Preprints
https://doi.org/10.5194/nhess-2021-155
https://doi.org/10.5194/nhess-2021-155

  06 Jul 2021

06 Jul 2021

Review status: this preprint is currently under review for the journal NHESS.

Brief communication: monitoring a soft-rock coastal cliff using webcams and strain sensors

Diego Guenzi1, Danilo Godone1, Paolo Allasia1, Nunzio Luciano Fazio2, Michele Perrotti2, and Piernicola Lollino2 Diego Guenzi et al.
  • 1CNR (Italian National Research Council) IRPI (Research Institute for Geo-Hydrological Protection), Turin office, Strada delle Cacce 73, 10135 Torino, Italy
  • 2CNR (Italian National Research Council) IRPI (Research Institute for Geo-Hydrological Protection), Bari office, Via Amendola 122 I, 70126 Bari, Italy

Abstract. In this brief communication, we describe a case study about monitoring a soft-rock coastal cliff using webcams and strain sensors, located in the Apulia region (southeastern Italy). In this urban and touristic area, coastal recession is extremely rapid and rockfalls are very frequent. Using low-cost and open source hardware and software, we are monitoring the area, trying to correlate both meteorological information with measures obtained from crack-meters and webcams, aiming to recognize potential precursor signals that may trigger instability phenomena.

Diego Guenzi et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2021-155', Anonymous Referee #1, 07 Jul 2021
    • AC1: 'Reply on RC1', Diego Guenzi, 22 Jul 2021
  • RC2: 'Comment on nhess-2021-155', Anonymous Referee #2, 14 Jul 2021
    • AC2: 'Reply on RC2', Diego Guenzi, 22 Jul 2021
      • AC3: 'Reply on AC2', Diego Guenzi, 22 Jul 2021

Diego Guenzi et al.

Diego Guenzi et al.

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Short summary
In the Apulia region (southeastern Italy) we are monitoring a soft-rock coastal cliff using webcams and strain sensors. In this urban and touristic area, coastal recession is extremely rapid and rockfalls are very frequent. In our work we are using low-cost and open source hardware and software, trying to correlate both meteorological information with measures obtained from crack-meters and webcams, aiming to recognize potential precursor signals that may trigger instability phenomena.
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