Golestan UniversityClimate Change Research2717-206672620260723Historical Changes and Future Projections of Radiative Flux and Surface Energy Balance Over IranHistorical Changes and Future Projections of Radiative Flux and Surface Energy Balance Over Iran11624281910.30488/ccr.2026.575657.1324FAAbbasaliDadashi-RoudbariAssistant Professor, Department of Geography, Faculty of Letters and Humanities, Ferdowsi University of Mashhad, Mashhad, Iran.0000-0002-9308-1019Journal Article20260213Surface downwelling shortwave radiation (RSDS) and surface downwelling longwave radiation (RLDS) are fundamental drivers of the surface energy balance and the broader hydrological cycle. Recognizing their pivotal role, this study investigates surface radiative flux dynamics across the arid and semi-arid landscapes of Iran. The investigation is grounded in data from Phase 6 of the Coupled Model Intercomparison Project (CMIP6). To mitigate structural uncertainties, a Multi-Model Ensemble Mean (MMEM) was constructed using five optimally selected models. Projections of these radiative components were evaluated across three future periods (near-, mid-, and far-future) extending to 2100, utilizing Shared Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5) relative to a historical baseline (1990–2014). Subsequent data analysis employed bilinear interpolation and spatial anomaly calculations. Findings indicate that during the historical period, the radiative variables exhibited an inverse spatial gradient. In future periods, RLDS shows a consistent upward trend in both scenarios, driven by tropospheric warming and water vapor feedback mechanisms. Notably, under SSP5-8.5, by the end of the century, southern coastal regions are projected to experience a substantial increase of 30–50 W m⁻². Conversely, RSDS exhibits primarily diminishing or fluctuating trends; however, positive anomalies were observed within the Central Plateau, a pattern attributed to the region’s inherent aridity. Consequently, the aggregate outcome of these shifts is the dominance of intensifying longwave radiation over variations in shortwave irradiance. The study demonstrates that this resulting radiative imbalance precipitates an enhanced positive surface net energy balance and exacerbates potential evapotranspiration, posing a severe threat to Iran’s water resources. Furthermore, the analysis reveals that low-lying coastal zones exhibit the highest thermodynamic sensitivity, whereas the Zagros and Alborz highlands play a pivotal role in modulating these radiative changes. The projections generated here offer immediate, practical value for strategic infrastructure planning. Specifically, these findings can directly inform the site selection, expansion, and efficiency forecasting of solar power facilities, while providing a critical scientific foundation for overhauling climate-responsive design standards and optimizing building energy management protocolsSurface downwelling shortwave radiation (RSDS) and surface downwelling longwave radiation (RLDS) are fundamental drivers of the surface energy balance and the broader hydrological cycle. Recognizing their pivotal role, this study investigates surface radiative flux dynamics across the arid and semi-arid landscapes of Iran. The investigation is grounded in data from Phase 6 of the Coupled Model Intercomparison Project (CMIP6). To mitigate structural uncertainties, a Multi-Model Ensemble Mean (MMEM) was constructed using five optimally selected models. Projections of these radiative components were evaluated across three future periods (near-, mid-, and far-future) extending to 2100, utilizing Shared Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5) relative to a historical baseline (1990–2014). Subsequent data analysis employed bilinear interpolation and spatial anomaly calculations. Findings indicate that during the historical period, the radiative variables exhibited an inverse spatial gradient. In future periods, RLDS shows a consistent upward trend in both scenarios, driven by tropospheric warming and water vapor feedback mechanisms. Notably, under SSP5-8.5, by the end of the century, southern coastal regions are projected to experience a substantial increase of 30–50 W m⁻². Conversely, RSDS exhibits primarily diminishing or fluctuating trends; however, positive anomalies were observed within the Central Plateau, a pattern attributed to the region’s inherent aridity. Consequently, the aggregate outcome of these shifts is the dominance of intensifying longwave radiation over variations in shortwave irradiance. The study demonstrates that this resulting radiative imbalance precipitates an enhanced positive surface net energy balance and exacerbates potential evapotranspiration, posing a severe threat to Iran’s water resources. Furthermore, the analysis reveals that low-lying coastal zones exhibit the highest thermodynamic sensitivity, whereas the Zagros and Alborz highlands play a pivotal role in modulating these radiative changes. The