Identification of Spatial Variations of Snow-covered days over Iran based on Remote Sensing Data

Document Type : مقاله پژوهشی

Authors

• Mohammad Sadegh Keikhosravi Kiany ¹
• SeyedAbolfazl Masoodian ²

¹ University of Isfahan

² Esfahan

10.22067/geo.v5i1.49715

Abstract

1. Introduction
Glaciers and snow covers in the mountains play an important role in the water budget of many areas of the world (Ramage and Isacks, 2003). In high altitudes and mountainous regions, snowmelt is a great contributor to the yearly runoff. The snow melt supplies 1/6 of people's needed water but due to global warming these glaciers may be at risk (Barnett, et al. 2005). For the regions of the world that snowmelt provides the needed water, trend analysis of snow cover is of great importance. Therefore, using data and information that is limited to stations cannot be satisfactory as in many high lands no station exists and the density of them is not enough to make us able to monitor snow cover changes. But snow cover information based on remote sensing data is an alternative way to obtain necessary information in both regional and global scale (hall et al. 2005; Brown and Armstrong, 2010). For this purpose remote sensing products have been introduced to scientific community based on geosynchronous and pole orbiting satellites(Romanov, et al. 2003; de Ruyter, et al. 2006; Zhao and Fernandes , 2009; Hall et al. 2010). In this way a lot of research has been conducted to analyze snow cover changes that have been noticed as follow: Maskey et al. (2011) investigated snow cover trend in Nepal and nearby areas using MODIS terra data from the years 2000 to 2008. The analysis indicated that in elevations below 6000 m in January there is a downward trend (Maskey et al. 2011, 391). Ke and Liu (2014) applied MODIS Terra and MODIS Aqua data from 2000 to 2012 in order to analyze snow cover in Xingzan in china. The findings showed that in winter for the elevations below 2000 m and above 4000 m negative trend exists (Ke and Liu, 2014, 22). Akyurek et al. (2011) investigated snow cover area in Karasu basin as the headwater of Uphrate River from 2000 to 2009. Therefore MODIS data were applied for this purpose. The results indicated that in the study period no negative trend is detected (Akyurek, et al. 2011, 3647 and 3637). Sonmez et al. (2014) applied IMS data over Turkey from 2004 to 2012 to analyze snow cover trend. Using Mann-kendall trend test revealed that in general a decreasing trend can be seen in the country but in fall a positive trend and in spring and summer a negative trend was detected.



2. Study area
Iran is located between 25° and 40°N and 44° and 64°E and is a mountainous country bordering the Gulf of Oman, the Persian Gulf, and the Caspian Sea. Overall, sixty percent of Iran is covered by mountains, with the central part of the country consisting of two dry deserts: the Dasht-e-Kavir and the Dasht-e-Lut. The Alborz range in the north, close to the Caspian Sea, extends in an east–west direction with a maximum elevation of approximately 5000 m. The Zagros Mountains are aligned in a northwest to southeast direction and reach a maximum elevation of approximately 3500 m. These two ranges play a significant role in determining the non-uniform spatial and temporal distribution of precipitation across the entire country (Javanmard et al., 2010).

3. Material and methods
In the present paper MODIS Terra and MODIS Aqua data were used to identify the trend of snow-covered days across Iran. The selected study period covers the years from 1382 to 1393. As MODIS Aqua data are missing before the year 1382, 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 analyzing the data by creating different codes in Matlab. Application of cloud removal methods have been suggested by many researchers (Dietz et al. 2014, Ke and Liu 2014, Wang et al. 2009). In the numerical format of remote sensing data a especial code has been introduced for each feature, for instance the code 200 represent snow, the code 50 represent cloud and etc. 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. Thus a DEM with the aforementioned attributes was provided from NASA. By using this DEM we also were able to calculate the mean altitude of regions that have had trend whether positive or negative. To examine the trend of snow-covered days the monthly frequency of snow-covered days were calculated for the period from 1382 to 1393 and in the next step the monthly matrices were converted to seasonal ones and then the slope of regression equations were calculated for each of the pixels and finally the slopes that had the same signs were considered as the regions with significant trend and these pixels were converted to maps.

