseline period was detected in certain northern and northwestern regions. This finding contradicts the initial expectation based on the theory that atmospheric moisture-holding capacity increases with temperature, and indicates that under very high emission scenarios, dynamic and thermodynamic atmospheric patterns may alter the trajectories of precipitation systems or the development of convective clouds. Therefore, the distinct response of humid regions under the SSP8.5 scenario highlights the complexity of climate change impacts and underscores the need for rigorous regional-scale analyses. On the other hand, the results showed that in the temperate and humid regions of the north, as well as the western slopes of the Zagros, although the Probable Maximum Precipitation (PMP) is high, the relative risk of flooding is lower than in hot and dry regions due to higher soil permeability, dense vegetation cover, and more efficient drainage networks. Conclusion The results of this study demonstrated that climate change exerts a substantial influence on variations in Probable Maximum Precipitation (PMP) across Iran, and that the spatial patterns of these changes differ markedly among various regions of the country. In the near future, the intensity of PMP is expected to increase across most regions of the country, with the greatest rise projected under the SSP4.5 scenario. A decrease in PMP in some northern parts of the country under the SSP8.5 scenario reflects the complexity and nonlinear behavior of precipitation systems under conditions of intense global warming. The results also showed that a high PMP does not necessarily imply a greater hazard, as geomorphological and hydrological conditions play a decisive role. Accordingly, strengthening flood-management strategies—particularly the design of hydrological structures in the arid regions of the country—is of critical importance, while in humid regions, runoff management and soil-erosion reduction are essential. The findings of this study highlight the need to revise water-resources management policies, hydraulic-structure design standards, and urban-planning strategies by incorporating the dynamic behavior of PMP under future climate conditions. Introduction In recent decades, climate change has substantially intensified environmental and hydrological challenges worldwide. Rising temperatures, increased frequency of extreme events, and altered precipitation patterns have a significant impact on the stability and performance of hydraulic structures. One of the most important parameters used in the design of dams, reservoirs, channels, spillways, and other critical hydraulic structures is the Probable Maximum Precipitation (PMP), which plays a key role in estimating the likelihood of extreme floods and in designing structures capable of withstanding such extreme conditions. In arid and semi-arid regions such as Iran, the impacts of climate change on Probable Maximum Precipitation (PMP) are complex. Therefore, evaluating changes in Probable Maximum Precipitation (PMP) during the baseline and future periods under various climate scenarios is crucial for flood risk management, the design of resilient hydraulic structures, and reducing vulnerability across different regions of the country. Materials and methods In this study, daily precipitation data from 96 Iranian synoptic stations for the baseline period 1990–2020 and outputs from CMIP6 models for the future period 2030–2060 were used under the SSP2.6, SSP4.5, and SSP8.5 scenarios. First, the performance of the GCMs at each station in simulating precipitation was evaluated using Taylor diagrams, and the optimal model for each station was identified. After selecting the optimal climate model for each station based on the Taylor diagram, daily precipitation data for the future period (2030–2060) were downscaled using the LARS-WG model under three emission scenarios. The modified Desa-Hirschfield method was used to estimate the Probable Maximum Precipitation (PMP). In this method, extreme values are excluded from the data series, after which the revised mean and standard deviation are calculated. This process improves the accuracy of the results and reduces estimation errors. Previous studies in Iran have shown that the classical Hershfield method overestimates PMP, whereas the Desa-Hershfield method produces more reasonable results that are better aligned with the characteristics of extreme precipitation. Therefore, in this study, the baseline and future PMP were estimated for each of the 96 stations using the modified Desa method. Then, the obtained values for both the baseline and future periods were mapped using the Kriging method to reveal the spatial patterns of PMP changes in Iran. Results and discussion The results indicated that the spatial distribution of PMP in Iran is highly heterogeneous, and factors such as topography, elevation, distance from moisture sources, proximity to the sea, and humid atmospheric flows play a key role in determining PMP values. The highest PMP values during the baseline period were observed in the humid regions along the Caspian Sea coast, the northwestern highlands, and parts of the western slopes of the Zagros Mountains. In contrast, the central, eastern, and southeastern regions of Iran exhibited the lowest PMP values due to their distance from moisture sources and relatively flat topography. The results of Probable Maximum Precipitation (PMP) for the future period indicated that the spatial distribution pattern of PMP is similar to that of the baseline period, with an increase in PMP values expected in most parts of Iran compared to the baseline. The largest increase occurs under the SSP4.5 scenario. According to this scenario, the greatest rise in PMP relative to the baseline period is expected in the humid