projections generated here offer immediate, practical value for strategic infrastructure planning. Specifically, these findings can directly inform the site selection, expansion, and efficiency forecasting of solar power facilities, while providing a critical scientific foundation for overhauling climate-responsive design standards and optimizing building energy management protocolshttps://ccr.gu.ac.ir/article_242819_7bac8405245a59d000955ee370d4cecf.pdfGolestan UniversityClimate Change Research2717-206672620260723Modeling Sea Surface Temperature Using the WRF Numerical Model: Application in a Coastal Arid Region (Case Study: Northern Coasts of the Persian Gulf)Modeling Sea Surface Temperature Using the WRF Numerical Model: Application in a Coastal Arid Region (Case Study: Northern Coasts of the Persian Gulf)517024486910.30488/ccr.2026.581093.1334FAHamidAbbaspourMaster of Science in Marine Environment, Faculty of Nano and Biological Sciences and Technology, Persian Gulf University, Bushehr, IranEsmaeilAbbasiAssistant professor of Environment Department, Persian Gulf Research Institute, Persian Gulf University, Bushehr, IranHanaEtemadiAssistant professor of Environment Department, Persian Gulf Research Institute, Persian Gulf University, Bushehr, IranImanRoustaAssociate Professor, Department of Geography, Yazd University, Yazd, Iran0000-0002-3694-6936Journal Article20260510Climate change, as one of the most pressing challenges of the twenty-first century, has profoundly affected marine and coastal ecosystems, particularly coastal drylands. Rising sea surface temperatures (SST), the increased frequency of extreme climate events, and intensified thermal stresses are among the key consequences of this global phenomenon. In this context, the application of downscaling numerical models such as the Weather Research and Forecasting (WRF) model offers a novel approach for SST modeling in these environmentally sensitive regions. The Persian Gulf, recognized as one of the world's most significant coastal dryland ecosystems, is characterized by high annual evaporation, elevated salinity, shallow depth, and strong seasonal temperature fluctuations, rendering it particularly vulnerable to climate change. The primary objective of this study is to identify the most optimal configuration of physical parameterization schemes within the WRF model for simulating SST in the Persian Gulf. SST observational data were obtained from the Iran Meteorological Organization. The WRF model was executed using eight distinct configurations, and the model outputs were validated using the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The results indicated that Configuration No. 5, which comprises the WSM 5-class microphysics, RRTM longwave radiation, Dudhia shortwave radiation, the Revised MM5 Monin Obukhov surface layer scheme, the Unified Noah land surface model, the YSU planetary boundary layer scheme, and the Kain–Fritsch cumulus parameterization scheme, exhibited the lowest error metrics and the highest correlation coefficient (0.985), and was therefore identified as the optimal configuration. The RMSE values for spring, summer, autumn, and winter were calculated as 0.036°C, 0.012°C, 0.028°C, and 0.011°C, respectively. Based on these findings, the WRF model outputs can serve as a suitable, high-precision alternative to the Bushehr meteorological buoy data for a range of applications, including climate change monitoring, fisheries management, the protection of coral reefs against bleaching induced by global warming, and the prediction of extreme temperature events in this coastal dryland region.Climate change, as one of the most pressing challenges of the twenty-first century, has profoundly affected marine and coastal ecosystems, particularly coastal drylands. Rising sea surface temperatures (SST), the increased frequency of extreme climate events, and intensified thermal stresses are among the key consequences of this global phenomenon. In this context, the application of downscaling numerical models such as the Weather Research and Forecasting (WRF) model offers a novel approach for SST modeling in these environmentally sensitive regions. The Persian Gulf, recognized as one of the world's most significant coastal dryland ecosystems, is characterized by high annual evaporation, elevated salinity, shallow depth, and strong seasonal temperature fluctuations, rendering it particularly vulnerable to climate change. The primary objective of this study is to identify the most optimal configuration of physical parameterization schemes within the WRF model for simulating SST in the Persian Gulf. SST observational data were obtained from the Iran Meteorological Organization. The WRF model was executed using eight distinct configurations, and the model outputs were validated using the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The results indicated that Configuration No. 5, which comprises the WSM 5-class microphysics, RRTM longwave radiation, Dudhia