4. Results and Discussion
The results of this study indicated the presence of trend in different seasons of Iran both positive and negative. The findings showed that in spring 1.1 and 0.32 percent of Iran’s overall territory has experienced negative and positive trends, respectively. In summer only 0.001 percent of Iran’s extent has had trend whether positive or negative in the number of snow-covered days. In the season of fall 3.8 and 3.2 percent of Iran’s was proved to have negative and positive trend respectively. In this season the areas having trend showed counterpart patterns of changes in the number of snow-covered days. For instance eastern regions of Iran indicates downward trend but conversely western counterparts have experienced upward trend in the number of snow-covered days. This similar pattern was noticed in some other parts of the country. In the season of winter the highest rate of trend was noticed. In this season some areas of mountainous regions especially those located in western Zagros have had the most downward trend in the number of snow-covered days. Some of these areas have significant trend of decrease equal 4 days or more. In this season the mean elevation of regions having decreasing trend was 1790 meters from sea level while the mean elevation of areas having upward trend was 2030 meters from sea level. In this season nearly 50 percent of the areas having trend have had the rate of trend equals -1 to 0 days annually. And the rate of decrease in over 30 percent of other areas was -2 to -1 days annually.

5. Conclusion
In this study MODIS Terra and MODIS Aqua data were applied to examine the trend of snow-covered days across the country. Before taking the daily data in to the analyses some pre-processing analyses were applied on the raw data to minimize cloud cover effects. The findings of the recent paper confirmed the existence of both downward and upward trend in all of the seasons in the country with the greatest rate of trend in winter. In this season nearly 22 percent of Iran’s territories were proved to have a significant decreasing trend in the number of snow-covered days. These areas are mainly located along Zagros and Alborz ranges that are considered to be Iran’s water supply. But only almost 2.6 percent of Iran has had an increasing trend in the number of snow-covered days and most are positioned in lower altitudes. It seems that the recent droughts in Iran stem from the noticeable decrease in the number of snow-covered days. And accordingly crucial steps should be taken and needed policies must be applied to mitigate the adverse effects of this phenomenon across the country.

Keywords

• Snow-covered days
• Trend
• MODIS Terra
• MODIS Aqua
• Iran

---

References

Akyurek, Z., Surer, S., & Beser, O. (2011). Investigation of the snow-cover dynamics in the Upper Euphrates Basin of Turkey using remotely sensed snow-cover products and hydro meteorological data. Hydrological Process, 25(23), 3637-3648.

Anderton, S. P., White, S. M., & Alvera, B. (2002). Micro-scale spatial variability and the timing of snow melt runoff in a high mountain catchment. Journal of Hydrology, 268(1), 158–176.

Barnett, T. P., Adam, J. C., & Lettenmaier, D. P. (2005). Potential impacts of a warming climate on water variability in snow-dominated regions. Nature, 438, 303–309.

Bergeron, J., Royer, A., Turcotte, R., & Roy, A. (2013). Snow cover estimation using blended MODIS and AMSR-E data for improved watershed-scale spring stream flow simulation in Quebec, Canada. Hydrological Processes, 28(16), 4626–4639.

Blöschl, G. (1999). Scaling issues in snow hydrology. Hydrological Process, 13(14), 2149–2175.

Brown R., & Armstrong R. L. (2010). Snow-cover data measurement, products and sources in snow and climate. In Physical processes, surface energy exchange and modeling, Armstrong RL, Brun E. Cambridge, UK: Cambridge University Press.

Brown, R. D., & Derksen, C. (2013). Is Eurasian October snow cover extent increasing. Environmental Research Letters, 8(2), 1-7.

MR de Wildt, G Seiz, & A Grün. (2006). Snow mapping using multi-temporal Meteosat-8 data. EARSeL eProceedings, 5, 18–31.

Dietz, A., Conrad, C., Kuenzer, C., Gesell, G., & Dech, S. (2014). Identifying changing snow cover characteristics in Central Asia between 1986 and 2014 from remote sensing data. Remote Sensing, 6(12), 12752-12775.

Gafurov, A., & Bardossy, A . (2009). Cloud removal methodology from MODIS snow cover product. Hydrology and Earth System Sciences, 13(7), 1361–1373.