temperate regions of northern Iran and the semi-humid areas of western and northwestern Iran. An increase in PMP was also observed in the arid and semi-arid central and southern regions, but it is less intense compared to the humid climates. According to the SSP2.6 scenario, the greatest increase in PMP relative to the baseline period is observed primarily in the southern and southeastern regions. This difference in the spatial pattern of PMP increase is likely due to the differing responses of climate models to changes in temperature, pressure, and synoptic circulations. The results also indicated that the Probable Maximum Precipitation (PMP) under the SSP8.5 scenario is lower than under the other two scenarios. Under the SSP8.5 scenario, a relative decline in PMP compared to the baseline period was detected in certain northern and northwestern regions. This finding contradicts the initial expectation based on the theory that atmospheric moisture-holding capacity increases with temperature, and indicates that under very high emission scenarios, dynamic and thermodynamic atmospheric patterns may alter the trajectories of precipitation systems or the development of convective clouds. Therefore, the distinct response of humid regions under the SSP8.5 scenario highlights the complexity of climate change impacts and underscores the need for rigorous regional-scale analyses. On the other hand, the results showed that in the temperate and humid regions of the north, as well as the western slopes of the Zagros, although the Probable Maximum Precipitation (PMP) is high, the relative risk of flooding is lower than in hot and dry regions due to higher soil permeability, dense vegetation cover, and more efficient drainage networks. Conclusion The results of this study demonstrated that climate change exerts a substantial influence on variations in Probable Maximum Precipitation (PMP) across Iran, and that the spatial patterns of these changes differ markedly among various regions of the country. In the near future, the intensity of PMP is expected to increase across most regions of the country, with the greatest rise projected under the SSP4.5 scenario. A decrease in PMP in some northern parts of the country under the SSP8.5 scenario reflects the complexity and nonlinear behavior of precipitation systems under conditions of intense global warming. The results also showed that a high PMP does not necessarily imply a greater hazard, as geomorphological and hydrological conditions play a decisive role. Accordingly, strengthening flood-management strategies—particularly the design of hydrological structures in the arid regions of the country—is of critical importance, while in humid regions, runoff management and soil-erosion reduction are essential. The findings of this study highlight the need to revise water-resources management policies, hydraulic-structure design standards, and urban-planning strategies by incorporating the dynamic behavior of PMP under future climate conditions.

https://clima.irimo.ir/article_244708_f7cba20da7c8ce58eb60e8531743984d.pdf

https://www.irimo.ir/ Journal of Climate Research 2228-5040 1405 17 2026 05 22 Evaluation of the capability of ERA5 and AgMERRA to simulate long-term air temperature and solar radiation over the Khorasan Provinces, Iran Evaluation of the capability of ERA5 and AgMERRA to simulate long-term air temperature and solar radiation over the Khorasan Provinces, Iran 87 104 246351 10.22034/jcr.2026.505340.1683 FA Mahdi Soltani M.Sc. Student in Agroecology, Faculty of Agriculture, Ferdowsi University of Mashhad 0009-0004-3948-0467 Mohsen Jahan Professor, Department of Agrotechnology, Faculty of Agriculture, Ferdowsi University of Mashhad 0000-0003-2259-5124 Fatemeh Yaghoubi Ph.D. in Agroecology, Faculty of Agriculture, Ferdowsi University of Mashhad 0000-0003-2735-3837 Journal Article 2025 02 08 Introduction Understanding climate change and its impacts on agriculture requires access to long and reliable daily weather records. For robust estimation of crop yield levels and variability, daily climate data over at least 10–20 years are needed. Reanalysis datasets, which combine observations with numerical weather prediction models, have therefore become key inputs for agro-climatic applications, including climate change assessment, water-resource management and crop modelling. Products such as ERA5, MERRA-2, NCEP/NCAR and the agriculture-oriented AgMERRA provide spatially continuous information on temperature, solar radiation and other variables, and have been successfully used to drive models such as DSSAT and WOFOST in different regions. Previous studies generally report good performance of ERA5 and AgMERRA for temperature and, to a lesser extent, for solar radiation, although biases and spatial heterogeneity in skill are frequently noted. In Iran, and particularly in the three Khorasan provinces in the north-east, the evaluation and use of both datasets remain limited despite their potential to support climate-change impact assessment and agricultural planning. The study therefore aims not to prove the superiority of one product over the other, but to provide an unbiased assessment of the performance and practical usability of both for daily temperature and solar radiation in this data-sparse region. Materials and Methods The study area comprises the three Khorasan provinces in north-eastern Iran, extending from 30°50′ to 38°30′N and 55°35′ to 61°25′E, with an area of about 294,000 km² (Fig. 1). Daily data from 42 synoptic stations (Table 2) were used, including minimum, maximum and mean air temperature and incoming solar radiation. Gridded reanalysis data were obtained in NetCDF format from ERA5 and AgMERRA (Table 1). ERA5 provides hourly temperature and radiation at 0.25° spatial resolution for 1940–2022, while