Abstract
The Himalayan region is a critical focal point for understanding the anticipated hydrological and geomorphological variations induced by climate change in proglacial areas. Remote sensing, particularly multispectral satellite data on the Google Earth Engine platform, was employed to develop a novel supervised classification model. This model, was the first of its kind applied in the Himalayan region, successfully classified submerged base-flow channels, vegetated surfaces, and emerged sediment bars in proglacial rivers. Focusing on training sites like Langtang-Khola (Nepal), Saltoro (Pakistan), and Nubra (Jammu and Kashmir), and a testing site, the Ganga-Bhagirathi River (India), the model achieved high accuracy (average overall accuracy of 96% and a kappa index of 0.94). This tool provides a reliable means to detect past and current morphological changes in Himalayan proglacial rivers for research and river management purposes. Shifting focus to morphological dynamics, the study investigates channel adjustments under the influence of vegetation, and hydroclimatic variations. Employing a supervised classification model extended to multiple spatial resolutions, the study reveals a general increase in riparian vegetation cover during the early 21st century. Unexpectedly, the subsequent period of 2016-20 witnesses noticeable channel widening across the three proglacial river segments. Despite varied river behaviors across locations, the Langtang-Khola River experienced significant changes during the 2019 earthquake, with evident effects on sediment transport and widening of the river channel. The Nubra river segment experienced vegetation erosion and channel widening during few peak meltwater runoff years from 1989 to 2003, while the Ganga-Bhagirathi River maintains stable vegetation cover despite the 2012 flood event. This collective work enhances our understanding of the intricate dynamics of channels and vegetation in Himalayan proglacial rivers, offering insights into their correlation with climate change factors and guiding future research and management strategies.