Role of Elevation and Aspect in Snow Distribution in Western Himalaya

Transcription

1 Water Resour Manage DOI /s Role of Elevation and Aspect in Snow Distribution in Western Himalaya Sanjay K. Jain & Ajanta Goswami & A. K. Saraf Received: 23 November 2006 / Accepted: 6 March 2008 # Springer Science + Business Media B.V Abstract Snow is a dynamic natural element, the distribution of which is largely controlled by latitude and altitude. In the tropical country like India, snow distribution is mostly controlled by altitude. The present study aims to identify the relationship between snow accumulation with elevation and aspect in rugged terrain in the Himalayan region. The river basins of four tributaries of the River Indus i.e. Satluj, Chenab, Ravi and Beas located in the western Himalaya were considered for study. Snow covered area was estimated for a period of 2 years (01 Jan 2003 to 17 Dec 2004) using MODIS 8 days maximum snow cover products. Aspect and classified relief maps were prepared using the USGS DEM. The interrelationship between aspect, elevation and snow cover area was determined for all the four river basins and comparative analysis has been made. A 2 years average shows that Satluj has the minimum snow covered area 23%, while Chenab has the highest snow covered area i.e. 42%, Ravi and Beas has 33% and 38% respectively. The minimum elevation from where snow covered area appears has been calculated and it has been observed that in case of Satluj, snow appears at a higher elevation (1,369 m) while in Chenab snow appears at an elevation of 834 m, followed by Ravi (1,058 m) and Beas (1,264 m). It was found that aspect has a major impact on snow accumulation in the lower elevations in all the basins as compared to higher elevations. Snow accumulates most in the northwest and northeast aspect. The rate of change in snow cover with elevation is determined for all the river basins and it has been concluded that Satluj has the lowest rate of change of snow cover with elevation (1.3% per 100 m), Chenab 1.8% per 100 m, followed by Ravi 2% per 100 m and Beas (2% per 100 m). Keywords Snow. Elevation. Aspect. Himalaya. DEM. Basin S. K. Jain (*) National Institute of Hydrology, Roorkee, India sjain@nih.ernet.in A. Goswami : A. K. Saraf Department of Earth Sciences, IIT, Roorkee, India

2 S.K. Jain et al. 1 Introduction Snow accumulation and loss is controlled primarily by atmospheric conditions, elevation and slope of the terrain. Atmospheric processes of interest are precipitation, deposition, condensation, turbulent transfer of heat and moisture, radiative exchange and air movement. Land features influence snow accumulation by slope, orientation, and by shadowing properties. These factors act together and are related to each other. For example, mountain ranges interrupt the winds that can redistribute the snow into drifts, slope and aspect influence incoming solar radiation and humidity, and latitude and elevation control air and ground temperature. Most of the perennial rivers flowing through the thickly populated and fertile Indo-Gangetic plain have its source in Himalaya. These drainage systems can be rightly designated as the lifeline of the Indian agriculture sustained economy. The mountainous area covered by snow is about 80% of the total area of Himalayas. In Himalayas, the western part gets more snow than the total area of Himalayas. The snow line, the lowest line on a mountain at which snow exists through out the year, is about 5,490 m at the equator and 610 m in Greenland. In temperate zones, it is about 3,050 m. (Jain et al. 2006). The weather over the entire Indian Subcontinent, of which the Himalaya and Karakoram form the northern border, can be broken down into two basic regimes: the winter and summer monsoons. During the winter monsoon the low level winds are out of the north, while in summer they are out of the south-to-south west. By April temperature over the Himalaya start to warm up dramatically. May is considered the hottest month in the northern Subcontinent because of the increase in surface heating as the Sun moves higher in the sky, and because compared to June September, cloud cover is limited. This period corresponds to significant snowmelt in the Himalaya, which causes the rivers and streams to rise substantially. The Himalayas contain over half the permanent snow and ice field outside the Polar Regions and it is estimated that roughly 50,000 km 2 of glaciers into the world s largest water drainage system of the Indus, Ganga and Brahmputra rivers. This perennial river system of the Himalayas is fed by melt water contributions from snow cover, glaciers and permafrost regions. The total amount of water flowing from the Himalayas to the plains of the Indian subcontinent is estimated to be about m 3 per year (IPCC 2001; Singh and Jain 2003). The Alps themselves have three geographic climate zones: the northern slopes, the inner Alpine valleys, and the southern slopes. The northern slopes block cool northern slopes, the inner Alpine valleys, and then southern slopes. The northern slopes block cool northwest or northerly winds from reaching the Mediterranean; conversely the southern slopes limit the amount of warm Mediterranean air reaching continental Europe. As a result the northern slopes tend to be cooler and cloudier than the corresponding elevation on the southern slopes. The inner Alps, especially the valleys, tend to be considerably drier than any either the southern or northern slopes. Overall, the southern slopes are sunnier and have fewer days with precipitation than the northern slopes. However, when precipitation does occur over the southern slopes, the intensity is frequently quite high (Lang and Rohrer 1987). The traditional method of collecting snow data is time consuming, expensive and sometimes hazardous. Consequently, there are only few ground data collecting stations in the Himalayas. Satellites now provide valuable data with higher spatial and temporal resolution. Nevertheless, field measurements are still required to validate the satellite data (Saraf et al. 1999). The principal applicability of remote sensing of snow properties is in snow-extent estimation. The medium spatial resolution sensors such as NOAA-AVHRR,

