The adjustments in channel morphology under influence of vegetation dynamics, impacting natural sediment and flow regimes at local, catchment, and regional scales, are primarily driven by natural and anthropogenic factors. Limited knowledge exists regarding the historical channel adjustments along Himalayan glacier-dominated rivers. This study specifically concentrates on three distinct glacier-dominated river segments: Nubra in Jammu and Kashmir, Ganga-Bhagirathi in India, and Langtang-Khola in Nepal. The research adopts a supervised classification model initially developed by Mukhtar and extends the technique by applying it to four additional sources of satellite data with spatial resolutions ranging from 2.4 m to 30 m. This extension of the model is accomplished using the Google Earth Engine (GEE) platform to extract three main macro-units (base-flow channels, emerged sediment bars and vegetated surfaces) in fluvial corridors. Across different locations, the behavior of the rivers exhibited variability; however, possibly cyclic behavior in riparian vegetation cover was observed during the studied period. Surprisingly, in the subsequent period of 2016–2020, noticeable channel widening was observed in almost all reaches of the three river segments. Notably, the high meltwater runoff periods from 1989 to 2003 in the Nubra River segment induced vegetation erosion and channel widening. On the contrary, flood events during the early 21st century possibly lacked the duration and intensity required to impact vegetation growth in river corridors. This trend was also evident in the Ganga-Bhagirathi River, where the stable vegetation cover showed no major effects from the 2012 flood event. Despite the susceptibility of the Langtang-Khola river to landslides and earthquakes, the study reaches in Langtang-Khola River remained unaffected by these catastrophic events. Briefly, this study contributes to an enhanced understanding of the intricate dynamics of channels and vegetation in Himalayan glacier-dominated rivers, spanning diverse spatial and temporal scales, and elucidates their correlation with factors related to climate change.
Mukhtar et al. (Tue,) studied this question.