Integrated Monitoring of Volcanic Ash and Forecasting at Sakurajima Volcano, Japan

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Author(s)

Abstract

<p>The Sakurajima volcano is characterized by frequent vulcanian eruptions at the Minamidake or Showa crater in the summit area. We installed an integrated monitoring system for the detection of volcanic ash (composed of remote sensing sensors XMP radars, lidar, and GNSS with different wave lengths) and 13 optical disdrometers on the ground covering all directions from the crater to measure drop size distribution and falling velocity. Campaign sampling of volcanic ash supports the conversion of particle counts measured by the disdrometer to the weight of volcanic ash. Seismometers and tilt/strain sensors were used to estimate the discharge rate of volcanic ash from the vents. XMP radar can detect volcanic ash clouds even under visual difficulty because of weather such as fog or clouds. A vulcanian eruption on November 13 was the largest event at the Sakurajima volcano in 2017; however, the volcanic plume was not visible due to clouds covering the summit. Radar revealed that the volcanic plume reached an elevation of 4.2–6.2 km. Post-fit phase residuals (PPR) from the GNSS analysis increased suddenly after the eruption, and large-PPR paths from the satellites to the ground-based receivers intersected each other at an elevation of 4.2 km. The height of the volcanic plume was also estimated from the discharge rate of volcanic ash to be 4.5 km, which is empirically related to seismic energy and the deflation volume obtained via ground deformation monitoring. Using the PUFF model, the weight of the ash-fall deposit was accurately forecast in the main direction of transport of the volcanic ash, which was verified by disdrometers. For further advances in forecasting of the ash-fall deposit, we must consider high-resolution wind field, shape of volcanic plume as the initial value, and the particle number distribution along the volcanic plume.</p>

Journal

  • Journal of Disaster Research

    Journal of Disaster Research 14(5), 798-809, 2019

    Fuji Technology Press Ltd.

Codes

  • NII Article ID (NAID)
    130007687217
  • Text Lang
    ENG
  • ISSN
    1881-2473
  • NDL Article ID
    029840754
  • NDL Call No.
    Z78-A454
  • Data Source
    NDL  J-STAGE 
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