shortwave radiation, the Revised MM5 Monin Obukhov surface layer scheme, the Unified Noah land surface model, the YSU planetary boundary layer scheme, and the Kain–Fritsch cumulus parameterization scheme, exhibited the lowest error metrics and the highest correlation coefficient (0.985), and was therefore identified as the optimal configuration. The RMSE values for spring, summer, autumn, and winter were calculated as 0.036°C, 0.012°C, 0.028°C, and 0.011°C, respectively. Based on these findings, the WRF model outputs can serve as a suitable, high-precision alternative to the Bushehr meteorological buoy data for a range of applications, including climate change monitoring, fisheries management, the protection of coral reefs against bleaching induced by global warming, and the prediction of extreme temperature events in this coastal dryland region.https://ccr.gu.ac.ir/article_244869_c4b7f68e5c28685ddc35241305e3a429.pdfGolestan UniversityClimate Change Research2717-206672620260723The Impact of Climate Change on Residential Architectural Design Strategies in Iran's Climatic Zones: Analysis of Thermal Comfort Diagrams in 11 Selected CitiesThe Impact of Climate Change on Residential Architectural Design Strategies in Iran's Climatic Zones: Analysis of Thermal Comfort Diagrams in 11 Selected Cities173424600810.30488/ccr.2026.579046.1328FAMotahareKarimabadiMaster's degree in Architectural Engineering, Department of Architecture, Faculty of Engineering, Golestan University, Gorgan, IranMohammadFarrokhzadAssociate Professor and Academic Member, Department of Architecture, Faculty of Engineering, Golestan University, Gorgan, IranJournal Article20260418Climate change, as one of the main challenges of the current century, has extensive impacts on energy performance and thermal comfort of buildings. Given Iran’s climatic diversity and the lack of comprehensive research on redefining residential architectural design strategies based on future climate scenarios, this study aims to identify changes in thermal comfort and develop climate-responsive design guidelines for Iran’s five main climatic zones (Caspian, cold mountainous, hot-dry, hot semi-humid, and hot-humid) under the influence of climate change. For this purpose, 11 representative cities were selected from the mentioned zones, and current (2020) and future (2100) climate data were extracted using Meteonorm 8 software based on the moderate RCP4.5 scenario. Data analysis was conducted using Climate Consultant 6 software, employing ASHRAE-55 and Givoni thermal comfort charts. The findings indicate that the thermal comfort threshold will decrease in most studied cities by 2100, particularly in hot-humid (Bandar Lengeh with a 6.2% decrease) and Caspian (Ramsar with a 3.3% decrease) zones. The need for active cooling systems and solar shading devices increases across all zones, while the need for active heating decreases significantly. In hot-dry zones, two-stage evaporative coolers replace single-stage ones, and in hot-humid zones, cooling with dehumidification becomes the most important strategy. Based on the analyses, zone-specific design guidelines are proposed, including optimization of window dimensions and orientation, shading device type, appropriate materials, and use of semi-open spaces. The results of this study can serve as a basis for revising climate-responsive housing design standards in Iran, considering future climate changesClimate change, as one of the main challenges of the current century, has extensive impacts on energy performance and thermal comfort of buildings. Given Iran’s climatic diversity and the lack of comprehensive research on redefining residential architectural design strategies based on future climate scenarios, this study aims to identify changes in thermal comfort and develop climate-responsive design guidelines for Iran’s five main climatic zones (Caspian, cold mountainous, hot-dry, hot semi-humid, and hot-humid) under the influence of climate change. For this purpose, 11 representative cities were selected from the mentioned zones, and current (2020) and future (2100) climate data were extracted using Meteonorm 8 software based on the moderate RCP4.5 scenario. Data analysis was conducted using Climate Consultant 6 software, employing ASHRAE-55 and Givoni thermal comfort charts. The findings indicate that the thermal comfort threshold will decrease in most studied cities by 2100, particularly in hot-humid (Bandar Lengeh with a 6.2% decrease) and Caspian (Ramsar with a 3.3% decrease) zones. The need for active cooling systems and solar shading devices increases across all zones, while the need for active heating decreases significantly. In hot-dry zones, two-stage evaporative coolers replace single-stage ones, and in hot-humid zones, cooling with dehumidification becomes the most important strategy. Based on the analyses, zone-specific design guidelines are proposed, including optimization of window dimensions and orientation, shading device type, appropriate