Change in snow cover area of Brahmaputra river basin and its sensitivity to temperature

Abstract

Background:The discharge of the River Brahmaputra 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. Results: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. Conclusions: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.

Google Earth Engine for Large-Scale Flood Mapping Using SAR Data and Impact Assessment on Agriculture and Population of Ganga-Brahmaputra Basin

“…Moreover, these results are consistent with the latest study, which has reported based on the MODIS NRT flood product that about 17.7% of the geographical area of North Bihar was inundated during 2020 flood events and the adversely affected districts were Muzzafarpur, Darbhanga, Saharsa, Khagaria, Madhepura, Bhagalpur, Katihar, and Purnea [62]. Previously, during the flood event of August-September 2017, 12% to 17% of the geographical area of North Bihar was inundated during the peak flood period [57]. The composite floods maps over 2000-2020 indicated that about 34% of the area was affected by floods, as derived from the MODIS-based NRT flood product [57].…”

“…The impact of climate change on the water resources of the snow and glacier-dominated river basins is also important for water resource management. The river discharge in the Brahmaputra river is highly influenced by the snowmelt at the upper part of the catchment due to global warming [57]. The snow cover area changes under the recent climatic conditions are expected to have a larger effect on the hydrological cycle [58].…”

Climate and topographic controls on snow cover dynamics in the Hindu Kush Himalaya

“…The inverse correlation between SCA and temperature is well established (Bednorz, ; Hosaka et al , ; Gurung et al , ; Maskey et al , ; Barman and Bhattacharjya, ), whereas the spatial variability across elevations is not well understood. Table shows the correlation ( τ ) between the daily average temperature and daily SCA of individual basins during the period between 2000 and 2007.…”

“…The 7000m asl elevation marks the transition from negative to positive correlation which has also been reported in Nepal. This pattern may be due to temperatures being well below critical above 7000 m asl, and as a result small changes in temperature do not lead to perceptible changes in SCA (Barman and Bhattacharjya, 2015). The other possible reason could be ablation at higher elevation due to wind erosion and sublimation.…”

“…The accuracy of MODIS products reported by many researchers based on comparisons with in situ data (Klein and Bernett 2003;Simic et al, 2004;Parajka and Bloeschl 2008;Wang et al, 2008) is as high as 94-95%, although the accuracy is low (<39%) where the snow depth is less than 4 cm . MODIS snow products has been found suitable and used for such basinwide analysis (Barman and Bhattacharjya, 2015).…”