Drone based quantification of channel response to an extreme flood for a piedmont stream

Heritage, G; Entwistle, NS

Abstract

The influence of extreme floods on the form and functioning of upland systems has concentrated on the erosive impact of these flows. They are seen to be highly competent with coarse sediment transport rates limited by upstream supply and moderated by the ‘blanketing’ effect of an armour layer. This study investigates the effect of extreme events on the upland sediment cascade subjected to a recent extreme rainfall-induced flood event. The drone-based survey generated orthophotography and a DEM surface, which was compared with historic LiDAR data. This allowed erosion and deposition to be quantified and the surface micro-variation used to characterise stable and mobile sediment. The idealised model of sediment residence time increasing downstream is questioned by the findings of this study as relatively little coarse bedload sediment appears to have been transferred downstream in favour of initial local channel erosion (moderated by legacy large sediment), mid-reach palaeo-channel reactivation, sub-channel infilling and downstream deposition of the majority of mobilised sediment across berm and bar surfaces within the active inset channel margins. Channel margin erosion was largely limited to fine sediment stripping moderated by the re-exposure of post-glacial sediment. Only a weak relationship was found between local channel slope and deposition, with storage linked more to the presence of inset berm and bar areas within the inset active channel. Downstream fining of sediment is apparent as is a strong contrast between coarser active sub-channels and finer bar and berm areas.

Citation

Heritage, G., & Entwistle, N. (2019). Drone based quantification of channel response to an extreme flood for a piedmont stream. Remote Sensing, 11(17), 2031. https://doi.org/10.3390/rs11172031

• Journal Article Type: Article
• Acceptance Date: Aug 17, 2019
• Online Publication Date: Aug 29, 2019
• Publication Date: Aug 29, 2019
• Deposit Date: Aug 19, 2019
• Publicly Available Date: Oct 10, 2019
• Journal: Remote Sensing
• Publisher: MDPI
• Volume: 11
• Issue: 17
• Pages: 2031
• DOI: https://doi.org/10.3390/rs11172031
• Publisher URL: https://doi.org/10.3390/rs11172031
• Related Public URLs: https://www.mdpi.com/journal/remotesensing