AgMERRA provides daily data at 0.5° resolution for 1980–2010; ERA5 data were aggregated to daily values. Because the evaluation periods differ but partly overlap, direct comparison of skill metrics is only approximate and long-term analysis focuses on ERA5. For each station, the nearest grid cell was extracted in R and daily reanalysis values were compared with observations. Performance was assessed using Pearson’s correlation coefficient (r), root mean square error (RMSE), normalized RMSE (NRMSE, %), mean bias error (MBE) and Willmott’s index of agreement (d). For solar radiation, both reanalysis products were first evaluated against pyranometer measurements at stations with sufficiently long records. In a second step, ERA5 and AgMERRA were compared with daily radiation estimated from the Angström–Prescott relationship at nine synoptic stations in Razavi Khorasan. This two-stage approach was adopted because direct radiation measurements are sparse in the region and Angström–Prescott estimates are widely used as a surrogate in data-scarce agro-climatic studies. Results and Discussion Daily minimum temperature (tmin) was generally well reproduced by both reanalysis products. Across the 42 stations, ERA5 showed r mostly around 0.96–0.98 (mean ≈0.97) and AgMERRA around 0.92–0.96 (mean ≈0.94). The index of agreement d was high for both datasets (mean ≈0.98 for ERA5 and 0.95 for AgMERRA). However, NRMSE for tmin in ERA5 was typically about 4–9% (mean ≈5%), compared with roughly 5–13% (mean ≈7%) for AgMERRA, and both products exhibited a predominantly cold bias. In ERA5, most stations showed MBE close to zero or down to about −2 to −3 °C, whereas in AgMERRA some stations underestimated tmin by about 3–5 °C, which can be critical for frost-related applications. For daily maximum temperature (tmax), performance was even more robust. In almost all stations, r was close to 0.98–1.00 in ERA5 and about 0.97–0.98 in AgMERRA, with d generally above 0.97 in both datasets. Relative errors were mostly within 3–7% for ERA5 and about 4–9% for AgMERRA. Despite this high skill, both products tended to underestimate tmax by roughly 1–4 °C at many stations, while a few sites showed mild warm biases. Mean daily temperature (tm) also showed very high correlations (r >0.98 for ERA5 and >0.97 for AgMERRA), but NRMSE reached about 3–9% in ERA5 and 3–16% in AgMERRA, with larger relative errors at some northern and southern stations. Bias in tm was again predominantly negative, implying that degree-day indices derived from these data may be underestimated if no bias correction is applied. From a climatic perspective, the spatial pattern of skill is consistent with the geography of the Khorasan region. In open, relatively homogeneous plains, where temperature variability is mainly controlled by large-scale synoptic systems and regional temperature gradients, both datasets—especially ERA5—reproduce daily temperatures with high r, low NRMSE and limited bias. In contrast, in mountainous areas, valley margins and desert fringes, complex topography, strong nocturnal radiative cooling and large diurnal ranges increase the discrepancy between point measurements and grid-cell means, particularly for tmin and tm. This reflects the limited spatial resolution and inherent smoothing of reanalysis fields and explains why some stations in northern highlands and southern arid zones show larger cold biases and relative errors. These temperature results are broadly consistent with previous evaluations of AgMERRA in North and Razavi Khorasan and with recent national-scale assessments indicating that ERA5 generally provides the most accurate air-temperature estimates over Iran, with larger errors in complex mountain and coastal regions. Considering the longer evaluation period for ERA5 compared with AgMERRA, direct comparison of skill metrics remains approximate. Nevertheless, the combined evidence indicates that both datasets offer acceptable accuracy for daily tmin, tmax and tm at station scale in the three Khorasan provinces, and that statistical bias correction can further enhance their suitability for climate, hydrological and agricultural impact studies. Conclusion This study showed that both datasets reproduce daily tmin, tmax and tm over the three Khorasan provinces with high correlations and generally small relative errors, although a cold bias remains at some mountainous and desert stations and for tmin and tm. For solar radiation, ERA5 performs acceptably at most sites, whereas AgMERRA has more limited spatial coverage and a tendency to underestimate. Considering its longer temporal coverage, finer spatial resolution and ongoing updates, ERA5 emerges as the preferred dataset for long-term climate, water and agricultural applications in the region, while both products should ideally be used with local bias correction. Introduction Understanding climate change and its impacts on agriculture requires access to long and reliable daily weather records. For robust estimation of crop yield levels and variability, daily climate data over at least 10–20 years are needed. Reanalysis datasets, which combine observations with numerical weather prediction models, have therefore become key inputs for agro-climatic applications, including climate change assessment, water-resource management and crop modelling. Products such as ERA5, MERRA-2, NCEP/NCAR and the agriculture-oriented AgMERRA provide spatially continuous information on temperature, solar radiation and other variables, and have been successfully used to drive models such as DSSAT and WOFOST in different regions. Previous studies generally report good performance of ERA5 and AgMERRA for temperature and, to a lesser extent, for solar radiation, although biases and spatial heterogeneity in skill are frequently noted. In Iran, and