Preprints
https://doi.org/10.5194/esurf-2020-78
https://doi.org/10.5194/esurf-2020-78

  08 Oct 2020

08 Oct 2020

Review status: a revised version of this preprint is currently under review for the journal ESurf.

Development of smart boulders to monitor mass movements via the Internet of Things: A pilot study in Nepal

Benedetta Dini1, Georgina L. Bennett2, Aldina M. A. Franco1, Michael R. Z. Whitworth3, Kristen L. Cook4, Andreas Senn5, and John M. Reynolds6 Benedetta Dini et al.
  • 1School of Environmental Sciences, University of East Anglia, Norwich Research Park, UK
  • 2College of Life and Environmental Sciences, University of Exeter, UK
  • 3AECOM, UK
  • 4GFZ-Potsdam, Germany
  • 5Miromico AG, Zurich, Switzerland
  • 6Reynolds International Ltd, UK

Abstract. Boulder movement can be observed not only in rock fall activity, but also in association with other landslide types such as rock slides, soil slides in colluvium originated from previous rock slides and debris flows. Large boulders pose a direct threat to life and key infrastructure, amplifying landslide and flood hazards, as they move from the slopes to the river network. Despite the hazard they pose, boulders have not been directly targeted as a mean to detect landslide movement or used in dedicated early warning systems. We use an innovative monitoring system to observe boulder movement occurring in different geomorphological settings, before reaching the river system. Our study focuses on an area in the upper Bhote Koshi catchment northeast of Kathmandu, where the Araniko highway is subjected to periodic landsliding and floods during the monsoons and was heavily affected by coseismic landslides during the 2015 Gorkha earthquake. In the area, damage by boulders to properties, roads and other key infrastructure, such as hydropower plants, is observed every year. We embedded trackers in 23 boulders spread between a landslide body and two debris flow channels, before the monsoon season of 2019. The trackers, equipped with accelerometers, can detect small angular changes in boulders orientation and large forces acting on them. The data can be transmitted in real time, via a long-range wide area network (LoRaWAN®) gateway to a server. Nine of the tagged boulders registered patterns in the accelerometer data compatible with downslope movements. Of these, six lying within the landslide body show small angular changes, indicating a reactivation during the rainfall period and a movement consistent with the landslide mass. Three boulders, located in a debris flow channel, show sharp changes in orientation, likely corresponding to larger free movements and sudden rotations. This study highlights that this innovative, cost-effective technology can be used to monitor boulders in hazard prone sites, identifying in real time the onset of movement, and may thus set the basis for early warning systems, particularly in developing countries, where expensive hazard mitigation strategies may be unfeasible.

Benedetta Dini et al.

 
Status: final response (author comments only)
Status: final response (author comments only)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

Benedetta Dini et al.

Video supplement

Hindi Landslide Timelapse Benedetta Dini, Georgina L. Bennett, Aldina M. A. Franco, and Kristen L. Cook https://doi.org/10.5446/48980

Benedetta Dini et al.

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Short summary
We use Long Range smart sensors connected to a network based on the Internet of Things to explore the possibility of detecting hazardous boulder movements in real time. Prior to the 2019 monsoon season we inserted the devices in 23 boulders spread over debris flow channels and a landslide in northeastern Nepal. The data obtained in this pilot study shows the potential of this technology to be used in remote, hazard prone areas in future early warning systems.