Coastal communities exposed to major earthquakes often face a second, compounding threat: tsunamis that strike damaged buildings minutes to hours later. Accurately estimating risk in these settings is a question not only of predicting hazard intensity but also of understanding how uncertainty accumulates across multiple models and sequential events. New research offers a probabilistic framework that helps engineers better track and quantify the inherent randomness of these events and the limitations in scientific knowledge, model selection, and parameters.
Outlined in “Uncertainty Quantification for Earthquake and Tsunami Risk Analysis with Application to Valparaíso, Chile,” the work by Hugo Rosero-Velásquez, Sven Harig, Juan Camilo Gómez-Zapata, and Daniel Straub builds on established seismic and tsunami risk frameworks by explicitly linking them, recognizing that tsunami damage depends on the level of earthquake damage already sustained. Applying the methodology to residential building portfolios in Valparaíso and Viña del Mar, the authors use sensitivity analysis to identify which assumptions most strongly influence loss estimates.
This work shows how uncertainty affects commonly used risk metrics, such as annual average loss, value at risk, and expected shortfall, and offers civil engineers a clearer basis for prioritizing data collection, refining models, and supporting risk-informed decisions. Read more about how this framework helps engineers communicate risk more transparently, even when future earthquakes and tsunamis cannot be predicted with certainty, in the ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering at https://ascelibrary.org/doi/10.1061/AJRUA6.RUENG-1655. The abstract is below.
Abstract
In a probabilistic analysis, epistemic uncertainties associated with modeling choices and limited data are accounted for, and their effect on the resulting risk metrics is quantified. In this paper, we address the challenge of identifying, classifying, quantifying, and comparing different sources of uncertainty and their influence on the losses and risk metrics of spatially distributed systems under sequential hazards, such as an earthquake event followed by a tsunami or a tropical cyclone followed by a storm surge. Through the example of earthquake and tsunami risk of the residential-building stock of the communes of Valparaíso and Viña del Mar in Chile, we investigate and discuss the relation between aleatory and epistemic uncertainties and their effects on different risk metrics. We identify epistemic uncertainty factors and perform a sensitivity analysis to assess their influence on the annual average loss, value at risk, and expected shortfall. Additionally, we investigate the influence of the considered time period on the uncertainty of the risk metrics and demonstrate that epistemic uncertainty dominates the total variance of the risk for long time periods. In the specific application, we observe that the risk metrics are most sensitive to the uncertainty of the earthquake fragility parameters and the ground motion model.
Learn more about how to reduce the uncertainty in tsunami forecasting in the ASCE Library: https://ascelibrary.org/doi/10.1061/AJRUA6.RUENG-1655.