3 Role of elevation and aspect in snow distribution TERRA/AQUA-MODIS etc provide a good opportunity to study snow distribution on daily basis. The high reflectivity of snow in visible band makes it one of the most easily distinguishable features in images. The elevation of the terrain seems to be paramount criteria in snow accumulations. The elevation plays an important role in snow accumulation during winter months but the latter is principally governed by topography during ablation period. The present study has been carried out in the river basins of Satluj, Chenab, Ravi and Beas, which are the main tributaries of river Indus, using MODIS/TERRA Snow over 8-Day L3 Global 500 m Grid (MOD10A2). The work was aimed to calculate weekly maximum percent snow cover (PCA). The change in snow-extent has been observed over a period of 2 years (01 Jan 2003 to 17 Dec 2004) in the four river basins. The spatial relationship between digital elevation model (DEM) data, including relief and aspect was evaluated and related to snow cover data. The influence of aspect and relief in snow cover was analyzed. 2 The Study Area and Data Used The four river basins used in this study (Fig. 1) namely, Satluj, Chenab, Ravi and Beas are the important tributaries of the Indus river system, which flows from the Tibetan plateau, through India and Pakistan and ultimately drains into Arabian Sea. The southwest part of the study area does not receive any snow and therefore the present study concentrates only on the region above 500 m of elevation. The details about the four river basins are given in Table 1. Depending upon broad climatic conditions the following four seasons prevail over the basin (Jain 2001; Singh and Jain 2003): Fig. 1 Hill shaded topography of the study area, which includes four river basins : Satluj, Beas, Ravi and Chenab

4 S.K. Jain et al. Table 1 The details of the 4 tributaries of Indus River (adopted from Chandra 1994) No. Name of the River Basin Mountain area Glacier area % Glaciation 1 Satluj 47,915 1, Chenab 27,195 2, Ravi 8, Beas 14, Winter Season (December March) The precipitation during this season is caused by extratropical weather system of midlatitude region originating from Caspian sea and moving eastward. This winter weather system is known as western disturbances and approach India from the west through Iran, Afghanistan and Pakistan. With the setting of the winter season these western disturbances have the tendency to move along lower latitudes. Ordinarily these disturbances remain at high latitudes and do not influence the Himalayas. But, as the season advances they come lower and lower and by the end of December these cover more or less whole Himalayas. Such disturbances recede to their original position, which lies beyond the Himalayan Mountains by the end of winter season. The precipitation during this season is generally in the form of snow in the greater Himalayas, snow and rain in the middle Himalayas, and light to moderate rain over the outer Himalayas and the adjoining north Indian plains. Precipitation occurs at intervals throughout the winter season. It is found that average frequency of occurrences of these disturbances is about three to five each month, which reduces as the season advances. The higher precipitation in the western Himalayas during these months is the combined effect of the nearly east west configuration of the Himalayas and eastward movement of the winter weather system. The precipitation associated with this weather system decreases considerably as they move eastward along the Himalaya because of increasing distance from the source of moisture. These weather systems cause snowfall at higher elevations. 2.2 Premonsoon Season (April June) Generally, pre-monsoon season lasts for about a period of 3 months from April to June and is considered as transit period between winter and southwest monsoon. Light to moderate rains are essentially caused by air mass convective storms. Convection increases because of increasing trend of temperature in the Himalayan region in this season. 2.3 Monsoon Season (July September) Normally, the moist air currents cause precipitation over the Himalayas from Bay of Bengal in this season. Sometimes, in association with certain weather situations both branches of monsoon (i.e., the Bay of Bengal and Arabian sea) arrive simultaneously in this region heralding the onset of monsoon. These currents, after striking the Burma and the eastern Himalayas are deflected westwards and travel along the Himalayas. Rainfall decreases westward because of increasing distance from the source of moisture i.e. Bay of Bengal or Arabian Sea, which results in less amount of moisture content in the air

