Flood Scenario Generation Using the Norta Model

Several stochastic programming models have been developed for critical infrastructure's resilience decision-making to extreme flood events. Generating flood scenarios for such models requires running advanced flood models on a sophisticated computing infrastructure for different parameterizations (for example, different hurricane intensity levels, tracks, etc.), which may not always be practical. To address this issue, in this study, we propose a Normal-to-Anything (NORTA) model-based flood scenario generation scheme, which requires significantly fewer computing resources. The scenarios we generate using the proposed approach preserve correlation in flood height at locations of interest, in our case, the power transmission grid's substation locations. We demonstrate our approach's efficacy with a case study using a synthetic power grid with statistical similarities with the actual Texas grid and the flood maps developed by the National Atmospheric and Oceanic Administration that represent the storm-surge risk in Texas.