materials, and use of semi-open spaces. The results of this study can serve as a basis for revising climate-responsive housing design standards in Iran, considering future climate changeshttps://ccr.gu.ac.ir/article_246008_f84b389d7a59eb4bc352767cb7a075ba.pdfGolestan UniversityClimate Change Research2717-206672620260723Analyzing the Performance of Machine Learning Models in Detecting Teleconnection Signals Affecting Temperature and Precipitation in Northwest IranAnalyzing the Performance of Machine Learning Models in Detecting Teleconnection Signals Affecting Temperature and Precipitation in Northwest Iran718824631310.30488/ccr.2026.579366.1337FASaeedArmaghan BostanabadDepartment of Climatology, Faculty of Planning and Environmental Sciences, University of Tabriz, Tabriz, IranBehrouzSari SarrafDepartment of Climatology, Faculty of Planning and Environmental Sciences, University of Tabriz, Tabriz, Iran0000-0003-3923-0412MajidRezaei Banafsheh DaraghDepartment of Climatology, Faculty of Planning and Environmental Sciences, University of Tabriz, Tabriz, Iran0009-0002-1204-2518Ali MohmmadKhorshiddoostDepartment of Climatology, Faculty of Planning and Environmental Sciences, University of Tabriz, Tabriz, Iran.0000-0003-3020-7603Journal Article20260523This study aimed to develop an intelligent and interpretable modeling framework for the monthly prediction of precipitation and temperature at 24 synoptic stations in northwest Iran over a 30‑year period. For this purpose, the performance of three different approaches was evaluated: statistical modeling (Multivariate Linear Regression, MLR), fuzzy inference (Adaptive Neuro‑Fuzzy Inference System, ANFIS), and artificial intelligence (Multilayer Perceptron neural network, MLP). To overcome the limitations of classical methods in parameter optimization, a genetic algorithm (GA) was employed to design the optimal network architecture and determine the connection weights (resulting in the hybrid GA‑MLP model). The results showed that the proposed GA-MLP hybrid model delivered the most efficient performance by reducing the RMSE (e.g., demonstrating a 30% improvement in temperature and 23.5% in precipitation compared to the MLR model) and successfully overcoming the non-convergence issue of the baseline MLP model. Specifically, it lowered the temperature prediction error at the Parsabad station to 1.3°C and the precipitation prediction error at the Jolfa station to 10.9 mm. Sensitivity analysis using a feature‑importance approach revealed the key role of six‑month time lags and teleconnections in the region’s climatic fluctuations, confirming the model’s ability to grasp the underlying physics of the problem. Furthermore, uncertainty assessment using a calibrated bootstrap method indicated a high coverage rate above 95%, ensuring the model’s reliability for operational decision‑making. Finally, topographic analysis demonstrated no significant correlation between model error and elevation, which attests to the spatial robustness of the proposed model across heterogeneous climates.This study aimed to develop an intelligent and interpretable modeling framework for the monthly prediction of precipitation and temperature at 24 synoptic stations in northwest Iran over a 30‑year period. For this purpose, the performance of three different approaches was evaluated: statistical modeling (Multivariate Linear Regression, MLR), fuzzy inference (Adaptive Neuro‑Fuzzy Inference System, ANFIS), and artificial intelligence (Multilayer Perceptron neural network, MLP). To overcome the limitations of classical methods in parameter optimization, a genetic algorithm (GA) was employed to design the optimal network architecture and determine the connection weights (resulting in the hybrid GA‑MLP model). The results showed that the proposed GA-MLP hybrid model delivered the most efficient performance by reducing the RMSE (e.g., demonstrating a 30% improvement in temperature and 23.5% in precipitation compared to the MLR model) and successfully overcoming the non-convergence issue of the baseline MLP model. Specifically, it lowered the temperature prediction error at the Parsabad station to 1.3°C and the precipitation prediction error at the Jolfa station to 10.9 mm. Sensitivity analysis using a feature‑importance approach revealed the key role of six‑month time lags and teleconnections in the region’s climatic fluctuations, confirming the model’s ability to grasp the underlying physics of the problem. Furthermore, uncertainty assessment using a calibrated bootstrap method indicated a high coverage rate above 95%, ensuring the model’s reliability for operational decision‑making. Finally, topographic analysis demonstrated no significant correlation between model error and elevation, which attests to the spatial robustness of the proposed model across heterogeneous climates.https://ccr.gu.ac.ir/article_246313_8cab9db68c0e7006e76a67847de637ac.pdfGolestan UniversityClimate Change Research2717-206672620260723Analysis of