Classification of the new raster-based method for Iranian regional climate

Introduction
Climate is a fundamental component of the Earth system that governs a wide array of environmental, ecological, and socio-economic processes. The diversity of climatic elements—including temperature, precipitation, humidity, wind, and solar radiation—plays a pivotal role in shaping regional and global climatic patterns. This variability results in the emergence of distinct climatic zones, each with unique environmental and socio-economic characteristics. Historically, the study of climate has intrigued scholars, scientists, and policy-makers alike, dating back to ancient civilizations that sought to understand weather phenomena to improve agricultural practices, navigation, and settlement planning. In contemporary times, with the escalation of global climate change, the importance of accurately understanding and classifying regional climates has grown exponentially. However, the diverse nature of climatic elements, coupled with their spatial and temporal variability, presents significant challenges in conducting integrated and simultaneous analyses. These challenges are further intensified in regional studies that span large geographical extents, diverse topographical features, and variable data quality from multiple stations or monitoring systems. Consequently, climate classification has emerged as an essential scientific tool, aiming to simplify this complexity by categorizing regions into coherent climatic zones based on statistical and environmental indicators. Such classifications are foundational for various applications, including ecological zoning, water resource management, urban planning, and climate adaptation strategies. In countries like Iran, where climatic diversity is pronounced due to a wide range of elevation zones, proximity to seas, and interaction with different atmospheric circulation systems, the need for accurate and region-specific climate classification becomes even more critical. Previous studies on Iran’s climate have employed traditional classification systems such as Köppen-Geiger, De Martonne, or Emberger indices, which, while useful, often fall short in capturing localized microclimatic variations and the dynamic influence of topographic features. Moreover, these classifications typically rely on long-term averages and may not incorporate recent trends linked to global climate change. This study addresses these gaps by adopting a data-driven and spatially explicit approach to classify the Iranian climate using long-term meteorological observations. By integrating advanced geostatistical methods and clustering techniques, this research aims to delineate coherent climatic zones across the country that reflect both macro- and micro-climatic influences. The outcomes not only contribute to the refinement of climatic classification in Iran but also serve as a crucial baseline for evaluating future climate trends and guiding decision-making in sectors such as agriculture, water resource management, tourism, and urban development.
 
Materials and Methods
The study area encompasses the entire territory of Iran, situated in the southwest of Asia and characterized by its complex topography, including mountain ranges such as Alborz and Zagros, vast deserts like Dasht-e Kavir and Dasht-e Lut, and coastal zones along the Caspian Sea, Persian Gulf, and the Sea of Oman.




Classification of the new raster-based method for Iranian regional climate                                                              Ahmadi and Kamangar / 72




This geomorphological diversity significantly influences the distribution of climatic variables across the country. The research utilized ground-based meteorological data collected from 92 synoptic stations maintained by the Iran Meteorological Organization (IRIMO), covering a 40-year period from 1980 to 2019. The selected parameters included daily minimum temperature, maximum temperature, total precipitation, and relative humidity—each being a critical determinant of climatic conditions. To create continuous climatic surfaces from the discrete point data, the CoKriging interpolation technique was employed. This geostatistical method allows for the estimation of spatially distributed climatic variables by considering both the primary and secondary variables, thereby improving the accuracy of spatial predictions. The interpolation results were validated using cross-validation techniques to ensure reliability and minimize spatial bias. For classification, a multivariate clustering approach was adopted. Initially, all variables were normalized to ensure uniformity in the scale and to avoid dominance by any single variable. Then, the Euclidean distance metric was used to calculate the dissimilarity matrix among the observations. Hierarchical clustering with Ward's linkage method was applied, which minimizes the variance within each cluster. To determine the optimal number of clusters (i.e., climatic zones), various validity indices such as the Davies-Bouldin Index and the Silhouette Score were evaluated. Spatial analysis and visualization were performed in GIS environments using tools such as ArcGIS and QGIS, allowing for the integration of climatic data with elevation models, land cover maps, and hydrographic networks. This facilitated a nuanced understanding of the spatial relationships between climate zones and physiographic features.
 
Results and Discussion
The spatial distribution of climatic variables across Iran reveals substantial heterogeneity that reflects the interplay between latitude, elevation, proximity to water bodies, and the influence of prevailing wind patterns and atmospheric systems. The analysis showed that:

Temperature: Maximum temperature values exhibit a clear gradient from north to south and from west to east. The southern and southeastern regions, including Sistan-Baluchestan, Kerman, and parts of Khuzestan, experience the highest maximum temperatures, often exceeding 45°C in summer. In contrast, the northwestern provinces such as West Azerbaijan and Kurdistan, influenced by higher elevations and continental air masses, record the lowest maximum temperatures, with winter temperatures frequently dropping below zero.
Precipitation: Precipitation patterns are largely dictated by elevation and the presence of orographic barriers. The highest precipitation occurs along the southern Caspian coast and the western slopes of the Zagros Mountains. These regions benefit from moist air masses from the Caspian Sea and the Mediterranean, respectively. In contrast, central and southeastern Iran are characterized by hyper-arid conditions, with annual rainfall often below 100 mm, making them among the driest areas in the world.
Humidity: Relative humidity is markedly higher in coastal regions—particularly the northern Caspian belt—due to maritime influences. In inland desert regions, low humidity values correspond with high evaporation rates and limited vegetation cover, intensifying aridity.