5 Role of elevation and aspect in snow distribution currents. Consequently, lesser precipitation is observed as one moves further west. This is the season of abundant rain and rivers are generally flooded. Snow and glaciers at very high altitudes continue melting during this season. The monsoon normally starts withdrawing from this region towards the end of September. It was observed that while the monsoon currents give copious rainfall over the Indian plains and lower Himalayas. At the time of crossing greater Himalayan ranges and approaching trans-himalayan regions, these currents become practically dry as most of the moisture content they initially carried is precipitated during their passage over the plains and mountain ranges of the Himalayas. It results in insignificant rainfall in the trans- Himalayan region. 2.4 Post Monsoon (October November) During this season clear autumn weather sets in and there is generally little rainfall. This is the driest season in the entire Himalayas as well as in the plain areas. The presence of glacier and snowfields over an extensive area in the western Himalayan is not only a dominant feature relevant to the climate alone, but is factor that significantly enters into many hydrological implications. Accumulation of snow during winter and period of snowmelt coinciding with the gradual rise in temperature, actually regulate the flow and make it available at the time of year when it is needed most. The primary data sets used includes: MODIS (MODerate Resolution Imaging Spectroradiometer) 8 days maximum snow cover data of the study area for a period of 2 years (01 Jan 2003 to 17 Dec 2004) from NSIDC. The MODIS data products were received through ftp from DAAC. The data products were available in HDF-EOS format, which is a standard archive format for EOS Data Information System (EOSDIS) products. MODIS is a key instrument launched by Earth Observation System (EOS) on the Terra (EOS AM) and Aqua (EOS PM) plateform. Terra, which was successfully, launched on December 18, 1999 from Vandenberg Air Force Base, California, and started collection scientific data on February 24, The satellite orbits in a sunsynchronous polar orbit at an altitude of 705 km. Descending southward, Terra crosses the equator at the same local time every day it passes from north to south across the equator in the morning. The Aqua (EOS PM-1) spacecraft carrying the second MODIS satellite was successfully launched on May 4, Aqua passes south to north over the equator in the afternoon (MODIS 2007). DEM of the study area extracted from the GTOPO30 DEM for India which is downloaded by FTP from the USGS Eros Data Centre web site: gtopo30/gtopo30.html. 3 Methodology 3.1 Creation of Data Base The base map of the study area containing drainage map was prepared at a scale of 1:10,00,000 from the River basin Atlas map of India (1999). The polygon map containing different river basins in the form of polygons was prepared. The total percentage of area covered by the snow in Satluj, Chenab, Ravi and Beas river basin has been calculated using GIS extension in ERDAS Imagine 8.6. Thus, a continuous variation in snow cover in the

6 S.K. Jain et al. four river basins was calculated for a period of 2 years in 8 days interval. The snow cover distribution with respect to elevation and aspect has been computed. The different steps involved in the study are summarized in Fig. 2. The brief description of generation of DEM and snow cover area maps is given below. 3.2 Digital Elevation Model (DEM) The USGS DEM is a global DEM, resulting from a collaborative effort of the staff at the USGS s Eros Data Center. The basic aim in developing such a global DEM was to meet the needs of the geospatial data user community for regional and continental scale topographic data. The DEM data are stored in 16-bit binary format. This DEM is a global dataset covering the full extent of latitude from 90 S to 90 N and longitude from 180 W to 180 E. The horizontal coordinate system is decimal degrees of latitude and longitude referenced to WGS84. The vertical units represent elevation in meters above mean sea level. The entire DEM has been divided into 33 smaller pieces. Each piece is known as a tile. The area from 60 S latitude to 90 N latitude and from 180 W longitude to 180 E longitudes is covered by 27 tiles, with each tile covering 50 of latitude and 40 of longitude. Antarctica (90 S latitude to 60 S latitude and 180 W longitude to 180 E longitude) is covered by six tiles, with each tile covering 30 of latitude and 60 of longitude (Saraf et al. 1999). The process of naming each tile is such that the name represents the latitude and longitude of the upper left corner of the tile. 3.3 Snow Cover Area The MODIS snow-mapping algorithm is a fully automated and computationally frugal approach to snow detection. It is based on a series of tests for snow detection including Normalized Difference Snow Index (NDSI) and other criteria tests. The Fig. 2 Flow chart illustrating different steps involved in studying the relationship between aspect elevation and % SCA

