Record Flooding Risk and Power Outage Restoration

We need to predict the probability of unprecedented flooding of lands and coastlines due to unexpected storms,overflowing rivers,hurricanes,tidal surges and dam failures.This paper addresses new record floods that exceed all prior“historic”levels and are invariably due to extreme or severe weather and/or unexpected precipitation,defeating barriers and causing extensive power system outages.Given their inherently low occurrence,the probabilities of new(rare)record floods are treated as random outcomes and independent events using classical statistical mechanics and related hypergeometric sampling.This analysis straightforwardly replaces tuning or fitting to“normal”precipitation,regular tides and prior flood data and the traditional use of multi-parameter extreme value distributions(EVDs)used for weather-induced flood forecasting and estimating“return periods”.The approach is not reliant on geographic computer models,meteorological forecasting,published“flood zone”charts,or hydrological techniques and images.We illustrate the universal applicability of this Bayesian-style approach of solely addressing new records for a wide range of specific flooding case studies for rivers,major hurricanes,quasi-periodic coastal tides,and dam failures.The quantitative link is shown between extreme event extent and power outage duration,and the results impact disaster resilience,infrastructure vulnerability and emergency preparedness measures.

Power Restoration Prediction Following Extreme Events and Disasters

This article examines electric power restoration following catastrophic damage in modern cities and regions due to extreme events and disasters. Recovery time and non-restoration probability are derived using new data from a comprehensive range of recent massive hurricanes,extensive wildfires, severe snowstorms, and damaging cyclones. Despite their totally disparate origins, over three orders of magnitude severe wildfires and hurricanes have the same non-restoration probability trends, which are of simple exponential form. The results fall into categories that are dependent on and grouped by the degree of damage and social disruption. The implications are discussed for emergency response planning. These new results demonstrate that the scientific laws of probability and human learning, which dominate risk in modern technologies and societies are also applicable to a wide range of disasters and extreme events.