Change in snow cover area of Brahmaputra river basin and its sensitivity to temperature
© Barman and Bhattacharjya. 2015
Received: 24 July 2015
Accepted: 28 September 2015
Published: 9 October 2015
The discharge of the River Brah maputra is highly affected by the melting of snow at the upper part of its catchment. Increase in discharge due to significant retreat of snow in turn affects the downstream flow characteristics of the river giving rise to severe catastrophic problems such as flood and erosion. Rising temperature is one of the major reasons of melting of snow at the upper Brahmaputra catchment. Keeping in mind such issue, in this paper, a study has been conducted to see how the snow cover area of the Brahmaputra river basin changes with respect to the change in temperature. MODIS image MOD09A1.5 (MODIS/Terra Surface Reflectance 8-Day L3 Global 500 m SIN Grid) of 500 m resolution consisting of seven bands has been taken to prepare the normalized difference snow index maps of the study area. The normalized difference snow index map is then used to obtain the areal extent of snow in the Brahmaputra catchment area. The normalized difference snow index maps were prepared starting from 2002 to 2012 for four different months, viz. January, April, July and October. For temperature data, HadCM3 data of spatial resolution 2.5° × 3.75° (latitude by longitude) has been used.
The evaluation of the results shows that the snow cover area of the basin shows decreasing trends with respect to the increasing trend of temperature except for the month of January. Further, this study also shows that MODIS data can efficiently be used for snow cover area variation study.
Variation of snow cover area is an indication of climate change as the melting of snow clearly reflects the rise in temperature. The attenuation of river flow due to the melting of snow in the river Brahmaputra basin may affect the downstream discharge of the river giving rise to severe flood and erosion problem. It has also been observed that MODIS data can efficiently be used in mapping snow cover of large areas, because of its good spatial as well as high temporal resolution.
The River Brahmaputra, one of the major river systems of the Indian subcontinent, which originates in the Himalayas, is expected to be much vulnerable to climate change because of substantial contribution from snow and glaciers (Singh et al. 1997; Singh and Jain 2002). During winter, a large extent of mountainous area of Himalayan river basins is covered by snow which starts ablating in the spring due to rise in temperature. IPCC (2001a) has indicated that the average global surface air temperature has increased by 0.6 ± 0.2 °C since the late 19th century and it is projected to increase by 1.4–5.8° C over the period 1990–2100. Lal and Singh (2001) and Lal (2001) have reported that the average annual mean surface temperature over Indian sub-continent is likely to increase by about 2.7 and 3.8 °C during the decades of 2050 and 2080 s, respectively. Dyurgerov and Meier (2005) and Prasad et al. (2009) mentioned that in recent years, the glaciers and snowfields of the Himalaya–Karakoram–Hindu Kush (HKH) mountain belts and Tibetan plateau are found to be amongst the fastest receding glacial and snow covers in the world. Again, Kulkarni et al. (2007) and Raina (2009) said that the simultaneous fragmentation of the glaciers along with glacial retreats has also degraded the total areal coverage of perennial snow and ice in the HKH region.
Mountain glaciers are used to detect and monitor local climate change in regions not typically monitored by instrumentation, as they are considered to be sensitive indicators of climate (Haeberli et al. 2007). Due to the large extent and difficult accessibility of high mountainous terrain, remote-sensing techniques provide an efficient way to collect data in such regions. Satellites are well suited to the measurement of snow cover because the high albedo of snow presents a good contrast with most other natural surfaces except clouds (Hall et al. 2002).
