Morphologic parameters of a watershed could help in segregating critical sub-watersheds for taking up conservation practices and mitigation interventions. Determination of critical watersheds or prioritization of sub-watersheds is inevitable for efficient and sustainable watershed management programs and allocation of its natural resources. The traditional methods of determination of morphologic parameters are time consuming, expensive and requires huge labor. However, the process becomes easier, cheaper and faster with the advent of Geographical Information System (GIS) and remote sensing technologies. In the present study, a combined approach of using toposheet, remotely sensed digital elevation model and morphometric ArcGIS toolbox has been adopted to determine morphometric parameters in Dudhnai river basin, a sub-basin of river Brahmaputra which is prone to both erosion and sedimentation. Seven sub-watersheds of Dudhnai have been prioritized by using the morphometric parameters and ranked them according to its vulnerability to soil erosion. The results of bifurcation ratio, drainage density, drainage intensity and constant of channel maintenance showed that Dudhnai watershed is a well-dissected watershed with less risk to flooding and soil erosion. However, significantly high values of infiltration number and ruggedness number obtained are indicative of very low infiltration which may result in high surface runoff and soil erosion. The study also revealed that channel erosion is stronger than sheet erosion in the basin. The prioritization of the sub-watersheds implied that Chil sub-watershed is the most susceptible sub-watershed that needs greater attention for soil and water conservation measures. The results of the present study could aid various stakeholders who are involved in the watershed development and management programs.
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.
The River Subansiri, one of the largest tributaries of the Brahmaputra, makes a significant contribution towards the discharge at its confluence with the Brahmaputra. This study aims to investigate an appropriate model to predict the future flow scenario of the river Subansiri. Two models have been developed. The first model is an artificial neural network (ANN) based rainfall-runoff model where rainfall has been considered as the input. The future rainfall of the basin is calculated using a multiple non-linear regression-based statistical downscaling technique. The proposed second model is a hybrid model developed using ANN and the Soil Conservation Service (SCS) curve number (CN) method. In this model, both rainfall and land use/land cover have been incorporated as the inputs. The ANN models were run using time series analysis and the method selected is the non-linear autoregressive model with exogenous inputs. Using Sen's slope values, the future trend of rainfall and runoff over the basin have been analyzed. The results showed that the hybrid model outperformed the simple ANN model. The ANN-SCS-based hybrid model has been run for different land use/land cover scenarios to study the future flow scenario of the River Subansiri.
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