Modeling and spatial analysis of snow depth in Northern Iran based on database from European Centre for Medium-Range Weather Forecasts (ECMWF)

Document Type : Full length article


1 Professor of Climatology, Tabriz University, Tabriz, Iran

2 PhD Student in Climatology, Tabriz University, Tabriz, Iran

3 Assistant Professor of Climatology, Tabriz University, Tabriz, Iran

4 Assistant Professor of Climatology, Research Group on Climate Change, Meteorological Organization, Mashhad, Iran


Snow is an important component of the climate system over the mid- and high-latitude regions of the Earth. Its high shortwave albedo and low heat conductivity modulate heat and radiation fluxes at the Earth’s surface. Thus, it can directly modulate regional temperature evolution and ultimately atmospheric circulation patterns. Moreover, because snow serves as a temporary water reservoir, snow variability impacts soil moisture, evaporation and ultimately precipitation processes). As a result, snow cover has an essential influence on ecological) and economical systems. Vice versa, snow cover itself is determined by climate variations. Recent Arctic warming has severely impacted spring snow cover. This study aimed to evaluate the snow depth it the north of Iran. The results of this study can be used in the field of water resources, flooding and climate change will be useful.
Materials and methods
The present study aimed at evaluating modeling and spatial analysis of snow depth based on the data from the European Centre for Medium-Range Weather Forecasts (ECMWF) of the ERA-Interim version with a 0.125 × 0.125 arc-spatial resolution. In this regard, the temporal changes of the snow depth in the North Country were also evaluated. This study examined the monthly data of the 6-level 3 product (MYD08_M3_6), Normalized Difference Vegetation Index (NDVI) of the Terra Satellite. Modeling of spatial relationships between snow depth and geographic parameters and NDVI was obtained by OLS and GWR models. The coefficients of regression equations obtained for the relationships were used in the area after calculation. Several criteria have been proposed for selecting the appropriate bandwidth. In this study, the Akaike information criterion (AIC) was used to select the core bandwidth.
Results and discussion
The average depth of snow in the northern zone of Iran ranges from 0.006 to 1.748 cm for winter and autumn, respectively. The northern area of Iran in this season is 1.34 cm. In winter, the maximum average snow depth in the northern zone of Iran in February is 1.748 cm. The maximum amount of standard deviation occurred in the same season. In general, in winter, the maximum snow depth in the northern zone of Iran is more than that in the other months of the year. The third quartile can be considered as the maximum snowfall and the first quartile is the northern border of the northern northwest of Iran, which can then be classified as the northern part of Iran's snowfall. In winter one-fourth of the year, the northern zone of Iran has a snow depth of more than 1.98 cm. The significant difference between Moran's I and Geary's c expected and Moran's I and Geary's measured has shown that the spatial autocorrelation values calculated for each month really fluctuate and the value cannot be due to the magnitude of the data and changes caused by around the mean.
The results indicated that the winter season with mantle cubes show 1.34 cm maximum snow depth during the seasons. Winter also has the highest snow depth variability. The highest snow depth was obtained with an average of 1.74 cm in February. Based on the results of the study, using quartz statistics, in winter one-quarter of the northern zone of Iran has a snow depth of more than 1.98 cm, which is the maximum value among seasons. The spatial dependence of the depth of snow on universal Moran methods has been rejected by the hypothesis that there is no relation between the depth of snow in the northern zone of Iran, and the Geary's c method has also shown that snowfall areas with high snow depth are relatively relative in terms of geographic patterns and a behavior They show clusters of their own. Correlations obtained with snow depth with longitude and vegetation index of NDVI have a significant reverse relationship and its relationship with latitude and elevation is a significant direct relationship. Modeling with GWR and OLS has also documented that the GWR method has a higher ability to justify the spatial association of snow depth with geographic parameters. The results of the GWR model show that the relationship between snow depth and geographic parameters, especially elevation, does not follow a linear model. Elevation in the mountain range of Alborz and northwestern Iran covers mountainous areas with significant snow depth.


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Volume 51, Issue 4
January 2020
Pages 651-671
  • Receive Date: 22 October 2018
  • Revise Date: 14 October 2019
  • Accept Date: 14 October 2019
  • First Publish Date: 22 December 2019