particularly in the three Khorasan provinces in the north-east, the evaluation and use of both datasets remain limited despite their potential to support climate-change impact assessment and agricultural planning. The study therefore aims not to prove the superiority of one product over the other, but to provide an unbiased assessment of the performance and practical usability of both for daily temperature and solar radiation in this data-sparse region. Materials and Methods The study area comprises the three Khorasan provinces in north-eastern Iran, extending from 30°50′ to 38°30′N and 55°35′ to 61°25′E, with an area of about 294,000 km² (Fig. 1). Daily data from 42 synoptic stations (Table 2) were used, including minimum, maximum and mean air temperature and incoming solar radiation. Gridded reanalysis data were obtained in NetCDF format from ERA5 and AgMERRA (Table 1). ERA5 provides hourly temperature and radiation at 0.25° spatial resolution for 1940–2022, while AgMERRA provides daily data at 0.5° resolution for 1980–2010; ERA5 data were aggregated to daily values. Because the evaluation periods differ but partly overlap, direct comparison of skill metrics is only approximate and long-term analysis focuses on ERA5. For each station, the nearest grid cell was extracted in R and daily reanalysis values were compared with observations. Performance was assessed using Pearson’s correlation coefficient (r), root mean square error (RMSE), normalized RMSE (NRMSE, %), mean bias error (MBE) and Willmott’s index of agreement (d). For solar radiation, both reanalysis products were first evaluated against pyranometer measurements at stations with sufficiently long records. In a second step, ERA5 and AgMERRA were compared with daily radiation estimated from the Angström–Prescott relationship at nine synoptic stations in Razavi Khorasan. This two-stage approach was adopted because direct radiation measurements are sparse in the region and Angström–Prescott estimates are widely used as a surrogate in data-scarce agro-climatic studies. Results and Discussion Daily minimum temperature (tmin) was generally well reproduced by both reanalysis products. Across the 42 stations, ERA5 showed r mostly around 0.96–0.98 (mean ≈0.97) and AgMERRA around 0.92–0.96 (mean ≈0.94). The index of agreement d was high for both datasets (mean ≈0.98 for ERA5 and 0.95 for AgMERRA). However, NRMSE for tmin in ERA5 was typically about 4–9% (mean ≈5%), compared with roughly 5–13% (mean ≈7%) for AgMERRA, and both products exhibited a predominantly cold bias. In ERA5, most stations showed MBE close to zero or down to about −2 to −3 °C, whereas in AgMERRA some stations underestimated tmin by about 3–5 °C, which can be critical for frost-related applications. For daily maximum temperature (tmax), performance was even more robust. In almost all stations, r was close to 0.98–1.00 in ERA5 and about 0.97–0.98 in AgMERRA, with d generally above 0.97 in both datasets. Relative errors were mostly within 3–7% for ERA5 and about 4–9% for AgMERRA. Despite this high skill, both products tended to underestimate tmax by roughly 1–4 °C at many stations, while a few sites showed mild warm biases. Mean daily temperature (tm) also showed very high correlations (r >0.98 for ERA5 and >0.97 for AgMERRA), but NRMSE reached about 3–9% in ERA5 and 3–16% in AgMERRA, with larger relative errors at some northern and southern stations. Bias in tm was again predominantly negative, implying that degree-day indices derived from these data may be underestimated if no bias correction is applied. From a climatic perspective, the spatial pattern of skill is consistent with the geography of the Khorasan region. In open, relatively homogeneous plains, where temperature variability is mainly controlled by large-scale synoptic systems and regional temperature gradients, both datasets—especially ERA5—reproduce daily temperatures with high r, low NRMSE and limited bias. In contrast, in mountainous areas, valley margins and desert fringes, complex topography, strong nocturnal radiative cooling and large diurnal ranges increase the discrepancy between point measurements and grid-cell means, particularly for tmin and tm. This reflects the limited spatial resolution and inherent smoothing of reanalysis fields and explains why some stations in northern highlands and southern arid zones show larger cold biases and relative errors. These temperature results are broadly consistent with previous evaluations of AgMERRA in North and Razavi Khorasan and with recent national-scale assessments indicating that ERA5 generally provides the most accurate air-temperature estimates over Iran, with larger errors in complex mountain and coastal regions. Considering the longer evaluation period for ERA5 compared with AgMERRA, direct comparison of skill metrics remains approximate. Nevertheless, the combined evidence indicates that both datasets offer acceptable accuracy for daily tmin, tmax and tm at station scale in the three Khorasan provinces, and that statistical bias correction can further enhance their suitability for climate, hydrological and agricultural impact studies. Conclusion This study showed that both datasets reproduce daily tmin, tmax and tm over the three Khorasan provinces with high correlations and generally small relative errors, although a cold bias remains at some mountainous and desert stations and for tmin and tm. For solar radiation, ERA5 performs acceptably at most sites, whereas AgMERRA has more limited spatial coverage and a tendency to underestimate. Considering its longer temporal coverage, finer spatial resolution and ongoing updates, ERA5 emerges as the preferred dataset for long-term climate, water and agricultural applications in the region, while both products should ideally be used with local bias correction.

