Quantifying Uncertainty and Variable Sensitivity within the U.S. Billion-Dollar Weather and Climate Disaster Cost Estimates

Research examining natural disaster costs on social and economic systems is substantial. However, there are few empirical studies that seek to quantify the uncertainty and establish confidence intervals surrounding natural disaster cost estimates (ex-post). To better frame the data limitations associated with natural disaster loss estimates, a range of losses can be evaluated by conducting multiple analyses and varying certain input parameters to which the losses are most sensitive. This paper contributes to the literature by examining new approaches for better understanding the uncertainty surrounding three U.S. natural disaster cost estimate case studies, via Monte Carlo simulations to quantify the 95%, 90% and 75% confidence intervals. This research also performs a sensitivity analysis for one of the case studies examining which input data variables and assumptions are the most sensitive and contribute most to the overall uncertainty of the estimate. The Monte Carlo simulations for all three of the natural disaster events examined provide additional confidence in the U.S. Billion-dollar weather and climate disaster loss estimate report (NCDC 2014), since these estimates are within the confidence limits and near the mean and median of the example simulations. The normalized sensitivity analysis of Hurricane Ike damage costs determined that commercial losses in Texas are the most sensitive to assumption variability. Therefore, improvements in quantifying the commercial insurance participation rate for Texas will result in the largest reduction of uncertainty in the total loss estimate for Hurricane Ike. Further minimization of uncertainty would continue with improved measurement of subsequent cost parameters in order of descending sensitivity