Abstract
Risk identification is the first step on a comprehensive disaster risk management strategy, and nowadays, when new open-source tools to conduct those analyses are becoming widely available, the interest and need to increase their transparency has increased. Catastrophic risk due to natural hazards should be considered in a prospective way quantifying the damages and losses before the real event occurs, and for that task, it is necessary to consider events that have not yet occurred. Since there are uncertainties related to when and where the next hazardous event will happen, how severe will it be, and how can it affect the exposed elements, it is important to adopt a probabilistic approach that considers those uncertainties and propagates them through the damage and loss calculation process following a rigorous methodology. This chapter develops the theoretical catastrophe risk model considering both retrospective and prospective analyses. In addition, it summarizes the methodology for the inclusion of second-order effects (nonphysical risk drivers), the approach to rationally incorporate background trends (e.g., climate change), an extension of the model to incorporate non-probabilistic uncertainty, and a methodology to define management actions that fit resilience targets. The work presented herein serves to provide a ground base for the minimum requirements of probabilistic risk assessment models.
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Notes
- 1.
A renewal process is a type of time-continuous, increasing, point process in which the inter-event times are mutually independent and identically distributed random variables, with an expected value equal to the inverse of the mean occurrence rate.
- 2.
Var{Λ} = λ2/(n − 2). The Cramér-Rao Lower Bound CRLB{Λ} = λ2/n.
- 3.
In particular, the increments of the process will not be independent.
- 4.
In both cases for RCP 8.5 and selecting the climate model that best fits the historical observations.
- 5.
See Alvarez (2008) for a summary of techniques to practically apply the extension principle.
- 6.
I[∙] = 1 if ∙ is true and I[∙] = 0 if ∙ is false.
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Bernal, G.A., Cardona, OD., Marulanda, M.C., Carreño, ML. (2021). Dealing with Uncertainty Using Fully Probabilistic Risk Assessment for Decision-Making. In: Eslamian, S., Eslamian, F. (eds) Handbook of Disaster Risk Reduction for Resilience. Springer, Cham. https://doi.org/10.1007/978-3-030-61278-8_14
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