Vice President Verisk Extreme Event Solutions, United States
Abstract Submission: Floods are one of the most devastating catastrophes. Flood models are essential tools to quantify the magnitude and likelihood of flood hazard across large regions. To ensure financial soundness of their businesses, the insurance industry is increasingly relying on the use of natural catastrophe models that reflect a much wider spectrum of plausible event extents, hazard intensities and probabilities. Typically, catastrophe flood models are developed at country or continental scales, enabling flood risk assessment at a high spatial resolution, and are based on thousands of years of simulations. For this, comprehensive high-resolution continuous precipitation modeling, including running global circulation and regional numerical weather models in a coupled framework, is done. The modeling community has generally found and opined that physically-based simulation models are better suited for catastrophe flood risk models as these adequately preserve the spatio-temporal meteorological and river basin dynamics inherent in the flooding process. However, such modeling effort comes with an excessive time, computational, and economic costs. Statistical simulation approaches have the potential to significantly reduce these costs but generating realistic flood events exhibiting inherent correlation has been challenging. This study presents a more thorough treatment of inherent spatio-temporal correlations and river dynamics in statistically generating flood events employing meta-gaussian approach. Application of the framework is illustrated by statistically generating thousands of Peak-over-Threshold (PoT) flood events for a sub-basin in the Ohio river basin. The advantages, challenges, and limitations of this statistical approach over the physically-based modeling are also presented.