Identification of snow cover using only the visible reflected light may be difficult because many things appear as white, such as clouds or even rocks. However, comparing the reflectance of other wavelength, such as infrared, it is possible to differentiate snow from clouds. Snow reflectance is high in the visible (0.5–0.7 µm) wavelengths and has low reflectance in the shortwave infrared (1–4 µm) wavelengths (Nolin and Liang 2000) which enables to distinguish snow from clouds and other non-snow-covered conditions. Use of the ratio of a short-wave IR channel to a visible channel was determined by Kyle et al. (1978) and Bunting and d’Entremont (1982) to be useful for snow cover mapping, and later utilized by Dozier (1989) to map snow in the Sierra Nevada Mountains. This method is known as Normalize Difference Snow Index (NDSI) method and is one of the very potential methods to map snow cover of an area. Moderate-Resolution Imaging Spectroradiometer (MODIS) imageries have successfully been applied in monitoring snow cover from space using the Normalize Difference Snow Index (NDSI). MODIS data, available since 2000, have proven useful for a large variety of land, ocean and atmospheric applications. Relative to similar sensors such as the Advanced Very High Resolution Radiometer (AVHRR), the MODIS sensor offers some significant advantages. For example, the MODIS provides observations at a nominal spatial resolution of 500-m versus the 1.1-km spatial resolution of the AVHRR. MODIS’s data are available continuously (spatially and temporally) and has also several spectral band observations that span the visible and short-wave infrared wavelengths, useful for distinguishing the extent of snow cover (Salomonson and Appel 2004). Most of the MODIS accuracy assessment reported the overall accuracy between 85 and 99 % during clear sky conditions (Parajka and Bloschl 2012).
The impact of climate change at regional scale can be studied effectively by the General Circulation Models (GCM), which describes the atmospheric process by mathematical equations. The spatial resolution of these models varies from 2.5° × 2.5° up to 8° × 10° latitude and longitude. There is considerable confidence in global climate model simulations mainly because GCM principles are based on well-established fundamental laws of physics such as conservation of mass, energy and momentum (Pitman and Perkins 2008). In addition, another source of confidence lies in the models’ ability to simulate important aspects of the current and past climates as well as their changes (Randall et al. 2007). GCMs simulate temperature better than rainfall and therefore there is more confidence in predictions of temperature than rainfall (Masanganise et al. 2013). McMahon et al. (2004) in their study have found that the monthly pattern of temperature is generally well reproduced by the GCMs compared to the monthly pattern of precipitation. They calculated the Nash–Sutcliffe efficiency (NSE) (Nash and Sutcliffe 1970), to compare GCM climate data and observed data and found that the median NSEs of all monthly temperature patterns are greater than 0.75, with 41 % greater than 0.90. They also concluded that along with few other GCMs, HadCM3 is also a better performing GCM.
There has been no long-term comprehensive in situ monitoring of snow/glacial melt contributing to the knowledge of hydrology in the river basins of the Himalayan region. As a result, studies on snow melt/glacial melt that describe a basin’s hydrology lack direct evidence and sometimes appear to be inconsistent (Armstrong 2010; Kaser et al. 2010). Most of the upper parts of the Brahmaputra catchment is covered by Himalayan snow and have major effect on the downstream flow characteristics of the river. Apart from temperature, other climatic factors such as heavy precipitation, wind speed etc. also effect on melting of snow. The effect of wind is important for the melting process of snow or chipped ice (Hasebe and Kumekawa 1994). According to the result of the laboratory experiment, performed by Hasebe and Kumekawa (1994), the relation between the ratio volume of snowmelt water to air temperature and wind speed is nearly linear. They also found that peak discharge is sharp as the wind speed increases and lag time from the beginning of snowmelt runoff to peak discharge is faster according to an increase in wind speed. Rainfall on existing snow cover frequently occurs in spring. At the onset of a rain storm, the snow cover often stores a portion of the rain and consequently attenuates runoff formation. However if the rain persists and the snow cover becomes saturated, any additional melt water will increase runoff formation.
Many studies state that the melting of glaciers is a clear indicator of climate change (Xu et al. 2009) and note that glacier change is the most visible and obvious indicator of changing temperatures (Armstrong 2010; Winkler et al. 2010). The rising temperature in the Himalayas would affect glacier melt (Barnett et al. 2005). In the present study, thus the temperature factor has been taken into consideration to study its impact on the change in snow cover area of the Brahmaputra river basin.