https://clima.irimo.ir/article_246351_e60180bf6f3475b648ddfc94c4b3b59e.pdf

https://www.irimo.ir/ Journal of Climate Research 2228-5040 1405 17 2026 05 22 Study of the role of vegetation on the formation of heat islands in Tehran City (district 6) Study of the role of vegetation on the formation of heat islands in Tehran City (district 6) 105 120 246352 10.22034/jcr.2026.269386.1408 FA Arian Zarfeshani University of Tehran Mohammad Hossein Jahangir Faraz Alijani University of Tehran Journal Article 2021 01 19 Extended Abstract: Introduction A simple study of the environment reveals that cities have expanded greatly in both the horizontal and vertical directions over the past two or three decades. This problematic phenomenon has caused the areas that in the not so distant past were full of various plants to be replaced with dark concrete and asphalt. This has a tremendous impact on the weather in different parts of the city, from a micro-meteorological point of view (small-scale meteorology). One of the obvious consequences is an increase in temperature in places that have more and more dense buildings in particular, and vegetation has a low density. Urban heat island development occurs when a large portion of the earth's natural vegetation in an area is replaced by man-made surfaces, resulting in low moisture due to lack of vegetation and impermeable surfaces absorbing large amounts of solar radiation during the day and then during the night Is reflected. In one city, the temperature is often 3 to 4 degrees Celsius higher than in other non-urban areas.The difference between the temperature of the heat waves and the environment around the intensity of the heat waves is called so that the higher the temperature of the heat waves than the environment, the higher the intensity of the heat waves, the intensity of urban heat waves can vary between 0 to 7 degrees Celsius depending on the season, sunlight and city characteristics. On a hot summer day, the sun warms homeless, dry surfaces such as rooftops and hard street surfaces, sometimes up to 20 to 50 degrees Celsius, while shady or humid surfaces, often on the outskirts of the city, have temperatures around the temperature. They have air. Urban heat islands exist during the day and night, but are more intense during the day when the sun shines. Materials and Methods Tehran province is located in the center of Tehran, with an area of about 12981 square kilometers, between 34 to 36.5 degrees north latitude and 50 to 53 degrees east longitude. This province is limited to Mazandaran province from the north, Qom province from the south, Markazi province from the southwest, Alborz province from the west and Semnan province from the east. The capital of this province is Tehran. Tehran is also the capital of Iran. District 6 of Tehran is one of the relatively old districts of Tehran, which is geographically located in the central part of Tehran. In order to verify the information obtained from this study, the obtained data were compared with the data of the Geophysic Synoptic Station of the University of Tehran. This station is very close to the study area and this is the reason for its selection. Land surface temperature can be described as; the sensation of heat when the ground is touched by hand or skin. The Land surface temperature is finally calculated by the following equation. T_s=TB/(1+(λ × TB/ρ)Lnε) The following equation is used to convert surface temperature to air temperature. Ta=0/14+6/44×LST Results and discussion Table 4, Validation of information obtained seasons Geophysical station air temperature (℃) Calculated air temperature (℃) Temperature difference (℃) Autumn 8/6 11/2 2/6 Winter 5/2 4/18 1/02 Spring 18/3 17/2 1/1 Summer 30/11 28/74 1/37 In order to validation the results obtained from the study and the data of the Geophysical Station of the University of Tehran were compare and the following table is presented. Figure 3 Temperature map of district 6 of Tehran Figure (3) shows the temperature map in summer, this map is classified into 5 temperature levels and temperatures above 30 ° C have been identified as thermal islands. Green areas indicate temperatures below 27.5 ° C, the average temperature in summer is 28.36 and the average temperature of the heat islands in summer is 30.24 ° C and the average intensity of the heat islands is 1.88 degrees Celsius. Figure 4 Map of vegetation and Heat islands of District 6 of Tehran City In places where there is good and dense vegetation, the temperature is lower and the reason for the low temperatures of the temperature map is the presence of vegetation. Conclusion Based on the results of this study, it can be summarized that one of the main reasons for the temperature balance in an environment is the presence of vegetation, and in the absence of it, the ambient temperature increases and using vegetation in areas where the intensity Heat islands are high and rising, can help lower the temperature. The reason for this can be summarized as follows: by changing the materials of urban surfaces and increasing the permeability of water and moisture, which is all achieved by replacing these surfaces with plants, and as a result, plants heat later than impermeable surfaces such as asphalt and concrete. Extended Abstract: Introduction A simple study of the environment reveals that cities have expanded greatly in both the horizontal and vertical directions over the past two or three decades. This problematic phenomenon has caused the areas that in the not so distant past were full of various plants to be replaced with dark concrete and asphalt. This has a tremendous impact on the weather in different parts of the city, from a micro-meteorological point of view (small-scale meteorology). One of the obvious consequences is an increase in temperature in places that have more and more dense buildings in particular, and vegetation has a low density. Urban heat island development occurs when a large portion of the earth's natural vegetation in an area is replaced by man-made surfaces, resulting in low moisture due to lack of vegetation and impermeable surfaces absorbing large amounts of solar radiation during the day and then during the night Is reflected. In one city, the temperature is