Hurricane-induced power outage risk under climate change is primarily driven by the uncertainty in projections of future hurricane frequency.

Nine in ten major outages in the US have been caused by hurricanes. Long-term outage risk is a function of climate change-triggered shifts in hurricane frequency and intensity; yet projections of both remain highly uncertain. However, outage risk models do not account for the epistemic uncertainties in physics-based hurricane projections under climate change, largely due to the extreme computational complexity. Instead they use simple probabilistic assumptions to model such uncertainties. Here, we propose a transparent and efficient framework to, for the first time, bridge the physics-based hurricane projections and intricate outage risk models. We find that uncertainty in projections of the frequency of weaker storms explains over 95% of the uncertainty in outage projections; thus, reducing this uncertainty will greatly improve outage risk management. We also show that the expected annual fraction of affected customers exhibits large variances, warranting the adoption of robust resilience investment strategies and climate-informed regulatory frameworks.

A variance decomposition approach to uncertainty quantification and sensitivity analysis of the Johnson and Ettinger model.

The Johnson and Ettinger (J&E) model is the most widely used vapor intrusion model in the United States. It is routinely used as part of hazardous waste site assessments to evaluate the potential for vapor intrusion exposure risks. This study incorporates mathematical approaches that allow sensitivity and uncertainty of the J&E model to be evaluated. In addition to performing Monte Carlo simulations to examine the uncertainty in the J&E model output, a powerful global sensitivity analysis technique based on Sobol indices is used to evaluate J&E model sensitivity to variations in the input parameters. The results suggest that the J&E model is most sensitive to the building air exchange rate, regardless of soil type and source depth. Building air exchange rate is not routinely measured during vapor intrusion investigations, but clearly improved estimates and/or measurements of the air exchange rate would lead to improved model predictions. It is also found that the J&E model is more sensitive to effective diffusivity than to effective permeability. Field measurements of effective diffusivity are not commonly collected during vapor intrusion investigations; however, consideration of this parameter warrants additional attention. Finally, the effects of input uncertainties on model predictions for different scenarios (e.g., sandy soil as compared to clayey soil, and "shallow" sources as compared to "deep" sources) are evaluated. Our results not only identify the range of variability to be expected depending on the scenario at hand, but also mark the important cases where special care is needed when estimating the input parameters to which the J&E model is most sensitive.