Exploring the Relation of Snow-Covered Days with Elevation, Slope and Aspect in Iran

Document Type : Full length article


1 PhD Student of Climatology, University of Esfahan, Iran

2 Professor of Climatology, University of Esfahan, Iran


Snow is a kind of precipitation that is formed by the condensation of moist air mass and in the condition that temperature is below freezing point. Although small areas of the world are mountainous regions, these small territories play an important role in the hydrological context of river basins. In some areas snow covers and glaciers supply drinking water. Monitoring and forecasting of snow cover areas are essential for the promotion of climatic predictions and water-related decisions, particularly in mountainous regions where a great proportion of needed water is provided. Some works have been conducted about the influence of elevation, slope and aspect on the distribution of snow-covered days. In this part, some of these works have been reviewed here. Endrizzi et al. (2006) have indicated that there is a relation between snow water balance, elevation and aspect. They found that the dependence of snow water balance on elevation is poor in fall and strong in the spring. Gurung et al. (2011) have indicated that in Bhutan the accumulation of snow is varied based on aspect. North-east and north-west facing slopes are favored areas for snow accumulation in the seasons of winter, summer and fall. 
Materials and Methods
In the present paper, MODIS Terra and MODIS Aqua data were used to explore the relation of snow-covered days with elevation, slope and aspect. The selected study period covers the years from 2003 to 2014. As MODIS Aqua data are missing before the year 2003, we had to limit the study period only to the aforementioned years. The data of these products were downloaded in daily time scale. Before the analysis of the data, we applied two different algorithms to minimize cloud contamination that is a big hindrance against snow cover monitoring. One of the applied algorithms is based on three days filtering and the second is made on the combination of the two products. By exploiting these algorithms, we managed to reduce cloud cover considerably. In the second step, we started analysis of the data by creating different codes in MATLAB. As the spatial resolution of the data was in 500 meters, we needed a Digital Elevation Model to be consistent with snow data both in spatial resolution and projection system. Therefore, a DEM with these conditions was obtained from NASA. In the applied Digital Elevation Model, the information of aspect and slope for each of the grids was available. In the next step, we developed some codes in MATLAB to explore the relation between elevation, aspect and slope.
Results and Discussion
The considered relation between the number of snow-covered days and elevation indicated that there are three patterns in this way. Up to the elevation of 700 meters the number of snow-covered days does not increase by the increase of elevations. In the elevations between 700-1700, the number of snow-covered days shows a gradual increase, but in the elevations between 1700-3200 the number of snow-covered days experiences a significant increase by the changes in the elevations. Above the elevation of 3200 meters, the behavior of snow-covered days does not show a clear pattern. Thus, it can be concluded that the number of snow-covered days does not show a linear pattern. The analysis of slope indicated that the snow-covered is the most frequent in the slope of 25 degree. Above this slope the snow-covered days become less frequent. The analysis of aspect indicated that the numbers of snow-covered days are observed frequently in the north facing slopes and less frequent in the south and south-west facing slopes. In another part of this paper, the profile of snow-covered days and other parameters like aspect and elevation was investigated over three mountains of Sahand, Karkas and Lalezar. The obtained results confirmed that in the east and north facing slopes the number of snow-covered days is more frequent than their western and southern counterparts.   
In this study, MODIS Terra and MODIS Aqua data were exploited in order to investigate the relation between snow-covered days with elevation, slope and aspect. The study period of the present study is ranged from 2003 to 2014. Before using the daily raw data, two fundamental algorithms were performed on the initial data to minimize cloud cover. After reducing cloud cover in the raw data, we started analysis by creating some codes in MATLAB. The obtained results indicate that in Iran the relation between snow-covered days and elevation does not depict a linear relation and in each of the elevation zones the behaviors are completely different. The most important direction of snow-covered days and elevation was seen in the elevations between 1700 to 3200 meters. The analysis of aspect show that north facing slopes has a good potential to be snow covered during the year. The analysis of slope indicated that in slope of 25 degree, the numbers of snow-covered days are the most frequent in comparison with the other slopes. Therefore, the slope of 25 degree is, indeed, the critical slope in this country. 