often 3 to 4 degrees Celsius higher than in other non-urban areas.The difference between the temperature of the heat waves and the environment around the intensity of the heat waves is called so that the higher the temperature of the heat waves than the environment, the higher the intensity of the heat waves, the intensity of urban heat waves can vary between 0 to 7 degrees Celsius depending on the season, sunlight and city characteristics. On a hot summer day, the sun warms homeless, dry surfaces such as rooftops and hard street surfaces, sometimes up to 20 to 50 degrees Celsius, while shady or humid surfaces, often on the outskirts of the city, have temperatures around the temperature. They have air. Urban heat islands exist during the day and night, but are more intense during the day when the sun shines. Materials and Methods Tehran province is located in the center of Tehran, with an area of about 12981 square kilometers, between 34 to 36.5 degrees north latitude and 50 to 53 degrees east longitude. This province is limited to Mazandaran province from the north, Qom province from the south, Markazi province from the southwest, Alborz province from the west and Semnan province from the east. The capital of this province is Tehran. Tehran is also the capital of Iran. District 6 of Tehran is one of the relatively old districts of Tehran, which is geographically located in the central part of Tehran. In order to verify the information obtained from this study, the obtained data were compared with the data of the Geophysic Synoptic Station of the University of Tehran. This station is very close to the study area and this is the reason for its selection. Land surface temperature can be described as; the sensation of heat when the ground is touched by hand or skin. The Land surface temperature is finally calculated by the following equation. T_s=TB/(1+(λ × TB/ρ)Lnε) The following equation is used to convert surface temperature to air temperature. Ta=0/14+6/44×LST Results and discussion Table 4, Validation of information obtained seasons Geophysical station air temperature (℃) Calculated air temperature (℃) Temperature difference (℃) Autumn 8/6 11/2 2/6 Winter 5/2 4/18 1/02 Spring 18/3 17/2 1/1 Summer 30/11 28/74 1/37 In order to validation the results obtained from the study and the data of the Geophysical Station of the University of Tehran were compare and the following table is presented. Figure 3 Temperature map of district 6 of Tehran Figure (3) shows the temperature map in summer, this map is classified into 5 temperature levels and temperatures above 30 ° C have been identified as thermal islands. Green areas indicate temperatures below 27.5 ° C, the average temperature in summer is 28.36 and the average temperature of the heat islands in summer is 30.24 ° C and the average intensity of the heat islands is 1.88 degrees Celsius. Figure 4 Map of vegetation and Heat islands of District 6 of Tehran City In places where there is good and dense vegetation, the temperature is lower and the reason for the low temperatures of the temperature map is the presence of vegetation. Conclusion Based on the results of this study, it can be summarized that one of the main reasons for the temperature balance in an environment is the presence of vegetation, and in the absence of it, the ambient temperature increases and using vegetation in areas where the intensity Heat islands are high and rising, can help lower the temperature. The reason for this can be summarized as follows: by changing the materials of urban surfaces and increasing the permeability of water and moisture, which is all achieved by replacing these surfaces with plants, and as a result, plants heat later than impermeable surfaces such as asphalt and concrete.

https://clima.irimo.ir/article_246352_2eefcaa674d87522aaa3d033ad13e1d7.pdf

https://www.irimo.ir/ Journal of Climate Research 2228-5040 1405 17 2026 05 22 Role of Big Data and Artificial Intelligence in Drought Monitoring and Climate Change Prediction Role of Big Data and Artificial Intelligence in Drought Monitoring and Climate Change Prediction 121 140 251341 10.22034/jcr.2026.586963.1746 FA Gholamreza Farrokhi Assistant Prof., Faculty of Agriculture, Payame Noor University, Tehran, Iran 0000-0002-5645-5907 Mahboobeh Gapeleh Instructor, Faculty of Computer, Taf.C., Islamic Azad University, Tafresh, Iran. 0000-0002-9338-0035 Journal Article 2026 06 17 Introduction The accelerating development of data generation and processing technologies—ranging from environmental sensor networks and the Internet of Things to remote sensing systems and cloud computing infrastructure—has produced an unprecedented volume of environmental data, enabling a more precise understanding of complex climatic processes. Within this context, big data and artificial intelligence (AI) have become fundamental pillars of climate science, not merely as auxiliary computational tools but as infrastructure that has transformed the very nature of research in this field. This transformation is particularly significant given that the Earth, as a complex and dynamic system, is shaped by the continuous interaction of atmospheric, hydrological, biological, and anthropogenic components—interactions that cannot be adequately understood without access to extensive, multi-source, and multi-scale data. Drought and the intensification of extreme climatic events have emerged as one of the most critical application domains for these technologies. As one of the most consequential environmental, economic, and social challenges of the present era, drought's gradual onset, extensive spatial coverage, and multidimensional consequences make its effective monitoring and prediction dependent on the analysis of vast volumes of heterogeneous data across multiple temporal and spatial scales. Despite growing research on environmental big data, existing studies have predominantly focused either on algorithmic and infrastructural development or on fragmented, discipline-specific applications related to drought, leaving a persistent need for analytical reviews that offer an integrated picture of trends, opportunities, and challenges in this field. The present study aims to provide an integrated analytical framework examining