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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/nhess-2018-294
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-2018-294
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 12 Nov 2018

Research article | 12 Nov 2018

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Natural Hazards and Earth System Sciences (NHESS).

Probabilistic forecasting of plausible debris flows from Nevado de Colima (México) using data from the Atenquique debris flow, 1955

Andrea Bevilacqua1,2, Abani K. Patra3,2, Marcus I. Bursik1, E. Bruce Pitman4, José Luis Macías5, Ricardo Saucedo6, and David Hyman1 Andrea Bevilacqua et al.
  • 1Department of Earth Sciences, SUNY at Buffalo, NY, 14260
  • 2Computational Data Science and Engineering program, SUNY at Buffalo, NY, 14260
  • 3Department of Mechanical and Aerospace Engineering, SUNY at Buffalo, NY, 14260
  • 4Department of Materials Design and Innovation, SUNY at Buffalo, NY, 14260
  • 5Departamento de Vulcanología, Instituto de Geofísica, UNAM, DF, 04510
  • 6Instituto de Geología, Facultad de Ingeniería, UASLP, SLP, 78240

Abstract. We detail a new prediction-oriented procedure aimed at volcanic hazard assessment based on geophysical mass flow models constrained with heterogeneous and poorly defined data. Our method relies on an itemized application of the empirical falsification principle over an arbitrarily wide envelope of possible input conditions. We thus provide a first step towards a objective and partially automated experimental design construction. In particular, instead of fully calibrating model inputs on past observations, we create and explore more general requirements of consistency, and then we separately use each piece of empirical data to remove those input values that are not compatible with it, hence defining partial solutions to the inverse problem. This has several advantages compared to a traditionally posed inverse problem: (i) the potentially non-empty inverse images of partial solutions of multiple possible forward models characterize the solutions to the inverse problem; (ii) the partial solutions can provide hazard estimates under weaker constraints, potentially including extreme cases that are important for hazard analysis; (iii) if multiple models are applicable, specific performance scores against each piece of empirical information can be calculated. We apply our procedure to the case study of the Atenquique volcaniclastic debris flow, which occurred on the flanks of Nevado de Colima volcano (México), 1955. We adopt and compare three depth averaged models currently implemented in the TITAN2D solver, available from vhub.org. The associated inverse problem is not well-posed if approached in a traditional way. We show that our procedure can extract valuable information for hazard assessment, allowing the exploration of the impact of synthetic flows similar to those that occurred in the past, but different in plausible ways. The implementation of multiple models is thus a crucial aspect of our approach, as they can allow the covering of other plausible flows. We also observe that model selection is inherently linked to the inversion problem.

Andrea Bevilacqua et al.
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Andrea Bevilacqua et al.
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Short summary
We introduce a new prediction-oriented method for hazard assessment of volcaniclastic debris flows, based on multiple models. We apply our procedure to a case study of the 1955 Atenquique flow, using three widely used depth averaged models. Depending on how it is looked at, the exercise provides useful information in either model selection or data inversion. Connecting inverse problems and model uncertainty represents a fundamental challenge in the future development of multi-model solvers.
We introduce a new prediction-oriented method for hazard assessment of volcaniclastic debris...
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