Spatiotemporal analysis of snow cover in Iran based on topographic characteristics

Ghasemifar, Elham; Mohammadi, Chenour; Farajzadeh, Manuchehr

doi:10.1007/s00704-018-2690-3

Cited by 5 publications

(4 citation statements)

References 30 publications

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“…Previous studies have demonstrated a relationship between snow cover and elevation, which generally indicates that snow cover duration increases with elevation [6,62,[78][79][80][81]. Moreover, snow cover duration is lower in sunny slopes relative to shaded slopes due to solar irradiation [78,80]. The absolute accuracy of the SPSA algorithm can also be attributed to the consistent spatial distribution of snow, which is consistent with our previous observations.…”

Section: Impact Factor Analysis Of the Spsa Algorithmsupporting

confidence: 89%

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Gap-Filling of a MODIS Normalized Difference Snow Index Product Based on the Similar Pixel Selecting Algorithm: A Case Study on the Qinghai–Tibetan Plateau

Zhu

et al. 2020

Remote Sensing

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Cloud contamination has largely limited the application of the Moderate Resolution Imaging Spectroradiometer(MODIS) normalized difference snow index (NDSI). Here, a novel gap-filling method based on spatial-temporal similar pixel interpolation was proposed to remove cloud occlusions in MODIS NDSI products. First, the widely used Terra and Aqua combination and three-day temporal filter methods were applied. The remaining missing NDSI information was estimated by using similar eligible pixels in the remaining cloud-free portion of a target image through a spatial-temporal similar pixel selecting algorithm (SPSA). The MODIS daily NDSI product data from 2003 to 2018 in the Qinghai–Tibetan Plateau (China) was used as a case study. The results demonstrate that the three-step methodology can generate almost completely cloud-free, daily MODIS NDSI images, reducing the cloud-gap fraction from >45% to less than 1.5% on average. The validation results of the SPSA method exhibited a high accuracy, with a high R2 exceeding 0.78, a low mean absolute error of 2.77%, a root mean square error of 3.78%, and a 96.92% overall accuracy. The proposed method can fill cloud gaps without a significant loss of accuracy, especially during snow cover transition periods (autumn and spring), which may provide more accurate cloud-free NDSI data for climate change and energy balance studies.

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Section: Impact Factor Analysis Of the Spsa Algorithmsupporting

confidence: 89%

Section: Impact Factor Analysis Of the Spsa Algorithmmentioning

confidence: 99%

Gap-Filling of a MODIS Normalized Difference Snow Index Product Based on the Similar Pixel Selecting Algorithm: A Case Study on the Qinghai–Tibetan Plateau

Zhu

et al. 2020

Remote Sensing

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“…Previous studies on historical snow resource patterns in Iran have primarily focused on spatiotemporal changes in snow cover area and duration estimated by remote sensing data sets (e.g., Ghasemifar et al., 2019; Keikhosravi Kiany et al., 2017; Safarianzengir et al., 2020). However, to the best of our knowledge, the present study is the primary research that applied daily snow depth measurements to investigate long‐term (1982–2019) variability and trends in snowpack dynamics across Iranian mountain ranges.…”

Section: Discussionmentioning

confidence: 99%

“…Although Iran is located in the arid and semi‐arid regions (Tabari et al., 2014), its major rivers (e.g., Karun, Karkheh, Zayandeh‐rud, and Ghezel Ozan) are principally fed by snowmelt water originating from the mountains throughout the country (Ghorbani, 2012; Sabzevari et al., 2015). Despite such great hydrological importance, however, spatio‐temporal changes in snow cover patterns in Iran have been investigated only by a few previous studies (e.g., Ghadimi et al., 2019; Ghasemifar et al., 2019), and mostly limited to the major river or lake basins throughout the country (e.g., Choubin et al., 2019; Keikhosravi, 2021; Seyedielmabad & Moradi, 2016). To our knowledge, hence, a comprehensive analysis of historical variability and changes in snow resources throughout different mountainous regions in Iran is still lacking and well‐motivated.…”

Section: Introductionmentioning

confidence: 99%

Long‐Term Variability and Trends in Snow Depth and Cover Days Throughout Iranian Mountain Ranges

Sadeqi,

Irannezhad,

Bahmani

et al. 2024

Water Resources Research

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In Iran, the mountain snow cover generally feeds major rivers and thereby largely provides water resources required for improving human lives and protecting nature. Hence, understanding historical variability and trends in mountainous snowpack water resources in Iran in response to global warming and climate change can play a critical role in the sustainable development of this country. Accordingly, this study investigated long‐term (1982–2018) snowpack climatology at 13 hydrometeorological measurement stations scattered throughout the Iranian mountain ranges, with a focus on Elburz, Azerbaijan, Zagros, and Khorasan mountainous regions. The non‐parametric Mann‐Kendall test was used to detect statistically significant (p < 0.05) trends, the Pettitt test to identify possible abrupt shift years, the Pearson's correlation coefficient to measure relationships among different time series, and the partial correlation to determine the most important climate factor influencing snowpack dynamics The annual snow depth (maximum snow depth) significantly declined throughout Iranian mountain ranges during 1982–2018, with an average rate of 1.0 (3.4) cm decade−1. The annual snow cover days (SCDs) also showed significant decreasing trends, ranging from 3 to 15 days decade−1 during 1982–2018, in 69% of the stations studied. Such considerable reductions in snow depth and cover days were mainly related to the compound effects of substantial increases in temperature, sunshine, and wind speed as well as decreases in precipitation and cloudiness during the SCDs across the Iranian mountain ranges. However, precipitation was the most influential climate factor controlling snow resources throughout both the Elburz and Zagros mountains in Iran.

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The impact of climate change on snowfall in Iran Basins using the satellite-derived snow products and CMIP6 Bias Corrected model

Ahmadi

Rostami

Dadashi-Roudbari

2022

Theor Appl Climatol

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Spatiotemporal analysis of snow cover in Iran based on topographic characteristics

Cited by 5 publications

References 30 publications

Gap-Filling of a MODIS Normalized Difference Snow Index Product Based on the Similar Pixel Selecting Algorithm: A Case Study on the Qinghai–Tibetan Plateau

Gap-Filling of a MODIS Normalized Difference Snow Index Product Based on the Similar Pixel Selecting Algorithm: A Case Study on the Qinghai–Tibetan Plateau

Long‐Term Variability and Trends in Snow Depth and Cover Days Throughout Iranian Mountain Ranges

The impact of climate change on snowfall in Iran Basins using the satellite-derived snow products and CMIP6 Bias Corrected model

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