نوع مقاله : مقاله پژوهشی
عنوان مقاله English
نویسندگان English
Snow cover area (SCA) and snow water equivalent (SWE) are critical variables for understanding water resource status and river flows from snowmelt in mountainous regions. However, accurate estimation of the spatial and temporal variations of these variables remains a major challenge in hydrological and climatic studies due to the scarcity of ground-based observations and the inherent limitations of satellite data. This study investigated the long-term spatial and temporal variations of SCA and SWE over the 2000–2025 period in Razavi Khorasan Province, Iran, utilizing MODIS satellite data and the FLDAS in the Google Earth Engine (GEE). To evaluate the accuracy of the results, the estimated data were validated against field measurements from snow-gauge stations. The satellite-derived estimates were evaluated using statistical metrics, including linear correlation (R), root mean square error (RMSE), mean absolute error (MAE), and bias. Furthermore, the SWE-to-precipitation ratio (SPR) index was calculated to investigate long-term changes in the contribution of snowfall to total precipitation. The annual Standardized Precipitation Index (SPI) was also employed to analyze the relationship between SCA and SWE, with drought and wet events. The results indicated that the maximum snow cover areas occurred in 2005 and 2008, 10775.7 km2 and 6697.7 km2, respectively. Conversely, the minimum snow cover area was recorded in 2018 at 632.4 km2. The overall trend of both SCA and SWE in the study area was downward. The negative slope of trend revealed a significant annual decrease of approximately -31.9 km2/year in snow cover area (at the 5% statistical significance level). The long-term SPR index also exhibited a decreasing trend with a slope of -0.0004/year, indicating a declining share of snowfall relative to total precipitation in the region. Overall, the persistence of this declining trend and the gradual reduction in strategic snow storage, even at a low rate, may affect snow reserves, snowmelt-driven runoff, and the management of snow-dependent water resources in the region over longer time scales.
کلیدواژهها English
Snow is a dynamic natural element and a vital component of the hydrological cycle. In arid and semi-arid regions, water availability during dry seasons heavily depends on mountain snow storages. Monitoring snow dynamics in mountainous watersheds via traditional field measurement methods is highly challenging due to complex topography and data scarcity. Satellite remote sensing has emerged as an effective and practical tool for the monitoring and assessment of mountainous watersheds. The Snow Water Equivalent to Precipitation Ratio (SPR) represents the ratio of snow water equivalent (SWE) to total precipitation during the cold season. The SPR provides a more comprehensive indicator for evaluating snow storage efficiency and the actual contribution of snow to water resources in mountainous regions. In contrast, traditional indices such as S/P and S/(S+R) express only the proportion of solid precipitation relatie to total precipitation and are primarily used to assess precipitation phase transitions (snow versus rain) and the impacts of climate warming on precipitation type.
This study investigates the long-term changes in snow cover area and snow water equivalent (SWE) in Razavi Khorasan Province using MODIS satellite imagery and the FLDAS system (Famine Early Warning Systems Network Land Data Assimilation System). To delineate snow-covered areas, the Normalized Difference Snow Index (NDSI) was calculated. By applying a threshold value of 0.5, monthly and annual raster maps of snow cover area were generated. The FLDAS-estimated SWE data were evaluated against in-situ SWE measurements using statistical metrics, including the Pearson correlation coefficient, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Bias. To investigate long-term trends in the partitioning of precipitation into snowfall and rainfall, the SWE-to-Precipitation Ratio (SPR) was calculated for the study area. In addition, the Standardized Precipitation Index (SPI) was used to monitor meteorological drought conditions and to examine their relationship with snow cover area and snow water equivalent (SWE).
The maximum annual snow cover area was recorded in 2005, 2008, and 2012, with areas of 10775.7, 6697.7, and 7745.7 km², respectively. The minimum snow cover area was observed in 2018 (632.4 km²). Trend analysis showed a significant decline (p = 0.03) in snow cover area over the study period, with a slope of −31.9 km² year⁻¹. The results revealed a strong and statistically significant correlation between the annual snow cover variations of the Binalud and Hezar Masjed mountain ranges (r = 0.95, P < 0.01), indicating that they exhibited similar temporal patterns throughout the study period. The SWE time series exhibited a decreasing trend during 2000–2025; however, the trend was not statistically significant. Similarly, the SWE-to-precipitation ratio (SPR) showed a negative trend with a slope of −0.0004 year⁻¹. This trend was not statistically significant (p = 0.34), and the coefficient of determination was low (R² = 0.01). To examine the relationship between snow conditions and drought, annual SPI values from the Bolghor and Dizbadeolya stations were analyzed. Moderate to severe drought conditions were identified during 2000, 2010, 2016, 2017, and 2020 to 2024. These periods coincided with lower snow cover area and SWE values. The accuracy of SWE data of FLDAS was evaluated against observations from the Karimabad and Ferizi snow stations using correlation coefficient (R), RMSE, MAE, and Bias statistics. The results showed good agreement between FLDAS and ground observations, with correlation coefficients more than 0.79. The RMSE values were 11.1 mm and 6.8 mm for the Karimabad and Ferizi stations, respectively, while negative Bias values at both stations indicated an underestimation of SWE by FLDAS.
This study investigated the long-term variations in snow cover area (SCA), snow water equivalent (SWE), and the snow water equivalent-to-precipitation ratio (SPR) under meteorological drought conditions in Razavi Khorasan Province during 2000–2025. The highest annual snow cover areas were observed in 2005, 2008, and 2012, whereas the minimum value occurred in 2018. The annual snow cover dynamics extracted for the Binalud and Hezar Masjed mountain ranges showed a strong and statistically significant correlation (r = 0.95, P < 0.01).The long-term decline in SPR indicates a gradual reduction in the contribution of snow to total cold-season precipitation, suggesting a progressive shift from snowfall toward rainfall under a warming climate. The validation results demonstrated that FLDAS provides reliable estimates of SWE (R > 0.79) despite a systematic underestimation associated with the coarse spatial resolution of the model over complex mountainous terrain. Therefore, FLDAS can be considered a useful dataset for monitoring snow variables in data-scarce mountainous regions, although bias correction is recommended for applications requiring higher accuracy.
This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
Majid Kazemzadeh: Conceptualization, methodology, software, validation, formal analysis, investigation, writing—original draft preparation, writing—review and editing. Zahra Noori: methodology, software, validation, formal analysis, visualization, investigation, data curation, writing—original draft preparation. Sahar Khazaei: data curation, writing—original draft preparation. Akbar Alipoor: data curation, writing—review and editing.
No artificial intelligence (AI) tools or AI-assisted technologies were used in the design, analysis, interpretation, or preparation of this study. The authors take full responsibility for the content of this publication.
The data that support the findings of this study are available from the corresponding author upon reasonable request.
The authors would like to express their gratitude to the Regional Water Company of Khorasan Razavi for providing the rain and snow data used in this research.
The authors declare that they have followed all ethical guidelines in the conduct and publication of this research, and all authors have approved the final manuscript.
The authors declare that they have no conflict of interest.