Main Subjects

1. Balk BC, Elder K. )2000(. Combining binary decision tree and geostatistical methods to estimate snow distribution in a mountain watershed. Water Resources Research, No. 36, pp.13–26.
2. Bergeron, J . Royer, A . Turcotte, R . Roy, A . (2013); Snow cover estimation using blended MODIS and AMSR-E data for improved watershed-scale spring streamflow simulation in Quebec, Canada; Hydrological Processes, No. 1,  pp. 1-14
3. Dietz, A . Conrad, C . Kuenzer, C . Gesell, G and Dech, S . 2014 . Identifying Changing Snow Cover Characteristics in Central Asia between 1986 and 2014 from Remote Sensing Data, Remote Sens, No. 6, pp.12752-12775
4. Dietz, A . Kuenzer, C . Conrad, C . (2013); Snow-cover variability in central Asia between 2000 and 2011 derived from improved MODIS daily snow-cover products; International Journal of Remote Sensing, No.34 pp. 3879–3902
5. Endrizzi, S .  Bertoldi, G .  Neteler, M . Rigon, R . 2006,  Snow Cover Patterns and Evolution at Basin Scale: GEOtop Model Simulations and Remote Sensing Observations; Eastern snow conference, pp.195-209
6. Gafurov, A .  and Bardossy, A . (2009); Cloud removal methodology from MODIS snow cover product, Hydrol. Earth Syst. Sci., No. 13 , pp. 1361–1373
7. Gurung, D . Kulkarni, A . Giriraj, A . Aung, K . Shrestha, B . 2011, Monitoring of seasonal snow cover in Bhutan using remote sensing technique; Current Science, No. 101, pp.1364:1370
8. Helfrich, S . McNamara, D . Ramsay, B . Baldwin, T . Kasheta, T . 2007. Enhancements to, and forthcoming developments in the Interactive Multisensor Snow and Ice Mapping System (IMS); Hydrol. Process. No. 21, pp.1576–1586
9. Jain, S . Goswami, A . Saraf, K . (2009); Role of Elevation and Aspect in snow distribution in western Himalaya; Water Resour Manage; No. 23, pp. 71-83
10. Kivinen, S . Kaarlejarvi, E . Jylh¨a, K . R¨ais¨anen, J . 2012. Spatiotemporal distribution of threatened high-latitude snow bed and snow patch habitats in warming climate; Environmental research letter, No. 7 , pp. 1-9
11. Ke, C . Liu, X . (2014); Modis-observed spatial and temporal variation in snow cover in Xinjiang, China; Climate Research, No. 59, pp. 15-26
12. Leathers, D .  Luff,  B . 1997 . Characteristics of snow cover duration across the northeast United States of America. International Journal ofClimatology, No. 17 , pp.1535−1547.
13. Marchand W-D, Killingtveit AA. 2001. Analyses of the relation between spatial snow distribution and terrain characteristics. Proceedings of the58th Eastern Snow Conference. Ottawa, Canada, 14–17 May, 2001.
14. Marks DG, Winstral AH, Seyfried MS. 2002. Simulation of terrain and forest shelter effects on patterns of snow deposition, snowmelt and runoff over a semi-arid mountain catchment. Hydrological Processes, No.16, pp. 3605–3626.
15. Mote, P . Hamlet,  A . Clark,  M . Lettenmaier, D . 2005. Declining mountain snowpack in western North America. Bulletin of the American Meteorological Society, No. 86, pp. 39-49
16. Parajka, J . Bloschi, G . (2008); The value of MODIS snow cover data in validating and calibrating conceptual hydrological models, Journal of Hydrology, No. 358, pp. 240-258
17. Sensoy, A . Uysal, G . Sorman, A . Sorman, U . 2010. Topographic effects on snow depletion curves of Upper Euphrates river basin, Turkey , Republic of Macedonia - 25, 29 May 2010pp.1-10
18. She, J . Zhang, Y . Li, X , Chen, Y . (2014); Changes in snow and glacier cover in an arid watershed of the western Kunlun Mountains using multisource remote sensing data; International Journal of Remote Sensing; No. 35, pp. 234-252
19. Wang, X . Xie, H . (2009); New methods for studying the spatiotemporal variation of snow cover based on combination products of MODIS Terra and Aqua; Journal of Hydrology, No. 371, pp. 192-200
20. Wang.X ,  Xie. H , Liang . T  and Huang. X (2009); Comparison and validation of MODIS standard and new combination of Terra and Aqua snow cover products in northern Xinjiang, China. Hydro.Process. No. 23, pp. 419-429
21. Zhang, G . Xie, H . Yao, T . Liang, T . Kang, S . (2012); Snow cover dynamics of four lake basins over Tibetan Plateau using time series MODIS data(2001-2010), Water resources reaserch, No. 48 pp. 1-22
  • Receive Date: 01 July 2015
  • Revise Date: 15 March 2016
  • Accept Date: 08 September 2015
  • First Publish Date: 20 March 2016