نوع مقاله : مقاله پژوهشی
نویسندگان
1 پژوهشکده سنجش از دور و GIS محیطی دانشگاه علوم کشاورزی و منابع طبیعی ساری، ساری، ایران
2 پارک علم و فناوری مازندران، ساری، ایران
3 پژوهشکده سنجش ازدور و GIS محیطی دانشگاه علوم کشاورزی و منابع طبیعی ساری، ساری، ایران
چکیده
کلیدواژهها
عنوان مقاله [English]
نویسندگان [English]
Background and Objective: Snow cover is one of the most influential surface components, playing a key role in regulating energy exchange, controlling land surface temperature, and governing climatic dynamics in mountainous regions. In recent decades, particularly at mid-latitudes and high elevations, rising temperatures have altered snow accumulation and melt patterns, with significant consequences for water resources, natural hazards, and local climate. The high-altitude areas of Mazandaran Province, located on the slopes of the Central Alborz Mountains, are especially vulnerable due to steep thermal gradients, altitudinal variability, and high dependence on winter snowfall. Despite its importance, a quantitative, multi-metric, and spatiotemporal analysis of the relationship between LST and snow depth in this region has been limited. The main objective of this study is to quantify the intensity, direction, and nature of the relationship between LST and snow depth and to identify dominant thermal–snow regimes in the highlands of Mazandaran Province.
Materials and Methods: Snow depth data from the GLDAS model and LST data derived from the MODIS sensor for the period 2018–2024 were used in this study. Analyses were conducted for five high-altitude stations—Alasht, Baladeh, Kiasar, Kojur, and Siah-Bisheh—representing diverse elevation conditions in the Central Alborz region. To examine the dependency between variables, complementary statistical indices including Pearson and Spearman correlations, MI, and R² were employed. Additionally, K-means clustering was applied to identify common behavioral patterns and thermal thresholds. To investigate spatiotemporal variations, annual LST and snow depth maps were produced and analyzed for the entire Central Alborz region of Mazandaran.
Results and Discussion: Statistical analyses consistently revealed a strong, significant, and negative relationship between LST and snow depth across all stations. Pearson correlation coefficients ranged from -0.58 to -0.77, and Spearman coefficients ranged from -0.76 to -0.91, indicating a stable inverse dependency between rising temperatures and decreasing snow depth. The notable differences between Pearson and Spearman coefficients suggest that the relationship is not purely linear, with nonlinear components playing a significant role. This finding was further supported by MI values ranging from 0.3 to 0.61, highlighting the influence of complex feedback mechanisms such as albedo effects, latent heat of melt storage, and variations in surface heat fluxes on the snow–temperature interaction. The strongest relationship was observed at the Siah-Bisheh station, where Pearson and Spearman coefficients reached -0.77 and -0.91, respectively, with an R² value of approximately 60%. This emphasizes the high sensitivity of the snow–temperature system at higher elevations and the key role of snow in moderating land surface temperature. K-means clustering identified three distinct thermal–snow regimes across all stations: warm snow-free conditions, transitional periods with unstable snow, and cold conditions with stable snow accumulation. These clusters clearly delineated temperature thresholds governing snow accumulation and melt, indicating that at temperatures near or below 0°C, snow significantly reduces LST through increased albedo and decreased surface energy absorption. Spatiotemporal analysis of annual maps revealed pronounced interannual variations. In 2019, with maximum snow depths reaching approximately 1.2 meters, the lowest LST values were recorded over extensive high-elevation areas. Conversely, in 2021 and 2022, reduced snow depths (maximum ~0.08 meters) coincided with LST increases exceeding 2°C. These patterns directly confirm the negative snow–temperature feedback and the high vulnerability of mountainous systems to reductions in snow cover.
Conclusion: The findings indicate that reductions in snow depth and coverage in the highlands of Mazandaran Province have a direct impact on increasing land surface temperature, potentially intensifying melt processes, altering runoff timing, and reducing water resource stability. The high sensitivity of the snow–temperature relationship in high-altitude stations underscores the importance of precise monitoring and modeling of these systems under warming climate conditions. Accordingly, the application of regional climate models based on global warming scenarios, along with the expansion of ground-based monitoring networks in high-altitude areas, is recommended as a key strategy for water resource management and mitigating climate change impacts in the region.
کلیدواژهها [English]
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