Hall, D. K., Kelly, R. E., Foster, J., & Chang A. T. (2005). Estimation of snow extent and snow properties. In Encyclopedia of Hydrological Sciences, 2, 811–830.

Hall, D. K., Riggs, G. A., Foster J. L, & Kumar S. V. (2010). Development and evaluation of a cloud-gap-filled modis daily snow-cover product. Remote Sensing of Environment, 114(3), 496–503.

Immerzeel, W., Droogers, P., Jong, S., & Bierkens, M. (2009). Large-scale monitoring of snow cover and runoff simulation in Himalayan river basins using remote sensing; Remote Sensing of Environment, 113(1), 40-49.

Javanmard, S., Yatagai, A., Nodzu, MI., Bodagh, Jamali., & Kawamoto H. (2010). Comparing high-resolution gridded precipitation data with satellite rainfall estimates of TRMM 3B42 over Iran. Advances in Geosciences, 25, 119–125.

Khadka, D., Babel, M., Shrestha, S., & Tripathi, N. (2014). Climate change impact on glacier and snow melt and runoff in Tamakoshi basin in the Hindu Kush Himalayan (HKH) region. Journal of Hydrology, 511(16), 49–60.

Ke, C., & Liu, X. (2014). Modis-observed spatial and temporal variation in snow cover in Xinjiang, China. Climate Research, 59, 15-26.

Lehning, M., Löwe, H., Ryser, M., & Raderschall, N. (2008). Inhomogeneous precipitation distribution and snow transport in steep terrain. Water Resources Research, 44(7), 1-19.

Manes, C., Guala, M., Löwe, H., Bartlett, S., Egli, L., & Lehning, M. (2008). Statistical properties of fresh snow roughness. Water Resources Research, 44(11), 1-9.

Maskey, S., Unlenbrook, S., & Ojha., S. (2011). An analysis of snow cover changes in the Himalayan region using MODIS snow products and in-situ temperature data. Climate Change, 108, 391-400.

Parajka, J., & Bloschi, G. (2006). Validation of MODIS snow cover images over Austria. Hydrology and Earth System Sciences, 3(5), 1569-1601.

Ramage, J. M., & Isacks, B. L. (2003). Interannual variations of snowmelt and refreeze timing in southeast Alaskan ice fields, USA. Journal of Glaciology, 49(164), 102–116.

Romanov, P., Tarpley, D., Gutman, G., & Carroll, TR. ( 2003). Mapping and monitoring of the snow cover fraction over North America. Journal of Geophysical Research, 108(16), 1-15.

Sharma, V., Mishra, V., Joshi, P. (2012). Snow cover variation and stream flow simulation in a snow-fed river basin of the Northwest Himalaya. Journal of Mountain Science, 9(6), 853-868.

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, 35(1), 234-252.

Sonmez, I., Tekeli, A., & Erdi, E. (2014). Snow cover trend analysis using Interactive Multisensor Snow and Ice Mapping System data over Turkey. International Journal of Climatology, 34(7), 2349-2361.

Tani, M. (1996). An approach to annual water balance for small mountainous catchments with wide spatial distributions of rainfall and snow water equivalent. Journal of Hydrology, 183(3), 205–225.

Udnaes, H., Alfnes, C. E., & Andreassen, L. M. ( 2007). Improving runoff modeling using satellite-derived snow cover area. Hydrology Research, 38(1), 21–32.

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, 371(1), 192-200.

Wang, X., Xie, H., Liang, T., & Huang, X. (2009). Comparison and validation of MODIS standard and new combination of Terra and Aqua snow cover products in northern Xinjiang, China. Hydrological Processes, 23(3), 419-429.

Yuang, D., & Woo, M. (1999). Representativeness of local snow data for large scale hydrologic investigations. Hydrological Processes, 13(12), 1977–1988.

Zhao, H., & Fernandes, R. (2009). Daily snow cover estimation from advanced very high resolution radiometer polar pathfinder data over Northern Hemisphere land surfaces during 1982–2004. Journal of Geophysical Research, 114(5), 1-14.

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 research, 48(10), 1-22.