Probabilistic forecasting of plausible debris flows from Nevado de Colima (Mexico) using data from the Atenquique debris flow, 1955
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, partial solutions are defined to the inverse
problem. This has several advantages compared to a traditionally posed
inverse problem: (i) the potentially nonempty 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 (Mexico), 1955. We adopt and compare three depth-averaged models
currently implemented in the TITAN2D solver, available from https://vhub.org
(Version 4.0.0 – last access: 23 June 2016). 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 that are 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.
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