Through cluster analysis, Iran was categorized into ten major climatic zones, each with distinctive climatic characteristics. These include:

Western Caspian Coastal – High rainfall (>1100 mm), mild winters, and moderate summers.
Zagros Highlands – Moderate rainfall and cold winters; high topographic variability.
Northwestern Cold – Characterized by long, cold winters and moderate precipitation.
Central Arid – Low precipitation (<100 mm), large temperature range.
Eastern Highlands – Moderate elevation, low humidity, relatively cooler than adjacent deserts.
Southeastern Arid – High temperatures (>25°C annual mean), minimal rainfall.
Kerman-Sistan Semi-Arid – Transitional zone with variable rainfall and high temperature extremes.
Southern Coastal – Maritime influence from the Persian Gulf; hot and humid summers.
Khorasan Semi-Humid – Influenced by northern air masses; moderate rainfall.
Central Plateau Margin – A zone of transition with mixed climatic signatures.

Elevation and topographic complexity emerged as major determinants in shaping climatic diversity. The Alborz and Zagros Mountain ranges act as significant barriers, redirecting air flows and creating rain shadows that contribute to the development of microclimates. These features explain why even regions at similar latitudes can have vastly different climates, as is the case in southern Iran, where some areas are hot and humid while others are hot and arid.
The classification also highlighted the influence of large-scale atmospheric systems such as the Subtropical Jet Stream, Mediterranean cyclones, and Indian Monsoon incursions, which periodically affect parts of Iran, contributing to seasonal rainfall variability and interannual extremes.
Conclusion
This study presents a novel, data-driven framework for the climatic classification of Iran based on long-term observational records and advanced spatial analysis. By integrating ground-based meteorological data from 92 synoptic stations over a 40-year period and employing geostatistical and clustering techniques, the research successfully delineated ten distinct climatic zones across the country. This classification reflects not only large-scale atmospheric circulation patterns but also the pronounced impact of topography, elevation, and proximity to water bodies, which contribute to the development of localized microclimates. Among the identified zones, the central-eastern arid region emerged as the most extensive, covering nearly one-third of the country, characterized by hot, dry conditions and minimal annual precipitation (~90 mm). In stark contrast, the western Caspian coastal zone, the smallest in spatial extent (~0.44% of Iran's area), was found to be the most humid and rain-rich region, receiving more than 1137 mm of precipitation annually. These sharp contrasts illustrate the climatic heterogeneity of Iran, driven by elevation gradients, wind systems, and land-sea interactions. Moreover, the study identified temperature and precipitation gradients across the country: temperature increases generally from north to south and west to east, while precipitation shows a reverse gradient, increasing from south to north and from east to west. The southeastern region was identified as the hottest zone, with average annual temperatures exceeding 25°C, whereas the northwestern highlands of Azerbaijan and Kurdistan exhibited the coldest conditions, with annual mean temperatures around 12°C. One of the major achievements of this study lies in overcoming the limitations of classical climate classification methods by using high-resolution spatial modeling techniques. The use of CoKriging interpolation minimized the errors typically associated with point-based station data, and the clustering method enabled the recognition of transitional climatic zones that are often overlooked in rigid classification systems like Köppen or Emberger. The implications of this work are far-reaching. Accurate identification of climatic zones provides a foundation for climate-informed decision-making in key sectors such as agriculture, water management, urban development, health, and disaster risk reduction. For example, agricultural planning can benefit from knowing the precise climatic needs of crops, urban infrastructure can be designed to better withstand local climatic stressors, and water resource allocations can be tailored to match the precipitation and evaporation patterns of each zone. Furthermore, this classification provides a baseline for monitoring future climate change. As global temperatures rise and precipitation patterns shift, tracking how and where Iran’s climatic zones evolve will be crucial for building adaptive capacity and resilience in both natural ecosystems and human systems. From a scientific perspective, the approach adopted here—combining long-term ground observations with spatial modeling and multivariate clustering—offers a replicable and scalable method for climatic classification in other topographically and climatically diverse regions. It can also serve as a base layer for more complex environmental modeling, such as hydrological simulations, ecological niche modeling, and climate change impact assessments.