the role of big data and AI in drought monitoring, climate change prediction, and decision support for sustainable natural resource management, with its principal contribution lying in the simultaneous and interconnected examination of big data infrastructure, AI algorithms, and decision-making processes in natural resource governance. Materials and Methods This study was conducted using a narrative-analytical review approach. Unlike systematic reviews, which rely on formal screening procedures, strict inclusion/exclusion criteria, and quantitative evidence synthesis, this approach aims to combine, interpret, and critically analyze dispersed findings within the field to construct an integrated conceptual framework of trends, opportunities, and challenges. Given the breadth and interdisciplinary nature of the domain—spanning meteorology, hydrology, data science, and natural resource policy—this approach allows for greater flexibility in thematic analysis and the synthesis of multiple perspectives compared to systematic review methodologies. Relevant literature was retrieved from Scopus, Web of Science, and Google Scholar using keyword combinations including "Big Data AND Drought," "Machine Learning AND Climate Change," "Remote Sensing AND Drought Monitoring," and "Artificial Intelligence AND Climate Prediction." The analytical process involved three main steps: thematic classification of studies according to their primary application domains (monitoring, prediction, modeling, and decision support); identification of methods, data sources, and technologies employed in each study; and comparative analysis of capabilities, limitations, and research gaps within each domain. In total, 57 sources were directly cited in developing the analytical framework of this study. Results and Discussion The review results indicate that the integration of multi-source data—derived from Earth observation systems, ground-based monitoring networks, and big data infrastructure—with AI algorithms enables more precise and real-time drought monitoring, identification of spatiotemporal patterns of climate change, improved prediction of climatic variables, and the development of early warning and decision-support systems. AI methods applied in drought and climate studies were classified into five categories: classical machine learning (e.g., Random Forest, Support Vector Machine), deep learning (e.g., LSTM, CNN), Transformer-based models, Explainable AI (XAI), and physics-informed AI, complemented by hybrid models that integrate physical and data-driven approaches to enhance both accuracy and generalizability. A comparative examination of widely used climate data repositories—including ERA5, CMIP6, CHIRPS, CRU, MODIS, Sentinel, Landsat, GPM, and GLDAS—revealed substantial variation in spatial and temporal resolution, directly affecting the reliability of AI-based analyses. A comparative table of seven representative applied studies further demonstrated the diversity of algorithms, data types, and evaluation metrics employed across the literature, while highlighting that no single study had comprehensively integrated big data infrastructure, AI algorithms, and decision-making processes—the gap this review specifically addresses. Despite these advances, persistent challenges were identified across four interconnected levels: data-related challenges (heterogeneity and uncertainty in environmental data), modeling challenges (overfitting and limited interpretability of deep learning models), computational/infrastructural challenges (dependence on costly computational resources, particularly in developing countries), and institutional challenges (the gap between the scientific community and policymakers, and the fragmentation of data across organizations). The analysis further revealed three principal research gaps: limited spatial transferability of AI models across different climatic regions, disproportionate emphasis on meteorological drought relative to hydrological, ecological, and socioeconomic drought types, and the absence of standardized frameworks for evaluating and comparing AI model performance. Conclusion The convergence of big data and AI has fundamentally transformed the traditional paradigm of drought and climate change monitoring, prediction, and management, marking a shift from static, retrospective approaches toward dynamic, multi-source, and forward-looking systems. However, fully realizing this potential requires addressing challenges that extend beyond purely technological dimensions to encompass data quality, model interpretability, computational infrastructure, and institutional data governance. Future research directions include the development of hybrid physics-informed AI models, the expansion of explainable AI to enhance trust in decision-critical applications, the advancement of climate digital twins, the integration of big data with IoT and edge computing for real-time monitoring, and the standardization of evaluation frameworks to improve comparability across studies. Ultimately, the effective deployment of big data and AI for sustainable natural resource management and climate resilience depends on aligning technological progress with institutional reform and stronger integration among climate science, data science, and public policy. Introduction The accelerating development of data generation and processing technologies—ranging from environmental sensor networks and the Internet of Things to remote sensing systems and cloud computing infrastructure—has produced an unprecedented volume of environmental data, enabling a more precise understanding of complex climatic processes. Within this context, big data and artificial intelligence (AI) have become fundamental pillars of climate science, not merely as auxiliary computational tools but as infrastructure that has transformed the very nature of research in this field. This transformation is particularly significant given that the Earth, as a complex and dynamic system, is shaped by the continuous interaction of atmospheric, hydrological, biological, and anthropogenic components—interactions that cannot be adequately understood without access to extensive, multi-source, and multi-scale data. Drought and the intensification of