Monitoring and Characterising Grain Scale Fluvial Bed-load Transport Behaviour Using Passive and Active Sensors

Clark, Miles (2023) Monitoring and Characterising Grain Scale Fluvial Bed-load Transport Behaviour Using Passive and Active Sensors. Doctoral thesis, University of East Anglia.

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Abstract

Bedload transport is a fundamental process by which coarse sediment is transferred through landscapes by river networks. Understanding how individual grains move within fluvial systems is essential for accurately modelling and predicting sediment fluxes and the evolution of sedimentary environments. Large wood is a major component of many forested rivers and to date, the impact of the presence of in stream wood on grain-scale bedload transport has not been well studied. In this thesis, passive (RFID) and active tracers (smart stones) are used to investigate and model the influence of wood on grain-scale bedload transport during long term deployment. In addition, this research examines the effectiveness of novel, Internet of Things (IoT) enabled smart stones for monitoring bedload transport.

First, 957 RFID tracers were inserted into a wood-loaded stream in Colorado and monitored over three years. Statistical modelling revealed a significant influence of wood on transport behaviour, where a reduction in entrainment likelihood, shorter transport distances, and premature deposition were recorded in tracers interacting with wood pieces. Next, a smart stone was designed embedded with 9-axis IMU sensors, integrated into an IoT network with Long Range Wide Area Network (LoRaWAN) capabilities. Laboratory experiments were conducted with the smart stones to replicate typical bedload movement behaviour, building unique IMU signatures associated with specific movement types. Smart stones were subsequently deployed at a range of field sites for remote real-time monitoring of transport behaviour. 57% of deployed tracers captured IMU data for tracer movement events, including one entrainment event captured by LoRaWAN, though the limited interaction between in stream wood and tracers precluded analysis of wood-sediment interaction. Overall, this research highlights the role of wood in altering the transport distances and entrainment likelihood of sediments. Furthermore, it demonstrates the potential for an integration of LoRaWAN networks and smart stones for remotely monitoring fluvial bedload dynamics.

Item Type:	Thesis (Doctoral)
Faculty \ School:	Faculty of Science > School of Environmental Sciences
Depositing User:	Chris White
Date Deposited:	12 Dec 2023 11:34
Last Modified:	12 Dec 2023 11:34
URI:	https://ueaeprints.uea.ac.uk/id/eprint/93970
DOI:

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