7 Role of elevation and aspect in snow distribution MODIS snow-mapping algorithm utilizes four (bands 1, 2, 4 and 6) of the seven MODIS bands designed especially to image the land surface. The Snowmap algorithm used in MODIS snow product is based on a long lineage of the NDSI technique for detection of snow and was developed using Landsat Thematic Mapper (TM) data (Dozier 1989; Halletal.1995; Klein et al. 1998; Halletal.2001, 2002; Klein and Barnett 2003). In the Snowmap algorithm, pixels must satisfy the following criteria: 1. Pixels have nominal Level-1B radiance data. 2. Pixels are on land or inland water. 3. Pixels are in daylight. 4. Pixels are unobstructed by cloud. 5. Pixels have an estimated surface temperature less than 283 K. The automated snow-mapping algorithm uses at-satellite reflectance in MODIS bands four ( μm) and six ( μm) to calculate the normalized difference snow index (NDSI) (Hall et al. 1995). Compared to most of the terrestrial objects snow has high reflectance in the visible wavelength and strong absorption in the infrared spectral regions. These characteristics make reflectance ratios the key in detecting snow (Riggs and Hall 2002). The index also hold good in snow/cloud discrimination since clouds tend to have high reflectance in both visible and mid-infrared wavelengths. Band 4 Band 6 NDSI ¼ Band 4 þ Band 6 The algorithm also uses MODIS bands 1 and 2 to calculate the NDVI (normalised difference in vegetation index) values to use with the NDSI values to map snow in dense forests (Klein et al. 1998). Band 2 Band 1 NDVI ¼ Band 2 þ Band 1 Snow detection is achieved by using two groups of grouped criteria tests for snow reflectance characteristics in the visible and near-infrared regions. One group is for detection of snow in many conditions. Criteria for snow in that group of tests is that a pixel has a NDSI, greater than 0.4 and near-infrared reflectance (band 2) greater than 0.11 and band 4 reflectance greater than If a pixel passes this group of criteria tests, it is identified as snow in the data product. The other group of criteria tests is used to better detect snow in dense vegetation. In this other group, if a pixel has NDSI and NDVI values in a defined polygon of a scatter plot of the two indices and near-infrared reflectance in band 2 greater than 0.11 and band 1 reflectance greater than 0.1, it is also determined to be snow (Riggs et al. 2003). A total of 88 MOD10A2 scenes of the study area covering a period of 2 years from 01 Jan Dec 2004 were utilized for the study. The MOD10A2 image of 01 Jan 2003 was first geo-referenced with the base map of the area using second order transformation technique. Geographic lat/long projection was adopted and WGS84 was selected as the datum plane. The remaining MOD10A2 scenes were geo-corrected by image-to-image transformation techniques using the corrected image of the month of January. After registration of all the maps, DEM was also geo-referenced. The re-sampling was done using nearest neighbourhood method, which uses the value of the nearest pixel to assign to the output pixel value. For SCA estimation MODIS products give better results as discussed in the study carried out by Jain et al Therefore in this study SCA was estimated from MODIS data (Jain et al. 2007).