extreme climatic events have emerged as one of the most critical application domains for these technologies. As one of the most consequential environmental, economic, and social challenges of the present era, drought's gradual onset, extensive spatial coverage, and multidimensional consequences make its effective monitoring and prediction dependent on the analysis of vast volumes of heterogeneous data across multiple temporal and spatial scales. Despite growing research on environmental big data, existing studies have predominantly focused either on algorithmic and infrastructural development or on fragmented, discipline-specific applications related to drought, leaving a persistent need for analytical reviews that offer an integrated picture of trends, opportunities, and challenges in this field. The present study aims to provide an integrated analytical framework examining the role of big data and AI in drought monitoring, climate change prediction, and decision support for sustainable natural resource management, with its principal contribution lying in the simultaneous and interconnected examination of big data infrastructure, AI algorithms, and decision-making processes in natural resource governance. Materials and Methods This study was conducted using a narrative-analytical review approach. Unlike systematic reviews, which rely on formal screening procedures, strict inclusion/exclusion criteria, and quantitative evidence synthesis, this approach aims to combine, interpret, and critically analyze dispersed findings within the field to construct an integrated conceptual framework of trends, opportunities, and challenges. Given the breadth and interdisciplinary nature of the domain—spanning meteorology, hydrology, data science, and natural resource policy—this approach allows for greater flexibility in thematic analysis and the synthesis of multiple perspectives compared to systematic review methodologies. Relevant literature was retrieved from Scopus, Web of Science, and Google Scholar using keyword combinations including "Big Data AND Drought," "Machine Learning AND Climate Change," "Remote Sensing AND Drought Monitoring," and "Artificial Intelligence AND Climate Prediction." The analytical process involved three main steps: thematic classification of studies according to their primary application domains (monitoring, prediction, modeling, and decision support); identification of methods, data sources, and technologies employed in each study; and comparative analysis of capabilities, limitations, and research gaps within each domain. In total, 57 sources were directly cited in developing the analytical framework of this study. Results and Discussion The review results indicate that the integration of multi-source data—derived from Earth observation systems, ground-based monitoring networks, and big data infrastructure—with AI algorithms enables more precise and real-time drought monitoring, identification of spatiotemporal patterns of climate change, improved prediction of climatic variables, and the development of early warning and decision-support systems. AI methods applied in drought and climate studies were classified into five categories: classical machine learning (e.g., Random Forest, Support Vector Machine), deep learning (e.g., LSTM, CNN), Transformer-based models, Explainable AI (XAI), and physics-informed AI, complemented by hybrid models that integrate physical and data-driven approaches to enhance both accuracy and generalizability. A comparative examination of widely used climate data repositories—including ERA5, CMIP6, CHIRPS, CRU, MODIS, Sentinel, Landsat, GPM, and GLDAS—revealed substantial variation in spatial and temporal resolution, directly affecting the reliability of AI-based analyses. A comparative table of seven representative applied studies further demonstrated the diversity of algorithms, data types, and evaluation metrics employed across the literature, while highlighting that no single study had comprehensively integrated big data infrastructure, AI algorithms, and decision-making processes—the gap this review specifically addresses. Despite these advances, persistent challenges were identified across four interconnected levels: data-related challenges (heterogeneity and uncertainty in environmental data), modeling challenges (overfitting and limited interpretability of deep learning models), computational/infrastructural challenges (dependence on costly computational resources, particularly in developing countries), and institutional challenges (the gap between the scientific community and policymakers, and the fragmentation of data across organizations). The analysis further revealed three principal research gaps: limited spatial transferability of AI models across different climatic regions, disproportionate emphasis on meteorological drought relative to hydrological, ecological, and socioeconomic drought types, and the absence of standardized frameworks for evaluating and comparing AI model performance. Conclusion The convergence of big data and AI has fundamentally transformed the traditional paradigm of drought and climate change monitoring, prediction, and management, marking a shift from static, retrospective approaches toward dynamic, multi-source, and forward-looking systems. However, fully realizing this potential requires addressing challenges that extend beyond purely technological dimensions to encompass data quality, model interpretability, computational infrastructure, and institutional data governance. Future research directions include the development of hybrid physics-informed AI models, the expansion of explainable AI to enhance trust in decision-critical applications, the advancement of climate digital twins, the integration of big data with IoT and edge computing for real-time monitoring, and the standardization of evaluation frameworks to improve comparability across studies. Ultimately, the effective deployment of big data and AI for sustainable natural resource management and climate resilience depends on aligning technological progress with institutional reform and stronger integration among climate science, data science, and public policy.

https://clima.irimo.ir/article_251341_0a2388627db42a15565ad1e47daa6025.pdf