Towards the assessment of sediment connectivity in a large Himalayan river basin

Geomorphic connectivity and its application for understanding landscape complexities: a focus on the hydro‐geomorphic systems of India

“…The impact of changing LULC on connectivity structures has been studied for a large catchment of the Kosi River by Mishra et al . (2019) (for upper Kosi basin, in the Himalayan mountains in Nepal). The connectivity indices (Borselli et al ., 2008; Cavalli et al ., 2013) utilized for this study considered the LULC as a governing factor to estimate the potential connectivity of the catchment.…”

“…Dudh Kosi, Bhote Kosi, and Tama Kosi) were found to be well‐connected because the LULC, along with slope and channel morphology, plays a significant role in mobilizing sediments from source to the main channel. The Arun sub‐basin shows a contrasting behaviour (Figure 6) where the land use along the steep and dissected terrain favour sediment transport but the variable slope in a large basin results in an overall low sediment contribution at the outlet (Mishra et al ., 2019). In general, the authors found an agreement between the predicted sediment connectivity and corresponding measured sediment fluxes at the outlets of different sub‐basins.…”

“…A significant point emerging from this study by Mishra et al . (2019) is that the integration of the connectivity analysis with the stream power data can help in explaining sediment dynamics in large basins. This is the first application of sediment connectivity in such a large basin (52 731 km 2 ) and provides an opportunity to understand how sediment connectivity could influence the river‐related hazards such as landslides, debris flow and floods in large river basins under the modern hydrological regime.…”

“…An estimation of longitudinal connectivity by means of stream‐power method (e.g. Bawa et al ., 2014; Mishra et al ., 2019) and indices methods (Cavalli et al ., 2013; Sinha et al ., 2013) can provide specific zones of the river with varying degree of material fluxes along its length. The zones of low and/or high lateral connectivity can be identified using the CRU method (Singh et al ., 2017; Singh and Sinha, 2019) and site‐specific management approaches can be implemented.…”

A method for evaluating the longitudinal functional connectivity of a river–lake–marsh system and its application in China

“…This parameter is the crop/vegetation and management factor used to determine the relative effectiveness of crop management systems in terms of soil loss, thereby explicitly representing impedance to the transport of material. We used the Normalized Difference Vegetation Index (NDVI) to calculate the C factor in each raster cell (Mishra et al, 2019;Bingzhe et al, 2020), with low values of the C factor indicating that the surface is well protected and soil roughness is high. The higher the surface roughness, the lower the efficiency of material transport, whilst a cell with high structural connectivity tends to transport material more efficiently.…”