8 S.K. Jain et al. 4 Snow Cover and Elevation The elevation of the terrain has a decisive role in snow accumulation. High elevation in general terms witness lesser temperature, which in terms leads to higher snow accumulation. The DEM was classified into eight relief classes: Class1 >6,000 m, Class2 6,000 5,000 m, Class3 4,000 5,000 m, Class4 3,000 4,000 m, Class5 2,500 3,000 m, Class6 2,000 2,500 m, Class7 1,500 2,000 m, Class8 <1,500 m. The area with elevation between 6,000 and 3,000 m were classified in 1,000 m interval and that between 3,000 to 1,500 m are classified in 500 m interval because lower elevation shows higher sensitivity to snow cover with time and need details study. The average of the percent snow cover with respect to eight relief classes were plotted against relief classes in a graphical form as shown in Fig. 3a d to establish relationship between elevation and snow cover. A linear regression relation has been drawn for percent snow cover in all the four river basins with relief. 5 Snow Cover and Aspect Aspect, defined, as the orientation of the slope face, is an important constituent of topography. Aspect maps display the general orientation of each area of land typically according to eight regimes: N, NW, W, SW, S, SE, E, and NE. The classified aspect map of the study area was prepared from the USGS-DEM. The classified aspect map was crossed Fig. 3 Scatter plot showing the % SCA elevation relationship in a Satluj, b Chenab, c Ravi and d Beas River Basin

9 Role of elevation and aspect in snow distribution with the classified elevation map to find the distribution of aspect classes with respect to different relief zone. The percent snow covered area in terms of different aspect classes was calculated for a period of 2 years (01 Jan 2003 to 17 Dec 2004) at weekly interval. 6 Results In the present study mainly two aspects have been studied and these are snow cover/ elevation and snow cover/aspect for four Himalayan basins. The results obtained for these basins are discussed here under. Percent snow covered area is least in case of Satluj river basin with 23% while Chenab, Ravi and Beas have 42%, 33% and 38% respectively and these are provided in Table 2. A strong linear regression relation is observed between percent snow cover and elevation in all the four river basins. The coefficient of correlation (R) for Ravi river basin, was best i.e followed by Beas (0.98), Satluj (0.96) and Chenab (Table 3) and shown in Fig. 3. The average rate of increase in snow cover percentage is calculated for all the four river basins and it has been observed that in Satluj river basin snow percent increases at a slower rate of 1.3% per 100 m, Chenab (1.8% per 100 m), followed by Ravi (2% per 100 m) and Beas river basin has the rate of 2% per 100 m (Table 3) From the above results the minimum elevation where snow fall starts was estimated and it has been observed that in case of Satluj snow fall starts at a higher elevation (1,369 m), in Chenab snow fall starts at an elevation of 834 m, followed by Ravi (1,058 m) and Beas (1,264 m) (Table 3). The following observations regarding snow cover area and aspects (as given in Table 4) may be made from the analysis: 1. In Satluj river basin, 15% of the slopes are oriented in North side followed by West (14.5%) and Southeast (12.5%). In general, about 42% of the total slope area is oriented in Southwest to North direction. In South direction, 26.4% of the area is covered by snow, followed by 26% in SE direction and 23.6% in E direction. 2. In Chenab river basin, 18.2% of the slopes are facing in West direction, 16% in Southwest direction and 12.3% in Northeast direction. In general, it is found that Southeast to west direction accounts for 46% of the total area of the river basin. Here the percentage snow cover is higher in case of N direction (46.5%), NW (46.3%) and E direction (46%). Table 2 Elevation wise percent snow cover area in Satluj, Chenab, Ravi and Beas River basins Elevation (m) Satluj Chenab Ravi Beas Area (km 2 ) SCA (%) Area (km 2 ) SCA (%) Area (km 2 ) SCA (%) Area (km 2 ) SCA (%) >6, Nil Nil ,000 6,000 9, , ,000 5,000 17, , , ,000 4,000 2, , , , ,500 3, , ,000 2, , ,500 2, , <1,500 3, , , , Total 35, , , ,