Temporal Trends and Seasonal Variability in Column-Averaged Methane Concentrations (XCH4) over the Middle East Using Sentinel-5P/TROPOMI Data during 2018–2023Analysis of Temporal Trends and Seasonal Variability in Column-Averaged Methane Concentrations (XCH4) over the Middle East Using Sentinel-5P/TROPOMI Data during 2018–2023355024678210.30488/ccr.2026.580388.1332FAAzizAzizpourM.Sc. in Environmental Engineering, Department of Environmental Engineering, Faculty of Environment, University of Tehran, Tehran, Iran0009-0004-4968-6434MohsenMirmohammadiAssistant Professor, Department of Environmental Engineering, Faculty of Environment, University of Tehran, Tehran, Iran0009-0004-4968-6434RoohollahNooriAssistant Professor, Department of Environmental Engineering, Faculty of Environment, University of Tehran, Tehran, Iran0000-0002-7463-8563SeyedMohsen MousaviDepartment of Environmental Sciences, Faculty of Natural Resources, University of Guilan, Rasht, IranJournal Article20260504Methane (CH₄) is one of the most important short-lived greenhouse gases affecting climate change, and its monitoring in energy-intensive regions is of particular importance. The aim of this research is to analyze the temporal trend and seasonal pattern of XCH₄ column concentration in the Middle East during the period 2018-2023 using Sentinel-5P/TROPOMI satellite data. In this study, daily Level 2 XCH<sub>4</sub> data were used, and after processing in a Python environment, XCH<sub>4</sub> values were aggregated at monthly, seasonal, and annual scales. The trend of changes was then evaluated using the Mann-Kendall test, Sen's slope estimator, and Ordinary Least Squares (OLS) regression. The results showed that XCH₄ in the Middle East during the study period has an increasing, monotonic, and statistically significant trend; its annual average increased from 1862.14 ppb in 2018 to 1918.77 ppb in 2023. The Mann-Kendall test showed Z = 9.64, p-value = 3.04×10⁻²¹, and τ = 0.75, indicating a strong and significant increasing trend over the entire study period. Sen's slope estimator and the OLS model also showed that XCH₄ in the Middle East increased by an average of about 12 to 13 ppb annually. Seasonally, the minimum mean XCH₄ was observed in winter (1876.2 ppb) and its maximum in summer (1898.3 ppb). Monthly analysis also confirmed this pattern, with the lowest value recorded in February and the highest in August–September. Additionally, annual and seasonal average maps indicated spatial heterogeneity of XCH₄ and higher values around the Persian Gulf and parts of the Arabian Peninsula. In summary, the results showed that the methane column concentration in the Middle East has followed a consistent increasing trend and a regular seasonal cycle in recent years. These findings indicate the suitable capacity of satellite monitoring for continuous observation of the temporal and spatial behavior of methane at a regional scaleMethane (CH₄) is one of the most important short-lived greenhouse gases affecting climate change, and its monitoring in energy-intensive regions is of particular importance. The aim of this research is to analyze the temporal trend and seasonal pattern of XCH₄ column concentration in the Middle East during the period 2018-2023 using Sentinel-5P/TROPOMI satellite data. In this study, daily Level 2 XCH<sub>4</sub> data were used, and after processing in a Python environment, XCH<sub>4</sub> values were aggregated at monthly, seasonal, and annual scales. The trend of changes was then evaluated using the Mann-Kendall test, Sen's slope estimator, and Ordinary Least Squares (OLS) regression. The results showed that XCH₄ in the Middle East during the study period has an increasing, monotonic, and statistically significant trend; its annual average increased from 1862.14 ppb in 2018 to 1918.77 ppb in 2023. The Mann-Kendall test showed Z = 9.64, p-value = 3.04×10⁻²¹, and τ = 0.75, indicating a strong and significant increasing trend over the entire study period. Sen's slope estimator and the OLS model also showed that XCH₄ in the Middle East increased by an average of about 12 to 13 ppb annually. Seasonally, the minimum mean XCH₄ was observed in winter (1876.2 ppb) and its maximum in summer (1898.3 ppb). Monthly analysis also confirmed this pattern, with the lowest value recorded in February and the highest in August–September. Additionally, annual and seasonal average maps indicated spatial heterogeneity of XCH₄ and higher values around the Persian Gulf and parts of the Arabian Peninsula. In summary, the results showed that the methane column concentration in the Middle East has followed a consistent increasing trend and a regular seasonal cycle in recent years. These findings indicate the suitable capacity of satellite monitoring for continuous observation of the temporal and spatial behavior of methane at a regional scalehttps://ccr.gu.ac.ir/article_246782_7471fcbdf30507b3c8b14fecb0c25b6e.pdfGolestan UniversityClimate Change Research2717-206672620260723Analysis