Journal of Arid Regions Geographic Studies

Journal of Arid Regions Geographic Studies

Analysis of Tow Decades of Snow Cover Changes in the Alborz Mountain Range

Document Type : Original Article

Authors
Bayan Khaledi 1
Manuchehr Farajzadeh 1
Yusef Ghavidel Rahimi 2
1 Department of Natural Geography, Faculty of Humanities, Tarbiat Modares University, Tehran, Iran
2 Department of Natural Geography, Faculty of Humanities, Tarbiat Modares University, Tehran, Iran.
10.22034/jargs.2025.518567.1195
Abstract
Aim: This study aims to investigate the trend of snow cover changes as a climatic factor in the Alborz Mountain Range during the period 2001 to 2023 and analyze the role of spatial and temporal components in these changes.
Materials and Methods: To conduct this study, daily data from the MODIS sensor were used. The Normalized Difference Snow Index (NDSI) was calculated to extract and zone snow-covered areas. The trend changes were analyzed using the nonparametric Mann-Kendall test and Sen’s Slope method. The relationship between the NDSI index and snow cover area was also evaluated through linear regression.
Findings: Snow cover in the Alborz Mountains showed a decreasing trend across most seasons between 2001 and 2023, particularly in winter and spring. This decline was statistically significant for both the mean and the minimum snow cover extent during winter, whereas in summer and autumn the trend was more variable. Furthermore, comparison of snow cover trends with seasonal temperature and precipitation patterns indicated that increased winter temperatures and reduced precipitation—especially at lower elevations—had a noticeable impact on the reduction in snow persistence and spatial extent. These patterns were also reflected in the time series analyses and elevation profiles.
Conclusion: Spatial distribution of snow cover was directly related to elevation and geographic location, with the highest NDSI values observed in the western and central highlands. Although climatic variables were not directly analyzed in this study, the concurrent decline in snow cover with documented increases in seasonal temperatures reported in previous research suggests potential indirect impacts of climate conditions on snow dynamics in the region.
Innovation: The use of daily and long-term MODIS data along with accurate statistical methods for analyzing trends and transition points is considered an innovation of this study.
Keywords
Snow cover
MODIS sensor
NDSI index
Alborz mountain range
Mann Kendall test
Climate change
Subjects

1. Introduction

Snow cover is one of the most critical components in hydrological and climatic systems, particularly in mountainous regions. Acting as a natural reservoir, snow contributes significantly to surface runoff regulation, groundwater recharge, and long-term water resource management. In many semi-arid and mountainous regions, snowmelt constitutes a primary source of water during the spring and early summer seasons. However, in recent decades, global warming and climate change have disrupted traditional snow accumulation and melt patterns. Rising average temperatures and changes in precipitation regimes have led to a general decrease in the duration and extent of snow cover worldwide. The Alborz mountain range, located in northern Iran, is one of the most vital hydrological zones in the region. It plays a pivotal role in supplying freshwater to downstream areas, including urban, agricultural, and industrial zones. Given the importance of this region and the increasing threats posed by climate change, long-term monitoring of snow cover using robust methods is essential. Remote sensing, particularly the use of MODIS data, offers a valuable opportunity for observing snow patterns over time. This study aims to analyze the spatial and temporal changes in snow cover in the Alborz Mountains during the period 2001–2023, using satellite data and statistical techniques.

2. Materials and Methods

The study area spans the entire Alborz mountain system, extending from approximately 48° to 57° east longitude and 35.5° to 37.5° north latitude. The region includes a variety of topographical and climatic zones, from lowlands to high elevations exceeding 5,000 meters. Daily surface reflectance data (MOD09GA) from the MODIS Terra satellite were used for this research. To detect snow-covered areas, the Normalized Difference Snow Index (NDSI) was calculated using green and near-infrared spectral bands. A threshold value of 0.4 was used to classify snow pixels. Water bodies were masked using the MOD44W.005 product to reduce misclassification. The study focused on seasonal analysis (autumn, winter, spring, and summer) of maximum, minimum, and mean NDSI values across 23 years (2001–2023). The Mann–Kendall trend test was applied to detect monotonic trends in the time series, while Sen’s slope estimator was used to quantify the magnitude of change. In addition to temporal analysis, spatial trends were evaluated using snow distribution maps and elevation profiles. A linear regression model was used to assess the correlation between NDSI values and total snow-covered area on an annual basis.

3. Results and Discussion

The analysis revealed that the highest snow cover values, based on NDSI, occurred in winter, followed by spring and autumn. In winter, a statistically significant decreasing trend was observed in both mean and minimum NDSI values. This suggests a reduction in both the extent and stability of snow cover during the coldest season. In contrast, the other seasons showed fluctuating patterns without statistically significant trends. The Mann–Kendall test confirmed a consistent downward trend in minimum NDSI across all seasons, with the most pronounced changes in winter and summer. Elevation profile analysis indicated that snow cover was primarily concentrated at elevations between 2,000 and 5,500 meters. Snow distribution maps showed a reduction in snow-covered area after 2008, with 2008 being the peak year (approximately 48,000 km²). Subsequent years displayed more spatial fragmentation and interannual variability, especially in autumn and spring. Western and central parts of the Alborz Mountains exhibited higher NDSI values, indicating denser and more persistent snow cover, whereas eastern regions had NDSI values typically below 0.4, reflecting limited snow presence. The regression analysis showed a strong positive correlation between NDSI and snow-covered area, highlighting the reliability of this index in monitoring snow extent over time. Moreover, lower elevations were found to lose snow earlier in the season, while higher elevations retained snow for longer durations, extending into early summer in some years.

4. Conclusion

This study confirms a significant reduction in snow cover in the Alborz Mountains over the period 2001–2023, especially during winter. The trend indicates a shift in snow behavior, likely influenced by regional warming and changing precipitation regimes. Although this research did not directly analyze meteorological data, the observed patterns align with previous studies reporting increasing winter temperatures and reduced snowfall in the region. The concentration of snow at higher elevations and retreat from lower altitudes serves as indirect evidence of climatic impacts. The use of MODIS data and the NDSI index has proven effective in detecting and quantifying these changes. The demonstrated correlation between snow index values and snow-covered area supports the use of remote sensing as a robust tool for long-term environmental monitoring. These findings can inform future hydrological modeling, resource planning, and climate adaptation strategies, particularly in areas that depend heavily on snowmelt for water supply. The study emphasizes the necessity of integrating satellite observations with climate models to better anticipate future changes in snow dynamics under continued global warming.

5. Acknowledgments & Funding

·        The authors are grateful to the Iranian Meteorological Organization for providing some weather station data for this research.

·        The manuscript did not receive a grant from any organization.

6. Conflict of Interest

·        The authors declare no conflict of interest.

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  • Receive Date 22 April 2025
  • Revise Date 01 July 2025
  • Accept Date 02 July 2025
  • Publish Date 21 April 2026