University of Tabriz Journal of Geography and Planning 2008-8078 30 96 2026 08 23 "Analysis of Spatial and Temporal Distribution of Middle Clouds in Khuzestan Province" "Analysis of Spatial and Temporal Distribution of Middle Clouds in Khuzestan Province" 20460 10.22034/gp.2025.67295.3406 FA Fatemeh Saghi Department of Geography Management Ahv. C. Islamic Azad university. Ahvaz. Iran MOHAMMAD BAFGHIZADEH Department of Geography, Payame Noor Universtiy, PO BOX 19395-3697 Tehran,IRAN 0000-0003-4663-9072 Gebraeil Ghorbanian Department of Geography Management, Ahv.C., Islamic Azad university, Ahvaz, Iran Reza Borna Department of Geography Management, Ahv.C., Islamic Azad university, Ahvaz, Iran Journal Article 2025 05 17 Introduction: Clouds are among the most important climatic elements affecting atmospheric and hydrological systems and play a key role in Earth's energy balance and hydrological cycle. Understanding the spatial and temporal patterns of mid-level clouds is crucial due to their influence on precipitation, temperature, and radiative balance, which is vital for water resources management, agriculture, and weather forecasting.<br /><br />Objective: This study aimed to investigate the spatial distribution and long-term (30-year, 1990–2020) trends of mid-level clouds in Khuzestan Province and to analyze their variations during warm and cold seasons.<br /><br />Methodology: Mid-level cloud data were obtained from the ERA5 reanalysis dataset with a spatial resolution of 0.25° for the 30-year period. Analyses included calculating spatial and seasonal averages of cloudiness, examining 30-year time series trends using the Sen’s slope estimator and the non-parametric Mann-Kendall test, and visualizing the data using GIS software.<br /><br />Findings: Results indicated that mid-level clouds have a higher contribution to total cloudiness during the cold season, with an average cloudiness of about 37% compared to only 0.5% in the warm season. Spatial distribution varied, with the northern and northeastern regions exhibiting the highest cloudiness and the southern and southeastern regions the lowest. Although annual fluctuations were observed over the 30-year period, no statistically significant long-term trend was detected.<br /><br />Conclusion: Understanding the spatial and temporal patterns of mid-level clouds can support water resource management, agricultural planning, precipitation forecasting, and drought risk reduction. However, limitations such as spatial and temporal errors in reanalysis data and unconsidered short-term fluctuations exist. Future studies could provide more precise analyses using higher-resolution satellite and ground-based observations. Introduction: Clouds are among the most important climatic elements affecting atmospheric and hydrological systems and play a key role in Earth's energy balance and hydrological cycle. Understanding the spatial and temporal patterns of mid-level clouds is crucial due to their influence on precipitation, temperature, and radiative balance, which is vital for water resources management, agriculture, and weather forecasting.<br /><br />Objective: This study aimed to investigate the spatial distribution and long-term (30-year, 1990–2020) trends of mid-level clouds in Khuzestan Province and to analyze their variations during warm and cold seasons.<br /><br />Methodology: Mid-level cloud data were obtained from the ERA5 reanalysis dataset with a spatial resolution of 0.25° for the 30-year period. Analyses included calculating spatial and seasonal averages of cloudiness, examining 30-year time series trends using the Sen’s slope estimator and the non-parametric Mann-Kendall test, and visualizing the data using GIS software.<br /><br />Findings: Results indicated that mid-level clouds have a higher contribution to total cloudiness during the cold season, with an average cloudiness of about 37% compared to only 0.5% in the warm season. Spatial distribution varied, with the northern and northeastern regions exhibiting the highest cloudiness and the southern and southeastern regions the lowest. Although annual fluctuations were observed over the 30-year period, no statistically significant long-term trend was detected.<br /><br />Conclusion: Understanding the spatial and temporal patterns of mid-level clouds can support water resource management, agricultural planning, precipitation forecasting, and drought risk reduction. However, limitations such as spatial and temporal errors in reanalysis data and unconsidered short-term fluctuations exist. Future studies could provide more precise analyses using higher-resolution satellite and ground-based observations.