An Optimization-Based Approach to Developing Computationally Efficient Long-Term Event-Loss Scenario Ensembles

Abstract

Long-term hazard and risk analyses can rely on ensembles of multiyear event sequences to represent uncertainty in hazard occurrence, event timing, and cumulative loss. These ensembles capture temporal dependence and long-horizon loss behavior, but they can be computationally demanding to propagate through hazard, exposure, and loss models when repeated evaluation is required. This paper presents a framework for reducing ensembles of long-term hazard-loss scenarios to a compact, weighted subset while preserving statistical characteristics relevant to engineering risk assessment. The approach operates on multidecade event sequences rather than individual hazard events. The reduction preserves the distribution of cumulative loss, the temporal spacing of events, the annual occurrence rates of hazard events, and the first and second moments of annual loss. These characteristics are computed from the full scenario set and enforced as fidelity targets in the reduced representation. The framework is demonstrated using simulated 30-year hurricane-loss sequences for eastern North Carolina. Results show that a reduced set of representative sequences reproduces the temporal, probabilistic, and spatial characteristics of the full set with limited deviation. Sensitivity analyses examine how accuracy varies with the number of retained sequences and the weighting structure. Although illustrated for hurricanes, the framework is applicable to other hazards characterized by long-term event sequences.