10 S.K. Jain et al. Table 3 Elevation vs. percent snow cover relation in Satluj, Chenab, Ravi and Beas River basins Elevation vs. % snow cover relation Satluj Chenab Ravi Beas Linear equation (where y=% snow cover & x=elevation) Y ¼ 0:0134x 18:351 Y ¼ 0:0183x 15:276 Y ¼ 0:0201x 21:276 Y ¼ 0:0205x 25:921 Coefficient of determination R 2 Coefficient of correlation R Rate of snow increase per 100 m (in %) Minimum elevation where snow fall starts in m 1, ,058 1, In Ravi river basin Southwest slopes have a share of 21.6% of the total area followed by 7.8% in West facing slopes and 13.7% in south facing slopes. As a whole Southeast to west facing slopes account for 53% of the total river basin area. A total of 54% of the area in N direction is covered by snow followed by 50.7% in NE and 49% in E direction. 4. Similarly, Beas river basin has 22.4% of the total slopes facing Southwest direction, 20% of the slopes are oriented in West direction and 13.6% of the slopes are facing south. As a whole 56% of the total area of the river basin is covered by the slopes facing southeast to West direction. However, if percent snow cover is considered it is observed that in NE direction 45% of the area is snow covered, in N 43% and in SE 38% of the area is snow covered. Table 4 Aspect wise average area and percent snow cover distribution in Satluj, Chenab, Ravi and Beas River Basins Aspect Classes Satluj Chenab Ravi Beas Average area in % % Snow cover Average area in % % Snow cover Average area in % % Snow cover Average area in % % Snow cover E SE S SW W NW N NE

11 Role of elevation and aspect in snow distribution 7 Discussion The great contrast in the topographical relief results in variety of climate in the Himalayas. Such regions are characterized by numerous small climatic differences over short horizontal distances. Principal controls producing such differences are those of altitude, local relief and mountain barrier effect. The most important factors controlling the weather and climate in the Himalayas are the altitude and aspect. Largely due to variations in altitude, the climate varies from hot and moist tropical climate in lower valleys, to cool temperate climate at about 2,000 m and tends towards polar as the altitude increases beyond 2,000 m. Altitude controls not only temperature but rainfall also. The second factor controlling the climate is aspect of slopes. Usually the south facing slopes are sunnier and also get more rain. Further, in each individual range the snowline is higher on southern aspect as these slopes have more sunshine. Also, the snow line in the eastern Himalayas is higher than in the western Himalayas. It is well known that northern or southern aspect and site exposure can have a big influence on snow and ice melt. A clear indication is given by the glacier development in sheltered valleys on north facing slopes and by the delayed snowmelt on northern aspects. In calculating the shortwave radiation inputs, it is therefore important to allow for the orientation of the mountain slopes and the surrounding elevation angle of the mountain barrier which determines the local sunrise and sunset (Quick and Singh 1992). The result of this study showed that during winter months north facing slopes receive significantly less radiant energy. However, during the snowmelt months of May, June and July, the elevation is so high and the sun rises and sets so far to the north that north facing slopes receive nearly as much solar energy during the whole day as a south facing slope. Change in aspect and elevation highly influences snow accumulation. The study conducted in the four river basins showed that average snow cover increases linearly with increase in elevation. The following observations were made from the results: (a) In Satluj river basin 42% of the total slopes is oriented in southwest to north direction, which is a favorable site for snow accumulation. But snow covered area is only 23% of the total land area. Rate of snow cover accumulation is lowest (1.3% per 100 m). This can be attributed to the fact that origin of Satluj River is Rakastal and Mansarovar Lake in Tibet region and flows through the Tibetan plain where precipitation is very less. In this part of the basin which is about 50% of the total basin, snow cover area is very less and therefore total SCA in this river basin is very less. (b) Although the rate of increase in percent snow cover in Chenab river basin is 1.8% per 100 m, but snowfall starts accumulating from an elevation of 834 m. Hence it has on an average 42% of the total area covered by snow. In this river basin 46% of the slopes are oriented in southeast to west direction but it is quite less compared to Ravi 53% and Beas 56%. Hence it has comparatively more percent land area oriented in northwest to southeast direction, which is a favorable site for snow accumulation. This factor has a strong impact on higher percentage of snow accumulation in Chenab river basin. (c) In case of Ravi river basin, 53% of the total slopes area oriented in southeast to west direction. Here snow starts to accumulate from an elevation of 1,058 m. But the rate snow cover accumulation is quite high (2%). The total area covered by snow is 33%. (d) In Beas river basin 56% of the slopes are oriented in southeast to west direction, which is not a favorable site for snow accumulation. Snow starts to accumulate from an elevation of 1,264 m, which is higher compared, to Chenab and Ravi. But rate of accumulation of snow cover with elevation is highest in this river basin 2% per 100 m, compared to others. Hence, total snow covered area in this river basin is quite high 38%.

