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Tag: Weather

Natural Hazard Monitoring with Global Navigation Satellite Systems (GNSS)

Illustration of GNSS-enabled natural hazard monitoring and early warning.

GNSS-enabled natural hazard monitoring and early warning. Networks of GNSS stations and seismometers are used to detect strong ground motion from large earthquakes. Buoys equipped with GNSS sensors monitor tsunami wave heights. Earthquake and tsunami waves can trigger acoustic and gravity waves, which are observable by GNSS in the ionosphere. In the troposphere, GNSS sensors measure signal delays induced by water vapour variations associated with severe weather events. Landslide and volcanic hazards can be monitored with GNSS stations, as well as acoustic-gravity waves induced by volcanic eruptions. The monitoring data is processed by warning centres, which can then broadcast warning signals in the event of a potential natural hazard-induced threat.

  • Creators: Roland Hohensinn & Mirjam Jobst
  • This version: 30.08.2024
  • License: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
  • Specific citation: This graphic by Roland Hohensinn and Mirjam Jobst is available via the open-access s-ink.org repository.
  • Related reference: Hohensinn, R., Aichinger-Rosenberger, M., Wareyka-Glaner, M.F., & Ravanelli, M. (2024). Natural-hazard monitoring with global navigation satellite systems (GNSS). In Advances in Geophysics, Vol. 65, Space Geodesy for Environmental Monitoring. Elsevier.

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UK Wintertime multi-hazard risk

Illustration of plausible effects when the activity of hazard(s) switches between climatologically controlled modes of behaviour, based on Great Britain.

Illustration of plausible effects when the activity of hazard(s) switches between climatologically controlled modes of behaviour, based on Great Britain. The impact-centric conceptualisation of the multi-hazard system (left-hand side panel) with two hazard modes, each associated with a dominant wind direction (blue arrows), that drive six hazards (circles). Rail infrastructure (red) is exposed to all six hazards, whilst (re)insurance (orange) is primarily concerned with only two in Mode 2. Losses in terms of magnitude and frequency (right-hand side panel), are illustrated with rare ‘worst cases’ on the righthand side (grey band). A conventional view that does not consider dependencies (grey line) might underestimate risk if two perils (e.g. flood and wind) compound. However, where exposed assets are subject to hazards driven by two opposing modes (red line) compounding effects are suppressed, so care is needed to avoid overestimating risk. Solid arrows represent effect magnitudes seen within the Network Rail loss data, with dashed ones indicating plausible stronger effects.

  • Creator: John Hillier
  • This version: 05.05.2023
  • License: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
  • Specific citation: This graphics by John Hillier from Hillier et al. (2020) are available via the open-access s-Ink.org repository.
  • Related reference: Hillier, J. K. , Matthews, T., Wilby, R., Murphy, C. (2020) Multi-hazard dependencies can increase or decrease risk Nature Climate Change, 10, 595–598 doi:10.1038/s41558-020-0832-y

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