A Monte Carlo methodology for modelling ashfall hazards

被引:27
|
作者
Hurst, T [1 ]
Smith, W [1 ]
机构
[1] Inst Geol & Nucl Sci, Lower Hutt, New Zealand
关键词
volcanic hazard; probabilistic hazard; ashfall; New Zealand; Monte Carlo;
D O I
10.1016/j.jvolgeores.2004.08.001
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
We have developed a methodology for quantifying the probability of particular thicknesses of tephra at any given site, using Monte Carlo methods. This is a part of the development of a probabilistic volcanic hazard model (PVHM) for New Zealand, for hazards planning and insurance purposes. We use an established program (ASHFALL) to model individual eruptions, where the likely thickness of ash deposited at selected sites depends on the location of the volcano, eruptive volume, column height and ash size, and the wind conditions. A Monte Carlo procedure allows us to simulate the variations in eruptive volume and in wind conditions by analysing repeat eruptions, each time allowing the parameters to vary randomly according to known or assumed distributions. Actual wind velocity profiles are used, with randomness included by selection of a starting date. This method can handle the effects of multiple volcanic sources, each source with its own characteristics. We accumulate the tephra thicknesses from all sources to estimate the combined ashfall hazard, expressed as the frequency with which any given depth of tephra is likely to be deposited at selected sites. These numbers are expressed as annual probabilities or as mean return periods. We can also use this method for obtaining an estimate of how often and how large the eruptions from a particular volcano have been. Results from sediment cores in Auckland give useful bounds for the likely total volumes erupted from Egmont Volcano (Mt. Taranaki), 280 km away, during the last 130,000 years. (C) 2004 Elsevier B.V. All rights reserved.
引用
收藏
页码:393 / 403
页数:11
相关论文
共 50 条
  • [31] Monte-Carlo Modelling of Severe Wind Gust
    Sanabria, L. A.
    Cechet, R. P.
    MODSIM 2007: INTERNATIONAL CONGRESS ON MODELLING AND SIMULATION: LAND, WATER AND ENVIRONMENTAL MANAGEMENT: INTEGRATED SYSTEMS FOR SUSTAINABILITY, 2007, : 2931 - 2937
  • [32] Monte Carlo modelling of energy deposition in trabecular bone
    Gersh, J. A.
    Dingfelder, M.
    Toburen, L. H.
    RADIATION PROTECTION DOSIMETRY, 2006, 122 (1-4) : 549 - 550
  • [33] User subjectivity in Monte Carlo modelling of pesticide exposure
    Trevisan, Marco
    UNCERTAINTIES IN ENVIRONMENTAL MODELLING AND CONSEQUENCES FOR POLICY MAKING, 2009, : 155 - 181
  • [34] The application of reverse Monte Carlo modelling to a polymeric melt
    Swenson, J
    Carlsson, P
    Börjesson, L
    Torell, LM
    McGreevy, RL
    Howells, WS
    COMPUTATIONAL AND THEORETICAL POLYMER SCIENCE, 2000, 10 (06): : 465 - 472
  • [35] System uncertainty modelling using Monte Carlo simulation
    Coughlan, L
    Basil, M
    Cox, P
    MEASUREMENT & CONTROL, 2000, 33 (03): : 78 - 81
  • [36] Monte Carlo modelling of glass dissolution: Comparison with experiments
    Lobanova, M
    Maurer, L
    Barboux, P
    Devreux, F
    Minet, Y
    SCIENTIFIC BASIS FOR NUCLEAR WASTE MANAGEMENT XXIV, 2000, 663 : 237 - 245
  • [37] Monte Carlo modelling of particle resuspension on a flat surface
    Benito, J. G.
    Aracena, K. A. Valenzuela
    Unac, R. O.
    Vidales, A. M.
    Ippolito, I.
    JOURNAL OF AEROSOL SCIENCE, 2015, 79 : 126 - 139
  • [38] Modelling of PMU Uncertainty by Means of Monte Carlo Method
    Sira, Martin
    Maslan, Stanislav
    Zachovalova, Vera Novakova
    Crotti, Gabriella
    Giordano, Domenico
    2016 CONFERENCE ON PRECISION ELECTROMAGNETIC MEASUREMENTS (CPEM 2016), 2016,
  • [39] Monte Carlo modelling of daylight activated photodynamic therapy
    Campbell, C. L.
    Wood, K.
    Valentine, R. M.
    Brown, C. T. A.
    Moseley, H.
    PHYSICS IN MEDICINE AND BIOLOGY, 2015, 60 (10): : 4059 - 4073
  • [40] Response surface modelling of Monte Carlo fire data
    Hasofer, AM
    Qu, J
    FIRE SAFETY JOURNAL, 2002, 37 (08) : 772 - 784