To what extent can we treat topographic metrics such as river long profiles as a long‐term record of multiple extreme geomorphic events and hence use them for hazard prediction? We demonstrate that in an area of rapid mountain erosion where the landscape is highly reactive to extreme events, channel steepness measured by integrating area over upstream distance (chi analysis) can be used as an indicator of geomorphic change during flash floods. We compare normalized channel steepness to the impact of devastating floods in the upper Ganga Basin in Uttarakhand, northern India, in June 2013. The pattern of sediment accumulation and erosion is broadly predictable from the distribution of normalized channel steepness; in reaches of high steepness, channel lowering up to 5 m undercut buildings causing collapse; in low steepness reaches, channels aggraded up to 30 m and widened causing flooding and burial by sediment. Normalized channel steepness provides a first‐order prediction of the signal of geomorphic change during extreme flood events. Sediment aggradation in lower gradient reaches is a predictable characteristic of floods with a proportion of discharge fed by point sources such as glacial lakes.

This study explores paleoflood deposits of the Siang River, known as the Tsangpo in Tibet. The river that often experiences large floods brings down huge amount of sediment and water that adversely affect the downstream regions with large human populations in the states of northeast Himalaya and its foreland. Along it's ~300 km mountainous stretch we collected samples for sedimentological, petrographic and Sr–Nd isotopic study to explore sediment provenance and dated the paleofloods (via optically stimulated luminescence, OSL). Geomorphic indices including precipitation and a geomorphic swath profile across the Brahmaputra catchment were studied to understand the interplay of mountain relief and rainfall that determine potential zones of high erosion and sediment supply. The OSL technique indicated the Siang River experienced at least eight large floods between 7 and 1 ka, possibly under the influence of warm and wet climatic conditions. The petrographic and isotopic data suggests that the eastern Himalayan syntaxis, which has the highest uplift and exhumation rate in the area, is not always the highest sediment producing zone. In some instances, the Tibetan plateau produces higher fluxes of sediments via glacial and landslide lake outburst floods (GLOFs and LLOFs). © 2020 John Wiley & Sons, Ltd.

Floods are known to be one of the greatest disasters in documented human history. In the Indian subcontinent, the Ganga-Brahmaputra plains annually accommodate monsoon-driven flooding of the Brahmaputra River and its tributaries, causing widespread flood inundation and geomorphic changes. In the present study, we present flood inundation maps of Assam and suggest a possible geomorphic explanation for the frequent flood inundation areas. We have used a novel SAR (Synthetic Aperture Radar)based approach for a comprehensive flood inundation mapping of Assam. The Google Earth Engine (GEE), a cloud-based data computing and analysis platform, is used to access and analyse the Sentinel SAR 1 GRD datasets for 30 events from 2018 to 2020.The Sentinel-2 imagery, WWF Hydrosheds, 90 m DEM (SRTM), and JRC Global Surface Water Dataset are also used to conduct the flood inundation mapping for 80,236(1 Â 1 km) grids in Assam and morphometric analysis in the lower Brahmaputra River Basin. We also carried out detailed mapping and geomorphic analysis on channel and sediment bar area and channel width in 100 (6.25 km wide) grids on the Brahmaputra River in Assam. Our results suggest that high flood inundation areas are well scattered in Assam, and Lakhimpur, Nagaon, Sibsagar, Sonitpur, and Barpeta are the most affected districts from 2018 to 2020. We also observe that the geomorphology can act as primary control over the frequent flood inundation in highlighted areas in Assam. On a basin scale, we recognize a low correlation between the stream power index, topographic wetness index, plan curvature, slope, and flood inundated areas, mainly due to the coarse resolution of the digital elevation model. In a braided river system like the Brahmaputra River, the channel and bar area and channel width are well correlated during flood events. Finally, the channel sinuosity also controls channel and bar area and width during regular and influential flood events.

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