جستجوی مقالات مرتبط با کلیدواژه « Snow-covered days » در نشریات گروه « جغرافیا »

The aim of this study is to explore the role of land surface temperature on distribution of snow-covered days in Iran. Firstly, the land surface temperature data were obtained from NASA for 2003 to 2014 in the spatial resolution of 1×1 km. MODIS Terra and MODIS Aqua data were also downloaded from 2003 to 2014 in the spatial resolution of 500 * 500. After preparation of the data in MATLAB software, Digital Elevation Model of the country was applied to calculate the annual LST for each of the elevations grouped by 1 meter. The findings of this study revealed that in the extents of the country which annul LST is below 30 º C the environmental condition is suitable for the accumulation of snow cover. And it was also found that the LST is 30 º C for the elevations above 1700 m. findings also revealed that there is no snow-covered day for the extents that LST is 37 º C and the most number of snow-covered days can be seen in the regions that LST is 0 º C.

Snow-covered areas and its variations are important parameters in hydrological and climatic systems. For the regions that snowmelts supply needed water, snow cover trend analysis is of great importance. Many big rivers of Iran originate from snow glaciers in mountains. So, investigation of snow cover changes is actually important. In the present study, the changes of snow-covered days in different elevation zones in Iran have been calculated via using MODIS Terra and MODIS Aqua data. The highest resolution data of these products that are available at 500m resolution were applied from 1382 to 1393. The Digital Elevation Model (Dem) of Iran that is in accordance to snow data both in resolution and projection system was exploited from NASA web site. In the first step, the frequency of snow covered days was calculated for each of the elevation zones from 1500 to 5500 in the intervals of 100 m and, as the second step, the trend of each zone was analyzed using Mann-Kendall test. The results indicated that in the months of Farvardin, Tir, Amordad, Shahrivar, Azar, Bahman, and Esfand no significant trend exists in any of the elevation zones. However in the months of Ordibehest and Khorad, there were negative trend in twenty one and ten elevation zones, respectively. It was also noticed that some positive trend exist in some of the individual elevation zones in the months of Mehr, Aban and Dey. In these aforementioned months, the numbers of elevation zones with positive trend were 2, 1 and 1 respectively.

In this paper daily MODIS Terra and MODIS Aqua data were applied to calculate seasonal clustering of snow-covered days in Iran. These data are available in 500 * 500 meters. The applied data in this study are the finest available data for MODIS products. As the first step, numerical snow data were collected in Matlab software and then some algorithms were exploited to reduce cloud cover. After preparation of data the mean matrix of solar months from Farvardin to Esfand was prepared in Matlab. The dimension of the Matrix was 12 * 7541502 that rows represent each month and the columns depict spatial pixels. In the second step, the Euclidean distance was then calculated using ward method in Matlab. The findings of this study indicated that there are four snow-covered seasons in Iran. No snow season includes the months of Ordibehesht to Khordad, the transient months that are Farvardin and Aban, the shell of snow-covered season includes the months of Azar and Esfand. And the core of snow-covered season that incorporates the months of Dey and Bahman. And we also calculated for each of the climate season and each of solar months the number of snow-covered days and their areas in Iran. Investigations revealed that the highest area of one day snow-covered day is in the month of Dey. In this month the extent of one day snow-covered days covers 15 percent of the country. In the last step, the relation between snow-covered days and altitude was explored using Iran’s Digital Elevation Model. The results confirmed that in each of season this relation is different.