A widely cited estimate shows considerable variation in the contribution of melt-water across the river basins fed by Himalayan glaciers (Eriksson et al. 2009; Xu et al. 2008) although this varies seasonally and spatially. The importance of melt-water contribution also varies by basin by basin. It is extremely important to the Indus basin and it is important for the Brahmaputra basin. But it plays modest roles for the Ganges, Yangtze and Yellow Rivers (Immerzeel et al. 2010). By region, melt-water contributes 30 % to the total water flow in the eastern Himalayas, 50 % in the central and western Himalayas and 80 % in Karakoram (Xu et al. 2009).
Description of study area
The variation of snow cover area in the Brahmaputra river basin has been studied starting from 2002 to 2012 for four different months viz. January, April, July and October using the MODIS image, MOD09A1.5 (MODIS/Terra Surface Reflectance 8-Day L3 Global 500 m SIN Grid)of 500 m resolution consisting of seven bands (band-1 to band-7). The climate change scenario data used in this study are based on simulations carried out using General Circulation Models (GCMs) for the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC 2007). The gridded temperature data of HadCM3 (Hadley center Coupled Model version 3) model of A2 scenario having spatial resolution of 2.5° × 3.75° (latitude by longitude) were used to get the average temperature of the upper portion of the Brahmaputra river basin. A2 scenario considers the forcing effect of greenhouse gases and Sulphate aerosol direct effect, which are based on IPCC SRES-A2 (Special Report of Emission Scenario A2). HadCM3 model was selected since it has the highest ‘skill scores’ for both precipitation and temperature of all the models used for the AR4 (Cai et al. 2009). This model does not require flux adjustment and has higher resolution compared to other models. The raw data were downloaded from the website of IPCC Data Distribution Centre (http://www.ipcc-data.org/ar4/gcm_data.html).
The area of Brahmaputra river basin is very large and it is not easy to get the actual temperature data at the upper part of the catchment. Hence, the average temperature values of the GCM points that fall in and around the upper catchment have been used to study the temperature variations at different time period. Miao et al. (2012), assessed the performance of the AR4 GCMs in simulating precipitation and temperature in China from 1960 to 1999 by comparison with observed data, using system bias (B), root-mean-square error (RMSE), Pearson correlation coefficient (R) and Nash–Sutcliffe model efficiency (E) metrics. They observed that GCMs gave reasonably accurate predictions of the temperature, but were less successful at reproducing the precipitation. Again, Mavromatis and Jones (1999) evaluated precipitation and near surface air temperature in two successive versions of the Hadley Centre General Circulation Model (GCM) to consider to what extent GCMs are capable of simulating the mean and variability of local climates. Their conclusion too was positive towards direct use of raw data of HadCM2. Taking the accuracy of GCMs’ temperature data as the ground, in this paper, we have used directly the HadCM3 temperature data to study its impact on snowmelt of the Brahmaputra river basin.
Normalized Difference Snow Index (NDSI)
Preparation of snow map
The Erdas Imagine remote sensing software has been used to prepare the snow map of the study area. The various operations performed were: importing the original, hdf format of the image to .img format, mosaicing of all the tiles that cover the Brahmaputra river basin, re-projection of image to geographic latitude longitude and WGS84, sub-setting of the mosaiced image as per the area of interest to get the shape of the Brahmaputra river basin and finally stacking operation was performed to get the image consisting of seven bands.
The created water, forest and NDVI masks were finally applied to the NDSI product to get the final snow map from which the snow cover area can be determined. The values within the NDSI, water, forest and NDVI masks are 1 if true (snow pixel) or 0 if false (non-snow pixel).