of Chemical Weathering Intensity and Paleoclimatic Interpretation of Quaternary Sediments Using CIA and PIA Geochemical Indices in an Arid Environment (Case Study: Abarkuh Playa, Iran)Analysis of Chemical Weathering Intensity and Paleoclimatic Interpretation of Quaternary Sediments Using CIA and PIA Geochemical Indices in an Arid Environment (Case Study: Abarkuh Playa, Iran)8910724726410.30488/ccr.2026.588183.1346FAMohamadُاSharifi PaichoonAssociate Professor of Geomorphology, Department of Geography, Yazd University, Yazd, Iran0000-0002-9456-5285AliShahriarAssistant Professor of Geomorphology, Department of Geography, Yazd University, Yazd, IranMahdiehMasoudiM.Sc. Graduate, Department of Geography, Yazd University, Yazd, IranJournal Article20260622This study examines the degree of chemical weathering in Quaternary sediments using geochemical proxies, particularly the CIA and PIA. The primary aim is to reconstruct past environmental and climatic conditions and to assess the intensity of chemical alteration in sediments in relation to weathering processes and sedimentary evolution. The investigation is based on geochemical and mineralogical data obtained from an 8-meter sedimentary trench comprising 15 samples from the Abarkouh playa. Major and trace elements, along with their corresponding oxides, were analyzed using XRF, and additional indices, including CIA, PIA, and a calcification index, were computed to evaluate weathering intensity and paleo-lake level fluctuations. The CIA is derived from the proportion of alumina relative to alkali and alkaline earth oxides, whereas the PIA reflects the extent of plagioclase feldspar alteration. The results indicate CIA values ranging from 20 to 50 and PIA values between 15 and 38, both of which point to weak to moderate chemical weathering conditions. Such relatively low values suggest limited hydrolytic processes, weak leaching of mobile elements, and a predominance of physical weathering over chemical alteration. Variations in these indices throughout the stratigraphic sequence further imply short-term climatic oscillations during the Quaternary, characterized by alternating dry and humid phases. Stratigraphic analysis of the trench reveals pronounced environmental changes. The lower and older sediments, enriched in stable oxides such as Al₂O₃, Fe₂O₃, and SiO₂, correspond to warm and humid conditions during the early Quaternary. The middle section reflects fluctuating humid, cold, and arid phases, evidenced by carbonate precipitation in sample 11 and elevated lake levels accompanied by enhanced biogeochemical activity and magnesium enrichment in samples 13 and 14. In contrast, the uppermost layer shows a marked increase in Na₂O and Cl, indicating progressive desiccation of the lake system and the establishment of present-day hyper-arid environmental conditionsThis study examines the degree of chemical weathering in Quaternary sediments using geochemical proxies, particularly the CIA and PIA. The primary aim is to reconstruct past environmental and climatic conditions and to assess the intensity of chemical alteration in sediments in relation to weathering processes and sedimentary evolution. The investigation is based on geochemical and mineralogical data obtained from an 8-meter sedimentary trench comprising 15 samples from the Abarkouh playa. Major and trace elements, along with their corresponding oxides, were analyzed using XRF, and additional indices, including CIA, PIA, and a calcification index, were computed to evaluate weathering intensity and paleo-lake level fluctuations. The CIA is derived from the proportion of alumina relative to alkali and alkaline earth oxides, whereas the PIA reflects the extent of plagioclase feldspar alteration. The results indicate CIA values ranging from 20 to 50 and PIA values between 15 and 38, both of which point to weak to moderate chemical weathering conditions. Such relatively low values suggest limited hydrolytic processes, weak leaching of mobile elements, and a predominance of physical weathering over chemical alteration. Variations in these indices throughout the stratigraphic sequence further imply short-term climatic oscillations during the Quaternary, characterized by alternating dry and humid phases. Stratigraphic analysis of the trench reveals pronounced environmental changes. The lower and older sediments, enriched in stable oxides such as Al₂O₃, Fe₂O₃, and SiO₂, correspond to warm and humid conditions during the early Quaternary. The middle section reflects fluctuating humid, cold, and arid phases, evidenced by carbonate precipitation in sample 11 and elevated lake levels accompanied by enhanced biogeochemical activity and magnesium enrichment in samples 13 and 14. In contrast, the uppermost layer shows a marked increase in Na₂O and Cl, indicating progressive desiccation of the lake system and the establishment of present-day hyper-arid environmental conditionshttps://ccr.gu.ac.ir/article_247264_d7f1b406b8cbd1400c1555541514c330.pdf