12 S.K. Jain et al. 8 Conclusions In this study, aspect and slope of four basins have been studied and a comparative analysis has been made. It was observed that aspect has a major impact in snow accumulation in lower elevations as compared to higher elevations. If majority of the slopes in lower altitudes are oriented in between north-west and north-east direction, snow accumulation will start from lower elevations. This is because of the fact that north facing slopes are less exposed to insulation and warm wind, which provides favorable environment for snow accumulation. The exception lies in case of Satluj river basin where percent snow cover is more in slopes oriented in north-east to south facing slopes. From this study it was found that four basins located in western Himalayan region have different characteristics and have different snow cover with respect to elevation and aspects. This type of information can be utilized in snowmelt runoff studies. References Atlas (1999) Integrated water resources development a plan for action, report of the National commission for integrated water resources development. Ministry of water resources, Govt. of India, India, September 1999 Chandra S (1994) Recent trends in Himalayan hydrology. Proceedings of Snowsymp September, Snow Avalanche Study Establishment (SASE) Manali, pp Dozier J (1989) Spectral signature of alpine snow cover from the Landsat Thematic Mapper. Remote Sens Environ 28:9 22 Hall DK, Riggs GA, Salomonson VV (1995) Development of methods for mapping global snow cover using Moderate Resolution Imaging Spectrometer (MODIS) data. Remote Sens Environ 4: Hall DK, Riggs GA, Salomonson VV, Barton JS, Casey K, Chien JYL, DiGirolamo NE, Klein AG, Powell HW, Tait AB (2001) Algorithm theoretical basis document (ATBD) for the MODIS snow and sea icemapping algorithms. Available online at (accessed on December 18, 2007) Hall DK, Riggs GA, Salomonson VV, DiGiorlamo NE, Bayr KJ (2002) MODIS snow-cover products. Remote Sens Environ 83(1 2): IPCC (2001) Climate change 2001, The scientific basis. In: Houghton JT, Meira Fiho LG, Callender BA, Harris N, Kattenberg A, Maskel K (eds) The third assessment report of working group I of the intergovernmental panel on climate change (IPCC). Cambridge University Press, Cambridge Jain SK (2001) Snowmelt runoff modeling and sedimentation studies in Satluj basin using remote sensing and GIS, PhD Thesis unpublished, University of Roorkee Jain SK, Agarwal PK, Singh VP (2006) Hydrology and water resources of India. Water Science and Technology Library, Springer, The Netherlands Jain SK, Ajanta G, Saraf AK (2007) Accuracy Assessment of MODIS, NOAA and IRS Data in Snow Cover Mapping Under Himalayan Condition, International Journal of Remote Sensing, (in press) Klein AG, Barnett AG (2003) Validation of daily MODIS snow cover maps of the Upper Rio Grande River Basin for the snow year. Remote Sens Environ 86: Klein AG, Hall DK, Seidel K (1998) Algorithm intercomparison for accuracy assessment of the MODIS snow-mapping algorithm. Proceedings of the 55th Eastern Snow Conference, 3 5 June 1998, Jackson, New Hampshire Lang H, Rohrer M (1987) Temporal and spatial variations of the snow cover in the Swiss Alps, proceedings of the Vancouver symposium on Large scale effects of seasonal snow cover, IAHS Publ. No. 166 MODIS (2007) About MODIS, NASA, available online at (accessed on December 22, 2007) Quick MC, Singh P (1992) Watershed modeling in the Himalayan region, Proceedings of International Symposium on hydrology of mountainous areas, May 28 30, 1992, Shimla, India Riggs GA, Hall DK (2002) Reduction of Cloud Obscuration in the MODIS Snow Data Product, Presentation at the 59th Eastern Snow Conference, 5 7 June 2002, Stowe, VT

13 Role of elevation and aspect in snow distribution Riggs GA, Barton JS, Casey KA, Hall DK, Salomonson VV (2003) MODIS Snow Products User s Guide Saraf AK, Foster JL, Singh P, Tarafdar S (1999) Passive microwave data for snow-depth and snow-extent estimations in the Himalayan Mountains. Int J Remote Sens 20(1):83 95 Singh P, Jain SK (2003) Modelling of stream flow and its components for a large Himalayan basin with predominant snowmelt yields. J Hydrol Sci 48(2):