Comparison with Landsat data
The HadCM3 Model
Results and discussion
Snow cover area and temperature for different months
Snow cover area (km2)
Snow cover area (sq. km)
Snow cover area (sq. km)
Snow cover area (sq. km)
For the other three months i.e. April, July and October, the results plotted in graphs (Fig. 8b–d) show a positive correlation between increase in temperature and decrease in snow cover. Maximum changes have been observed in the month of October. For the month of April, minimum snow cover was observed in the year 2004 (56,236 km2) and maximum was observed in the year 2002 (114,588 km2). For July, the minimum was observed in the year 2007 (7881 km2) and the maximum was observed in the year 2003 (25,851 km2). Again, for the month of October, the minimum was observed in the year 2007 (12,347 km2) and the maximum was observed in the year 2002 (84,551 km2). In case of temperature, the minimum in the month of April was observed in the year 2006 (−3.81 °C) and maximum was observed in the year 2007 (0.81 °C). In case of July, the minimum and maximum temperatures were observed in 2004 (7.20 °C) and 2006 (8.85 °C), respectively. On the other hand, for the month of October, the minimum temperature was observed in the year 2002 (−0.67 °C) and maximum was observed in the year 2011 (1.43 °C). Apart from the role of temperature, other factors should also be taken into account to explain the reason behind the difference in snow melt trend of the other 2 months from that of January. For example, if the snow is older, its albedo will be lower and in such situation it absorbs more solar radiation. Again, if the temperature variation is not significant, snow melts faster in the windy condition than in the calm conditions. However, temperature being a dominant factor, this study evaluated the impact of increase in temperature on the snow cover.
Melting of snow with respect to the rise in temperature has a major impact on the hydrology of the rivers. The flood and river bank erosion caused by the river Brahmaputra at its downstream are the main calamities that affect the livelihood of the people. Though precipitation is considered as the main cause of flood in this region, attention should also be paid to the impact of contribution of snowmelt water to the discharge of the river Brahmaputra. From this study, it has been found that the melting of snow is proportional to rise in temperature. Here a quantitative analysis has been performed to show how much area covered by snow has been reduced due to the rise in temperature from 2002 to 2012 for four different months viz. January, April, July and October. On the basis of these findings, in future, further analysis could be carried out to determine the volume of snowmelt water that contributes to the discharge of the river Brahmaputra. In mountain region particularly, global mean temperature has not been spatially homogeneous. A minor variation in temperature under low magnitude warming is more effective to changes in volume and area of glacier than high magnitude warming. Increase in glacier lakes due to rise in temperature should also be a serious matter of concern. Glacier lakes are formed when the glacier erodes the land, and then melts, filling the hole or space that it has created. A retreating glacier often left behind large deposits of ice in hollows between hills. As the ice age ended, these melted to create lakes (http://en.wikipedia.org/wiki/Glacial_lake).
In this study, an attempt has been made to correlate change in snow cover area of the Brahmaputra river basin with the change in temperature. It is found that except for the month of January, the snow cover area for other two months i.e. April and October show a decreasing trend with increase in temperature. Thus, significant warming in the snowbound higher Himalayas is causing the growth of existing glacial lakes. The formations of new water bodies have a huge potential of causing damage at the downstream of the river. It has also been found from the study that MODIS data can efficiently be used in mapping snow cover of large areas, because of its good spatial as well as temporal resolution relative to similar sensors such as AVHRR. Since snow reflects more energy in the visible part than in the mid-infrared, the NDSI ratio enhances the contrast between snow and bare ground. Additionally, the reflectance of clouds remains high in MODIS band-6. Thus, the NDSI can discriminate clouds and snow. MODIS snow map prepared by the method of NDSI was found to be accurate while comparing with the high resolution LANDSAT data of same date. In both the images, the area covered by snow for the selected portions were found to be the same.
SB downloaded all the required data for the study to prepare the snow cover maps and then analyzed the result. She also prepared a draft manuscript for publication. RKB gave the guidance throughout the entire study, reviewed the manuscript and corrected it by giving his own inputs for the betterment of the paper. Both authors read and approved the final manuscript.
The authors are extremely grateful to the editor and the anonymous reviewers of this paper for their constructive and incisive comments and suggestions.
Compliance with ethical guidelines
Competing interests The authors declare that they have no competing interests.
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