Smart cobbles and boulders for monitoring movement in rivers and on hillslopes

Constraining the initiation of bedload sediment transport in rivers is of fundamental importance to understanding a range of geomorphic processes. Likewise, on hillslopes, identifying the initiation of movement is a vital first step towards developing early warning systems for hazards such as landslides. Several studies have previously experimented with embedding sensors within cobbles and boulders to capture and characterise their initiation and subsequent movement in the laboratory and in the field (both for hillslopes and riverbeds). However, these sensors have been limited by their battery life and/or lack of wireless sensor communication in their ability to monitor movement in natural settings over extended time periods. Accelerometers have been most widely applied, e.g. to detect bedload movement on a river bed, but can only measure vibrations and partial changes in orientation between stationary periods, which can occur simply during shaking of a cobble in its pocket on the bed. Gyroscopes, which can assist in continuous orientation tracking and therefore identification of actual transport (e.g. rolling of a cobble along a riverbed), have higher power consumption.

On SENSUM (smart SENSing of landscapes Undergoing hazardous hydrogeomorphic Movement), we have leveraged advances in micro-electronics and Internet of Things technologies to develop a low-power inertial measurement sensor that communicates in near real-time via Long Range Wide Area Network (LoRaWAN). The sensor includes accelerometers, gyroscopes and magnetometers and laboratory experiments have already shown their potential to differentiate between sliding and rolling behaviour. We have embedded sensors in natural and manmade boulders (SlideCubes), cobbles and wood debris within several landslide and flood prone sites across the UK. The sensors form part of Wireless Sensor Networks that also consist of LoRaWAN gateways and other sensors such as discharge gauges.

We present field data captured from smart cobbles installed in upland rivers on Dartmoor and Cumbria that demonstrate the potential of SENSUM sensors to detect initiation of bedload transport, i.e. the transition from shaking of a cobble in its pocket to downstream transport by rolling and/or saltation. We also present preliminary data of landslide movement captured by sensors installed in SlideCubes at Lyme Regis and Isle of Wight. Moving forwards, we will use machine learning methods to analyse sensor data on the server in near real-time in order to characterise and alert of hazardous movement.